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0000000000000000000000000000000000000000..b8b13896b64a43786f94b60e50361f79d6e6f974 --- /dev/null +++ b/.gitgnore @@ -0,0 +1,49 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg + +# Virtual Environment +venv/ +env/ +ENV/ +.venv/ + +# IDE & Editors +.idea/ +*.swp +*.swo +.DS_Store +.vscode/ +*.sublime-project +*.sublime-workspace + +# Logs & Databases +*.log +*.sqlite3 + +# Generated by app +response.wav +sandbox/*.py +!sandbox/.gitkeep + +# Hugging Face cache +~/.cache/huggingface/ +.gitattributes diff --git a/.gradio/certificate.pem b/.gradio/certificate.pem new file mode 100644 index 0000000000000000000000000000000000000000..30aa93639f903461a0f91745c832895eb0b370f8 --- /dev/null +++ b/.gradio/certificate.pem @@ -0,0 +1,31 @@ +-----BEGIN CERTIFICATE----- +MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw 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6af93c660533f8225b402989ed81408fcbba5cb3..e1e5db89b78f86ab2853a1e7838cec66a239706d 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,6 @@ --- -title: AI Coding Genius -emoji: 🏃 -colorFrom: pink -colorTo: gray -sdk: gradio -sdk_version: 6.2.0 +title: AI-Coding-Genius app_file: app.py -pinned: false +sdk: gradio +sdk_version: 5.42.0 --- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/agents/__init__.py b/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/agents/__init__.py @@ -0,0 +1 @@ + diff --git a/agents/coder.py b/agents/coder.py new file mode 100644 index 0000000000000000000000000000000000000000..65681a0e2b4c64108d02372110e112a2ab22a5ac --- /dev/null +++ b/agents/coder.py @@ -0,0 +1,23 @@ +# agents/coder.py +from transformers import AutoTokenizer, AutoModelForCausalLM + +class CoderAgent: + def __init__(self, model_name="deepseek-ai/deepseek-coder-6.7b-instruct"): + self.tokenizer = AutoTokenizer.from_pretrained(model_name) + self.model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" + ) + + def generate(self, prompt): + full_prompt = f""" +You're a brilliant, friendly AI coder. Explain clearly and write clean Python. +Include comments and use best practices. + +Task: {prompt} + """.strip() + inputs = self.tokenizer(full_prompt, return_tensors="pt").to(self.model.device) + outputs = self.model.generate(**inputs, max_new_tokens=1024, temperature=0.4) + code = self.tokenizer.decode(outputs[0], skip_special_tokens=True) + return code[len(full_prompt):].strip() diff --git a/agents/reviewer.py b/agents/reviewer.py new file mode 100644 index 0000000000000000000000000000000000000000..f872b8b5861b4cd9c99535e26ab24e61a1ee8ed3 --- /dev/null +++ b/agents/reviewer.py @@ -0,0 +1,27 @@ +# agents/reviewer.py +from transformers import pipeline + +class ReviewerAgent: + def __init__(self, model_name="Qwen/Qwen2-7B-Instruct"): + self.pipe = pipeline( + "text-generation", + model=model_name, + torch_dtype="auto", + device_map="auto" + ) + + def review(self, code): + prompt = f""" +Review this Python code for: +- Bugs +- Performance +- Readability +- Best practices + +Code: +{code} + +Provide a clear, constructive review. + """ + result = self.pipe(prompt, max_new_tokens=512) + return result[0]['generated_text'] diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..2b801983ee06fccc89f12dce6dba5025dbe343e6 --- /dev/null +++ b/app.py @@ -0,0 +1,194 @@ +import os +import torch +import gradio as gr +from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech +from transformers import WhisperProcessor, WhisperForConditionalGeneration +import librosa +import soundfile as sf +import numpy as np +from llama_cpp import Llama +from huggingface_hub import hf_hub_download # Needed to get the model + +# ───────────────────────────────────────────────────────────── +# 🧠 Load Qwen Coder (The Brain) - INSIDE Python now +# ───────────────────────────────────────────────────────────── +print("🧠 Downloading/Loading Qwen Model...") +try: + # 1. Download the model file from Hugging Face automatically + model_path = hf_hub_download( + repo_id="Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF", + filename="qwen2.5-coder-1.5b-instruct-q8_0.gguf" + ) + + # 2. Load it directly into memory + llm = Llama( + model_path=model_path, + n_ctx=4096, # Context window + n_threads=2, # Use 2 CPU threads (good for free tier) + verbose=False + ) + print("✅ Qwen Model Loaded Successfully!") + llm_ready = True +except Exception as e: + print(f"❌ Failed to load Qwen: {e}") + llm_ready = False + +# ───────────────────────────────────────────────────────────── +# 🔊 Load Text-to-Speech (TTS) +# ───────────────────────────────────────────────────────────── +print("🔊 Loading TTS model...") +try: + tts_processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") + tts_model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts") + speaker_embeddings = torch.zeros(1, 512) # Safe fallback + tts_ready = True + print("✅ TTS loaded!") +except Exception as e: + print(f"❌ TTS failed to load: {e}") + tts_ready = False + +# ───────────────────────────────────────────────────────────── +# 🎤 Load Speech-to-Text (STT) - Whisper Tiny +# ───────────────────────────────────────────────────────────── +print("🎤 Loading STT model (whisper-tiny)...") +try: + stt_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") + stt_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") + stt_model.eval() + stt_ready = True + print("✅ STT loaded!") +except Exception as e: + print(f"❌ STT failed to load: {e}") + stt_ready = False + +# Create folders +os.makedirs("assets", exist_ok=True) + +# ───────────────────────────────────────────────────────────── +# 🎤 Convert Speech to Text +# ───────────────────────────────────────────────────────────── +def speech_to_text(audio): + if not stt_ready or audio is None: + return "Voice input not available." + + try: + sample_rate, y = audio + if y.dtype != np.float32: + y = y.astype(np.float32) / 32768.0 + if len(y.shape) > 1: + y = y.mean(axis=1) + if sample_rate != 16000: + y = librosa.resample(y, orig_sr=sample_rate, target_sr=16000) + + inputs = stt_processor(y, sampling_rate=16000, return_tensors="pt") + outputs = stt_model.generate(inputs["input_features"]) + text = stt_processor.batch_decode(outputs, skip_special_tokens=True)[0] + return text.strip() + except Exception as e: + return f"❌ STT Error: {str(e)}" + +# ───────────────────────────────────────────────────────────── +# 💬 Generate Code (Now using Internal LLM) +# ───────────────────────────────────────────────────────────── +def generate_code(prompt): + if not prompt.strip(): + yield "# 👋 Hello!", "Hi! I'm your AI coding partner.", None + return + + if not llm_ready: + yield "# Error", "❌ Model failed to load. Check logs.", None + return + + yield "# Thinking...", "🧠 AI is thinking...", None + + try: + # Create the prompt in ChatML format + messages = [ + {"role": "system", "content": "You are a helpful AI coder."}, + {"role": "user", "content": prompt} + ] + + # Ask the internal model to generate + output = llm.create_chat_completion( + messages=messages, + max_tokens=512, + temperature=0.4, + top_p=0.95, + stream=True + ) + + # Stream the response + raw_code = "" + for chunk in output: + if "content" in chunk["choices"][0]["delta"]: + text_chunk = chunk["choices"][0]["delta"]["content"] + raw_code += text_chunk + # Live update the code block + yield f"```python\n{raw_code}\n```", "🚀 Generating...", None + + # Clean up code + clean_code = raw_code + if "```python" in clean_code: + clean_code = clean_code.split("```python")[1].split("```")[0].strip() + elif "```" in clean_code: + clean_code = clean_code.split("```")[1].split("```")[0].strip() + + final_display = f"```python\n{clean_code}\n```" + + # 🎙️ Generate voice + audio_path = None + if tts_ready: + try: + voice_text = f"Here is the code for {prompt[:20]}" + inputs_tts = tts_processor(text=voice_text, return_tensors="pt") + speech = tts_model.generate_speech(inputs_tts["input_ids"], speaker_embeddings) + audio_path = os.path.abspath("assets/response.wav") + sf.write(audio_path, speech.cpu().numpy(), samplerate=16000) + except Exception as e: + print(f"⚠️ TTS failed: {e}") + + yield final_display, f"✅ Done!", audio_path + + except Exception as e: + yield "print('Error')", f"❌ Error: {str(e)}", None + +# ───────────────────────────────────────────────────────────── +# 💾 Save As Function +# ───────────────────────────────────────────────────────────── +def save_as_code(code, filename): + if not filename.strip(): + filename = "ai_generated_code.py" + elif not filename.endswith(".py"): + filename += ".py" + try: + clean_code = code.replace("```python", "").replace("```", "").strip() + # In cloud, we just save to volatile memory, but this works for the demo + with open(filename, "w", encoding="utf-8") as f: + f.write(clean_code) + return f"💾 Saved (Temporary): {filename}" + except Exception as e: + return f"❌ Save failed: {str(e)}" + +# ───────────────────────────────────────────────────────────── +# 🚀 Gradio UI +# ───────────────────────────────────────────────────────────── +with gr.Blocks(title="AI Coding Genius", theme=gr.themes.Soft()) as demo: + gr.Markdown("# 🤖 AI Coding Genius (Cloud Edition)") + + with gr.Row(): + stt_input = gr.Audio(label="🎤 Speak", type="numpy", format="wav") + + inp = gr.Textbox(label="💬 Prompt", placeholder="Make a snake game...") + btn = gr.Button("🚀 Generate", variant="primary") + + code_out = gr.Code(label="💻 Code", language="python", lines=15) + status_out = gr.Textbox(label="Status") + audio_out = gr.Audio(label="Voice", autoplay=True) + + # Wiring + stt_input.change(speech_to_text, stt_input, inp) + btn.click(generate_code, inp, [code_out, status_out, audio_out]) + +if __name__ == "__main__": + # Removed share=True for Cloud deployment + demo.launch() \ No newline at end of file diff --git a/assets/.gitkeep b/assets/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/assets/.gitkeep @@ -0,0 +1 @@ + diff --git a/assets/response.wav b/assets/response.wav new file mode 100644 index 0000000000000000000000000000000000000000..5a39ae591e66341453b04dd2fd4af92311258f97 Binary files /dev/null and b/assets/response.wav differ diff --git a/llama.cpp/.clang-format b/llama.cpp/.clang-format new file mode 100644 index 0000000000000000000000000000000000000000..d03c483e6ae95f4065bb2844de1a47016fa64a53 --- /dev/null +++ b/llama.cpp/.clang-format @@ -0,0 +1,164 @@ +--- +Language: Cpp +AlignAfterOpenBracket: Align +AlignArrayOfStructures: Left +AlignConsecutiveAssignments: AcrossComments +AlignConsecutiveBitFields: AcrossComments +AlignConsecutiveDeclarations: AcrossComments +AlignConsecutiveMacros: AcrossComments +# AlignConsecutiveShortCaseStatements: AcrossComments +AlignEscapedNewlines: Left # LeftWithLastLine +AlignOperands: Align +AlignTrailingComments: + Kind: Always + OverEmptyLines: 1 +AllowAllArgumentsOnNextLine: true +AllowAllParametersOfDeclarationOnNextLine: false +# AllowBreakBeforeNoexceptSpecifier: OnlyWithParen +AllowShortBlocksOnASingleLine: Never +AllowShortCaseLabelsOnASingleLine: false +AllowShortFunctionsOnASingleLine: Inline +AllowShortIfStatementsOnASingleLine: Never +AllowShortLambdasOnASingleLine: Inline +AllowShortLoopsOnASingleLine: false +AlwaysBreakBeforeMultilineStrings: true +BinPackArguments: false +BinPackParameters: false # OnePerLine +BitFieldColonSpacing: Both +BreakBeforeBraces: Custom # Attach +BraceWrapping: + AfterCaseLabel: true + AfterClass: false + AfterControlStatement: false + AfterEnum: false + AfterFunction: false + AfterNamespace: false + AfterObjCDeclaration: false + AfterStruct: false + AfterUnion: false + AfterExternBlock: false + BeforeCatch: false + BeforeElse: false + BeforeLambdaBody: false + BeforeWhile: false + IndentBraces: false + SplitEmptyFunction: false + SplitEmptyRecord: false + SplitEmptyNamespace: false +# BreakAdjacentStringLiterals: true +BreakAfterAttributes: Never +BreakBeforeBinaryOperators: None +BreakBeforeInlineASMColon: OnlyMultiline +BreakBeforeTernaryOperators: false +# BreakBinaryOperations: Never +BreakConstructorInitializers: AfterColon +# BreakFunctionDefinitionParameters: false +BreakInheritanceList: AfterComma +BreakStringLiterals: true +# BreakTemplateDeclarations: Yes +ColumnLimit: 120 +CommentPragmas: '^ IWYU pragma:' +CompactNamespaces: false +ConstructorInitializerIndentWidth: 4 +ContinuationIndentWidth: 4 +Cpp11BracedListStyle: false +DerivePointerAlignment: false +DisableFormat: false +EmptyLineBeforeAccessModifier: Leave +EmptyLineAfterAccessModifier: Never +ExperimentalAutoDetectBinPacking: false +FixNamespaceComments: true +IncludeBlocks: Regroup +IncludeCategories: + - Regex: '".*"' + Priority: 1 + SortPriority: 0 + - Regex: '^<.*\.h>' + Priority: 2 + SortPriority: 0 + - Regex: '^<.*' + Priority: 3 + SortPriority: 0 + - Regex: '.*' + Priority: 4 + SortPriority: 0 +IncludeIsMainRegex: '([-_](test|unittest))?$' +IncludeIsMainSourceRegex: '' +IndentAccessModifiers: false +IndentCaseBlocks: true +IndentCaseLabels: true +IndentExternBlock: NoIndent +IndentGotoLabels: false +IndentPPDirectives: AfterHash +IndentWidth: 4 +IndentWrappedFunctionNames: false +InsertBraces: true # NOTE: may lead to incorrect formatting +InsertNewlineAtEOF: true +JavaScriptQuotes: Leave +JavaScriptWrapImports: true +KeepEmptyLinesAtTheStartOfBlocks: false +LambdaBodyIndentation: Signature +LineEnding: LF +MacroBlockBegin: '' +MacroBlockEnd: '' +MaxEmptyLinesToKeep: 1 +NamespaceIndentation: None +ObjCBinPackProtocolList: Auto +ObjCBlockIndentWidth: 4 +ObjCSpaceAfterProperty: true +ObjCSpaceBeforeProtocolList: true +PPIndentWidth: -1 +PackConstructorInitializers: CurrentLine +PenaltyBreakAssignment: 2 +PenaltyBreakBeforeFirstCallParameter: 1 +PenaltyBreakComment: 300 +PenaltyBreakFirstLessLess: 120 +PenaltyBreakString: 1000 +PenaltyBreakTemplateDeclaration: 10 +PenaltyExcessCharacter: 1000000 +PenaltyReturnTypeOnItsOwnLine: 200 +PointerAlignment: Middle +QualifierAlignment: Left +#QualifierOrder: ['static', 'inline', 'friend', 'constexpr', 'const', 'volatile', 'type', 'restrict'] +RawStringFormats: + - Language: Cpp + Delimiters: + - cc + - CC + - cpp + - Cpp + - CPP + - 'c++' + - 'C++' + CanonicalDelimiter: '' +ReferenceAlignment: Middle +ReflowComments: false # IndentOnly +SeparateDefinitionBlocks: Always +SortIncludes: CaseInsensitive +SortUsingDeclarations: LexicographicNumeric +SpaceAfterCStyleCast: true +SpaceAfterLogicalNot: false +SpaceAfterTemplateKeyword: true +SpaceBeforeAssignmentOperators: true +SpaceBeforeCpp11BracedList: false +SpaceBeforeCtorInitializerColon: true +SpaceBeforeInheritanceColon: true +SpaceBeforeParens: ControlStatements +SpaceBeforeRangeBasedForLoopColon: true +SpaceInEmptyBlock: false +SpaceInEmptyParentheses: false +SpacesBeforeTrailingComments: 2 +SpacesInAngles: Never +SpacesInContainerLiterals: true +SpacesInLineCommentPrefix: + Minimum: 1 + Maximum: -1 +SpacesInParentheses: false +SpacesInSquareBrackets: false +SpaceBeforeSquareBrackets: false +Standard: c++17 +TabWidth: 4 +UseTab: Never +WhitespaceSensitiveMacros: ['STRINGIZE'] +... + diff --git a/llama.cpp/.clang-tidy b/llama.cpp/.clang-tidy new file mode 100644 index 0000000000000000000000000000000000000000..7f17d87bbfbb6b99362b7086c1c547ccd55e986d --- /dev/null +++ b/llama.cpp/.clang-tidy @@ -0,0 +1,27 @@ +--- +Checks: > + bugprone-*, + -bugprone-easily-swappable-parameters, + -bugprone-implicit-widening-of-multiplication-result, + -bugprone-misplaced-widening-cast, + -bugprone-narrowing-conversions, + readability-*, + -readability-avoid-unconditional-preprocessor-if, + -readability-function-cognitive-complexity, + -readability-identifier-length, + -readability-implicit-bool-conversion, + -readability-magic-numbers, + -readability-uppercase-literal-suffix, + -readability-simplify-boolean-expr, + -readability-math-missing-parentheses, + clang-analyzer-*, + -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling, + performance-*, + portability-*, + -portability-simd-intrinsics, + misc-*, + -misc-const-correctness, + -misc-non-private-member-variables-in-classes, + -misc-no-recursion, + -misc-use-anonymous-namespace, +FormatStyle: none diff --git a/llama.cpp/.devops/cann.Dockerfile b/llama.cpp/.devops/cann.Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..e577b863a24d757d17aac63b2daf791a76124a36 --- /dev/null +++ b/llama.cpp/.devops/cann.Dockerfile @@ -0,0 +1,130 @@ +# ============================================================================== +# ARGUMENTS +# ============================================================================== + +# Define the CANN base image for easier version updates later +ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.1.rc1-910b-openeuler22.03-py3.10 + +# ============================================================================== +# BUILD STAGE +# Compile all binary files and libraries +# ============================================================================== +FROM ${CANN_BASE_IMAGE} AS build + +# Define the Ascend chip model for compilation. Default is Ascend910B3 +ARG ASCEND_SOC_TYPE=Ascend910B3 + +# -- Install build dependencies -- +RUN yum install -y gcc g++ cmake make git libcurl-devel python3 python3-pip && \ + yum clean all && \ + rm -rf /var/cache/yum + +# -- Set the working directory -- +WORKDIR /app + +# -- Copy project files -- +COPY . . + +# -- Set CANN environment variables (required for compilation) -- +# Using ENV instead of `source` allows environment variables to persist across the entire image layer +ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest +ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${LD_LIBRARY_PATH} +ENV PATH=${ASCEND_TOOLKIT_HOME}/bin:${PATH} +ENV ASCEND_OPP_PATH=${ASCEND_TOOLKIT_HOME}/opp +ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH +# ... You can add other environment variables from the original file as needed ... +# For brevity, only core variables are listed here. You can paste the original ENV list here. + +# -- Build llama.cpp -- +# Use the passed ASCEND_SOC_TYPE argument and add general build options +RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh --force \ + && \ + cmake -B build \ + -DGGML_CANN=ON \ + -DCMAKE_BUILD_TYPE=Release \ + -DSOC_TYPE=${ASCEND_SOC_TYPE} \ + . && \ + cmake --build build --config Release -j$(nproc) + +# -- Organize build artifacts for copying in later stages -- +# Create a lib directory to store all .so files +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +# Create a full directory to store all executables and Python scripts +RUN mkdir -p /app/full && \ + cp build/bin/* /app/full/ && \ + cp *.py /app/full/ && \ + cp -r gguf-py /app/full/ && \ + cp -r requirements /app/full/ && \ + cp requirements.txt /app/full/ + # If you have a tools.sh script, make sure it is copied here + # cp .devops/tools.sh /app/full/tools.sh + +# ============================================================================== +# BASE STAGE +# Create a minimal base image with CANN runtime and common libraries +# ============================================================================== +FROM ${CANN_BASE_IMAGE} AS base + +# -- Install runtime dependencies -- +RUN yum install -y libgomp curl && \ + yum clean all && \ + rm -rf /var/cache/yum + +# -- Set CANN environment variables (required for runtime) -- +ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest +ENV LD_LIBRARY_PATH=/app:${ASCEND_TOOLKIT_HOME}/lib64:${LD_LIBRARY_PATH} +ENV PATH=${ASCEND_TOOLKIT_HOME}/bin:${PATH} +ENV ASCEND_OPP_PATH=${ASCEND_TOOLKIT_HOME}/opp +# ... You can add other environment variables from the original file as needed ... + +WORKDIR /app + +# Copy compiled .so files from the build stage +COPY --from=build /app/lib/ /app + +# ============================================================================== +# FINAL STAGES (TARGETS) +# ============================================================================== + +### Target: full +# Complete image with all tools, Python bindings, and dependencies +# ============================================================================== +FROM base AS full + +COPY --from=build /app/full /app + +# Install Python dependencies +RUN yum install -y git python3 python3-pip && \ + pip3 install --no-cache-dir --upgrade pip setuptools wheel && \ + pip3 install --no-cache-dir -r requirements.txt && \ + yum clean all && \ + rm -rf /var/cache/yum + +# You need to provide a tools.sh script as the entrypoint +ENTRYPOINT ["/app/tools.sh"] +# If there is no tools.sh, you can set the default to start the server +# ENTRYPOINT ["/app/llama-server"] + +### Target: light +# Lightweight image containing only llama-cli +# ============================================================================== +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Target: server +# Dedicated server image containing only llama-server +# ============================================================================== +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +HEALTHCHECK --interval=5m CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/llama.cpp/.devops/cloud-v-pipeline b/llama.cpp/.devops/cloud-v-pipeline new file mode 100644 index 0000000000000000000000000000000000000000..abcb97bfb73cd73e320f0ff7fbf38b13c8c32ae9 --- /dev/null +++ b/llama.cpp/.devops/cloud-v-pipeline @@ -0,0 +1,22 @@ +node('x86_runner1'){ // Running on x86 runner containing latest vector qemu, latest vector gcc and all the necessary libraries + stage('Cleanup'){ + cleanWs() // Cleaning previous CI build in workspace + } + stage('checkout repo'){ + retry(5){ // Retry if the cloning fails due to some reason + checkout scm // Clone the repo on Runner + } + } + stage('Compiling llama.cpp'){ + sh'''#!/bin/bash + make RISCV=1 RISCV_CROSS_COMPILE=1 # Compiling llama for RISC-V + ''' + } + stage('Running llama.cpp'){ + sh'''#!/bin/bash + module load gnu-bin2/0.1 # loading latest versions of vector qemu and vector gcc + qemu-riscv64 -L /softwares/gnu-bin2/sysroot -cpu rv64,v=true,vlen=256,elen=64,vext_spec=v1.0 ./llama-cli -m /home/alitariq/codellama-7b.Q4_K_M.gguf -p "Anything" -n 9 > llama_log.txt # Running llama.cpp on vector qemu-riscv64 + cat llama_log.txt # Printing results + ''' + } +} diff --git a/llama.cpp/.devops/cpu.Dockerfile b/llama.cpp/.devops/cpu.Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..4ed9cad00ad0fdb1895193db49719296f89ebcef --- /dev/null +++ b/llama.cpp/.devops/cpu.Dockerfile @@ -0,0 +1,92 @@ +ARG UBUNTU_VERSION=22.04 + +FROM ubuntu:$UBUNTU_VERSION AS build + +ARG TARGETARCH + +ARG GGML_CPU_ARM_ARCH=armv8-a + +RUN apt-get update && \ + apt-get install -y build-essential git cmake libcurl4-openssl-dev + +WORKDIR /app + +COPY . . + +RUN if [ "$TARGETARCH" = "amd64" ]; then \ + cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \ + elif [ "$TARGETARCH" = "arm64" ]; then \ + cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_TESTS=OFF -DGGML_CPU_ARM_ARCH=${GGML_CPU_ARM_ARCH}; \ + else \ + echo "Unsupported architecture"; \ + exit 1; \ + fi && \ + cmake --build build -j $(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ubuntu:$UBUNTU_VERSION AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/llama.cpp/.devops/cuda.Dockerfile b/llama.cpp/.devops/cuda.Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..00cf04a661e7ca2d31d10bf13cc58df5f2fa6480 --- /dev/null +++ b/llama.cpp/.devops/cuda.Dockerfile @@ -0,0 +1,94 @@ +ARG UBUNTU_VERSION=22.04 +# This needs to generally match the container host's environment. +ARG CUDA_VERSION=12.4.0 +# Target the CUDA build image +ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} + +ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} + +FROM ${BASE_CUDA_DEV_CONTAINER} AS build + +# CUDA architecture to build for (defaults to all supported archs) +ARG CUDA_DOCKER_ARCH=default + +RUN apt-get update && \ + apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1 + +WORKDIR /app + +COPY . . + +RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ + export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ + fi && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ${BASE_CUDA_RUN_CONTAINER} AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/llama.cpp/.devops/intel.Dockerfile b/llama.cpp/.devops/intel.Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..0349211a5e5ff71cba9b1f8607cb27ce685bd341 --- /dev/null +++ b/llama.cpp/.devops/intel.Dockerfile @@ -0,0 +1,95 @@ +ARG ONEAPI_VERSION=2025.1.1-0-devel-ubuntu24.04 + +## Build Image + +FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build + +ARG GGML_SYCL_F16=OFF +RUN apt-get update && \ + apt-get install -y git libcurl4-openssl-dev + +WORKDIR /app + +COPY . . + +RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \ + echo "GGML_SYCL_F16 is set" \ + && export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \ + fi && \ + echo "Building with dynamic libs" && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${OPT_SYCL_F16} && \ + cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +FROM intel/oneapi-basekit:$ONEAPI_VERSION AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +### Full +FROM base AS full + +COPY --from=build /app/lib/ /app +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update && \ + apt-get install -y \ + git \ + python3 \ + python3-pip \ + python3-venv && \ + python3 -m venv /opt/venv && \ + . /opt/venv/bin/activate && \ + pip install --upgrade pip setuptools wheel && \ + pip install -r requirements.txt && \ + apt autoremove -y && \ + apt clean -y && \ + rm -rf /tmp/* /var/tmp/* && \ + find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete && \ + find /var/cache -type f -delete + +ENV PATH="/opt/venv/bin:$PATH" + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/lib/ /app +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/lib/ /app +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] + diff --git a/llama.cpp/.devops/llama-cli-cann.Dockerfile b/llama.cpp/.devops/llama-cli-cann.Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..a356ca4f7382cf80ef06746dc930fabc4555e492 --- /dev/null +++ b/llama.cpp/.devops/llama-cli-cann.Dockerfile @@ -0,0 +1,44 @@ +ARG ASCEND_VERSION=8.1.RC1.alpha001-910b-openeuler22.03-py3.10 + +FROM ascendai/cann:$ASCEND_VERSION AS build + +WORKDIR /app + +COPY . . + +RUN yum install -y gcc g++ cmake make libcurl-devel +ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest +ENV LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:$LIBRARY_PATH +ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/lib64/plugin/opskernel:${ASCEND_TOOLKIT_HOME}/lib64/plugin/nnengine:${ASCEND_TOOLKIT_HOME}/opp/built-in/op_impl/ai_core/tbe/op_tiling:${LD_LIBRARY_PATH} +ENV PYTHONPATH=${ASCEND_TOOLKIT_HOME}/python/site-packages:${ASCEND_TOOLKIT_HOME}/opp/built-in/op_impl/ai_core/tbe:${PYTHONPATH} +ENV PATH=${ASCEND_TOOLKIT_HOME}/bin:${ASCEND_TOOLKIT_HOME}/compiler/ccec_compiler/bin:${PATH} +ENV ASCEND_AICPU_PATH=${ASCEND_TOOLKIT_HOME} +ENV ASCEND_OPP_PATH=${ASCEND_TOOLKIT_HOME}/opp +ENV TOOLCHAIN_HOME=${ASCEND_TOOLKIT_HOME}/toolkit +ENV ASCEND_HOME_PATH=${ASCEND_TOOLKIT_HOME} + +# find libascend_hal.so, because the drive hasn`t been mounted. +ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH + +RUN echo "Building with static libs" && \ + source /usr/local/Ascend/ascend-toolkit/set_env.sh --force && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF -DLLAMA_BUILD_TESTS=OFF && \ + cmake --build build --config Release --target llama-cli + +# TODO: use image with NNRT +FROM ascendai/cann:$ASCEND_VERSION AS runtime +COPY --from=build /app/build/bin/llama-cli /llama-cli + +ENV LC_ALL=C.utf8 + +ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest +ENV LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:$LIBRARY_PATH +ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/lib64/plugin/opskernel:${ASCEND_TOOLKIT_HOME}/lib64/plugin/nnengine:${ASCEND_TOOLKIT_HOME}/opp/built-in/op_impl/ai_core/tbe/op_tiling:${LD_LIBRARY_PATH} +ENV PYTHONPATH=${ASCEND_TOOLKIT_HOME}/python/site-packages:${ASCEND_TOOLKIT_HOME}/opp/built-in/op_impl/ai_core/tbe:${PYTHONPATH} +ENV PATH=${ASCEND_TOOLKIT_HOME}/bin:${ASCEND_TOOLKIT_HOME}/compiler/ccec_compiler/bin:${PATH} +ENV ASCEND_AICPU_PATH=${ASCEND_TOOLKIT_HOME} +ENV ASCEND_OPP_PATH=${ASCEND_TOOLKIT_HOME}/opp +ENV TOOLCHAIN_HOME=${ASCEND_TOOLKIT_HOME}/toolkit +ENV ASCEND_HOME_PATH=${ASCEND_TOOLKIT_HOME} + +ENTRYPOINT ["/llama-cli" ] diff --git a/llama.cpp/.devops/llama-cpp-cuda.srpm.spec b/llama.cpp/.devops/llama-cpp-cuda.srpm.spec new file mode 100644 index 0000000000000000000000000000000000000000..6858dc217465eed192ac251b3757b0feefbd1c05 --- /dev/null +++ b/llama.cpp/.devops/llama-cpp-cuda.srpm.spec @@ -0,0 +1,83 @@ +# SRPM for building from source and packaging an RPM for RPM-based distros. +# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages +# Built and maintained by John Boero - boeroboy@gmail.com +# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal + +# Notes for llama.cpp: +# 1. Tags are currently based on hash - which will not sort asciibetically. +# We need to declare standard versioning if people want to sort latest releases. +# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies. +# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed. +# Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo +# 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries. +# It is up to the user to install the correct vendor-specific support. + +Name: llama.cpp-cuda +Version: %( date "+%%Y%%m%%d" ) +Release: 1%{?dist} +Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL) +License: MIT +Source0: https://github.com/ggml-org/llama.cpp/archive/refs/heads/master.tar.gz +BuildRequires: coreutils make gcc-c++ git cuda-toolkit +Requires: cuda-toolkit +URL: https://github.com/ggml-org/llama.cpp + +%define debug_package %{nil} +%define source_date_epoch_from_changelog 0 + +%description +CPU inference for Meta's Lllama2 models using default options. + +%prep +%setup -n llama.cpp-master + +%build +make -j GGML_CUDA=1 + +%install +mkdir -p %{buildroot}%{_bindir}/ +cp -p llama-cli %{buildroot}%{_bindir}/llama-cuda-cli +cp -p llama-server %{buildroot}%{_bindir}/llama-cuda-server +cp -p llama-simple %{buildroot}%{_bindir}/llama-cuda-simple + +mkdir -p %{buildroot}/usr/lib/systemd/system +%{__cat} < %{buildroot}/usr/lib/systemd/system/llamacuda.service +[Unit] +Description=Llama.cpp server, CPU only (no GPU support in this build). +After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target + +[Service] +Type=simple +EnvironmentFile=/etc/sysconfig/llama +ExecStart=/usr/bin/llama-cuda-server $LLAMA_ARGS +ExecReload=/bin/kill -s HUP $MAINPID +Restart=never + +[Install] +WantedBy=default.target +EOF + +mkdir -p %{buildroot}/etc/sysconfig +%{__cat} < %{buildroot}/etc/sysconfig/llama +LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin" +EOF + +%clean +rm -rf %{buildroot} +rm -rf %{_builddir}/* + +%files +%{_bindir}/llama-cuda-cli +%{_bindir}/llama-cuda-server +%{_bindir}/llama-cuda-simple +/usr/lib/systemd/system/llamacuda.service +%config /etc/sysconfig/llama + +%pre + +%post + +%preun +%postun + +%changelog diff --git a/llama.cpp/.devops/llama-cpp.srpm.spec b/llama.cpp/.devops/llama-cpp.srpm.spec new file mode 100644 index 0000000000000000000000000000000000000000..4f6d5c961ff12eece7e631f57f37798c929b7ef1 --- /dev/null +++ b/llama.cpp/.devops/llama-cpp.srpm.spec @@ -0,0 +1,85 @@ +# SRPM for building from source and packaging an RPM for RPM-based distros. +# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages +# Built and maintained by John Boero - boeroboy@gmail.com +# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal + +# Notes for llama.cpp: +# 1. Tags are currently based on hash - which will not sort asciibetically. +# We need to declare standard versioning if people want to sort latest releases. +# In the meantime, YYYYMMDD format will be used. +# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies. +# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed. +# Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo +# 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries. +# It is up to the user to install the correct vendor-specific support. + +Name: llama.cpp +Version: %( date "+%%Y%%m%%d" ) +Release: 1%{?dist} +Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL) +License: MIT +Source0: https://github.com/ggml-org/llama.cpp/archive/refs/heads/master.tar.gz +BuildRequires: coreutils make gcc-c++ git libstdc++-devel +Requires: libstdc++ +URL: https://github.com/ggml-org/llama.cpp + +%define debug_package %{nil} +%define source_date_epoch_from_changelog 0 + +%description +CPU inference for Meta's Lllama2 models using default options. +Models are not included in this package and must be downloaded separately. + +%prep +%setup -n llama.cpp-master + +%build +make -j + +%install +mkdir -p %{buildroot}%{_bindir}/ +cp -p llama-cli %{buildroot}%{_bindir}/llama-cli +cp -p llama-server %{buildroot}%{_bindir}/llama-server +cp -p llama-simple %{buildroot}%{_bindir}/llama-simple + +mkdir -p %{buildroot}/usr/lib/systemd/system +%{__cat} < %{buildroot}/usr/lib/systemd/system/llama.service +[Unit] +Description=Llama.cpp server, CPU only (no GPU support in this build). +After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target + +[Service] +Type=simple +EnvironmentFile=/etc/sysconfig/llama +ExecStart=/usr/bin/llama-server $LLAMA_ARGS +ExecReload=/bin/kill -s HUP $MAINPID +Restart=never + +[Install] +WantedBy=default.target +EOF + +mkdir -p %{buildroot}/etc/sysconfig +%{__cat} < %{buildroot}/etc/sysconfig/llama +LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin" +EOF + +%clean +rm -rf %{buildroot} +rm -rf %{_builddir}/* + +%files +%{_bindir}/llama-cli +%{_bindir}/llama-server +%{_bindir}/llama-simple +/usr/lib/systemd/system/llama.service +%config /etc/sysconfig/llama + +%pre + +%post + +%preun +%postun + +%changelog diff --git a/llama.cpp/.devops/musa.Dockerfile b/llama.cpp/.devops/musa.Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..811d0e9be7fa7e801ed5f5d4ff02ee4ecdfd33a0 --- /dev/null +++ b/llama.cpp/.devops/musa.Dockerfile @@ -0,0 +1,101 @@ +ARG UBUNTU_VERSION=22.04 +# This needs to generally match the container host's environment. +ARG MUSA_VERSION=rc4.2.0 +# Target the MUSA build image +ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}-amd64 + +ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}-amd64 + +FROM ${BASE_MUSA_DEV_CONTAINER} AS build + +# MUSA architecture to build for (defaults to all supported archs) +ARG MUSA_DOCKER_ARCH=default + +RUN apt-get update && \ + apt-get install -y \ + build-essential \ + cmake \ + python3 \ + python3-pip \ + git \ + libcurl4-openssl-dev \ + libgomp1 + +WORKDIR /app + +COPY . . + +RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \ + export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \ + fi && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ${BASE_MUSA_RUN_CONTAINER} AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/llama.cpp/.devops/nix/apps.nix b/llama.cpp/.devops/nix/apps.nix new file mode 100644 index 0000000000000000000000000000000000000000..3dad556578adecd35a63f9d4ded876da7d236a83 --- /dev/null +++ b/llama.cpp/.devops/nix/apps.nix @@ -0,0 +1,21 @@ +{ + perSystem = + { config, lib, ... }: + { + apps = + let + inherit (config.packages) default; + binaries = [ + "llama-cli" + "llama-embedding" + "llama-server" + "llama-quantize" + ]; + mkApp = name: { + type = "app"; + program = "${default}/bin/${name}"; + }; + in + lib.genAttrs binaries mkApp; + }; +} diff --git a/llama.cpp/.devops/nix/devshells.nix b/llama.cpp/.devops/nix/devshells.nix new file mode 100644 index 0000000000000000000000000000000000000000..f469c14c2aa7c497a47c4dcdc07e31fb4a295698 --- /dev/null +++ b/llama.cpp/.devops/nix/devshells.nix @@ -0,0 +1,52 @@ +{ inputs, ... }: + +{ + perSystem = + { + config, + lib, + system, + ... + }: + { + devShells = + let + pkgs = import inputs.nixpkgs { inherit system; }; + stdenv = pkgs.stdenv; + scripts = config.packages.python-scripts; + in + lib.pipe (config.packages) [ + (lib.concatMapAttrs ( + name: package: { + ${name} = pkgs.mkShell { + name = "${name}"; + inputsFrom = [ package ]; + shellHook = '' + echo "Entering ${name} devShell" + ''; + }; + "${name}-extra" = + if (name == "python-scripts") then + null + else + pkgs.mkShell { + name = "${name}-extra"; + inputsFrom = [ + package + scripts + ]; + # Extra packages that *may* be used by some scripts + packages = [ + pkgs.python3Packages.tiktoken + ]; + shellHook = '' + echo "Entering ${name} devShell" + addToSearchPath "LD_LIBRARY_PATH" "${lib.getLib stdenv.cc.cc}/lib" + ''; + }; + } + )) + (lib.filterAttrs (name: value: value != null)) + ]; + }; +} diff --git a/llama.cpp/.devops/nix/docker.nix b/llama.cpp/.devops/nix/docker.nix new file mode 100644 index 0000000000000000000000000000000000000000..8af69133ecbd68c9445c8a70aa3eb599c8b6cdb2 --- /dev/null +++ b/llama.cpp/.devops/nix/docker.nix @@ -0,0 +1,37 @@ +{ + lib, + dockerTools, + buildEnv, + llama-cpp, + interactive ? true, + coreutils, +}: + +# A tar that can be fed into `docker load`: +# +# $ nix build .#llamaPackages.docker +# $ docker load < result + +# For details and variations cf. +# - https://nixos.org/manual/nixpkgs/unstable/#ssec-pkgs-dockerTools-buildLayeredImage +# - https://discourse.nixos.org/t/a-faster-dockertools-buildimage-prototype/16922 +# - https://nixery.dev/ + +# Approximate (compressed) sizes, at the time of writing, are: +# +# .#llamaPackages.docker: 125M; +# .#llamaPackagesCuda.docker: 537M; +# .#legacyPackages.aarch64-linux.llamaPackagesXavier.docker: 415M. + +dockerTools.buildLayeredImage { + name = llama-cpp.pname; + tag = "latest"; + + contents = + [ llama-cpp ] + ++ lib.optionals interactive [ + coreutils + dockerTools.binSh + dockerTools.caCertificates + ]; +} diff --git a/llama.cpp/.devops/nix/jetson-support.nix b/llama.cpp/.devops/nix/jetson-support.nix new file mode 100644 index 0000000000000000000000000000000000000000..8e341bad87df1a9796d7de803e8e77ff6349ce86 --- /dev/null +++ b/llama.cpp/.devops/nix/jetson-support.nix @@ -0,0 +1,39 @@ +{ inputs, ... }: +{ + perSystem = + { + config, + system, + lib, + pkgsCuda, + ... + }: + { + legacyPackages = + let + caps.llamaPackagesXavier = "7.2"; + caps.llamaPackagesOrin = "8.7"; + caps.llamaPackagesTX2 = "6.2"; + caps.llamaPackagesNano = "5.3"; + + pkgsFor = + cap: + import inputs.nixpkgs { + inherit system; + config = { + cudaSupport = true; + cudaCapabilities = [ cap ]; + cudaEnableForwardCompat = false; + inherit (pkgsCuda.config) allowUnfreePredicate; + }; + }; + in + builtins.mapAttrs (name: cap: (pkgsFor cap).callPackage ./scope.nix { }) caps; + + packages = lib.optionalAttrs (system == "aarch64-linux") { + jetson-xavier = config.legacyPackages.llamaPackagesXavier.llama-cpp; + jetson-orin = config.legacyPackages.llamaPackagesOrin.llama-cpp; + jetson-nano = config.legacyPackages.llamaPackagesNano.llama-cpp; + }; + }; +} diff --git a/llama.cpp/.devops/nix/nixpkgs-instances.nix b/llama.cpp/.devops/nix/nixpkgs-instances.nix new file mode 100644 index 0000000000000000000000000000000000000000..5268dfb4b792b8b12ece6e4003c2a78049a0feab --- /dev/null +++ b/llama.cpp/.devops/nix/nixpkgs-instances.nix @@ -0,0 +1,45 @@ +{ inputs, ... }: +{ + # The _module.args definitions are passed on to modules as arguments. E.g. + # the module `{ pkgs ... }: { /* config */ }` implicitly uses + # `_module.args.pkgs` (defined in this case by flake-parts). + perSystem = + { system, ... }: + { + _module.args = { + # Note: bringing up https://zimbatm.com/notes/1000-instances-of-nixpkgs + # again, the below creates several nixpkgs instances which the + # flake-centric CLI will be forced to evaluate e.g. on `nix flake show`. + # + # This is currently "slow" and "expensive", on a certain scale. + # This also isn't "right" in that this hinders dependency injection at + # the level of flake inputs. This might get removed in the foreseeable + # future. + # + # Note that you can use these expressions without Nix + # (`pkgs.callPackage ./devops/nix/scope.nix { }` is the entry point). + + pkgsCuda = import inputs.nixpkgs { + inherit system; + # Ensure dependencies use CUDA consistently (e.g. that openmpi, ucc, + # and ucx are built with CUDA support) + config.cudaSupport = true; + config.allowUnfreePredicate = + p: + builtins.all ( + license: + license.free + || builtins.elem license.shortName [ + "CUDA EULA" + "cuDNN EULA" + ] + ) (p.meta.licenses or [ p.meta.license ]); + }; + # Ensure dependencies use ROCm consistently + pkgsRocm = import inputs.nixpkgs { + inherit system; + config.rocmSupport = true; + }; + }; + }; +} diff --git a/llama.cpp/.devops/nix/package-gguf-py.nix b/llama.cpp/.devops/nix/package-gguf-py.nix new file mode 100644 index 0000000000000000000000000000000000000000..c74b9a3386e94bc5b1cc19dd53a1976085d7aa0e --- /dev/null +++ b/llama.cpp/.devops/nix/package-gguf-py.nix @@ -0,0 +1,36 @@ +{ + lib, + llamaVersion, + numpy, + tqdm, + sentencepiece, + pyyaml, + poetry-core, + buildPythonPackage, + pytestCheckHook, +}: + +buildPythonPackage { + pname = "gguf"; + version = llamaVersion; + pyproject = true; + nativeBuildInputs = [ poetry-core ]; + propagatedBuildInputs = [ + numpy + tqdm + sentencepiece + pyyaml + ]; + src = lib.cleanSource ../../gguf-py; + pythonImportsCheck = [ + "numpy" + "gguf" + ]; + nativeCheckInputs = [ pytestCheckHook ]; + doCheck = true; + meta = with lib; { + description = "Python package for writing binary files in the GGUF format"; + license = licenses.mit; + maintainers = [ maintainers.ditsuke ]; + }; +} diff --git a/llama.cpp/.devops/nix/package.nix b/llama.cpp/.devops/nix/package.nix new file mode 100644 index 0000000000000000000000000000000000000000..bf2e2032aab7f0b464454eb6fe55468438c37460 --- /dev/null +++ b/llama.cpp/.devops/nix/package.nix @@ -0,0 +1,248 @@ +{ + lib, + glibc, + config, + stdenv, + runCommand, + cmake, + ninja, + pkg-config, + git, + mpi, + blas, + cudaPackages, + autoAddDriverRunpath, + darwin, + rocmPackages, + vulkan-headers, + vulkan-loader, + curl, + shaderc, + useBlas ? + builtins.all (x: !x) [ + useCuda + useMetalKit + useRocm + useVulkan + ] + && blas.meta.available, + useCuda ? config.cudaSupport, + useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin, + # Increases the runtime closure size by ~700M + useMpi ? false, + useRocm ? config.rocmSupport, + rocmGpuTargets ? builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets, + enableCurl ? true, + useVulkan ? false, + llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake + + # It's necessary to consistently use backendStdenv when building with CUDA support, + # otherwise we get libstdc++ errors downstream. + effectiveStdenv ? if useCuda then cudaPackages.backendStdenv else stdenv, + enableStatic ? effectiveStdenv.hostPlatform.isStatic, + precompileMetalShaders ? false, +}: + +let + inherit (lib) + cmakeBool + cmakeFeature + optionalAttrs + optionals + strings + ; + + stdenv = throw "Use effectiveStdenv instead"; + + suffices = + lib.optionals useBlas [ "BLAS" ] + ++ lib.optionals useCuda [ "CUDA" ] + ++ lib.optionals useMetalKit [ "MetalKit" ] + ++ lib.optionals useMpi [ "MPI" ] + ++ lib.optionals useRocm [ "ROCm" ] + ++ lib.optionals useVulkan [ "Vulkan" ]; + + pnameSuffix = + strings.optionalString (suffices != [ ]) + "-${strings.concatMapStringsSep "-" strings.toLower suffices}"; + descriptionSuffix = strings.optionalString ( + suffices != [ ] + ) ", accelerated with ${strings.concatStringsSep ", " suffices}"; + + xcrunHost = runCommand "xcrunHost" { } '' + mkdir -p $out/bin + ln -s /usr/bin/xcrun $out/bin + ''; + + # apple_sdk is supposed to choose sane defaults, no need to handle isAarch64 + # separately + darwinBuildInputs = + with darwin.apple_sdk.frameworks; + [ + Accelerate + CoreVideo + CoreGraphics + ] + ++ optionals useMetalKit [ MetalKit ]; + + cudaBuildInputs = with cudaPackages; [ + cuda_cudart + cuda_cccl # + libcublas + ]; + + rocmBuildInputs = with rocmPackages; [ + clr + hipblas + rocblas + ]; + + vulkanBuildInputs = [ + vulkan-headers + vulkan-loader + shaderc + ]; +in + +effectiveStdenv.mkDerivation (finalAttrs: { + pname = "llama-cpp${pnameSuffix}"; + version = llamaVersion; + + # Note: none of the files discarded here are visible in the sandbox or + # affect the output hash. This also means they can be modified without + # triggering a rebuild. + src = lib.cleanSourceWith { + filter = + name: type: + let + noneOf = builtins.all (x: !x); + baseName = baseNameOf name; + in + noneOf [ + (lib.hasSuffix ".nix" name) # Ignore *.nix files when computing outPaths + (lib.hasSuffix ".md" name) # Ignore *.md changes whe computing outPaths + (lib.hasPrefix "." baseName) # Skip hidden files and directories + (baseName == "flake.lock") + ]; + src = lib.cleanSource ../../.; + }; + + postPatch = '' + substituteInPlace ./ggml/src/ggml-metal/ggml-metal.m \ + --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";" + substituteInPlace ./ggml/src/ggml-metal/ggml-metal.m \ + --replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";" + ''; + + # With PR#6015 https://github.com/ggml-org/llama.cpp/pull/6015, + # `default.metallib` may be compiled with Metal compiler from XCode + # and we need to escape sandbox on MacOS to access Metal compiler. + # `xcrun` is used find the path of the Metal compiler, which is varible + # and not on $PATH + # see https://github.com/ggml-org/llama.cpp/pull/6118 for discussion + __noChroot = effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders; + + nativeBuildInputs = + [ + cmake + ninja + pkg-config + git + ] + ++ optionals useCuda [ + cudaPackages.cuda_nvcc + + autoAddDriverRunpath + ] + ++ optionals (effectiveStdenv.hostPlatform.isGnu && enableStatic) [ glibc.static ] + ++ optionals (effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders) [ xcrunHost ]; + + buildInputs = + optionals effectiveStdenv.isDarwin darwinBuildInputs + ++ optionals useCuda cudaBuildInputs + ++ optionals useMpi [ mpi ] + ++ optionals useRocm rocmBuildInputs + ++ optionals useBlas [ blas ] + ++ optionals useVulkan vulkanBuildInputs + ++ optionals enableCurl [ curl ]; + + cmakeFlags = + [ + (cmakeBool "LLAMA_BUILD_SERVER" true) + (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic)) + (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true) + (cmakeBool "LLAMA_CURL" enableCurl) + (cmakeBool "GGML_NATIVE" false) + (cmakeBool "GGML_BLAS" useBlas) + (cmakeBool "GGML_CUDA" useCuda) + (cmakeBool "GGML_HIP" useRocm) + (cmakeBool "GGML_METAL" useMetalKit) + (cmakeBool "GGML_VULKAN" useVulkan) + (cmakeBool "GGML_STATIC" enableStatic) + ] + ++ optionals useCuda [ + ( + with cudaPackages.flags; + cmakeFeature "CMAKE_CUDA_ARCHITECTURES" ( + builtins.concatStringsSep ";" (map dropDot cudaCapabilities) + ) + ) + ] + ++ optionals useRocm [ + (cmakeFeature "CMAKE_HIP_COMPILER" "${rocmPackages.llvm.clang}/bin/clang") + (cmakeFeature "CMAKE_HIP_ARCHITECTURES" rocmGpuTargets) + ] + ++ optionals useMetalKit [ + (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1") + (cmakeBool "GGML_METAL_EMBED_LIBRARY" (!precompileMetalShaders)) + ]; + + # Environment variables needed for ROCm + env = optionalAttrs useRocm { + ROCM_PATH = "${rocmPackages.clr}"; + HIP_DEVICE_LIB_PATH = "${rocmPackages.rocm-device-libs}/amdgcn/bitcode"; + }; + + # TODO(SomeoneSerge): It's better to add proper install targets at the CMake level, + # if they haven't been added yet. + postInstall = '' + mkdir -p $out/include + cp $src/include/llama.h $out/include/ + ''; + + meta = { + # Configurations we don't want even the CI to evaluate. Results in the + # "unsupported platform" messages. This is mostly a no-op, because + # cudaPackages would've refused to evaluate anyway. + badPlatforms = optionals useCuda lib.platforms.darwin; + + # Configurations that are known to result in build failures. Can be + # overridden by importing Nixpkgs with `allowBroken = true`. + broken = (useMetalKit && !effectiveStdenv.isDarwin); + + description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}"; + homepage = "https://github.com/ggml-org/llama.cpp/"; + license = lib.licenses.mit; + + # Accommodates `nix run` and `lib.getExe` + mainProgram = "llama-cli"; + + # These people might respond, on the best effort basis, if you ping them + # in case of Nix-specific regressions or for reviewing Nix-specific PRs. + # Consider adding yourself to this list if you want to ensure this flake + # stays maintained and you're willing to invest your time. Do not add + # other people without their consent. Consider removing people after + # they've been unreachable for long periods of time. + + # Note that lib.maintainers is defined in Nixpkgs, but you may just add + # an attrset following the same format as in + # https://github.com/NixOS/nixpkgs/blob/f36a80e54da29775c78d7eff0e628c2b4e34d1d7/maintainers/maintainer-list.nix + maintainers = with lib.maintainers; [ + philiptaron + SomeoneSerge + ]; + + # Extend `badPlatforms` instead + platforms = lib.platforms.all; + }; +}) diff --git a/llama.cpp/.devops/nix/python-scripts.nix b/llama.cpp/.devops/nix/python-scripts.nix new file mode 100644 index 0000000000000000000000000000000000000000..5b088ff519c3aa323e67e6f0a6ae8df61c22c059 --- /dev/null +++ b/llama.cpp/.devops/nix/python-scripts.nix @@ -0,0 +1,66 @@ +{ + lib, + stdenv, + buildPythonPackage, + poetry-core, + mkShell, + python3Packages, + gguf-py, +}@inputs: + +let + llama-python-deps = with python3Packages; [ + numpy + sentencepiece + transformers + protobuf + torchWithoutCuda + gguf-py + tqdm + + # for scripts/compare-llama-bench.py + gitpython + tabulate + + # for examples/pydantic-models-to-grammar-examples.py + docstring-parser + pydantic + + ]; + + llama-python-test-deps = with python3Packages; [ + # Server bench + matplotlib + + # server tests + openai + pytest + prometheus-client + ]; +in + +buildPythonPackage ({ + pname = "llama-scripts"; + version = "0.0.0"; + pyproject = true; + + # NOTE: The files filtered out here are not visible in the build sandbox, neither + # do they affect the output hash. They can be modified without triggering a rebuild. + src = lib.cleanSourceWith { + filter = + name: type: + let + any = builtins.any (x: x); + baseName = builtins.baseNameOf name; + in + any [ + (lib.hasSuffix ".py" name) + (baseName == "README.md") + (baseName == "pyproject.toml") + ]; + src = lib.cleanSource ../../.; + }; + nativeBuildInputs = [ poetry-core ]; + nativeCheckInputs = llama-python-test-deps; + dependencies = llama-python-deps; +}) diff --git a/llama.cpp/.devops/nix/scope.nix b/llama.cpp/.devops/nix/scope.nix new file mode 100644 index 0000000000000000000000000000000000000000..23884553de608f97026d7620796b943222edea22 --- /dev/null +++ b/llama.cpp/.devops/nix/scope.nix @@ -0,0 +1,41 @@ +{ + lib, + newScope, + python3, + llamaVersion ? "0.0.0", +}: + +let + pythonPackages = python3.pkgs; + buildPythonPackage = pythonPackages.buildPythonPackage; + numpy = pythonPackages.numpy; + tqdm = pythonPackages.tqdm; + sentencepiece = pythonPackages.sentencepiece; + pyyaml = pythonPackages.pyyaml; + poetry-core = pythonPackages.poetry-core; + pytestCheckHook = pythonPackages.pytestCheckHook; +in + +# We're using `makeScope` instead of just writing out an attrset +# because it allows users to apply overlays later using `overrideScope'`. +# Cf. https://noogle.dev/f/lib/makeScope + +lib.makeScope newScope (self: { + inherit llamaVersion; + gguf-py = self.callPackage ./package-gguf-py.nix { + inherit + buildPythonPackage + numpy + tqdm + sentencepiece + poetry-core + pyyaml + pytestCheckHook + ; + }; + python-scripts = self.callPackage ./python-scripts.nix { inherit buildPythonPackage poetry-core; }; + llama-cpp = self.callPackage ./package.nix { }; + docker = self.callPackage ./docker.nix { }; + docker-min = self.callPackage ./docker.nix { interactive = false; }; + sif = self.callPackage ./sif.nix { }; +}) diff --git a/llama.cpp/.devops/nix/sif.nix b/llama.cpp/.devops/nix/sif.nix new file mode 100644 index 0000000000000000000000000000000000000000..c3ecf33b6ce20ad6773a7bf8c782e8ce1f396969 --- /dev/null +++ b/llama.cpp/.devops/nix/sif.nix @@ -0,0 +1,27 @@ +{ + lib, + singularity-tools, + llama-cpp, + bashInteractive, + interactive ? false, +}: + +let + optionalInt = cond: x: if cond then x else 0; +in +singularity-tools.buildImage rec { + inherit (llama-cpp) name; + contents = [ llama-cpp ] ++ lib.optionals interactive [ bashInteractive ]; + + # These are excessive (but safe) for most variants. Building singularity + # images requires superuser privileges, so we build them inside a VM in a + # writable image of pre-determined size. + # + # ROCm is currently affected by https://github.com/NixOS/nixpkgs/issues/276846 + # + # Expected image sizes: + # - cpu/blas: 150M, + # - cuda, all gencodes: 560M, + diskSize = 4096 + optionalInt llama-cpp.useRocm 16384; + memSize = diskSize; +} diff --git a/llama.cpp/.devops/rocm.Dockerfile b/llama.cpp/.devops/rocm.Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..a4fa9b24b15bfa3ea56f8e3bebade7a57f399348 --- /dev/null +++ b/llama.cpp/.devops/rocm.Dockerfile @@ -0,0 +1,113 @@ +ARG UBUNTU_VERSION=24.04 + +# This needs to generally match the container host's environment. +ARG ROCM_VERSION=6.4 +ARG AMDGPU_VERSION=6.4 + +# Target the CUDA build image +ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete + +### Build image +FROM ${BASE_ROCM_DEV_CONTAINER} AS build + +# Unless otherwise specified, we make a fat build. +# List from https://github.com/ggml-org/llama.cpp/pull/1087#issuecomment-1682807878 +# This is mostly tied to rocBLAS supported archs. +# gfx803, gfx900, gfx1032, gfx1101, gfx1102,not officialy supported +# gfx906 is deprecated +#check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.2.4/reference/system-requirements.html + +ARG ROCM_DOCKER_ARCH='gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102' +#ARG ROCM_DOCKER_ARCH=gfx1100 + +# Set nvcc architectured +ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH} +# Enable ROCm +# ENV CC=/opt/rocm/llvm/bin/clang +# ENV CXX=/opt/rocm/llvm/bin/clang++ + +RUN apt-get update \ + && apt-get install -y \ + build-essential \ + cmake \ + git \ + libcurl4-openssl-dev \ + curl \ + libgomp1 + +WORKDIR /app + +COPY . . + +RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ + cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DCMAKE_BUILD_TYPE=Release -DLLAMA_BUILD_TESTS=OFF \ + && cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib \ + && find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ${BASE_ROCM_DEV_CONTAINER} AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3-pip \ + python3 \ + python3-wheel\ + && pip install --break-system-packages --upgrade setuptools \ + && pip install --break-system-packages -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/llama.cpp/.devops/tools.sh b/llama.cpp/.devops/tools.sh new file mode 100644 index 0000000000000000000000000000000000000000..d5cebb4d2509c5e5b3cd0cde6a3520b04788f585 --- /dev/null +++ b/llama.cpp/.devops/tools.sh @@ -0,0 +1,49 @@ +#!/usr/bin/env bash +set -e + +# Read the first argument into a variable +arg1="$1" + +# Shift the arguments to remove the first one +shift + +if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then + exec python3 ./convert_hf_to_gguf.py "$@" +elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then + exec ./llama-quantize "$@" +elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then + exec ./llama-cli "$@" +elif [[ "$arg1" == '--bench' || "$arg1" == '-b' ]]; then + exec ./llama-bench "$@" +elif [[ "$arg1" == '--perplexity' || "$arg1" == '-p' ]]; then + exec ./llama-perplexity "$@" +elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then + echo "Converting PTH to GGML..." + for i in $(ls $1/$2/ggml-model-f16.bin*); do + if [ -f "${i/f16/q4_0}" ]; then + echo "Skip model quantization, it already exists: ${i/f16/q4_0}" + else + echo "Converting PTH to GGML: $i into ${i/f16/q4_0}..." + exec ./llama-quantize "$i" "${i/f16/q4_0}" q4_0 + fi + done +elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then + exec ./llama-server "$@" +else + echo "Unknown command: $arg1" + echo "Available commands: " + echo " --run (-r): Run a model previously converted into ggml" + echo " ex: -m /models/7B/ggml-model-q4_0.bin -p \"Building a website can be done in 10 simple steps:\" -n 512" + echo " --bench (-b): Benchmark the performance of the inference for various parameters." + echo " ex: -m model.gguf" + echo " --perplexity (-p): Measure the perplexity of a model over a given text." + echo " ex: -m model.gguf -f file.txt" + echo " --convert (-c): Convert a llama model into ggml" + echo " ex: --outtype f16 \"/models/7B/\" " + echo " --quantize (-q): Optimize with quantization process ggml" + echo " ex: \"/models/7B/ggml-model-f16.bin\" \"/models/7B/ggml-model-q4_0.bin\" 2" + echo " --all-in-one (-a): Execute --convert & --quantize" + echo " ex: \"/models/\" 7B" + echo " --server (-s): Run a model on the server" + echo " ex: -m /models/7B/ggml-model-q4_0.bin -c 2048 -ngl 43 -mg 1 --port 8080" +fi diff --git a/llama.cpp/.devops/vulkan.Dockerfile b/llama.cpp/.devops/vulkan.Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..e55082af6b3b7e183a40ab6909de7fc439d19604 --- /dev/null +++ b/llama.cpp/.devops/vulkan.Dockerfile @@ -0,0 +1,89 @@ +ARG UBUNTU_VERSION=24.04 + +FROM ubuntu:$UBUNTU_VERSION AS build + +# Install build tools +RUN apt update && apt install -y git build-essential cmake wget + +# Install Vulkan SDK and cURL +RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ + wget -qO /etc/apt/sources.list.d/lunarg-vulkan-noble.list https://packages.lunarg.com/vulkan/lunarg-vulkan-noble.list && \ + apt update -y && \ + apt-get install -y vulkan-sdk libcurl4-openssl-dev curl + +# Build it +WORKDIR /app + +COPY . . + +RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON && \ + cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ubuntu:$UBUNTU_VERSION AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl libvulkan-dev \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + python3-wheel \ + && pip install --break-system-packages --upgrade setuptools \ + && pip install --break-system-packages -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/llama.cpp/.dockerignore b/llama.cpp/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..6185a2f1215fee5c020851c746fb527b7973dd36 --- /dev/null +++ b/llama.cpp/.dockerignore @@ -0,0 +1,20 @@ +*.o +*.a +.cache/ +# Do not ignore .git directory, otherwise the reported build number will always be 0 +.github/ +.gitignore +.vs/ +.vscode/ +.DS_Store + +build*/ + +models/* + +/llama-cli +/llama-quantize + +arm_neon.h +compile_commands.json +Dockerfile diff --git a/llama.cpp/.ecrc b/llama.cpp/.ecrc new file mode 100644 index 0000000000000000000000000000000000000000..7f0f82aff385a8c1131b78f92ee9ac3a788a4800 --- /dev/null +++ b/llama.cpp/.ecrc @@ -0,0 +1,6 @@ +{ + "Exclude": ["^\\.gitmodules$", "stb_image\\.h"], + "Disable": { + "IndentSize": true + } +} diff --git a/llama.cpp/.editorconfig b/llama.cpp/.editorconfig new file mode 100644 index 0000000000000000000000000000000000000000..17d20fb39ca967d20f89fe125dee028a28914ad3 --- /dev/null +++ b/llama.cpp/.editorconfig @@ -0,0 +1,54 @@ +# https://EditorConfig.org + +# Top-most EditorConfig file +root = true + +# Unix-style newlines with a newline ending every file, utf-8 charset +[*] +end_of_line = lf +insert_final_newline = true +trim_trailing_whitespace = true +charset = utf-8 +indent_style = space +indent_size = 4 + +[Makefile] +indent_style = tab + +[scripts/*.mk] +indent_style = tab + +[prompts/*.txt] +insert_final_newline = unset + +[tools/server/public/*] +indent_size = 2 + +[tools/server/public/deps_*] +trim_trailing_whitespace = unset +indent_style = unset +indent_size = unset + +[tools/server/deps_*] +trim_trailing_whitespace = unset +indent_style = unset +indent_size = unset + +[examples/llama.swiftui/llama.swiftui.xcodeproj/*] +indent_style = tab + +[tools/cvector-generator/*.txt] +trim_trailing_whitespace = unset +insert_final_newline = unset + +[models/templates/*.jinja] +indent_style = unset +indent_size = unset +end_of_line = unset +charset = unset +trim_trailing_whitespace = unset +insert_final_newline = unset + +[vendor/miniaudio/miniaudio.h] +trim_trailing_whitespace = unset +insert_final_newline = unset diff --git a/llama.cpp/.flake8 b/llama.cpp/.flake8 new file mode 100644 index 0000000000000000000000000000000000000000..d0fa661e7867a0e19cd3802688cd1811a7d46820 --- /dev/null +++ b/llama.cpp/.flake8 @@ -0,0 +1,18 @@ +[flake8] +max-line-length = 125 +ignore = E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503 +exclude = + # Do not traverse examples and tools + examples, + tools, + # Do not include package initializers + __init__.py, + # No need to traverse our git directory + .git, + # There's no value in checking cache directories + __pycache__, + # No need to include the build path + build, + # This contains builds that we don't want to check + dist # This is generated with `python build .` for package releases +# max-complexity = 10 diff --git a/llama.cpp/.github/ISSUE_TEMPLATE/010-bug-compilation.yml b/llama.cpp/.github/ISSUE_TEMPLATE/010-bug-compilation.yml new file mode 100644 index 0000000000000000000000000000000000000000..be2a5f22b71b1b49cdb4ab4a741d400ed47eb584 --- /dev/null +++ b/llama.cpp/.github/ISSUE_TEMPLATE/010-bug-compilation.yml @@ -0,0 +1,87 @@ +name: Bug (compilation) +description: Something goes wrong when trying to compile llama.cpp. +title: "Compile bug: " +labels: ["bug-unconfirmed", "compilation"] +body: + - type: markdown + attributes: + value: > + Thanks for taking the time to fill out this bug report! + This issue template is intended for bug reports where the compilation of llama.cpp fails. + Before opening an issue, please confirm that the compilation still fails with `-DGGML_CCACHE=OFF`. + If the compilation succeeds with ccache disabled you should be able to permanently fix the issue + by clearing `~/.cache/ccache` (on Linux). + - type: textarea + id: commit + attributes: + label: Git commit + description: Which commit are you trying to compile? + placeholder: | + $git rev-parse HEAD + 84a07a17b1b08cf2b9747c633a2372782848a27f + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: Operating systems + description: Which operating systems do you know to be affected? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: true + - type: dropdown + id: backends + attributes: + label: GGML backends + description: Which GGML backends do you know to be affected? + options: [AMX, BLAS, CPU, CUDA, HIP, Metal, Musa, RPC, SYCL, Vulkan, OpenCL] + multiple: true + validations: + required: true + - type: textarea + id: info + attributes: + label: Problem description & steps to reproduce + description: > + Please give us a summary of the problem and tell us how to reproduce it. + If you can narrow down the bug to specific compile flags, that information would be very much appreciated by us. + placeholder: > + I'm trying to compile llama.cpp with CUDA support on a fresh install of Ubuntu and get error XY. + Here are the exact commands that I used: ... + validations: + required: true + - type: textarea + id: first_bad_commit + attributes: + label: First Bad Commit + description: > + If the bug was not present on an earlier version: when did it start appearing? + If possible, please do a git bisect and identify the exact commit that introduced the bug. + validations: + required: false + - type: textarea + id: command + attributes: + label: Compile command + description: > + Please provide the exact command you used to compile llama.cpp. For example: `cmake -B ...`. + This will be automatically formatted into code, so no need for backticks. + render: shell + validations: + required: true + - type: textarea + id: logs + attributes: + label: Relevant log output + description: > + Please copy and paste any relevant log output, including any generated text. + This will be automatically formatted into code, so no need for backticks. + render: shell + validations: + required: true diff --git a/llama.cpp/.github/ISSUE_TEMPLATE/011-bug-results.yml b/llama.cpp/.github/ISSUE_TEMPLATE/011-bug-results.yml new file mode 100644 index 0000000000000000000000000000000000000000..78fcbc4a958c060d679a22743a999087b7d04f0e --- /dev/null +++ b/llama.cpp/.github/ISSUE_TEMPLATE/011-bug-results.yml @@ -0,0 +1,101 @@ +name: Bug (model use) +description: Something goes wrong when using a model (in general, not specific to a single llama.cpp module). +title: "Eval bug: " +labels: ["bug-unconfirmed", "model evaluation"] +body: + - type: markdown + attributes: + value: > + Thanks for taking the time to fill out this bug report! + This issue template is intended for bug reports where the model evaluation results + (i.e. the generated text) are incorrect or llama.cpp crashes during model evaluation. + If you encountered the issue while using an external UI (e.g. ollama), + please reproduce your issue using one of the examples/binaries in this repository. + The `llama-cli` binary can be used for simple and reproducible model inference. + - type: textarea + id: version + attributes: + label: Name and Version + description: Which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./llama-cli --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: Operating systems + description: Which operating systems do you know to be affected? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: true + - type: dropdown + id: backends + attributes: + label: GGML backends + description: Which GGML backends do you know to be affected? + options: [AMX, BLAS, CPU, CUDA, HIP, Metal, Musa, RPC, SYCL, Vulkan, OpenCL] + multiple: true + validations: + required: true + - type: textarea + id: hardware + attributes: + label: Hardware + description: Which CPUs/GPUs are you using? + placeholder: > + e.g. Ryzen 5950X + 2x RTX 4090 + validations: + required: true + - type: textarea + id: model + attributes: + label: Models + description: > + Which model(s) at which quantization were you using when encountering the bug? + If you downloaded a GGUF file off of Huggingface, please provide a link. + placeholder: > + e.g. Meta LLaMA 3.1 Instruct 8b q4_K_M + validations: + required: false + - type: textarea + id: info + attributes: + label: Problem description & steps to reproduce + description: > + Please give us a summary of the problem and tell us how to reproduce it. + If you can narrow down the bug to specific hardware, compile flags, or command line arguments, + that information would be very much appreciated by us. + placeholder: > + e.g. when I run llama-cli with -ngl 99 I get garbled outputs. + When I use -ngl 0 it works correctly. + Here are the exact commands that I used: ... + validations: + required: true + - type: textarea + id: first_bad_commit + attributes: + label: First Bad Commit + description: > + If the bug was not present on an earlier version: when did it start appearing? + If possible, please do a git bisect and identify the exact commit that introduced the bug. + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: > + Please copy and paste any relevant log output, including the command that you entered and any generated text. + This will be automatically formatted into code, so no need for backticks. + render: shell + validations: + required: true diff --git a/llama.cpp/.github/ISSUE_TEMPLATE/019-bug-misc.yml b/llama.cpp/.github/ISSUE_TEMPLATE/019-bug-misc.yml new file mode 100644 index 0000000000000000000000000000000000000000..4503e9629d575b19a3b1d1859f58447230ee0eeb --- /dev/null +++ b/llama.cpp/.github/ISSUE_TEMPLATE/019-bug-misc.yml @@ -0,0 +1,91 @@ +name: Bug (misc.) +description: Something is not working the way it should (and it's not covered by any of the above cases). +title: "Misc. bug: " +labels: ["bug-unconfirmed"] +body: + - type: markdown + attributes: + value: > + Thanks for taking the time to fill out this bug report! + This issue template is intended for miscellaneous bugs that don't fit into any other category. + If you encountered the issue while using an external UI (e.g. ollama), + please reproduce your issue using one of the examples/binaries in this repository. + - type: textarea + id: version + attributes: + label: Name and Version + description: Which version of our software is affected? (You can use `--version` to get a version string.) + placeholder: | + $./llama-cli --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: Operating systems + description: Which operating systems do you know to be affected? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: false + - type: dropdown + id: module + attributes: + label: Which llama.cpp modules do you know to be affected? + multiple: true + options: + - Documentation/Github + - libllama (core library) + - llama-cli + - llama-server + - llama-bench + - llama-quantize + - Python/Bash scripts + - Test code + - Other (Please specify in the next section) + validations: + required: false + - type: textarea + id: command + attributes: + label: Command line + description: > + Please provide the exact commands you entered, if applicable. For example: `llama-server -m ... -c ...`, `llama-cli -m ...`, etc. + This will be automatically formatted into code, so no need for backticks. + render: shell + validations: + required: false + - type: textarea + id: info + attributes: + label: Problem description & steps to reproduce + description: > + Please give us a summary of the problem and tell us how to reproduce it (if applicable). + validations: + required: true + - type: textarea + id: first_bad_commit + attributes: + label: First Bad Commit + description: > + If the bug was not present on an earlier version and it's not trivial to track down: when did it start appearing? + If possible, please do a git bisect and identify the exact commit that introduced the bug. + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: > + If applicable, please copy and paste any relevant log output, including any generated text. + This will be automatically formatted into code, so no need for backticks. + render: shell + validations: + required: false diff --git a/llama.cpp/.github/ISSUE_TEMPLATE/020-enhancement.yml b/llama.cpp/.github/ISSUE_TEMPLATE/020-enhancement.yml new file mode 100644 index 0000000000000000000000000000000000000000..ee8318519e32886de8f493b662e146991c1dd8e5 --- /dev/null +++ b/llama.cpp/.github/ISSUE_TEMPLATE/020-enhancement.yml @@ -0,0 +1,51 @@ +name: Enhancement +description: Used to request enhancements for llama.cpp. +title: "Feature Request: " +labels: ["enhancement"] +body: + - type: markdown + attributes: + value: | + [Please post your idea first in Discussion if there is not yet a consensus for this enhancement request. This will help to keep this issue tracker focused on enhancements that the community has agreed needs to be implemented.](https://github.com/ggml-org/llama.cpp/discussions/categories/ideas) + + - type: checkboxes + id: prerequisites + attributes: + label: Prerequisites + description: Please confirm the following before submitting your enhancement request. + options: + - label: I am running the latest code. Mention the version if possible as well. + required: true + - label: I carefully followed the [README.md](https://github.com/ggml-org/llama.cpp/blob/master/README.md). + required: true + - label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed). + required: true + - label: I reviewed the [Discussions](https://github.com/ggml-org/llama.cpp/discussions), and have a new and useful enhancement to share. + required: true + + - type: textarea + id: feature-description + attributes: + label: Feature Description + description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement. + placeholder: Detailed description of the enhancement + validations: + required: true + + - type: textarea + id: motivation + attributes: + label: Motivation + description: Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users. + placeholder: Explanation of why this feature is needed and its benefits + validations: + required: true + + - type: textarea + id: possible-implementation + attributes: + label: Possible Implementation + description: If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better. + placeholder: Detailed description of potential implementation + validations: + required: false diff --git a/llama.cpp/.github/ISSUE_TEMPLATE/030-research.yml b/llama.cpp/.github/ISSUE_TEMPLATE/030-research.yml new file mode 100644 index 0000000000000000000000000000000000000000..fd360391c2a1e4a22c7d71171c611e019e29ea3a --- /dev/null +++ b/llama.cpp/.github/ISSUE_TEMPLATE/030-research.yml @@ -0,0 +1,52 @@ +name: Research +description: Track new technical research area. +title: "Research: " +labels: ["research 🔬"] +body: + - type: markdown + attributes: + value: | + Don't forget to check for any [duplicate research issue tickets](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22) + + - type: checkboxes + id: research-stage + attributes: + label: Research Stage + description: Track general state of this research ticket + options: + - label: Background Research (Let's try to avoid reinventing the wheel) + - label: Hypothesis Formed (How do you think this will work and it's effect?) + - label: Strategy / Implementation Forming + - label: Analysis of results + - label: Debrief / Documentation (So people in the future can learn from us) + + - type: textarea + id: background + attributes: + label: Previous existing literature and research + description: Whats the current state of the art and whats the motivation for this research? + + - type: textarea + id: hypothesis + attributes: + label: Hypothesis + description: How do you think this will work and it's effect? + + - type: textarea + id: implementation + attributes: + label: Implementation + description: Got an approach? e.g. a PR ready to go? + + - type: textarea + id: analysis + attributes: + label: Analysis + description: How does the proposed implementation behave? + + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/llama.cpp/.github/ISSUE_TEMPLATE/040-refactor.yml b/llama.cpp/.github/ISSUE_TEMPLATE/040-refactor.yml new file mode 100644 index 0000000000000000000000000000000000000000..a9ed617c8083e817f8d0643daa712edef7160b89 --- /dev/null +++ b/llama.cpp/.github/ISSUE_TEMPLATE/040-refactor.yml @@ -0,0 +1,28 @@ +name: Refactor (Maintainers) +description: Used to track refactoring opportunities. +title: "Refactor: " +labels: ["refactor"] +body: + - type: markdown + attributes: + value: | + Don't forget to [check for existing refactor issue tickets](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3Arefactoring) in case it's already covered. + Also you may want to check [Pull request refactor label as well](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Aopen+is%3Apr+label%3Arefactoring) for duplicates too. + + - type: textarea + id: background-description + attributes: + label: Background Description + description: Please provide a detailed written description of the pain points you are trying to solve. + placeholder: Detailed description behind your motivation to request refactor + validations: + required: true + + - type: textarea + id: possible-approaches + attributes: + label: Possible Refactor Approaches + description: If you have some idea of possible approaches to solve this problem. You may want to make it a todo list. + placeholder: Your idea of possible refactoring opportunity/approaches + validations: + required: false diff --git a/llama.cpp/.github/ISSUE_TEMPLATE/config.yml b/llama.cpp/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..b56a240a64f3475382d2a745d4df3dac0a4bbc1e --- /dev/null +++ b/llama.cpp/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,11 @@ +blank_issues_enabled: true +contact_links: + - name: Got an idea? + url: https://github.com/ggml-org/llama.cpp/discussions/categories/ideas + about: Pop it there. It may then become an enhancement ticket. + - name: Got a question? + url: https://github.com/ggml-org/llama.cpp/discussions/categories/q-a + about: Ask a question there! + - name: Want to contribute? + url: https://github.com/ggml-org/llama.cpp/wiki/contribute + about: Head to the contribution guide page of the wiki for areas you can help with diff --git a/llama.cpp/.github/actions/get-tag-name/action.yml b/llama.cpp/.github/actions/get-tag-name/action.yml new file mode 100644 index 0000000000000000000000000000000000000000..7cd97c186114eb40240bd045677e8cb3dd3c8899 --- /dev/null +++ b/llama.cpp/.github/actions/get-tag-name/action.yml @@ -0,0 +1,22 @@ +name: "Determine tag name" +description: "Determine the tag name to use for a release" +outputs: + name: + description: "The name of the tag" + value: ${{ steps.tag.outputs.name }} + +runs: + using: "composite" + steps: + - name: Determine tag name + id: tag + shell: bash + run: | + BUILD_NUMBER="$(git rev-list --count HEAD)" + SHORT_HASH="$(git rev-parse --short=7 HEAD)" + if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then + echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT + else + SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-') + echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT + fi diff --git a/llama.cpp/.github/actions/windows-setup-cuda/action.yml b/llama.cpp/.github/actions/windows-setup-cuda/action.yml new file mode 100644 index 0000000000000000000000000000000000000000..5dad3e3ac3cda0e39bc1edbb06e998b4d66f5d11 --- /dev/null +++ b/llama.cpp/.github/actions/windows-setup-cuda/action.yml @@ -0,0 +1,67 @@ +name: "Windows - Setup CUDA Toolkit" +description: "Setup CUDA Toolkit for Windows" +inputs: + cuda_version: + description: "CUDA toolkit version" + required: true + +runs: + using: "composite" + steps: + - name: Install Cuda Toolkit 11.7 + if: ${{ inputs.cuda_version == '11.7' }} + shell: pwsh + run: | + mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" + choco install unzip -y + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cudart/windows-x86_64/cuda_cudart-windows-x86_64-11.7.99-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/windows-x86_64/cuda_nvcc-windows-x86_64-11.7.99-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvrtc/windows-x86_64/cuda_nvrtc-windows-x86_64-11.7.99-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-x86_64/libcublas-windows-x86_64-11.7.4.6-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvtx/windows-x86_64/cuda_nvtx-windows-x86_64-11.7.91-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/visual_studio_integration/windows-x86_64/visual_studio_integration-windows-x86_64-11.7.91-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvprof/windows-x86_64/cuda_nvprof-windows-x86_64-11.7.101-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cccl/windows-x86_64/cuda_cccl-windows-x86_64-11.7.91-archive.zip" + unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_cudart-windows-x86_64-11.7.99-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvcc-windows-x86_64-11.7.99-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvrtc-windows-x86_64-11.7.99-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\libcublas-windows-x86_64-11.7.4.6-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvtx-windows-x86_64-11.7.91-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\visual_studio_integration-windows-x86_64-11.7.91-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvprof-windows-x86_64-11.7.101-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_cccl-windows-x86_64-11.7.91-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\libnvvp" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8 + echo "CUDA_PATH_V11_7=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8 + + - name: Install Cuda Toolkit 12.4 + if: ${{ inputs.cuda_version == '12.4' }} + shell: pwsh + run: | + mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" + choco install unzip -y + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cudart/windows-x86_64/cuda_cudart-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/windows-x86_64/cuda_nvcc-windows-x86_64-12.4.131-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvrtc/windows-x86_64/cuda_nvrtc-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-x86_64/libcublas-windows-x86_64-12.4.5.8-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvtx/windows-x86_64/cuda_nvtx-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_profiler_api/windows-x86_64/cuda_profiler_api-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/visual_studio_integration/windows-x86_64/visual_studio_integration-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvprof/windows-x86_64/cuda_nvprof-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cccl/windows-x86_64/cuda_cccl-windows-x86_64-12.4.127-archive.zip" + unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_cudart-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvcc-windows-x86_64-12.4.131-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvrtc-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\libcublas-windows-x86_64-12.4.5.8-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvtx-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_profiler_api-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\visual_studio_integration-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvprof-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_cccl-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\libnvvp" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8 + echo "CUDA_PATH_V12_4=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8 diff --git a/llama.cpp/.github/actions/windows-setup-curl/action.yml b/llama.cpp/.github/actions/windows-setup-curl/action.yml new file mode 100644 index 0000000000000000000000000000000000000000..988ee1745434785bcbfc83b65a48e3040f0d1dbf --- /dev/null +++ b/llama.cpp/.github/actions/windows-setup-curl/action.yml @@ -0,0 +1,30 @@ +name: 'Windows - Setup CURL' +description: 'Composite action, to be reused in other workflow' +inputs: + curl_version: + description: 'CURL version' + required: false + default: '8.6.0_6' + architecture: + description: 'Architecture of the libcurl to download' + required: false + default: 'win64' +outputs: + curl_path: + description: "Path to the downloaded libcurl" + value: ${{ steps.get_libcurl.outputs.curl_path }} + +runs: + using: "composite" + steps: + - name: libCURL + id: get_libcurl + shell: powershell + env: + CURL_VERSION: ${{ inputs.curl_version }} + ARCHITECTURE: ${{ inputs.architecture }} + run: | + curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-${env:ARCHITECTURE}-mingw.zip" + mkdir $env:RUNNER_TEMP/libcurl + tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl + echo "curl_path=$env:RUNNER_TEMP/libcurl" >> $env:GITHUB_OUTPUT diff --git a/llama.cpp/.github/labeler.yml b/llama.cpp/.github/labeler.yml new file mode 100644 index 0000000000000000000000000000000000000000..7993e439ffe8edd39c4b76ddb6fd7915953f57ac --- /dev/null +++ b/llama.cpp/.github/labeler.yml @@ -0,0 +1,94 @@ +# https://github.com/actions/labeler +Apple Metal: + - changed-files: + - any-glob-to-any-file: + - ggml/include/ggml-metal.h + - ggml/src/ggml-metal/** + - README-metal.md +SYCL: + - changed-files: + - any-glob-to-any-file: + - ggml/include/ggml-sycl.h + - ggml/src/ggml-sycl/** + - docs/backend/SYCL.md + - examples/sycl/** +Nvidia GPU: + - changed-files: + - any-glob-to-any-file: + - ggml/include/ggml-cuda.h + - ggml/src/ggml-cuda/** +Vulkan: + - changed-files: + - any-glob-to-any-file: + - ggml/include/ggml-vulkan.h + - ggml/src/ggml-vulkan/** +documentation: + - changed-files: + - any-glob-to-any-file: + - docs/** + - media/** +testing: + - changed-files: + - any-glob-to-any-file: + - tests/** +build: + - changed-files: + - any-glob-to-any-file: + - cmake/** + - CMakeLists.txt + - CMakePresets.json +examples: + - changed-files: + - any-glob-to-any-file: + - examples/** + - tools/** +devops: + - changed-files: + - any-glob-to-any-file: + - .devops/** + - .github/** + - ci/** +python: + - changed-files: + - any-glob-to-any-file: + - "**/*.py" + - requirements/** + - gguf-py/** + - .flake8 +script: + - changed-files: + - any-glob-to-any-file: + - scripts/** +android: + - changed-files: + - any-glob-to-any-file: + - examples/llama.android/** +server: + - changed-files: + - any-glob-to-any-file: + - tools/server/** +ggml: + - changed-files: + - any-glob-to-any-file: + - ggml/** +nix: + - changed-files: + - any-glob-to-any-file: + - "**/*.nix" + - .github/workflows/nix-*.yml + - .devops/nix/nixpkgs-instances.nix +embedding: + - changed-files: + - any-glob-to-any-file: examples/embedding/ + +Ascend NPU: + - changed-files: + - any-glob-to-any-file: + - ggml/include/ggml-cann.h + - ggml/src/ggml-cann/** + - docs/backend/CANN.md +OpenCL: + - changed-files: + - any-glob-to-any-file: + - ggml/include/ggml-opencl.h + - ggml/src/ggml-opencl/** diff --git a/llama.cpp/.github/pull_request_template.md b/llama.cpp/.github/pull_request_template.md new file mode 100644 index 0000000000000000000000000000000000000000..93f6bc0e35af81959433037a7ca20c0f308a1daf --- /dev/null +++ b/llama.cpp/.github/pull_request_template.md @@ -0,0 +1 @@ +*Make sure to read the [contributing guidelines](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md) before submitting a PR* diff --git a/llama.cpp/.github/workflows/bench.yml.disabled b/llama.cpp/.github/workflows/bench.yml.disabled new file mode 100644 index 0000000000000000000000000000000000000000..3d848136633d227655bf5e639bc49336f56f43d6 --- /dev/null +++ b/llama.cpp/.github/workflows/bench.yml.disabled @@ -0,0 +1,304 @@ +# TODO: there have been some issues with the workflow, so disabling for now +# https://github.com/ggml-org/llama.cpp/issues/7893 +# +# Benchmark +name: Benchmark + +on: + workflow_dispatch: + inputs: + gpu-series: + description: 'Azure GPU series to run with' + required: true + type: choice + options: + - Standard_NC4as_T4_v3 + - Standard_NC24ads_A100_v4 + - Standard_NC80adis_H100_v5 + sha: + description: 'Commit SHA1 to build' + required: false + type: string + duration: + description: 'Duration of the bench' + type: string + default: 10m + + push: + branches: + - master + paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'tools/server/*.h*', 'tools/server/*.cpp'] + pull_request_target: + types: [opened, synchronize, reopened] + paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'tools/server/*.h*', 'tools/server/*.cpp'] + schedule: + - cron: '04 2 * * *' + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}-${{ github.event.inputs.sha }} + cancel-in-progress: true + +jobs: + bench-server-baseline: + runs-on: Standard_NC4as_T4_v3 + env: + RUNNER_LABEL: Standard_NC4as_T4_v3 # FIXME Do not find a way to not duplicate it + N_USERS: 8 + DURATION: 10m + + strategy: + matrix: + model: [phi-2] + ftype: [q4_0, q8_0, f16] + include: + - model: phi-2 + ftype: q4_0 + pr_comment_enabled: "true" + + if: | + inputs.gpu-series == 'Standard_NC4as_T4_v3' + || github.event_name == 'pull_request_target' + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }} + + - name: Install python env + id: pipenv + run: | + cd tools/server/bench + python3 -m venv venv + source venv/bin/activate + pip install -r requirements.txt + + - name: Prometheus + id: install_prometheus + run: | + wget --quiet https://github.com/prometheus/prometheus/releases/download/v2.51.0/prometheus-2.51.0.linux-amd64.tar.gz + tar xzf prometheus*.tar.gz --strip-components=1 + ./prometheus --config.file=tools/server/bench/prometheus.yml & + while ! nc -z localhost 9090; do + sleep 0.1 + done + + - name: Set up Go + uses: actions/setup-go@v5 + with: + go-version: '1.21' + + - name: Install k6 and xk6-sse + id: k6_installation + run: | + cd tools/server/bench + go install go.k6.io/xk6/cmd/xk6@latest + xk6 build master \ + --with github.com/phymbert/xk6-sse + + - name: Build + id: cmake_build + run: | + set -eux + cmake -B build \ + -DGGML_NATIVE=OFF \ + -DLLAMA_BUILD_SERVER=ON \ + -DLLAMA_CUBLAS=ON \ + -DCUDAToolkit_ROOT=/usr/local/cuda \ + -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc \ + -DCMAKE_CUDA_ARCHITECTURES=75 \ + -DLLAMA_FATAL_WARNINGS=OFF \ + -DLLAMA_ALL_WARNINGS=OFF \ + -DCMAKE_BUILD_TYPE=Release; + cmake --build build --config Release -j $(nproc) --target llama-server + + - name: Download the dataset + id: download_dataset + run: | + cd tools/server/bench + wget --quiet https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json + + - name: Server bench + id: server_bench + env: + HEAD_REF: ${{ github.head_ref || github.ref_name }} + run: | + set -eux + + cd tools/server/bench + source venv/bin/activate + python bench.py \ + --runner-label ${{ env.RUNNER_LABEL }} \ + --name ${{ github.job }} \ + --branch $HEAD_REF \ + --commit ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha }} \ + --scenario script.js \ + --duration ${{ github.event.inputs.duration || env.DURATION }} \ + --hf-repo ggml-org/models \ + --hf-file ${{ matrix.model }}/ggml-model-${{ matrix.ftype }}.gguf \ + --model-path-prefix /models \ + --parallel ${{ env.N_USERS }} \ + -ngl 33 \ + --batch-size 2048 \ + --ubatch-size 256 \ + --ctx-size 16384 \ + --n-prompts 1000 \ + --max-prompt-tokens 1024 \ + --max-tokens 2048 + + cat results.github.env >> $GITHUB_ENV + + # Remove dataset as we do not want it in the artefact + rm ShareGPT_V3_unfiltered_cleaned_split.json + + - uses: actions/upload-artifact@v4 + with: + name: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }} + compression-level: 9 + path: | + tools/server/bench/*.jpg + tools/server/bench/*.json + tools/server/bench/*.log + + - name: Commit status + uses: Sibz/github-status-action@v1 + with: + authToken: ${{secrets.GITHUB_TOKEN}} + sha: ${{ inputs.sha || github.event.pull_request.head.sha || github.sha }} + context: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }} + description: | + ${{ env.BENCH_RESULTS }} + state: 'success' + + - name: Upload benchmark images + uses: devicons/public-upload-to-imgur@v2.2.2 + continue-on-error: true # Important as it looks unstable: 503 + id: imgur_step + with: + client_id: ${{secrets.IMGUR_CLIENT_ID}} + path: | + tools/server/bench/prompt_tokens_seconds.jpg + tools/server/bench/predicted_tokens_seconds.jpg + tools/server/bench/kv_cache_usage_ratio.jpg + tools/server/bench/requests_processing.jpg + + - name: Extract mermaid + id: set_mermaid + run: | + set -eux + + cd tools/server/bench + PROMPT_TOKENS_SECONDS=$(cat prompt_tokens_seconds.mermaid) + echo "PROMPT_TOKENS_SECONDS<> $GITHUB_ENV + echo "$PROMPT_TOKENS_SECONDS" >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV + + PREDICTED_TOKENS_SECONDS=$(cat predicted_tokens_seconds.mermaid) + echo "PREDICTED_TOKENS_SECONDS<> $GITHUB_ENV + echo "$PREDICTED_TOKENS_SECONDS" >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV + + KV_CACHE_USAGE_RATIO=$(cat kv_cache_usage_ratio.mermaid) + echo "KV_CACHE_USAGE_RATIO<> $GITHUB_ENV + echo "$KV_CACHE_USAGE_RATIO" >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV + + REQUESTS_PROCESSING=$(cat requests_processing.mermaid) + echo "REQUESTS_PROCESSING<> $GITHUB_ENV + echo "$REQUESTS_PROCESSING" >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV + + - name: Extract image url + id: extract_image_url + continue-on-error: true + run: | + set -eux + + echo "IMAGE_O=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[0] }}" >> $GITHUB_ENV + echo "IMAGE_1=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[1] }}" >> $GITHUB_ENV + echo "IMAGE_2=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[2] }}" >> $GITHUB_ENV + echo "IMAGE_3=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[3] }}" >> $GITHUB_ENV + + - name: Comment PR + uses: mshick/add-pr-comment@v2 + id: comment_pr + if: ${{ github.event.pull_request != '' && matrix.pr_comment_enabled == 'true' }} + with: + message-id: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }} + message: | +

+ + 📈 **llama.cpp server** for _${{ github.job }}_ on _${{ env.RUNNER_LABEL }}_ for `${{ matrix.model }}`-`${{ matrix.ftype }}`: **${{ env.BENCH_ITERATIONS}} iterations** 🚀 + +

+ +
+ + Expand details for performance related PR only + + - Concurrent users: ${{ env.N_USERS }}, duration: ${{ github.event.inputs.duration || env.DURATION }} + - HTTP request : avg=${{ env.HTTP_REQ_DURATION_AVG }}ms p(95)=${{ env.HTTP_REQ_DURATION_P_95_ }}ms fails=${{ env.HTTP_REQ_FAILED_PASSES }}, finish reason: stop=${{ env.LLAMACPP_COMPLETIONS_STOP_RATE_PASSES }} truncated=${{ env.LLAMACPP_COMPLETIONS_TRUNCATED_RATE_PASSES }} + - Prompt processing (pp): avg=${{ env.LLAMACPP_PROMPT_PROCESSING_SECOND_AVG }}tk/s p(95)=${{ env.LLAMACPP_PROMPT_PROCESSING_SECOND_P_95_ }}tk/s + - Token generation (tg): avg=${{ env.LLAMACPP_TOKENS_SECOND_AVG }}tk/s p(95)=${{ env.LLAMACPP_TOKENS_SECOND_P_95_ }}tk/s + - ${{ env.BENCH_GRAPH_XLABEL }} + + +

+ + prompt_tokens_seconds + +

+ + More + + ```mermaid + ${{ env.PROMPT_TOKENS_SECONDS }} + ``` + +
+ + predicted_tokens_seconds + +
+ More + + ```mermaid + ${{ env.PREDICTED_TOKENS_SECONDS }} + ``` + +
+ +

+ +
+ + Details + +

+ + kv_cache_usage_ratio + +

+ More + + ```mermaid + ${{ env.KV_CACHE_USAGE_RATIO }} + ``` + +
+ + requests_processing + +
+ More + + ```mermaid + ${{ env.REQUESTS_PROCESSING }} + ``` + +
+ +

+
+
diff --git a/llama.cpp/.github/workflows/build-cmake-pkg.yml b/llama.cpp/.github/workflows/build-cmake-pkg.yml new file mode 100644 index 0000000000000000000000000000000000000000..1a67dcb90e64feb21b363fab23b83a988525fe65 --- /dev/null +++ b/llama.cpp/.github/workflows/build-cmake-pkg.yml @@ -0,0 +1,51 @@ +name: Build relocatable cmake package +on: + workflow_dispatch: + workflow_call: + +jobs: + linux: + runs-on: ubuntu-24.04 + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Install dependencies + run: | + sudo apt update + sudo apt install -y build-essential tcl + + - name: Build + run: | + PREFIX="$(pwd)"/inst + cmake -S . -B build -DCMAKE_PREFIX_PATH="$PREFIX" \ + -DLLAMA_CURL=OFF -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_TOOLS=OFF \ + -DLLAMA_BUILD_EXAMPLES=OFF -DCMAKE_BUILD_TYPE=Release + cmake --build build --config Release + cmake --install build --prefix "$PREFIX" --config Release + + export LLAMA_CONFIG="$PREFIX"/lib/cmake/llama/llama-config.cmake + tclsh <<'EOF' + set build(commit) [string trim [exec git rev-parse --short HEAD]] + set build(number) [string trim [exec git rev-list --count HEAD]] + set build(version) "0.0.$build(number)" + + set llamaconfig [read [open "$env(LLAMA_CONFIG)" r]] + set checks [list "set\\(LLAMA_VERSION \\s+$build(version)\\)" \ + "set\\(LLAMA_BUILD_COMMIT\\s+$build(commit)\\)" \ + "set\\(LLAMA_BUILD_NUMBER\\s+$build(number)\\)"] + + puts -nonewline "Checking llama-config.cmake version... " + foreach check $checks { + if {![regexp -expanded -- $check $llamaconfig]} { + puts "\"$check\" failed!" + exit 1 + } + } + puts "success." + EOF + + cd examples/simple-cmake-pkg + cmake -S . -B build -DCMAKE_PREFIX_PATH="$PREFIX"/lib/cmake + cmake --build build diff --git a/llama.cpp/.github/workflows/build-linux-cross.yml b/llama.cpp/.github/workflows/build-linux-cross.yml new file mode 100644 index 0000000000000000000000000000000000000000..ed6b0a342550267782525cceb903b5a9e8c8dc08 --- /dev/null +++ b/llama.cpp/.github/workflows/build-linux-cross.yml @@ -0,0 +1,346 @@ +name: Build on Linux using cross-compiler +on: + workflow_dispatch: + workflow_call: + +jobs: + ubuntu-24-riscv64-cpu-cross: + runs-on: ubuntu-24.04 + + steps: + - uses: actions/checkout@v4 + - name: Setup Riscv + run: | + sudo dpkg --add-architecture riscv64 + + # Add arch-specific repositories for non-amd64 architectures + cat << EOF | sudo tee /etc/apt/sources.list.d/riscv64-ports.list + deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe + deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe + deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe + deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe + EOF + + sudo apt-get update || true ;# Prevent failure due to missing URLs. + + sudo apt-get install -y --no-install-recommends \ + build-essential \ + gcc-14-riscv64-linux-gnu \ + g++-14-riscv64-linux-gnu + + - name: Build + run: | + cmake -B build -DLLAMA_CURL=OFF \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_OPENMP=OFF \ + -DLLAMA_BUILD_EXAMPLES=ON \ + -DLLAMA_BUILD_TOOLS=ON \ + -DLLAMA_BUILD_TESTS=OFF \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=riscv64 \ + -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \ + -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 \ + -DCMAKE_POSITION_INDEPENDENT_CODE=ON \ + -DCMAKE_FIND_ROOT_PATH=/usr/lib/riscv64-linux-gnu \ + -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \ + -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \ + -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH + + cmake --build build --config Release -j $(nproc) + + # ubuntu-24-riscv64-vulkan-cross: + # runs-on: ubuntu-24.04 + + # steps: + # - uses: actions/checkout@v4 + # - name: Setup Riscv + # run: | + # sudo dpkg --add-architecture riscv64 + + # # Add arch-specific repositories for non-amd64 architectures + # cat << EOF | sudo tee /etc/apt/sources.list.d/riscv64-ports.list + # deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe + # deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe + # deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe + # deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe + # EOF + + # sudo apt-get update || true ;# Prevent failure due to missing URLs. + + # sudo apt-get install -y --no-install-recommends \ + # build-essential \ + # glslc \ + # gcc-14-riscv64-linux-gnu \ + # g++-14-riscv64-linux-gnu \ + # libvulkan-dev:riscv64 + + # - name: Build + # run: | + # cmake -B build -DLLAMA_CURL=OFF \ + # -DCMAKE_BUILD_TYPE=Release \ + # -DGGML_VULKAN=ON \ + # -DGGML_OPENMP=OFF \ + # -DLLAMA_BUILD_EXAMPLES=ON \ + # -DLLAMA_BUILD_TOOLS=ON \ + # -DLLAMA_BUILD_TESTS=OFF \ + # -DCMAKE_SYSTEM_NAME=Linux \ + # -DCMAKE_SYSTEM_PROCESSOR=riscv64 \ + # -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \ + # -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 \ + # -DCMAKE_POSITION_INDEPENDENT_CODE=ON \ + # -DCMAKE_FIND_ROOT_PATH=/usr/lib/riscv64-linux-gnu \ + # -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \ + # -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \ + # -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH + + # cmake --build build --config Release -j $(nproc) + + # ubuntu-24-arm64-vulkan-cross: + # runs-on: ubuntu-24.04 + + # steps: + # - uses: actions/checkout@v4 + # - name: Setup Arm64 + # run: | + # sudo dpkg --add-architecture arm64 + + # # Add arch-specific repositories for non-amd64 architectures + # cat << EOF | sudo tee /etc/apt/sources.list.d/arm64-ports.list + # deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe + # deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe + # deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe + # deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe + # EOF + + # sudo apt-get update || true ;# Prevent failure due to missing URLs. + + # sudo apt-get install -y --no-install-recommends \ + # build-essential \ + # glslc \ + # crossbuild-essential-arm64 \ + # libvulkan-dev:arm64 + + # - name: Build + # run: | + # cmake -B build -DLLAMA_CURL=OFF \ + # -DCMAKE_BUILD_TYPE=Release \ + # -DGGML_VULKAN=ON \ + # -DGGML_OPENMP=OFF \ + # -DLLAMA_BUILD_EXAMPLES=ON \ + # -DLLAMA_BUILD_TOOLS=ON \ + # -DLLAMA_BUILD_TESTS=OFF \ + # -DCMAKE_SYSTEM_NAME=Linux \ + # -DCMAKE_SYSTEM_PROCESSOR=aarch64 \ + # -DCMAKE_C_COMPILER=aarch64-linux-gnu-gcc \ + # -DCMAKE_CXX_COMPILER=aarch64-linux-gnu-g++ \ + # -DCMAKE_POSITION_INDEPENDENT_CODE=ON \ + # -DCMAKE_FIND_ROOT_PATH=/usr/lib/aarch64-linux-gnu \ + # -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \ + # -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \ + # -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH + + # cmake --build build --config Release -j $(nproc) + + ubuntu-24-ppc64el-cpu-cross: + runs-on: ubuntu-24.04 + + steps: + - uses: actions/checkout@v4 + - name: Setup PowerPC64le + run: | + sudo dpkg --add-architecture ppc64el + + # Add arch-specific repositories for non-amd64 architectures + cat << EOF | sudo tee /etc/apt/sources.list.d/ppc64el-ports.list + deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble main universe + deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe + deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe + deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe + EOF + + sudo apt-get update || true ;# Prevent failure due to missing URLs. + + sudo apt-get install -y --no-install-recommends \ + build-essential \ + gcc-14-powerpc64le-linux-gnu \ + g++-14-powerpc64le-linux-gnu + + - name: Build + run: | + cmake -B build -DLLAMA_CURL=OFF \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_OPENMP=OFF \ + -DLLAMA_BUILD_EXAMPLES=ON \ + -DLLAMA_BUILD_TOOLS=ON \ + -DLLAMA_BUILD_TESTS=OFF \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=ppc64 \ + -DCMAKE_C_COMPILER=powerpc64le-linux-gnu-gcc-14 \ + -DCMAKE_CXX_COMPILER=powerpc64le-linux-gnu-g++-14 \ + -DCMAKE_POSITION_INDEPENDENT_CODE=ON \ + -DCMAKE_FIND_ROOT_PATH=/usr/lib/powerpc64le-linux-gnu \ + -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \ + -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \ + -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH + + cmake --build build --config Release -j $(nproc) + + # ubuntu-24-ppc64el-vulkan-cross: + # runs-on: ubuntu-24.04 + + # steps: + # - uses: actions/checkout@v4 + # - name: Setup PowerPC64le + # run: | + # sudo dpkg --add-architecture ppc64el + + # # Add arch-specific repositories for non-amd64 architectures + # cat << EOF | sudo tee /etc/apt/sources.list.d/ppc64el-ports.list + # deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble main universe + # deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe + # deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe + # deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe + # EOF + + # sudo apt-get update || true ;# Prevent failure due to missing URLs. + + # sudo apt-get install -y --no-install-recommends \ + # build-essential \ + # glslc \ + # gcc-14-powerpc64le-linux-gnu \ + # g++-14-powerpc64le-linux-gnu \ + # libvulkan-dev:ppc64el + + # - name: Build + # run: | + # cmake -B build -DLLAMA_CURL=OFF \ + # -DCMAKE_BUILD_TYPE=Release \ + # -DGGML_VULKAN=ON \ + # -DGGML_OPENMP=OFF \ + # -DLLAMA_BUILD_EXAMPLES=ON \ + # -DLLAMA_BUILD_TOOLS=ON \ + # -DLLAMA_BUILD_TESTS=OFF \ + # -DCMAKE_SYSTEM_NAME=Linux \ + # -DCMAKE_SYSTEM_PROCESSOR=ppc64 \ + # -DCMAKE_C_COMPILER=powerpc64le-linux-gnu-gcc-14 \ + # -DCMAKE_CXX_COMPILER=powerpc64le-linux-gnu-g++-14 \ + # -DCMAKE_POSITION_INDEPENDENT_CODE=ON \ + # -DCMAKE_FIND_ROOT_PATH=/usr/lib/powerpc64le-linux-gnu \ + # -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \ + # -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \ + # -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH + + # cmake --build build --config Release -j $(nproc) + + debian-13-loongarch64-cpu-cross: + runs-on: ubuntu-24.04 + container: debian@sha256:653dfb9f86c3782e8369d5f7d29bb8faba1f4bff9025db46e807fa4c22903671 + + steps: + - uses: actions/checkout@v4 + - name: Setup LoongArch + run: | + rm -f /etc/apt/sources.list.d/* + cat << EOF | tee /etc/apt/sources.list.d/debian-ports.list + deb http://snapshot.debian.org/archive/debian/20250515T202920Z/ trixie main + EOF + ( echo 'quiet "true";'; \ + echo 'APT::Get::Assume-Yes "true";'; \ + echo 'APT::Install-Recommends "false";'; \ + echo 'Acquire::Check-Valid-Until "false";'; \ + echo 'Acquire::Retries "5";'; \ + ) > /etc/apt/apt.conf.d/99snapshot-repos + + apt-get update + apt-get install -y ca-certificates debian-ports-archive-keyring cmake git zip + dpkg --add-architecture loong64 + + # Add arch-specific repositories for non-amd64 architectures + cat << EOF | tee /etc/apt/sources.list.d/loong64-ports.list + deb [arch=loong64] http://snapshot.debian.org/archive/debian-ports/20250515T194251Z/ sid main + EOF + + apt-get update || true ;# Prevent failure due to missing URLs. + + apt-get install -y --no-install-recommends \ + build-essential \ + gcc-14-loongarch64-linux-gnu \ + g++-14-loongarch64-linux-gnu + + - name: Build + run: | + cmake -B build -DLLAMA_CURL=OFF \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_OPENMP=OFF \ + -DLLAMA_BUILD_EXAMPLES=ON \ + -DLLAMA_BUILD_TOOLS=ON \ + -DLLAMA_BUILD_TESTS=OFF \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=loongarch64 \ + -DCMAKE_C_COMPILER=loongarch64-linux-gnu-gcc-14 \ + -DCMAKE_CXX_COMPILER=loongarch64-linux-gnu-g++-14 \ + -DCMAKE_POSITION_INDEPENDENT_CODE=ON \ + -DCMAKE_FIND_ROOT_PATH=/usr/lib/loongarch64-linux-gnu \ + -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \ + -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \ + -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH + + cmake --build build --config Release -j $(nproc) + + debian-13-loongarch64-vulkan-cross: + runs-on: ubuntu-24.04 + container: debian@sha256:653dfb9f86c3782e8369d5f7d29bb8faba1f4bff9025db46e807fa4c22903671 + + steps: + - uses: actions/checkout@v4 + - name: Setup LoongArch + run: | + rm -f /etc/apt/sources.list.d/* + cat << EOF | tee /etc/apt/sources.list.d/debian-ports.list + deb http://snapshot.debian.org/archive/debian/20250515T202920Z/ trixie main + EOF + ( echo 'quiet "true";'; \ + echo 'APT::Get::Assume-Yes "true";'; \ + echo 'APT::Install-Recommends "false";'; \ + echo 'Acquire::Check-Valid-Until "false";'; \ + echo 'Acquire::Retries "5";'; \ + ) > /etc/apt/apt.conf.d/99snapshot-repos + + apt-get update + apt-get install -y ca-certificates debian-ports-archive-keyring cmake git zip + dpkg --add-architecture loong64 + + # Add arch-specific repositories for non-amd64 architectures + cat << EOF | tee /etc/apt/sources.list.d/loong64-ports.list + deb [arch=loong64] http://snapshot.debian.org/archive/debian-ports/20250515T194251Z/ sid main + EOF + + apt-get update || true ;# Prevent failure due to missing URLs. + + apt-get install -y --no-install-recommends \ + build-essential \ + glslc \ + gcc-14-loongarch64-linux-gnu \ + g++-14-loongarch64-linux-gnu \ + libvulkan-dev:loong64 + + - name: Build + run: | + cmake -B build -DLLAMA_CURL=OFF \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_VULKAN=ON \ + -DGGML_OPENMP=OFF \ + -DLLAMA_BUILD_EXAMPLES=ON \ + -DLLAMA_BUILD_TOOLS=ON \ + -DLLAMA_BUILD_TESTS=OFF \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=loongarch64 \ + -DCMAKE_C_COMPILER=loongarch64-linux-gnu-gcc-14 \ + -DCMAKE_CXX_COMPILER=loongarch64-linux-gnu-g++-14 \ + -DCMAKE_POSITION_INDEPENDENT_CODE=ON \ + -DCMAKE_FIND_ROOT_PATH=/usr/lib/loongarch64-linux-gnu \ + -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \ + -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \ + -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH + + cmake --build build --config Release -j $(nproc) diff --git a/llama.cpp/.github/workflows/build.yml b/llama.cpp/.github/workflows/build.yml new file mode 100644 index 0000000000000000000000000000000000000000..12e3fae3dcde907436bb4db7791421200423ea2e --- /dev/null +++ b/llama.cpp/.github/workflows/build.yml @@ -0,0 +1,1245 @@ +name: CI + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: [ + '.github/workflows/build.yml', + '.github/workflows/build-linux-cross.yml', + '.github/workflows/build-cmake-pkg.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + '**/*.cu', + '**/*.cuh', + '**/*.swift', + '**/*.m', + '**/*.metal', + '**/*.comp' + ] + + pull_request: + types: [opened, synchronize, reopened] + paths: [ + '.github/workflows/build.yml', + '.github/workflows/build-linux-cross.yml', + '.github/workflows/build-cmake-pkg.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + '**/*.cu', + '**/*.cuh', + '**/*.swift', + '**/*.m', + '**/*.metal', + '**/*.comp' + ] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + LLAMA_LOG_COLORS: 1 + LLAMA_LOG_PREFIX: 1 + LLAMA_LOG_TIMESTAMPS: 1 + +jobs: + macOS-latest-cmake-arm64: + runs-on: macos-14 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-arm64 + evict-old-files: 1d + + - name: Dependencies + id: depends + continue-on-error: true + run: | + brew update + brew install curl + + - name: Build + id: cmake_build + run: | + sysctl -a + cmake -B build \ + -DCMAKE_BUILD_RPATH="@loader_path" \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=OFF \ + -DGGML_METAL_SHADER_DEBUG=ON \ + -DGGML_RPC=ON + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L 'main|curl' --verbose --timeout 900 + + macOS-latest-cmake-x64: + runs-on: macos-13 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-x64 + evict-old-files: 1d + + - name: Dependencies + id: depends + continue-on-error: true + run: | + brew update + brew install curl + + - name: Build + id: cmake_build + run: | + sysctl -a + # Metal is disabled due to intermittent failures with Github runners not having a GPU: + # https://github.com/ggml-org/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313 + cmake -B build \ + -DCMAKE_BUILD_RPATH="@loader_path" \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DGGML_METAL=OFF \ + -DGGML_RPC=ON + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L main --verbose --timeout 900 + + macOS-latest-cmake-arm64-webgpu: + runs-on: macos-14 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-arm64-webgpu + evict-old-files: 1d + + - name: Dependencies + id: depends + continue-on-error: true + run: | + brew update + brew install curl + + - name: Dawn Dependency + id: dawn-depends + run: | + ARTIFACTS_JSON=$(curl -s -L \ + -H "Accept: application/vnd.github+json" \ + -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \ + -H "X-GitHub-Api-Version: 2022-11-28" \ + "https://api.github.com/repos/google/dawn/actions/artifacts") + echo "Finding latest macos-latest-Release artifact..." + DOWNLOAD_URL=$(echo "$ARTIFACTS_JSON" | jq -r '.artifacts + | sort_by(.created_at) + | reverse + | map(select(.name | test("macos-latest-Release$"))) + | .[0].archive_download_url') + if [ "$DOWNLOAD_URL" = "null" ] || [ -z "$DOWNLOAD_URL" ]; then + echo "No suitable Dawn artifact found!" + exit 1 + fi + echo "Downloading from: $DOWNLOAD_URL" + curl -L \ + -H "Accept: application/vnd.github+json" \ + -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \ + -o artifact.zip "$DOWNLOAD_URL" + unzip artifact.zip + mkdir dawn + tar_file=$(find . -name '*.tar.gz' | head -n 1) + echo "Extracting: $tar_file" + tar -xvf "$tar_file" -C dawn --strip-components=1 + + - name: Build + id: cmake_build + run: | + export CMAKE_PREFIX_PATH=dawn + cmake -B build -DGGML_WEBGPU=ON -DGGML_METAL=OFF -DGGML_BLAS=OFF + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L main --verbose --timeout 900 + + ubuntu-cpu-cmake: + strategy: + matrix: + include: + - build: 'x64' + os: ubuntu-22.04 + - build: 'arm64' + os: ubuntu-22.04-arm + + runs-on: ${{ matrix.os }} + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-cpu-cmake + evict-old-files: 1d + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential libcurl4-openssl-dev + + - name: Build + id: cmake_build + run: | + cmake -B build \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DGGML_RPC=ON + cmake --build build --config Release -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L 'main|curl' --verbose --timeout 900 + + - name: Test llama2c conversion + id: llama2c_test + run: | + cd build + echo "Fetch tokenizer" + wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/tok512.bin + echo "Fetch llama2c model" + wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin + ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf + ./bin/llama-cli -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 + + ubuntu-latest-cmake-sanitizer: + runs-on: ubuntu-latest + + continue-on-error: true + + strategy: + matrix: + sanitizer: [ADDRESS, THREAD, UNDEFINED] + build_type: [Debug] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-latest-cmake-sanitizer-${{ matrix.sanitizer }} + evict-old-files: 1d + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential libcurl4-openssl-dev + + - name: Build + id: cmake_build + if: ${{ matrix.sanitizer != 'THREAD' }} + run: | + cmake -B build \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) + + - name: Build (no OpenMP) + id: cmake_build_no_openmp + if: ${{ matrix.sanitizer == 'THREAD' }} + run: | + cmake -B build \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ + -DGGML_OPENMP=OFF + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L main --verbose --timeout 900 + + ubuntu-latest-llguidance: + runs-on: ubuntu-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential libcurl4-openssl-dev + + - name: Build + id: cmake_build + run: | + mkdir build + cd build + cmake .. \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_LLGUIDANCE=ON + cmake --build . --config Release -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L main --verbose --timeout 900 + + ubuntu-latest-cmake-rpc: + runs-on: ubuntu-latest + + continue-on-error: true + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-latest-cmake-rpc + evict-old-files: 1d + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential libcurl4-openssl-dev + + - name: Build + id: cmake_build + run: | + cmake -B build \ + -DGGML_RPC=ON + cmake --build build --config Release -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L main --verbose + + ubuntu-22-cmake-vulkan: + runs-on: ubuntu-22.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-vulkan + evict-old-files: 1d + + - name: Dependencies + id: depends + run: | + wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add - + sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list + sudo apt-get update -y + sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk libcurl4-openssl-dev + + - name: Build + id: cmake_build + run: | + cmake -B build \ + -DGGML_VULKAN=ON + cmake --build build --config Release -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + export GGML_VK_VISIBLE_DEVICES=0 + # This is using llvmpipe and runs slower than other backends + ctest -L main --verbose --timeout 4200 + + ubuntu-22-cmake-webgpu: + runs-on: ubuntu-22.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-webgpu + evict-old-files: 1d + + - name: Vulkan SDK Dependencies + id: vulkan-depends + run: | + wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add - + sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list + sudo apt-get update -y + sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk libcurl4-openssl-dev + + - name: Dawn Dependency + id: dawn-depends + run: | + sudo apt-get install -y libxrandr-dev libxinerama-dev libxcursor-dev mesa-common-dev libx11-xcb-dev libxi-dev + ARTIFACTS_JSON=$(curl -s -L \ + -H "Accept: application/vnd.github+json" \ + -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \ + -H "X-GitHub-Api-Version: 2022-11-28" \ + "https://api.github.com/repos/google/dawn/actions/artifacts") + echo "Finding latest ubuntu-latest-Release artifact..." + DOWNLOAD_URL=$(echo "$ARTIFACTS_JSON" | jq -r '.artifacts + | sort_by(.created_at) + | reverse + | map(select(.name | test("ubuntu-latest-Release$"))) + | .[0].archive_download_url') + if [ "$DOWNLOAD_URL" = "null" ] || [ -z "$DOWNLOAD_URL" ]; then + echo "No suitable Dawn artifact found!" + exit 1 + fi + echo "Downloading from: $DOWNLOAD_URL" + curl -L \ + -H "Accept: application/vnd.github+json" \ + -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \ + -o artifact.zip "$DOWNLOAD_URL" + unzip artifact.zip + mkdir dawn + tar_file=$(find . -name '*.tar.gz' | head -n 1) + echo "Extracting: $tar_file" + tar -xvf "$tar_file" -C dawn --strip-components=1 + + - name: Build + id: cmake_build + run: | + export Dawn_DIR=dawn/lib64/cmake/Dawn + cmake -B build -DGGML_WEBGPU=ON + cmake --build build --config Release -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + # This is using llvmpipe and runs slower than other backends + ctest -L main --verbose --timeout 3600 + + ubuntu-22-cmake-hip: + runs-on: ubuntu-22.04 + container: rocm/dev-ubuntu-22.04:6.0.2 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev libcurl4-openssl-dev + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-hip + evict-old-files: 1d + + - name: Build with native CMake HIP support + id: cmake_build + run: | + cmake -B build -S . \ + -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \ + -DGGML_HIP_ROCWMMA_FATTN=ON \ + -DGGML_HIP=ON + cmake --build build --config Release -j $(nproc) + + - name: Build with legacy HIP support + id: cmake_build_legacy_hip + run: | + cmake -B build2 -S . \ + -DCMAKE_C_COMPILER=hipcc \ + -DCMAKE_CXX_COMPILER=hipcc \ + -DGGML_HIP_ROCWMMA_FATTN=ON \ + -DGGML_HIP=ON + cmake --build build2 --config Release -j $(nproc) + + ubuntu-22-cmake-musa: + runs-on: ubuntu-22.04 + container: mthreads/musa:rc4.2.0-devel-ubuntu22.04-amd64 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Dependencies + id: depends + run: | + apt-get update + apt-get install -y build-essential git cmake libcurl4-openssl-dev + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-musa + evict-old-files: 1d + + - name: Build with native CMake MUSA support + id: cmake_build + run: | + cmake -B build -S . \ + -DGGML_MUSA=ON + cmake --build build --config Release -j $(nproc) + + ubuntu-22-cmake-sycl: + runs-on: ubuntu-22.04 + + continue-on-error: true + + steps: + - uses: actions/checkout@v4 + + - name: add oneAPI to apt + shell: bash + run: | + cd /tmp + wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main" + + - name: install oneAPI dpcpp compiler + shell: bash + run: | + sudo apt update + sudo apt install intel-oneapi-compiler-dpcpp-cpp libcurl4-openssl-dev + + - name: install oneAPI MKL library + shell: bash + run: | + sudo apt install intel-oneapi-mkl-devel + + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-sycl + evict-old-files: 1d + + - name: Build + id: cmake_build + run: | + source /opt/intel/oneapi/setvars.sh + cmake -B build \ + -DGGML_SYCL=ON \ + -DCMAKE_C_COMPILER=icx \ + -DCMAKE_CXX_COMPILER=icpx + cmake --build build --config Release -j $(nproc) + + ubuntu-22-cmake-sycl-fp16: + runs-on: ubuntu-22.04 + + continue-on-error: true + + steps: + - uses: actions/checkout@v4 + + - name: add oneAPI to apt + shell: bash + run: | + cd /tmp + wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main" + + - name: install oneAPI dpcpp compiler + shell: bash + run: | + sudo apt update + sudo apt install intel-oneapi-compiler-dpcpp-cpp libcurl4-openssl-dev + + - name: install oneAPI MKL library + shell: bash + run: | + sudo apt install intel-oneapi-mkl-devel + + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-sycl-fp16 + evict-old-files: 1d + + - name: Build + id: cmake_build + run: | + source /opt/intel/oneapi/setvars.sh + cmake -B build \ + -DGGML_SYCL=ON \ + -DCMAKE_C_COMPILER=icx \ + -DCMAKE_CXX_COMPILER=icpx \ + -DGGML_SYCL_F16=ON + cmake --build build --config Release -j $(nproc) + + build-linux-cross: + uses: ./.github/workflows/build-linux-cross.yml + + build-cmake-pkg: + uses: ./.github/workflows/build-cmake-pkg.yml + + macOS-latest-cmake-ios: + runs-on: macos-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-ios + evict-old-files: 1d + + - name: Dependencies + id: depends + continue-on-error: true + run: | + brew update + + - name: Build + id: cmake_build + run: | + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_BUILD_COMMON=OFF \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_SYSTEM_NAME=iOS \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ + -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + + macOS-latest-cmake-tvos: + runs-on: macos-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-tvos + evict-old-files: 1d + + - name: Dependencies + id: depends + continue-on-error: true + run: | + brew update + + - name: Build + id: cmake_build + run: | + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_BUILD_COMMON=OFF \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_SYSTEM_NAME=tvOS \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ + -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + + macOS-latest-cmake-visionos: + runs-on: macos-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Dependencies + id: depends + continue-on-error: true + run: | + brew update + + - name: Build + id: cmake_build + run: | + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_BUILD_COMMON=OFF \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_SYSTEM_NAME=visionOS \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=1.0 \ + -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + + macOS-latest-swift: + runs-on: macos-latest + + strategy: + matrix: + destination: ['generic/platform=macOS', 'generic/platform=iOS', 'generic/platform=tvOS'] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-swift + evict-old-files: 1d + + - name: Dependencies + id: depends + continue-on-error: true + run: | + brew update + + - name: Build llama.cpp with CMake + id: cmake_build + run: | + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_CURL=OFF \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) + + - name: xcodebuild for swift package + id: xcodebuild + run: | + ./build-xcframework.sh + + windows-msys2: + runs-on: windows-2025 + + strategy: + fail-fast: false + matrix: + include: + - { sys: UCRT64, env: ucrt-x86_64, build: Release } + - { sys: CLANG64, env: clang-x86_64, build: Release } + + steps: + - name: Clone + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-msys2 + variant: ccache + evict-old-files: 1d + + - name: Setup ${{ matrix.sys }} + uses: msys2/setup-msys2@v2 + with: + update: true + msystem: ${{matrix.sys}} + install: >- + base-devel + git + mingw-w64-${{matrix.env}}-toolchain + mingw-w64-${{matrix.env}}-cmake + mingw-w64-${{matrix.env}}-openblas + + - name: Build using CMake + shell: msys2 {0} + run: | + cmake -B build + cmake --build build --config ${{ matrix.build }} -j $(nproc) + + - name: Clean after building using CMake + shell: msys2 {0} + run: | + rm -rf build + + - name: Build using CMake w/ OpenBLAS + shell: msys2 {0} + run: | + cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS + cmake --build build --config ${{ matrix.build }} -j $(nproc) + + windows-latest-cmake: + runs-on: windows-2025 + + env: + OPENBLAS_VERSION: 0.3.23 + SDE_VERSION: 9.33.0-2024-01-07 + VULKAN_VERSION: 1.4.313.2 + + strategy: + matrix: + include: + - build: 'cpu-x64 (static)' + arch: 'x64' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF' + - build: 'openblas-x64' + arch: 'x64' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' + - build: 'vulkan-x64' + arch: 'x64' + defines: '-DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_VULKAN=ON' + - build: 'llvm-arm64' + arch: 'arm64' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON' + - build: 'llvm-arm64-opencl-adreno' + arch: 'arm64' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON' + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-latest-cmake-${{ matrix.build }} + variant: ccache + evict-old-files: 1d + + - name: Download OpenBLAS + id: get_openblas + if: ${{ matrix.build == 'openblas-x64' }} + run: | + curl.exe -o $env:RUNNER_TEMP/openblas.zip -L "https://github.com/xianyi/OpenBLAS/releases/download/v${env:OPENBLAS_VERSION}/OpenBLAS-${env:OPENBLAS_VERSION}-x64.zip" + curl.exe -o $env:RUNNER_TEMP/OpenBLAS.LICENSE.txt -L "https://github.com/xianyi/OpenBLAS/raw/v${env:OPENBLAS_VERSION}/LICENSE" + mkdir $env:RUNNER_TEMP/openblas + tar.exe -xvf $env:RUNNER_TEMP/openblas.zip -C $env:RUNNER_TEMP/openblas + $vcdir = $(vswhere -latest -products * -requires Microsoft.VisualStudio.Component.VC.Tools.x86.x64 -property installationPath) + $msvc = $(join-path $vcdir $('VC\Tools\MSVC\'+$(gc -raw $(join-path $vcdir 'VC\Auxiliary\Build\Microsoft.VCToolsVersion.default.txt')).Trim())) + $lib = $(join-path $msvc 'bin\Hostx64\x64\lib.exe') + & $lib /machine:x64 "/def:${env:RUNNER_TEMP}/openblas/lib/libopenblas.def" "/out:${env:RUNNER_TEMP}/openblas/lib/openblas.lib" /name:openblas.dll + + - name: Install Vulkan SDK + id: get_vulkan + if: ${{ matrix.build == 'vulkan-x64' }} + run: | + curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/vulkansdk-windows-X64-${env:VULKAN_VERSION}.exe" + & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install + Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}" + Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin" + + - name: Install Ninja + id: install_ninja + run: | + choco install ninja + + - name: Install OpenCL Headers and Libs + id: install_opencl + if: ${{ matrix.build == 'llvm-arm64-opencl-adreno' }} + run: | + git clone https://github.com/KhronosGroup/OpenCL-Headers + cd OpenCL-Headers + cmake -B build ` + -DBUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF ` + -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release" + cmake --build build --target install + git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader + cd OpenCL-ICD-Loader + cmake -B build-arm64-release ` + -A arm64 ` + -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" ` + -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release" + cmake --build build-arm64-release --target install --config release + + - name: libCURL + id: get_libcurl + uses: ./.github/actions/windows-setup-curl + with: + architecture: ${{ matrix.arch == 'x64' && 'win64' || 'win64a' }} + + - name: Build + id: cmake_build + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} + run: | + cmake -S . -B build ${{ matrix.defines }} ` + -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include" + cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} + cp $env:CURL_PATH/bin/libcurl-*.dll build/bin/Release + + - name: Add libopenblas.dll + id: add_libopenblas_dll + if: ${{ matrix.build == 'openblas-x64' }} + run: | + cp $env:RUNNER_TEMP/openblas/bin/libopenblas.dll ./build/bin/Release/openblas.dll + cp $env:RUNNER_TEMP/OpenBLAS.LICENSE.txt ./build/bin/Release/OpenBLAS-${env:OPENBLAS_VERSION}.txt + + - name: Test + id: cmake_test + if: ${{ matrix.arch == 'x64' }} + run: | + cd build + ctest -L main -C Release --verbose --timeout 900 + + # TODO: disabled for now, consider adding tests for all CPU variants instead + # - name: Test (Intel SDE) + # id: cmake_test_sde + # if: ${{ matrix.build == 'avx512-x64' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation + # run: | + # curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L "https://downloadmirror.intel.com/813591/sde-external-${env:SDE_VERSION}-win.tar.xz" + # # for some weird reason windows tar doesn't like sde tar.xz + # 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar.xz + # 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar + # $sde = $(join-path $env:RUNNER_TEMP sde-external-${env:SDE_VERSION}-win/sde.exe) + # cd build + # $env:LLAMA_SKIP_TESTS_SLOW_ON_EMULATOR = 1 + # & $sde -future -- ctest -L main -C Release --verbose --timeout 900 + + ubuntu-latest-cmake-cuda: + runs-on: ubuntu-latest + container: nvidia/cuda:12.6.2-devel-ubuntu24.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Install dependencies + env: + DEBIAN_FRONTEND: noninteractive + run: | + apt update + apt install -y cmake build-essential ninja-build libgomp1 git libcurl4-openssl-dev + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-latest-cmake-cuda + evict-old-files: 1d + + - name: Build with CMake + run: | + cmake -S . -B build -G Ninja \ + -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_CUDA_ARCHITECTURES=89-real \ + -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DGGML_NATIVE=OFF \ + -DGGML_CUDA=ON + cmake --build build + + windows-2022-cmake-cuda: + runs-on: windows-2022 + + strategy: + matrix: + cuda: ['12.4'] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Install ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-cuda-${{ matrix.cuda }} + variant: ccache + evict-old-files: 1d + + - name: Install Cuda Toolkit + uses: ./.github/actions/windows-setup-cuda + with: + cuda_version: ${{ matrix.cuda }} + + - name: Install Ninja + id: install_ninja + run: | + choco install ninja + + - name: libCURL + id: get_libcurl + uses: ./.github/actions/windows-setup-curl + + - name: Build + id: cmake_build + shell: cmd + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} + run: | + call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64 + cmake -S . -B build -G "Ninja Multi-Config" ^ + -DLLAMA_BUILD_SERVER=ON ^ + -DGGML_NATIVE=OFF ^ + -DGGML_BACKEND_DL=ON ^ + -DGGML_CPU_ALL_VARIANTS=ON ^ + -DGGML_CUDA=ON ^ + -DGGML_RPC=ON ^ + -DCURL_LIBRARY="%CURL_PATH%/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="%CURL_PATH%/include" + set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1 + cmake --build build --config Release -j %NINJA_JOBS% -t ggml + cmake --build build --config Release + + windows-latest-cmake-sycl: + runs-on: windows-2022 + + defaults: + run: + shell: bash + + env: + WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/7cd9bba0-7aab-4e30-b3ae-2221006a4a05/intel-oneapi-base-toolkit-2025.1.1.34_offline.exe + WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel + ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI" + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-latest-cmake-sycl + variant: ccache + evict-old-files: 1d + + - name: Install + run: | + scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL + + # TODO: add libcurl support ; we will also need to modify win-build-sycl.bat to accept user-specified args + + - name: Build + id: cmake_build + run: examples/sycl/win-build-sycl.bat + + windows-latest-cmake-hip: + if: ${{ github.event.inputs.create_release != 'true' }} + runs-on: windows-2022 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Clone rocWMMA repository + id: clone_rocwmma + run: | + git clone https://github.com/rocm/rocwmma --branch rocm-6.2.4 --depth 1 + + - name: Install + id: depends + run: | + $ErrorActionPreference = "Stop" + write-host "Downloading AMD HIP SDK Installer" + Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe" + write-host "Installing AMD HIP SDK" + Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait + write-host "Completed AMD HIP SDK installation" + + - name: Verify ROCm + id: verify + run: | + & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version + + - name: Install ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ${{ github.job }} + evict-old-files: 1d + + - name: libCURL + id: get_libcurl + uses: ./.github/actions/windows-setup-curl + + - name: Build + id: cmake_build + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} + run: | + $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path) + $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}" + cmake -G "Unix Makefiles" -B build -S . ` + -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" ` + -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" ` + -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/rocwmma/library/include/" ` + -DCMAKE_BUILD_TYPE=Release ` + -DGGML_HIP=ON ` + -DGGML_HIP_ROCWMMA_FATTN=ON ` + -DGGML_RPC=ON ` + -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include" + cmake --build build -j ${env:NUMBER_OF_PROCESSORS} + + ios-xcode-build: + runs-on: macos-latest + + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Build + id: cmake_build + run: | + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_CURL=OFF \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_SYSTEM_NAME=iOS \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ + -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + + - name: xcodebuild for swift package + id: xcodebuild + run: | + ./build-xcframework.sh + + - name: Build Xcode project + run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' FRAMEWORK_FOLDER_PATH=./build-ios build + + android-build: + runs-on: ubuntu-latest + + steps: + - name: Clone + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: android-build + evict-old-files: 1d + + - name: Set up JDK + uses: actions/setup-java@v3 + with: + java-version: 17 + distribution: zulu + + - name: Setup Android SDK + uses: android-actions/setup-android@v3 + with: + log-accepted-android-sdk-licenses: false + + - name: Build + run: | + cd examples/llama.android + ./gradlew build --no-daemon + + openEuler-latest-cmake-cann: + if: ${{ github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'Ascend NPU') }} + defaults: + run: + shell: bash -el {0} + strategy: + matrix: + arch: [x86, aarch64] + cann: + - '8.1.RC1.alpha001-910b-openeuler22.03-py3.10' + device: + - 'ascend910b3' + build: + - 'Release' + runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }} + container: ascendai/cann:${{ matrix.cann }} + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Dependencies + run: | + yum update -y + yum install -y git gcc gcc-c++ make cmake libcurl-devel + + - name: Build + run: | + export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH} + + cmake -S . -B build \ + -DCMAKE_BUILD_TYPE=${{ matrix.build }} \ + -DGGML_CANN=on \ + -DSOC_TYPE=${{ matrix.device }} + cmake --build build -j $(nproc) diff --git a/llama.cpp/.github/workflows/close-issue.yml b/llama.cpp/.github/workflows/close-issue.yml new file mode 100644 index 0000000000000000000000000000000000000000..54c745b036d1824e8604bad6151856b671456261 --- /dev/null +++ b/llama.cpp/.github/workflows/close-issue.yml @@ -0,0 +1,28 @@ +name: Close inactive issues +on: + schedule: + - cron: "42 0 * * *" + +# Fine-grant permission +# https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token +permissions: + issues: write + +jobs: + close-issues: + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + steps: + - uses: actions/stale@v5 + with: + exempt-issue-labels: "refactoring,help wanted,good first issue,research,bug,roadmap" + days-before-issue-stale: 30 + days-before-issue-close: 14 + stale-issue-label: "stale" + close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale." + days-before-pr-stale: -1 + days-before-pr-close: -1 + operations-per-run: 10000 + repo-token: ${{ secrets.GITHUB_TOKEN }} diff --git a/llama.cpp/.github/workflows/docker.yml b/llama.cpp/.github/workflows/docker.yml new file mode 100644 index 0000000000000000000000000000000000000000..dbf5e04145396147d666201c2d386d5b886f0586 --- /dev/null +++ b/llama.cpp/.github/workflows/docker.yml @@ -0,0 +1,178 @@ +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +# GitHub recommends pinning actions to a commit SHA. +# To get a newer version, you will need to update the SHA. +# You can also reference a tag or branch, but the action may change without warning. + +name: Publish Docker image + +on: + workflow_dispatch: # allows manual triggering + schedule: + # Rebuild daily rather than on every push because it is expensive + - cron: '12 4 * * *' + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +# Fine-grant permission +# https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token +permissions: + packages: write + +jobs: + push_to_registry: + name: Push Docker image to Docker Hub + + runs-on: ubuntu-22.04 + env: + COMMIT_SHA: ${{ github.sha }} + strategy: + fail-fast: false + matrix: + config: + # Multi-stage build + # Note: the arm64 images are failing, which prevents the amd64 images from being built + # https://github.com/ggml-org/llama.cpp/issues/11888 + #- { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: false } + - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false } + - { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false } + - { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true } + - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true } + - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false } + # Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete + #- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: true } + steps: + - name: Check out the repo + uses: actions/checkout@v4 + with: + fetch-depth: 0 # preserve git history, so we can determine the build number + + - name: Set up QEMU + uses: docker/setup-qemu-action@v3 + with: + image: tonistiigi/binfmt:qemu-v7.0.0-28 + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: Log in to Docker Hub + uses: docker/login-action@v2 + with: + registry: ghcr.io + username: ${{ github.repository_owner }} + password: ${{ secrets.GITHUB_TOKEN }} + + - name: Determine tag name + id: tag + shell: bash + run: | + BUILD_NUMBER="$(git rev-list --count HEAD)" + SHORT_HASH="$(git rev-parse --short=7 HEAD)" + REPO_OWNER="${GITHUB_REPOSITORY_OWNER@L}" # to lower case + REPO_NAME="${{ github.event.repository.name }}" + + # determine tag name postfix (build number, commit hash) + if [[ "${{ env.GITHUB_BRANCH_NAME }}" == "master" ]]; then + TAG_POSTFIX="-b${BUILD_NUMBER}" + else + SAFE_NAME=$(echo "${{ env.GITHUB_BRANCH_NAME }}" | tr '/' '-') + TAG_POSTFIX="-${SAFE_NAME}-${SHORT_HASH}" + fi + # list all tags possible + if [[ "${{ matrix.config.tag }}" == "cpu" ]]; then + TYPE="" + else + TYPE="-${{ matrix.config.tag }}" + fi + PREFIX="ghcr.io/${REPO_OWNER}/${REPO_NAME}:" + FULLTAGS="${PREFIX}full${TYPE},${PREFIX}full${TYPE}${TAG_POSTFIX}" + LIGHTTAGS="${PREFIX}light${TYPE},${PREFIX}light${TYPE}${TAG_POSTFIX}" + SERVERTAGS="${PREFIX}server${TYPE},${PREFIX}server${TYPE}${TAG_POSTFIX}" + echo "full_output_tags=$FULLTAGS" >> $GITHUB_OUTPUT + echo "light_output_tags=$LIGHTTAGS" >> $GITHUB_OUTPUT + echo "server_output_tags=$SERVERTAGS" >> $GITHUB_OUTPUT + echo "full_output_tags=$FULLTAGS" # print out for debugging + echo "light_output_tags=$LIGHTTAGS" # print out for debugging + echo "server_output_tags=$SERVERTAGS" # print out for debugging + env: + GITHUB_BRANCH_NAME: ${{ github.head_ref || github.ref_name }} + GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}' + + - name: Free Disk Space (Ubuntu) + if: ${{ matrix.config.free_disk_space == true }} + uses: ggml-org/free-disk-space@v1.3.1 + with: + # this might remove tools that are actually needed, + # if set to "true" but frees about 6 GB + tool-cache: false + + # all of these default to true, but feel free to set to + # "false" if necessary for your workflow + android: true + dotnet: true + haskell: true + large-packages: true + docker-images: true + swap-storage: true + + - name: Build and push Full Docker image (tagged + versioned) + if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.full == true }} + uses: docker/build-push-action@v6 + with: + context: . + push: true + platforms: ${{ matrix.config.platforms }} + # tag list is generated from step above + tags: ${{ steps.tag.outputs.full_output_tags }} + file: ${{ matrix.config.dockerfile }} + target: full + provenance: false + # using github experimental cache + cache-from: type=gha + cache-to: type=gha,mode=max + # return to this if the experimental github cache is having issues + #cache-to: type=local,dest=/tmp/.buildx-cache + #cache-from: type=local,src=/tmp/.buildx-cache + + - name: Build and push Light Docker image (tagged + versioned) + if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.light == true }} + uses: docker/build-push-action@v6 + with: + context: . + push: true + platforms: ${{ matrix.config.platforms }} + # tag list is generated from step above + tags: ${{ steps.tag.outputs.light_output_tags }} + file: ${{ matrix.config.dockerfile }} + target: light + provenance: false + # using github experimental cache + cache-from: type=gha + cache-to: type=gha,mode=max + # return to this if the experimental github cache is having issues + #cache-to: type=local,dest=/tmp/.buildx-cache + #cache-from: type=local,src=/tmp/.buildx-cache + + - name: Build and push Server Docker image (tagged + versioned) + if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.server == true }} + uses: docker/build-push-action@v6 + with: + context: . + push: true + platforms: ${{ matrix.config.platforms }} + # tag list is generated from step above + tags: ${{ steps.tag.outputs.server_output_tags }} + file: ${{ matrix.config.dockerfile }} + target: server + provenance: false + # using github experimental cache + cache-from: type=gha + cache-to: type=gha,mode=max + # return to this if the experimental github cache is having issues + #cache-to: type=local,dest=/tmp/.buildx-cache + #cache-from: type=local,src=/tmp/.buildx-cache diff --git a/llama.cpp/.github/workflows/editorconfig.yml b/llama.cpp/.github/workflows/editorconfig.yml new file mode 100644 index 0000000000000000000000000000000000000000..e2f74091c81b4105da3301d8004c96c357cc02b4 --- /dev/null +++ b/llama.cpp/.github/workflows/editorconfig.yml @@ -0,0 +1,29 @@ +name: EditorConfig Checker + +on: + workflow_dispatch: # allows manual triggering + inputs: + create_release: + description: 'Create new release' + required: true + type: boolean + push: + branches: + - master + pull_request: + branches: + - master + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +jobs: + editorconfig: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: editorconfig-checker/action-editorconfig-checker@v2 + with: + version: v3.0.3 + - run: editorconfig-checker diff --git a/llama.cpp/.github/workflows/gguf-publish.yml b/llama.cpp/.github/workflows/gguf-publish.yml new file mode 100644 index 0000000000000000000000000000000000000000..73854a5b0a1e746b50801bf07c313cd269d0fcfc --- /dev/null +++ b/llama.cpp/.github/workflows/gguf-publish.yml @@ -0,0 +1,44 @@ +# This workflow will upload a Python Package using Twine when a GGUF release is created +# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries + +# See `gguf-py/README.md` for how to make a release. + +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +name: Upload Python Package + +on: + workflow_dispatch: + push: + # Pattern matched against refs/tags + tags: + - 'gguf-v*' # Push events to every version tag + + +jobs: + deploy: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.9.x' + - name: Install dependencies + run: | + cd gguf-py + python -m pip install poetry + poetry install + + - name: Build package + run: cd gguf-py && poetry build + - name: Publish package + uses: pypa/gh-action-pypi-publish@release/v1 + with: + password: ${{ secrets.PYPI_API_TOKEN }} + packages-dir: gguf-py/dist diff --git a/llama.cpp/.github/workflows/labeler.yml b/llama.cpp/.github/workflows/labeler.yml new file mode 100644 index 0000000000000000000000000000000000000000..fda98b76756a66e58521bd84292f3b4f7b0db457 --- /dev/null +++ b/llama.cpp/.github/workflows/labeler.yml @@ -0,0 +1,17 @@ +name: "Pull Request Labeler" +on: +- pull_request_target + +jobs: + labeler: + permissions: + contents: read + pull-requests: write + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + repository: "ggml-org/llama.cpp" + - uses: actions/labeler@v5 + with: + configuration-path: '.github/labeler.yml' diff --git a/llama.cpp/.github/workflows/pre-tokenizer-hashes.yml b/llama.cpp/.github/workflows/pre-tokenizer-hashes.yml new file mode 100644 index 0000000000000000000000000000000000000000..ed1efdb120828666001f5e5f50aefed4ac9867ca --- /dev/null +++ b/llama.cpp/.github/workflows/pre-tokenizer-hashes.yml @@ -0,0 +1,45 @@ +name: Check Pre-Tokenizer Hashes + +on: + push: + paths: + - 'convert_hf_to_gguf.py' + - 'convert_hf_to_gguf_update.py' + pull_request: + paths: + - 'convert_hf_to_gguf.py' + - 'convert_hf_to_gguf_update.py' + +jobs: + pre-tokenizer-hashes: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install Python dependencies + run: | + python3 -m venv .venv + .venv/bin/pip install -r requirements/requirements-convert_hf_to_gguf_update.txt + + - name: Update pre-tokenizer hashes + run: | + cp convert_hf_to_gguf.py /tmp + .venv/bin/python convert_hf_to_gguf_update.py --check-missing + + - name: Check if committed pre-tokenizer hashes matches generated version + run: | + if ! diff -q convert_hf_to_gguf.py /tmp/convert_hf_to_gguf.py; then + echo "Model pre-tokenizer hashes (in convert_hf_to_gguf.py) do not match generated hashes (from convert_hf_to_gguf_update.py)." + echo "To fix: run ./convert_hf_to_gguf_update.py and commit the updated convert_hf_to_gguf.py along with your changes" + echo "Differences found:" + diff convert_hf_to_gguf.py /tmp/convert_hf_to_gguf.py || true + exit 1 + fi + echo "Model pre-tokenizer hashes are up to date." diff --git a/llama.cpp/.github/workflows/python-check-requirements.yml b/llama.cpp/.github/workflows/python-check-requirements.yml new file mode 100644 index 0000000000000000000000000000000000000000..798f6578ccf62afecd8a228fa6eeb126c3d7f98c --- /dev/null +++ b/llama.cpp/.github/workflows/python-check-requirements.yml @@ -0,0 +1,33 @@ +name: Python check requirements.txt + +on: + push: + paths: + - '.github/workflows/python-check-requirements.yml' + - 'scripts/check-requirements.sh' + - 'convert*.py' + - '**/requirements*.txt' + pull_request: + paths: + - '.github/workflows/python-check-requirements.yml' + - 'scripts/check-requirements.sh' + - 'convert*.py' + - '**/requirements*.txt' + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +jobs: + python-check-requirements: + runs-on: ubuntu-latest + name: check-requirements + steps: + - name: Check out source repository + uses: actions/checkout@v4 + - name: Set up Python environment + uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Run check-requirements.sh script + run: bash scripts/check-requirements.sh diff --git a/llama.cpp/.github/workflows/python-lint.yml b/llama.cpp/.github/workflows/python-lint.yml new file mode 100644 index 0000000000000000000000000000000000000000..57d56fa245a29c8fcb99ededa4320ce386ab6728 --- /dev/null +++ b/llama.cpp/.github/workflows/python-lint.yml @@ -0,0 +1,30 @@ +name: flake8 Lint + +on: + push: + branches: + - master + paths: ['.github/workflows/python-lint.yml', '**/*.py'] + pull_request: + types: [opened, synchronize, reopened] + paths: ['.github/workflows/python-lint.yml', '**/*.py'] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +jobs: + flake8-lint: + runs-on: ubuntu-latest + name: Lint + steps: + - name: Check out source repository + uses: actions/checkout@v4 + - name: Set up Python environment + uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: flake8 Lint + uses: py-actions/flake8@v2 + with: + plugins: "flake8-no-print" diff --git a/llama.cpp/.github/workflows/python-type-check.yml b/llama.cpp/.github/workflows/python-type-check.yml new file mode 100644 index 0000000000000000000000000000000000000000..a05abf3792f2bf250d8653e4ea5ff81984e3cbea --- /dev/null +++ b/llama.cpp/.github/workflows/python-type-check.yml @@ -0,0 +1,40 @@ +name: Python Type-Check + +on: + push: + paths: + - '.github/workflows/python-type-check.yml' + - 'pyrightconfig.json' + - '**.py' + - '**/requirements*.txt' + pull_request: + paths: + - '.github/workflows/python-type-check.yml' + - 'pyrightconfig.json' + - '**.py' + - '**/requirements*.txt' + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +jobs: + python-type-check: + runs-on: ubuntu-latest + name: pyright type-check + steps: + - name: Check out source repository + uses: actions/checkout@v4 + - name: Set up Python environment + uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Install Python dependencies + # TODO: use a venv + run: pip install -r requirements/requirements-all.txt + - name: Type-check with Pyright + uses: jakebailey/pyright-action@v2 + with: + version: 1.1.382 + level: warning + warnings: true diff --git a/llama.cpp/.github/workflows/release.yml b/llama.cpp/.github/workflows/release.yml new file mode 100644 index 0000000000000000000000000000000000000000..1e476769ea5bd9e6e9c3140293b7ef3b773eb594 --- /dev/null +++ b/llama.cpp/.github/workflows/release.yml @@ -0,0 +1,755 @@ +name: Release + +on: + workflow_dispatch: # allows manual triggering + inputs: + create_release: + description: 'Create new release' + required: true + type: boolean + push: + branches: + - master + paths: ['.github/workflows/release.yml', '**/CMakeLists.txt', '**/.cmake', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp'] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +env: + BRANCH_NAME: ${{ github.head_ref || github.ref_name }} + CMAKE_ARGS: "-DLLAMA_BUILD_EXAMPLES=OFF -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_TOOLS=ON -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON" + +jobs: + macOS-arm64: + runs-on: macos-14 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-arm64 + evict-old-files: 1d + + - name: Dependencies + id: depends + continue-on-error: true + run: | + brew update + brew install curl + + - name: Build + id: cmake_build + run: | + sysctl -a + cmake -B build \ + -DCMAKE_INSTALL_RPATH='@loader_path' \ + -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DGGML_RPC=ON \ + ${{ env.CMAKE_ARGS }} + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) + + - name: Determine tag name + id: tag + uses: ./.github/actions/get-tag-name + + - name: Pack artifacts + id: pack_artifacts + run: | + cp LICENSE ./build/bin/ + zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip ./build/bin/* + + - name: Upload artifacts + uses: actions/upload-artifact@v4 + with: + path: llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip + name: llama-bin-macos-arm64.zip + + macOS-x64: + runs-on: macos-13 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-x64 + evict-old-files: 1d + + - name: Dependencies + id: depends + continue-on-error: true + run: | + brew update + brew install curl + + - name: Build + id: cmake_build + run: | + sysctl -a + # Metal is disabled due to intermittent failures with Github runners not having a GPU: + # https://github.com/ggml-org/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313 + cmake -B build \ + -DCMAKE_INSTALL_RPATH='@loader_path' \ + -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DGGML_METAL=OFF \ + -DGGML_RPC=ON + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) + + - name: Determine tag name + id: tag + uses: ./.github/actions/get-tag-name + + - name: Pack artifacts + id: pack_artifacts + run: | + cp LICENSE ./build/bin/ + zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/* + + - name: Upload artifacts + uses: actions/upload-artifact@v4 + with: + path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip + name: llama-bin-macos-x64.zip + + ubuntu-22-cpu: + strategy: + matrix: + include: + - build: 'x64' + os: ubuntu-22.04 + # GGML_BACKEND_DL and GGML_CPU_ALL_VARIANTS are not currently supported on arm + # - build: 'arm64' + # os: ubuntu-22.04-arm + + runs-on: ${{ matrix.os }} + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-cpu-cmake + evict-old-files: 1d + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential libcurl4-openssl-dev + + - name: Build + id: cmake_build + run: | + cmake -B build \ + -DCMAKE_INSTALL_RPATH='$ORIGIN' \ + -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \ + -DGGML_BACKEND_DL=ON \ + -DGGML_NATIVE=OFF \ + -DGGML_CPU_ALL_VARIANTS=ON \ + -DLLAMA_FATAL_WARNINGS=ON \ + ${{ env.CMAKE_ARGS }} + cmake --build build --config Release -j $(nproc) + + - name: Determine tag name + id: tag + uses: ./.github/actions/get-tag-name + + - name: Pack artifacts + id: pack_artifacts + run: | + cp LICENSE ./build/bin/ + zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.zip ./build/bin/* + + - name: Upload artifacts + uses: actions/upload-artifact@v4 + with: + path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.zip + name: llama-bin-ubuntu-${{ matrix.build }}.zip + + ubuntu-22-vulkan: + runs-on: ubuntu-22.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-vulkan + evict-old-files: 1d + + - name: Dependencies + id: depends + run: | + wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add - + sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list + sudo apt-get update -y + sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk libcurl4-openssl-dev + + - name: Build + id: cmake_build + run: | + cmake -B build \ + -DCMAKE_INSTALL_RPATH='$ORIGIN' \ + -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \ + -DGGML_BACKEND_DL=ON \ + -DGGML_NATIVE=OFF \ + -DGGML_CPU_ALL_VARIANTS=ON \ + -DGGML_VULKAN=ON \ + ${{ env.CMAKE_ARGS }} + cmake --build build --config Release -j $(nproc) + + - name: Determine tag name + id: tag + uses: ./.github/actions/get-tag-name + + - name: Pack artifacts + id: pack_artifacts + run: | + cp LICENSE ./build/bin/ + zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip ./build/bin/* + + - name: Upload artifacts + uses: actions/upload-artifact@v4 + with: + path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip + name: llama-bin-ubuntu-vulkan-x64.zip + + windows-cpu: + runs-on: windows-2025 + + strategy: + matrix: + include: + - arch: 'x64' + - arch: 'arm64' + + steps: + - name: Clone + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-latest-cmake-cpu-${{ matrix.arch }} + variant: ccache + evict-old-files: 1d + + - name: Install Ninja + run: | + choco install ninja + + - name: libCURL + id: get_libcurl + uses: ./.github/actions/windows-setup-curl + with: + architecture: ${{ matrix.arch == 'x64' && 'win64' || 'win64a' }} + + - name: Build + shell: cmd + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} + run: | + call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" ${{ matrix.arch == 'x64' && 'x64' || 'amd64_arm64' }} + cmake -S . -B build -G "Ninja Multi-Config" ^ + -D CMAKE_TOOLCHAIN_FILE=cmake/${{ matrix.arch }}-windows-llvm.cmake ^ + -DGGML_NATIVE=OFF ^ + -DGGML_BACKEND_DL=ON ^ + -DGGML_CPU_ALL_VARIANTS=${{ matrix.arch == 'x64' && 'ON' || 'OFF' }} ^ + -DGGML_OPENMP=ON ^ + -DCURL_LIBRARY="%CURL_PATH%/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="%CURL_PATH%/include" ^ + ${{ env.CMAKE_ARGS }} + cmake --build build --config Release + + - name: Pack artifacts + id: pack_artifacts + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} + run: | + Copy-Item $env:CURL_PATH\bin\libcurl-${{ matrix.arch }}.dll .\build\bin\Release\ + Copy-Item "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Redist\MSVC\14.44.35112\debug_nonredist\${{ matrix.arch }}\Microsoft.VC143.OpenMP.LLVM\libomp140.${{ matrix.arch == 'x64' && 'x86_64' || 'aarch64' }}.dll" .\build\bin\Release\ + 7z a llama-bin-win-cpu-${{ matrix.arch }}.zip .\build\bin\Release\* + + - name: Upload artifacts + uses: actions/upload-artifact@v4 + with: + path: llama-bin-win-cpu-${{ matrix.arch }}.zip + name: llama-bin-win-cpu-${{ matrix.arch }}.zip + + windows: + runs-on: windows-2025 + + env: + OPENBLAS_VERSION: 0.3.23 + VULKAN_VERSION: 1.4.313.2 + + strategy: + matrix: + include: + - backend: 'vulkan' + arch: 'x64' + defines: '-DGGML_VULKAN=ON' + target: 'ggml-vulkan' + - backend: 'opencl-adreno' + arch: 'arm64' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON' + target: 'ggml-opencl' + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-latest-cmake-${{ matrix.backend }}-${{ matrix.arch }} + variant: ccache + evict-old-files: 1d + + - name: Install Vulkan SDK + id: get_vulkan + if: ${{ matrix.backend == 'vulkan' }} + run: | + curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/vulkansdk-windows-X64-${env:VULKAN_VERSION}.exe" + & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install + Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}" + Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin" + + - name: Install Ninja + id: install_ninja + run: | + choco install ninja + + - name: Install OpenCL Headers and Libs + id: install_opencl + if: ${{ matrix.backend == 'opencl-adreno' && matrix.arch == 'arm64' }} + run: | + git clone https://github.com/KhronosGroup/OpenCL-Headers + cd OpenCL-Headers + cmake -B build ` + -DBUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF ` + -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release" + cmake --build build --target install + git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader + cd OpenCL-ICD-Loader + cmake -B build-arm64-release ` + -A arm64 ` + -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" ` + -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release" + cmake --build build-arm64-release --target install --config release + + - name: Build + id: cmake_build + run: | + cmake -S . -B build ${{ matrix.defines }} -DGGML_NATIVE=OFF -DGGML_CPU=OFF -DGGML_BACKEND_DL=ON -DLLAMA_CURL=OFF + cmake --build build --config Release --target ${{ matrix.target }} + + - name: Pack artifacts + id: pack_artifacts + run: | + 7z a llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip .\build\bin\Release\${{ matrix.target }}.dll + + - name: Upload artifacts + uses: actions/upload-artifact@v4 + with: + path: llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip + name: llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip + + windows-cuda: + runs-on: windows-2022 + + strategy: + matrix: + cuda: ['12.4'] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Install ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-cuda-${{ matrix.cuda }} + variant: ccache + evict-old-files: 1d + + - name: Install Cuda Toolkit + uses: ./.github/actions/windows-setup-cuda + with: + cuda_version: ${{ matrix.cuda }} + + - name: Install Ninja + id: install_ninja + run: | + choco install ninja + + - name: Build + id: cmake_build + shell: cmd + run: | + call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64 + cmake -S . -B build -G "Ninja Multi-Config" ^ + -DGGML_BACKEND_DL=ON ^ + -DGGML_NATIVE=OFF ^ + -DGGML_CPU=OFF ^ + -DGGML_CUDA=ON ^ + -DLLAMA_CURL=OFF + set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1 + cmake --build build --config Release -j %NINJA_JOBS% --target ggml-cuda + + - name: Pack artifacts + id: pack_artifacts + run: | + 7z a llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip .\build\bin\Release\ggml-cuda.dll + + - name: Upload artifacts + uses: actions/upload-artifact@v4 + with: + path: llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip + name: llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip + + - name: Copy and pack Cuda runtime + run: | + echo "Cuda install location: ${{ env.CUDA_PATH }}" + $dst='.\build\bin\cudart\' + robocopy "${{env.CUDA_PATH}}\bin" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll + robocopy "${{env.CUDA_PATH}}\lib" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll + 7z a cudart-llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip $dst\* + + - name: Upload Cuda runtime + uses: actions/upload-artifact@v4 + with: + path: cudart-llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip + name: cudart-llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip + + windows-sycl: + runs-on: windows-2022 + + defaults: + run: + shell: bash + + env: + WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/7cd9bba0-7aab-4e30-b3ae-2221006a4a05/intel-oneapi-base-toolkit-2025.1.1.34_offline.exe + WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel + ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI" + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-latest-cmake-sycl + variant: ccache + evict-old-files: 1d + + - name: Install + run: | + scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL + + - name: Build + id: cmake_build + shell: cmd + run: | + call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force + cmake -G "Ninja" -B build ^ + -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx ^ + -DCMAKE_BUILD_TYPE=Release ^ + -DGGML_BACKEND_DL=ON -DBUILD_SHARED_LIBS=ON ^ + -DGGML_CPU=OFF -DGGML_SYCL=ON ^ + -DLLAMA_CURL=OFF + cmake --build build --target ggml-sycl -j + + - name: Build the release package + id: pack_artifacts + run: | + echo "cp oneAPI running time dll files in ${{ env.ONEAPI_ROOT }} to ./build/bin" + + cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_sycl_blas.5.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_core.2.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_tbb_thread.2.dll" ./build/bin + + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_level_zero.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_opencl.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_loader.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_win_proxy_loader.dll" ./build/bin + + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl8.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/svml_dispmd.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libmmd.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libiomp5md.dll" ./build/bin + + cp "${{ env.ONEAPI_ROOT }}/dnnl/latest/bin/dnnl.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/tbb/latest/bin/tbb12.dll" ./build/bin + + echo "cp oneAPI running time dll files to ./build/bin done" + 7z a llama-bin-win-sycl-x64.zip ./build/bin/* + + - name: Upload the release package + uses: actions/upload-artifact@v4 + with: + path: llama-bin-win-sycl-x64.zip + name: llama-bin-win-sycl-x64.zip + + windows-hip: + runs-on: windows-2022 + + strategy: + matrix: + include: + - name: "radeon" + gpu_targets: "gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032" + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Clone rocWMMA repository + id: clone_rocwmma + run: | + git clone https://github.com/rocm/rocwmma --branch rocm-6.2.4 --depth 1 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-latest-cmake-hip-${{ matrix.name }}-x64 + evict-old-files: 1d + + - name: Install + id: depends + run: | + $ErrorActionPreference = "Stop" + write-host "Downloading AMD HIP SDK Installer" + Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe" + write-host "Installing AMD HIP SDK" + Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait + write-host "Completed AMD HIP SDK installation" + + - name: Verify ROCm + id: verify + run: | + & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version + + - name: Build + id: cmake_build + run: | + $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path) + $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}" + cmake -G "Unix Makefiles" -B build -S . ` + -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" ` + -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" ` + -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/rocwmma/library/include/ -Wno-ignored-attributes -Wno-nested-anon-types" ` + -DCMAKE_BUILD_TYPE=Release ` + -DGGML_BACKEND_DL=ON ` + -DGGML_NATIVE=OFF ` + -DGGML_CPU=OFF ` + -DAMDGPU_TARGETS="${{ matrix.gpu_targets }}" ` + -DGGML_HIP_ROCWMMA_FATTN=ON ` + -DGGML_HIP=ON ` + -DLLAMA_CURL=OFF + cmake --build build --target ggml-hip -j ${env:NUMBER_OF_PROCESSORS} + md "build\bin\rocblas\library\" + cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\" + cp "${env:HIP_PATH}\bin\rocblas.dll" "build\bin\" + cp "${env:HIP_PATH}\bin\rocblas\library\*" "build\bin\rocblas\library\" + + - name: Pack artifacts + id: pack_artifacts + run: | + 7z a llama-bin-win-hip-${{ matrix.name }}-x64.zip .\build\bin\* + + - name: Upload artifacts + uses: actions/upload-artifact@v4 + with: + path: llama-bin-win-hip-${{ matrix.name }}-x64.zip + name: llama-bin-win-hip-${{ matrix.name }}-x64.zip + + ios-xcode-build: + runs-on: macos-latest + + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Build + id: cmake_build + run: | + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_CURL=OFF \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_SYSTEM_NAME=iOS \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ + -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + + - name: xcodebuild for swift package + id: xcodebuild + run: | + ./build-xcframework.sh + + - name: Build Xcode project + run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' FRAMEWORK_FOLDER_PATH=./build-ios build + + - name: Determine tag name + id: tag + uses: ./.github/actions/get-tag-name + + - name: Pack artifacts + id: pack_artifacts + run: | + zip --symlinks -r llama-${{ steps.tag.outputs.name }}-xcframework.zip build-apple/llama.xcframework + + - name: Upload artifacts + uses: actions/upload-artifact@v4 + with: + path: llama-${{ steps.tag.outputs.name }}-xcframework.zip + name: llama-${{ steps.tag.outputs.name }}-xcframework + + release: + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + + # Fine-grant permission + # https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token + permissions: + contents: write # for creating release + + runs-on: ubuntu-latest + + needs: + - windows + - windows-cpu + - windows-cuda + - windows-sycl + - windows-hip + - ubuntu-22-cpu + - ubuntu-22-vulkan + - macOS-arm64 + - macOS-x64 + - ios-xcode-build + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Determine tag name + id: tag + uses: ./.github/actions/get-tag-name + + - name: Download artifacts + id: download-artifact + uses: actions/download-artifact@v4 + with: + path: ./artifact + merge-multiple: true + + - name: Move artifacts + id: move_artifacts + run: | + mkdir -p release + + echo "Adding CPU backend files to existing zips..." + for arch in x64 arm64; do + cpu_zip="artifact/llama-bin-win-cpu-${arch}.zip" + temp_dir=$(mktemp -d) + echo "Extracting CPU backend for $arch..." + unzip "$cpu_zip" -d "$temp_dir" + + echo "Adding CPU files to $arch zips..." + for target_zip in artifact/llama-bin-win-*-${arch}.zip; do + if [[ "$target_zip" == "$cpu_zip" ]]; then + continue + fi + echo "Adding CPU backend to $(basename "$target_zip")" + realpath_target_zip=$(realpath "$target_zip") + (cd "$temp_dir" && zip -r "$realpath_target_zip" .) + done + + rm -rf "$temp_dir" + done + + echo "Renaming and moving zips to release..." + for zip_file in artifact/llama-bin-win-*.zip; do + base_name=$(basename "$zip_file" .zip) + zip_name="llama-${{ steps.tag.outputs.name }}-${base_name#llama-}.zip" + echo "Moving $zip_file to release/$zip_name" + mv "$zip_file" "release/$zip_name" + done + + echo "Moving other artifacts..." + mv -v artifact/*.zip release + + - name: Create release + id: create_release + uses: ggml-org/action-create-release@v1 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + tag_name: ${{ steps.tag.outputs.name }} + + - name: Upload release + id: upload_release + uses: actions/github-script@v3 + with: + github-token: ${{secrets.GITHUB_TOKEN}} + script: | + const path = require('path'); + const fs = require('fs'); + const release_id = '${{ steps.create_release.outputs.id }}'; + for (let file of await fs.readdirSync('./release')) { + if (path.extname(file) === '.zip') { + console.log('uploadReleaseAsset', file); + await github.repos.uploadReleaseAsset({ + owner: context.repo.owner, + repo: context.repo.repo, + release_id: release_id, + name: file, + data: await fs.readFileSync(`./release/${file}`) + }); + } + } diff --git a/llama.cpp/.github/workflows/server.yml b/llama.cpp/.github/workflows/server.yml new file mode 100644 index 0000000000000000000000000000000000000000..1c17c583942b0142d257aafe6853a84bce3daf82 --- /dev/null +++ b/llama.cpp/.github/workflows/server.yml @@ -0,0 +1,237 @@ +# Server build and tests +name: Server + +on: + workflow_dispatch: # allows manual triggering + inputs: + sha: + description: 'Commit SHA1 to build' + required: false + type: string + slow_tests: + description: 'Run slow tests' + required: true + type: boolean + push: + branches: + - master + paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*'] + pull_request: + types: [opened, synchronize, reopened] + paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*'] + +env: + LLAMA_LOG_COLORS: 1 + LLAMA_LOG_PREFIX: 1 + LLAMA_LOG_TIMESTAMPS: 1 + LLAMA_LOG_VERBOSITY: 10 + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }} + cancel-in-progress: true + +jobs: + server: + runs-on: ubuntu-latest + + strategy: + matrix: + sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken + build_type: [RelWithDebInfo] + include: + - build_type: Release + sanitizer: "" + fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken + + steps: + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get -y install \ + build-essential \ + xxd \ + git \ + cmake \ + curl \ + wget \ + language-pack-en \ + libcurl4-openssl-dev + + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }} + + - name: Python setup + id: setup_python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Tests dependencies + id: test_dependencies + run: | + pip install -r tools/server/tests/requirements.txt + + # Setup nodejs (to be used for verifying bundled index.html) + - uses: actions/setup-node@v4 + with: + node-version: '22.11.0' + + - name: WebUI - Install dependencies + id: webui_lint + run: | + cd tools/server/webui + npm ci + + - name: WebUI - Check code format + id: webui_format + run: | + git config --global --add safe.directory $(realpath .) + cd tools/server/webui + git status + + npm run format + git status + modified_files="$(git status -s)" + echo "Modified files: ${modified_files}" + if [ -n "${modified_files}" ]; then + echo "Files do not follow coding style. To fix: npm run format" + echo "${modified_files}" + exit 1 + fi + + - name: Verify bundled index.html + id: verify_server_index_html + run: | + git config --global --add safe.directory $(realpath .) + cd tools/server/webui + git status + + npm run build + git status + modified_files="$(git status -s)" + echo "Modified files: ${modified_files}" + if [ -n "${modified_files}" ]; then + echo "Repository is dirty or server/webui is not built as expected" + echo "Hint: You may need to follow Web UI build guide in server/README.md" + echo "${modified_files}" + exit 1 + fi + + - name: Build (no OpenMP) + id: cmake_build_no_openmp + if: ${{ matrix.sanitizer == 'THREAD' }} + run: | + cmake -B build \ + -DGGML_NATIVE=OFF \ + -DLLAMA_BUILD_SERVER=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ + -DGGML_OPENMP=OFF ; + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server + + - name: Build (sanitizers) + id: cmake_build_sanitizers + if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }} + run: | + cmake -B build \ + -DGGML_NATIVE=OFF \ + -DLLAMA_BUILD_SERVER=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server + + - name: Build (sanitizers) + id: cmake_build + if: ${{ matrix.sanitizer == '' }} + run: | + cmake -B build \ + -DGGML_NATIVE=OFF \ + -DLLAMA_BUILD_SERVER=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ; + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server + + - name: Tests + id: server_integration_tests + if: ${{ matrix.sanitizer == '' }} + env: + GITHUB_ACTIONS: "true" + run: | + cd tools/server/tests + ./tests.sh + + - name: Tests (sanitizers) + id: server_integration_tests_sanitizers + if: ${{ matrix.sanitizer != '' }} + run: | + cd tools/server/tests + LLAMA_SANITIZE=1 ./tests.sh + + - name: Slow tests + id: server_integration_tests_slow + if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }} + run: | + cd tools/server/tests + SLOW_TESTS=1 ./tests.sh + + + server-windows: + runs-on: windows-2022 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }} + + - name: libCURL + id: get_libcurl + uses: ./.github/actions/windows-setup-curl + + - name: Build + id: cmake_build + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} + run: | + cmake -B build -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include" + cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target llama-server + + - name: Python setup + id: setup_python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Tests dependencies + id: test_dependencies + run: | + pip install -r tools/server/tests/requirements.txt + + - name: Copy Libcurl + id: prepare_libcurl + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} + run: | + cp $env:CURL_PATH/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll + + - name: Tests + id: server_integration_tests + if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }} + run: | + cd tools/server/tests + $env:PYTHONIOENCODING = ":replace" + pytest -v -x -m "not slow" + + - name: Slow tests + id: server_integration_tests_slow + if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }} + run: | + cd tools/server/tests + $env:SLOW_TESTS = "1" + pytest -v -x diff --git a/llama.cpp/.github/workflows/update-ops-docs.yml b/llama.cpp/.github/workflows/update-ops-docs.yml new file mode 100644 index 0000000000000000000000000000000000000000..259777cff52307b7a0fef9c6ab22386030b4f527 --- /dev/null +++ b/llama.cpp/.github/workflows/update-ops-docs.yml @@ -0,0 +1,40 @@ +name: Update Operations Documentation + +on: + push: + paths: + - 'docs/ops/**' + - 'scripts/create_ops_docs.py' + pull_request: + paths: + - 'docs/ops/**' + - 'scripts/create_ops_docs.py' + +jobs: + update-ops-docs: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.x' + + - name: Generate operations documentation to temporary file + run: | + mkdir -p /tmp/ops_check + ./scripts/create_ops_docs.py /tmp/ops_check/ops.md + + - name: Check if docs/ops.md matches generated version + run: | + if ! diff -q docs/ops.md /tmp/ops_check/ops.md; then + echo "Operations documentation (docs/ops.md) is not up to date with the backend CSV files." + echo "To fix: run ./scripts/create_ops_docs.py and commit the updated docs/ops.md along with your changes" + echo "Differences found:" + diff docs/ops.md /tmp/ops_check/ops.md || true + exit 1 + fi + echo "Operations documentation is up to date." diff --git a/llama.cpp/.github/workflows/winget.yml b/llama.cpp/.github/workflows/winget.yml new file mode 100644 index 0000000000000000000000000000000000000000..8204ff9ca960b29acfb3316ab98ea573bcf2ba56 --- /dev/null +++ b/llama.cpp/.github/workflows/winget.yml @@ -0,0 +1,42 @@ +name: Update Winget Package + +on: + workflow_dispatch: # allows manual triggering + schedule: + - cron: '28 5 * * *' # Update every day at 5:28 UTC + +jobs: + update: + name: Update Winget Package + runs-on: ubuntu-latest + + steps: + - name: Install cargo binstall + uses: cargo-bins/cargo-binstall@268643a6b5ea099f5718ee5cd3ff7dc89a5eb49b + + - name: Install komac + run: | + cargo binstall komac@2.11.2 -y + + - name: Find latest release + id: find_latest_release + uses: actions/github-script@v6 + with: + script: | + const { data: releases } = await github.rest.repos.listReleases({ + owner: context.repo.owner, + repo: context.repo.repo, + }); + console.log("Latest release:", releases[0].tag_name); + return releases[0].tag_name; + + - name: Update manifest + env: + VERSION: ${{ steps.find_latest_release.outputs.result }} + run: | + echo "Updating manifest..." + komac update --version ${{ env.VERSION }} \ + --urls "https://github.com/ggml-org/llama.cpp/releases/download/${{ env.VERSION }}/llama-${{ env.VERSION }}-bin-win-vulkan-x64.zip" \ + --token ${{ secrets.WINGET_GITHUB_TOKEN }} \ + --submit \ + ggml.llamacpp diff --git a/llama.cpp/.gitignore b/llama.cpp/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..7af23f56ec0c7455f18824cefe0d731a936e6003 --- /dev/null +++ b/llama.cpp/.gitignore @@ -0,0 +1,149 @@ +# Extensions + +*.a +*.bat +*.bin +*.d +*.dll +*.dot +*.etag +*.exe +*.gcda +*.gcno +*.gcov +*.gguf +*.gguf.json +*.lastModified +*.log +*.metallib +*.o +*.so +*.swp +*.tmp + +# IDE / OS + +.cache/ +.ccls-cache/ +.direnv/ +.DS_Store +.envrc +.idea/ +.swiftpm +.vs/ +.vscode/ +nppBackup + + +# Coverage + +gcovr-report/ +lcov-report/ + +# Build Artifacts + +tags +.build/ +build* +release +debug +!build-info.cmake +!build-info.cpp.in +!build-info.sh +!build.zig +!docs/build.md +/libllama.so +/llama-* +/vulkan-shaders-gen +android-ndk-* +arm_neon.h +cmake-build-* +CMakeSettings.json +compile_commands.json +ggml-metal-embed.metal +llama-batched-swift +/rpc-server +out/ +tmp/ +autogen-*.md + +# Deprecated + +/main +/server + +# CI + +!.github/workflows/*.yml + +# Models + +models/* +models-mnt +!models/.editorconfig +!models/ggml-vocab-*.gguf* +!models/templates + +# Zig +zig-out/ +zig-cache/ + +# Logs + +ppl-*.txt +qnt-*.txt +perf-*.txt + +# Examples + +examples/jeopardy/results.txt +tools/server/*.css.hpp +tools/server/*.html.hpp +tools/server/*.js.hpp +tools/server/*.mjs.hpp +tools/server/*.gz.hpp +!build_64.sh +!examples/*.bat +!examples/*/*.kts +!examples/*/*/*.kts +!examples/sycl/*.bat +!examples/sycl/*.sh + +# Server Web UI temporary files +node_modules +tools/server/webui/dist + +# Python + +/.venv +__pycache__/ +*/poetry.lock +poetry.toml + +# Nix +/result + +# Test binaries +/tests/test-backend-ops +/tests/test-double-float +/tests/test-grad0 +/tests/test-grammar-parser +/tests/test-llama-grammar +/tests/test-opt +/tests/test-quantize-fns +/tests/test-quantize-perf +/tests/test-rope +/tests/test-sampling +/tests/test-tokenizer-0 +/tests/test-tokenizer-1-bpe +/tests/test-tokenizer-1-spm + +# Scripts +!/scripts/install-oneapi.bat + +# Test models for lora adapters +/lora-tests + +# Local scripts +/run-vim.sh +/run-chat.sh diff --git a/llama.cpp/.gitmodules b/llama.cpp/.gitmodules new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/llama.cpp/.pre-commit-config.yaml b/llama.cpp/.pre-commit-config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0dff5f584658563caf2103df46685f2c401c3967 --- /dev/null +++ b/llama.cpp/.pre-commit-config.yaml @@ -0,0 +1,16 @@ +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +exclude: prompts/.*.txt +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.6.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-added-large-files +- repo: https://github.com/PyCQA/flake8 + rev: 7.0.0 + hooks: + - id: flake8 + additional_dependencies: [flake8-no-print] diff --git a/llama.cpp/.vs/CMake Overview b/llama.cpp/.vs/CMake Overview new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/llama.cpp/.vs/ProjectSettings.json b/llama.cpp/.vs/ProjectSettings.json new file mode 100644 index 0000000000000000000000000000000000000000..8b095887d0aae59f79cbae5a82d740292ca1c889 --- /dev/null +++ b/llama.cpp/.vs/ProjectSettings.json @@ -0,0 +1,3 @@ +{ + "CurrentProjectSetting": "arm64-apple-clang+static-release" +} \ No newline at end of file diff --git a/llama.cpp/.vs/VSWorkspaceState.json b/llama.cpp/.vs/VSWorkspaceState.json new file mode 100644 index 0000000000000000000000000000000000000000..c3a85c82a7ab3eb2822ca386d51acad52f3c3fd2 --- /dev/null +++ b/llama.cpp/.vs/VSWorkspaceState.json @@ -0,0 +1,33 @@ +{ + "OutputFoldersPerTargetSystem": { + "Local Machine": [ + "build-x64-linux-gcc-debug", + "build-x64-linux-gcc-release", + "build-x64-linux-gcc-reldbg", + "build-x64-linux-gcc+static-release", + "build-arm64-windows-llvm-debug", + "build-arm64-windows-llvm-release", + "build-arm64-windows-llvm+static-release", + "build-arm64-apple-clang-debug", + "build-arm64-apple-clang-release", + "build-arm64-apple-clang+static-release", + "build-x64-windows-llvm-debug", + "build-x64-windows-llvm-release", + "build-x64-windows-llvm-reldbg", + "build-x64-windows-llvm+static-release", + "build-x64-windows-msvc-debug", + "build-x64-windows-msvc-release", + 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<391746016@qq.com> +wbpxre150 <100937007+wbpxre150@users.noreply.github.com> +whoreson <139810751+whoreson@users.noreply.github.com> +woachk <24752637+woachk@users.noreply.github.com> +wonjun Jang +woodx <124784234+woodx9@users.noreply.github.com> +wwoodsTM <104587230+wwoodsTM@users.noreply.github.com> +wzy <32936898+Freed-Wu@users.noreply.github.com> +xaedes +xaedes +xctan +xiaobing318 <71554036+xiaobing318@users.noreply.github.com> +xiaofei +xloem <0xloem@gmail.com> +yangli2 +ymcki <84055651+ymcki@users.noreply.github.com> +yuiseki +yuri@FreeBSD +zakkor +zhangkaihuo +zhentaoyu +zhouwg <6889919+zhouwg@users.noreply.github.com> +zhouwg +zrm +Ștefan-Gabriel Muscalu +杨朱 · Kiki +源文雨 <41315874+fumiama@users.noreply.github.com> +蕭澧邦 <45505768+shou692199@users.noreply.github.com> +谢乃闻 +Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com> diff --git a/llama.cpp/CMakeLists.txt b/llama.cpp/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..a6f1a4deb0d97bc9c63833f7022d8802dadf6ac0 --- /dev/null +++ b/llama.cpp/CMakeLists.txt @@ -0,0 +1,264 @@ +cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories. +project("llama.cpp" C CXX) +include(CheckIncludeFileCXX) + +#set(CMAKE_WARN_DEPRECATED YES) +set(CMAKE_WARN_UNUSED_CLI YES) + +set(CMAKE_EXPORT_COMPILE_COMMANDS ON) + +if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE) + set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE) + set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo") +endif() + +# Add path to modules +list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/") + +set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin) +set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin) + +if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR) + set(LLAMA_STANDALONE ON) + + include(git-vars) + + # configure project version + # TODO +else() + set(LLAMA_STANDALONE OFF) +endif() + +option(LLAMA_USE_SYSTEM_GGML "Use system libggml" OFF) + +if (EMSCRIPTEN) + set(BUILD_SHARED_LIBS_DEFAULT OFF) + + option(LLAMA_WASM_SINGLE_FILE "llama: embed WASM inside the generated llama.js" ON) +else() + if (MINGW) + set(BUILD_SHARED_LIBS_DEFAULT OFF) + else() + set(BUILD_SHARED_LIBS_DEFAULT ON) + endif() +endif() + +option(BUILD_SHARED_LIBS "build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT}) + +if (WIN32) + add_compile_definitions(_CRT_SECURE_NO_WARNINGS) +endif() + +if (MSVC) + add_compile_options("$<$:/utf-8>") + add_compile_options("$<$:/utf-8>") + add_compile_options("$<$:/bigobj>") + add_compile_options("$<$:/bigobj>") +endif() + +# +# option list +# + +# debug +option(LLAMA_ALL_WARNINGS "llama: enable all compiler warnings" ON) +option(LLAMA_ALL_WARNINGS_3RD_PARTY "llama: enable all compiler warnings in 3rd party libs" OFF) + +# build +option(LLAMA_FATAL_WARNINGS "llama: enable -Werror flag" OFF) + +# sanitizers +option(LLAMA_SANITIZE_THREAD "llama: enable thread sanitizer" OFF) +option(LLAMA_SANITIZE_ADDRESS "llama: enable address sanitizer" OFF) +option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF) + +# utils +option(LLAMA_BUILD_COMMON "llama: build common utils library" ${LLAMA_STANDALONE}) + +# extra artifacts +option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) +option(LLAMA_BUILD_TOOLS "llama: build tools" ${LLAMA_STANDALONE}) +option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) +option(LLAMA_BUILD_SERVER "llama: build server example" ${LLAMA_STANDALONE}) + +# 3rd party libs +option(LLAMA_CURL "llama: use libcurl to download model from an URL" ON) +option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF) + +# Required for relocatable CMake package +include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake) +include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/common.cmake) + +if (NOT DEFINED LLAMA_BUILD_NUMBER) + set(LLAMA_BUILD_NUMBER ${BUILD_NUMBER}) +endif() +if (NOT DEFINED LLAMA_BUILD_COMMIT) + set(LLAMA_BUILD_COMMIT ${BUILD_COMMIT}) +endif() +set(LLAMA_INSTALL_VERSION 0.0.${LLAMA_BUILD_NUMBER}) + +# override ggml options +set(GGML_ALL_WARNINGS ${LLAMA_ALL_WARNINGS}) +set(GGML_FATAL_WARNINGS ${LLAMA_FATAL_WARNINGS}) + +# change the default for these ggml options +if (NOT DEFINED GGML_LLAMAFILE) + set(GGML_LLAMAFILE_DEFAULT ON) +endif() + +if (NOT DEFINED GGML_CUDA_GRAPHS) + set(GGML_CUDA_GRAPHS_DEFAULT ON) +endif() + +# transition helpers +function (llama_option_depr TYPE OLD NEW) + if (${OLD}) + message(${TYPE} "${OLD} is deprecated and will be removed in the future.\nUse ${NEW} instead\n") + set(${NEW} ON PARENT_SCOPE) + endif() +endfunction() + +llama_option_depr(FATAL_ERROR LLAMA_CUBLAS GGML_CUDA) +llama_option_depr(WARNING LLAMA_CUDA GGML_CUDA) +llama_option_depr(WARNING LLAMA_METAL GGML_METAL) +llama_option_depr(WARNING LLAMA_METAL_EMBED_LIBRARY GGML_METAL_EMBED_LIBRARY) +llama_option_depr(WARNING LLAMA_NATIVE GGML_NATIVE) +llama_option_depr(WARNING LLAMA_RPC GGML_RPC) +llama_option_depr(WARNING LLAMA_SYCL GGML_SYCL) +llama_option_depr(WARNING LLAMA_SYCL_F16 GGML_SYCL_F16) +llama_option_depr(WARNING LLAMA_CANN GGML_CANN) + +if (NOT MSVC) + if (LLAMA_SANITIZE_THREAD) + message(STATUS "Using -fsanitize=thread") + + add_compile_options(-fsanitize=thread) + link_libraries (-fsanitize=thread) + endif() + + if (LLAMA_SANITIZE_ADDRESS) + message(STATUS "Using -fsanitize=address") + + add_compile_options(-fsanitize=address -fno-omit-frame-pointer) + link_libraries (-fsanitize=address) + endif() + + if (LLAMA_SANITIZE_UNDEFINED) + message(STATUS "Using -fsanitize=undefined") + + add_compile_options(-fsanitize=undefined) + link_libraries (-fsanitize=undefined) + endif() +endif() + +# +# 3rd-party +# + +if (LLAMA_USE_SYSTEM_GGML) + message(STATUS "Using system-provided libggml, skipping ggml build") + find_package(ggml REQUIRED) + add_library(ggml ALIAS ggml::ggml) +endif() + +if (NOT TARGET ggml AND NOT LLAMA_USE_SYSTEM_GGML) + set(GGML_BUILD_NUMBER ${LLAMA_BUILD_NUMBER}) + set(GGML_BUILD_COMMIT ${LLAMA_BUILD_COMMIT}) + add_subdirectory(ggml) + # ... otherwise assume ggml is added by a parent CMakeLists.txt +endif() + +if (MINGW) + # Target Windows 8 for PrefetchVirtualMemory + add_compile_definitions(_WIN32_WINNT=${GGML_WIN_VER}) +endif() + +# +# build the library +# + +add_subdirectory(src) + +# +# utils, programs, examples and tests +# + +if (NOT LLAMA_BUILD_COMMON) + message(STATUS "LLAMA_BUILD_COMMON is OFF, disabling LLAMA_CURL") + set(LLAMA_CURL OFF) +endif() + +if (LLAMA_BUILD_COMMON) + add_subdirectory(common) +endif() + +if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION) + include(CTest) + add_subdirectory(tests) +endif() + +if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES) + add_subdirectory(examples) + add_subdirectory(pocs) +endif() + +if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TOOLS) + add_subdirectory(tools) +endif() + +# +# install +# + +include(GNUInstallDirs) +include(CMakePackageConfigHelpers) + +set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location of header files") +set(LLAMA_LIB_INSTALL_DIR ${CMAKE_INSTALL_LIBDIR} CACHE PATH "Location of library files") +set(LLAMA_BIN_INSTALL_DIR ${CMAKE_INSTALL_BINDIR} CACHE PATH "Location of binary files") + +set(LLAMA_PUBLIC_HEADERS + ${CMAKE_CURRENT_SOURCE_DIR}/include/llama.h + ${CMAKE_CURRENT_SOURCE_DIR}/include/llama-cpp.h) + +set_target_properties(llama + PROPERTIES + PUBLIC_HEADER "${LLAMA_PUBLIC_HEADERS}") + +install(TARGETS llama LIBRARY PUBLIC_HEADER) + +configure_package_config_file( + ${CMAKE_CURRENT_SOURCE_DIR}/cmake/llama-config.cmake.in + ${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake + INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama + PATH_VARS LLAMA_INCLUDE_INSTALL_DIR + LLAMA_LIB_INSTALL_DIR + LLAMA_BIN_INSTALL_DIR ) + +write_basic_package_version_file( + ${CMAKE_CURRENT_BINARY_DIR}/llama-version.cmake + VERSION ${LLAMA_INSTALL_VERSION} + COMPATIBILITY SameMajorVersion) + +install(FILES ${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake + ${CMAKE_CURRENT_BINARY_DIR}/llama-version.cmake + DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama) + +install( + FILES convert_hf_to_gguf.py + PERMISSIONS + OWNER_READ + OWNER_WRITE + OWNER_EXECUTE + GROUP_READ + GROUP_EXECUTE + WORLD_READ + WORLD_EXECUTE + DESTINATION ${CMAKE_INSTALL_BINDIR}) + +configure_file(cmake/llama.pc.in + "${CMAKE_CURRENT_BINARY_DIR}/llama.pc" + @ONLY) + +install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc" + DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig) diff --git a/llama.cpp/CMakePresets.json b/llama.cpp/CMakePresets.json new file mode 100644 index 0000000000000000000000000000000000000000..f67fada373728b77e22903cf7efd3039daef1f1e --- /dev/null +++ b/llama.cpp/CMakePresets.json @@ -0,0 +1,95 @@ +{ + "version": 4, + "configurePresets": [ + { + "name": "base", + "hidden": true, + "generator": "Ninja", + "binaryDir": "${sourceDir}/build-${presetName}", + "cacheVariables": { + "CMAKE_EXPORT_COMPILE_COMMANDS": "ON", + "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.." + } + }, + { + "name": "sycl-base", + "hidden": true, + "generator": "Ninja", + "binaryDir": "${sourceDir}/build-${presetName}", + "cacheVariables": { + "CMAKE_EXPORT_COMPILE_COMMANDS": "ON", + "CMAKE_CXX_COMPILER": "icx", + "CMAKE_C_COMPILER": "cl", + "GGML_SYCL": "ON", + "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.." + } + }, + { "name": "debug", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } }, + { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } }, + { "name": "reldbg", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } }, + { "name": "static", "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } }, + { "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16": "ON" } }, + { "name": "vulkan", "hidden": true, "cacheVariables": { "GGML_VULKAN": "ON" } }, + + { + "name": "x64-windows-llvm", "hidden": true, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/x64-windows-llvm.cmake" + } + }, + + { + "name": "arm64-windows-llvm", "hidden": true, + "architecture": { "value": "arm64", "strategy": "external" }, + "toolset": { "value": "host=x64", "strategy": "external" }, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-llvm.cmake" + } + }, + + { + "name": "arm64-apple-clang", "hidden": true, + "architecture": { "value": "arm64", "strategy": "external" }, + "toolset": { "value": "host=x64", "strategy": "external" }, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-apple-clang.cmake" + } + }, + { + "name": "x64-linux-gcc", "hidden": true, + "cacheVariables": { + "CMAKE_C_COMPILER": "gcc", + "CMAKE_CXX_COMPILER": "g++" + } + }, + { "name": "x64-linux-gcc-debug", "inherits": [ "base", "x64-linux-gcc", "debug" ] }, + { "name": "x64-linux-gcc-release", "inherits": [ "base", "x64-linux-gcc", "release" ] }, + { "name": "x64-linux-gcc-reldbg", "inherits": [ "base", "x64-linux-gcc", "reldbg" ] }, + { "name": "x64-linux-gcc+static-release", "inherits": [ "base", "x64-linux-gcc", "release", "static" ] }, + + { "name": "arm64-windows-llvm-debug", "inherits": [ "base", "arm64-windows-llvm", "debug" ] }, + { "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg" ] }, + { "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg", "static" ] }, + + { "name": "arm64-apple-clang-debug", "inherits": [ "base", "arm64-apple-clang", "debug" ] }, + { "name": "arm64-apple-clang-release", "inherits": [ "base", "arm64-apple-clang", "reldbg" ] }, + { "name": "arm64-apple-clang+static-release", "inherits": [ "base", "arm64-apple-clang", "reldbg", "static" ] }, + + { "name": "x64-windows-llvm-debug", "inherits": [ "base", "x64-windows-llvm", "debug" ] }, + { "name": "x64-windows-llvm-release", "inherits": [ "base", "x64-windows-llvm", "release" ] }, + { "name": "x64-windows-llvm-reldbg", "inherits": [ "base", "x64-windows-llvm", "reldbg" ] }, + { "name": "x64-windows-llvm+static-release", "inherits": [ "base", "x64-windows-llvm", "reldbg", "static" ] }, + + { "name": "x64-windows-msvc-debug", "inherits": [ "base", "debug" ] }, + { "name": "x64-windows-msvc-release", "inherits": [ "base", "reldbg" ] }, + { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] }, + + { "name": "x64-windows-sycl-debug", "inherits": [ "sycl-base", "debug" ] }, + { "name": "x64-windows-sycl-debug-f16", "inherits": [ "sycl-base", "debug", "sycl_f16" ] }, + { "name": "x64-windows-sycl-release", "inherits": [ "sycl-base", "release" ] }, + { "name": "x64-windows-sycl-release-f16", "inherits": [ "sycl-base", "release", "sycl_f16" ] }, + + { "name": "x64-windows-vulkan-debug", "inherits": [ "base", "vulkan", "debug" ] }, + { "name": "x64-windows-vulkan-release", "inherits": [ "base", "vulkan", "release" ] } + ] +} diff --git a/llama.cpp/CODEOWNERS b/llama.cpp/CODEOWNERS new file mode 100644 index 0000000000000000000000000000000000000000..9b0cedc2d0a54ea92555c06e2c18a5bf749ea078 --- /dev/null +++ b/llama.cpp/CODEOWNERS @@ -0,0 +1,12 @@ +# collaborators can optionally add themselves here to indicate their availability for reviewing related PRs + +/ci/ @ggerganov +/.devops/*.Dockerfile @ngxson +/tools/server/ @ngxson +/ggml/src/ggml-cuda/fattn* @JohannesGaessler +/ggml/src/ggml-cuda/mmq.* @JohannesGaessler +/ggml/src/ggml-cuda/mmv.* @JohannesGaessler +/ggml/src/ggml-cuda/mmvq.* @JohannesGaessler +/ggml/src/ggml-opt.cpp @JohannesGaessler +/ggml/src/gguf.cpp @JohannesGaessler +/ggml/src/ggml-vulkan/ @0cc4m diff --git a/llama.cpp/CONTRIBUTING.md b/llama.cpp/CONTRIBUTING.md new file mode 100644 index 0000000000000000000000000000000000000000..9ca8fe1a716e11851eb4a11d1c67031666b48b32 --- /dev/null +++ b/llama.cpp/CONTRIBUTING.md @@ -0,0 +1,127 @@ +# Pull requests (for contributors) + +- llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml repository](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-2) has minimal implementations for language model inference using GPT-2. [mnist](https://github.com/ggml-org/ggml/tree/master/examples/mnist) demonstrates how to train and evaluate a simple image classifier +- Test your changes: + - Execute [the full CI locally on your machine](ci/README.md) before publishing + - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`) + - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends) + - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops` +- Create separate PRs for each feature or fix. Avoid combining unrelated changes in a single PR +- Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly +- If your PR becomes stale, don't hesitate to ping the maintainers in the comments + +# Pull requests (for collaborators) + +- Squash-merge PRs +- Use the following format for the squashed commit title: ` : (#)`. For example: `utils : fix typo in utils.py (#1234)` +- Optionally pick a `` from here: https://github.com/ggml-org/llama.cpp/wiki/Modules +- Consider adding yourself to [CODEOWNERS](CODEOWNERS) + +# Coding guidelines + +- Avoid adding third-party dependencies, extra files, extra headers, etc. +- Always consider cross-compatibility with other operating systems and architectures +- Avoid fancy-looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple +- Vertical alignment makes things more readable and easier to batch edit +- Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a` +- Use sized integer types such as `int32_t` in the public API, e.g. `size_t` may also be appropriate for allocation sizes or byte offsets +- Declare structs with `struct foo {}` instead of `typedef struct foo {} foo` + - In C++ code omit optional `struct` and `enum` keyword whenever they are not necessary + ```cpp + // OK + llama_context * ctx; + const llama_rope_type rope_type; + + // not OK + struct llama_context * ctx; + const enum llama_rope_type rope_type; + ``` + + _(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline.)_ + +- Try to follow the existing patterns in the code (indentation, spaces, etc.). In case of doubt use `clang-format` (from clang-tools v15+) to format the added code +- For anything not covered in the current guidelines, refer to the [C++ Core Guidelines](https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines) +- Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices +- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggml-org/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$ + +![matmul](media/matmul.png) + +# Naming guidelines + +- Use `snake_case` for function, variable and type names +- Naming usually optimizes for longest common prefix (see https://github.com/ggml-org/ggml/pull/302#discussion_r1243240963) + + ```cpp + // not OK + int small_number; + int big_number; + + // OK + int number_small; + int number_big; + ``` + +- Enum values are always in upper case and prefixed with the enum name + + ```cpp + enum llama_vocab_type { + LLAMA_VOCAB_TYPE_NONE = 0, + LLAMA_VOCAB_TYPE_SPM = 1, + LLAMA_VOCAB_TYPE_BPE = 2, + LLAMA_VOCAB_TYPE_WPM = 3, + LLAMA_VOCAB_TYPE_UGM = 4, + LLAMA_VOCAB_TYPE_RWKV = 5, + }; + ``` + +- The general naming pattern is `_`, with `` being `_` + + ```cpp + llama_model_init(); // class: "llama_model", method: "init" + llama_sampler_chain_remove(); // class: "llama_sampler_chain", method: "remove" + llama_sampler_get_seed(); // class: "llama_sampler", method: "get_seed" + llama_set_embeddings(); // class: "llama_context", method: "set_embeddings" + llama_n_threads(); // class: "llama_context", method: "n_threads" + llama_adapter_lora_free(); // class: "llama_adapter_lora", method: "free" + ``` + + - The `get` `` can be omitted + - The `` can be omitted if not necessary + - The `_context` suffix of the `` is optional. Use it to disambiguate symbols when needed + - Use `init`/`free` for constructor/destructor `` + +- Use the `_t` suffix when a type is supposed to be opaque to the user - it's not relevant to them if it is a struct or anything else + + ```cpp + typedef struct llama_context * llama_context_t; + + enum llama_pooling_type llama_pooling_type(const llama_context_t ctx); + ``` + + _(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline)_ + +- C/C++ filenames are all lowercase with dashes. Headers use the `.h` extension. Source files use the `.c` or `.cpp` extension +- Python filenames are all lowercase with underscores + +- _(TODO: abbreviations usage)_ + +# Preprocessor directives + +- _(TODO: add guidelines with examples and apply them to the codebase)_ + + ```cpp + #ifdef FOO + #endif // FOO + ``` + +# Documentation + +- Documentation is a community effort +- When you need to look into the source code to figure out how to use an API consider adding a short summary to the header file for future reference +- When you notice incorrect or outdated documentation, please update it + +# Resources + +The Github issues, PRs and discussions contain a lot of information that can be useful to get familiar with the codebase. For convenience, some of the more important information is referenced from Github projects: + +https://github.com/ggml-org/llama.cpp/projects diff --git a/llama.cpp/LICENSE b/llama.cpp/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..25cc5b74ae592e8236dd32355684c14c7fd1aa92 --- /dev/null +++ b/llama.cpp/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023-2024 The ggml authors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/llama.cpp/Makefile b/llama.cpp/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..d985686968fed5186cf9405f78ee3b8d202376c6 --- /dev/null +++ b/llama.cpp/Makefile @@ -0,0 +1,1608 @@ +ifndef LLAMA_MAKEFILE +$(error The Makefile build is deprecated. Use the CMake build instead. For more details, see https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md) +endif + +# Define the default target now so that it is always the first target +BUILD_TARGETS = \ + libllava.a \ + llama-batched \ + llama-batched-bench \ + llama-bench \ + llama-cli \ + llama-convert-llama2c-to-ggml \ + llama-embedding \ + llama-eval-callback \ + llama-export-lora \ + llama-gbnf-validator \ + llama-gguf \ + llama-gguf-hash \ + llama-gguf-split \ + llama-gritlm \ + llama-imatrix \ + llama-infill \ + llama-llava-cli \ + llama-minicpmv-cli\ + llama-qwen2vl-cli\ + llama-lookahead \ + llama-lookup \ + llama-lookup-create \ + llama-lookup-merge \ + llama-lookup-stats \ + llama-parallel \ + llama-passkey \ + llama-perplexity \ + llama-q8dot \ + llama-quantize \ + llama-quantize-stats \ + llama-retrieval \ + llama-save-load-state \ + llama-server \ + llama-simple \ + llama-simple-chat \ + llama-run \ + llama-speculative \ + llama-tokenize \ + llama-vdot \ + llama-cvector-generator \ + llama-gen-docs \ + tests/test-c.o + +# Binaries only useful for tests +TEST_TARGETS = \ + tests/test-arg-parser \ + tests/test-autorelease \ + tests/test-backend-ops \ + tests/test-chat \ + tests/test-chat-template \ + tests/test-double-float \ + tests/test-grammar-integration \ + tests/test-grammar-parser \ + tests/test-json-schema-to-grammar \ + tests/test-llama-grammar \ + tests/test-log \ + tests/test-model-load-cancel \ + tests/test-quantize-fns \ + tests/test-quantize-perf \ + tests/test-rope \ + tests/test-sampling \ + tests/test-tokenizer-0 \ + tests/test-tokenizer-1-bpe \ + tests/test-tokenizer-1-spm +# tests/test-opt \ + +# Legacy build targets that were renamed in #7809, but should still be removed when the project is cleaned +LEGACY_TARGETS_CLEAN = main quantize quantize-stats perplexity imatrix embedding vdot q8dot convert-llama2c-to-ggml \ + simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama \ + retrieval speculative infill tokenize parallel export-lora lookahead lookup passkey gritlm + +# Legacy build targets that were renamed in #7809, but we want to build binaries that for them that output a deprecation warning if people try to use them. +# We don't want to clutter things too much, so we only build replacements for the most commonly used binaries. +LEGACY_TARGETS_BUILD = main quantize perplexity embedding server + +# Deprecation aliases +ifdef LLAMA_CUBLAS +$(error LLAMA_CUBLAS is removed. Use GGML_CUDA instead.) +endif + +ifdef LLAMA_CUDA +GGML_CUDA := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_KOMPUTE +GGML_KOMPUTE := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_METAL +GGML_METAL := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_RPC +GGML_RPC := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_SYCL +GGML_SYCL := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_SYCL_F16 +GGML_SYCL_F16 := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_OPENBLAS +GGML_OPENBLAS := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_OPENBLAS64 +GGML_OPENBLAS64 := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_BLIS +GGML_BLIS := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_NO_LLAMAFILE +GGML_NO_LLAMAFILE := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_NO_ACCELERATE +GGML_NO_ACCELERATE := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_NO_OPENMP +GGML_NO_OPENMP := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_NO_METAL +GGML_NO_METAL := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_DISABLE_LOGS +REMOVE_WARNING := 1 +endif + +ifdef LLAMA_SERVER_VERBOSE +REMOVE_WARNING := 1 +endif + +ifndef UNAME_S +UNAME_S := $(shell uname -s) +endif + +ifndef UNAME_P +UNAME_P := $(shell uname -p) +endif + +ifndef UNAME_M +UNAME_M := $(shell uname -m) +endif + +# In GNU make default CXX is g++ instead of c++. Let's fix that so that users +# of non-gcc compilers don't have to provide g++ alias or wrapper. +DEFCC := cc +DEFCXX := c++ +ifeq ($(origin CC),default) +CC := $(DEFCC) +endif +ifeq ($(origin CXX),default) +CXX := $(DEFCXX) +endif + +# Mac OS + Arm can report x86_64 +# ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789 +ifeq ($(UNAME_S),Darwin) + ifndef GGML_NO_METAL + GGML_METAL := 1 + endif + + GGML_NO_OPENMP := 1 + + ifneq ($(UNAME_P),arm) + SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null) + ifeq ($(SYSCTL_M),1) + # UNAME_P := arm + # UNAME_M := arm64 + warn := $(warning Your arch is announced as x86_64, but it seems to actually be ARM64. Not fixing that can lead to bad performance. For more info see: https://github.com/ggerganov/whisper.cpp/issues/66\#issuecomment-1282546789) + endif + endif +endif + +ifdef GGML_METAL + GGML_METAL_EMBED_LIBRARY := 1 +endif + +ifdef GGML_RPC + BUILD_TARGETS += rpc-server +endif + +ifdef GGML_VULKAN + BUILD_TARGETS += vulkan-shaders-gen +endif + +default: $(BUILD_TARGETS) $(LEGACY_TARGETS_BUILD) + +test: $(TEST_TARGETS) + @failures=0; \ + for test_target in $(TEST_TARGETS); do \ + if [ "$$test_target" = "tests/test-tokenizer-0" ]; then \ + ./$$test_target $(CURDIR)/models/ggml-vocab-llama-spm.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-llama-bpe.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-phi-3.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-falcon.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-bert-bge.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-starcoder.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-gpt-2.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-refact.gguf; \ + elif [ "$$test_target" = "tests/test-tokenizer-1-spm" ]; then \ + continue; \ + elif [ "$$test_target" = "tests/test-tokenizer-1-bpe" ]; then \ + continue; \ + else \ + echo "Running test $$test_target..."; \ + ./$$test_target; \ + fi; \ + if [ $$? -ne 0 ]; then \ + printf 'Test %s FAILED!\n\n' $$test_target; \ + failures=$$(( failures + 1 )); \ + else \ + printf 'Test %s passed.\n\n' $$test_target; \ + fi; \ + done; \ + if [ $$failures -gt 0 ]; then \ + printf '\n%s tests failed.\n' $$failures; \ + exit 1; \ + fi + @echo 'All tests passed.' + +all: $(BUILD_TARGETS) $(TEST_TARGETS) $(LEGACY_TARGETS_BUILD) + +ifdef RISCV_CROSS_COMPILE +CC := riscv64-unknown-linux-gnu-gcc +CXX := riscv64-unknown-linux-gnu-g++ +endif + +# +# Compile flags +# + +# keep standard at C11 and C++17 +MK_CPPFLAGS = -Iggml/include -Iggml/src -Iinclude -Isrc -Icommon -DGGML_USE_CPU +MK_CFLAGS = -std=c11 -fPIC +MK_CXXFLAGS = -std=c++17 -fPIC +MK_NVCCFLAGS = -std=c++17 + +ifdef LLAMA_NO_CCACHE +GGML_NO_CCACHE := 1 +DEPRECATE_WARNING := 1 +endif + +ifndef GGML_NO_CCACHE +CCACHE := $(shell which ccache) +ifdef CCACHE +export CCACHE_SLOPPINESS = time_macros +$(info I ccache found, compilation results will be cached. Disable with GGML_NO_CCACHE.) +CC := $(CCACHE) $(CC) +CXX := $(CCACHE) $(CXX) +else +$(info I ccache not found. Consider installing it for faster compilation.) +endif # CCACHE +endif # GGML_NO_CCACHE + +# clock_gettime came in POSIX.1b (1993) +# CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional +# posix_memalign came in POSIX.1-2001 / SUSv3 +# M_PI is an XSI extension since POSIX.1-2001 / SUSv3, came in XPG1 (1985) +MK_CPPFLAGS += -D_XOPEN_SOURCE=600 + +# Somehow in OpenBSD whenever POSIX conformance is specified +# some string functions rely on locale_t availability, +# which was introduced in POSIX.1-2008, forcing us to go higher +ifeq ($(UNAME_S),OpenBSD) + MK_CPPFLAGS += -U_XOPEN_SOURCE -D_XOPEN_SOURCE=700 +endif + +# Data types, macros and functions related to controlling CPU affinity and +# some memory allocation are available on Linux through GNU extensions in libc +ifeq ($(UNAME_S),Linux) + MK_CPPFLAGS += -D_GNU_SOURCE + MK_LDFLAGS += -ldl +endif + +# RLIMIT_MEMLOCK came in BSD, is not specified in POSIX.1, +# and on macOS its availability depends on enabling Darwin extensions +# similarly on DragonFly, enabling BSD extensions is necessary +ifeq ($(UNAME_S),Darwin) + MK_CPPFLAGS += -D_DARWIN_C_SOURCE +endif +ifeq ($(UNAME_S),DragonFly) + MK_CPPFLAGS += -D__BSD_VISIBLE +endif + +# alloca is a non-standard interface that is not visible on BSDs when +# POSIX conformance is specified, but not all of them provide a clean way +# to enable it in such cases +ifeq ($(UNAME_S),FreeBSD) + MK_CPPFLAGS += -D__BSD_VISIBLE +endif +ifeq ($(UNAME_S),NetBSD) + MK_CPPFLAGS += -D_NETBSD_SOURCE +endif +ifeq ($(UNAME_S),OpenBSD) + MK_CPPFLAGS += -D_BSD_SOURCE +endif + +ifdef GGML_SCHED_MAX_COPIES + MK_CPPFLAGS += -DGGML_SCHED_MAX_COPIES=$(GGML_SCHED_MAX_COPIES) +endif + +ifdef LLAMA_DEBUG + MK_CFLAGS += -O0 -g + MK_CXXFLAGS += -O0 -g + MK_LDFLAGS += -g + MK_NVCCFLAGS += -O0 -g + + ifeq ($(UNAME_S),Linux) + MK_CPPFLAGS += -D_GLIBCXX_ASSERTIONS + endif +else + MK_CPPFLAGS += -DNDEBUG + MK_CFLAGS += -O3 -g + MK_CXXFLAGS += -O3 -g + MK_NVCCFLAGS += -O3 -g +endif + +ifdef LLAMA_SANITIZE_THREAD + MK_CFLAGS += -fsanitize=thread -g + MK_CXXFLAGS += -fsanitize=thread -g + MK_LDFLAGS += -fsanitize=thread -g +endif + +ifdef LLAMA_SANITIZE_ADDRESS + MK_CFLAGS += -fsanitize=address -fno-omit-frame-pointer -g + MK_CXXFLAGS += -fsanitize=address -fno-omit-frame-pointer -g + MK_LDFLAGS += -fsanitize=address -fno-omit-frame-pointer -g +endif + +ifdef LLAMA_SANITIZE_UNDEFINED + MK_CFLAGS += -fsanitize=undefined -g + MK_CXXFLAGS += -fsanitize=undefined -g + MK_LDFLAGS += -fsanitize=undefined -g +endif + +ifdef LLAMA_SERVER_SSL + MK_CPPFLAGS += -DCPPHTTPLIB_OPENSSL_SUPPORT + MK_LDFLAGS += -lssl -lcrypto +endif + +ifndef GGML_NO_CPU_AARCH64 + MK_CPPFLAGS += -DGGML_USE_CPU_REPACK +endif + +# warnings +WARN_FLAGS = \ + -Wall \ + -Wextra \ + -Wpedantic \ + -Wcast-qual \ + -Wno-unused-function + +MK_CFLAGS += \ + $(WARN_FLAGS) \ + -Wshadow \ + -Wstrict-prototypes \ + -Wpointer-arith \ + -Wmissing-prototypes \ + -Werror=implicit-int \ + -Werror=implicit-function-declaration + +MK_CXXFLAGS += \ + $(WARN_FLAGS) \ + -Wmissing-declarations \ + -Wmissing-noreturn + +ifeq ($(LLAMA_FATAL_WARNINGS),1) + MK_CFLAGS += -Werror + MK_CXXFLAGS += -Werror +endif + +# this version of Apple ld64 is buggy +ifneq '' '$(findstring dyld-1015.7,$(shell $(CC) $(LDFLAGS) -Wl,-v 2>&1))' + MK_CPPFLAGS += -DHAVE_BUGGY_APPLE_LINKER +endif + +# OS specific +# TODO: support Windows +ifneq '' '$(filter $(UNAME_S),Linux Darwin FreeBSD NetBSD OpenBSD Haiku)' + MK_CFLAGS += -pthread + MK_CXXFLAGS += -pthread +endif + +# detect Windows +ifneq ($(findstring _NT,$(UNAME_S)),) + _WIN32 := 1 +endif + +# library name prefix +ifneq ($(_WIN32),1) + LIB_PRE := lib +endif + +# Dynamic Shared Object extension +ifneq ($(_WIN32),1) + DSO_EXT := .so +else + DSO_EXT := .dll +endif + +# Windows Sockets 2 (Winsock) for network-capable apps +ifeq ($(_WIN32),1) + LWINSOCK2 := -lws2_32 +endif + +ifdef LLAMA_GPROF + MK_CFLAGS += -pg + MK_CXXFLAGS += -pg +endif + +# Architecture specific +# TODO: probably these flags need to be tweaked on some architectures +# feel free to update the Makefile for your architecture and send a pull request or issue + +ifndef RISCV_CROSS_COMPILE + +ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64)) + # Use all CPU extensions that are available: + MK_CFLAGS += -march=native -mtune=native + HOST_CXXFLAGS += -march=native -mtune=native + + # Usage AMX build test + #MK_CFLAGS += -march=graniterapids -mtune=graniterapids + #HOST_CXXFLAGS += -march=graniterapids -mtune=graniterapids + + # Usage AVX-only + #MK_CFLAGS += -mfma -mf16c -mavx + #MK_CXXFLAGS += -mfma -mf16c -mavx + + # Usage SSSE3-only (Not is SSE3!) + #MK_CFLAGS += -mssse3 + #MK_CXXFLAGS += -mssse3 +endif + +ifneq '' '$(findstring mingw,$(shell $(CC) -dumpmachine))' + # The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves. + # https://gcc.gnu.org/bugzilla/show_bug.cgi?id=54412 + # https://github.com/ggml-org/llama.cpp/issues/2922 + MK_CFLAGS += -Xassembler -muse-unaligned-vector-move + MK_CXXFLAGS += -Xassembler -muse-unaligned-vector-move + + # Target Windows 8 for PrefetchVirtualMemory + MK_CPPFLAGS += -D_WIN32_WINNT=0x602 +endif + +ifneq ($(filter aarch64%,$(UNAME_M)),) + # Apple M1, M2, etc. + # Raspberry Pi 3, 4, Zero 2 (64-bit) + # Nvidia Jetson + MK_CFLAGS += -mcpu=native + MK_CXXFLAGS += -mcpu=native + JETSON_RELEASE_INFO = $(shell jetson_release) + ifdef JETSON_RELEASE_INFO + ifneq ($(filter TX2%,$(JETSON_RELEASE_INFO)),) + JETSON_EOL_MODULE_DETECT = 1 + CC = aarch64-unknown-linux-gnu-gcc + cxx = aarch64-unknown-linux-gnu-g++ + endif + endif +endif + +ifneq ($(filter armv6%,$(UNAME_M)),) + # Raspberry Pi 1, Zero + MK_CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access + MK_CXXFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access +endif + +ifneq ($(filter armv7%,$(UNAME_M)),) + # Raspberry Pi 2 + MK_CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations + MK_CXXFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations +endif + +ifneq ($(filter armv8%,$(UNAME_M)),) + # Raspberry Pi 3, 4, Zero 2 (32-bit) + MK_CFLAGS += -mfp16-format=ieee -mno-unaligned-access + MK_CXXFLAGS += -mfp16-format=ieee -mno-unaligned-access +endif + +ifneq ($(filter ppc64%,$(UNAME_M)),) + POWER9_M := $(shell grep "POWER9" /proc/cpuinfo) + ifneq (,$(findstring POWER9,$(POWER9_M))) + MK_CFLAGS += -mcpu=power9 + MK_CXXFLAGS += -mcpu=power9 + endif +endif + +ifneq ($(filter ppc64le%,$(UNAME_M)),) + MK_CFLAGS += -mcpu=powerpc64le + MK_CXXFLAGS += -mcpu=powerpc64le + CUDA_POWER_ARCH = 1 +endif + +ifneq ($(filter loongarch64%,$(UNAME_M)),) + MK_CFLAGS += -mlasx + MK_CXXFLAGS += -mlasx +endif + +ifneq ($(filter riscv64%,$(UNAME_M)),) + MK_CFLAGS += -march=rv64gcv -mabi=lp64d + MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d +endif + +else # RISC-V CROSS COMPILATION + MK_CFLAGS += -march=rv64gcv -mabi=lp64d + MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d +endif + +ifndef GGML_NO_ACCELERATE + # Mac OS - include Accelerate framework. + # `-framework Accelerate` works both with Apple Silicon and Mac Intel + ifeq ($(UNAME_S),Darwin) + MK_CPPFLAGS += -DGGML_USE_ACCELERATE -DGGML_USE_BLAS -DGGML_BLAS_USE_ACCELERATE + MK_CPPFLAGS += -DACCELERATE_NEW_LAPACK + MK_CPPFLAGS += -DACCELERATE_LAPACK_ILP64 + MK_LDFLAGS += -framework Accelerate + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o + endif +endif # GGML_NO_ACCELERATE + +ifndef GGML_NO_OPENMP + MK_CPPFLAGS += -DGGML_USE_OPENMP + MK_CFLAGS += -fopenmp + MK_CXXFLAGS += -fopenmp +endif # GGML_NO_OPENMP + +ifdef GGML_OPENBLAS + MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas) + MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas) + MK_LDFLAGS += $(shell pkg-config --libs openblas) + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o +endif # GGML_OPENBLAS + +ifdef GGML_OPENBLAS64 + MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas64) + MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas64) + MK_LDFLAGS += $(shell pkg-config --libs openblas64) + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o +endif # GGML_OPENBLAS64 + +ifdef GGML_BLIS + MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_BLIS -I/usr/local/include/blis -I/usr/include/blis + MK_LDFLAGS += -lblis -L/usr/local/lib + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o +endif # GGML_BLIS + +ifdef GGML_NVPL + MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_NVPL -DNVPL_ILP64 -I/usr/local/include/nvpl_blas -I/usr/include/nvpl_blas + MK_LDFLAGS += -L/usr/local/lib -lnvpl_blas_core -lnvpl_blas_ilp64_gomp + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o +endif # GGML_NVPL + +ifndef GGML_NO_LLAMAFILE + MK_CPPFLAGS += -DGGML_USE_LLAMAFILE + OBJ_GGML_EXT += ggml/src/ggml-cpu/llamafile/sgemm.o +endif + +ifndef GGML_NO_AMX + MK_CPPFLAGS += -DGGML_USE_AMX + OBJ_GGML_EXT += ggml/src/ggml-cpu/amx/amx.o ggml/src/ggml-cpu/amx/mmq.o +endif + +# only necessary for the CPU backend files +MK_CPPFLAGS += -Iggml/src/ggml-cpu + +ifdef GGML_RPC + MK_CPPFLAGS += -DGGML_USE_RPC + OBJ_GGML_EXT += ggml/src/ggml-rpc.o +endif # GGML_RPC + +OBJ_CUDA_TMPL = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-mma*.cu)) +OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/mmq*.cu)) + +ifdef GGML_CUDA_FA_ALL_QUANTS + OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*.cu)) +else + OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu)) + OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu)) + OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*f16-f16.cu)) +endif # GGML_CUDA_FA_ALL_QUANTS + +ifdef GGML_CUDA + ifneq ('', '$(wildcard /opt/cuda)') + CUDA_PATH ?= /opt/cuda + else + CUDA_PATH ?= /usr/local/cuda + endif + + MK_CPPFLAGS += -DGGML_USE_CUDA -DGGML_CUDA_USE_GRAPHS -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include + MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L$(CUDA_PATH)/lib64/stubs -L/usr/lib/wsl/lib + MK_NVCCFLAGS += -use_fast_math + + OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o + OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu)) + OBJ_GGML_EXT += $(OBJ_CUDA_TMPL) + +ifdef LLAMA_FATAL_WARNINGS + MK_NVCCFLAGS += -Werror all-warnings +endif # LLAMA_FATAL_WARNINGS + +ifndef JETSON_EOL_MODULE_DETECT + MK_NVCCFLAGS += --forward-unknown-to-host-compiler +endif # JETSON_EOL_MODULE_DETECT + +ifdef LLAMA_DEBUG + MK_NVCCFLAGS += -lineinfo +endif # LLAMA_DEBUG + +ifdef GGML_CUDA_DEBUG + MK_NVCCFLAGS += --device-debug +endif # GGML_CUDA_DEBUG + +ifdef GGML_CUDA_NVCC + NVCC = $(CCACHE) $(GGML_CUDA_NVCC) +else + NVCC = $(CCACHE) nvcc +endif # GGML_CUDA_NVCC + +ifdef CUDA_DOCKER_ARCH + MK_NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH) +else ifndef CUDA_POWER_ARCH + MK_NVCCFLAGS += -arch=native +endif # CUDA_DOCKER_ARCH + +ifdef GGML_CUDA_FORCE_MMQ + MK_NVCCFLAGS += -DGGML_CUDA_FORCE_MMQ +endif # GGML_CUDA_FORCE_MMQ + +ifdef GGML_CUDA_FORCE_CUBLAS + MK_NVCCFLAGS += -DGGML_CUDA_FORCE_CUBLAS +endif # GGML_CUDA_FORCE_CUBLAS + +ifdef GGML_CUDA_F16 + MK_NVCCFLAGS += -DGGML_CUDA_F16 +endif # GGML_CUDA_F16 + +ifdef GGML_CUDA_DMMV_F16 + MK_NVCCFLAGS += -DGGML_CUDA_F16 +endif # GGML_CUDA_DMMV_F16 + +ifdef GGML_CUDA_PEER_MAX_BATCH_SIZE + MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(GGML_CUDA_PEER_MAX_BATCH_SIZE) +else + MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 +endif # GGML_CUDA_PEER_MAX_BATCH_SIZE + +ifdef GGML_CUDA_NO_PEER_COPY + MK_NVCCFLAGS += -DGGML_CUDA_NO_PEER_COPY +endif # GGML_CUDA_NO_PEER_COPY + +ifdef GGML_CUDA_CCBIN + MK_NVCCFLAGS += -ccbin $(GGML_CUDA_CCBIN) +endif # GGML_CUDA_CCBIN + +ifdef GGML_CUDA_NO_FA + MK_NVCCFLAGS += -DGGML_CUDA_NO_FA +endif # GGML_CUDA_NO_FA + +ifdef GGML_CUDA_FA_ALL_QUANTS + MK_NVCCFLAGS += -DGGML_CUDA_FA_ALL_QUANTS +endif # GGML_CUDA_FA_ALL_QUANTS + +ifdef JETSON_EOL_MODULE_DETECT +define NVCC_COMPILE + $(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUDA -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ +endef # NVCC_COMPILE +else +define NVCC_COMPILE + $(NVCC) $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ +endef # NVCC_COMPILE +endif # JETSON_EOL_MODULE_DETECT + +ggml/src/ggml-cuda/%.o: \ + ggml/src/ggml-cuda/%.cu \ + ggml/include/ggml.h \ + ggml/src/ggml-common.h \ + ggml/src/ggml-cuda/common.cuh + $(NVCC_COMPILE) + +ggml/src/ggml-cuda/ggml-cuda.o: \ + ggml/src/ggml-cuda/ggml-cuda.cu \ + ggml/include/ggml-cuda.h \ + ggml/include/ggml.h \ + ggml/include/ggml-backend.h \ + ggml/src/ggml-backend-impl.h \ + ggml/src/ggml-common.h \ + $(wildcard ggml/src/ggml-cuda/*.cuh) + $(NVCC_COMPILE) +endif # GGML_CUDA + +ifdef GGML_VULKAN + MK_CPPFLAGS += -DGGML_USE_VULKAN + MK_LDFLAGS += $(shell pkg-config --libs vulkan) + OBJ_GGML_EXT += ggml/src/ggml-vulkan.o ggml/src/ggml-vulkan-shaders.o + +ifdef GGML_VULKAN_CHECK_RESULTS + MK_CPPFLAGS += -DGGML_VULKAN_CHECK_RESULTS +endif + +ifdef GGML_VULKAN_DEBUG + MK_CPPFLAGS += -DGGML_VULKAN_DEBUG +endif + +ifdef GGML_VULKAN_MEMORY_DEBUG + MK_CPPFLAGS += -DGGML_VULKAN_MEMORY_DEBUG +endif + +ifdef GGML_VULKAN_PERF + MK_CPPFLAGS += -DGGML_VULKAN_PERF +endif + +ifdef GGML_VULKAN_VALIDATE + MK_CPPFLAGS += -DGGML_VULKAN_VALIDATE +endif + +ifdef GGML_VULKAN_RUN_TESTS + MK_CPPFLAGS += -DGGML_VULKAN_RUN_TESTS +endif + +GLSLC_CMD = glslc +_ggml_vk_genshaders_cmd = $(shell pwd)/vulkan-shaders-gen +_ggml_vk_header = ggml/src/ggml-vulkan-shaders.hpp +_ggml_vk_source = ggml/src/ggml-vulkan-shaders.cpp +_ggml_vk_input_dir = ggml/src/ggml-vulkan/vulkan-shaders +_ggml_vk_shader_deps = $(echo $(_ggml_vk_input_dir)/*.comp) + +ggml/src/ggml-vulkan.o: ggml/src/ggml-vulkan/ggml-vulkan.cpp ggml/include/ggml-vulkan.h $(_ggml_vk_header) $(_ggml_vk_source) + $(CXX) $(CXXFLAGS) $(shell pkg-config --cflags vulkan) -c $< -o $@ + +$(_ggml_vk_header): $(_ggml_vk_source) + +$(_ggml_vk_source): $(_ggml_vk_shader_deps) vulkan-shaders-gen + $(_ggml_vk_genshaders_cmd) \ + --glslc $(GLSLC_CMD) \ + --input-dir $(_ggml_vk_input_dir) \ + --target-hpp $(_ggml_vk_header) \ + --target-cpp $(_ggml_vk_source) + +vulkan-shaders-gen: ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp + $(CXX) $(CXXFLAGS) -o $@ $(LDFLAGS) ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp + +endif # GGML_VULKAN + +ifdef GGML_HIP + ifeq ($(wildcard /opt/rocm),) + ROCM_PATH ?= /usr + AMDGPU_TARGETS ?= $(shell $(shell which amdgpu-arch)) + else + ROCM_PATH ?= /opt/rocm + AMDGPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch) + endif + + MK_CPPFLAGS += -DGGML_USE_HIP -DGGML_USE_CUDA + + MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib + MK_LDFLAGS += -L$(ROCM_PATH)/lib64 -Wl,-rpath=$(ROCM_PATH)/lib64 + MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas + + HIPCC ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc + + HIPFLAGS += $(addprefix --offload-arch=,$(AMDGPU_TARGETS)) + +ifdef GGML_CUDA_FORCE_MMQ + HIPFLAGS += -DGGML_CUDA_FORCE_MMQ +endif # GGML_CUDA_FORCE_MMQ + +ifdef GGML_CUDA_FORCE_CUBLAS + HIPFLAGS += -DGGML_CUDA_FORCE_CUBLAS +endif # GGML_CUDA_FORCE_CUBLAS + +ifdef GGML_CUDA_NO_PEER_COPY + HIPFLAGS += -DGGML_CUDA_NO_PEER_COPY +endif # GGML_CUDA_NO_PEER_COPY + +ifdef GGML_CUDA_NO_FA + HIPFLAGS += -DGGML_CUDA_NO_FA +endif # GGML_CUDA_NO_FA + + OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o + OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu)) + OBJ_GGML_EXT += $(OBJ_CUDA_TMPL) + +ggml/src/ggml-cuda/ggml-cuda.o: \ + ggml/src/ggml-cuda/ggml-cuda.cu \ + ggml/include/ggml-cuda.h \ + ggml/include/ggml.h \ + ggml/include/ggml-backend.h \ + ggml/src/ggml-backend-impl.h \ + ggml/src/ggml-common.h \ + $(wildcard ggml/src/ggml-cuda/*.cuh) + $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< + +ggml/src/ggml-cuda/%.o: \ + ggml/src/ggml-cuda/%.cu \ + ggml/include/ggml.h \ + ggml/src/ggml-common.h \ + ggml/src/ggml-cuda/common.cuh + $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< +endif # GGML_HIP + +ifdef GGML_MUSA + ifeq ($(wildcard /opt/musa),) + MUSA_PATH ?= /usr/local/musa + else + MUSA_PATH ?= /opt/musa + endif + MUSA_ARCHITECTURES ?= 21;22;31 + + MK_CPPFLAGS += -DGGML_USE_MUSA -DGGML_USE_CUDA + MK_LDFLAGS += -L$(MUSA_PATH)/lib -Wl,-rpath=$(MUSA_PATH)/lib + MK_LDFLAGS += -lmusa -lmusart -lmublas + + ifndef GGML_NO_OPENMP + # For Ubuntu Focal + MK_CPPFLAGS += -I/usr/lib/llvm-10/include/openmp + MK_LDFLAGS += -L/usr/lib/llvm-10/lib + # For Ubuntu Jammy + MK_CPPFLAGS += -I/usr/lib/llvm-14/lib/clang/14.0.0/include + MK_LDFLAGS += -L/usr/lib/llvm-14/lib + endif # GGML_NO_OPENMP + + CC := $(MUSA_PATH)/bin/clang + CXX := $(MUSA_PATH)/bin/clang++ + MCC := $(CCACHE) $(MUSA_PATH)/bin/mcc + + MUSAFLAGS = -fsigned-char -x musa -mtgpu + MUSAFLAGS += $(foreach arch,$(subst ;, ,$(MUSA_ARCHITECTURES)),--cuda-gpu-arch=mp_$(arch)) + +ifdef GGML_CUDA_FORCE_MMQ + MUSAFLAGS += -DGGML_CUDA_FORCE_MMQ +endif # GGML_CUDA_FORCE_MMQ + +ifdef GGML_CUDA_FORCE_CUBLAS + MUSAFLAGS += -DGGML_CUDA_FORCE_CUBLAS +endif # GGML_CUDA_FORCE_CUBLAS + +ifdef GGML_CUDA_F16 + MUSAFLAGS += -DGGML_CUDA_F16 +endif # GGML_CUDA_F16 + +ifdef GGML_CUDA_DMMV_F16 + MUSAFLAGS += -DGGML_CUDA_F16 +endif # GGML_CUDA_DMMV_F16 + +ifdef GGML_CUDA_PEER_MAX_BATCH_SIZE + MUSAFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(GGML_CUDA_PEER_MAX_BATCH_SIZE) +else + MUSAFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 +endif # GGML_CUDA_PEER_MAX_BATCH_SIZE + +ifdef GGML_CUDA_NO_PEER_COPY + MUSAFLAGS += -DGGML_CUDA_NO_PEER_COPY +endif # GGML_CUDA_NO_PEER_COPY + +ifdef GGML_CUDA_NO_FA + MUSAFLAGS += -DGGML_CUDA_NO_FA +endif # GGML_CUDA_NO_FA + +ifdef GGML_CUDA_FA_ALL_QUANTS + MUSAFLAGS += -DGGML_CUDA_FA_ALL_QUANTS +endif # GGML_CUDA_FA_ALL_QUANTS + + OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o + OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu)) + OBJ_GGML_EXT += $(OBJ_CUDA_TMPL) + +ggml/src/ggml-cuda/ggml-cuda.o: \ + ggml/src/ggml-cuda/ggml-cuda.cu \ + ggml/include/ggml-cuda.h \ + ggml/include/ggml.h \ + ggml/include/ggml-backend.h \ + ggml/src/ggml-backend-impl.h \ + ggml/src/ggml-common.h \ + $(wildcard ggml/src/ggml-cuda/*.cuh) + $(MCC) $(CXXFLAGS) $(MUSAFLAGS) -c -o $@ $< + +ggml/src/ggml-cuda/%.o: \ + ggml/src/ggml-cuda/%.cu \ + ggml/include/ggml.h \ + ggml/src/ggml-common.h \ + ggml/src/ggml-cuda/common.cuh + $(MCC) $(CXXFLAGS) $(MUSAFLAGS) -c -o $@ $< +endif # GGML_MUSA + +ifdef GGML_METAL + MK_CPPFLAGS += -DGGML_USE_METAL + MK_LDFLAGS += -framework Foundation -framework Metal -framework MetalKit + OBJ_GGML_EXT += ggml/src/ggml-metal/ggml-metal.o + +ifdef GGML_METAL_USE_BF16 + MK_CPPFLAGS += -DGGML_METAL_USE_BF16 +endif # GGML_METAL_USE_BF16 +ifdef GGML_METAL_NDEBUG + MK_CPPFLAGS += -DGGML_METAL_NDEBUG +endif +ifdef GGML_METAL_EMBED_LIBRARY + MK_CPPFLAGS += -DGGML_METAL_EMBED_LIBRARY + OBJ_GGML_EXT += ggml/src/ggml-metal-embed.o +endif +endif # GGML_METAL + +ifdef GGML_METAL +ggml/src/ggml-metal/ggml-metal.o: \ + ggml/src/ggml-metal/ggml-metal.m \ + ggml/src/ggml-metal/ggml-metal-impl.h \ + ggml/include/ggml-metal.h \ + ggml/include/ggml.h + $(CC) $(CFLAGS) -c $< -o $@ + +ifdef GGML_METAL_EMBED_LIBRARY +ggml/src/ggml-metal-embed.o: \ + ggml/src/ggml-metal/ggml-metal.metal \ + ggml/src/ggml-metal/ggml-metal-impl.h \ + ggml/src/ggml-common.h + @echo "Embedding Metal library" + @sed -e '/__embed_ggml-common.h__/r ggml/src/ggml-common.h' -e '/__embed_ggml-common.h__/d' < ggml/src/ggml-metal/ggml-metal.metal > ggml/src/ggml-metal/ggml-metal-embed.metal.tmp + @sed -e '/#include "ggml-metal-impl.h"/r ggml/src/ggml-metal/ggml-metal-impl.h' -e '/#include "ggml-metal-impl.h"/d' < ggml/src/ggml-metal/ggml-metal-embed.metal.tmp > ggml/src/ggml-metal/ggml-metal-embed.metal + $(eval TEMP_ASSEMBLY=$(shell mktemp -d)) + @echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo ".incbin \"ggml/src/ggml-metal/ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + $(CC) $(CFLAGS) -c $(TEMP_ASSEMBLY)/ggml-metal-embed.s -o $@ + @rm -f ${TEMP_ASSEMBLY}/ggml-metal-embed.s + @rmdir ${TEMP_ASSEMBLY} +endif +endif # GGML_METAL + +DIR_GGML = ggml +DIR_LLAMA = src +DIR_COMMON = common + +OBJ_GGML = \ + $(DIR_GGML)/src/ggml.o \ + $(DIR_GGML)/src/ggml-alloc.o \ + $(DIR_GGML)/src/ggml-backend.o \ + $(DIR_GGML)/src/ggml-backend-reg.o \ + $(DIR_GGML)/src/ggml-opt.o \ + $(DIR_GGML)/src/ggml-quants.o \ + $(DIR_GGML)/src/ggml-threading.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu_cpp.o \ + $(DIR_GGML)/src/ggml-cpu/repack.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu-hbm.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu-quants.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu-traits.o \ + $(OBJ_GGML_EXT) + +OBJ_LLAMA = \ + $(DIR_LLAMA)/llama.o \ + $(DIR_LLAMA)/llama-vocab.o \ + $(DIR_LLAMA)/llama-grammar.o \ + $(DIR_LLAMA)/llama-sampling.o \ + $(DIR_LLAMA)/unicode.o \ + $(DIR_LLAMA)/unicode-data.o + +OBJ_COMMON = \ + $(DIR_COMMON)/common.o \ + $(DIR_COMMON)/arg.o \ + $(DIR_COMMON)/log.o \ + $(DIR_COMMON)/console.o \ + $(DIR_COMMON)/ngram-cache.o \ + $(DIR_COMMON)/sampling.o \ + $(DIR_COMMON)/speculative.o \ + $(DIR_COMMON)/chat.o \ + $(DIR_COMMON)/build-info.o \ + $(DIR_COMMON)/json-schema-to-grammar.o + +OBJ_ALL = $(OBJ_GGML) $(OBJ_LLAMA) $(OBJ_COMMON) + +LIB_GGML = $(LIB_PRE)ggml$(DSO_EXT) +LIB_GGML_S = $(LIB_PRE)ggml.a + +LIB_LLAMA = $(LIB_PRE)llama$(DSO_EXT) +LIB_LLAMA_S = $(LIB_PRE)llama.a + +LIB_COMMON = $(LIB_PRE)common$(DSO_EXT) +LIB_COMMON_S = $(LIB_PRE)common.a + +LIB_ALL = $(LIB_GGML) $(LIB_LLAMA) $(LIB_COMMON) +LIB_ALL_S = $(LIB_GGML_S) $(LIB_LLAMA_S) $(LIB_COMMON_S) + +GF_CC := $(CC) +include scripts/get-flags.mk + +# combine build flags with cmdline overrides +override CPPFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) +override CFLAGS := $(CPPFLAGS) $(MK_CFLAGS) $(GF_CFLAGS) $(CFLAGS) +BASE_CXXFLAGS := $(MK_CXXFLAGS) $(CXXFLAGS) +override CXXFLAGS := $(BASE_CXXFLAGS) $(HOST_CXXFLAGS) $(GF_CXXFLAGS) $(CPPFLAGS) +override NVCCFLAGS := $(MK_NVCCFLAGS) $(NVCCFLAGS) +override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS) + +# identify CUDA host compiler +ifdef GGML_CUDA +GF_CC := $(NVCC) $(NVCCFLAGS) 2>/dev/null .c -Xcompiler +include scripts/get-flags.mk +CUDA_CXXFLAGS := $(BASE_CXXFLAGS) $(GF_CXXFLAGS) -Wno-pedantic +endif + +ifdef LLAMA_CURL +override CXXFLAGS := $(CXXFLAGS) -DLLAMA_USE_CURL +override LDFLAGS := $(LDFLAGS) -lcurl +endif + +# +# Print build information +# + +$(info I llama.cpp build info: ) +$(info I UNAME_S: $(UNAME_S)) +$(info I UNAME_P: $(UNAME_P)) +$(info I UNAME_M: $(UNAME_M)) +$(info I CFLAGS: $(CFLAGS)) +$(info I CXXFLAGS: $(CXXFLAGS)) +$(info I NVCCFLAGS: $(NVCCFLAGS)) +$(info I LDFLAGS: $(LDFLAGS)) +$(info I CC: $(shell $(CC) --version | head -n 1)) +$(info I CXX: $(shell $(CXX) --version | head -n 1)) +ifdef GGML_CUDA +$(info I NVCC: $(shell $(NVCC) --version | tail -n 1)) +CUDA_VERSION := $(shell $(NVCC) --version | grep -oP 'release (\K[0-9]+\.[0-9])') +ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1) + +ifndef CUDA_DOCKER_ARCH +ifndef CUDA_POWER_ARCH +$(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via environment variable CUDA_DOCKER_ARCH, e.g. by running "export CUDA_DOCKER_ARCH=compute_XX" on Unix-like systems, where XX is the minimum compute capability that the code needs to run on. A list with compute capabilities can be found here: https://developer.nvidia.com/cuda-gpus ) +endif # CUDA_POWER_ARCH +endif # CUDA_DOCKER_ARCH + +endif # eq ($(shell echo "$(CUDA_VERSION) < 11.7" | bc),1) +endif # GGML_CUDA +$(info ) + +ifdef DEPRECATE_WARNING +$(info !!! DEPRECATION WARNING !!!) +$(info The following LLAMA_ options are deprecated and will be removed in the future. Use the GGML_ prefix instead) +$(info - LLAMA_CUDA) +$(info - LLAMA_METAL) +$(info - LLAMA_METAL_EMBED_LIBRARY) +$(info - LLAMA_OPENMP) +$(info - LLAMA_RPC) +$(info - LLAMA_SYCL) +$(info - LLAMA_SYCL_F16) +$(info - LLAMA_OPENBLAS) +$(info - LLAMA_OPENBLAS64) +$(info - LLAMA_BLIS) +$(info - LLAMA_NO_LLAMAFILE) +$(info - LLAMA_NO_ACCELERATE) +$(info - LLAMA_NO_OPENMP) +$(info - LLAMA_NO_METAL) +$(info - LLAMA_NO_CCACHE) +$(info ) +endif + +ifdef REMOVE_WARNING +$(info !!! REMOVAL WARNING !!!) +$(info The following LLAMA_ options have been removed and are no longer supported) +$(info - LLAMA_DISABLE_LOGS (https://github.com/ggml-org/llama.cpp/pull/9418)) +$(info - LLAMA_SERVER_VERBOSE (https://github.com/ggml-org/llama.cpp/pull/9418)) +$(info ) +endif + +# +# Build libraries +# + +# Libraries +LIB_GGML = libggml.so +LIB_GGML_S = libggml.a + +LIB_LLAMA = libllama.so +LIB_LLAMA_S = libllama.a + +LIB_COMMON = libcommon.so +LIB_COMMON_S = libcommon.a + +# Targets +BUILD_TARGETS += $(LIB_GGML) $(LIB_GGML_S) $(LIB_LLAMA) $(LIB_LLAMA_S) $(LIB_COMMON) $(LIB_COMMON_S) + +# Dependency files +DEP_FILES = $(OBJ_GGML:.o=.d) $(OBJ_LLAMA:.o=.d) $(OBJ_COMMON:.o=.d) + +# Default target +all: $(BUILD_TARGETS) + +# force c++ build for source file that have same name as c file +# Note: need this exception because `ggml-cpu.c` and `ggml-cpu.cpp` both produce the same obj/dep files +$(DIR_GGML)/%_cpp.o: $(DIR_GGML)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ + +# Rules for building object files +$(DIR_GGML)/%.o: $(DIR_GGML)/%.c + $(CC) $(CFLAGS) -MMD -c $< -o $@ + +$(DIR_GGML)/%.o: $(DIR_GGML)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ + +$(DIR_LLAMA)/%.o: $(DIR_LLAMA)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ + +$(DIR_COMMON)/%.o: $(DIR_COMMON)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ + +# Rules for building libraries +$(LIB_GGML): $(OBJ_GGML) + $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) + +$(LIB_GGML_S): $(OBJ_GGML) + ar rcs $(LIB_GGML_S) $^ + +$(LIB_LLAMA): $(OBJ_LLAMA) $(LIB_GGML) + $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) + +$(LIB_LLAMA_S): $(OBJ_LLAMA) + ar rcs $(LIB_LLAMA_S) $^ + +$(LIB_COMMON): $(OBJ_COMMON) $(LIB_LLAMA) $(LIB_GGML) + $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) + +$(LIB_COMMON_S): $(OBJ_COMMON) + ar rcs $(LIB_COMMON_S) $^ + +# Include dependency files +-include $(DEP_FILES) + +# Clean generated server assets +clean-server-assets: + find tools/server -type f -name "*.js.hpp" -delete + find tools/server -type f -name "*.mjs.hpp" -delete + find tools/server -type f -name "*.css.hpp" -delete + find tools/server -type f -name "*.html.hpp" -delete + +# Clean rule +clean: clean-server-assets + rm -vrf $(BUILD_TARGETS) $(TEST_TARGETS) + rm -rvf *.a *.dll *.so *.dot + find ggml src common tests examples pocs -type f -name "*.o" -delete + find ggml src common tests examples pocs -type f -name "*.d" -delete + +# +# Examples +# + +# $< is the first prerequisite, i.e. the source file. +# Explicitly compile this to an object file so that it can be cached with ccache. +# The source file is then filtered out from $^ (the list of all prerequisites) and the object file is added instead. + +# Helper function that replaces .c, .cpp, and .cu file endings with .o: +GET_OBJ_FILE = $(patsubst %.c,%.o,$(patsubst %.cpp,%.o,$(patsubst %.cu,%.o,$(1)))) + +llama-cli: tools/main/main.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + @echo + @echo '==== Run ./llama-cli -h for help. ====' + @echo + +llama-run: tools/run/run.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-simple: examples/simple/simple.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-simple-chat: examples/simple-chat/simple-chat.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-tokenize: tools/tokenize/tokenize.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-batched: examples/batched/batched.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-batched-bench: tools/batched-bench/batched-bench.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-quantize: tools/quantize/quantize.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-quantize-stats: tools/quantize-stats/quantize-stats.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-perplexity: tools/perplexity/perplexity.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-imatrix: tools/imatrix/imatrix.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-embedding: examples/embedding/embedding.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-gritlm: examples/gritlm/gritlm.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-save-load-state: examples/save-load-state/save-load-state.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-gguf: examples/gguf/gguf.cpp \ + $(OBJ_GGML) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +examples/gguf-hash/deps/sha1/sha1.o: \ + examples/gguf-hash/deps/sha1/sha1.c + $(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@ + +examples/gguf-hash/deps/xxhash/xxhash.o: \ + examples/gguf-hash/deps/xxhash/xxhash.c + $(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@ + +examples/gguf-hash/deps/sha256/sha256.o: \ + examples/gguf-hash/deps/sha256/sha256.c + $(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@ + +llama-gguf-hash: examples/gguf-hash/gguf-hash.cpp examples/gguf-hash/deps/sha1/sha1.o examples/gguf-hash/deps/xxhash/xxhash.o examples/gguf-hash/deps/sha256/sha256.o\ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -Iexamples/gguf-hash/deps -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-gguf-split: tools/gguf-split/gguf-split.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-eval-callback: examples/eval-callback/eval-callback.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-cvector-generator: tools/cvector-generator/cvector-generator.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-bench: tools/llama-bench/llama-bench.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-export-lora: tools/export-lora/export-lora.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-retrieval: examples/retrieval/retrieval.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-speculative: examples/speculative/speculative.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-parallel: examples/parallel/parallel.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-lookahead: examples/lookahead/lookahead.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-lookup: examples/lookup/lookup.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-lookup-create: examples/lookup/lookup-create.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-lookup-merge: examples/lookup/lookup-merge.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-lookup-stats: examples/lookup/lookup-stats.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-passkey: examples/passkey/passkey.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-gbnf-validator: examples/gbnf-validator/gbnf-validator.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +ifdef GGML_RPC +rpc-server: tools/rpc/rpc-server.cpp \ + $(OBJ_GGML) + $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) +endif # GGML_RPC + +llama-server: \ + tools/server/server.cpp \ + tools/server/utils.hpp \ + tools/server/httplib.h \ + tools/server/index.html.hpp \ + tools/server/loading.html.hpp \ + common/chat.cpp \ + common/chat.h \ + common/chat-template.hpp \ + common/json.hpp \ + common/minja.hpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Itools/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2) + +# Portable equivalent of `cd tools/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`: +tools/server/%.hpp: tools/server/public/% FORCE Makefile + @( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \ + echo "unsigned char $${NAME}[] = {" && \ + cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \ + echo "};" && \ + echo "unsigned int $${NAME}_len = $(shell cat $< | wc -c );" \ + ) > $@ + +llama-gen-docs: examples/gen-docs/gen-docs.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +libllava.a: tools/mtmd/llava.cpp \ + tools/mtmd/llava.h \ + tools/mtmd/clip.cpp \ + tools/mtmd/clip.h \ + common/stb_image.h \ + common/base64.hpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual + +llama-llava-cli: tools/mtmd/llava-cli.cpp \ + tools/mtmd/llava.cpp \ + tools/mtmd/llava.h \ + tools/mtmd/clip.cpp \ + tools/mtmd/clip.h \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual + +llama-minicpmv-cli: tools/mtmd/minicpmv-cli.cpp \ + tools/mtmd/llava.cpp \ + tools/mtmd/llava.h \ + tools/mtmd/clip.cpp \ + tools/mtmd/clip.h \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual + +llama-qwen2vl-cli: tools/mtmd/qwen2vl-cli.cpp \ + tools/mtmd/llava.cpp \ + tools/mtmd/llava.h \ + tools/mtmd/clip.cpp \ + tools/mtmd/clip.h \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual + +ifeq ($(UNAME_S),Darwin) +swift: examples/batched.swift + (cd examples/batched.swift; make build) +endif + +common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh + @sh scripts/build-info.sh "$(CC)" > $@.tmp + @if ! cmp -s $@.tmp $@; then \ + mv $@.tmp $@; \ + else \ + rm $@.tmp; \ + fi + +common/build-info.o: common/build-info.cpp + $(CXX) $(CXXFLAGS) -c $(filter-out %.h,$^) -o $@ + +# +# Tests +# + +tests: $(TEST_TARGETS) + +tests/test-arg-parser: tests/test-arg-parser.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-llama-grammar: tests/test-llama-grammar.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-log: tests/test-log.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-grammar-parser: tests/test-grammar-parser.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-grammar-integration: tests/test-grammar-integration.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-double-float: tests/test-double-float.cpp + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -Itools/server -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-chat: tests/test-chat.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -Itools/server -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-opt: tests/test-opt.cpp \ + $(OBJ_GGML) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-quantize-fns: tests/test-quantize-fns.cpp \ + $(OBJ_GGML) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-quantize-perf: tests/test-quantize-perf.cpp \ + $(OBJ_GGML) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-sampling: tests/test-sampling.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-tokenizer-0: tests/test-tokenizer-0.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-tokenizer-1-spm: tests/test-tokenizer-1-spm.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-rope: tests/test-rope.cpp ggml/src/ggml.o \ + $(OBJ_GGML) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-c.o: tests/test-c.c include/llama.h + $(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@ + +tests/test-backend-ops: tests/test-backend-ops.cpp \ + $(OBJ_GGML) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-model-load-cancel: tests/test-model-load-cancel.cpp tests/get-model.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-autorelease: tests/test-autorelease.cpp tests/get-model.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-chat-template: tests/test-chat-template.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +# +# PoCs +# + +llama-vdot: pocs/vdot/vdot.cpp ggml/src/ggml.o \ + $(OBJ_GGML) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-q8dot: pocs/vdot/q8dot.cpp ggml/src/ggml.o \ + $(OBJ_GGML) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +# +# Deprecated binaries that we want to keep around long enough for people to migrate to the new filenames, then these can be removed. +# +# Mark legacy binary targets as .PHONY so that they are always checked. +.PHONY: FORCE main quantize perplexity embedding server + +# Define the object file target +examples/deprecation-warning/deprecation-warning.o: examples/deprecation-warning/deprecation-warning.cpp + $(CXX) $(CXXFLAGS) -c $< -o $@ + +# NOTE: We currently will always build the deprecation-warning `main` and `server` binaries to help users migrate. +# Eventually we will want to remove these target from building all the time. +main: examples/deprecation-warning/deprecation-warning.o + $(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS) + @echo "NOTICE: The 'main' binary is deprecated. Please use 'llama-cli' instead." + +server: examples/deprecation-warning/deprecation-warning.o + $(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS) + @echo "NOTICE: The 'server' binary is deprecated. Please use 'llama-server' instead." + +quantize: examples/deprecation-warning/deprecation-warning.o +ifneq (,$(wildcard quantize)) + $(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS) + @echo "#########" + @echo "WARNING: The 'quantize' binary is deprecated. Please use 'llama-quantize' instead." + @echo " Remove the 'quantize' binary to remove this warning." + @echo "#########" +endif + +perplexity: examples/deprecation-warning/deprecation-warning.o +ifneq (,$(wildcard perplexity)) + $(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS) + @echo "#########" + @echo "WARNING: The 'perplexity' binary is deprecated. Please use 'llama-perplexity' instead." + @echo " Remove the 'perplexity' binary to remove this warning." + @echo "#########" +endif + +embedding: examples/deprecation-warning/deprecation-warning.o +ifneq (,$(wildcard embedding)) + $(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS) + @echo "#########" + @echo "WARNING: The 'embedding' binary is deprecated. Please use 'llama-embedding' instead." + @echo " Remove the 'embedding' binary to remove this warning." + @echo "#########" +endif diff --git a/llama.cpp/README.md b/llama.cpp/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d2caf84f99b5f9dea21a909eadd4ab37d5903a5b --- /dev/null +++ b/llama.cpp/README.md @@ -0,0 +1,603 @@ +# llama.cpp + +![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png) + +[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) +[![Release](https://img.shields.io/github/v/release/ggml-org/llama.cpp)](https://github.com/ggml-org/llama.cpp/releases) +[![Server](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml/badge.svg)](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml) + +[Manifesto](https://github.com/ggml-org/llama.cpp/discussions/205) / [ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) + +LLM inference in C/C++ + +## Recent API changes + +- [Changelog for `libllama` API](https://github.com/ggml-org/llama.cpp/issues/9289) +- [Changelog for `llama-server` REST API](https://github.com/ggml-org/llama.cpp/issues/9291) + +## Hot topics + +- Hot PRs: [All](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+) | [Open](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+is%3Aopen) +- Multimodal support arrived in `llama-server`: [#12898](https://github.com/ggml-org/llama.cpp/pull/12898) | [documentation](./docs/multimodal.md) +- VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode +- Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim +- Introducing GGUF-my-LoRA https://github.com/ggml-org/llama.cpp/discussions/10123 +- Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggml-org/llama.cpp/discussions/9669 +- Hugging Face GGUF editor: [discussion](https://github.com/ggml-org/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor) + +---- + +## Quick start + +Getting started with llama.cpp is straightforward. Here are several ways to install it on your machine: + +- Install `llama.cpp` using [brew, nix or winget](docs/install.md) +- Run with Docker - see our [Docker documentation](docs/docker.md) +- Download pre-built binaries from the [releases page](https://github.com/ggml-org/llama.cpp/releases) +- Build from source by cloning this repository - check out [our build guide](docs/build.md) + +Once installed, you'll need a model to work with. Head to the [Obtaining and quantizing models](#obtaining-and-quantizing-models) section to learn more. + +Example command: + +```sh +# Use a local model file +llama-cli -m my_model.gguf + +# Or download and run a model directly from Hugging Face +llama-cli -hf ggml-org/gemma-3-1b-it-GGUF + +# Launch OpenAI-compatible API server +llama-server -hf ggml-org/gemma-3-1b-it-GGUF +``` + +## Description + +The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide +range of hardware - locally and in the cloud. + +- Plain C/C++ implementation without any dependencies +- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks +- AVX, AVX2, AVX512 and AMX support for x86 architectures +- 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use +- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA) +- Vulkan and SYCL backend support +- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity + +The `llama.cpp` project is the main playground for developing new features for the [ggml](https://github.com/ggml-org/ggml) library. + +
+Models + +Typically finetunes of the base models below are supported as well. + +Instructions for adding support for new models: [HOWTO-add-model.md](docs/development/HOWTO-add-model.md) + +#### Text-only + +- [X] LLaMA 🦙 +- [x] LLaMA 2 🦙🦙 +- [x] LLaMA 3 🦙🦙🦙 +- [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) +- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) +- [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct) +- [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon) +- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) +- [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne) +- [X] [BERT](https://github.com/ggml-org/llama.cpp/pull/5423) +- [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/) +- [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft) +- [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila) +- [X] [Starcoder models](https://github.com/ggml-org/llama.cpp/pull/3187) +- [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim) +- [X] [MPT](https://github.com/ggml-org/llama.cpp/pull/3417) +- [X] [Bloom](https://github.com/ggml-org/llama.cpp/pull/3553) +- [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi) +- [X] [StableLM models](https://huggingface.co/stabilityai) +- [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek) +- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen) +- [x] [PLaMo-13B](https://github.com/ggml-org/llama.cpp/pull/3557) +- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi) +- [x] [PhiMoE](https://github.com/ggml-org/llama.cpp/pull/11003) +- [x] [GPT-2](https://huggingface.co/gpt2) +- [x] [Orion 14B](https://github.com/ggml-org/llama.cpp/pull/5118) +- [x] [InternLM2](https://huggingface.co/models?search=internlm2) +- [x] [CodeShell](https://github.com/WisdomShell/codeshell) +- [x] [Gemma](https://ai.google.dev/gemma) +- [x] [Mamba](https://github.com/state-spaces/mamba) +- [x] [Grok-1](https://huggingface.co/keyfan/grok-1-hf) +- [x] [Xverse](https://huggingface.co/models?search=xverse) +- [x] [Command-R models](https://huggingface.co/models?search=CohereForAI/c4ai-command-r) +- [x] [SEA-LION](https://huggingface.co/models?search=sea-lion) +- [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B) +- [x] [OLMo](https://allenai.org/olmo) +- [x] [OLMo 2](https://allenai.org/olmo) +- [x] [OLMoE](https://huggingface.co/allenai/OLMoE-1B-7B-0924) +- [x] [Granite models](https://huggingface.co/collections/ibm-granite/granite-code-models-6624c5cec322e4c148c8b330) +- [x] [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) + [Pythia](https://github.com/EleutherAI/pythia) +- [x] [Snowflake-Arctic MoE](https://huggingface.co/collections/Snowflake/arctic-66290090abe542894a5ac520) +- [x] [Smaug](https://huggingface.co/models?search=Smaug) +- [x] [Poro 34B](https://huggingface.co/LumiOpen/Poro-34B) +- [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM) +- [x] [Flan T5](https://huggingface.co/models?search=flan-t5) +- [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca) +- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat) +- [x] [GLM-4-0414](https://huggingface.co/collections/THUDM/glm-4-0414-67f3cbcb34dd9d252707cb2e) +- [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966) +- [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct) +- [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a) +- [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat) +- [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a) +- [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM) +- [x] [QRWKV-6](https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1) +- [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct) +- [X] [Trillion-7B-preview](https://huggingface.co/trillionlabs/Trillion-7B-preview) +- [x] [Ling models](https://huggingface.co/collections/inclusionAI/ling-67c51c85b34a7ea0aba94c32) +- [x] [LFM2 models](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38) + +#### Multimodal + +- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e), [LLaVA 1.6 models](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2) +- [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava) +- [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5) +- [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V) +- [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM) +- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL) +- [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM) +- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2) +- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny) +- [x] [GLM-EDGE](https://huggingface.co/models?search=glm-edge) +- [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d) + +
+ +
+Bindings + +- Python: [ddh0/easy-llama](https://github.com/ddh0/easy-llama) +- Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python) +- Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp) +- Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp) +- JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp) +- JS/TS (Programmable Prompt Engine CLI): [offline-ai/cli](https://github.com/offline-ai/cli) +- JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm) +- Typescript/Wasm (nicer API, available on npm): [ngxson/wllama](https://github.com/ngxson/wllama) +- Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb) +- Rust (more features): [edgenai/llama_cpp-rs](https://github.com/edgenai/llama_cpp-rs) +- Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp) +- Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs) +- Rust (automated build from crates.io): [ShelbyJenkins/llm_client](https://github.com/ShelbyJenkins/llm_client) +- C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp) +- C#/VB.NET (more features - community license): [LM-Kit.NET](https://docs.lm-kit.com/lm-kit-net/index.html) +- Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s) +- Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj) +- React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn) +- Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp) +- Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig) +- Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart) +- Flutter: [xuegao-tzx/Fllama](https://github.com/xuegao-tzx/Fllama) +- PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggml-org/llama.cpp/pull/6326) +- Guile Scheme: [guile_llama_cpp](https://savannah.nongnu.org/projects/guile-llama-cpp) +- Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift) +- Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama) +- Delphi [Embarcadero/llama-cpp-delphi](https://github.com/Embarcadero/llama-cpp-delphi) + +
+ +
+UIs + +*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* + +- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT) +- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT) +- [Dot](https://github.com/alexpinel/Dot) (GPL) +- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT) +- [iohub/collama](https://github.com/iohub/coLLaMA) (Apache-2.0) +- [janhq/jan](https://github.com/janhq/jan) (AGPL) +- [johnbean393/Sidekick](https://github.com/johnbean393/Sidekick) (MIT) +- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file) (Apache-2.0) +- [KodiBot](https://github.com/firatkiral/kodibot) (GPL) +- [llama.vim](https://github.com/ggml-org/llama.vim) (MIT) +- [LARS](https://github.com/abgulati/LARS) (AGPL) +- [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL) +- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT) +- [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT) +- [LMStudio](https://lmstudio.ai/) (proprietary) +- [LocalAI](https://github.com/mudler/LocalAI) (MIT) +- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL) +- [MindMac](https://mindmac.app) (proprietary) +- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT) +- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT) +- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) (Apache-2.0) +- [nat/openplayground](https://github.com/nat/openplayground) (MIT) +- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) (MIT) +- [ollama/ollama](https://github.com/ollama/ollama) (MIT) +- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL) +- [PocketPal AI](https://github.com/a-ghorbani/pocketpal-ai) (MIT) +- [psugihara/FreeChat](https://github.com/psugihara/FreeChat) (MIT) +- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) (MIT) +- [pythops/tenere](https://github.com/pythops/tenere) (AGPL) +- [ramalama](https://github.com/containers/ramalama) (MIT) +- [semperai/amica](https://github.com/semperai/amica) (MIT) +- [withcatai/catai](https://github.com/withcatai/catai) (MIT) +- [Autopen](https://github.com/blackhole89/autopen) (GPL) + +
+ +
+Tools + +- [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML +- [akx/ollama-dl](https://github.com/akx/ollama-dl) – download models from the Ollama library to be used directly with llama.cpp +- [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption +- [gpustack/gguf-parser](https://github.com/gpustack/gguf-parser-go/tree/main/cmd/gguf-parser) - review/check the GGUF file and estimate the memory usage +- [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers and a model example) + +
+ +
+Infrastructure + +- [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp +- [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs +- [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly +- [llama-swap](https://github.com/mostlygeek/llama-swap) - transparent proxy that adds automatic model switching with llama-server +- [Kalavai](https://github.com/kalavai-net/kalavai-client) - Crowdsource end to end LLM deployment at any scale +- [llmaz](https://github.com/InftyAI/llmaz) - ☸️ Easy, advanced inference platform for large language models on Kubernetes. +
+ +
+Games + +- [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you. + +
+ + +## Supported backends + +| Backend | Target devices | +| --- | --- | +| [Metal](docs/build.md#metal-build) | Apple Silicon | +| [BLAS](docs/build.md#blas-build) | All | +| [BLIS](docs/backend/BLIS.md) | All | +| [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU | +| [MUSA](docs/build.md#musa) | Moore Threads GPU | +| [CUDA](docs/build.md#cuda) | Nvidia GPU | +| [HIP](docs/build.md#hip) | AMD GPU | +| [Vulkan](docs/build.md#vulkan) | GPU | +| [CANN](docs/build.md#cann) | Ascend NPU | +| [OpenCL](docs/backend/OPENCL.md) | Adreno GPU | +| [WebGPU [In Progress]](docs/build.md#webgpu) | All | +| [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All | + +## Obtaining and quantizing models + +The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](https://huggingface.co/models?library=gguf&sort=trending) compatible with `llama.cpp`: + +- [Trending](https://huggingface.co/models?library=gguf&sort=trending) +- [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf) + +You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from [Hugging Face](https://huggingface.co/) or other model hosting sites, such as [ModelScope](https://modelscope.cn/), by using this CLI argument: `-hf /[:quant]`. For example: + +```sh +llama-cli -hf ggml-org/gemma-3-1b-it-GGUF +``` + +By default, the CLI would download from Hugging Face, you can switch to other options with the environment variable `MODEL_ENDPOINT`. For example, you may opt to downloading model checkpoints from ModelScope or other model sharing communities by setting the environment variable, e.g. `MODEL_ENDPOINT=https://www.modelscope.cn/`. + +After downloading a model, use the CLI tools to run it locally - see below. + +`llama.cpp` requires the model to be stored in the [GGUF](https://github.com/ggml-org/ggml/blob/master/docs/gguf.md) file format. Models in other data formats can be converted to GGUF using the `convert_*.py` Python scripts in this repo. + +The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with `llama.cpp`: + +- Use the [GGUF-my-repo space](https://huggingface.co/spaces/ggml-org/gguf-my-repo) to convert to GGUF format and quantize model weights to smaller sizes +- Use the [GGUF-my-LoRA space](https://huggingface.co/spaces/ggml-org/gguf-my-lora) to convert LoRA adapters to GGUF format (more info: https://github.com/ggml-org/llama.cpp/discussions/10123) +- Use the [GGUF-editor space](https://huggingface.co/spaces/CISCai/gguf-editor) to edit GGUF meta data in the browser (more info: https://github.com/ggml-org/llama.cpp/discussions/9268) +- Use the [Inference Endpoints](https://ui.endpoints.huggingface.co/) to directly host `llama.cpp` in the cloud (more info: https://github.com/ggml-org/llama.cpp/discussions/9669) + +To learn more about model quantization, [read this documentation](tools/quantize/README.md) + +## [`llama-cli`](tools/main) + +#### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality. + +-
+ Run in conversation mode + + Models with a built-in chat template will automatically activate conversation mode. If this doesn't occur, you can manually enable it by adding `-cnv` and specifying a suitable chat template with `--chat-template NAME` + + ```bash + llama-cli -m model.gguf + + # > hi, who are you? + # Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today? + # + # > what is 1+1? + # Easy peasy! The answer to 1+1 is... 2! + ``` + +
+ +-
+ Run in conversation mode with custom chat template + + ```bash + # use the "chatml" template (use -h to see the list of supported templates) + llama-cli -m model.gguf -cnv --chat-template chatml + + # use a custom template + llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:' + ``` + +
+ +-
+ Run simple text completion + + To disable conversation mode explicitly, use `-no-cnv` + + ```bash + llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128 -no-cnv + + # I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey. + ``` + +
+ +-
+ Constrain the output with a custom grammar + + ```bash + llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:' + + # {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"} + ``` + + The [grammars/](grammars/) folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](grammars/README.md). + + For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/ + +
+ + +## [`llama-server`](tools/server) + +#### A lightweight, [OpenAI API](https://github.com/openai/openai-openapi) compatible, HTTP server for serving LLMs. + +-
+ Start a local HTTP server with default configuration on port 8080 + + ```bash + llama-server -m model.gguf --port 8080 + + # Basic web UI can be accessed via browser: http://localhost:8080 + # Chat completion endpoint: http://localhost:8080/v1/chat/completions + ``` + +
+ +-
+ Support multiple-users and parallel decoding + + ```bash + # up to 4 concurrent requests, each with 4096 max context + llama-server -m model.gguf -c 16384 -np 4 + ``` + +
+ +-
+ Enable speculative decoding + + ```bash + # the draft.gguf model should be a small variant of the target model.gguf + llama-server -m model.gguf -md draft.gguf + ``` + +
+ +-
+ Serve an embedding model + + ```bash + # use the /embedding endpoint + llama-server -m model.gguf --embedding --pooling cls -ub 8192 + ``` + +
+ +-
+ Serve a reranking model + + ```bash + # use the /reranking endpoint + llama-server -m model.gguf --reranking + ``` + +
+ +-
+ Constrain all outputs with a grammar + + ```bash + # custom grammar + llama-server -m model.gguf --grammar-file grammar.gbnf + + # JSON + llama-server -m model.gguf --grammar-file grammars/json.gbnf + ``` + +
+ + +## [`llama-perplexity`](tools/perplexity) + +#### A tool for measuring the [perplexity](tools/perplexity/README.md) [^1] (and other quality metrics) of a model over a given text. + +-
+ Measure the perplexity over a text file + + ```bash + llama-perplexity -m model.gguf -f file.txt + + # [1]15.2701,[2]5.4007,[3]5.3073,[4]6.2965,[5]5.8940,[6]5.6096,[7]5.7942,[8]4.9297, ... + # Final estimate: PPL = 5.4007 +/- 0.67339 + ``` + +
+ +-
+ Measure KL divergence + + ```bash + # TODO + ``` + +
+ +[^1]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity) + +## [`llama-bench`](tools/llama-bench) + +#### Benchmark the performance of the inference for various parameters. + +-
+ Run default benchmark + + ```bash + llama-bench -m model.gguf + + # Output: + # | model | size | params | backend | threads | test | t/s | + # | ------------------- | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: | + # | qwen2 1.5B Q4_0 | 885.97 MiB | 1.54 B | Metal,BLAS | 16 | pp512 | 5765.41 ± 20.55 | + # | qwen2 1.5B Q4_0 | 885.97 MiB | 1.54 B | Metal,BLAS | 16 | tg128 | 197.71 ± 0.81 | + # + # build: 3e0ba0e60 (4229) + ``` + +
+ +## [`llama-run`](tools/run) + +#### A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama [^3]. + +-
+ Run a model with a specific prompt (by default it's pulled from Ollama registry) + + ```bash + llama-run granite-code + ``` + +
+ +[^3]: [RamaLama](https://github.com/containers/ramalama) + +## [`llama-simple`](examples/simple) + +#### A minimal example for implementing apps with `llama.cpp`. Useful for developers. + +-
+ Basic text completion + + ```bash + llama-simple -m model.gguf + + # Hello my name is Kaitlyn and I am a 16 year old girl. I am a junior in high school and I am currently taking a class called "The Art of + ``` + +
+ + +## Contributing + +- Contributors can open PRs +- Collaborators can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch +- Collaborators will be invited based on contributions +- Any help with managing issues, PRs and projects is very appreciated! +- See [good first issues](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions +- Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information +- Make sure to read this: [Inference at the edge](https://github.com/ggml-org/llama.cpp/discussions/205) +- A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532) + +## Other documentation + +- [main (cli)](tools/main/README.md) +- [server](tools/server/README.md) +- [GBNF grammars](grammars/README.md) + +#### Development documentation + +- [How to build](docs/build.md) +- [Running on Docker](docs/docker.md) +- [Build on Android](docs/android.md) +- [Performance troubleshooting](docs/development/token_generation_performance_tips.md) +- [GGML tips & tricks](https://github.com/ggml-org/llama.cpp/wiki/GGML-Tips-&-Tricks) + +#### Seminal papers and background on the models + +If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT: +- LLaMA: + - [Introducing LLaMA: A foundational, 65-billion-parameter large language model](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) + - [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) +- GPT-3 + - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) +- GPT-3.5 / InstructGPT / ChatGPT: + - [Aligning language models to follow instructions](https://openai.com/research/instruction-following) + - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155) + +## XCFramework +The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS, +and macOS. It can be used in Swift projects without the need to compile the +library from source. For example: +```swift +// swift-tools-version: 5.10 +// The swift-tools-version declares the minimum version of Swift required to build this package. + +import PackageDescription + +let package = Package( + name: "MyLlamaPackage", + targets: [ + .executableTarget( + name: "MyLlamaPackage", + dependencies: [ + "LlamaFramework" + ]), + .binaryTarget( + name: "LlamaFramework", + url: "https://github.com/ggml-org/llama.cpp/releases/download/b5046/llama-b5046-xcframework.zip", + checksum: "c19be78b5f00d8d29a25da41042cb7afa094cbf6280a225abe614b03b20029ab" + ) + ] +) +``` +The above example is using an intermediate build `b5046` of the library. This can be modified +to use a different version by changing the URL and checksum. + +## Completions +Command-line completion is available for some environments. + +#### Bash Completion +```bash +$ build/bin/llama-cli --completion-bash > ~/.llama-completion.bash +$ source ~/.llama-completion.bash +``` +Optionally this can be added to your `.bashrc` or `.bash_profile` to load it +automatically. For example: +```console +$ echo "source ~/.llama-completion.bash" >> ~/.bashrc +``` + +## Dependencies + +- [yhirose/cpp-httplib](https://github.com/yhirose/cpp-httplib) - Single-header HTTP server, used by `llama-server` - MIT license +- [stb-image](https://github.com/nothings/stb) - Single-header image format decoder, used by multimodal subsystem - Public domain +- [nlohmann/json](https://github.com/nlohmann/json) - Single-header JSON library, used by various tools/examples - MIT License +- [minja](https://github.com/google/minja) - Minimal Jinja parser in C++, used by various tools/examples - MIT License +- [linenoise.cpp](./tools/run/linenoise.cpp/linenoise.cpp) - C++ library that provides readline-like line editing capabilities, used by `llama-run` - BSD 2-Clause License +- [curl](https://curl.se/) - Client-side URL transfer library, used by various tools/examples - [CURL License](https://curl.se/docs/copyright.html) +- [miniaudio.h](https://github.com/mackron/miniaudio) - Single-header audio format decoder, used by multimodal subsystem - Public domain diff --git a/llama.cpp/SECURITY.md b/llama.cpp/SECURITY.md new file mode 100644 index 0000000000000000000000000000000000000000..520df4373cf3388424cc2c8058a94ca7ed0bd96e --- /dev/null +++ b/llama.cpp/SECURITY.md @@ -0,0 +1,68 @@ +# Security Policy + + - [**Using llama.cpp securely**](#using-llamacpp-securely) + - [Untrusted models](#untrusted-models) + - [Untrusted inputs](#untrusted-inputs) + - [Data privacy](#data-privacy) + - [Untrusted environments or networks](#untrusted-environments-or-networks) + - [Multi-Tenant environments](#multi-tenant-environments) + - [**Reporting a vulnerability**](#reporting-a-vulnerability) + +## Using llama.cpp securely + +### Untrusted models +Be careful when running untrusted models. This classification includes models created by unknown developers or utilizing data obtained from unknown sources. + +*Always execute untrusted models within a secure, isolated environment such as a sandbox* (e.g., containers, virtual machines). This helps protect your system from potentially malicious code. + +> [!NOTE] +> The trustworthiness of a model is not binary. You must always determine the proper level of caution depending on the specific model and how it matches your use case and risk tolerance. + +### Untrusted inputs + +Some models accept various input formats (text, images, audio, etc.). The libraries converting these inputs have varying security levels, so it's crucial to isolate the model and carefully pre-process inputs to mitigate script injection risks. + +For maximum security when handling untrusted inputs, you may need to employ the following: + +* Sandboxing: Isolate the environment where the inference happens. +* Pre-analysis: Check how the model performs by default when exposed to prompt injection (e.g. using [fuzzing for prompt injection](https://github.com/FonduAI/awesome-prompt-injection?tab=readme-ov-file#tools)). This will give you leads on how hard you will have to work on the next topics. +* Updates: Keep both LLaMA C++ and your libraries updated with the latest security patches. +* Input Sanitation: Before feeding data to the model, sanitize inputs rigorously. This involves techniques such as: + * Validation: Enforce strict rules on allowed characters and data types. + * Filtering: Remove potentially malicious scripts or code fragments. + * Encoding: Convert special characters into safe representations. + * Verification: Run tooling that identifies potential script injections (e.g. [models that detect prompt injection attempts](https://python.langchain.com/docs/guides/safety/hugging_face_prompt_injection)). + +### Data privacy + +To protect sensitive data from potential leaks or unauthorized access, it is crucial to sandbox the model execution. This means running the model in a secure, isolated environment, which helps mitigate many attack vectors. + +### Untrusted environments or networks + +If you can't run your models in a secure and isolated environment or if it must be exposed to an untrusted network, make sure to take the following security precautions: +* Do not use the RPC backend, [rpc-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) and [llama-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/server) functionality (see https://github.com/ggml-org/llama.cpp/pull/13061). +* Confirm the hash of any downloaded artifact (e.g. pre-trained model weights) matches a known-good value. +* Encrypt your data if sending it over the network. + +### Multi-Tenant environments + +If you intend to run multiple models in parallel with shared memory, it is your responsibility to ensure the models do not interact or access each other's data. The primary areas of concern are tenant isolation, resource allocation, model sharing and hardware attacks. + +1. Tenant Isolation: Models should run separately with strong isolation methods to prevent unwanted data access. Separating networks is crucial for isolation, as it prevents unauthorized access to data or models and malicious users from sending graphs to execute under another tenant's identity. + +2. Resource Allocation: A denial of service caused by one model can impact the overall system health. Implement safeguards like rate limits, access controls, and health monitoring. + +3. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk. + +4. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time. + +## Reporting a vulnerability + +Beware that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities of LLaMA C++. + + +However, If you have discovered a security vulnerability in this project, please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released. + +Please disclose it as a private [security advisory](https://github.com/ggml-org/llama.cpp/security/advisories/new). + +A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure. diff --git a/llama.cpp/build-arm64-apple-clang+static-release/.cmake/api/v1/query/client-MicrosoftVS/query.json b/llama.cpp/build-arm64-apple-clang+static-release/.cmake/api/v1/query/client-MicrosoftVS/query.json new file mode 100644 index 0000000000000000000000000000000000000000..7d776af01723298a21119ae90e63529e03a8c8a4 --- /dev/null +++ b/llama.cpp/build-arm64-apple-clang+static-release/.cmake/api/v1/query/client-MicrosoftVS/query.json @@ -0,0 +1 @@ +{"requests":[{"kind":"cache","version":2},{"kind":"cmakeFiles","version":1},{"kind":"codemodel","version":2},{"kind":"toolchains","version":1}]} \ No newline at end of file diff --git a/llama.cpp/build-arm64-apple-clang+static-release/CMakeCache.txt b/llama.cpp/build-arm64-apple-clang+static-release/CMakeCache.txt new file mode 100644 index 0000000000000000000000000000000000000000..596b71d52dcdd67111df508f07d2763d0a2dfe43 --- /dev/null +++ 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+#if defined(__VERSIONNUM__) + /* __VERSIONNUM__ = 0xVVRRPPTT */ +# define COMPILER_VERSION_MAJOR DEC(__VERSIONNUM__ >> 24 & 0xFF) +# define COMPILER_VERSION_MINOR DEC(__VERSIONNUM__ >> 16 & 0xFF) +# define COMPILER_VERSION_PATCH DEC(__VERSIONNUM__ >> 8 & 0xFF) +# define COMPILER_VERSION_TWEAK DEC(__VERSIONNUM__ & 0xFF) +#endif + +#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) +# define COMPILER_ID "IAR" +# if defined(__VER__) && defined(__ICCARM__) +# define COMPILER_VERSION_MAJOR DEC((__VER__) / 1000000) +# define COMPILER_VERSION_MINOR DEC(((__VER__) / 1000) % 1000) +# define COMPILER_VERSION_PATCH DEC((__VER__) % 1000) +# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) +# elif defined(__VER__) && (defined(__ICCAVR__) || defined(__ICCRX__) || defined(__ICCRH850__) || defined(__ICCRL78__) || defined(__ICC430__) || defined(__ICCRISCV__) || defined(__ICCV850__) || defined(__ICC8051__) || defined(__ICCSTM8__)) +# define COMPILER_VERSION_MAJOR DEC((__VER__) / 100) +# define COMPILER_VERSION_MINOR DEC((__VER__) - (((__VER__) / 100)*100)) +# define COMPILER_VERSION_PATCH DEC(__SUBVERSION__) +# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) +# endif + +#elif defined(__SDCC_VERSION_MAJOR) || defined(SDCC) +# define COMPILER_ID "SDCC" +# if defined(__SDCC_VERSION_MAJOR) +# define COMPILER_VERSION_MAJOR DEC(__SDCC_VERSION_MAJOR) +# define COMPILER_VERSION_MINOR DEC(__SDCC_VERSION_MINOR) +# define COMPILER_VERSION_PATCH DEC(__SDCC_VERSION_PATCH) +# else + /* SDCC = VRP */ +# define COMPILER_VERSION_MAJOR DEC(SDCC/100) +# define COMPILER_VERSION_MINOR DEC(SDCC/10 % 10) +# define COMPILER_VERSION_PATCH DEC(SDCC % 10) +# endif + + +/* These compilers are either not known or too old to define an + identification macro. Try to identify the platform and guess that + it is the native compiler. */ +#elif defined(__hpux) || defined(__hpua) +# define COMPILER_ID "HP" + +#else /* unknown compiler */ +# define COMPILER_ID "" +#endif + +/* Construct the string literal in pieces to prevent the source from + getting matched. Store it in a pointer rather than an array + because some compilers will just produce instructions to fill the + array rather than assigning a pointer to a static array. */ +char const* info_compiler = "INFO" ":" "compiler[" COMPILER_ID "]"; +#ifdef SIMULATE_ID +char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]"; +#endif + +#ifdef __QNXNTO__ +char const* qnxnto = "INFO" ":" "qnxnto[]"; +#endif + +#if defined(__CRAYXT_COMPUTE_LINUX_TARGET) +char const *info_cray = "INFO" ":" "compiler_wrapper[CrayPrgEnv]"; +#endif + +#define STRINGIFY_HELPER(X) #X +#define STRINGIFY(X) STRINGIFY_HELPER(X) + +/* Identify known platforms by name. */ +#if defined(__linux) || defined(__linux__) || defined(linux) +# define PLATFORM_ID "Linux" + +#elif defined(__MSYS__) +# define PLATFORM_ID "MSYS" + +#elif defined(__CYGWIN__) +# define PLATFORM_ID "Cygwin" + +#elif defined(__MINGW32__) +# define PLATFORM_ID "MinGW" + +#elif defined(__APPLE__) +# define PLATFORM_ID "Darwin" + +#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32) +# define PLATFORM_ID "Windows" + +#elif defined(__FreeBSD__) || defined(__FreeBSD) +# define PLATFORM_ID "FreeBSD" + +#elif defined(__NetBSD__) || defined(__NetBSD) +# define PLATFORM_ID "NetBSD" + +#elif defined(__OpenBSD__) || defined(__OPENBSD) +# define PLATFORM_ID "OpenBSD" + +#elif defined(__sun) || defined(sun) +# define PLATFORM_ID "SunOS" + +#elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__) +# define PLATFORM_ID "AIX" + +#elif defined(__hpux) || defined(__hpux__) +# define PLATFORM_ID "HP-UX" + +#elif defined(__HAIKU__) +# define PLATFORM_ID "Haiku" + +#elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS) +# define PLATFORM_ID "BeOS" + +#elif defined(__QNX__) || defined(__QNXNTO__) +# define PLATFORM_ID "QNX" + +#elif defined(__tru64) || defined(_tru64) || defined(__TRU64__) +# define PLATFORM_ID "Tru64" + +#elif defined(__riscos) || defined(__riscos__) +# define PLATFORM_ID "RISCos" + +#elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__) +# define PLATFORM_ID "SINIX" + +#elif defined(__UNIX_SV__) +# define PLATFORM_ID "UNIX_SV" + +#elif defined(__bsdos__) +# define PLATFORM_ID "BSDOS" + +#elif defined(_MPRAS) || defined(MPRAS) +# define PLATFORM_ID "MP-RAS" + +#elif defined(__osf) || defined(__osf__) +# define PLATFORM_ID "OSF1" + +#elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv) +# define PLATFORM_ID "SCO_SV" + +#elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX) +# define PLATFORM_ID "ULTRIX" + +#elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX) +# define PLATFORM_ID "Xenix" + +#elif defined(__WATCOMC__) +# if defined(__LINUX__) +# define PLATFORM_ID "Linux" + +# elif defined(__DOS__) +# define PLATFORM_ID "DOS" + +# elif defined(__OS2__) +# define PLATFORM_ID "OS2" + +# elif defined(__WINDOWS__) +# define PLATFORM_ID "Windows3x" + +# elif defined(__VXWORKS__) +# define PLATFORM_ID "VxWorks" + +# else /* unknown platform */ +# define PLATFORM_ID +# endif + +#elif defined(__INTEGRITY) +# if defined(INT_178B) +# define PLATFORM_ID "Integrity178" + +# else /* regular Integrity */ +# define PLATFORM_ID "Integrity" +# endif + +# elif defined(_ADI_COMPILER) +# define PLATFORM_ID "ADSP" + +#else /* unknown platform */ +# define PLATFORM_ID + +#endif + +/* For windows compilers MSVC and Intel we can determine + the architecture of the compiler being used. This is because + the compilers do not have flags that can change the architecture, + but rather depend on which compiler is being used +*/ +#if defined(_WIN32) && defined(_MSC_VER) +# if defined(_M_IA64) +# define ARCHITECTURE_ID "IA64" + +# elif defined(_M_ARM64EC) +# define ARCHITECTURE_ID "ARM64EC" + +# elif defined(_M_X64) || defined(_M_AMD64) +# define ARCHITECTURE_ID "x64" + +# elif defined(_M_IX86) +# define ARCHITECTURE_ID "X86" + +# elif defined(_M_ARM64) +# define ARCHITECTURE_ID "ARM64" + +# elif defined(_M_ARM) +# if _M_ARM == 4 +# define ARCHITECTURE_ID "ARMV4I" +# elif _M_ARM == 5 +# define ARCHITECTURE_ID "ARMV5I" +# else +# define ARCHITECTURE_ID "ARMV" STRINGIFY(_M_ARM) +# endif + +# elif defined(_M_MIPS) +# define ARCHITECTURE_ID "MIPS" + +# elif defined(_M_SH) +# define ARCHITECTURE_ID "SHx" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__WATCOMC__) +# if defined(_M_I86) +# define ARCHITECTURE_ID "I86" + +# elif defined(_M_IX86) +# define ARCHITECTURE_ID "X86" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) +# if defined(__ICCARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__ICCRX__) +# define ARCHITECTURE_ID "RX" + +# elif defined(__ICCRH850__) +# define ARCHITECTURE_ID "RH850" + +# elif defined(__ICCRL78__) +# define ARCHITECTURE_ID "RL78" + +# elif defined(__ICCRISCV__) +# define ARCHITECTURE_ID "RISCV" + +# elif defined(__ICCAVR__) +# define ARCHITECTURE_ID "AVR" + +# elif defined(__ICC430__) +# define ARCHITECTURE_ID "MSP430" + +# elif defined(__ICCV850__) +# define ARCHITECTURE_ID "V850" + +# elif defined(__ICC8051__) +# define ARCHITECTURE_ID "8051" + +# elif defined(__ICCSTM8__) +# define ARCHITECTURE_ID "STM8" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__ghs__) +# if defined(__PPC64__) +# define ARCHITECTURE_ID "PPC64" + +# elif defined(__ppc__) +# define ARCHITECTURE_ID "PPC" + +# elif defined(__ARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__x86_64__) +# define ARCHITECTURE_ID "x64" + +# elif defined(__i386__) +# define ARCHITECTURE_ID "X86" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__clang__) && defined(__ti__) +# if defined(__ARM_ARCH) +# define ARCHITECTURE_ID "ARM" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__TI_COMPILER_VERSION__) +# if defined(__TI_ARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__MSP430__) +# define ARCHITECTURE_ID "MSP430" + +# elif defined(__TMS320C28XX__) +# define ARCHITECTURE_ID "TMS320C28x" + +# elif defined(__TMS320C6X__) || defined(_TMS320C6X) +# define ARCHITECTURE_ID "TMS320C6x" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +# elif defined(__ADSPSHARC__) +# define ARCHITECTURE_ID "SHARC" + +# elif defined(__ADSPBLACKFIN__) +# define ARCHITECTURE_ID "Blackfin" + +#elif defined(__TASKING__) + +# if defined(__CTC__) || defined(__CPTC__) +# define ARCHITECTURE_ID "TriCore" + +# elif defined(__CMCS__) +# define ARCHITECTURE_ID "MCS" + +# elif defined(__CARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__CARC__) +# define ARCHITECTURE_ID "ARC" + +# elif defined(__C51__) +# define ARCHITECTURE_ID "8051" + +# elif defined(__CPCP__) +# define ARCHITECTURE_ID "PCP" + +# else +# define ARCHITECTURE_ID "" +# endif + +#else +# define ARCHITECTURE_ID +#endif + +/* Convert integer to decimal digit literals. */ +#define DEC(n) \ + ('0' + (((n) / 10000000)%10)), \ + ('0' + (((n) / 1000000)%10)), \ + ('0' + (((n) / 100000)%10)), \ + ('0' + (((n) / 10000)%10)), \ + ('0' + (((n) / 1000)%10)), \ + ('0' + (((n) / 100)%10)), \ + ('0' + (((n) / 10)%10)), \ + ('0' + ((n) % 10)) + +/* Convert integer to hex digit literals. */ +#define HEX(n) \ + ('0' + ((n)>>28 & 0xF)), \ + ('0' + ((n)>>24 & 0xF)), \ + ('0' + ((n)>>20 & 0xF)), \ + ('0' + ((n)>>16 & 0xF)), \ + ('0' + ((n)>>12 & 0xF)), \ + ('0' + ((n)>>8 & 0xF)), \ + ('0' + ((n)>>4 & 0xF)), \ + ('0' + ((n) & 0xF)) + +/* Construct a string literal encoding the version number. */ +#ifdef COMPILER_VERSION +char const* info_version = "INFO" ":" "compiler_version[" COMPILER_VERSION "]"; + +/* Construct a string literal encoding the version number components. */ +#elif defined(COMPILER_VERSION_MAJOR) +char const info_version[] = { + 'I', 'N', 'F', 'O', ':', + 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[', + COMPILER_VERSION_MAJOR, +# ifdef COMPILER_VERSION_MINOR + '.', COMPILER_VERSION_MINOR, +# ifdef COMPILER_VERSION_PATCH + '.', COMPILER_VERSION_PATCH, +# ifdef COMPILER_VERSION_TWEAK + '.', COMPILER_VERSION_TWEAK, +# endif +# endif +# endif + ']','\0'}; +#endif + +/* Construct a string literal encoding the internal version number. */ +#ifdef COMPILER_VERSION_INTERNAL +char const info_version_internal[] = { + 'I', 'N', 'F', 'O', ':', + 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','_', + 'i','n','t','e','r','n','a','l','[', + COMPILER_VERSION_INTERNAL,']','\0'}; +#elif defined(COMPILER_VERSION_INTERNAL_STR) +char const* info_version_internal = "INFO" ":" "compiler_version_internal[" COMPILER_VERSION_INTERNAL_STR "]"; +#endif + +/* Construct a string literal encoding the version number components. */ +#ifdef SIMULATE_VERSION_MAJOR +char const info_simulate_version[] = { + 'I', 'N', 'F', 'O', ':', + 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[', + SIMULATE_VERSION_MAJOR, +# ifdef SIMULATE_VERSION_MINOR + '.', SIMULATE_VERSION_MINOR, +# ifdef SIMULATE_VERSION_PATCH + '.', SIMULATE_VERSION_PATCH, +# ifdef SIMULATE_VERSION_TWEAK + '.', SIMULATE_VERSION_TWEAK, +# endif +# endif +# endif + ']','\0'}; +#endif + +/* Construct the string literal in pieces to prevent the source from + getting matched. Store it in a pointer rather than an array + because some compilers will just produce instructions to fill the + array rather than assigning a pointer to a static array. */ +char const* info_platform = "INFO" ":" "platform[" PLATFORM_ID "]"; +char const* info_arch = "INFO" ":" "arch[" ARCHITECTURE_ID "]"; + + + +#define C_STD_99 199901L +#define C_STD_11 201112L +#define C_STD_17 201710L +#define C_STD_23 202311L + +#ifdef __STDC_VERSION__ +# define C_STD __STDC_VERSION__ +#endif + +#if !defined(__STDC__) && !defined(__clang__) +# if defined(_MSC_VER) || defined(__ibmxl__) || defined(__IBMC__) +# define C_VERSION "90" +# else +# define C_VERSION +# endif +#elif C_STD > C_STD_17 +# define C_VERSION "23" +#elif C_STD > C_STD_11 +# define C_VERSION "17" +#elif C_STD > C_STD_99 +# define C_VERSION "11" +#elif C_STD >= C_STD_99 +# define C_VERSION "99" +#else +# define C_VERSION "90" +#endif +const char* info_language_standard_default = + "INFO" ":" "standard_default[" C_VERSION "]"; + +const char* info_language_extensions_default = "INFO" ":" "extensions_default[" +#if (defined(__clang__) || defined(__GNUC__) || defined(__xlC__) || \ + defined(__TI_COMPILER_VERSION__)) && \ + !defined(__STRICT_ANSI__) + "ON" +#else + "OFF" +#endif +"]"; + +/*--------------------------------------------------------------------------*/ + +#ifdef ID_VOID_MAIN +void main() {} +#else +# if defined(__CLASSIC_C__) +int main(argc, argv) int argc; char *argv[]; +# else +int main(int argc, char* argv[]) +# endif +{ + int require = 0; + require += info_compiler[argc]; + require += info_platform[argc]; + require += info_arch[argc]; +#ifdef COMPILER_VERSION_MAJOR + require += info_version[argc]; +#endif +#ifdef COMPILER_VERSION_INTERNAL + require += info_version_internal[argc]; +#endif +#ifdef SIMULATE_ID + require += info_simulate[argc]; +#endif +#ifdef SIMULATE_VERSION_MAJOR + require += info_simulate_version[argc]; +#endif +#if defined(__CRAYXT_COMPUTE_LINUX_TARGET) + require += info_cray[argc]; +#endif + require += info_language_standard_default[argc]; + require += info_language_extensions_default[argc]; + (void)argv; + return require; +} +#endif diff --git a/llama.cpp/build-arm64-apple-clang+static-release/CMakeFiles/3.31.6-msvc6/CompilerIdCXX/CMakeCXXCompilerId.cpp b/llama.cpp/build-arm64-apple-clang+static-release/CMakeFiles/3.31.6-msvc6/CompilerIdCXX/CMakeCXXCompilerId.cpp new file mode 100644 index 0000000000000000000000000000000000000000..75f207b7a4b4a59a0dd471097989560429b99395 --- /dev/null +++ b/llama.cpp/build-arm64-apple-clang+static-release/CMakeFiles/3.31.6-msvc6/CompilerIdCXX/CMakeCXXCompilerId.cpp @@ -0,0 +1,919 @@ +/* This source file must have a .cpp extension so that all C++ compilers + recognize the extension without flags. Borland does not know .cxx for + example. */ +#ifndef __cplusplus +# error "A C compiler has been selected for C++." +#endif + +#if !defined(__has_include) +/* If the compiler does not have __has_include, pretend the answer is + always no. */ +# define __has_include(x) 0 +#endif + + +/* Version number components: V=Version, R=Revision, P=Patch + Version date components: YYYY=Year, MM=Month, DD=Day */ + +#if defined(__INTEL_COMPILER) || defined(__ICC) +# define COMPILER_ID "Intel" +# if defined(_MSC_VER) +# define SIMULATE_ID "MSVC" +# endif +# if defined(__GNUC__) +# define SIMULATE_ID "GNU" +# endif + /* __INTEL_COMPILER = VRP prior to 2021, and then VVVV for 2021 and later, + except that a few beta releases use the old format with V=2021. */ +# if __INTEL_COMPILER < 2021 || __INTEL_COMPILER == 202110 || __INTEL_COMPILER == 202111 +# define COMPILER_VERSION_MAJOR DEC(__INTEL_COMPILER/100) +# define COMPILER_VERSION_MINOR DEC(__INTEL_COMPILER/10 % 10) +# if defined(__INTEL_COMPILER_UPDATE) +# define COMPILER_VERSION_PATCH DEC(__INTEL_COMPILER_UPDATE) +# else +# define COMPILER_VERSION_PATCH DEC(__INTEL_COMPILER % 10) +# endif +# else +# define COMPILER_VERSION_MAJOR DEC(__INTEL_COMPILER) +# define COMPILER_VERSION_MINOR DEC(__INTEL_COMPILER_UPDATE) + /* The third version component from --version is an update index, + but no macro is provided for it. */ +# define COMPILER_VERSION_PATCH DEC(0) +# endif +# if defined(__INTEL_COMPILER_BUILD_DATE) + /* __INTEL_COMPILER_BUILD_DATE = YYYYMMDD */ +# define COMPILER_VERSION_TWEAK DEC(__INTEL_COMPILER_BUILD_DATE) +# endif +# if defined(_MSC_VER) + /* _MSC_VER = VVRR */ +# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) +# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) +# endif +# if defined(__GNUC__) +# define SIMULATE_VERSION_MAJOR DEC(__GNUC__) +# elif defined(__GNUG__) +# define SIMULATE_VERSION_MAJOR DEC(__GNUG__) +# endif +# if defined(__GNUC_MINOR__) +# define SIMULATE_VERSION_MINOR DEC(__GNUC_MINOR__) +# endif +# if defined(__GNUC_PATCHLEVEL__) +# define SIMULATE_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__) +# endif + +#elif (defined(__clang__) && defined(__INTEL_CLANG_COMPILER)) || defined(__INTEL_LLVM_COMPILER) +# define COMPILER_ID "IntelLLVM" +#if defined(_MSC_VER) +# define SIMULATE_ID "MSVC" +#endif +#if defined(__GNUC__) +# define SIMULATE_ID "GNU" +#endif +/* __INTEL_LLVM_COMPILER = VVVVRP prior to 2021.2.0, VVVVRRPP for 2021.2.0 and + * later. Look for 6 digit vs. 8 digit version number to decide encoding. + * VVVV is no smaller than the current year when a version is released. + */ +#if __INTEL_LLVM_COMPILER < 1000000L +# define COMPILER_VERSION_MAJOR DEC(__INTEL_LLVM_COMPILER/100) +# define COMPILER_VERSION_MINOR DEC(__INTEL_LLVM_COMPILER/10 % 10) +# define COMPILER_VERSION_PATCH DEC(__INTEL_LLVM_COMPILER % 10) +#else +# define COMPILER_VERSION_MAJOR DEC(__INTEL_LLVM_COMPILER/10000) +# define COMPILER_VERSION_MINOR DEC(__INTEL_LLVM_COMPILER/100 % 100) +# define COMPILER_VERSION_PATCH DEC(__INTEL_LLVM_COMPILER % 100) +#endif +#if defined(_MSC_VER) + /* _MSC_VER = VVRR */ +# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) +# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) +#endif +#if defined(__GNUC__) +# define SIMULATE_VERSION_MAJOR DEC(__GNUC__) +#elif defined(__GNUG__) +# define SIMULATE_VERSION_MAJOR DEC(__GNUG__) +#endif +#if defined(__GNUC_MINOR__) +# define SIMULATE_VERSION_MINOR DEC(__GNUC_MINOR__) +#endif +#if defined(__GNUC_PATCHLEVEL__) +# define SIMULATE_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__) +#endif + +#elif defined(__PATHCC__) +# define COMPILER_ID "PathScale" +# define COMPILER_VERSION_MAJOR DEC(__PATHCC__) +# define COMPILER_VERSION_MINOR DEC(__PATHCC_MINOR__) +# if defined(__PATHCC_PATCHLEVEL__) +# define COMPILER_VERSION_PATCH DEC(__PATHCC_PATCHLEVEL__) +# endif + +#elif defined(__BORLANDC__) && defined(__CODEGEARC_VERSION__) +# define COMPILER_ID "Embarcadero" +# define COMPILER_VERSION_MAJOR HEX(__CODEGEARC_VERSION__>>24 & 0x00FF) +# define COMPILER_VERSION_MINOR HEX(__CODEGEARC_VERSION__>>16 & 0x00FF) +# define COMPILER_VERSION_PATCH DEC(__CODEGEARC_VERSION__ & 0xFFFF) + +#elif defined(__BORLANDC__) +# define COMPILER_ID "Borland" + /* __BORLANDC__ = 0xVRR */ +# define COMPILER_VERSION_MAJOR HEX(__BORLANDC__>>8) +# define COMPILER_VERSION_MINOR HEX(__BORLANDC__ & 0xFF) + +#elif defined(__WATCOMC__) && __WATCOMC__ < 1200 +# define COMPILER_ID "Watcom" + /* __WATCOMC__ = VVRR */ +# define COMPILER_VERSION_MAJOR DEC(__WATCOMC__ / 100) +# define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10) +# if (__WATCOMC__ % 10) > 0 +# define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10) +# endif + +#elif defined(__WATCOMC__) +# define COMPILER_ID "OpenWatcom" + /* __WATCOMC__ = VVRP + 1100 */ +# define COMPILER_VERSION_MAJOR DEC((__WATCOMC__ - 1100) / 100) +# define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10) +# if (__WATCOMC__ % 10) > 0 +# define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10) +# endif + +#elif defined(__SUNPRO_CC) +# define COMPILER_ID "SunPro" +# if __SUNPRO_CC >= 0x5100 + /* __SUNPRO_CC = 0xVRRP */ +# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_CC>>12) +# define COMPILER_VERSION_MINOR HEX(__SUNPRO_CC>>4 & 0xFF) +# define COMPILER_VERSION_PATCH HEX(__SUNPRO_CC & 0xF) +# else + /* __SUNPRO_CC = 0xVRP */ +# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_CC>>8) +# define COMPILER_VERSION_MINOR HEX(__SUNPRO_CC>>4 & 0xF) +# define COMPILER_VERSION_PATCH HEX(__SUNPRO_CC & 0xF) +# endif + +#elif defined(__HP_aCC) +# define COMPILER_ID "HP" + /* __HP_aCC = VVRRPP */ +# define COMPILER_VERSION_MAJOR DEC(__HP_aCC/10000) +# define COMPILER_VERSION_MINOR DEC(__HP_aCC/100 % 100) +# define COMPILER_VERSION_PATCH DEC(__HP_aCC % 100) + +#elif defined(__DECCXX) +# define COMPILER_ID "Compaq" + /* __DECCXX_VER = VVRRTPPPP */ +# define COMPILER_VERSION_MAJOR DEC(__DECCXX_VER/10000000) +# define COMPILER_VERSION_MINOR DEC(__DECCXX_VER/100000 % 100) +# define COMPILER_VERSION_PATCH DEC(__DECCXX_VER % 10000) + +#elif defined(__IBMCPP__) && defined(__COMPILER_VER__) +# define COMPILER_ID "zOS" + /* __IBMCPP__ = VRP */ +# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100) +# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10) +# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10) + +#elif defined(__open_xl__) && defined(__clang__) +# define COMPILER_ID "IBMClang" +# define COMPILER_VERSION_MAJOR DEC(__open_xl_version__) +# define COMPILER_VERSION_MINOR DEC(__open_xl_release__) +# define COMPILER_VERSION_PATCH DEC(__open_xl_modification__) +# define COMPILER_VERSION_TWEAK DEC(__open_xl_ptf_fix_level__) + + +#elif defined(__ibmxl__) && defined(__clang__) +# define COMPILER_ID "XLClang" +# define COMPILER_VERSION_MAJOR DEC(__ibmxl_version__) +# define COMPILER_VERSION_MINOR DEC(__ibmxl_release__) +# define COMPILER_VERSION_PATCH DEC(__ibmxl_modification__) +# define COMPILER_VERSION_TWEAK DEC(__ibmxl_ptf_fix_level__) + + +#elif defined(__IBMCPP__) && !defined(__COMPILER_VER__) && __IBMCPP__ >= 800 +# define COMPILER_ID "XL" + /* __IBMCPP__ = VRP */ +# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100) +# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10) +# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10) + +#elif defined(__IBMCPP__) && !defined(__COMPILER_VER__) && __IBMCPP__ < 800 +# define COMPILER_ID "VisualAge" + /* __IBMCPP__ = VRP */ +# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100) +# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10) +# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10) + +#elif defined(__NVCOMPILER) +# define COMPILER_ID "NVHPC" +# define COMPILER_VERSION_MAJOR DEC(__NVCOMPILER_MAJOR__) +# define COMPILER_VERSION_MINOR DEC(__NVCOMPILER_MINOR__) +# if defined(__NVCOMPILER_PATCHLEVEL__) +# define COMPILER_VERSION_PATCH DEC(__NVCOMPILER_PATCHLEVEL__) +# endif + +#elif defined(__PGI) +# define COMPILER_ID "PGI" +# define COMPILER_VERSION_MAJOR DEC(__PGIC__) +# define COMPILER_VERSION_MINOR DEC(__PGIC_MINOR__) +# if defined(__PGIC_PATCHLEVEL__) +# define COMPILER_VERSION_PATCH DEC(__PGIC_PATCHLEVEL__) +# endif + +#elif defined(__clang__) && defined(__cray__) +# define COMPILER_ID "CrayClang" +# define COMPILER_VERSION_MAJOR DEC(__cray_major__) +# define COMPILER_VERSION_MINOR DEC(__cray_minor__) +# define COMPILER_VERSION_PATCH DEC(__cray_patchlevel__) +# define COMPILER_VERSION_INTERNAL_STR __clang_version__ + + +#elif defined(_CRAYC) +# define COMPILER_ID "Cray" +# define COMPILER_VERSION_MAJOR DEC(_RELEASE_MAJOR) +# define COMPILER_VERSION_MINOR DEC(_RELEASE_MINOR) + +#elif defined(__TI_COMPILER_VERSION__) +# define COMPILER_ID "TI" + /* __TI_COMPILER_VERSION__ = VVVRRRPPP */ +# define COMPILER_VERSION_MAJOR DEC(__TI_COMPILER_VERSION__/1000000) +# define COMPILER_VERSION_MINOR DEC(__TI_COMPILER_VERSION__/1000 % 1000) +# define COMPILER_VERSION_PATCH DEC(__TI_COMPILER_VERSION__ % 1000) + +#elif defined(__CLANG_FUJITSU) +# define COMPILER_ID "FujitsuClang" +# define COMPILER_VERSION_MAJOR DEC(__FCC_major__) +# define COMPILER_VERSION_MINOR DEC(__FCC_minor__) +# define COMPILER_VERSION_PATCH DEC(__FCC_patchlevel__) +# define COMPILER_VERSION_INTERNAL_STR __clang_version__ + + +#elif defined(__FUJITSU) +# define COMPILER_ID "Fujitsu" +# if defined(__FCC_version__) +# define COMPILER_VERSION __FCC_version__ +# elif defined(__FCC_major__) +# define COMPILER_VERSION_MAJOR DEC(__FCC_major__) +# define COMPILER_VERSION_MINOR DEC(__FCC_minor__) +# define COMPILER_VERSION_PATCH DEC(__FCC_patchlevel__) +# endif +# if defined(__fcc_version) +# define COMPILER_VERSION_INTERNAL DEC(__fcc_version) +# elif defined(__FCC_VERSION) +# define COMPILER_VERSION_INTERNAL DEC(__FCC_VERSION) +# endif + + +#elif defined(__ghs__) +# define COMPILER_ID "GHS" +/* __GHS_VERSION_NUMBER = VVVVRP */ +# ifdef __GHS_VERSION_NUMBER +# define COMPILER_VERSION_MAJOR DEC(__GHS_VERSION_NUMBER / 100) +# define COMPILER_VERSION_MINOR DEC(__GHS_VERSION_NUMBER / 10 % 10) +# define COMPILER_VERSION_PATCH DEC(__GHS_VERSION_NUMBER % 10) +# endif + +#elif defined(__TASKING__) +# define COMPILER_ID "Tasking" + # define COMPILER_VERSION_MAJOR DEC(__VERSION__/1000) + # define COMPILER_VERSION_MINOR DEC(__VERSION__ % 100) +# define COMPILER_VERSION_INTERNAL DEC(__VERSION__) + +#elif defined(__ORANGEC__) +# define COMPILER_ID "OrangeC" +# define COMPILER_VERSION_MAJOR DEC(__ORANGEC_MAJOR__) +# define COMPILER_VERSION_MINOR DEC(__ORANGEC_MINOR__) +# define COMPILER_VERSION_PATCH DEC(__ORANGEC_PATCHLEVEL__) + +#elif defined(__SCO_VERSION__) +# define COMPILER_ID "SCO" + +#elif defined(__ARMCC_VERSION) && !defined(__clang__) +# define COMPILER_ID "ARMCC" +#if __ARMCC_VERSION >= 1000000 + /* __ARMCC_VERSION = VRRPPPP */ + # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/1000000) + # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 100) + # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION % 10000) +#else + /* __ARMCC_VERSION = VRPPPP */ + # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/100000) + # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 10) + # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION % 10000) +#endif + + +#elif defined(__clang__) && defined(__apple_build_version__) +# define COMPILER_ID "AppleClang" +# if defined(_MSC_VER) +# define SIMULATE_ID "MSVC" +# endif +# define COMPILER_VERSION_MAJOR DEC(__clang_major__) +# define COMPILER_VERSION_MINOR DEC(__clang_minor__) +# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__) +# if defined(_MSC_VER) + /* _MSC_VER = VVRR */ +# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) +# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) +# endif +# define COMPILER_VERSION_TWEAK DEC(__apple_build_version__) + +#elif defined(__clang__) && defined(__ARMCOMPILER_VERSION) +# define COMPILER_ID "ARMClang" + # define COMPILER_VERSION_MAJOR DEC(__ARMCOMPILER_VERSION/1000000) + # define COMPILER_VERSION_MINOR DEC(__ARMCOMPILER_VERSION/10000 % 100) + # define COMPILER_VERSION_PATCH DEC(__ARMCOMPILER_VERSION/100 % 100) +# define COMPILER_VERSION_INTERNAL DEC(__ARMCOMPILER_VERSION) + +#elif defined(__clang__) && defined(__ti__) +# define COMPILER_ID "TIClang" + # define COMPILER_VERSION_MAJOR DEC(__ti_major__) + # define COMPILER_VERSION_MINOR DEC(__ti_minor__) + # define COMPILER_VERSION_PATCH DEC(__ti_patchlevel__) +# define COMPILER_VERSION_INTERNAL DEC(__ti_version__) + +#elif defined(__clang__) +# define COMPILER_ID "Clang" +# if defined(_MSC_VER) +# define SIMULATE_ID "MSVC" +# endif +# define COMPILER_VERSION_MAJOR DEC(__clang_major__) +# define COMPILER_VERSION_MINOR DEC(__clang_minor__) +# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__) +# if defined(_MSC_VER) + /* _MSC_VER = VVRR */ +# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) +# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) +# endif + +#elif defined(__LCC__) && (defined(__GNUC__) || defined(__GNUG__) || defined(__MCST__)) +# define COMPILER_ID "LCC" +# define COMPILER_VERSION_MAJOR DEC(__LCC__ / 100) +# define COMPILER_VERSION_MINOR DEC(__LCC__ % 100) +# if defined(__LCC_MINOR__) +# define COMPILER_VERSION_PATCH DEC(__LCC_MINOR__) +# endif +# if defined(__GNUC__) && defined(__GNUC_MINOR__) +# define SIMULATE_ID "GNU" +# define SIMULATE_VERSION_MAJOR DEC(__GNUC__) +# define SIMULATE_VERSION_MINOR DEC(__GNUC_MINOR__) +# if defined(__GNUC_PATCHLEVEL__) +# define SIMULATE_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__) +# endif +# endif + +#elif defined(__GNUC__) || defined(__GNUG__) +# define COMPILER_ID "GNU" +# if defined(__GNUC__) +# define COMPILER_VERSION_MAJOR DEC(__GNUC__) +# else +# define COMPILER_VERSION_MAJOR DEC(__GNUG__) +# endif +# if defined(__GNUC_MINOR__) +# define COMPILER_VERSION_MINOR DEC(__GNUC_MINOR__) +# endif +# if defined(__GNUC_PATCHLEVEL__) +# define COMPILER_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__) +# endif + +#elif defined(_MSC_VER) +# define COMPILER_ID "MSVC" + /* _MSC_VER = VVRR */ +# define COMPILER_VERSION_MAJOR DEC(_MSC_VER / 100) +# define COMPILER_VERSION_MINOR DEC(_MSC_VER % 100) +# if defined(_MSC_FULL_VER) +# if _MSC_VER >= 1400 + /* _MSC_FULL_VER = VVRRPPPPP */ +# define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 100000) +# else + /* _MSC_FULL_VER = VVRRPPPP */ +# define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 10000) +# endif +# endif +# if defined(_MSC_BUILD) +# define COMPILER_VERSION_TWEAK DEC(_MSC_BUILD) +# endif + +#elif defined(_ADI_COMPILER) +# define COMPILER_ID "ADSP" +#if defined(__VERSIONNUM__) + /* __VERSIONNUM__ = 0xVVRRPPTT */ +# define COMPILER_VERSION_MAJOR DEC(__VERSIONNUM__ >> 24 & 0xFF) +# define COMPILER_VERSION_MINOR DEC(__VERSIONNUM__ >> 16 & 0xFF) +# define COMPILER_VERSION_PATCH DEC(__VERSIONNUM__ >> 8 & 0xFF) +# define COMPILER_VERSION_TWEAK DEC(__VERSIONNUM__ & 0xFF) +#endif + +#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) +# define COMPILER_ID "IAR" +# if defined(__VER__) && defined(__ICCARM__) +# define COMPILER_VERSION_MAJOR DEC((__VER__) / 1000000) +# define COMPILER_VERSION_MINOR DEC(((__VER__) / 1000) % 1000) +# define COMPILER_VERSION_PATCH DEC((__VER__) % 1000) +# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) +# elif defined(__VER__) && (defined(__ICCAVR__) || defined(__ICCRX__) || defined(__ICCRH850__) || defined(__ICCRL78__) || defined(__ICC430__) || defined(__ICCRISCV__) || defined(__ICCV850__) || defined(__ICC8051__) || defined(__ICCSTM8__)) +# define COMPILER_VERSION_MAJOR DEC((__VER__) / 100) +# define COMPILER_VERSION_MINOR DEC((__VER__) - (((__VER__) / 100)*100)) +# define COMPILER_VERSION_PATCH DEC(__SUBVERSION__) +# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) +# endif + + +/* These compilers are either not known or too old to define an + identification macro. Try to identify the platform and guess that + it is the native compiler. */ +#elif defined(__hpux) || defined(__hpua) +# define COMPILER_ID "HP" + +#else /* unknown compiler */ +# define COMPILER_ID "" +#endif + +/* Construct the string literal in pieces to prevent the source from + getting matched. Store it in a pointer rather than an array + because some compilers will just produce instructions to fill the + array rather than assigning a pointer to a static array. */ +char const* info_compiler = "INFO" ":" "compiler[" COMPILER_ID "]"; +#ifdef SIMULATE_ID +char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]"; +#endif + +#ifdef __QNXNTO__ +char const* qnxnto = "INFO" ":" "qnxnto[]"; +#endif + +#if defined(__CRAYXT_COMPUTE_LINUX_TARGET) +char const *info_cray = "INFO" ":" "compiler_wrapper[CrayPrgEnv]"; +#endif + +#define STRINGIFY_HELPER(X) #X +#define STRINGIFY(X) STRINGIFY_HELPER(X) + +/* Identify known platforms by name. */ +#if defined(__linux) || defined(__linux__) || defined(linux) +# define PLATFORM_ID "Linux" + +#elif defined(__MSYS__) +# define PLATFORM_ID "MSYS" + +#elif defined(__CYGWIN__) +# define PLATFORM_ID "Cygwin" + +#elif defined(__MINGW32__) +# define PLATFORM_ID "MinGW" + +#elif defined(__APPLE__) +# define PLATFORM_ID "Darwin" + +#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32) +# define PLATFORM_ID "Windows" + +#elif defined(__FreeBSD__) || defined(__FreeBSD) +# define PLATFORM_ID "FreeBSD" + +#elif defined(__NetBSD__) || defined(__NetBSD) +# define PLATFORM_ID "NetBSD" + +#elif defined(__OpenBSD__) || defined(__OPENBSD) +# define PLATFORM_ID "OpenBSD" + +#elif defined(__sun) || defined(sun) +# define PLATFORM_ID "SunOS" + +#elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__) +# define PLATFORM_ID "AIX" + +#elif defined(__hpux) || defined(__hpux__) +# define PLATFORM_ID "HP-UX" + +#elif defined(__HAIKU__) +# define PLATFORM_ID "Haiku" + +#elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS) +# define PLATFORM_ID "BeOS" + +#elif defined(__QNX__) || defined(__QNXNTO__) +# define PLATFORM_ID "QNX" + +#elif defined(__tru64) || defined(_tru64) || defined(__TRU64__) +# define PLATFORM_ID "Tru64" + +#elif defined(__riscos) || defined(__riscos__) +# define PLATFORM_ID "RISCos" + +#elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__) +# define PLATFORM_ID "SINIX" + +#elif defined(__UNIX_SV__) +# define PLATFORM_ID "UNIX_SV" + +#elif defined(__bsdos__) +# define PLATFORM_ID "BSDOS" + +#elif defined(_MPRAS) || defined(MPRAS) +# define PLATFORM_ID "MP-RAS" + +#elif defined(__osf) || defined(__osf__) +# define PLATFORM_ID "OSF1" + +#elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv) +# define PLATFORM_ID "SCO_SV" + +#elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX) +# define PLATFORM_ID "ULTRIX" + +#elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX) +# define PLATFORM_ID "Xenix" + +#elif defined(__WATCOMC__) +# if defined(__LINUX__) +# define PLATFORM_ID "Linux" + +# elif defined(__DOS__) +# define PLATFORM_ID "DOS" + +# elif defined(__OS2__) +# define PLATFORM_ID "OS2" + +# elif defined(__WINDOWS__) +# define PLATFORM_ID "Windows3x" + +# elif defined(__VXWORKS__) +# define PLATFORM_ID "VxWorks" + +# else /* unknown platform */ +# define PLATFORM_ID +# endif + +#elif defined(__INTEGRITY) +# if defined(INT_178B) +# define PLATFORM_ID "Integrity178" + +# else /* regular Integrity */ +# define PLATFORM_ID "Integrity" +# endif + +# elif defined(_ADI_COMPILER) +# define PLATFORM_ID "ADSP" + +#else /* unknown platform */ +# define PLATFORM_ID + +#endif + +/* For windows compilers MSVC and Intel we can determine + the architecture of the compiler being used. This is because + the compilers do not have flags that can change the architecture, + but rather depend on which compiler is being used +*/ +#if defined(_WIN32) && defined(_MSC_VER) +# if defined(_M_IA64) +# define ARCHITECTURE_ID "IA64" + +# elif defined(_M_ARM64EC) +# define ARCHITECTURE_ID "ARM64EC" + +# elif defined(_M_X64) || defined(_M_AMD64) +# define ARCHITECTURE_ID "x64" + +# elif defined(_M_IX86) +# define ARCHITECTURE_ID "X86" + +# elif defined(_M_ARM64) +# define ARCHITECTURE_ID "ARM64" + +# elif defined(_M_ARM) +# if _M_ARM == 4 +# define ARCHITECTURE_ID "ARMV4I" +# elif _M_ARM == 5 +# define ARCHITECTURE_ID "ARMV5I" +# else +# define ARCHITECTURE_ID "ARMV" STRINGIFY(_M_ARM) +# endif + +# elif defined(_M_MIPS) +# define ARCHITECTURE_ID "MIPS" + +# elif defined(_M_SH) +# define ARCHITECTURE_ID "SHx" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__WATCOMC__) +# if defined(_M_I86) +# define ARCHITECTURE_ID "I86" + +# elif defined(_M_IX86) +# define ARCHITECTURE_ID "X86" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) +# if defined(__ICCARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__ICCRX__) +# define ARCHITECTURE_ID "RX" + +# elif defined(__ICCRH850__) +# define ARCHITECTURE_ID "RH850" + +# elif defined(__ICCRL78__) +# define ARCHITECTURE_ID "RL78" + +# elif defined(__ICCRISCV__) +# define ARCHITECTURE_ID "RISCV" + +# elif defined(__ICCAVR__) +# define ARCHITECTURE_ID "AVR" + +# elif defined(__ICC430__) +# define ARCHITECTURE_ID "MSP430" + +# elif defined(__ICCV850__) +# define ARCHITECTURE_ID "V850" + +# elif defined(__ICC8051__) +# define ARCHITECTURE_ID "8051" + +# elif defined(__ICCSTM8__) +# define ARCHITECTURE_ID "STM8" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__ghs__) +# if defined(__PPC64__) +# define ARCHITECTURE_ID "PPC64" + +# elif defined(__ppc__) +# define ARCHITECTURE_ID "PPC" + +# elif defined(__ARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__x86_64__) +# define ARCHITECTURE_ID "x64" + +# elif defined(__i386__) +# define ARCHITECTURE_ID "X86" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__clang__) && defined(__ti__) +# if defined(__ARM_ARCH) +# define ARCHITECTURE_ID "ARM" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__TI_COMPILER_VERSION__) +# if defined(__TI_ARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__MSP430__) +# define ARCHITECTURE_ID "MSP430" + +# elif defined(__TMS320C28XX__) +# define ARCHITECTURE_ID "TMS320C28x" + +# elif defined(__TMS320C6X__) || defined(_TMS320C6X) +# define ARCHITECTURE_ID "TMS320C6x" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +# elif defined(__ADSPSHARC__) +# define ARCHITECTURE_ID "SHARC" + +# elif defined(__ADSPBLACKFIN__) +# define ARCHITECTURE_ID "Blackfin" + +#elif defined(__TASKING__) + +# if defined(__CTC__) || defined(__CPTC__) +# define ARCHITECTURE_ID "TriCore" + +# elif defined(__CMCS__) +# define ARCHITECTURE_ID "MCS" + +# elif defined(__CARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__CARC__) +# define ARCHITECTURE_ID "ARC" + +# elif defined(__C51__) +# define ARCHITECTURE_ID "8051" + +# elif defined(__CPCP__) +# define ARCHITECTURE_ID "PCP" + +# else +# define ARCHITECTURE_ID "" +# endif + +#else +# define ARCHITECTURE_ID +#endif + +/* Convert integer to decimal digit literals. */ +#define DEC(n) \ + ('0' + (((n) / 10000000)%10)), \ + ('0' + (((n) / 1000000)%10)), \ + ('0' + (((n) / 100000)%10)), \ + ('0' + (((n) / 10000)%10)), \ + ('0' + (((n) / 1000)%10)), \ + ('0' + (((n) / 100)%10)), \ + ('0' + (((n) / 10)%10)), \ + ('0' + ((n) % 10)) + +/* Convert integer to hex digit literals. */ +#define HEX(n) \ + ('0' + ((n)>>28 & 0xF)), \ + ('0' + ((n)>>24 & 0xF)), \ + ('0' + ((n)>>20 & 0xF)), \ + ('0' + ((n)>>16 & 0xF)), \ + ('0' + ((n)>>12 & 0xF)), \ + ('0' + ((n)>>8 & 0xF)), \ + ('0' + ((n)>>4 & 0xF)), \ + ('0' + ((n) & 0xF)) + +/* Construct a string literal encoding the version number. */ +#ifdef COMPILER_VERSION +char const* info_version = "INFO" ":" "compiler_version[" COMPILER_VERSION "]"; + +/* Construct a string literal encoding the version number components. */ +#elif defined(COMPILER_VERSION_MAJOR) +char const info_version[] = { + 'I', 'N', 'F', 'O', ':', + 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[', + COMPILER_VERSION_MAJOR, +# ifdef COMPILER_VERSION_MINOR + '.', COMPILER_VERSION_MINOR, +# ifdef COMPILER_VERSION_PATCH + '.', COMPILER_VERSION_PATCH, +# ifdef COMPILER_VERSION_TWEAK + '.', COMPILER_VERSION_TWEAK, +# endif +# endif +# endif + ']','\0'}; +#endif + +/* Construct a string literal encoding the internal version number. */ +#ifdef COMPILER_VERSION_INTERNAL +char const info_version_internal[] = { + 'I', 'N', 'F', 'O', ':', + 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','_', + 'i','n','t','e','r','n','a','l','[', + COMPILER_VERSION_INTERNAL,']','\0'}; +#elif defined(COMPILER_VERSION_INTERNAL_STR) +char const* info_version_internal = "INFO" ":" "compiler_version_internal[" COMPILER_VERSION_INTERNAL_STR "]"; +#endif + +/* Construct a string literal encoding the version number components. */ +#ifdef SIMULATE_VERSION_MAJOR +char const info_simulate_version[] = { + 'I', 'N', 'F', 'O', ':', + 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[', + SIMULATE_VERSION_MAJOR, +# ifdef SIMULATE_VERSION_MINOR + '.', SIMULATE_VERSION_MINOR, +# ifdef SIMULATE_VERSION_PATCH + '.', SIMULATE_VERSION_PATCH, +# ifdef SIMULATE_VERSION_TWEAK + '.', SIMULATE_VERSION_TWEAK, +# endif +# endif +# endif + ']','\0'}; +#endif + +/* Construct the string literal in pieces to prevent the source from + getting matched. Store it in a pointer rather than an array + because some compilers will just produce instructions to fill the + array rather than assigning a pointer to a static array. */ +char const* info_platform = "INFO" ":" "platform[" PLATFORM_ID "]"; +char const* info_arch = "INFO" ":" "arch[" ARCHITECTURE_ID "]"; + + + +#define CXX_STD_98 199711L +#define CXX_STD_11 201103L +#define CXX_STD_14 201402L +#define CXX_STD_17 201703L +#define CXX_STD_20 202002L +#define CXX_STD_23 202302L + +#if defined(__INTEL_COMPILER) && defined(_MSVC_LANG) +# if _MSVC_LANG > CXX_STD_17 +# define CXX_STD _MSVC_LANG +# elif _MSVC_LANG == CXX_STD_17 && defined(__cpp_aggregate_paren_init) +# define CXX_STD CXX_STD_20 +# elif _MSVC_LANG > CXX_STD_14 && __cplusplus > CXX_STD_17 +# define CXX_STD CXX_STD_20 +# elif _MSVC_LANG > CXX_STD_14 +# define CXX_STD CXX_STD_17 +# elif defined(__INTEL_CXX11_MODE__) && defined(__cpp_aggregate_nsdmi) +# define CXX_STD CXX_STD_14 +# elif defined(__INTEL_CXX11_MODE__) +# define CXX_STD CXX_STD_11 +# else +# define CXX_STD CXX_STD_98 +# endif +#elif defined(_MSC_VER) && defined(_MSVC_LANG) +# if _MSVC_LANG > __cplusplus +# define CXX_STD _MSVC_LANG +# else +# define CXX_STD __cplusplus +# endif +#elif defined(__NVCOMPILER) +# if __cplusplus == CXX_STD_17 && defined(__cpp_aggregate_paren_init) +# define CXX_STD CXX_STD_20 +# else +# define CXX_STD __cplusplus +# endif +#elif defined(__INTEL_COMPILER) || defined(__PGI) +# if __cplusplus == CXX_STD_11 && defined(__cpp_namespace_attributes) +# define CXX_STD CXX_STD_17 +# elif __cplusplus == CXX_STD_11 && defined(__cpp_aggregate_nsdmi) +# define CXX_STD CXX_STD_14 +# else +# define CXX_STD __cplusplus +# endif +#elif (defined(__IBMCPP__) || defined(__ibmxl__)) && defined(__linux__) +# if __cplusplus == CXX_STD_11 && defined(__cpp_aggregate_nsdmi) +# define CXX_STD CXX_STD_14 +# else +# define CXX_STD __cplusplus +# endif +#elif __cplusplus == 1 && defined(__GXX_EXPERIMENTAL_CXX0X__) +# define CXX_STD CXX_STD_11 +#else +# define CXX_STD __cplusplus +#endif + +const char* info_language_standard_default = "INFO" ":" "standard_default[" +#if CXX_STD > CXX_STD_23 + "26" +#elif CXX_STD > CXX_STD_20 + "23" +#elif CXX_STD > CXX_STD_17 + "20" +#elif CXX_STD > CXX_STD_14 + "17" +#elif CXX_STD > CXX_STD_11 + "14" +#elif CXX_STD >= CXX_STD_11 + "11" +#else + "98" +#endif +"]"; + +const char* info_language_extensions_default = "INFO" ":" "extensions_default[" +#if (defined(__clang__) || defined(__GNUC__) || defined(__xlC__) || \ + defined(__TI_COMPILER_VERSION__)) && \ + !defined(__STRICT_ANSI__) + "ON" +#else + "OFF" +#endif +"]"; + +/*--------------------------------------------------------------------------*/ + +int main(int argc, char* argv[]) +{ + int require = 0; + require += info_compiler[argc]; + require += info_platform[argc]; + require += info_arch[argc]; +#ifdef COMPILER_VERSION_MAJOR + require += info_version[argc]; +#endif +#ifdef COMPILER_VERSION_INTERNAL + require += info_version_internal[argc]; +#endif +#ifdef SIMULATE_ID + require += info_simulate[argc]; +#endif +#ifdef SIMULATE_VERSION_MAJOR + require += info_simulate_version[argc]; +#endif +#if defined(__CRAYXT_COMPUTE_LINUX_TARGET) + require += info_cray[argc]; +#endif + require += info_language_standard_default[argc]; + require += info_language_extensions_default[argc]; + (void)argv; + return require; +} diff --git a/llama.cpp/build-arm64-apple-clang+static-release/CMakeFiles/CMakeConfigureLog.yaml b/llama.cpp/build-arm64-apple-clang+static-release/CMakeFiles/CMakeConfigureLog.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d728f22d94536b575ca99f1d3a1f1937ecd2f447 --- /dev/null +++ b/llama.cpp/build-arm64-apple-clang+static-release/CMakeFiles/CMakeConfigureLog.yaml @@ -0,0 +1,524 @@ + +--- +events: + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineSystem.cmake:200 (message)" + - "CMakeLists.txt:2 (project)" + message: | + The target system is: Darwin - - arm64 + The host system is: Windows - 10.0.26100 - AMD64 + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the C compiler identification source file "CMakeCCompilerId.c" failed. + Compiler: clang + Build flags: -march=armv8.4-a;-fvectorize;-ffp-model=fast;-fno-finite-math-only;-Wno-format;-Wno-unused-variable;-Wno-unused-function + Id flags: -c;--target=arm64-apple-darwin-macho + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the C compiler identification source file "CMakeCCompilerId.c" failed. + Compiler: clang + Build flags: -march=armv8.4-a;-fvectorize;-ffp-model=fast;-fno-finite-math-only;-Wno-format;-Wno-unused-variable;-Wno-unused-function + Id flags: + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the C compiler identification source file "CMakeCCompilerId.c" failed. + Compiler: clang + Build flags: -march=armv8.4-a;-fvectorize;-ffp-model=fast;-fno-finite-math-only;-Wno-format;-Wno-unused-variable;-Wno-unused-function + Id flags: -c + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the C compiler identification source file "CMakeCCompilerId.c" failed. + Compiler: clang + Build flags: -march=armv8.4-a;-fvectorize;-ffp-model=fast;-fno-finite-math-only;-Wno-format;-Wno-unused-variable;-Wno-unused-function + Id flags: -Aa + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the C compiler identification source file "CMakeCCompilerId.c" failed. + Compiler: clang + Build flags: -march=armv8.4-a;-fvectorize;-ffp-model=fast;-fno-finite-math-only;-Wno-format;-Wno-unused-variable;-Wno-unused-function + Id flags: -D__CLASSIC_C__ + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the C compiler identification source file "CMakeCCompilerId.c" failed. + Compiler: clang + Build flags: -march=armv8.4-a;-fvectorize;-ffp-model=fast;-fno-finite-math-only;-Wno-format;-Wno-unused-variable;-Wno-unused-function + Id flags: --target=arm-arm-none-eabi;-mcpu=cortex-m3 + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the C compiler identification source file "CMakeCCompilerId.c" failed. + Compiler: clang + Build flags: -march=armv8.4-a;-fvectorize;-ffp-model=fast;-fno-finite-math-only;-Wno-format;-Wno-unused-variable;-Wno-unused-function + Id flags: -c;-I__does_not_exist__ + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the C compiler identification source file "CMakeCCompilerId.c" failed. + Compiler: clang + Build flags: + Id flags: -c;--target=arm64-apple-darwin-macho + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the C compiler identification source file "CMakeCCompilerId.c" failed. + Compiler: clang + Build flags: + Id flags: + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the C compiler identification source file "CMakeCCompilerId.c" failed. + Compiler: clang + Build flags: + Id flags: -c + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the C compiler identification source file "CMakeCCompilerId.c" failed. + Compiler: clang + Build flags: + Id flags: -Aa + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the C compiler identification source file "CMakeCCompilerId.c" failed. + Compiler: clang + Build flags: + Id flags: -D__CLASSIC_C__ + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the C compiler identification source file "CMakeCCompilerId.c" failed. + Compiler: clang + Build flags: + Id flags: --target=arm-arm-none-eabi;-mcpu=cortex-m3 + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the C compiler identification source file "CMakeCCompilerId.c" failed. + Compiler: clang + Build flags: + Id flags: -c;-I__does_not_exist__ + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:1250 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:86 (CMAKE_DETERMINE_COMPILER_ID_VENDOR)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Checking whether the C compiler is IAR using "" did not match "IAR .+ Compiler": + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:1250 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:86 (CMAKE_DETERMINE_COMPILER_ID_VENDOR)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCCompiler.cmake:123 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Checking whether the C compiler is IAR using "" did not match "IAR .+ Compiler": + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the CXX compiler identification source file "CMakeCXXCompilerId.cpp" failed. + Compiler: clang++ + Build flags: -march=armv8.4-a;-fvectorize;-ffp-model=fast;-fno-finite-math-only;-Wno-format;-Wno-unused-variable;-Wno-unused-function + Id flags: -c;--target=arm64-apple-darwin-macho + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the CXX compiler identification source file "CMakeCXXCompilerId.cpp" failed. + Compiler: clang++ + Build flags: -march=armv8.4-a;-fvectorize;-ffp-model=fast;-fno-finite-math-only;-Wno-format;-Wno-unused-variable;-Wno-unused-function + Id flags: + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the CXX compiler identification source file "CMakeCXXCompilerId.cpp" failed. + Compiler: clang++ + Build flags: -march=armv8.4-a;-fvectorize;-ffp-model=fast;-fno-finite-math-only;-Wno-format;-Wno-unused-variable;-Wno-unused-function + Id flags: -c + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the CXX compiler identification source file "CMakeCXXCompilerId.cpp" failed. + Compiler: clang++ + Build flags: -march=armv8.4-a;-fvectorize;-ffp-model=fast;-fno-finite-math-only;-Wno-format;-Wno-unused-variable;-Wno-unused-function + Id flags: --c++ + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the CXX compiler identification source file "CMakeCXXCompilerId.cpp" failed. + Compiler: clang++ + Build flags: -march=armv8.4-a;-fvectorize;-ffp-model=fast;-fno-finite-math-only;-Wno-format;-Wno-unused-variable;-Wno-unused-function + Id flags: --ec++ + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the CXX compiler identification source file "CMakeCXXCompilerId.cpp" failed. + Compiler: clang++ + Build flags: -march=armv8.4-a;-fvectorize;-ffp-model=fast;-fno-finite-math-only;-Wno-format;-Wno-unused-variable;-Wno-unused-function + Id flags: --target=arm-arm-none-eabi;-mcpu=cortex-m3 + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the CXX compiler identification source file "CMakeCXXCompilerId.cpp" failed. + Compiler: clang++ + Build flags: -march=armv8.4-a;-fvectorize;-ffp-model=fast;-fno-finite-math-only;-Wno-format;-Wno-unused-variable;-Wno-unused-function + Id flags: -c;-I__does_not_exist__ + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the CXX compiler identification source file "CMakeCXXCompilerId.cpp" failed. + Compiler: clang++ + Build flags: + Id flags: -c;--target=arm64-apple-darwin-macho + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the CXX compiler identification source file "CMakeCXXCompilerId.cpp" failed. + Compiler: clang++ + Build flags: + Id flags: + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the CXX compiler identification source file "CMakeCXXCompilerId.cpp" failed. + Compiler: clang++ + Build flags: + Id flags: -c + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the CXX compiler identification source file "CMakeCXXCompilerId.cpp" failed. + Compiler: clang++ + Build flags: + Id flags: --c++ + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the CXX compiler identification source file "CMakeCXXCompilerId.cpp" failed. + Compiler: clang++ + Build flags: + Id flags: --ec++ + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the CXX compiler identification source file "CMakeCXXCompilerId.cpp" failed. + Compiler: clang++ + Build flags: + Id flags: --target=arm-arm-none-eabi;-mcpu=cortex-m3 + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:17 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Compiling the CXX compiler identification source file "CMakeCXXCompilerId.cpp" failed. + Compiler: clang++ + Build flags: + Id flags: -c;-I__does_not_exist__ + + The output was: + no such file or directory + + + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:1250 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:86 (CMAKE_DETERMINE_COMPILER_ID_VENDOR)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Checking whether the CXX compiler is IAR using "" did not match "IAR .+ Compiler": + - + kind: "message-v1" + backtrace: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:1250 (message)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCompilerId.cmake:86 (CMAKE_DETERMINE_COMPILER_ID_VENDOR)" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/share/cmake-3.31/Modules/CMakeDetermineCXXCompiler.cmake:126 (CMAKE_DETERMINE_COMPILER_ID)" + - "CMakeLists.txt:2 (project)" + message: | + Checking whether the CXX compiler is IAR using "" did not match "IAR .+ Compiler": +... diff --git a/llama.cpp/build-arm64-apple-clang+static-release/CMakeFiles/cmake.check_cache b/llama.cpp/build-arm64-apple-clang+static-release/CMakeFiles/cmake.check_cache new file mode 100644 index 0000000000000000000000000000000000000000..56c437b9b7acb76284e54ca56e02374aac6338f5 --- /dev/null +++ b/llama.cpp/build-arm64-apple-clang+static-release/CMakeFiles/cmake.check_cache @@ -0,0 +1 @@ +# This file is generated by cmake for dependency checking of the CMakeCache.txt file diff --git a/llama.cpp/build-arm64-apple-clang+static-release/VSInheritEnvironments.txt b/llama.cpp/build-arm64-apple-clang+static-release/VSInheritEnvironments.txt new file mode 100644 index 0000000000000000000000000000000000000000..61e577ecd7d08624be9a5ce8acf25d6df5e7d1e3 --- /dev/null +++ b/llama.cpp/build-arm64-apple-clang+static-release/VSInheritEnvironments.txt @@ -0,0 +1 @@ +msvc_arm64_x64 \ No newline at end of file diff --git a/llama.cpp/build-xcframework.sh b/llama.cpp/build-xcframework.sh new file mode 100644 index 0000000000000000000000000000000000000000..7d363672952826d0a79132b0daad50697d2c902a --- /dev/null +++ b/llama.cpp/build-xcframework.sh @@ -0,0 +1,541 @@ +#!/usr/bin/env bash +# +# Options +IOS_MIN_OS_VERSION=16.4 +MACOS_MIN_OS_VERSION=13.3 +VISIONOS_MIN_OS_VERSION=1.0 +TVOS_MIN_OS_VERSION=16.4 + +BUILD_SHARED_LIBS=OFF +LLAMA_BUILD_EXAMPLES=OFF +LLAMA_BUILD_TOOLS=OFF +LLAMA_BUILD_TESTS=OFF +LLAMA_BUILD_SERVER=OFF +GGML_METAL=ON +GGML_METAL_EMBED_LIBRARY=ON +GGML_BLAS_DEFAULT=ON +GGML_METAL_USE_BF16=ON +GGML_OPENMP=OFF + +COMMON_C_FLAGS="-Wno-macro-redefined -Wno-shorten-64-to-32 -Wno-unused-command-line-argument -g" +COMMON_CXX_FLAGS="-Wno-macro-redefined -Wno-shorten-64-to-32 -Wno-unused-command-line-argument -g" + +# Common options for all builds +COMMON_CMAKE_ARGS=( + -DCMAKE_XCODE_ATTRIBUTE_CODE_SIGNING_REQUIRED=NO + -DCMAKE_XCODE_ATTRIBUTE_CODE_SIGN_IDENTITY="" + -DCMAKE_XCODE_ATTRIBUTE_CODE_SIGNING_ALLOWED=NO + -DCMAKE_XCODE_ATTRIBUTE_DEBUG_INFORMATION_FORMAT="dwarf-with-dsym" + -DCMAKE_XCODE_ATTRIBUTE_GCC_GENERATE_DEBUGGING_SYMBOLS=YES + -DCMAKE_XCODE_ATTRIBUTE_COPY_PHASE_STRIP=NO + -DCMAKE_XCODE_ATTRIBUTE_STRIP_INSTALLED_PRODUCT=NO + -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml + -DBUILD_SHARED_LIBS=${BUILD_SHARED_LIBS} + -DLLAMA_BUILD_EXAMPLES=${LLAMA_BUILD_EXAMPLES} + -DLLAMA_BUILD_TOOLS=${LLAMA_BUILD_TOOLS} + -DLLAMA_BUILD_TESTS=${LLAMA_BUILD_TESTS} + -DLLAMA_BUILD_SERVER=${LLAMA_BUILD_SERVER} + -DGGML_METAL_EMBED_LIBRARY=${GGML_METAL_EMBED_LIBRARY} + -DGGML_BLAS_DEFAULT=${GGML_BLAS_DEFAULT} + -DGGML_METAL=${GGML_METAL} + -DGGML_METAL_USE_BF16=${GGML_METAL_USE_BF16} + -DGGML_NATIVE=OFF + -DGGML_OPENMP=${GGML_OPENMP} +) + +XCODE_VERSION=$(xcodebuild -version 2>/dev/null | head -n1 | awk '{ print $2 }') +MAJOR_VERSION=$(echo $XCODE_VERSION | cut -d. -f1) +MINOR_VERSION=$(echo $XCODE_VERSION | cut -d. -f2) +echo "Detected Xcode version: $XCODE_VERSION" + +check_required_tool() { + local tool=$1 + local install_message=$2 + + if ! command -v $tool &> /dev/null; then + echo "Error: $tool is required but not found." + echo "$install_message" + exit 1 + fi +} +echo "Checking for required tools..." +check_required_tool "cmake" "Please install CMake 3.28.0 or later (brew install cmake)" +check_required_tool "xcodebuild" "Please install Xcode and Xcode Command Line Tools (xcode-select --install)" +check_required_tool "libtool" "Please install libtool which should be available with Xcode Command Line Tools (CLT). Make sure Xcode CLT is installed (xcode-select --install)" +check_required_tool "dsymutil" "Please install Xcode and Xcode Command Line Tools (xcode-select --install)" + +set -e + +## Clean up previous builds +rm -rf build-apple +rm -rf build-ios-sim +rm -rf build-ios-device +rm -rf build-macos +rm -rf build-visionos +rm -rf build-visionos-sim +rm -rf build-tvos-sim +rm -rf build-tvos-device + +# Setup the xcframework build directory structure +setup_framework_structure() { + local build_dir=$1 + local min_os_version=$2 + local platform=$3 # "ios", "macos", "visionos", or "tvos" + local framework_name="llama" + + echo "Creating ${platform}-style framework structure for ${build_dir}" + + if [[ "$platform" == "macos" ]]; then + # macOS versioned structure uses versioned directories + mkdir -p ${build_dir}/framework/${framework_name}.framework/Versions/A/Headers + mkdir -p ${build_dir}/framework/${framework_name}.framework/Versions/A/Modules + mkdir -p ${build_dir}/framework/${framework_name}.framework/Versions/A/Resources + + # Create symbolic links + ln -sf A ${build_dir}/framework/${framework_name}.framework/Versions/Current + ln -sf Versions/Current/Headers ${build_dir}/framework/${framework_name}.framework/Headers + ln -sf Versions/Current/Modules ${build_dir}/framework/${framework_name}.framework/Modules + ln -sf Versions/Current/Resources ${build_dir}/framework/${framework_name}.framework/Resources + ln -sf Versions/Current/${framework_name} ${build_dir}/framework/${framework_name}.framework/${framework_name} + + # Set header and module paths + local header_path=${build_dir}/framework/${framework_name}.framework/Versions/A/Headers/ + local module_path=${build_dir}/framework/${framework_name}.framework/Versions/A/Modules/ + else + # iOS/VisionOS/tvOS use a flat structure + mkdir -p ${build_dir}/framework/${framework_name}.framework/Headers + mkdir -p ${build_dir}/framework/${framework_name}.framework/Modules + + # Remove any existing structure to ensure clean build + rm -rf ${build_dir}/framework/${framework_name}.framework/Versions + + # Set header and module paths + local header_path=${build_dir}/framework/${framework_name}.framework/Headers/ + local module_path=${build_dir}/framework/${framework_name}.framework/Modules/ + fi + + # Copy all required headers (common for all platforms) + cp include/llama.h ${header_path} + cp ggml/include/ggml.h ${header_path} + cp ggml/include/ggml-opt.h ${header_path} + cp ggml/include/ggml-alloc.h ${header_path} + cp ggml/include/ggml-backend.h ${header_path} + cp ggml/include/ggml-metal.h ${header_path} + cp ggml/include/ggml-cpu.h ${header_path} + cp ggml/include/ggml-blas.h ${header_path} + cp ggml/include/gguf.h ${header_path} + + # Create module map (common for all platforms) + cat > ${module_path}module.modulemap << EOF +framework module llama { + header "llama.h" + header "ggml.h" + header "ggml-alloc.h" + header "ggml-backend.h" + header "ggml-metal.h" + header "ggml-cpu.h" + header "ggml-blas.h" + header "gguf.h" + + link "c++" + link framework "Accelerate" + link framework "Metal" + link framework "Foundation" + + export * +} +EOF + + # Platform-specific settings for Info.plist + local platform_name="" + local sdk_name="" + local supported_platform="" + + case "$platform" in + "ios") + platform_name="iphoneos" + sdk_name="iphoneos${min_os_version}" + supported_platform="iPhoneOS" + local plist_path="${build_dir}/framework/${framework_name}.framework/Info.plist" + local device_family=' UIDeviceFamily + + 1 + 2 + ' + ;; + "macos") + platform_name="macosx" + sdk_name="macosx${min_os_version}" + supported_platform="MacOSX" + local plist_path="${build_dir}/framework/${framework_name}.framework/Versions/A/Resources/Info.plist" + local device_family="" + ;; + "visionos") + platform_name="xros" + sdk_name="xros${min_os_version}" + supported_platform="XRPlatform" + local plist_path="${build_dir}/framework/${framework_name}.framework/Info.plist" + local device_family="" + ;; + "tvos") + platform_name="appletvos" + sdk_name="appletvos${min_os_version}" + supported_platform="AppleTVOS" + local plist_path="${build_dir}/framework/${framework_name}.framework/Info.plist" + local device_family=' UIDeviceFamily + + 3 + ' + ;; + esac + + # Create Info.plist + cat > ${plist_path} << EOF + + + + + CFBundleDevelopmentRegion + en + CFBundleExecutable + llama + CFBundleIdentifier + org.ggml.llama + CFBundleInfoDictionaryVersion + 6.0 + CFBundleName + llama + CFBundlePackageType + FMWK + CFBundleShortVersionString + 1.0 + CFBundleVersion + 1 + MinimumOSVersion + ${min_os_version} + CFBundleSupportedPlatforms + + ${supported_platform} + ${device_family} + DTPlatformName + ${platform_name} + DTSDKName + ${sdk_name} + + +EOF +} + +# Create dynamic libraries from static libraries. +combine_static_libraries() { + local build_dir="$1" + local release_dir="$2" + local platform="$3" # "ios", "macos", "visionos", or "tvos" + local is_simulator="$4" + local base_dir="$(pwd)" + local framework_name="llama" + + # Determine output path based on platform + local output_lib="" + if [[ "$platform" == "macos" ]]; then + # macOS uses versioned structure + output_lib="${build_dir}/framework/${framework_name}.framework/Versions/A/${framework_name}" + else + # iOS, visionOS, and tvOS use a directory flat structure + output_lib="${build_dir}/framework/${framework_name}.framework/${framework_name}" + fi + + local libs=( + "${base_dir}/${build_dir}/src/${release_dir}/libllama.a" + "${base_dir}/${build_dir}/ggml/src/${release_dir}/libggml.a" + "${base_dir}/${build_dir}/ggml/src/${release_dir}/libggml-base.a" + "${base_dir}/${build_dir}/ggml/src/${release_dir}/libggml-cpu.a" + "${base_dir}/${build_dir}/ggml/src/ggml-metal/${release_dir}/libggml-metal.a" + "${base_dir}/${build_dir}/ggml/src/ggml-blas/${release_dir}/libggml-blas.a" + ) + + # Create temporary directory for processing + local temp_dir="${base_dir}/${build_dir}/temp" + mkdir -p "${temp_dir}" + + # Since we have multiple architectures libtool will find object files that do not + # match the target architecture. We suppress these warnings. + libtool -static -o "${temp_dir}/combined.a" "${libs[@]}" 2> /dev/null + + # Determine SDK, architectures, and install_name based on platform and simulator flag. + local sdk="" + local archs="" + local min_version_flag="" + local install_name="" + + case "$platform" in + "ios") + if [[ "$is_simulator" == "true" ]]; then + sdk="iphonesimulator" + archs="arm64 x86_64" + min_version_flag="-mios-simulator-version-min=${IOS_MIN_OS_VERSION}" + else + sdk="iphoneos" + archs="arm64" + min_version_flag="-mios-version-min=${IOS_MIN_OS_VERSION}" + fi + install_name="@rpath/llama.framework/llama" + ;; + "macos") + sdk="macosx" + archs="arm64 x86_64" + min_version_flag="-mmacosx-version-min=${MACOS_MIN_OS_VERSION}" + install_name="@rpath/llama.framework/Versions/Current/llama" + ;; + "visionos") + if [[ "$is_simulator" == "true" ]]; then + sdk="xrsimulator" + archs="arm64 x86_64" + min_version_flag="-mtargetos=xros${VISIONOS_MIN_OS_VERSION}-simulator" + else + sdk="xros" + archs="arm64" + min_version_flag="-mtargetos=xros${VISIONOS_MIN_OS_VERSION}" + fi + # Use flat structure for visionOS, same as iOS + install_name="@rpath/llama.framework/llama" + ;; + "tvos") + if [[ "$is_simulator" == "true" ]]; then + sdk="appletvsimulator" + archs="arm64 x86_64" + min_version_flag="-mtvos-simulator-version-min=${TVOS_MIN_OS_VERSION}" + else + sdk="appletvos" + archs="arm64" + min_version_flag="-mtvos-version-min=${TVOS_MIN_OS_VERSION}" + fi + install_name="@rpath/llama.framework/llama" + ;; + esac + + # Build architecture flags + local arch_flags="" + for arch in $archs; do + arch_flags+=" -arch $arch" + done + + # Create dynamic library + echo "Creating dynamic library for ${platform}." + xcrun -sdk $sdk clang++ -dynamiclib \ + -isysroot $(xcrun --sdk $sdk --show-sdk-path) \ + $arch_flags \ + $min_version_flag \ + -Wl,-force_load,"${temp_dir}/combined.a" \ + -framework Foundation -framework Metal -framework Accelerate \ + -install_name "$install_name" \ + -o "${base_dir}/${output_lib}" + + # Platform-specific post-processing for device builds + if [[ "$is_simulator" == "false" ]]; then + if command -v xcrun vtool &>/dev/null; then + case "$platform" in + "ios") + echo "Marking binary as a framework binary for iOS..." + xcrun vtool -set-build-version ios ${IOS_MIN_OS_VERSION} ${IOS_MIN_OS_VERSION} -replace \ + -output "${base_dir}/${output_lib}" "${base_dir}/${output_lib}" + ;; + "visionos") + echo "Marking binary as a framework binary for visionOS..." + if [[ "$MAJOR_VERSION" -gt 16 ]] || [[ "$MAJOR_VERSION" -eq 16 && "$MINOR_VERSION" -gt 2 ]]; then + echo "Xcode version greater than 16.2, using visionOS." + VISION_OS_BUILD_VERSION="visionos" + else + echo "Xcode version less than or equal to 16.2, using xros." + VISION_OS_BUILD_VERSION="xros" + fi + xcrun vtool -set-build-version ${VISION_OS_BUILD_VERSION} ${VISIONOS_MIN_OS_VERSION} ${VISIONOS_MIN_OS_VERSION} -replace \ + -output "${base_dir}/${output_lib}" "${base_dir}/${output_lib}" + ;; + "tvos") + echo "Marking binary as a framework binary for tvOS..." + xcrun vtool -set-build-version tvos ${TVOS_MIN_OS_VERSION} ${TVOS_MIN_OS_VERSION} -replace \ + -output "${base_dir}/${output_lib}" "${base_dir}/${output_lib}" + ;; + esac + else + echo "Warning: vtool not found. Binary may not pass App Store validation." + fi + fi + + echo "Creating properly formatted dSYM..." + # Create a separate directory for dSYMs for all platforms + mkdir -p "${base_dir}/${build_dir}/dSYMs" + + # iOS and visionOS style dSYM (flat structure) + if [[ "$platform" == "ios" || "$platform" == "visionos" || "$platform" == "tvos" ]]; then + # Generate dSYM in the dSYMs directory + xcrun dsymutil "${base_dir}/${output_lib}" -o "${base_dir}/${build_dir}/dSYMs/llama.dSYM" + + # Create a copy of the binary that will be stripped + cp "${base_dir}/${output_lib}" "${temp_dir}/binary_to_strip" + + # Strip debug symbols from the copy + xcrun strip -S "${temp_dir}/binary_to_strip" -o "${temp_dir}/stripped_lib" + + # Replace the original with the stripped version + mv "${temp_dir}/stripped_lib" "${base_dir}/${output_lib}" + else + # macOS style dSYM + # First strip debug info to a separate file + xcrun strip -S "${base_dir}/${output_lib}" -o "${temp_dir}/stripped_lib" + + # Generate dSYM in the dSYMs directory + xcrun dsymutil "${base_dir}/${output_lib}" -o "${base_dir}/${build_dir}/dSYMs/llama.dSYM" + + # Replace original binary with stripped version + mv "${temp_dir}/stripped_lib" "${base_dir}/${output_lib}" + fi + + # Remove any automatically generated dSYM files in the framework structure as they will + # otherwise case Invalid Bundle Structure validation errors. + if [ -d "${base_dir}/${output_lib}.dSYM" ]; then + echo "Removing generated dSYM file in framework structure: ${base_dir}/${output_lib}.dSYM" + rm -rf "${base_dir}/${output_lib}.dSYM" + fi + + # Clean up + rm -rf "${temp_dir}" +} + +echo "Building for iOS simulator..." +cmake -B build-ios-sim -G Xcode \ + "${COMMON_CMAKE_ARGS[@]}" \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=${IOS_MIN_OS_VERSION} \ + -DIOS=ON \ + -DCMAKE_SYSTEM_NAME=iOS \ + -DCMAKE_OSX_SYSROOT=iphonesimulator \ + -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \ + -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphonesimulator \ + -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \ + -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \ + -DLLAMA_CURL=OFF \ + -S . +cmake --build build-ios-sim --config Release -- -quiet + +echo "Building for iOS devices..." +cmake -B build-ios-device -G Xcode \ + "${COMMON_CMAKE_ARGS[@]}" \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=${IOS_MIN_OS_VERSION} \ + -DCMAKE_OSX_SYSROOT=iphoneos \ + -DCMAKE_OSX_ARCHITECTURES="arm64" \ + -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphoneos \ + -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \ + -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \ + -DLLAMA_CURL=OFF \ + -S . +cmake --build build-ios-device --config Release -- -quiet + +echo "Building for macOS..." +cmake -B build-macos -G Xcode \ + "${COMMON_CMAKE_ARGS[@]}" \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=${MACOS_MIN_OS_VERSION} \ + -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \ + -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \ + -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \ + -DLLAMA_CURL=OFF \ + -S . +cmake --build build-macos --config Release -- -quiet + +echo "Building for visionOS..." +cmake -B build-visionos -G Xcode \ + "${COMMON_CMAKE_ARGS[@]}" \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=${VISIONOS_MIN_OS_VERSION} \ + -DCMAKE_OSX_ARCHITECTURES="arm64" \ + -DCMAKE_SYSTEM_NAME=visionOS \ + -DCMAKE_OSX_SYSROOT=xros \ + -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xros \ + -DCMAKE_C_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_C_FLAGS}" \ + -DCMAKE_CXX_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_CXX_FLAGS}" \ + -DLLAMA_CURL=OFF \ + -S . +cmake --build build-visionos --config Release -- -quiet + +echo "Building for visionOS simulator..." +cmake -B build-visionos-sim -G Xcode \ + "${COMMON_CMAKE_ARGS[@]}" \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=${VISIONOS_MIN_OS_VERSION} \ + -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \ + -DCMAKE_SYSTEM_NAME=visionOS \ + -DCMAKE_OSX_SYSROOT=xrsimulator \ + -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xrsimulator \ + -DCMAKE_C_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_C_FLAGS}" \ + -DCMAKE_CXX_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_CXX_FLAGS}" \ + -DLLAMA_CURL=OFF \ + -S . +cmake --build build-visionos-sim --config Release -- -quiet + +# Add tvOS builds (might need the same u_int definitions as watchOS and visionOS) +echo "Building for tvOS simulator..." +cmake -B build-tvos-sim -G Xcode \ + "${COMMON_CMAKE_ARGS[@]}" \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=${TVOS_MIN_OS_VERSION} \ + -DCMAKE_SYSTEM_NAME=tvOS \ + -DCMAKE_OSX_SYSROOT=appletvsimulator \ + -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \ + -DGGML_METAL=ON \ + -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvsimulator \ + -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \ + -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \ + -DLLAMA_CURL=OFF \ + -S . +cmake --build build-tvos-sim --config Release -- -quiet + +echo "Building for tvOS devices..." +cmake -B build-tvos-device -G Xcode \ + "${COMMON_CMAKE_ARGS[@]}" \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=${TVOS_MIN_OS_VERSION} \ + -DCMAKE_SYSTEM_NAME=tvOS \ + -DCMAKE_OSX_SYSROOT=appletvos \ + -DCMAKE_OSX_ARCHITECTURES="arm64" \ + -DGGML_METAL=ON \ + -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvos \ + -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \ + -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \ + -DLLAMA_CURL=OFF \ + -S . +cmake --build build-tvos-device --config Release -- -quiet + +# Setup frameworks and copy binaries and headers +echo "Setting up framework structures..." +setup_framework_structure "build-ios-sim" ${IOS_MIN_OS_VERSION} "ios" +setup_framework_structure "build-ios-device" ${IOS_MIN_OS_VERSION} "ios" +setup_framework_structure "build-macos" ${MACOS_MIN_OS_VERSION} "macos" +setup_framework_structure "build-visionos" ${VISIONOS_MIN_OS_VERSION} "visionos" +setup_framework_structure "build-visionos-sim" ${VISIONOS_MIN_OS_VERSION} "visionos" +setup_framework_structure "build-tvos-sim" ${TVOS_MIN_OS_VERSION} "tvos" +setup_framework_structure "build-tvos-device" ${TVOS_MIN_OS_VERSION} "tvos" + +# Create dynamic libraries from static libraries +echo "Creating dynamic libraries from static libraries..." +combine_static_libraries "build-ios-sim" "Release-iphonesimulator" "ios" "true" +combine_static_libraries "build-ios-device" "Release-iphoneos" "ios" "false" +combine_static_libraries "build-macos" "Release" "macos" "false" +combine_static_libraries "build-visionos" "Release-xros" "visionos" "false" +combine_static_libraries "build-visionos-sim" "Release-xrsimulator" "visionos" "true" +combine_static_libraries "build-tvos-sim" "Release-appletvsimulator" "tvos" "true" +combine_static_libraries "build-tvos-device" "Release-appletvos" "tvos" "false" + +# Create XCFramework with correct debug symbols paths +echo "Creating XCFramework..." +xcodebuild -create-xcframework \ + -framework $(pwd)/build-ios-sim/framework/llama.framework \ + -debug-symbols $(pwd)/build-ios-sim/dSYMs/llama.dSYM \ + -framework $(pwd)/build-ios-device/framework/llama.framework \ + -debug-symbols $(pwd)/build-ios-device/dSYMs/llama.dSYM \ + -framework $(pwd)/build-macos/framework/llama.framework \ + -debug-symbols 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the editor. +# The syntax for the file is as follows: +# KEY:TYPE=VALUE +# KEY is the name of a variable in the cache. +# TYPE is a hint to GUIs for the type of VALUE, DO NOT EDIT TYPE!. +# VALUE is the current value for the KEY. + +######################## +# EXTERNAL cache entries +######################## + +//AVX2 flags +AVX2_FLAGS:STRING=' ' + +//AVX2 support +AVX2_FOUND:BOOL=TRUE + +//AVX512 flags +AVX512_FLAGS:STRING= + +//AVX512 support +AVX512_FOUND:BOOL=FALSE + +//AVX flags +AVX_FLAGS:STRING=' ' + +//AVX support +AVX_FOUND:BOOL=TRUE + +//No help, variable specified on the command line. +BUILD_LLAMA_SERVER:UNINITIALIZED=ON + +//ggml: build shared libraries +BUILD_SHARED_LIBS:BOOL=ON + +//Build the testing tree. +BUILD_TESTING:BOOL=ON + +//Path to a program. +CMAKE_AR:FILEPATH=C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/lib.exe + +//Choose the type of build, options are: None Debug Release RelWithDebInfo +// MinSizeRel ... 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compiler +CMAKE_C_COMPILER:FILEPATH=C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/cl.exe + +//Flags used by the C compiler during all build types. +CMAKE_C_FLAGS:STRING=/DWIN32 /D_WINDOWS /W3 + +//Flags used by the C compiler during DEBUG builds. +CMAKE_C_FLAGS_DEBUG:STRING=/MDd /Zi /Ob0 /Od /RTC1 + +//Flags used by the C compiler during MINSIZEREL builds. +CMAKE_C_FLAGS_MINSIZEREL:STRING=/MD /O1 /Ob1 /DNDEBUG + +//Flags used by the C compiler during RELEASE builds. +CMAKE_C_FLAGS_RELEASE:STRING=/MD /O2 /Ob2 /DNDEBUG + +//Flags used by the C compiler during RELWITHDEBINFO builds. +CMAKE_C_FLAGS_RELWITHDEBINFO:STRING=/MD /Zi /O2 /Ob1 /DNDEBUG + +//Libraries linked by default with all C applications. +CMAKE_C_STANDARD_LIBRARIES:STRING=kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + +//Flags used by the linker during all build types. 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architecture-independent data (DATAROOTDIR) +CMAKE_INSTALL_DATADIR:PATH= + +//Read-only architecture-independent data root (share) +CMAKE_INSTALL_DATAROOTDIR:PATH=share + +//Documentation root (DATAROOTDIR/doc/PROJECT_NAME) +CMAKE_INSTALL_DOCDIR:PATH= + +//C header files (include) +CMAKE_INSTALL_INCLUDEDIR:PATH=include + +//Info documentation (DATAROOTDIR/info) +CMAKE_INSTALL_INFODIR:PATH= + +//Object code libraries (lib) +CMAKE_INSTALL_LIBDIR:PATH=lib + +//Program executables (libexec) +CMAKE_INSTALL_LIBEXECDIR:PATH=libexec + +//Locale-dependent data (DATAROOTDIR/locale) +CMAKE_INSTALL_LOCALEDIR:PATH= + +//Modifiable single-machine data (var) +CMAKE_INSTALL_LOCALSTATEDIR:PATH=var + +//Man documentation (DATAROOTDIR/man) +CMAKE_INSTALL_MANDIR:PATH= + +//C header files for non-gcc (/usr/include) +CMAKE_INSTALL_OLDINCLUDEDIR:PATH=/usr/include + +//Install path prefix, prepended onto install directories. +CMAKE_INSTALL_PREFIX:PATH=C:/Program Files (x86)/llama.cpp + +//Run-time variable data (LOCALSTATEDIR/run) +CMAKE_INSTALL_RUNSTATEDIR:PATH= + +//System admin executables (sbin) +CMAKE_INSTALL_SBINDIR:PATH=sbin + +//Modifiable architecture-independent data (com) +CMAKE_INSTALL_SHAREDSTATEDIR:PATH=com + +//Read-only single-machine data (etc) +CMAKE_INSTALL_SYSCONFDIR:PATH=etc + +//Path to a program. +CMAKE_LINKER:FILEPATH=C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/link.exe + +//Program used to build from build.ninja files. +CMAKE_MAKE_PROGRAM:FILEPATH=C:/Users/Kids/AppData/Local/Programs/Python/Python311/Scripts/ninja.exe + +//Flags used by the linker during the creation of modules during +// all build types. +CMAKE_MODULE_LINKER_FLAGS:STRING=/machine:x64 + +//Flags used by the linker during the creation of modules during +// DEBUG builds. +CMAKE_MODULE_LINKER_FLAGS_DEBUG:STRING=/debug /INCREMENTAL + +//Flags used by the linker during the creation of modules during +// MINSIZEREL builds. 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the linker during the creation of shared libraries +// during MINSIZEREL builds. +CMAKE_SHARED_LINKER_FLAGS_MINSIZEREL:STRING=/INCREMENTAL:NO + +//Flags used by the linker during the creation of shared libraries +// during RELEASE builds. +CMAKE_SHARED_LINKER_FLAGS_RELEASE:STRING=/INCREMENTAL:NO + +//Flags used by the linker during the creation of shared libraries +// during RELWITHDEBINFO builds. +CMAKE_SHARED_LINKER_FLAGS_RELWITHDEBINFO:STRING=/debug /INCREMENTAL + +//If set, runtime paths are not added when installing shared libraries, +// but are added when building. +CMAKE_SKIP_INSTALL_RPATH:BOOL=NO + +//If set, runtime paths are not added when using shared libraries. +CMAKE_SKIP_RPATH:BOOL=NO + +//Flags used by the archiver during the creation of static libraries +// during all build types. +CMAKE_STATIC_LINKER_FLAGS:STRING=/machine:x64 + +//Flags used by the archiver during the creation of static libraries +// during DEBUG builds. +CMAKE_STATIC_LINKER_FLAGS_DEBUG:STRING= + +//Flags used by the archiver during the creation of static libraries +// during MINSIZEREL builds. +CMAKE_STATIC_LINKER_FLAGS_MINSIZEREL:STRING= + +//Flags used by the archiver during the creation of static libraries +// during RELEASE builds. +CMAKE_STATIC_LINKER_FLAGS_RELEASE:STRING= + +//Flags used by the archiver during the creation of static libraries +// during RELWITHDEBINFO builds. +CMAKE_STATIC_LINKER_FLAGS_RELWITHDEBINFO:STRING= + +//If this value is on, makefiles will be generated without the +// .SILENT directive, and all commands will be echoed to the console +// during the make. This is useful for debugging only. With Visual +// Studio IDE projects all commands are done without /nologo. +CMAKE_VERBOSE_MAKEFILE:BOOL=FALSE + +//Path to the coverage program that CTest uses for performing coverage +// inspection +COVERAGE_COMMAND:FILEPATH=COVERAGE_COMMAND-NOTFOUND + +//Extra command line flags to pass to the coverage tool +COVERAGE_EXTRA_FLAGS:STRING=-l + +//How many times to retry timed-out CTest submissions. +CTEST_SUBMIT_RETRY_COUNT:STRING=3 + +//How long to wait between timed-out CTest submissions. +CTEST_SUBMIT_RETRY_DELAY:STRING=5 + +//Maximum time allowed before CTest will kill the test. +DART_TESTING_TIMEOUT:STRING=1500 + +//FMA flags +FMA_FLAGS:STRING=' ' + +//FMA support +FMA_FOUND:BOOL=TRUE + +//ggml: enable Accelerate framework +GGML_ACCELERATE:BOOL=ON + +//ggml: enable all compiler warnings in 3rd party libs +GGML_ALL_WARNINGS_3RD_PARTY:BOOL=OFF + +//ggml: enable AVX +GGML_AVX:BOOL=OFF + +//ggml: enable AVX2 +GGML_AVX2:BOOL=OFF + +//ggml: enable AVX512F +GGML_AVX512:BOOL=OFF + +//ggml: enable AVX512-BF16 +GGML_AVX512_BF16:BOOL=OFF + +//ggml: enable AVX512-VBMI +GGML_AVX512_VBMI:BOOL=OFF + +//ggml: enable AVX512-VNNI +GGML_AVX512_VNNI:BOOL=OFF + +//ggml: enable AVX-VNNI +GGML_AVX_VNNI:BOOL=OFF + +//ggml: build backends as dynamic libraries (requires BUILD_SHARED_LIBS) +GGML_BACKEND_DL:BOOL=OFF + +//Location of binary files +GGML_BIN_INSTALL_DIR:PATH=bin + +//ggml: use BLAS +GGML_BLAS:BOOL=OFF + +//ggml: BLAS library vendor +GGML_BLAS_VENDOR:STRING=Generic + +//ggml: enable BMI2 +GGML_BMI2:BOOL=OFF + +//ggml: build examples +GGML_BUILD_EXAMPLES:BOOL=OFF + +//ggml: build tests +GGML_BUILD_TESTS:BOOL=OFF + +//ggml: use ccache if available +GGML_CCACHE:BOOL=ON + +//Path to a program. +GGML_CCACHE_FOUND:FILEPATH=GGML_CCACHE_FOUND-NOTFOUND + +//ggml: enable CPU backend +GGML_CPU:BOOL=ON + +//ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL) +GGML_CPU_ALL_VARIANTS:BOOL=OFF + +//ggml: CPU architecture for ARM +GGML_CPU_ARM_ARCH:STRING= + +//ggml: use memkind for CPU HBM +GGML_CPU_HBM:BOOL=OFF + +//ggml: use KleidiAI optimized kernels if applicable +GGML_CPU_KLEIDIAI:BOOL=OFF + +//ggml: CPU type for PowerPC +GGML_CPU_POWERPC_CPUTYPE:STRING= + +//ggml: use runtime weight conversion of Q4_0 to Q4_X_X +GGML_CPU_REPACK:BOOL=ON + +//ggml: use CUDA +GGML_CUDA:BOOL=OFF + +//ggml: cuda link binary compression mode; requires cuda 12.8+ +GGML_CUDA_COMPRESSION_MODE:STRING=size + +//ggml: use 16 bit floats for some calculations +GGML_CUDA_F16:BOOL=OFF + +//ggml: compile ggml FlashAttention CUDA kernels +GGML_CUDA_FA:BOOL=ON + +//ggml: compile all quants for FlashAttention +GGML_CUDA_FA_ALL_QUANTS:BOOL=OFF + +//ggml: always use cuBLAS instead of mmq kernels +GGML_CUDA_FORCE_CUBLAS:BOOL=OFF + +//ggml: use mmq kernels instead of cuBLAS +GGML_CUDA_FORCE_MMQ:BOOL=OFF + +//ggml: use CUDA graphs (llama.cpp only) +GGML_CUDA_GRAPHS:BOOL=ON + +//ggml: do not use peer to peer copies +GGML_CUDA_NO_PEER_COPY:BOOL=OFF + +//ggml: do not try to use CUDA VMM +GGML_CUDA_NO_VMM:BOOL=OFF + +//ggml: max. batch size for using peer access +GGML_CUDA_PEER_MAX_BATCH_SIZE:STRING=128 + +//ggml: enable gprof +GGML_GPROF:BOOL=OFF + +//ggml: use HIP +GGML_HIP:BOOL=OFF + +//ggml: enable rocWMMA FlashAttention on GFX12 +GGML_HIP_FORCE_ROCWMMA_FATTN_GFX12:BOOL=OFF + +//ggml: use HIP graph, experimental, slow +GGML_HIP_GRAPHS:BOOL=OFF + +//ggml: enable MFMA MMA for CDNA in MMQ +GGML_HIP_MMQ_MFMA:BOOL=ON + +//ggml: do not try to use HIP VMM +GGML_HIP_NO_VMM:BOOL=ON + +//ggml: enable rocWMMA for FlashAttention +GGML_HIP_ROCWMMA_FATTN:BOOL=OFF + +//Location of header files +GGML_INCLUDE_INSTALL_DIR:PATH=include + +//ggml: enable lasx +GGML_LASX:BOOL=ON + +//Location of library files +GGML_LIB_INSTALL_DIR:PATH=lib + +//ggml: use LLAMAFILE +GGML_LLAMAFILE:BOOL=ON + +//ggml: enable lsx +GGML_LSX:BOOL=ON + +//ggml: enable link time optimization +GGML_LTO:BOOL=OFF + +//ggml: use Metal +GGML_METAL:BOOL=OFF + +//ggml: embed Metal library +GGML_METAL_EMBED_LIBRARY:BOOL=OFF + +//ggml: metal minimum macOS version +GGML_METAL_MACOSX_VERSION_MIN:STRING= + +//ggml: disable Metal debugging +GGML_METAL_NDEBUG:BOOL=OFF + +//ggml: compile Metal with -fno-fast-math +GGML_METAL_SHADER_DEBUG:BOOL=OFF + +//ggml: metal standard version (-std flag) +GGML_METAL_STD:STRING= + +//ggml: use bfloat if available +GGML_METAL_USE_BF16:BOOL=OFF + +//ggml: use MUSA +GGML_MUSA:BOOL=OFF + +//ggml: use MUSA graph, experimental, unstable +GGML_MUSA_GRAPHS:BOOL=OFF + +//ggml: enable muDNN for accelerated copy +GGML_MUSA_MUDNN_COPY:BOOL=OFF + +//ggml: optimize the build for the current system +GGML_NATIVE:BOOL=ON + +//ggml: enable nnpa +GGML_NNPA:BOOL=OFF + +//ggml: use OpenCL +GGML_OPENCL:BOOL=OFF + +//ggml: embed kernels +GGML_OPENCL_EMBED_KERNELS:BOOL=ON + +//ggml: use OpenCL profiling (increases overhead) +GGML_OPENCL_PROFILING:BOOL=OFF + +//gmml: OpenCL API version to target +GGML_OPENCL_TARGET_VERSION:STRING=300 + +//ggml: use optimized kernels for Adreno +GGML_OPENCL_USE_ADRENO_KERNELS:BOOL=ON + +//ggml: use OpenMP +GGML_OPENMP:BOOL=ON + +//ggml: use RPC +GGML_RPC:BOOL=OFF + +//ggml: enable rvv +GGML_RVV:BOOL=ON + +//ggml: enable riscv zfh +GGML_RV_ZFH:BOOL=OFF + +//ggml: enable address sanitizer +GGML_SANITIZE_ADDRESS:BOOL=OFF + +//ggml: enable thread sanitizer +GGML_SANITIZE_THREAD:BOOL=OFF + +//ggml: enable undefined sanitizer +GGML_SANITIZE_UNDEFINED:BOOL=OFF + +//Path to a program. +GGML_SCCACHE_FOUND:FILEPATH=GGML_SCCACHE_FOUND-NOTFOUND + +//ggml: max input copies for pipeline parallelism +GGML_SCHED_MAX_COPIES:STRING=4 + +//ggml: enable SSE 4.2 +GGML_SSE42:BOOL=OFF + +//ggml: static link libraries +GGML_STATIC:BOOL=OFF + +//ggml: use SYCL +GGML_SYCL:BOOL=OFF + +//ggml: sycl device architecture +GGML_SYCL_DEVICE_ARCH:STRING= + +//ggml: enable oneDNN in the SYCL backend +GGML_SYCL_DNN:BOOL=ON + +//ggml: use 16 bit floats for sycl calculations +GGML_SYCL_F16:BOOL=OFF + +//ggml: enable graphs in the SYCL backend +GGML_SYCL_GRAPH:BOOL=ON + +//ggml: sycl target device +GGML_SYCL_TARGET:STRING=INTEL + +//ggml: use Vulkan +GGML_VULKAN:BOOL=OFF + +//ggml: run Vulkan op checks +GGML_VULKAN_CHECK_RESULTS:BOOL=OFF + +//ggml: enable Vulkan debug output +GGML_VULKAN_DEBUG:BOOL=OFF + +//ggml: enable Vulkan memory debug output +GGML_VULKAN_MEMORY_DEBUG:BOOL=OFF + +//ggml: run Vulkan tests +GGML_VULKAN_RUN_TESTS:BOOL=OFF + +//ggml: toolchain file for vulkan-shaders-gen +GGML_VULKAN_SHADERS_GEN_TOOLCHAIN:FILEPATH= + +//ggml: enable Vulkan shader debug info +GGML_VULKAN_SHADER_DEBUG_INFO:BOOL=OFF + +//ggml: enable Vulkan validation +GGML_VULKAN_VALIDATE:BOOL=OFF + +//ggml: enable vxe +GGML_VXE:BOOL=ON + +//ggml: use WebGPU +GGML_WEBGPU:BOOL=OFF + +//ggml: enable WebGPU debug output +GGML_WEBGPU_DEBUG:BOOL=OFF + +//ggml: enable xtheadvector +GGML_XTHEADVECTOR:BOOL=OFF + +//Path to a program. +GITCOMMAND:FILEPATH=C:/Users/Kids/AppData/Local/Programs/Git/cmd/git.exe + +//Git command line client 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COMPILER_VERSION_PATCH DEC(__ORANGEC_PATCHLEVEL__) + +#elif defined(__RENESAS__) +# define COMPILER_ID "Renesas" +/* __RENESAS_VERSION__ = 0xVVRRPP00 */ +# define COMPILER_VERSION_MAJOR HEX(__RENESAS_VERSION__ >> 24 & 0xFF) +# define COMPILER_VERSION_MINOR HEX(__RENESAS_VERSION__ >> 16 & 0xFF) +# define COMPILER_VERSION_PATCH HEX(__RENESAS_VERSION__ >> 8 & 0xFF) + +#elif defined(__TINYC__) +# define COMPILER_ID "TinyCC" + +#elif defined(__BCC__) +# define COMPILER_ID "Bruce" + +#elif defined(__SCO_VERSION__) +# define COMPILER_ID "SCO" + +#elif defined(__ARMCC_VERSION) && !defined(__clang__) +# define COMPILER_ID "ARMCC" +#if __ARMCC_VERSION >= 1000000 + /* __ARMCC_VERSION = VRRPPPP */ + # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/1000000) + # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 100) + # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION % 10000) +#else + /* __ARMCC_VERSION = VRPPPP */ + # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/100000) + # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 10) + # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION % 10000) +#endif + + +#elif defined(__clang__) && defined(__apple_build_version__) +# define COMPILER_ID "AppleClang" +# if defined(_MSC_VER) +# define SIMULATE_ID "MSVC" +# endif +# define COMPILER_VERSION_MAJOR DEC(__clang_major__) +# define COMPILER_VERSION_MINOR DEC(__clang_minor__) +# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__) +# if defined(_MSC_VER) + /* _MSC_VER = VVRR */ +# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) +# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) +# endif +# define COMPILER_VERSION_TWEAK DEC(__apple_build_version__) + +#elif defined(__clang__) && defined(__ARMCOMPILER_VERSION) +# define COMPILER_ID "ARMClang" + # define COMPILER_VERSION_MAJOR DEC(__ARMCOMPILER_VERSION/1000000) + # define COMPILER_VERSION_MINOR DEC(__ARMCOMPILER_VERSION/10000 % 100) + # define COMPILER_VERSION_PATCH DEC(__ARMCOMPILER_VERSION/100 % 100) +# define COMPILER_VERSION_INTERNAL DEC(__ARMCOMPILER_VERSION) + +#elif defined(__clang__) && defined(__ti__) +# define COMPILER_ID "TIClang" + # define COMPILER_VERSION_MAJOR DEC(__ti_major__) + # define COMPILER_VERSION_MINOR DEC(__ti_minor__) + # define COMPILER_VERSION_PATCH DEC(__ti_patchlevel__) +# define COMPILER_VERSION_INTERNAL DEC(__ti_version__) + +#elif defined(__clang__) +# define COMPILER_ID "Clang" +# if defined(_MSC_VER) +# define SIMULATE_ID "MSVC" +# endif +# define COMPILER_VERSION_MAJOR DEC(__clang_major__) +# define COMPILER_VERSION_MINOR DEC(__clang_minor__) +# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__) +# if defined(_MSC_VER) + /* _MSC_VER = VVRR */ +# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) +# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) +# endif + +#elif defined(__LCC__) && (defined(__GNUC__) || defined(__GNUG__) || defined(__MCST__)) +# define COMPILER_ID "LCC" +# define COMPILER_VERSION_MAJOR DEC(__LCC__ / 100) +# define COMPILER_VERSION_MINOR DEC(__LCC__ % 100) +# if defined(__LCC_MINOR__) +# define COMPILER_VERSION_PATCH DEC(__LCC_MINOR__) +# endif +# if defined(__GNUC__) && defined(__GNUC_MINOR__) +# define SIMULATE_ID "GNU" +# define SIMULATE_VERSION_MAJOR DEC(__GNUC__) +# define SIMULATE_VERSION_MINOR DEC(__GNUC_MINOR__) +# if defined(__GNUC_PATCHLEVEL__) +# define SIMULATE_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__) +# endif +# endif + +#elif defined(__GNUC__) +# define COMPILER_ID "GNU" +# define COMPILER_VERSION_MAJOR DEC(__GNUC__) +# if defined(__GNUC_MINOR__) +# define COMPILER_VERSION_MINOR DEC(__GNUC_MINOR__) +# endif +# if defined(__GNUC_PATCHLEVEL__) +# define COMPILER_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__) +# endif + +#elif defined(_MSC_VER) +# define COMPILER_ID "MSVC" + /* _MSC_VER = VVRR */ +# define COMPILER_VERSION_MAJOR DEC(_MSC_VER / 100) +# define COMPILER_VERSION_MINOR DEC(_MSC_VER % 100) +# if defined(_MSC_FULL_VER) +# if _MSC_VER >= 1400 + /* _MSC_FULL_VER = VVRRPPPPP */ +# define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 100000) +# else + /* _MSC_FULL_VER = VVRRPPPP */ +# define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 10000) +# endif +# endif +# if defined(_MSC_BUILD) +# define COMPILER_VERSION_TWEAK DEC(_MSC_BUILD) +# endif + +#elif defined(_ADI_COMPILER) +# define COMPILER_ID "ADSP" +#if defined(__VERSIONNUM__) + /* __VERSIONNUM__ = 0xVVRRPPTT */ +# define COMPILER_VERSION_MAJOR DEC(__VERSIONNUM__ >> 24 & 0xFF) +# define COMPILER_VERSION_MINOR DEC(__VERSIONNUM__ >> 16 & 0xFF) +# define COMPILER_VERSION_PATCH DEC(__VERSIONNUM__ >> 8 & 0xFF) +# define COMPILER_VERSION_TWEAK DEC(__VERSIONNUM__ & 0xFF) +#endif + +#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) +# define COMPILER_ID "IAR" +# if defined(__VER__) && defined(__ICCARM__) +# define COMPILER_VERSION_MAJOR DEC((__VER__) / 1000000) +# define COMPILER_VERSION_MINOR DEC(((__VER__) / 1000) % 1000) +# define COMPILER_VERSION_PATCH DEC((__VER__) % 1000) +# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) +# elif defined(__VER__) && (defined(__ICCAVR__) || defined(__ICCRX__) || defined(__ICCRH850__) || defined(__ICCRL78__) || defined(__ICC430__) || defined(__ICCRISCV__) || defined(__ICCV850__) || defined(__ICC8051__) || defined(__ICCSTM8__)) +# define COMPILER_VERSION_MAJOR DEC((__VER__) / 100) +# define COMPILER_VERSION_MINOR DEC((__VER__) - (((__VER__) / 100)*100)) +# define COMPILER_VERSION_PATCH DEC(__SUBVERSION__) +# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) +# endif + +#elif defined(__DCC__) && defined(_DIAB_TOOL) +# define COMPILER_ID "Diab" + # define COMPILER_VERSION_MAJOR DEC(__VERSION_MAJOR_NUMBER__) + # define COMPILER_VERSION_MINOR DEC(__VERSION_MINOR_NUMBER__) + # define COMPILER_VERSION_PATCH DEC(__VERSION_ARCH_FEATURE_NUMBER__) + # define COMPILER_VERSION_TWEAK DEC(__VERSION_BUG_FIX_NUMBER__) + + +#elif defined(__SDCC_VERSION_MAJOR) || defined(SDCC) +# define COMPILER_ID "SDCC" +# if defined(__SDCC_VERSION_MAJOR) +# define COMPILER_VERSION_MAJOR DEC(__SDCC_VERSION_MAJOR) +# define COMPILER_VERSION_MINOR DEC(__SDCC_VERSION_MINOR) +# define COMPILER_VERSION_PATCH DEC(__SDCC_VERSION_PATCH) +# else + /* SDCC = VRP */ +# define COMPILER_VERSION_MAJOR DEC(SDCC/100) +# define COMPILER_VERSION_MINOR DEC(SDCC/10 % 10) +# define COMPILER_VERSION_PATCH DEC(SDCC % 10) +# endif + + +/* These compilers are either not known or too old to define an + identification macro. Try to identify the platform and guess that + it is the native compiler. */ +#elif defined(__hpux) || defined(__hpua) +# define COMPILER_ID "HP" + +#else /* unknown compiler */ +# define COMPILER_ID "" +#endif + +/* Construct the string literal in pieces to prevent the source from + getting matched. Store it in a pointer rather than an array + because some compilers will just produce instructions to fill the + array rather than assigning a pointer to a static array. */ +char const* info_compiler = "INFO" ":" "compiler[" COMPILER_ID "]"; +#ifdef SIMULATE_ID +char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]"; +#endif + +#ifdef __QNXNTO__ +char const* qnxnto = "INFO" ":" "qnxnto[]"; +#endif + +#if defined(__CRAYXT_COMPUTE_LINUX_TARGET) +char const *info_cray = "INFO" ":" "compiler_wrapper[CrayPrgEnv]"; +#endif + +#define STRINGIFY_HELPER(X) #X +#define STRINGIFY(X) STRINGIFY_HELPER(X) + +/* Identify known platforms by name. */ +#if defined(__linux) || defined(__linux__) || defined(linux) +# define PLATFORM_ID "Linux" + +#elif defined(__MSYS__) +# define PLATFORM_ID "MSYS" + +#elif defined(__CYGWIN__) +# define PLATFORM_ID "Cygwin" + +#elif defined(__MINGW32__) +# define PLATFORM_ID "MinGW" + +#elif defined(__APPLE__) +# define PLATFORM_ID "Darwin" + +#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32) +# define PLATFORM_ID "Windows" + +#elif defined(__FreeBSD__) || defined(__FreeBSD) +# define PLATFORM_ID "FreeBSD" + +#elif defined(__NetBSD__) || defined(__NetBSD) +# define PLATFORM_ID "NetBSD" + +#elif defined(__OpenBSD__) || defined(__OPENBSD) +# define PLATFORM_ID "OpenBSD" + +#elif defined(__sun) || defined(sun) +# define PLATFORM_ID "SunOS" + +#elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__) +# define PLATFORM_ID "AIX" + +#elif defined(__hpux) || defined(__hpux__) +# define PLATFORM_ID "HP-UX" + +#elif defined(__HAIKU__) +# define PLATFORM_ID "Haiku" + +#elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS) +# define PLATFORM_ID "BeOS" + +#elif defined(__QNX__) || defined(__QNXNTO__) +# define PLATFORM_ID "QNX" + +#elif defined(__tru64) || defined(_tru64) || defined(__TRU64__) +# define PLATFORM_ID "Tru64" + +#elif defined(__riscos) || defined(__riscos__) +# define PLATFORM_ID "RISCos" + +#elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__) +# define PLATFORM_ID "SINIX" + +#elif defined(__UNIX_SV__) +# define PLATFORM_ID "UNIX_SV" + +#elif defined(__bsdos__) +# define PLATFORM_ID "BSDOS" + +#elif defined(_MPRAS) || defined(MPRAS) +# define PLATFORM_ID "MP-RAS" + +#elif defined(__osf) || defined(__osf__) +# define PLATFORM_ID "OSF1" + +#elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv) +# define PLATFORM_ID "SCO_SV" + +#elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX) +# define PLATFORM_ID "ULTRIX" + +#elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX) +# define PLATFORM_ID "Xenix" + +#elif defined(__WATCOMC__) +# if defined(__LINUX__) +# define PLATFORM_ID "Linux" + +# elif defined(__DOS__) +# define PLATFORM_ID "DOS" + +# elif defined(__OS2__) +# define PLATFORM_ID "OS2" + +# elif defined(__WINDOWS__) +# define PLATFORM_ID "Windows3x" + +# elif defined(__VXWORKS__) +# define PLATFORM_ID "VxWorks" + +# else /* unknown platform */ +# define PLATFORM_ID +# endif + +#elif defined(__INTEGRITY) +# if defined(INT_178B) +# define PLATFORM_ID "Integrity178" + +# else /* regular Integrity */ +# define PLATFORM_ID "Integrity" +# endif + +# elif defined(_ADI_COMPILER) +# define PLATFORM_ID "ADSP" + +#else /* unknown platform */ +# define PLATFORM_ID + +#endif + +/* For windows compilers MSVC and Intel we can determine + the architecture of the compiler being used. This is because + the compilers do not have flags that can change the architecture, + but rather depend on which compiler is being used +*/ +#if defined(_WIN32) && defined(_MSC_VER) +# if defined(_M_IA64) +# define ARCHITECTURE_ID "IA64" + +# elif defined(_M_ARM64EC) +# define ARCHITECTURE_ID "ARM64EC" + +# elif defined(_M_X64) || defined(_M_AMD64) +# define ARCHITECTURE_ID "x64" + +# elif defined(_M_IX86) +# define ARCHITECTURE_ID "X86" + +# elif defined(_M_ARM64) +# define ARCHITECTURE_ID "ARM64" + +# elif defined(_M_ARM) +# if _M_ARM == 4 +# define ARCHITECTURE_ID "ARMV4I" +# elif _M_ARM == 5 +# define ARCHITECTURE_ID "ARMV5I" +# else +# define ARCHITECTURE_ID "ARMV" STRINGIFY(_M_ARM) +# endif + +# elif defined(_M_MIPS) +# define ARCHITECTURE_ID "MIPS" + +# elif defined(_M_SH) +# define ARCHITECTURE_ID "SHx" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__WATCOMC__) +# if defined(_M_I86) +# define ARCHITECTURE_ID "I86" + +# elif defined(_M_IX86) +# define ARCHITECTURE_ID "X86" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) +# if defined(__ICCARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__ICCRX__) +# define ARCHITECTURE_ID "RX" + +# elif defined(__ICCRH850__) +# define ARCHITECTURE_ID "RH850" + +# elif defined(__ICCRL78__) +# define ARCHITECTURE_ID "RL78" + +# elif defined(__ICCRISCV__) +# define ARCHITECTURE_ID "RISCV" + +# elif defined(__ICCAVR__) +# define ARCHITECTURE_ID "AVR" + +# elif defined(__ICC430__) +# define ARCHITECTURE_ID "MSP430" + +# elif defined(__ICCV850__) +# define ARCHITECTURE_ID "V850" + +# elif defined(__ICC8051__) +# define ARCHITECTURE_ID "8051" + +# elif defined(__ICCSTM8__) +# define ARCHITECTURE_ID "STM8" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__ghs__) +# if defined(__PPC64__) +# define ARCHITECTURE_ID "PPC64" + +# elif defined(__ppc__) +# define ARCHITECTURE_ID "PPC" + +# elif defined(__ARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__x86_64__) +# define ARCHITECTURE_ID "x64" + +# elif defined(__i386__) +# define ARCHITECTURE_ID "X86" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__clang__) && defined(__ti__) +# if defined(__ARM_ARCH) +# define ARCHITECTURE_ID "ARM" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__TI_COMPILER_VERSION__) +# if defined(__TI_ARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__MSP430__) +# define ARCHITECTURE_ID "MSP430" + +# elif defined(__TMS320C28XX__) +# define ARCHITECTURE_ID "TMS320C28x" + +# elif defined(__TMS320C6X__) || defined(_TMS320C6X) +# define ARCHITECTURE_ID "TMS320C6x" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +# elif defined(__ADSPSHARC__) +# define ARCHITECTURE_ID "SHARC" + +# elif defined(__ADSPBLACKFIN__) +# define ARCHITECTURE_ID "Blackfin" + +#elif defined(__TASKING__) + +# if defined(__CTC__) || defined(__CPTC__) +# define ARCHITECTURE_ID "TriCore" + +# elif defined(__CMCS__) +# define ARCHITECTURE_ID "MCS" + +# elif defined(__CARM__) || defined(__CPARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__CARC__) +# define ARCHITECTURE_ID "ARC" + +# elif defined(__C51__) +# define ARCHITECTURE_ID "8051" + +# elif defined(__CPCP__) +# define ARCHITECTURE_ID "PCP" + +# else +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__RENESAS__) +# if defined(__CCRX__) +# define ARCHITECTURE_ID "RX" + +# elif defined(__CCRL__) +# define ARCHITECTURE_ID "RL78" + +# elif defined(__CCRH__) +# define ARCHITECTURE_ID "RH850" + +# else +# define ARCHITECTURE_ID "" +# endif + +#else +# define ARCHITECTURE_ID +#endif + +/* Convert integer to decimal digit literals. */ +#define DEC(n) \ + ('0' + (((n) / 10000000)%10)), \ + ('0' + (((n) / 1000000)%10)), \ + ('0' + (((n) / 100000)%10)), \ + ('0' + (((n) / 10000)%10)), \ + ('0' + (((n) / 1000)%10)), \ + ('0' + (((n) / 100)%10)), \ + ('0' + (((n) / 10)%10)), \ + ('0' + ((n) % 10)) + +/* Convert integer to hex digit literals. */ +#define HEX(n) \ + ('0' + ((n)>>28 & 0xF)), \ + ('0' + ((n)>>24 & 0xF)), \ + ('0' + ((n)>>20 & 0xF)), \ + ('0' + ((n)>>16 & 0xF)), \ + ('0' + ((n)>>12 & 0xF)), \ + ('0' + ((n)>>8 & 0xF)), \ + ('0' + ((n)>>4 & 0xF)), \ + ('0' + ((n) & 0xF)) + +/* Construct a string literal encoding the version number. */ +#ifdef COMPILER_VERSION +char const* info_version = "INFO" ":" "compiler_version[" COMPILER_VERSION "]"; + +/* Construct a string literal encoding the version number components. */ +#elif defined(COMPILER_VERSION_MAJOR) +char const info_version[] = { + 'I', 'N', 'F', 'O', ':', + 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[', + COMPILER_VERSION_MAJOR, +# ifdef COMPILER_VERSION_MINOR + '.', COMPILER_VERSION_MINOR, +# ifdef COMPILER_VERSION_PATCH + '.', COMPILER_VERSION_PATCH, +# ifdef COMPILER_VERSION_TWEAK + '.', COMPILER_VERSION_TWEAK, +# endif +# endif +# endif + ']','\0'}; +#endif + +/* Construct a string literal encoding the internal version number. */ +#ifdef COMPILER_VERSION_INTERNAL +char const info_version_internal[] = { + 'I', 'N', 'F', 'O', ':', + 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','_', + 'i','n','t','e','r','n','a','l','[', + COMPILER_VERSION_INTERNAL,']','\0'}; +#elif defined(COMPILER_VERSION_INTERNAL_STR) +char const* info_version_internal = "INFO" ":" "compiler_version_internal[" COMPILER_VERSION_INTERNAL_STR "]"; +#endif + +/* Construct a string literal encoding the version number components. */ +#ifdef SIMULATE_VERSION_MAJOR +char const info_simulate_version[] = { + 'I', 'N', 'F', 'O', ':', + 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[', + SIMULATE_VERSION_MAJOR, +# ifdef SIMULATE_VERSION_MINOR + '.', SIMULATE_VERSION_MINOR, +# ifdef SIMULATE_VERSION_PATCH + '.', SIMULATE_VERSION_PATCH, +# ifdef SIMULATE_VERSION_TWEAK + '.', SIMULATE_VERSION_TWEAK, +# endif +# endif +# endif + ']','\0'}; +#endif + +/* Construct the string literal in pieces to prevent the source from + getting matched. Store it in a pointer rather than an array + because some compilers will just produce instructions to fill the + array rather than assigning a pointer to a static array. */ +char const* info_platform = "INFO" ":" "platform[" PLATFORM_ID "]"; +char const* info_arch = "INFO" ":" "arch[" ARCHITECTURE_ID "]"; + + + +#define C_STD_99 199901L +#define C_STD_11 201112L +#define C_STD_17 201710L +#define C_STD_23 202311L + +#ifdef __STDC_VERSION__ +# define C_STD __STDC_VERSION__ +#endif + +#if !defined(__STDC__) && !defined(__clang__) && !defined(__RENESAS__) +# if defined(_MSC_VER) || defined(__ibmxl__) || defined(__IBMC__) +# define C_VERSION "90" +# else +# define C_VERSION +# endif +#elif C_STD > C_STD_17 +# define C_VERSION "23" +#elif C_STD > C_STD_11 +# define C_VERSION "17" +#elif C_STD > C_STD_99 +# define C_VERSION "11" +#elif C_STD >= C_STD_99 +# define C_VERSION "99" +#else +# define C_VERSION "90" +#endif +const char* info_language_standard_default = + "INFO" ":" "standard_default[" C_VERSION "]"; + +const char* info_language_extensions_default = "INFO" ":" "extensions_default[" +#if (defined(__clang__) || defined(__GNUC__) || defined(__xlC__) || \ + defined(__TI_COMPILER_VERSION__) || defined(__RENESAS__)) && \ + !defined(__STRICT_ANSI__) + "ON" +#else + "OFF" +#endif +"]"; + +/*--------------------------------------------------------------------------*/ + +#ifdef ID_VOID_MAIN +void main() {} +#else +# if defined(__CLASSIC_C__) +int main(argc, argv) int argc; char *argv[]; +# else +int main(int argc, char* argv[]) +# endif +{ + int require = 0; + require += info_compiler[argc]; + require += info_platform[argc]; + require += info_arch[argc]; +#ifdef COMPILER_VERSION_MAJOR + require += info_version[argc]; +#endif +#if defined(COMPILER_VERSION_INTERNAL) || defined(COMPILER_VERSION_INTERNAL_STR) + require += info_version_internal[argc]; +#endif +#ifdef SIMULATE_ID + require += info_simulate[argc]; +#endif +#ifdef SIMULATE_VERSION_MAJOR + require += info_simulate_version[argc]; +#endif +#if defined(__CRAYXT_COMPUTE_LINUX_TARGET) + require += info_cray[argc]; +#endif + require += info_language_standard_default[argc]; + require += info_language_extensions_default[argc]; + (void)argv; + return require; +} +#endif diff --git a/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdC/CMakeCCompilerId.exe b/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdC/CMakeCCompilerId.exe new file mode 100644 index 0000000000000000000000000000000000000000..4678ee6766e0ce33cef6cac3cf1998d734926e42 --- /dev/null +++ b/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdC/CMakeCCompilerId.exe @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:99a275b239c4d5ee96a807d5d0c6a409c509c26d2cd8c37746ecadbf9354d9ec +size 108032 diff --git a/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdC/CMakeCCompilerId.obj b/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdC/CMakeCCompilerId.obj new file mode 100644 index 0000000000000000000000000000000000000000..716887dd299fd45437e7acef6503a73604ec5f9f Binary files /dev/null and b/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdC/CMakeCCompilerId.obj differ diff --git a/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdCXX/CMakeCXXCompilerId.cpp b/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdCXX/CMakeCXXCompilerId.cpp new file mode 100644 index 0000000000000000000000000000000000000000..7ffcb5b476a7937f9fc4a8aa0eb7758f8d1c5221 --- /dev/null +++ b/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdCXX/CMakeCXXCompilerId.cpp @@ -0,0 +1,949 @@ +/* This source file must have a .cpp extension so that all C++ compilers + recognize the extension without flags. 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Look for 6 digit vs. 8 digit version number to decide encoding. + * VVVV is no smaller than the current year when a version is released. + */ +#if __INTEL_LLVM_COMPILER < 1000000L +# define COMPILER_VERSION_MAJOR DEC(__INTEL_LLVM_COMPILER/100) +# define COMPILER_VERSION_MINOR DEC(__INTEL_LLVM_COMPILER/10 % 10) +# define COMPILER_VERSION_PATCH DEC(__INTEL_LLVM_COMPILER % 10) +#else +# define COMPILER_VERSION_MAJOR DEC(__INTEL_LLVM_COMPILER/10000) +# define COMPILER_VERSION_MINOR DEC(__INTEL_LLVM_COMPILER/100 % 100) +# define COMPILER_VERSION_PATCH DEC(__INTEL_LLVM_COMPILER % 100) +#endif +#if defined(_MSC_VER) + /* _MSC_VER = VVRR */ +# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) +# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) +#endif +#if defined(__GNUC__) +# define SIMULATE_VERSION_MAJOR DEC(__GNUC__) +#elif defined(__GNUG__) +# define SIMULATE_VERSION_MAJOR DEC(__GNUG__) +#endif +#if defined(__GNUC_MINOR__) +# define SIMULATE_VERSION_MINOR DEC(__GNUC_MINOR__) +#endif +#if defined(__GNUC_PATCHLEVEL__) +# define SIMULATE_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__) +#endif + +#elif defined(__PATHCC__) +# define COMPILER_ID "PathScale" +# define COMPILER_VERSION_MAJOR DEC(__PATHCC__) +# define COMPILER_VERSION_MINOR DEC(__PATHCC_MINOR__) +# if defined(__PATHCC_PATCHLEVEL__) +# define COMPILER_VERSION_PATCH DEC(__PATHCC_PATCHLEVEL__) +# endif + +#elif defined(__BORLANDC__) && defined(__CODEGEARC_VERSION__) +# define COMPILER_ID "Embarcadero" +# define COMPILER_VERSION_MAJOR HEX(__CODEGEARC_VERSION__>>24 & 0x00FF) +# define COMPILER_VERSION_MINOR HEX(__CODEGEARC_VERSION__>>16 & 0x00FF) +# define COMPILER_VERSION_PATCH DEC(__CODEGEARC_VERSION__ & 0xFFFF) + +#elif defined(__BORLANDC__) +# define COMPILER_ID "Borland" + /* __BORLANDC__ = 0xVRR */ +# define COMPILER_VERSION_MAJOR HEX(__BORLANDC__>>8) +# define COMPILER_VERSION_MINOR HEX(__BORLANDC__ & 0xFF) + +#elif defined(__WATCOMC__) && __WATCOMC__ < 1200 +# define COMPILER_ID "Watcom" + /* __WATCOMC__ = VVRR */ +# define COMPILER_VERSION_MAJOR DEC(__WATCOMC__ / 100) +# define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10) +# if (__WATCOMC__ % 10) > 0 +# define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10) +# endif + +#elif defined(__WATCOMC__) +# define COMPILER_ID "OpenWatcom" + /* __WATCOMC__ = VVRP + 1100 */ +# define COMPILER_VERSION_MAJOR DEC((__WATCOMC__ - 1100) / 100) +# define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10) +# if (__WATCOMC__ % 10) > 0 +# define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10) +# endif + +#elif defined(__SUNPRO_CC) +# define COMPILER_ID "SunPro" +# if __SUNPRO_CC >= 0x5100 + /* __SUNPRO_CC = 0xVRRP */ +# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_CC>>12) +# define COMPILER_VERSION_MINOR HEX(__SUNPRO_CC>>4 & 0xFF) +# define COMPILER_VERSION_PATCH HEX(__SUNPRO_CC & 0xF) +# else + /* __SUNPRO_CC = 0xVRP */ +# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_CC>>8) +# define COMPILER_VERSION_MINOR HEX(__SUNPRO_CC>>4 & 0xF) +# define COMPILER_VERSION_PATCH HEX(__SUNPRO_CC & 0xF) +# endif + +#elif defined(__HP_aCC) +# define COMPILER_ID "HP" + /* __HP_aCC = VVRRPP */ +# define COMPILER_VERSION_MAJOR DEC(__HP_aCC/10000) +# define COMPILER_VERSION_MINOR DEC(__HP_aCC/100 % 100) +# define COMPILER_VERSION_PATCH DEC(__HP_aCC % 100) + +#elif defined(__DECCXX) +# define COMPILER_ID "Compaq" + /* __DECCXX_VER = VVRRTPPPP */ +# define COMPILER_VERSION_MAJOR DEC(__DECCXX_VER/10000000) +# define COMPILER_VERSION_MINOR DEC(__DECCXX_VER/100000 % 100) +# define COMPILER_VERSION_PATCH DEC(__DECCXX_VER % 10000) + +#elif defined(__IBMCPP__) && defined(__COMPILER_VER__) +# define COMPILER_ID "zOS" + /* __IBMCPP__ = VRP */ +# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100) +# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10) +# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10) + +#elif defined(__open_xl__) && defined(__clang__) +# define COMPILER_ID "IBMClang" +# define COMPILER_VERSION_MAJOR DEC(__open_xl_version__) +# define COMPILER_VERSION_MINOR DEC(__open_xl_release__) +# define COMPILER_VERSION_PATCH DEC(__open_xl_modification__) +# define COMPILER_VERSION_TWEAK DEC(__open_xl_ptf_fix_level__) +# define COMPILER_VERSION_INTERNAL_STR __clang_version__ + + +#elif defined(__ibmxl__) && defined(__clang__) +# define COMPILER_ID "XLClang" +# define COMPILER_VERSION_MAJOR DEC(__ibmxl_version__) +# define COMPILER_VERSION_MINOR DEC(__ibmxl_release__) +# define COMPILER_VERSION_PATCH DEC(__ibmxl_modification__) +# define COMPILER_VERSION_TWEAK DEC(__ibmxl_ptf_fix_level__) + + +#elif defined(__IBMCPP__) && !defined(__COMPILER_VER__) && __IBMCPP__ >= 800 +# define COMPILER_ID "XL" + /* __IBMCPP__ = VRP */ +# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100) +# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10) +# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10) + +#elif defined(__IBMCPP__) && !defined(__COMPILER_VER__) && __IBMCPP__ < 800 +# define COMPILER_ID "VisualAge" + /* __IBMCPP__ = VRP */ +# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100) +# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10) +# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10) + +#elif defined(__NVCOMPILER) +# define COMPILER_ID "NVHPC" +# define COMPILER_VERSION_MAJOR DEC(__NVCOMPILER_MAJOR__) +# define COMPILER_VERSION_MINOR DEC(__NVCOMPILER_MINOR__) +# if defined(__NVCOMPILER_PATCHLEVEL__) +# define COMPILER_VERSION_PATCH DEC(__NVCOMPILER_PATCHLEVEL__) +# endif + +#elif defined(__PGI) +# define COMPILER_ID "PGI" +# define COMPILER_VERSION_MAJOR DEC(__PGIC__) +# define COMPILER_VERSION_MINOR DEC(__PGIC_MINOR__) +# if defined(__PGIC_PATCHLEVEL__) +# define COMPILER_VERSION_PATCH DEC(__PGIC_PATCHLEVEL__) +# endif + +#elif defined(__clang__) && defined(__cray__) +# define COMPILER_ID "CrayClang" +# define COMPILER_VERSION_MAJOR DEC(__cray_major__) +# define COMPILER_VERSION_MINOR DEC(__cray_minor__) +# define COMPILER_VERSION_PATCH DEC(__cray_patchlevel__) +# define COMPILER_VERSION_INTERNAL_STR __clang_version__ + + +#elif defined(_CRAYC) +# define COMPILER_ID "Cray" +# define COMPILER_VERSION_MAJOR DEC(_RELEASE_MAJOR) +# define COMPILER_VERSION_MINOR DEC(_RELEASE_MINOR) + +#elif defined(__TI_COMPILER_VERSION__) +# define COMPILER_ID "TI" + /* __TI_COMPILER_VERSION__ = VVVRRRPPP */ +# define COMPILER_VERSION_MAJOR DEC(__TI_COMPILER_VERSION__/1000000) +# define COMPILER_VERSION_MINOR DEC(__TI_COMPILER_VERSION__/1000 % 1000) +# define COMPILER_VERSION_PATCH DEC(__TI_COMPILER_VERSION__ % 1000) + +#elif defined(__CLANG_FUJITSU) +# define COMPILER_ID "FujitsuClang" +# define COMPILER_VERSION_MAJOR DEC(__FCC_major__) +# define COMPILER_VERSION_MINOR DEC(__FCC_minor__) +# define COMPILER_VERSION_PATCH DEC(__FCC_patchlevel__) +# define COMPILER_VERSION_INTERNAL_STR __clang_version__ + + +#elif defined(__FUJITSU) +# define COMPILER_ID "Fujitsu" +# if defined(__FCC_version__) +# define COMPILER_VERSION __FCC_version__ +# elif defined(__FCC_major__) +# define COMPILER_VERSION_MAJOR DEC(__FCC_major__) +# define COMPILER_VERSION_MINOR DEC(__FCC_minor__) +# define COMPILER_VERSION_PATCH DEC(__FCC_patchlevel__) +# endif +# if defined(__fcc_version) +# define COMPILER_VERSION_INTERNAL DEC(__fcc_version) +# elif defined(__FCC_VERSION) +# define COMPILER_VERSION_INTERNAL DEC(__FCC_VERSION) +# endif + + +#elif defined(__ghs__) +# define COMPILER_ID "GHS" +/* __GHS_VERSION_NUMBER = VVVVRP */ +# ifdef __GHS_VERSION_NUMBER +# define COMPILER_VERSION_MAJOR DEC(__GHS_VERSION_NUMBER / 100) +# define COMPILER_VERSION_MINOR DEC(__GHS_VERSION_NUMBER / 10 % 10) +# define COMPILER_VERSION_PATCH DEC(__GHS_VERSION_NUMBER % 10) +# endif + +#elif defined(__TASKING__) +# define COMPILER_ID "Tasking" + # define COMPILER_VERSION_MAJOR DEC(__VERSION__/1000) + # define COMPILER_VERSION_MINOR DEC(__VERSION__ % 100) +# define COMPILER_VERSION_INTERNAL DEC(__VERSION__) + +#elif defined(__ORANGEC__) +# define COMPILER_ID "OrangeC" +# define COMPILER_VERSION_MAJOR DEC(__ORANGEC_MAJOR__) +# define COMPILER_VERSION_MINOR 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1000) +# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) +# elif defined(__VER__) && (defined(__ICCAVR__) || defined(__ICCRX__) || defined(__ICCRH850__) || defined(__ICCRL78__) || defined(__ICC430__) || defined(__ICCRISCV__) || defined(__ICCV850__) || defined(__ICC8051__) || defined(__ICCSTM8__)) +# define COMPILER_VERSION_MAJOR DEC((__VER__) / 100) +# define COMPILER_VERSION_MINOR DEC((__VER__) - (((__VER__) / 100)*100)) +# define COMPILER_VERSION_PATCH DEC(__SUBVERSION__) +# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) +# endif + +#elif defined(__DCC__) && defined(_DIAB_TOOL) +# define COMPILER_ID "Diab" + # define COMPILER_VERSION_MAJOR DEC(__VERSION_MAJOR_NUMBER__) + # define COMPILER_VERSION_MINOR DEC(__VERSION_MINOR_NUMBER__) + # define COMPILER_VERSION_PATCH DEC(__VERSION_ARCH_FEATURE_NUMBER__) + # define COMPILER_VERSION_TWEAK DEC(__VERSION_BUG_FIX_NUMBER__) + + + +/* These compilers are either not known or too old to define an + identification macro. Try to identify the platform and guess that + it is the native compiler. */ +#elif defined(__hpux) || defined(__hpua) +# define COMPILER_ID "HP" + +#else /* unknown compiler */ +# define COMPILER_ID "" +#endif + +/* Construct the string literal in pieces to prevent the source from + getting matched. Store it in a pointer rather than an array + because some compilers will just produce instructions to fill the + array rather than assigning a pointer to a static array. */ +char const* info_compiler = "INFO" ":" "compiler[" COMPILER_ID "]"; +#ifdef SIMULATE_ID +char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]"; +#endif + +#ifdef __QNXNTO__ +char const* qnxnto = "INFO" ":" "qnxnto[]"; +#endif + +#if defined(__CRAYXT_COMPUTE_LINUX_TARGET) +char const *info_cray = "INFO" ":" "compiler_wrapper[CrayPrgEnv]"; +#endif + +#define STRINGIFY_HELPER(X) #X +#define STRINGIFY(X) STRINGIFY_HELPER(X) + +/* Identify known platforms by name. */ +#if defined(__linux) || defined(__linux__) || defined(linux) +# define PLATFORM_ID "Linux" + +#elif defined(__MSYS__) +# define PLATFORM_ID "MSYS" + +#elif defined(__CYGWIN__) +# define PLATFORM_ID "Cygwin" + +#elif defined(__MINGW32__) +# define PLATFORM_ID "MinGW" + +#elif defined(__APPLE__) +# define PLATFORM_ID "Darwin" + +#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32) +# define PLATFORM_ID "Windows" + +#elif defined(__FreeBSD__) || defined(__FreeBSD) +# define PLATFORM_ID "FreeBSD" + +#elif defined(__NetBSD__) || defined(__NetBSD) +# define PLATFORM_ID "NetBSD" + +#elif defined(__OpenBSD__) || defined(__OPENBSD) +# define PLATFORM_ID "OpenBSD" + +#elif defined(__sun) || defined(sun) +# define PLATFORM_ID "SunOS" + +#elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__) +# define PLATFORM_ID "AIX" + +#elif defined(__hpux) || defined(__hpux__) +# define PLATFORM_ID "HP-UX" + +#elif defined(__HAIKU__) +# define PLATFORM_ID "Haiku" + +#elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS) +# define PLATFORM_ID "BeOS" + +#elif defined(__QNX__) || defined(__QNXNTO__) +# define PLATFORM_ID "QNX" + +#elif defined(__tru64) || defined(_tru64) || defined(__TRU64__) +# define PLATFORM_ID "Tru64" + +#elif defined(__riscos) || defined(__riscos__) +# define PLATFORM_ID "RISCos" + +#elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__) +# define PLATFORM_ID "SINIX" + +#elif defined(__UNIX_SV__) +# define PLATFORM_ID "UNIX_SV" + +#elif defined(__bsdos__) +# define PLATFORM_ID "BSDOS" + +#elif defined(_MPRAS) || defined(MPRAS) +# define PLATFORM_ID "MP-RAS" + +#elif defined(__osf) || defined(__osf__) +# define PLATFORM_ID "OSF1" + +#elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv) +# define PLATFORM_ID "SCO_SV" + +#elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX) +# define PLATFORM_ID "ULTRIX" + +#elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX) +# define PLATFORM_ID "Xenix" + +#elif defined(__WATCOMC__) +# if defined(__LINUX__) +# define PLATFORM_ID "Linux" + +# elif defined(__DOS__) +# define PLATFORM_ID "DOS" + +# elif defined(__OS2__) +# define PLATFORM_ID "OS2" + +# elif defined(__WINDOWS__) +# define PLATFORM_ID "Windows3x" + +# elif defined(__VXWORKS__) +# define PLATFORM_ID "VxWorks" + +# else /* unknown platform */ +# define PLATFORM_ID +# endif + +#elif defined(__INTEGRITY) +# if defined(INT_178B) +# define PLATFORM_ID "Integrity178" + +# else /* regular Integrity */ +# define PLATFORM_ID "Integrity" +# endif + +# elif defined(_ADI_COMPILER) +# define PLATFORM_ID "ADSP" + +#else /* unknown platform */ +# define PLATFORM_ID + +#endif + +/* For windows compilers MSVC and Intel we can determine + the architecture of the compiler being used. This is because + the compilers do not have flags that can change the architecture, + but rather depend on which compiler is being used +*/ +#if defined(_WIN32) && defined(_MSC_VER) +# if defined(_M_IA64) +# define ARCHITECTURE_ID "IA64" + +# elif defined(_M_ARM64EC) +# define ARCHITECTURE_ID "ARM64EC" + +# elif defined(_M_X64) || defined(_M_AMD64) +# define ARCHITECTURE_ID "x64" + +# elif defined(_M_IX86) +# define ARCHITECTURE_ID "X86" + +# elif defined(_M_ARM64) +# define ARCHITECTURE_ID "ARM64" + +# elif defined(_M_ARM) +# if _M_ARM == 4 +# define ARCHITECTURE_ID "ARMV4I" +# elif _M_ARM == 5 +# define ARCHITECTURE_ID "ARMV5I" +# else +# define ARCHITECTURE_ID "ARMV" STRINGIFY(_M_ARM) +# endif + +# elif defined(_M_MIPS) +# define ARCHITECTURE_ID "MIPS" + +# elif defined(_M_SH) +# define ARCHITECTURE_ID "SHx" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__WATCOMC__) +# if defined(_M_I86) +# define ARCHITECTURE_ID "I86" + +# elif defined(_M_IX86) +# define ARCHITECTURE_ID "X86" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) +# if defined(__ICCARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__ICCRX__) +# define ARCHITECTURE_ID "RX" + +# elif defined(__ICCRH850__) +# define ARCHITECTURE_ID "RH850" + +# elif defined(__ICCRL78__) +# define ARCHITECTURE_ID "RL78" + +# elif defined(__ICCRISCV__) +# define ARCHITECTURE_ID "RISCV" + +# elif defined(__ICCAVR__) +# define ARCHITECTURE_ID "AVR" + +# elif defined(__ICC430__) +# define ARCHITECTURE_ID "MSP430" + +# elif defined(__ICCV850__) +# define ARCHITECTURE_ID "V850" + +# elif defined(__ICC8051__) +# define ARCHITECTURE_ID "8051" + +# elif defined(__ICCSTM8__) +# define ARCHITECTURE_ID "STM8" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__ghs__) +# if defined(__PPC64__) +# define ARCHITECTURE_ID "PPC64" + +# elif defined(__ppc__) +# define ARCHITECTURE_ID "PPC" + +# elif defined(__ARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__x86_64__) +# define ARCHITECTURE_ID "x64" + +# elif defined(__i386__) +# define ARCHITECTURE_ID "X86" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__clang__) && defined(__ti__) +# if defined(__ARM_ARCH) +# define ARCHITECTURE_ID "ARM" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__TI_COMPILER_VERSION__) +# if defined(__TI_ARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__MSP430__) +# define ARCHITECTURE_ID "MSP430" + +# elif defined(__TMS320C28XX__) +# define ARCHITECTURE_ID "TMS320C28x" + +# elif defined(__TMS320C6X__) || defined(_TMS320C6X) +# define ARCHITECTURE_ID "TMS320C6x" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +# elif defined(__ADSPSHARC__) +# define ARCHITECTURE_ID "SHARC" + +# elif defined(__ADSPBLACKFIN__) +# define ARCHITECTURE_ID "Blackfin" + +#elif defined(__TASKING__) + +# if defined(__CTC__) || defined(__CPTC__) +# define ARCHITECTURE_ID "TriCore" + +# elif defined(__CMCS__) +# define ARCHITECTURE_ID "MCS" + +# elif defined(__CARM__) || defined(__CPARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__CARC__) +# define ARCHITECTURE_ID "ARC" + +# elif defined(__C51__) +# define ARCHITECTURE_ID "8051" + +# elif defined(__CPCP__) +# define ARCHITECTURE_ID "PCP" + +# else +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__RENESAS__) +# if defined(__CCRX__) +# define ARCHITECTURE_ID "RX" + +# elif defined(__CCRL__) +# define ARCHITECTURE_ID "RL78" + +# elif defined(__CCRH__) +# define ARCHITECTURE_ID "RH850" + +# else +# define ARCHITECTURE_ID "" +# endif + +#else +# define ARCHITECTURE_ID +#endif + +/* Convert integer to decimal digit literals. */ +#define DEC(n) \ + ('0' + (((n) / 10000000)%10)), \ + ('0' + (((n) / 1000000)%10)), \ + ('0' + (((n) / 100000)%10)), \ + ('0' + (((n) / 10000)%10)), \ + ('0' + (((n) / 1000)%10)), \ + ('0' + (((n) / 100)%10)), \ + ('0' + (((n) / 10)%10)), \ + ('0' + ((n) % 10)) + +/* Convert integer to hex digit literals. */ +#define HEX(n) \ + ('0' + ((n)>>28 & 0xF)), \ + ('0' + ((n)>>24 & 0xF)), \ + ('0' + ((n)>>20 & 0xF)), \ + ('0' + ((n)>>16 & 0xF)), \ + ('0' + ((n)>>12 & 0xF)), \ + ('0' + ((n)>>8 & 0xF)), \ + ('0' + ((n)>>4 & 0xF)), \ + ('0' + ((n) & 0xF)) + +/* Construct a string literal encoding the version number. */ +#ifdef COMPILER_VERSION +char const* info_version = "INFO" ":" "compiler_version[" COMPILER_VERSION "]"; + +/* Construct a string literal encoding the version number components. */ +#elif defined(COMPILER_VERSION_MAJOR) +char const info_version[] = { + 'I', 'N', 'F', 'O', ':', + 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[', + COMPILER_VERSION_MAJOR, +# ifdef COMPILER_VERSION_MINOR + '.', COMPILER_VERSION_MINOR, +# ifdef COMPILER_VERSION_PATCH + '.', COMPILER_VERSION_PATCH, +# ifdef COMPILER_VERSION_TWEAK + '.', COMPILER_VERSION_TWEAK, +# endif +# endif +# endif + ']','\0'}; +#endif + +/* Construct a string literal encoding the internal version number. */ +#ifdef COMPILER_VERSION_INTERNAL +char const info_version_internal[] = { + 'I', 'N', 'F', 'O', ':', + 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','_', + 'i','n','t','e','r','n','a','l','[', + COMPILER_VERSION_INTERNAL,']','\0'}; +#elif defined(COMPILER_VERSION_INTERNAL_STR) +char const* info_version_internal = "INFO" ":" "compiler_version_internal[" COMPILER_VERSION_INTERNAL_STR "]"; +#endif + +/* Construct a string literal encoding the version number components. */ +#ifdef SIMULATE_VERSION_MAJOR +char const info_simulate_version[] = { + 'I', 'N', 'F', 'O', ':', + 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[', + SIMULATE_VERSION_MAJOR, +# ifdef SIMULATE_VERSION_MINOR + '.', SIMULATE_VERSION_MINOR, +# ifdef SIMULATE_VERSION_PATCH + '.', SIMULATE_VERSION_PATCH, +# ifdef SIMULATE_VERSION_TWEAK + '.', SIMULATE_VERSION_TWEAK, +# endif +# endif +# endif + ']','\0'}; +#endif + +/* Construct the string literal in pieces to prevent the source from + getting matched. Store it in a pointer rather than an array + because some compilers will just produce instructions to fill the + array rather than assigning a pointer to a static array. */ +char const* info_platform = "INFO" ":" "platform[" PLATFORM_ID "]"; +char const* info_arch = "INFO" ":" "arch[" ARCHITECTURE_ID "]"; + + + +#define CXX_STD_98 199711L +#define CXX_STD_11 201103L +#define CXX_STD_14 201402L +#define CXX_STD_17 201703L +#define CXX_STD_20 202002L +#define CXX_STD_23 202302L + +#if defined(__INTEL_COMPILER) && defined(_MSVC_LANG) +# if _MSVC_LANG > CXX_STD_17 +# define CXX_STD _MSVC_LANG +# elif _MSVC_LANG == CXX_STD_17 && defined(__cpp_aggregate_paren_init) +# define CXX_STD CXX_STD_20 +# elif _MSVC_LANG > CXX_STD_14 && __cplusplus > CXX_STD_17 +# define CXX_STD CXX_STD_20 +# elif _MSVC_LANG > CXX_STD_14 +# define CXX_STD CXX_STD_17 +# elif defined(__INTEL_CXX11_MODE__) && defined(__cpp_aggregate_nsdmi) +# define CXX_STD CXX_STD_14 +# elif defined(__INTEL_CXX11_MODE__) +# define CXX_STD CXX_STD_11 +# else +# define CXX_STD CXX_STD_98 +# endif +#elif defined(_MSC_VER) && defined(_MSVC_LANG) +# if _MSVC_LANG > __cplusplus +# define CXX_STD _MSVC_LANG +# else +# define CXX_STD __cplusplus +# endif +#elif defined(__NVCOMPILER) +# if __cplusplus == CXX_STD_17 && defined(__cpp_aggregate_paren_init) +# define CXX_STD CXX_STD_20 +# else +# define CXX_STD __cplusplus +# endif +#elif defined(__INTEL_COMPILER) || defined(__PGI) +# if __cplusplus == CXX_STD_11 && defined(__cpp_namespace_attributes) +# define CXX_STD CXX_STD_17 +# elif __cplusplus == CXX_STD_11 && defined(__cpp_aggregate_nsdmi) +# define CXX_STD CXX_STD_14 +# else +# define CXX_STD __cplusplus +# endif +#elif (defined(__IBMCPP__) || defined(__ibmxl__)) && defined(__linux__) +# if __cplusplus == CXX_STD_11 && defined(__cpp_aggregate_nsdmi) +# define CXX_STD CXX_STD_14 +# else +# define CXX_STD __cplusplus +# endif +#elif __cplusplus == 1 && defined(__GXX_EXPERIMENTAL_CXX0X__) +# define CXX_STD CXX_STD_11 +#else +# define CXX_STD __cplusplus +#endif + +const char* info_language_standard_default = "INFO" ":" "standard_default[" +#if CXX_STD > CXX_STD_23 + "26" +#elif CXX_STD > CXX_STD_20 + "23" +#elif CXX_STD > CXX_STD_17 + "20" +#elif CXX_STD > CXX_STD_14 + "17" +#elif CXX_STD > CXX_STD_11 + "14" +#elif CXX_STD >= CXX_STD_11 + "11" +#else + "98" +#endif +"]"; + +const char* info_language_extensions_default = "INFO" ":" "extensions_default[" +#if (defined(__clang__) || defined(__GNUC__) || defined(__xlC__) || \ + defined(__TI_COMPILER_VERSION__) || defined(__RENESAS__)) && \ + !defined(__STRICT_ANSI__) + "ON" +#else + "OFF" +#endif +"]"; + +/*--------------------------------------------------------------------------*/ + +int main(int argc, char* argv[]) +{ + int require = 0; + require += info_compiler[argc]; + require += info_platform[argc]; + require += info_arch[argc]; +#ifdef COMPILER_VERSION_MAJOR + require += info_version[argc]; +#endif +#if defined(COMPILER_VERSION_INTERNAL) || defined(COMPILER_VERSION_INTERNAL_STR) + require += info_version_internal[argc]; +#endif +#ifdef SIMULATE_ID + require += info_simulate[argc]; +#endif +#ifdef SIMULATE_VERSION_MAJOR + require += info_simulate_version[argc]; +#endif +#if defined(__CRAYXT_COMPUTE_LINUX_TARGET) + require += info_cray[argc]; +#endif + require += info_language_standard_default[argc]; + require += info_language_extensions_default[argc]; + (void)argv; + return require; +} diff --git a/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdCXX/CMakeCXXCompilerId.exe b/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdCXX/CMakeCXXCompilerId.exe new file mode 100644 index 0000000000000000000000000000000000000000..4ad86161811bccea49d8da9f507c9f069325fffe --- /dev/null +++ b/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdCXX/CMakeCXXCompilerId.exe @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c82bafb5e58b8989b838d2aafbf0b5c2d17dfd187e2b0a899fc8b7f1a5c6ce60 +size 108032 diff --git a/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdCXX/CMakeCXXCompilerId.obj b/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdCXX/CMakeCXXCompilerId.obj new file mode 100644 index 0000000000000000000000000000000000000000..bca161c4eb1d44d8f09886474681e594b17710d6 Binary files /dev/null and b/llama.cpp/build/CMakeFiles/4.1.0-rc4/CompilerIdCXX/CMakeCXXCompilerId.obj differ diff --git a/llama.cpp/build/CMakeFiles/CMakeConfigureLog.yaml b/llama.cpp/build/CMakeFiles/CMakeConfigureLog.yaml new file mode 100644 index 0000000000000000000000000000000000000000..303609fcdf0b4fd5d77ed218337ecf46dd7b5b48 --- /dev/null +++ b/llama.cpp/build/CMakeFiles/CMakeConfigureLog.yaml @@ -0,0 +1,4604 @@ + +--- +events: + - 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"C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\CMake\\CMake\\bin" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\CMake\\Ninja" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\VC\\Linux\\bin\\ConnectionManagerExe" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\VC\\vcpkg" + - + kind: "find-v1" + backtrace: + - "C:/Program Files/CMake/share/cmake-4.1/Modules/CMakeDetermineCompiler.cmake:63 (find_program)" + - "C:/Program Files/CMake/share/cmake-4.1/Modules/CMakeDetermineCCompiler.cmake:64 (_cmake_find_compiler)" + - "CMakeLists.txt:2 (project)" + mode: "program" + variable: "CMAKE_C_COMPILER" + description: "C compiler" + settings: + SearchFramework: "NEVER" + SearchAppBundle: "NEVER" + CMAKE_FIND_USE_CMAKE_PATH: false + CMAKE_FIND_USE_CMAKE_ENVIRONMENT_PATH: false + CMAKE_FIND_USE_SYSTEM_ENVIRONMENT_PATH: true + CMAKE_FIND_USE_CMAKE_SYSTEM_PATH: false + CMAKE_FIND_USE_INSTALL_PREFIX: true + names: + - 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"C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/Tools/" + - "C:/Windows/System32/" + - "C:/Windows/" + - "C:/Windows/System32/wbem/" + - "C:/Windows/System32/WindowsPowerShell/v1.0/" + - "C:/Windows/System32/OpenSSH/" + - "C:/Program Files/Intel/WiFi/bin/" + - "C:/Program Files/Common Files/Intel/WirelessCommon/" + - "C:/Program Files/nodejs/" + - "C:/Program Files (x86)/Windows Kits/10/Windows Performance Toolkit/" + - "C:/Program Files/CMake/bin/" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python311/Scripts/" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python311/" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python313/Scripts/" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python313/" + - "C:/Users/Kids/AppData/Local/Microsoft/WindowsApps/" + - "C:/Users/Kids/AppData/Local/Programs/Windsurf/bin/" + - "C:/Users/Kids/AppData/Local/Programs/Microsoft VS Code/bin/" + - "C:/Users/Kids/AppData/Local/Programs/Git/cmd/" + - "C:/Users/Kids/AppData/Roaming/npm/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/bin/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/Ninja/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/VC/Linux/bin/ConnectionManagerExe/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/vcpkg/" + searched_directories: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/cc.com" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/cc.exe" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/cc" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/gcc.com" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/gcc.exe" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/gcc" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/cl.com" + found: "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/cl.exe" + search_context: + ENV{PATH}: + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\VC\\Tools\\MSVC\\14.44.35207\\bin\\HostX64\\x64" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\VC\\VCPackages" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\TestWindow" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\TeamFoundation\\Team Explorer" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\MSBuild\\Current\\bin\\Roslyn" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Team Tools\\DiagnosticsHub\\Collector" + - "C:\\Program Files (x86)\\Windows Kits\\10\\bin\\10.0.26100.0\\\\x64" + - 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"C:/Windows/System32/OpenSSH/lld-link" + - "C:/Program Files/Intel/WiFi/bin/lld-link.com" + - "C:/Program Files/Intel/WiFi/bin/lld-link.exe" + - "C:/Program Files/Intel/WiFi/bin/lld-link" + - "C:/Program Files/Common Files/Intel/WirelessCommon/lld-link.com" + - "C:/Program Files/Common Files/Intel/WirelessCommon/lld-link.exe" + - "C:/Program Files/Common Files/Intel/WirelessCommon/lld-link" + - "C:/Program Files/nodejs/lld-link.com" + - "C:/Program Files/nodejs/lld-link.exe" + - "C:/Program Files/nodejs/lld-link" + - "C:/Program Files (x86)/Windows Kits/10/Windows Performance Toolkit/lld-link.com" + - "C:/Program Files (x86)/Windows Kits/10/Windows Performance Toolkit/lld-link.exe" + - "C:/Program Files (x86)/Windows Kits/10/Windows Performance Toolkit/lld-link" + - "C:/Program Files/CMake/bin/lld-link.com" + - "C:/Program Files/CMake/bin/lld-link.exe" + - "C:/Program Files/CMake/bin/lld-link" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python311/Scripts/lld-link.com" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python311/Scripts/lld-link.exe" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python311/Scripts/lld-link" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python311/lld-link.com" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python311/lld-link.exe" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python311/lld-link" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python313/Scripts/lld-link.com" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python313/Scripts/lld-link.exe" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python313/Scripts/lld-link" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python313/lld-link.com" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python313/lld-link.exe" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python313/lld-link" + - "C:/Users/Kids/AppData/Local/Microsoft/WindowsApps/lld-link.com" + - "C:/Users/Kids/AppData/Local/Microsoft/WindowsApps/lld-link.exe" + - "C:/Users/Kids/AppData/Local/Microsoft/WindowsApps/lld-link" + - "C:/Users/Kids/AppData/Local/Programs/Windsurf/bin/lld-link.com" + - "C:/Users/Kids/AppData/Local/Programs/Windsurf/bin/lld-link.exe" + - "C:/Users/Kids/AppData/Local/Programs/Windsurf/bin/lld-link" + - "C:/Users/Kids/AppData/Local/Programs/Microsoft VS Code/bin/lld-link.com" + - "C:/Users/Kids/AppData/Local/Programs/Microsoft VS Code/bin/lld-link.exe" + - "C:/Users/Kids/AppData/Local/Programs/Microsoft VS Code/bin/lld-link" + - "C:/Users/Kids/AppData/Local/Programs/Git/cmd/lld-link.com" + - "C:/Users/Kids/AppData/Local/Programs/Git/cmd/lld-link.exe" + - "C:/Users/Kids/AppData/Local/Programs/Git/cmd/lld-link" + - "C:/Users/Kids/AppData/Roaming/npm/lld-link.com" + - "C:/Users/Kids/AppData/Roaming/npm/lld-link.exe" + - "C:/Users/Kids/AppData/Roaming/npm/lld-link" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/bin/lld-link.com" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/bin/lld-link.exe" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/bin/lld-link" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/Ninja/lld-link.com" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/Ninja/lld-link.exe" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/Ninja/lld-link" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/VC/Linux/bin/ConnectionManagerExe/lld-link.com" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/VC/Linux/bin/ConnectionManagerExe/lld-link.exe" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/VC/Linux/bin/ConnectionManagerExe/lld-link" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/vcpkg/lld-link.com" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/vcpkg/lld-link.exe" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/vcpkg/lld-link" + found: false + search_context: + ENV{PATH}: + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\VC\\Tools\\MSVC\\14.44.35207\\bin\\HostX64\\x64" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\VC\\VCPackages" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\TestWindow" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\TeamFoundation\\Team Explorer" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\MSBuild\\Current\\bin\\Roslyn" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Team Tools\\DiagnosticsHub\\Collector" + - "C:\\Program Files (x86)\\Windows Kits\\10\\bin\\10.0.26100.0\\\\x64" + - "C:\\Program Files (x86)\\Windows Kits\\10\\bin\\\\x64" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\\\MSBuild\\Current\\Bin\\amd64" + - "C:\\Windows\\Microsoft.NET\\Framework64\\v4.0.30319" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\Tools\\" + - "C:\\WINDOWS\\system32" + - "C:\\WINDOWS" + - "C:\\WINDOWS\\System32\\Wbem" + - "C:\\WINDOWS\\System32\\WindowsPowerShell\\v1.0\\" + - "C:\\WINDOWS\\System32\\OpenSSH\\" + - "C:\\Program Files\\Intel\\WiFi\\bin\\" + - "C:\\Program Files\\Common Files\\Intel\\WirelessCommon\\" + - "C:\\Program Files\\nodejs\\" + - "C:\\Program Files (x86)\\Windows Kits\\10\\Windows Performance Toolkit\\" + - "C:\\Program Files\\CMake\\bin" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Python\\Python311\\Scripts\\" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Python\\Python311\\" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Python\\Python313\\Scripts\\" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Python\\Python313\\" + - "C:\\Users\\Kids\\AppData\\Local\\Microsoft\\WindowsApps" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Windsurf\\bin" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Microsoft VS Code\\bin" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Git\\cmd" + - "C:\\Users\\Kids\\AppData\\Roaming\\npm" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\CMake\\CMake\\bin" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\CMake\\Ninja" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\VC\\Linux\\bin\\ConnectionManagerExe" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\VC\\vcpkg" + - + kind: "find-v1" + backtrace: + - "C:/Program Files/CMake/share/cmake-4.1/Modules/CMakeFindBinUtils.cmake:238 (find_program)" + - "C:/Program Files/CMake/share/cmake-4.1/Modules/CMakeDetermineCCompiler.cmake:200 (include)" + - "CMakeLists.txt:2 (project)" + mode: "program" + variable: "CMAKE_LINKER" + description: "Path to a program." + settings: + SearchFramework: "NEVER" + SearchAppBundle: "NEVER" + CMAKE_FIND_USE_CMAKE_PATH: false + CMAKE_FIND_USE_CMAKE_ENVIRONMENT_PATH: false + CMAKE_FIND_USE_SYSTEM_ENVIRONMENT_PATH: true + CMAKE_FIND_USE_CMAKE_SYSTEM_PATH: true + CMAKE_FIND_USE_INSTALL_PREFIX: true + names: + - "link" + candidate_directories: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/VC/vcpackages/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/TestWindow/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/TeamFoundation/Team Explorer/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/MSBuild/Current/Bin/Roslyn/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Team Tools/DiagnosticsHub/Collector/" + - "C:/Program Files (x86)/Windows Kits/10/bin/10.0.26100.0/x64/" + - "C:/Program Files (x86)/Windows Kits/10/bin/x64/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/MSBuild/Current/Bin/amd64/" + - "C:/Windows/Microsoft.NET/Framework64/v4.0.30319/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/Tools/" + - "C:/Windows/System32/" + - "C:/Windows/" + - "C:/Windows/System32/wbem/" + - "C:/Windows/System32/WindowsPowerShell/v1.0/" + - "C:/Windows/System32/OpenSSH/" + - "C:/Program Files/Intel/WiFi/bin/" + - "C:/Program Files/Common Files/Intel/WirelessCommon/" + - "C:/Program Files/nodejs/" + - "C:/Program Files (x86)/Windows Kits/10/Windows Performance Toolkit/" + - "C:/Program Files/CMake/bin/" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python311/Scripts/" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python311/" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python313/Scripts/" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python313/" + - "C:/Users/Kids/AppData/Local/Microsoft/WindowsApps/" + - "C:/Users/Kids/AppData/Local/Programs/Windsurf/bin/" + - "C:/Users/Kids/AppData/Local/Programs/Microsoft VS Code/bin/" + - "C:/Users/Kids/AppData/Local/Programs/Git/cmd/" + - "C:/Users/Kids/AppData/Roaming/npm/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/bin/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/Ninja/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/VC/Linux/bin/ConnectionManagerExe/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/vcpkg/" + searched_directories: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/link.com" + found: "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/link.exe" + search_context: + ENV{PATH}: + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\VC\\Tools\\MSVC\\14.44.35207\\bin\\HostX64\\x64" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\VC\\VCPackages" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\TestWindow" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\TeamFoundation\\Team Explorer" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\MSBuild\\Current\\bin\\Roslyn" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Team Tools\\DiagnosticsHub\\Collector" + - "C:\\Program Files (x86)\\Windows Kits\\10\\bin\\10.0.26100.0\\\\x64" + - "C:\\Program Files (x86)\\Windows Kits\\10\\bin\\\\x64" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\\\MSBuild\\Current\\Bin\\amd64" + - "C:\\Windows\\Microsoft.NET\\Framework64\\v4.0.30319" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\Tools\\" + - "C:\\WINDOWS\\system32" + - "C:\\WINDOWS" + - "C:\\WINDOWS\\System32\\Wbem" + - "C:\\WINDOWS\\System32\\WindowsPowerShell\\v1.0\\" + - "C:\\WINDOWS\\System32\\OpenSSH\\" + - "C:\\Program Files\\Intel\\WiFi\\bin\\" + - "C:\\Program Files\\Common Files\\Intel\\WirelessCommon\\" + - "C:\\Program Files\\nodejs\\" + - "C:\\Program Files (x86)\\Windows Kits\\10\\Windows Performance Toolkit\\" + - "C:\\Program Files\\CMake\\bin" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Python\\Python311\\Scripts\\" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Python\\Python311\\" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Python\\Python313\\Scripts\\" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Python\\Python313\\" + - "C:\\Users\\Kids\\AppData\\Local\\Microsoft\\WindowsApps" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Windsurf\\bin" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Microsoft VS Code\\bin" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Git\\cmd" + - "C:\\Users\\Kids\\AppData\\Roaming\\npm" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\CMake\\CMake\\bin" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\CMake\\Ninja" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\VC\\Linux\\bin\\ConnectionManagerExe" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\VC\\vcpkg" + - + kind: "find-v1" + backtrace: + - "C:/Program Files/CMake/share/cmake-4.1/Modules/CMakeFindBinUtils.cmake:238 (find_program)" + - "C:/Program Files/CMake/share/cmake-4.1/Modules/CMakeDetermineCCompiler.cmake:200 (include)" + - "CMakeLists.txt:2 (project)" + mode: "program" + variable: "CMAKE_MT" + description: "Path to a program." + settings: + SearchFramework: "NEVER" + SearchAppBundle: "NEVER" + CMAKE_FIND_USE_CMAKE_PATH: false + CMAKE_FIND_USE_CMAKE_ENVIRONMENT_PATH: false + CMAKE_FIND_USE_SYSTEM_ENVIRONMENT_PATH: true + CMAKE_FIND_USE_CMAKE_SYSTEM_PATH: true + CMAKE_FIND_USE_INSTALL_PREFIX: true + names: + - "mt" + candidate_directories: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/VC/vcpackages/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/TestWindow/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/TeamFoundation/Team Explorer/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/MSBuild/Current/Bin/Roslyn/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Team Tools/DiagnosticsHub/Collector/" + - "C:/Program Files (x86)/Windows Kits/10/bin/10.0.26100.0/x64/" + - "C:/Program Files (x86)/Windows Kits/10/bin/x64/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/MSBuild/Current/Bin/amd64/" + - "C:/Windows/Microsoft.NET/Framework64/v4.0.30319/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/Tools/" + - "C:/Windows/System32/" + - "C:/Windows/" + - "C:/Windows/System32/wbem/" + - "C:/Windows/System32/WindowsPowerShell/v1.0/" + - "C:/Windows/System32/OpenSSH/" + - "C:/Program Files/Intel/WiFi/bin/" + - "C:/Program Files/Common Files/Intel/WirelessCommon/" + - "C:/Program Files/nodejs/" + - "C:/Program Files (x86)/Windows Kits/10/Windows Performance Toolkit/" + - "C:/Program Files/CMake/bin/" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python311/Scripts/" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python311/" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python313/Scripts/" + - "C:/Users/Kids/AppData/Local/Programs/Python/Python313/" + - "C:/Users/Kids/AppData/Local/Microsoft/WindowsApps/" + - "C:/Users/Kids/AppData/Local/Programs/Windsurf/bin/" + - "C:/Users/Kids/AppData/Local/Programs/Microsoft VS Code/bin/" + - "C:/Users/Kids/AppData/Local/Programs/Git/cmd/" + - "C:/Users/Kids/AppData/Roaming/npm/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/bin/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/Ninja/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/VC/Linux/bin/ConnectionManagerExe/" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/vcpkg/" + searched_directories: + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/mt.com" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/mt.exe" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/mt" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/VC/vcpackages/mt.com" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/VC/vcpackages/mt.exe" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/VC/vcpackages/mt" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/TestWindow/mt.com" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/TestWindow/mt.exe" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/TestWindow/mt" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/TeamFoundation/Team Explorer/mt.com" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/TeamFoundation/Team Explorer/mt.exe" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/TeamFoundation/Team Explorer/mt" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/MSBuild/Current/Bin/Roslyn/mt.com" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/MSBuild/Current/Bin/Roslyn/mt.exe" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/MSBuild/Current/Bin/Roslyn/mt" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Team Tools/DiagnosticsHub/Collector/mt.com" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Team Tools/DiagnosticsHub/Collector/mt.exe" + - "C:/Program Files/Microsoft Visual Studio/2022/Community/Team Tools/DiagnosticsHub/Collector/mt" + - "C:/Program Files (x86)/Windows Kits/10/bin/10.0.26100.0/x64/mt.com" + found: "C:/Program Files (x86)/Windows Kits/10/bin/10.0.26100.0/x64/mt.exe" + search_context: + ENV{PATH}: + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\VC\\Tools\\MSVC\\14.44.35207\\bin\\HostX64\\x64" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\VC\\VCPackages" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\TestWindow" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\TeamFoundation\\Team Explorer" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\MSBuild\\Current\\bin\\Roslyn" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Team Tools\\DiagnosticsHub\\Collector" + - "C:\\Program Files (x86)\\Windows Kits\\10\\bin\\10.0.26100.0\\\\x64" + - "C:\\Program Files (x86)\\Windows Kits\\10\\bin\\\\x64" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\\\MSBuild\\Current\\Bin\\amd64" + - "C:\\Windows\\Microsoft.NET\\Framework64\\v4.0.30319" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\Tools\\" + - "C:\\WINDOWS\\system32" + - "C:\\WINDOWS" + - "C:\\WINDOWS\\System32\\Wbem" + - "C:\\WINDOWS\\System32\\WindowsPowerShell\\v1.0\\" + - "C:\\WINDOWS\\System32\\OpenSSH\\" + - "C:\\Program Files\\Intel\\WiFi\\bin\\" + - "C:\\Program Files\\Common Files\\Intel\\WirelessCommon\\" + - "C:\\Program Files\\nodejs\\" + - "C:\\Program Files (x86)\\Windows Kits\\10\\Windows Performance Toolkit\\" + - "C:\\Program Files\\CMake\\bin" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Python\\Python311\\Scripts\\" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Python\\Python311\\" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Python\\Python313\\Scripts\\" + - 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"C:\\Users\\Kids\\AppData\\Local\\Programs\\Python\\Python313\\Scripts\\" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Python\\Python313\\" + - "C:\\Users\\Kids\\AppData\\Local\\Microsoft\\WindowsApps" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Windsurf\\bin" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Microsoft VS Code\\bin" + - "C:\\Users\\Kids\\AppData\\Local\\Programs\\Git\\cmd" + - "C:\\Users\\Kids\\AppData\\Roaming\\npm" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\CMake\\CMake\\bin" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\CommonExtensions\\Microsoft\\CMake\\Ninja" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\IDE\\VC\\Linux\\bin\\ConnectionManagerExe" + - "C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\VC\\vcpkg" + CMAKE_INSTALL_PREFIX: "C:/Program Files (x86)/llama.cpp" + CMAKE_SYSTEM_PREFIX_PATH: + - "C:/Program Files" + - "C:/Program Files (x86)" + - "C:/Program Files/CMake" + - "C:/Program Files (x86)/llama.cpp" + - + kind: "try_compile-v1" + backtrace: + - "C:/Program Files/CMake/share/cmake-4.1/Modules/Internal/CheckSourceCompiles.cmake:104 (try_compile)" + - "C:/Program Files/CMake/share/cmake-4.1/Modules/CheckCSourceCompiles.cmake:103 (cmake_check_source_compiles)" + - "C:/Program Files/CMake/share/cmake-4.1/Modules/FindThreads.cmake:97 (check_c_source_compiles)" + - "C:/Program Files/CMake/share/cmake-4.1/Modules/FindThreads.cmake:163 (_threads_check_libc)" + - "ggml/CMakeLists.txt:234 (find_package)" + checks: + - "Performing Test CMAKE_HAVE_LIBC_PTHREAD" + directories: + source: "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/CMakeFiles/CMakeScratch/TryCompile-u02i7b" + binary: "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/CMakeFiles/CMakeScratch/TryCompile-u02i7b" + cmakeVariables: + CMAKE_C_FLAGS: "/DWIN32 /D_WINDOWS /W3" + CMAKE_C_FLAGS_DEBUG: "/MDd /Zi /Ob0 /Od /RTC1" + CMAKE_EXE_LINKER_FLAGS: "/machine:x64" + CMAKE_MODULE_PATH: "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/cmake/" + buildResult: + variable: "CMAKE_HAVE_LIBC_PTHREAD" + cached: true + stdout: | + Change Dir: 'C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/CMakeFiles/CMakeScratch/TryCompile-u02i7b' + + Run Build Command(s): C:/Users/Kids/AppData/Local/Programs/Python/Python311/Scripts/ninja.exe -v cmTC_0d2e7 + [1/2] C:\\PROGRA~1\\MIB055~1\\2022\\COMMUN~1\\VC\\Tools\\MSVC\\1444~1.352\\bin\\Hostx64\\x64\\cl.exe /nologo -DCMAKE_HAVE_LIBC_PTHREAD /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 -std:c11 /showIncludes /FoCMakeFiles\\cmTC_0d2e7.dir\\src.c.obj /FdCMakeFiles\\cmTC_0d2e7.dir\\ /FS -c "C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\build\\CMakeFiles\\CMakeScratch\\TryCompile-u02i7b\\src.c" + FAILED: CMakeFiles/cmTC_0d2e7.dir/src.c.obj + C:\\PROGRA~1\\MIB055~1\\2022\\COMMUN~1\\VC\\Tools\\MSVC\\1444~1.352\\bin\\Hostx64\\x64\\cl.exe /nologo -DCMAKE_HAVE_LIBC_PTHREAD /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 -std:c11 /showIncludes /FoCMakeFiles\\cmTC_0d2e7.dir\\src.c.obj /FdCMakeFiles\\cmTC_0d2e7.dir\\ /FS -c "C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\build\\CMakeFiles\\CMakeScratch\\TryCompile-u02i7b\\src.c" + C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\build\\CMakeFiles\\CMakeScratch\\TryCompile-u02i7b\\src.c(1): fatal error C1083: Cannot open include file: 'pthread.h': No such file or directory + ninja: build stopped: subcommand failed. + + exitCode: 1 + - + kind: "try_compile-v1" + backtrace: + - "C:/Program Files/CMake/share/cmake-4.1/Modules/CheckLibraryExists.cmake:154 (try_compile)" + - "C:/Program Files/CMake/share/cmake-4.1/Modules/FindThreads.cmake:112 (check_library_exists)" + - "C:/Program Files/CMake/share/cmake-4.1/Modules/FindThreads.cmake:175 (_threads_check_lib)" + - "ggml/CMakeLists.txt:234 (find_package)" + checks: + - "Looking for pthread_create in pthreads" + directories: + source: "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/CMakeFiles/CMakeScratch/TryCompile-mwfg5m" + binary: "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/CMakeFiles/CMakeScratch/TryCompile-mwfg5m" + cmakeVariables: + CMAKE_C_FLAGS: "/DWIN32 /D_WINDOWS /W3" + CMAKE_C_FLAGS_DEBUG: "/MDd /Zi /Ob0 /Od /RTC1" + CMAKE_EXE_LINKER_FLAGS: "/machine:x64" + CMAKE_MODULE_PATH: "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/cmake/" + buildResult: + variable: "CMAKE_HAVE_PTHREADS_CREATE" + cached: true + stdout: | + Change Dir: 'C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/CMakeFiles/CMakeScratch/TryCompile-mwfg5m' + + Run Build Command(s): C:/Users/Kids/AppData/Local/Programs/Python/Python311/Scripts/ninja.exe -v cmTC_8bacb + [1/2] C:\\PROGRA~1\\MIB055~1\\2022\\COMMUN~1\\VC\\Tools\\MSVC\\1444~1.352\\bin\\Hostx64\\x64\\cl.exe /nologo /DWIN32 /D_WINDOWS /W3 -DCHECK_FUNCTION_EXISTS=pthread_create /MDd /Zi /Ob0 /Od /RTC1 -std:c11 /showIncludes /FoCMakeFiles\\cmTC_8bacb.dir\\CheckFunctionExists.c.obj /FdCMakeFiles\\cmTC_8bacb.dir\\ /FS -c "C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\build\\CMakeFiles\\CMakeScratch\\TryCompile-mwfg5m\\CheckFunctionExists.c" + [2/2] C:\\WINDOWS\\system32\\cmd.exe /C "cd . && "C:\\Program Files\\CMake\\bin\\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=CMakeFiles\\cmTC_8bacb.dir --rc=C:\\PROGRA~2\\WI3CF2~1\\10\\bin\\100261~1.0\\x64\\rc.exe --mt=C:\\PROGRA~2\\WI3CF2~1\\10\\bin\\100261~1.0\\x64\\mt.exe --manifests -- C:\\PROGRA~1\\MIB055~1\\2022\\COMMUN~1\\VC\\Tools\\MSVC\\1444~1.352\\bin\\Hostx64\\x64\\link.exe /nologo CMakeFiles\\cmTC_8bacb.dir\\CheckFunctionExists.c.obj /out:cmTC_8bacb.exe /implib:cmTC_8bacb.lib /pdb:cmTC_8bacb.pdb /version:0.0 /machine:x64 /debug /INCREMENTAL /subsystem:console pthreads.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib && cd ." + FAILED: cmTC_8bacb.exe + C:\\WINDOWS\\system32\\cmd.exe /C "cd . && "C:\\Program Files\\CMake\\bin\\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=CMakeFiles\\cmTC_8bacb.dir --rc=C:\\PROGRA~2\\WI3CF2~1\\10\\bin\\100261~1.0\\x64\\rc.exe --mt=C:\\PROGRA~2\\WI3CF2~1\\10\\bin\\100261~1.0\\x64\\mt.exe --manifests -- C:\\PROGRA~1\\MIB055~1\\2022\\COMMUN~1\\VC\\Tools\\MSVC\\1444~1.352\\bin\\Hostx64\\x64\\link.exe /nologo CMakeFiles\\cmTC_8bacb.dir\\CheckFunctionExists.c.obj /out:cmTC_8bacb.exe /implib:cmTC_8bacb.lib /pdb:cmTC_8bacb.pdb /version:0.0 /machine:x64 /debug /INCREMENTAL /subsystem:console pthreads.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib && cd ." + LINK Pass 1: command "C:\\PROGRA~1\\MIB055~1\\2022\\COMMUN~1\\VC\\Tools\\MSVC\\1444~1.352\\bin\\Hostx64\\x64\\link.exe /nologo CMakeFiles\\cmTC_8bacb.dir\\CheckFunctionExists.c.obj /out:cmTC_8bacb.exe /implib:cmTC_8bacb.lib /pdb:cmTC_8bacb.pdb /version:0.0 /machine:x64 /debug /INCREMENTAL /subsystem:console pthreads.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib /MANIFEST /MANIFESTFILE:CMakeFiles\\cmTC_8bacb.dir/intermediate.manifest CMakeFiles\\cmTC_8bacb.dir/manifest.res" failed (exit code 1104) with the following output:\x0d + LINK : fatal error LNK1104: cannot open file 'pthreads.lib'\x0d\x0d + ninja: build stopped: subcommand failed. + + exitCode: 1 + - + kind: "try_compile-v1" + backtrace: + - "C:/Program Files/CMake/share/cmake-4.1/Modules/CheckLibraryExists.cmake:154 (try_compile)" + - "C:/Program Files/CMake/share/cmake-4.1/Modules/FindThreads.cmake:112 (check_library_exists)" + - "C:/Program Files/CMake/share/cmake-4.1/Modules/FindThreads.cmake:176 (_threads_check_lib)" + - "ggml/CMakeLists.txt:234 (find_package)" + checks: + - "Looking for pthread_create in pthread" + directories: + source: "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/CMakeFiles/CMakeScratch/TryCompile-vus2an" + binary: 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user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib && cd ." + FAILED: cmTC_0f0e6.exe + C:\\WINDOWS\\system32\\cmd.exe /C "cd . && "C:\\Program Files\\CMake\\bin\\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=CMakeFiles\\cmTC_0f0e6.dir --rc=C:\\PROGRA~2\\WI3CF2~1\\10\\bin\\100261~1.0\\x64\\rc.exe --mt=C:\\PROGRA~2\\WI3CF2~1\\10\\bin\\100261~1.0\\x64\\mt.exe --manifests -- C:\\PROGRA~1\\MIB055~1\\2022\\COMMUN~1\\VC\\Tools\\MSVC\\1444~1.352\\bin\\Hostx64\\x64\\link.exe /nologo CMakeFiles\\cmTC_0f0e6.dir\\CheckFunctionExists.c.obj /out:cmTC_0f0e6.exe /implib:cmTC_0f0e6.lib /pdb:cmTC_0f0e6.pdb /version:0.0 /machine:x64 /debug /INCREMENTAL /subsystem:console pthread.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib && cd ." + LINK Pass 1: command "C:\\PROGRA~1\\MIB055~1\\2022\\COMMUN~1\\VC\\Tools\\MSVC\\1444~1.352\\bin\\Hostx64\\x64\\link.exe /nologo 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included in the main 'build.ninja'. + +# ============================================================================= +# Project: llama.cpp +# Configurations: Debug +# ============================================================================= +# ============================================================================= + +############################################# +# localized /showIncludes string + +msvc_deps_prefix = Note: including file: + + +############################################# +# Rule for running custom commands. + +rule CUSTOM_COMMAND + command = $COMMAND + description = $DESC + + +############################################# +# Rule for compiling C files. + +rule C_COMPILER__ggml-base_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building C object $out + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__ggml-base_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX shared library. + +rule CXX_SHARED_LIBRARY_LINKER__ggml-base_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_dll --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /dll /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX shared library $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__ggml_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX shared library. + +rule CXX_SHARED_LIBRARY_LINKER__ggml_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_dll --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /dll /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX shared library $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling C files. + +rule C_COMPILER__ggml-cpu_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building C object $out + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__ggml-cpu_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX shared library. + +rule CXX_SHARED_LIBRARY_LINKER__ggml-cpu_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_dll --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /dll /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX shared library $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX shared library. + +rule CXX_SHARED_LIBRARY_LINKER__llama_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_dll --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /dll /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX shared library $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__build_info_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__common_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX static library. + +rule CXX_STATIC_LIBRARY_LINKER__common_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\lib.exe /nologo $LINK_FLAGS /out:$TARGET_FILE $in && $POST_BUILD" + description = Linking CXX static library $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-tokenizer-0_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-tokenizer-0_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-chat-parser_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-chat-parser_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-chat-template_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-chat-template_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-json-partial_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-json-partial_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-log_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-log_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-regex-partial_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-regex-partial_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-thread-safety_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-thread-safety_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-gguf_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-gguf_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-backend-ops_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-backend-ops_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-model-load-cancel_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-model-load-cancel_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-autorelease_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-autorelease_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-barrier_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-barrier_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-quantize-fns_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-quantize-fns_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-quantize-perf_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-quantize-perf_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-rope_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-rope_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling C files. + +rule C_COMPILER__test-mtmd-c-api_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building C object $out + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__test-mtmd-c-api_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__test-mtmd-c-api_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling C files. + +rule C_COMPILER__test-c_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building C object $out + + +############################################# +# Rule for linking C executable. + +rule C_EXECUTABLE_LINKER__test-c_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking C executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-batched_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-batched_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-embedding_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-embedding_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-eval-callback_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-eval-callback_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-gguf-hash_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-gguf-hash_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling C files. + +rule C_COMPILER__xxhash_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building C object $out + + +############################################# +# Rule for compiling C files. + +rule C_COMPILER__sha1_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building C object $out + + +############################################# +# Rule for compiling C files. + +rule C_COMPILER__sha256_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building C object $out + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-gguf_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-gguf_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-gritlm_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-gritlm_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-lookahead_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-lookahead_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-lookup_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-lookup_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-lookup-create_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-lookup-create_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-lookup-merge_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-lookup-merge_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-lookup-stats_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-lookup-stats_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-parallel_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-parallel_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-passkey_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-passkey_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-retrieval_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-retrieval_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-save-load-state_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-save-load-state_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-simple_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-simple_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-simple-chat_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-simple-chat_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-speculative_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-speculative_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-speculative-simple_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-speculative-simple_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-gen-docs_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-gen-docs_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-finetune_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-finetune_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-diffusion-cli_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-diffusion-cli_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-convert-llama2c-to-ggml_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-convert-llama2c-to-ggml_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-vdot_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-vdot_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-q8dot_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-q8dot_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-batched-bench_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-batched-bench_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-gguf-split_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-gguf-split_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-imatrix_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-imatrix_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-bench_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-bench_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-cli_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-cli_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-perplexity_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-perplexity_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-quantize_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-quantize_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-server_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-server_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-run_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-run_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-tokenize_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-tokenize_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-tts_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-tts_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__mtmd_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX shared library. + +rule CXX_SHARED_LIBRARY_LINKER__mtmd_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_dll --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /dll /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX shared library $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-llava-cli_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-llava-cli_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-gemma3-cli_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-gemma3-cli_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-minicpmv-cli_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-minicpmv-cli_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-qwen2vl-cli_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-qwen2vl-cli_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-mtmd-cli_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-mtmd-cli_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-cvector-generator_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-cvector-generator_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for compiling CXX files. + +rule CXX_COMPILER__llama-export-lora_unscanned_Debug + deps = msvc + command = ${LAUNCHER}${CODE_CHECK}C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\cl.exe /nologo /TP $DEFINES $INCLUDES $FLAGS /showIncludes /Fo$out /Fd$TARGET_COMPILE_PDB /FS -c $in + description = Building CXX object $out + + +############################################# +# Rule for linking CXX executable. + +rule CXX_EXECUTABLE_LINKER__llama-export-lora_Debug + command = C:\WINDOWS\system32\cmd.exe /C "$PRE_LINK && "C:\Program Files\CMake\bin\cmake.exe" -E vs_link_exe --msvc-ver=1944 --intdir=$OBJECT_DIR --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100261~1.0\x64\mt.exe --manifests $MANIFESTS -- C:\PROGRA~1\MIB055~1\2022\COMMUN~1\VC\Tools\MSVC\1444~1.352\bin\Hostx64\x64\link.exe /nologo $in /out:$TARGET_FILE /implib:$TARGET_IMPLIB /pdb:$TARGET_PDB /version:0.0 $LINK_FLAGS $LINK_PATH $LINK_LIBRARIES && $POST_BUILD" + description = Linking CXX executable $TARGET_FILE + restat = $RESTAT + + +############################################# +# Rule for re-running cmake. + +rule RERUN_CMAKE + command = "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" + description = Re-running CMake... + generator = 1 + + +############################################# +# Rule for cleaning all built files. + +rule CLEAN + command = C:\Users\Kids\AppData\Local\Programs\Python\Python311\Scripts\ninja.exe $FILE_ARG -t clean $TARGETS + description = Cleaning all built files... + + +############################################# +# Rule for printing all primary targets available. + +rule HELP + command = C:\Users\Kids\AppData\Local\Programs\Python\Python311\Scripts\ninja.exe -t targets + description = All primary targets available: + diff --git a/llama.cpp/build/CTestTestfile.cmake b/llama.cpp/build/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..8e19ebcc543b85b7f1ef71058259535e53d12c51 --- /dev/null +++ b/llama.cpp/build/CTestTestfile.cmake @@ -0,0 +1,13 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. +subdirs("ggml") +subdirs("src") +subdirs("common") +subdirs("tests") +subdirs("examples") +subdirs("pocs") +subdirs("tools") diff --git a/llama.cpp/build/DartConfiguration.tcl b/llama.cpp/build/DartConfiguration.tcl new file mode 100644 index 0000000000000000000000000000000000000000..76629cca7e68e4b7fdb76334c5e3843c8f97c16a --- /dev/null +++ b/llama.cpp/build/DartConfiguration.tcl @@ -0,0 +1,109 @@ +# This file is configured by CMake automatically as DartConfiguration.tcl +# If you choose not to use CMake, this file may be hand configured, by +# filling in the required variables. + + +# Configuration directories and files +SourceDirectory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp +BuildDirectory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build + +# Where to place the cost data store +CostDataFile: + +# Site is something like machine.domain, i.e. pragmatic.crd +Site: DESKTOP-ATRBRB2 + +# Build name is osname-revision-compiler, i.e. Linux-2.4.2-2smp-c++ +BuildName: Win32-ninja + +# Subprojects +LabelsForSubprojects: + +# Submission information +SubmitURL: http:// +SubmitInactivityTimeout: + +# Dashboard start time +NightlyStartTime: 00:00:00 EDT + +# Commands for the build/test/submit cycle +ConfigureCommand: "C:/Program Files/CMake/bin/cmake.exe" "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp" +MakeCommand: "C:\Program Files\CMake\bin\cmake.exe" --build . --config "${CTEST_CONFIGURATION_TYPE}" +DefaultCTestConfigurationType: Release + +# version control +UpdateVersionOnly: + +# CVS options +# Default is "-d -P -A" +CVSCommand: +CVSUpdateOptions: + +# Subversion options +SVNCommand: +SVNOptions: +SVNUpdateOptions: + +# Git options +GITCommand: C:/Users/Kids/AppData/Local/Programs/Git/cmd/git.exe +GITInitSubmodules: +GITUpdateOptions: +GITUpdateCustom: + +# Perforce options +P4Command: +P4Client: +P4Options: +P4UpdateOptions: +P4UpdateCustom: + +# Generic update command +UpdateCommand: C:/Users/Kids/AppData/Local/Programs/Git/cmd/git.exe +UpdateOptions: +UpdateType: git + +# Compiler info +Compiler: C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/cl.exe +CompilerVersion: 19.44.35213.0 + +# Dynamic analysis (MemCheck) +PurifyCommand: +ValgrindCommand: +ValgrindCommandOptions: +DrMemoryCommand: +DrMemoryCommandOptions: +CudaSanitizerCommand: +CudaSanitizerCommandOptions: +MemoryCheckType: +MemoryCheckSanitizerOptions: +MemoryCheckCommand: MEMORYCHECK_COMMAND-NOTFOUND +MemoryCheckCommandOptions: +MemoryCheckSuppressionFile: + +# Coverage +CoverageCommand: COVERAGE_COMMAND-NOTFOUND +CoverageExtraFlags: -l + +# Testing options +# TimeOut is the amount of time in seconds to wait for processes +# to complete during testing. After TimeOut seconds, the +# process will be summarily terminated. +# Currently set to 25 minutes +TimeOut: 1500 + +# During parallel testing CTest will not start a new test if doing +# so would cause the system load to exceed this value. +TestLoad: + +TLSVerify: +TLSVersion: + +UseLaunchers: +CurlOptions: +# warning, if you add new options here that have to do with submit, +# you have to update cmCTestSubmitCommand.cxx + +# For CTest submissions that timeout, these options +# specify behavior for retrying the submission +CTestSubmitRetryDelay: 5 +CTestSubmitRetryCount: 3 diff --git a/llama.cpp/build/bin/LICENSE-curl b/llama.cpp/build/bin/LICENSE-curl new file mode 100644 index 0000000000000000000000000000000000000000..ecaca48e687318a3a5be6e6b7bef37a3c0470d9c --- /dev/null +++ b/llama.cpp/build/bin/LICENSE-curl @@ -0,0 +1,9 @@ +Copyright (c) 1996 - 2025, Daniel Stenberg, daniel@haxx.se, and many contributors, see the THANKS file. + +All rights reserved. + +Permission to use, copy, modify, and distribute this software for any purpose with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF THIRD PARTY RIGHTS. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +Except as contained in this notice, the name of a copyright holder shall not be used in advertising or otherwise to promote the sale, use or other dealings in this Software without prior written authorization of the copyright holder. diff --git a/llama.cpp/build/bin/LICENSE-httplib b/llama.cpp/build/bin/LICENSE-httplib new file mode 100644 index 0000000000000000000000000000000000000000..d95fc43e8f9fdb98e4fb550273545542d44bae6d --- /dev/null +++ b/llama.cpp/build/bin/LICENSE-httplib @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright (c) 2017 yhirose + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/llama.cpp/build/bin/LICENSE-jsonhpp b/llama.cpp/build/bin/LICENSE-jsonhpp new file mode 100644 index 0000000000000000000000000000000000000000..7ea9a8d4f9a81081a69dd151581b3aa93c33f79d --- /dev/null +++ b/llama.cpp/build/bin/LICENSE-jsonhpp @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2013-2025 Niels Lohmann + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/llama.cpp/build/bin/LICENSE-linenoise b/llama.cpp/build/bin/LICENSE-linenoise new file mode 100644 index 0000000000000000000000000000000000000000..455160f181435bfd84c1923352e4a41e69cc2941 --- /dev/null +++ b/llama.cpp/build/bin/LICENSE-linenoise @@ -0,0 +1,26 @@ +Copyright (c) 2010-2014, Salvatore Sanfilippo +Copyright (c) 2010-2013, Pieter Noordhuis +Copyright (c) 2025, Eric Curtin + +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. 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sha256:a10b718234e84247e20196ad03bd75d0107461ce68cff18f2deefe9f56d4d9e4 +size 111616 diff --git a/llama.cpp/build/build.ninja b/llama.cpp/build/build.ninja new file mode 100644 index 0000000000000000000000000000000000000000..6f4da2cdc1dd5f119942be5817ccbd43e7b12973 --- /dev/null +++ b/llama.cpp/build/build.ninja @@ -0,0 +1,7780 @@ +# CMAKE generated file: DO NOT EDIT! +# Generated by "Ninja" Generator, CMake Version 4.1 + +# This file contains all the build statements describing the +# compilation DAG. + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# +# Which is the root file. +# ============================================================================= + +# ============================================================================= +# Project: llama.cpp +# Configurations: Debug +# ============================================================================= + +############################################# +# Minimal version of Ninja required by this file + +ninja_required_version = 1.5 + + +############################################# +# Set configuration variable for custom commands. + +CONFIGURATION = Debug +# ============================================================================= +# Include auxiliary files. + + +############################################# +# Include rules file. + +include CMakeFiles\rules.ninja + +# ============================================================================= + +############################################# +# Logical path to working directory; prefix for absolute paths. + +cmake_ninja_workdir = C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\ + +############################################# +# Utility command for Experimental + +build Experimental: phony CMakeFiles\Experimental + + +############################################# +# Utility command for Nightly + +build Nightly: phony CMakeFiles\Nightly + + +############################################# +# Utility command for Continuous + +build Continuous: phony CMakeFiles\Continuous + + +############################################# +# Utility command for NightlyMemoryCheck + +build NightlyMemoryCheck: phony CMakeFiles\NightlyMemoryCheck + + +############################################# +# Utility command for NightlyStart + +build NightlyStart: phony CMakeFiles\NightlyStart + + +############################################# +# Utility command for NightlyUpdate + +build NightlyUpdate: phony CMakeFiles\NightlyUpdate + + +############################################# +# Utility command for NightlyConfigure + +build NightlyConfigure: phony CMakeFiles\NightlyConfigure + + +############################################# +# Utility command for NightlyBuild + +build NightlyBuild: phony CMakeFiles\NightlyBuild + + +############################################# +# Utility command for NightlyTest + +build NightlyTest: phony CMakeFiles\NightlyTest + + +############################################# +# Utility command for NightlyCoverage + +build NightlyCoverage: phony CMakeFiles\NightlyCoverage + + +############################################# +# Utility command for NightlyMemCheck + +build NightlyMemCheck: phony CMakeFiles\NightlyMemCheck + + +############################################# +# Utility command for NightlySubmit + +build NightlySubmit: phony CMakeFiles\NightlySubmit + + +############################################# +# Utility command for ExperimentalStart + +build ExperimentalStart: phony CMakeFiles\ExperimentalStart + + +############################################# +# Utility command for ExperimentalUpdate + +build ExperimentalUpdate: phony CMakeFiles\ExperimentalUpdate + + +############################################# +# Utility command for ExperimentalConfigure + +build ExperimentalConfigure: phony CMakeFiles\ExperimentalConfigure + + +############################################# +# Utility command for ExperimentalBuild + +build ExperimentalBuild: phony CMakeFiles\ExperimentalBuild + + +############################################# +# Utility command for ExperimentalTest + +build ExperimentalTest: phony CMakeFiles\ExperimentalTest + + +############################################# +# Utility command for ExperimentalCoverage + +build ExperimentalCoverage: phony CMakeFiles\ExperimentalCoverage + + +############################################# +# Utility command for ExperimentalMemCheck + +build ExperimentalMemCheck: phony CMakeFiles\ExperimentalMemCheck + + +############################################# +# Utility command for ExperimentalSubmit + +build ExperimentalSubmit: phony CMakeFiles\ExperimentalSubmit + + +############################################# +# Utility command for ContinuousStart + +build ContinuousStart: phony CMakeFiles\ContinuousStart + + +############################################# +# Utility command for ContinuousUpdate + +build ContinuousUpdate: phony CMakeFiles\ContinuousUpdate + + +############################################# +# Utility command for ContinuousConfigure + +build ContinuousConfigure: phony CMakeFiles\ContinuousConfigure + + +############################################# +# Utility command for ContinuousBuild + +build ContinuousBuild: phony CMakeFiles\ContinuousBuild + + +############################################# +# Utility command for ContinuousTest + +build ContinuousTest: phony CMakeFiles\ContinuousTest + + +############################################# +# Utility command for ContinuousCoverage + +build ContinuousCoverage: phony CMakeFiles\ContinuousCoverage + + +############################################# +# Utility command for ContinuousMemCheck + +build ContinuousMemCheck: phony CMakeFiles\ContinuousMemCheck + + +############################################# +# Utility command for ContinuousSubmit + +build ContinuousSubmit: phony CMakeFiles\ContinuousSubmit + + +############################################# +# Utility command for test + +build CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build test: phony CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build edit_cache: phony CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build rebuild_cache: phony CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build list_install_components: phony + + +############################################# +# Utility command for install + +build CMakeFiles\install.util: CUSTOM_COMMAND all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build install: phony CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build CMakeFiles\install\local.util: CUSTOM_COMMAND all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build install\local: phony CMakeFiles\install\local.util + + +############################################# +# Custom command for CMakeFiles\Experimental + +build CMakeFiles\Experimental | ${cmake_ninja_workdir}CMakeFiles\Experimental: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Experimental -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\Nightly + +build CMakeFiles\Nightly | ${cmake_ninja_workdir}CMakeFiles\Nightly: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Nightly -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\Continuous + +build CMakeFiles\Continuous | ${cmake_ninja_workdir}CMakeFiles\Continuous: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Continuous -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\NightlyMemoryCheck + +build CMakeFiles\NightlyMemoryCheck | ${cmake_ninja_workdir}CMakeFiles\NightlyMemoryCheck: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=NightlyMemoryCheck -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\NightlyStart + +build CMakeFiles\NightlyStart | ${cmake_ninja_workdir}CMakeFiles\NightlyStart: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Nightly -DACTIONS=Start -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\NightlyUpdate + +build CMakeFiles\NightlyUpdate | ${cmake_ninja_workdir}CMakeFiles\NightlyUpdate: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Nightly -DACTIONS=Update -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\NightlyConfigure + +build CMakeFiles\NightlyConfigure | ${cmake_ninja_workdir}CMakeFiles\NightlyConfigure: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Nightly -DACTIONS=Configure -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\NightlyBuild + +build CMakeFiles\NightlyBuild | ${cmake_ninja_workdir}CMakeFiles\NightlyBuild: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Nightly -DACTIONS=Build -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\NightlyTest + +build CMakeFiles\NightlyTest | ${cmake_ninja_workdir}CMakeFiles\NightlyTest: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Nightly -DACTIONS=Test -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\NightlyCoverage + +build CMakeFiles\NightlyCoverage | ${cmake_ninja_workdir}CMakeFiles\NightlyCoverage: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Nightly -DACTIONS=Coverage -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\NightlyMemCheck + +build CMakeFiles\NightlyMemCheck | ${cmake_ninja_workdir}CMakeFiles\NightlyMemCheck: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Nightly -DACTIONS=MemCheck -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\NightlySubmit + +build CMakeFiles\NightlySubmit | ${cmake_ninja_workdir}CMakeFiles\NightlySubmit: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Nightly -DACTIONS=Submit -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ExperimentalStart + +build CMakeFiles\ExperimentalStart | ${cmake_ninja_workdir}CMakeFiles\ExperimentalStart: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Experimental -DACTIONS=Start -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ExperimentalUpdate + +build CMakeFiles\ExperimentalUpdate | ${cmake_ninja_workdir}CMakeFiles\ExperimentalUpdate: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Experimental -DACTIONS=Update -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ExperimentalConfigure + +build CMakeFiles\ExperimentalConfigure | ${cmake_ninja_workdir}CMakeFiles\ExperimentalConfigure: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Experimental -DACTIONS=Configure -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ExperimentalBuild + +build CMakeFiles\ExperimentalBuild | ${cmake_ninja_workdir}CMakeFiles\ExperimentalBuild: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Experimental -DACTIONS=Build -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ExperimentalTest + +build CMakeFiles\ExperimentalTest | ${cmake_ninja_workdir}CMakeFiles\ExperimentalTest: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Experimental -DACTIONS=Test -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ExperimentalCoverage + +build CMakeFiles\ExperimentalCoverage | ${cmake_ninja_workdir}CMakeFiles\ExperimentalCoverage: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Experimental -DACTIONS=Coverage -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ExperimentalMemCheck + +build CMakeFiles\ExperimentalMemCheck | ${cmake_ninja_workdir}CMakeFiles\ExperimentalMemCheck: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Experimental -DACTIONS=MemCheck -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ExperimentalSubmit + +build CMakeFiles\ExperimentalSubmit | ${cmake_ninja_workdir}CMakeFiles\ExperimentalSubmit: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Experimental -DACTIONS=Submit -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ContinuousStart + +build CMakeFiles\ContinuousStart | ${cmake_ninja_workdir}CMakeFiles\ContinuousStart: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Continuous -DACTIONS=Start -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ContinuousUpdate + +build CMakeFiles\ContinuousUpdate | ${cmake_ninja_workdir}CMakeFiles\ContinuousUpdate: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Continuous -DACTIONS=Update -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ContinuousConfigure + +build CMakeFiles\ContinuousConfigure | ${cmake_ninja_workdir}CMakeFiles\ContinuousConfigure: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Continuous -DACTIONS=Configure -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ContinuousBuild + +build CMakeFiles\ContinuousBuild | ${cmake_ninja_workdir}CMakeFiles\ContinuousBuild: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Continuous -DACTIONS=Build -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ContinuousTest + +build CMakeFiles\ContinuousTest | ${cmake_ninja_workdir}CMakeFiles\ContinuousTest: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Continuous -DACTIONS=Test -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ContinuousCoverage + +build CMakeFiles\ContinuousCoverage | ${cmake_ninja_workdir}CMakeFiles\ContinuousCoverage: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Continuous -DACTIONS=Coverage -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ContinuousMemCheck + +build CMakeFiles\ContinuousMemCheck | ${cmake_ninja_workdir}CMakeFiles\ContinuousMemCheck: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Continuous -DACTIONS=MemCheck -S CMakeFiles/CTestScript.cmake -V" + pool = console + + +############################################# +# Custom command for CMakeFiles\ContinuousSubmit + +build CMakeFiles\ContinuousSubmit | ${cmake_ninja_workdir}CMakeFiles\ContinuousSubmit: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build" && "C:\Program Files\CMake\bin\ctest.exe" -DMODEL=Continuous -DACTIONS=Submit -S CMakeFiles/CTestScript.cmake -V" + pool = console + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/CMakeLists.txt +# ============================================================================= + + +############################################# +# Utility command for test + +build ggml\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build ggml\test: phony ggml\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build ggml\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build ggml\edit_cache: phony ggml\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build ggml\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build ggml\rebuild_cache: phony ggml\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build ggml\list_install_components: phony + + +############################################# +# Utility command for install + +build ggml\CMakeFiles\install.util: CUSTOM_COMMAND ggml\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build ggml\install: phony ggml\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build ggml\CMakeFiles\install\local.util: CUSTOM_COMMAND ggml\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build ggml\install\local: phony ggml\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for SHARED_LIBRARY target ggml-base + + +############################################# +# Order-only phony target for ggml-base + +build cmake_object_order_depends_target_ggml-base: phony || . + +build ggml\src\CMakeFiles\ggml-base.dir\ggml.c.obj: C_COMPILER__ggml-base_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml.c || cmake_object_order_depends_target_ggml-base + CONFIG = Debug + DEFINES = -DGGML_BUILD -DGGML_COMMIT=\"03d46982\" -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_VERSION=\"0.0.6074\" -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_base_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 -std:c11 /utf-8 /bigobj /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-base.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-base.dir + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-base.dir\ + TARGET_PDB = bin\ggml-base.pdb + +build ggml\src\CMakeFiles\ggml-base.dir\ggml.cpp.obj: CXX_COMPILER__ggml-base_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml.cpp || cmake_object_order_depends_target_ggml-base + CONFIG = Debug + DEFINES = -DGGML_BUILD -DGGML_COMMIT=\"03d46982\" -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_VERSION=\"0.0.6074\" -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_base_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-base.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-base.dir + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-base.dir\ + TARGET_PDB = bin\ggml-base.pdb + +build ggml\src\CMakeFiles\ggml-base.dir\ggml-alloc.c.obj: C_COMPILER__ggml-base_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-alloc.c || cmake_object_order_depends_target_ggml-base + CONFIG = Debug + DEFINES = -DGGML_BUILD -DGGML_COMMIT=\"03d46982\" -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_VERSION=\"0.0.6074\" -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_base_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 -std:c11 /utf-8 /bigobj /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-base.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-base.dir + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-base.dir\ + TARGET_PDB = bin\ggml-base.pdb + +build ggml\src\CMakeFiles\ggml-base.dir\ggml-backend.cpp.obj: CXX_COMPILER__ggml-base_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-backend.cpp || cmake_object_order_depends_target_ggml-base + CONFIG = Debug + DEFINES = -DGGML_BUILD -DGGML_COMMIT=\"03d46982\" -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_VERSION=\"0.0.6074\" -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_base_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-base.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-base.dir + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-base.dir\ + TARGET_PDB = bin\ggml-base.pdb + +build ggml\src\CMakeFiles\ggml-base.dir\ggml-opt.cpp.obj: CXX_COMPILER__ggml-base_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-opt.cpp || cmake_object_order_depends_target_ggml-base + CONFIG = Debug + DEFINES = -DGGML_BUILD -DGGML_COMMIT=\"03d46982\" -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_VERSION=\"0.0.6074\" -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_base_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-base.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-base.dir + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-base.dir\ + TARGET_PDB = bin\ggml-base.pdb + +build ggml\src\CMakeFiles\ggml-base.dir\ggml-threading.cpp.obj: CXX_COMPILER__ggml-base_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-threading.cpp || cmake_object_order_depends_target_ggml-base + CONFIG = Debug + DEFINES = -DGGML_BUILD -DGGML_COMMIT=\"03d46982\" -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_VERSION=\"0.0.6074\" -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_base_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-base.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-base.dir + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-base.dir\ + TARGET_PDB = bin\ggml-base.pdb + +build ggml\src\CMakeFiles\ggml-base.dir\ggml-quants.c.obj: C_COMPILER__ggml-base_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-quants.c || cmake_object_order_depends_target_ggml-base + CONFIG = Debug + DEFINES = -DGGML_BUILD -DGGML_COMMIT=\"03d46982\" -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_VERSION=\"0.0.6074\" -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_base_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 -std:c11 /utf-8 /bigobj /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-base.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-base.dir + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-base.dir\ + TARGET_PDB = bin\ggml-base.pdb + +build ggml\src\CMakeFiles\ggml-base.dir\gguf.cpp.obj: CXX_COMPILER__ggml-base_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\gguf.cpp || cmake_object_order_depends_target_ggml-base + CONFIG = Debug + DEFINES = -DGGML_BUILD -DGGML_COMMIT=\"03d46982\" -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_VERSION=\"0.0.6074\" -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_base_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-base.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-base.dir + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-base.dir\ + TARGET_PDB = bin\ggml-base.pdb + + +# ============================================================================= +# Link build statements for SHARED_LIBRARY target ggml-base + + +############################################# +# Link the shared library bin\ggml-base.dll + +build bin\ggml-base.dll ggml\src\ggml-base.lib: CXX_SHARED_LIBRARY_LINKER__ggml-base_Debug ggml\src\CMakeFiles\ggml-base.dir\ggml.c.obj ggml\src\CMakeFiles\ggml-base.dir\ggml.cpp.obj ggml\src\CMakeFiles\ggml-base.dir\ggml-alloc.c.obj ggml\src\CMakeFiles\ggml-base.dir\ggml-backend.cpp.obj ggml\src\CMakeFiles\ggml-base.dir\ggml-opt.cpp.obj ggml\src\CMakeFiles\ggml-base.dir\ggml-threading.cpp.obj ggml\src\CMakeFiles\ggml-base.dir\ggml-quants.c.obj ggml\src\CMakeFiles\ggml-base.dir\gguf.cpp.obj + CONFIG = Debug + LANGUAGE_COMPILE_FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = -shared /machine:x64 /debug /INCREMENTAL + LINK_LIBRARIES = kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = ggml\src\CMakeFiles\ggml-base.dir + POST_BUILD = cd . + PRE_LINK = cd . + RESTAT = 1 + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-base.dir\ + TARGET_FILE = bin\ggml-base.dll + TARGET_IMPLIB = ggml\src\ggml-base.lib + TARGET_PDB = bin\ggml-base.pdb + +# ============================================================================= +# Object build statements for SHARED_LIBRARY target ggml + + +############################################# +# Order-only phony target for ggml + +build cmake_object_order_depends_target_ggml: phony || cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu + +build ggml\src\CMakeFiles\ggml.dir\ggml-backend-reg.cpp.obj: CXX_COMPILER__ggml_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-backend-reg.cpp || cmake_object_order_depends_target_ggml + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_BUILD -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml.dir + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml.dir\ + TARGET_PDB = bin\ggml.pdb + + +# ============================================================================= +# Link build statements for SHARED_LIBRARY target ggml + + +############################################# +# Link the shared library bin\ggml.dll + +build bin\ggml.dll ggml\src\ggml.lib: CXX_SHARED_LIBRARY_LINKER__ggml_Debug ggml\src\CMakeFiles\ggml.dir\ggml-backend-reg.cpp.obj | ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll + CONFIG = Debug + LANGUAGE_COMPILE_FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = -shared /machine:x64 /debug /INCREMENTAL + LINK_LIBRARIES = ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = ggml\src\CMakeFiles\ggml.dir + POST_BUILD = cd . + PRE_LINK = cd . + RESTAT = 1 + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml.dir\ + TARGET_FILE = bin\ggml.dll + TARGET_IMPLIB = ggml\src\ggml.lib + TARGET_PDB = bin\ggml.pdb + +# ============================================================================= +# Object build statements for SHARED_LIBRARY target ggml-cpu + + +############################################# +# Order-only phony target for ggml-cpu + +build cmake_object_order_depends_target_ggml-cpu: phony || cmake_object_order_depends_target_ggml-base + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\ggml-cpu.c.obj: C_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\ggml-cpu.c || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 -std:c11 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\ggml-cpu.cpp.obj: CXX_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\ggml-cpu.cpp || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\repack.cpp.obj: CXX_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\repack.cpp || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\hbm.cpp.obj: CXX_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\hbm.cpp || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\quants.c.obj: C_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\quants.c || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 -std:c11 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\traits.cpp.obj: CXX_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\traits.cpp || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\amx\amx.cpp.obj: CXX_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\amx\amx.cpp || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\amx + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\amx\mmq.cpp.obj: CXX_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\amx + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\binary-ops.cpp.obj: CXX_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\binary-ops.cpp || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\unary-ops.cpp.obj: CXX_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\unary-ops.cpp || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\vec.cpp.obj: CXX_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\vec.cpp || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\ops.cpp.obj: CXX_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\ops.cpp || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\llamafile\sgemm.cpp.obj: CXX_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\llamafile\sgemm.cpp || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\llamafile + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\arch\x86\quants.c.obj: C_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\arch\x86\quants.c || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 -std:c11 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\arch\x86 + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + +build ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\arch\x86\repack.cpp.obj: CXX_COMPILER__ggml-cpu_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu\arch\x86\repack.cpp || cmake_object_order_depends_target_ggml-cpu + CONFIG = Debug + DEFINES = -DGGML_AVX2 -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_F16C -DGGML_FMA -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -DGGML_USE_CPU_REPACK -DGGML_USE_LLAMAFILE -DGGML_USE_OPENMP -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cpu_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\ggml-cpu" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + OBJECT_FILE_DIR = ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\arch\x86 + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_PDB = bin\ggml-cpu.pdb + + +# ============================================================================= +# Link build statements for SHARED_LIBRARY target ggml-cpu + + +############################################# +# Link the shared library bin\ggml-cpu.dll + +build bin\ggml-cpu.dll ggml\src\ggml-cpu.lib: CXX_SHARED_LIBRARY_LINKER__ggml-cpu_Debug ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\ggml-cpu.c.obj ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\ggml-cpu.cpp.obj ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\repack.cpp.obj ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\hbm.cpp.obj ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\quants.c.obj ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\traits.cpp.obj ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\amx\amx.cpp.obj ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\amx\mmq.cpp.obj ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\binary-ops.cpp.obj ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\unary-ops.cpp.obj ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\vec.cpp.obj ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\ops.cpp.obj ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\llamafile\sgemm.cpp.obj ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\arch\x86\quants.c.obj ggml\src\CMakeFiles\ggml-cpu.dir\ggml-cpu\arch\x86\repack.cpp.obj | ggml\src\ggml-base.lib || bin\ggml-base.dll + CONFIG = Debug + LANGUAGE_COMPILE_FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = -shared /machine:x64 /debug /INCREMENTAL + LINK_LIBRARIES = ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = ggml\src\CMakeFiles\ggml-cpu.dir + POST_BUILD = cd . + PRE_LINK = cd . + RESTAT = 1 + TARGET_COMPILE_PDB = ggml\src\CMakeFiles\ggml-cpu.dir\ + TARGET_FILE = bin\ggml-cpu.dll + TARGET_IMPLIB = ggml\src\ggml-cpu.lib + TARGET_PDB = bin\ggml-cpu.pdb + + +############################################# +# Utility command for test + +build ggml\src\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml\src" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build ggml\src\test: phony ggml\src\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build ggml\src\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml\src" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build ggml\src\edit_cache: phony ggml\src\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build ggml\src\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml\src" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build ggml\src\rebuild_cache: phony ggml\src\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build ggml\src\list_install_components: phony + + +############################################# +# Utility command for install + +build ggml\src\CMakeFiles\install.util: CUSTOM_COMMAND ggml\src\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml\src" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build ggml\src\install: phony ggml\src\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build ggml\src\CMakeFiles\install\local.util: CUSTOM_COMMAND ggml\src\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml\src" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build ggml\src\install\local: phony ggml\src\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/CMakeLists.txt +# ============================================================================= + + +############################################# +# Utility command for test + +build ggml\src\ggml-cpu\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml\src\ggml-cpu" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build ggml\src\ggml-cpu\test: phony ggml\src\ggml-cpu\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build ggml\src\ggml-cpu\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml\src\ggml-cpu" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build ggml\src\ggml-cpu\edit_cache: phony ggml\src\ggml-cpu\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build ggml\src\ggml-cpu\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml\src\ggml-cpu" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build ggml\src\ggml-cpu\rebuild_cache: phony ggml\src\ggml-cpu\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build ggml\src\ggml-cpu\list_install_components: phony + + +############################################# +# Utility command for install + +build ggml\src\ggml-cpu\CMakeFiles\install.util: CUSTOM_COMMAND ggml\src\ggml-cpu\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml\src\ggml-cpu" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build ggml\src\ggml-cpu\install: phony ggml\src\ggml-cpu\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build ggml\src\ggml-cpu\CMakeFiles\install\local.util: CUSTOM_COMMAND ggml\src\ggml-cpu\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\ggml\src\ggml-cpu" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build ggml\src\ggml-cpu\install\local: phony ggml\src\ggml-cpu\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for SHARED_LIBRARY target llama + + +############################################# +# Order-only phony target for llama + +build cmake_object_order_depends_target_llama: phony || cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu + +build src\CMakeFiles\llama.dir\llama.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-adapter.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-adapter.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-arch.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-arch.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-batch.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-batch.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-chat.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-chat.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-context.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-context.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-cparams.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-cparams.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-grammar.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-grammar.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-graph.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-graph.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-hparams.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-hparams.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-impl.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-impl.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-io.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-io.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-kv-cache-unified.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-kv-cache-unified.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-kv-cache-unified-iswa.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-kv-cache-unified-iswa.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-memory.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-memory.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-memory-hybrid.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-memory-hybrid.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-memory-recurrent.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-memory-recurrent.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-mmap.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-mmap.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-model-loader.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-model-loader.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-model-saver.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-model-saver.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-model.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-model.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-quant.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-quant.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-sampling.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-sampling.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\llama-vocab.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\llama-vocab.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\unicode-data.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\unicode-data.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + +build src\CMakeFiles\llama.dir\unicode.cpp.obj: CXX_COMPILER__llama_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\src\unicode.cpp || cmake_object_order_depends_target_llama + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dllama_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = src\CMakeFiles\llama.dir + OBJECT_FILE_DIR = src\CMakeFiles\llama.dir + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_PDB = bin\llama.pdb + + +# ============================================================================= +# Link build statements for SHARED_LIBRARY target llama + + +############################################# +# Link the shared library bin\llama.dll + +build bin\llama.dll src\llama.lib: CXX_SHARED_LIBRARY_LINKER__llama_Debug src\CMakeFiles\llama.dir\llama.cpp.obj src\CMakeFiles\llama.dir\llama-adapter.cpp.obj src\CMakeFiles\llama.dir\llama-arch.cpp.obj src\CMakeFiles\llama.dir\llama-batch.cpp.obj src\CMakeFiles\llama.dir\llama-chat.cpp.obj src\CMakeFiles\llama.dir\llama-context.cpp.obj src\CMakeFiles\llama.dir\llama-cparams.cpp.obj src\CMakeFiles\llama.dir\llama-grammar.cpp.obj src\CMakeFiles\llama.dir\llama-graph.cpp.obj src\CMakeFiles\llama.dir\llama-hparams.cpp.obj src\CMakeFiles\llama.dir\llama-impl.cpp.obj src\CMakeFiles\llama.dir\llama-io.cpp.obj src\CMakeFiles\llama.dir\llama-kv-cache-unified.cpp.obj src\CMakeFiles\llama.dir\llama-kv-cache-unified-iswa.cpp.obj src\CMakeFiles\llama.dir\llama-memory.cpp.obj src\CMakeFiles\llama.dir\llama-memory-hybrid.cpp.obj src\CMakeFiles\llama.dir\llama-memory-recurrent.cpp.obj src\CMakeFiles\llama.dir\llama-mmap.cpp.obj src\CMakeFiles\llama.dir\llama-model-loader.cpp.obj src\CMakeFiles\llama.dir\llama-model-saver.cpp.obj src\CMakeFiles\llama.dir\llama-model.cpp.obj src\CMakeFiles\llama.dir\llama-quant.cpp.obj src\CMakeFiles\llama.dir\llama-sampling.cpp.obj src\CMakeFiles\llama.dir\llama-vocab.cpp.obj src\CMakeFiles\llama.dir\unicode-data.cpp.obj src\CMakeFiles\llama.dir\unicode.cpp.obj | ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll + CONFIG = Debug + LANGUAGE_COMPILE_FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = -shared /machine:x64 /debug /INCREMENTAL + LINK_LIBRARIES = ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = src\CMakeFiles\llama.dir + POST_BUILD = cd . + PRE_LINK = cd . + RESTAT = 1 + TARGET_COMPILE_PDB = src\CMakeFiles\llama.dir\ + TARGET_FILE = bin\llama.dll + TARGET_IMPLIB = src\llama.lib + TARGET_PDB = bin\llama.pdb + + +############################################# +# Utility command for test + +build src\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\src" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build src\test: phony src\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build src\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\src" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build src\edit_cache: phony src\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build src\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\src" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build src\rebuild_cache: phony src\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build src\list_install_components: phony + + +############################################# +# Utility command for install + +build src\CMakeFiles\install.util: CUSTOM_COMMAND src\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\src" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build src\install: phony src\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build src\CMakeFiles\install\local.util: CUSTOM_COMMAND src\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\src" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build src\install\local: phony src\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for OBJECT_LIBRARY target build_info + + +############################################# +# Order-only phony target for build_info + +build cmake_object_order_depends_target_build_info: phony || . + +build common\CMakeFiles\build_info.dir\build-info.cpp.obj: CXX_COMPILER__build_info_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\common\build-info.cpp || cmake_object_order_depends_target_build_info + CONFIG = Debug + DEFINES = -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 /utf-8 /bigobj + OBJECT_DIR = common\CMakeFiles\build_info.dir + OBJECT_FILE_DIR = common\CMakeFiles\build_info.dir + TARGET_COMPILE_PDB = common\CMakeFiles\build_info.dir\ + TARGET_PDB = "" + + + +############################################# +# Object library build_info + +build common\build_info: phony common\CMakeFiles\build_info.dir\build-info.cpp.obj + +# ============================================================================= +# Object build statements for STATIC_LIBRARY target common + + +############################################# +# Order-only phony target for common + +build cmake_object_order_depends_target_common: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build common\CMakeFiles\common.dir\arg.cpp.obj: CXX_COMPILER__common_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\common\arg.cpp || cmake_object_order_depends_target_common + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python 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"C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build common\edit_cache: phony common\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build common\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\common" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build common\rebuild_cache: phony common\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build common\list_install_components: phony + + +############################################# +# Utility command for install + +build common\CMakeFiles\install.util: CUSTOM_COMMAND common\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\common" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build common\install: phony common\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build common\CMakeFiles\install\local.util: CUSTOM_COMMAND common\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\common" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build common\install\local: phony common\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target test-tokenizer-0 + + +############################################# +# Order-only phony target for test-tokenizer-0 + +build cmake_object_order_depends_target_test-tokenizer-0: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-tokenizer-0.dir\test-tokenizer-0.cpp.obj: CXX_COMPILER__test-tokenizer-0_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-tokenizer-0.cpp || cmake_object_order_depends_target_test-tokenizer-0 + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-tokenizer-0.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-tokenizer-0.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-tokenizer-0.dir\ + TARGET_PDB = bin\test-tokenizer-0.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-tokenizer-0 + + +############################################# +# Link the executable bin\test-tokenizer-0.exe + +build bin\test-tokenizer-0.exe: CXX_EXECUTABLE_LINKER__test-tokenizer-0_Debug tests\CMakeFiles\test-tokenizer-0.dir\test-tokenizer-0.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-tokenizer-0.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-tokenizer-0.dir\ + TARGET_FILE = bin\test-tokenizer-0.exe + TARGET_IMPLIB = tests\test-tokenizer-0.lib + TARGET_PDB = bin\test-tokenizer-0.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-chat-parser + + +############################################# +# Order-only phony target for test-chat-parser + +build cmake_object_order_depends_target_test-chat-parser: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-chat-parser.dir\test-chat-parser.cpp.obj: CXX_COMPILER__test-chat-parser_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-chat-parser.cpp || cmake_object_order_depends_target_test-chat-parser + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-chat-parser.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-chat-parser.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-chat-parser.dir\ + TARGET_PDB = bin\test-chat-parser.pdb + +build tests\CMakeFiles\test-chat-parser.dir\get-model.cpp.obj: CXX_COMPILER__test-chat-parser_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-chat-parser + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-chat-parser.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-chat-parser.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-chat-parser.dir\ + TARGET_PDB = bin\test-chat-parser.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-chat-parser + + +############################################# +# Link the executable bin\test-chat-parser.exe + +build bin\test-chat-parser.exe: CXX_EXECUTABLE_LINKER__test-chat-parser_Debug tests\CMakeFiles\test-chat-parser.dir\test-chat-parser.cpp.obj tests\CMakeFiles\test-chat-parser.dir\get-model.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-chat-parser.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-chat-parser.dir\ + TARGET_FILE = bin\test-chat-parser.exe + TARGET_IMPLIB = tests\test-chat-parser.lib + TARGET_PDB = bin\test-chat-parser.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-chat-template + + +############################################# +# Order-only phony target for test-chat-template + +build cmake_object_order_depends_target_test-chat-template: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-chat-template.dir\test-chat-template.cpp.obj: CXX_COMPILER__test-chat-template_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-chat-template.cpp || cmake_object_order_depends_target_test-chat-template + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-chat-template.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-chat-template.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-chat-template.dir\ + TARGET_PDB = bin\test-chat-template.pdb + +build tests\CMakeFiles\test-chat-template.dir\get-model.cpp.obj: CXX_COMPILER__test-chat-template_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-chat-template + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-chat-template.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-chat-template.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-chat-template.dir\ + TARGET_PDB = bin\test-chat-template.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-chat-template + + +############################################# +# Link the executable bin\test-chat-template.exe + +build bin\test-chat-template.exe: CXX_EXECUTABLE_LINKER__test-chat-template_Debug tests\CMakeFiles\test-chat-template.dir\test-chat-template.cpp.obj tests\CMakeFiles\test-chat-template.dir\get-model.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-chat-template.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-chat-template.dir\ + TARGET_FILE = bin\test-chat-template.exe + TARGET_IMPLIB = tests\test-chat-template.lib + TARGET_PDB = bin\test-chat-template.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-json-partial + + +############################################# +# Order-only phony target for test-json-partial + +build cmake_object_order_depends_target_test-json-partial: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-json-partial.dir\test-json-partial.cpp.obj: CXX_COMPILER__test-json-partial_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-json-partial.cpp || cmake_object_order_depends_target_test-json-partial + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-json-partial.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-json-partial.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-json-partial.dir\ + TARGET_PDB = bin\test-json-partial.pdb + +build tests\CMakeFiles\test-json-partial.dir\get-model.cpp.obj: CXX_COMPILER__test-json-partial_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-json-partial + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-json-partial.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-json-partial.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-json-partial.dir\ + TARGET_PDB = bin\test-json-partial.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-json-partial + + +############################################# +# Link the executable bin\test-json-partial.exe + +build bin\test-json-partial.exe: CXX_EXECUTABLE_LINKER__test-json-partial_Debug tests\CMakeFiles\test-json-partial.dir\test-json-partial.cpp.obj tests\CMakeFiles\test-json-partial.dir\get-model.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-json-partial.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-json-partial.dir\ + TARGET_FILE = bin\test-json-partial.exe + TARGET_IMPLIB = tests\test-json-partial.lib + TARGET_PDB = bin\test-json-partial.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-log + + +############################################# +# Order-only phony target for test-log + +build cmake_object_order_depends_target_test-log: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-log.dir\test-log.cpp.obj: CXX_COMPILER__test-log_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-log.cpp || cmake_object_order_depends_target_test-log + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-log.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-log.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-log.dir\ + TARGET_PDB = bin\test-log.pdb + +build tests\CMakeFiles\test-log.dir\get-model.cpp.obj: CXX_COMPILER__test-log_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-log + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-log.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-log.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-log.dir\ + TARGET_PDB = bin\test-log.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-log + + +############################################# +# Link the executable bin\test-log.exe + +build bin\test-log.exe: CXX_EXECUTABLE_LINKER__test-log_Debug tests\CMakeFiles\test-log.dir\test-log.cpp.obj tests\CMakeFiles\test-log.dir\get-model.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-log.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-log.dir\ + TARGET_FILE = bin\test-log.exe + TARGET_IMPLIB = tests\test-log.lib + TARGET_PDB = bin\test-log.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-regex-partial + + +############################################# +# Order-only phony target for test-regex-partial + +build cmake_object_order_depends_target_test-regex-partial: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-regex-partial.dir\test-regex-partial.cpp.obj: CXX_COMPILER__test-regex-partial_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-regex-partial.cpp || cmake_object_order_depends_target_test-regex-partial + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-regex-partial.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-regex-partial.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-regex-partial.dir\ + TARGET_PDB = bin\test-regex-partial.pdb + +build tests\CMakeFiles\test-regex-partial.dir\get-model.cpp.obj: CXX_COMPILER__test-regex-partial_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-regex-partial + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-regex-partial.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-regex-partial.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-regex-partial.dir\ + TARGET_PDB = bin\test-regex-partial.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-regex-partial + + +############################################# +# Link the executable bin\test-regex-partial.exe + +build bin\test-regex-partial.exe: CXX_EXECUTABLE_LINKER__test-regex-partial_Debug tests\CMakeFiles\test-regex-partial.dir\test-regex-partial.cpp.obj tests\CMakeFiles\test-regex-partial.dir\get-model.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-regex-partial.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-regex-partial.dir\ + TARGET_FILE = bin\test-regex-partial.exe + TARGET_IMPLIB = tests\test-regex-partial.lib + TARGET_PDB = bin\test-regex-partial.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-thread-safety + + +############################################# +# Order-only phony target for test-thread-safety + +build cmake_object_order_depends_target_test-thread-safety: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-thread-safety.dir\test-thread-safety.cpp.obj: CXX_COMPILER__test-thread-safety_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-thread-safety.cpp || cmake_object_order_depends_target_test-thread-safety + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-thread-safety.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-thread-safety.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-thread-safety.dir\ + TARGET_PDB = bin\test-thread-safety.pdb + +build tests\CMakeFiles\test-thread-safety.dir\get-model.cpp.obj: CXX_COMPILER__test-thread-safety_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-thread-safety + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-thread-safety.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-thread-safety.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-thread-safety.dir\ + TARGET_PDB = bin\test-thread-safety.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-thread-safety + + +############################################# +# Link the executable bin\test-thread-safety.exe + +build bin\test-thread-safety.exe: CXX_EXECUTABLE_LINKER__test-thread-safety_Debug tests\CMakeFiles\test-thread-safety.dir\test-thread-safety.cpp.obj tests\CMakeFiles\test-thread-safety.dir\get-model.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-thread-safety.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-thread-safety.dir\ + TARGET_FILE = bin\test-thread-safety.exe + TARGET_IMPLIB = tests\test-thread-safety.lib + TARGET_PDB = bin\test-thread-safety.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-gguf + + +############################################# +# Order-only phony target for test-gguf + +build cmake_object_order_depends_target_test-gguf: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-gguf.dir\test-gguf.cpp.obj: CXX_COMPILER__test-gguf_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-gguf.cpp || cmake_object_order_depends_target_test-gguf + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-gguf.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-gguf.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-gguf.dir\ + TARGET_PDB = bin\test-gguf.pdb + +build tests\CMakeFiles\test-gguf.dir\get-model.cpp.obj: CXX_COMPILER__test-gguf_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-gguf + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-gguf.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-gguf.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-gguf.dir\ + TARGET_PDB = bin\test-gguf.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-gguf + + +############################################# +# Link the executable bin\test-gguf.exe + +build bin\test-gguf.exe: CXX_EXECUTABLE_LINKER__test-gguf_Debug tests\CMakeFiles\test-gguf.dir\test-gguf.cpp.obj tests\CMakeFiles\test-gguf.dir\get-model.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-gguf.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-gguf.dir\ + TARGET_FILE = bin\test-gguf.exe + TARGET_IMPLIB = tests\test-gguf.lib + TARGET_PDB = bin\test-gguf.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-backend-ops + + +############################################# +# Order-only phony target for test-backend-ops + +build cmake_object_order_depends_target_test-backend-ops: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-backend-ops.dir\test-backend-ops.cpp.obj: CXX_COMPILER__test-backend-ops_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-backend-ops.cpp || cmake_object_order_depends_target_test-backend-ops + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-backend-ops.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-backend-ops.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-backend-ops.dir\ + TARGET_PDB = bin\test-backend-ops.pdb + +build tests\CMakeFiles\test-backend-ops.dir\get-model.cpp.obj: CXX_COMPILER__test-backend-ops_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-backend-ops + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-backend-ops.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-backend-ops.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-backend-ops.dir\ + TARGET_PDB = bin\test-backend-ops.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-backend-ops + + +############################################# +# Link the executable bin\test-backend-ops.exe + +build bin\test-backend-ops.exe: CXX_EXECUTABLE_LINKER__test-backend-ops_Debug tests\CMakeFiles\test-backend-ops.dir\test-backend-ops.cpp.obj tests\CMakeFiles\test-backend-ops.dir\get-model.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-backend-ops.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-backend-ops.dir\ + TARGET_FILE = bin\test-backend-ops.exe + TARGET_IMPLIB = tests\test-backend-ops.lib + TARGET_PDB = bin\test-backend-ops.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-model-load-cancel + + +############################################# +# Order-only phony target for test-model-load-cancel + +build cmake_object_order_depends_target_test-model-load-cancel: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-model-load-cancel.dir\test-model-load-cancel.cpp.obj: CXX_COMPILER__test-model-load-cancel_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-model-load-cancel.cpp || cmake_object_order_depends_target_test-model-load-cancel + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-model-load-cancel.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-model-load-cancel.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-model-load-cancel.dir\ + TARGET_PDB = bin\test-model-load-cancel.pdb + +build tests\CMakeFiles\test-model-load-cancel.dir\get-model.cpp.obj: CXX_COMPILER__test-model-load-cancel_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-model-load-cancel + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-model-load-cancel.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-model-load-cancel.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-model-load-cancel.dir\ + TARGET_PDB = bin\test-model-load-cancel.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-model-load-cancel + + +############################################# +# Link the executable bin\test-model-load-cancel.exe + +build bin\test-model-load-cancel.exe: CXX_EXECUTABLE_LINKER__test-model-load-cancel_Debug tests\CMakeFiles\test-model-load-cancel.dir\test-model-load-cancel.cpp.obj tests\CMakeFiles\test-model-load-cancel.dir\get-model.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-model-load-cancel.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-model-load-cancel.dir\ + TARGET_FILE = bin\test-model-load-cancel.exe + TARGET_IMPLIB = tests\test-model-load-cancel.lib + TARGET_PDB = bin\test-model-load-cancel.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-autorelease + + +############################################# +# Order-only phony target for test-autorelease + +build cmake_object_order_depends_target_test-autorelease: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-autorelease.dir\test-autorelease.cpp.obj: CXX_COMPILER__test-autorelease_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-autorelease.cpp || cmake_object_order_depends_target_test-autorelease + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-autorelease.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-autorelease.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-autorelease.dir\ + TARGET_PDB = bin\test-autorelease.pdb + +build tests\CMakeFiles\test-autorelease.dir\get-model.cpp.obj: CXX_COMPILER__test-autorelease_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-autorelease + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-autorelease.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-autorelease.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-autorelease.dir\ + TARGET_PDB = bin\test-autorelease.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-autorelease + + +############################################# +# Link the executable bin\test-autorelease.exe + +build bin\test-autorelease.exe: CXX_EXECUTABLE_LINKER__test-autorelease_Debug tests\CMakeFiles\test-autorelease.dir\test-autorelease.cpp.obj tests\CMakeFiles\test-autorelease.dir\get-model.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-autorelease.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-autorelease.dir\ + TARGET_FILE = bin\test-autorelease.exe + TARGET_IMPLIB = tests\test-autorelease.lib + TARGET_PDB = bin\test-autorelease.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-barrier + + +############################################# +# Order-only phony target for test-barrier + +build cmake_object_order_depends_target_test-barrier: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-barrier.dir\test-barrier.cpp.obj: CXX_COMPILER__test-barrier_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-barrier.cpp || cmake_object_order_depends_target_test-barrier + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-barrier.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-barrier.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-barrier.dir\ + TARGET_PDB = bin\test-barrier.pdb + +build tests\CMakeFiles\test-barrier.dir\get-model.cpp.obj: CXX_COMPILER__test-barrier_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-barrier + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-barrier.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-barrier.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-barrier.dir\ + TARGET_PDB = bin\test-barrier.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-barrier + + +############################################# +# Link the executable bin\test-barrier.exe + +build bin\test-barrier.exe: CXX_EXECUTABLE_LINKER__test-barrier_Debug tests\CMakeFiles\test-barrier.dir\test-barrier.cpp.obj tests\CMakeFiles\test-barrier.dir\get-model.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-barrier.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-barrier.dir\ + TARGET_FILE = bin\test-barrier.exe + TARGET_IMPLIB = tests\test-barrier.lib + TARGET_PDB = bin\test-barrier.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-quantize-fns + + +############################################# +# Order-only phony target for test-quantize-fns + +build cmake_object_order_depends_target_test-quantize-fns: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-quantize-fns.dir\test-quantize-fns.cpp.obj: CXX_COMPILER__test-quantize-fns_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-quantize-fns.cpp || cmake_object_order_depends_target_test-quantize-fns + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-quantize-fns.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-quantize-fns.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-quantize-fns.dir\ + TARGET_PDB = bin\test-quantize-fns.pdb + +build tests\CMakeFiles\test-quantize-fns.dir\get-model.cpp.obj: CXX_COMPILER__test-quantize-fns_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-quantize-fns + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-quantize-fns.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-quantize-fns.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-quantize-fns.dir\ + TARGET_PDB = bin\test-quantize-fns.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-quantize-fns + + +############################################# +# Link the executable bin\test-quantize-fns.exe + +build bin\test-quantize-fns.exe: CXX_EXECUTABLE_LINKER__test-quantize-fns_Debug tests\CMakeFiles\test-quantize-fns.dir\test-quantize-fns.cpp.obj tests\CMakeFiles\test-quantize-fns.dir\get-model.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-quantize-fns.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-quantize-fns.dir\ + TARGET_FILE = bin\test-quantize-fns.exe + TARGET_IMPLIB = tests\test-quantize-fns.lib + TARGET_PDB = bin\test-quantize-fns.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-quantize-perf + + +############################################# +# Order-only phony target for test-quantize-perf + +build cmake_object_order_depends_target_test-quantize-perf: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-quantize-perf.dir\test-quantize-perf.cpp.obj: CXX_COMPILER__test-quantize-perf_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-quantize-perf.cpp || cmake_object_order_depends_target_test-quantize-perf + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-quantize-perf.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-quantize-perf.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-quantize-perf.dir\ + TARGET_PDB = bin\test-quantize-perf.pdb + +build tests\CMakeFiles\test-quantize-perf.dir\get-model.cpp.obj: CXX_COMPILER__test-quantize-perf_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-quantize-perf + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-quantize-perf.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-quantize-perf.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-quantize-perf.dir\ + TARGET_PDB = bin\test-quantize-perf.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-quantize-perf + + +############################################# +# Link the executable bin\test-quantize-perf.exe + +build bin\test-quantize-perf.exe: CXX_EXECUTABLE_LINKER__test-quantize-perf_Debug tests\CMakeFiles\test-quantize-perf.dir\test-quantize-perf.cpp.obj tests\CMakeFiles\test-quantize-perf.dir\get-model.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-quantize-perf.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-quantize-perf.dir\ + TARGET_FILE = bin\test-quantize-perf.exe + TARGET_IMPLIB = tests\test-quantize-perf.lib + TARGET_PDB = bin\test-quantize-perf.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-rope + + +############################################# +# Order-only phony target for test-rope + +build cmake_object_order_depends_target_test-rope: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-rope.dir\test-rope.cpp.obj: CXX_COMPILER__test-rope_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-rope.cpp || cmake_object_order_depends_target_test-rope + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-rope.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-rope.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-rope.dir\ + TARGET_PDB = bin\test-rope.pdb + +build tests\CMakeFiles\test-rope.dir\get-model.cpp.obj: CXX_COMPILER__test-rope_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-rope + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-rope.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-rope.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-rope.dir\ + TARGET_PDB = bin\test-rope.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-rope + + +############################################# +# Link the executable bin\test-rope.exe + +build bin\test-rope.exe: CXX_EXECUTABLE_LINKER__test-rope_Debug tests\CMakeFiles\test-rope.dir\test-rope.cpp.obj tests\CMakeFiles\test-rope.dir\get-model.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-rope.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-rope.dir\ + TARGET_FILE = bin\test-rope.exe + TARGET_IMPLIB = tests\test-rope.lib + TARGET_PDB = bin\test-rope.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-mtmd-c-api + + +############################################# +# Order-only phony target for test-mtmd-c-api + +build cmake_object_order_depends_target_test-mtmd-c-api: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama cmake_object_order_depends_target_mtmd + +build tests\CMakeFiles\test-mtmd-c-api.dir\test-mtmd-c-api.c.obj: C_COMPILER__test-mtmd-c-api_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-mtmd-c-api.c || cmake_object_order_depends_target_test-mtmd-c-api + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\." + OBJECT_DIR = tests\CMakeFiles\test-mtmd-c-api.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-mtmd-c-api.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-mtmd-c-api.dir\ + TARGET_PDB = bin\test-mtmd-c-api.pdb + +build tests\CMakeFiles\test-mtmd-c-api.dir\get-model.cpp.obj: CXX_COMPILER__test-mtmd-c-api_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\get-model.cpp || cmake_object_order_depends_target_test-mtmd-c-api + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\." + OBJECT_DIR = tests\CMakeFiles\test-mtmd-c-api.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-mtmd-c-api.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-mtmd-c-api.dir\ + TARGET_PDB = bin\test-mtmd-c-api.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-mtmd-c-api + + +############################################# +# Link the executable bin\test-mtmd-c-api.exe + +build bin\test-mtmd-c-api.exe: CXX_EXECUTABLE_LINKER__test-mtmd-c-api_Debug tests\CMakeFiles\test-mtmd-c-api.dir\test-mtmd-c-api.c.obj tests\CMakeFiles\test-mtmd-c-api.dir\get-model.cpp.obj | common\common.lib tools\mtmd\mtmd.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll bin\mtmd.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib tools\mtmd\mtmd.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-mtmd-c-api.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-mtmd-c-api.dir\ + TARGET_FILE = bin\test-mtmd-c-api.exe + TARGET_IMPLIB = tests\test-mtmd-c-api.lib + TARGET_PDB = bin\test-mtmd-c-api.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target test-c + + +############################################# +# Order-only phony target for test-c + +build cmake_object_order_depends_target_test-c: phony || cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tests\CMakeFiles\test-c.dir\test-c.c.obj: C_COMPILER__test-c_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tests\test-c.c || cmake_object_order_depends_target_test-c + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tests\CMakeFiles\test-c.dir + OBJECT_FILE_DIR = tests\CMakeFiles\test-c.dir + TARGET_COMPILE_PDB = tests\CMakeFiles\test-c.dir\ + TARGET_PDB = bin\test-c.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target test-c + + +############################################# +# Link the executable bin\test-c.exe + +build bin\test-c.exe: C_EXECUTABLE_LINKER__test-c_Debug tests\CMakeFiles\test-c.dir\test-c.c.obj | src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tests\CMakeFiles\test-c.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tests\CMakeFiles\test-c.dir\ + TARGET_FILE = bin\test-c.exe + TARGET_IMPLIB = tests\test-c.lib + TARGET_PDB = bin\test-c.pdb + + +############################################# +# Utility command for test + +build tests\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tests" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tests\test: phony tests\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tests\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tests" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tests\edit_cache: phony tests\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tests\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tests" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tests\rebuild_cache: phony tests\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tests\list_install_components: phony + + +############################################# +# Utility command for install + +build tests\CMakeFiles\install.util: CUSTOM_COMMAND tests\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tests" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tests\install: phony tests\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tests\CMakeFiles\install\local.util: CUSTOM_COMMAND tests\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tests" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tests\install\local: phony tests\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/CMakeLists.txt +# ============================================================================= + + +############################################# +# Utility command for test + +build examples\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\test: phony examples\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\edit_cache: phony examples\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\rebuild_cache: phony examples\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\CMakeFiles\install.util: CUSTOM_COMMAND examples\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\install: phony examples\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\install\local: phony examples\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-batched + + +############################################# +# Order-only phony target for llama-batched + +build cmake_object_order_depends_target_llama-batched: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\batched\CMakeFiles\llama-batched.dir\batched.cpp.obj: CXX_COMPILER__llama-batched_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\batched\batched.cpp || cmake_object_order_depends_target_llama-batched + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\batched\CMakeFiles\llama-batched.dir + OBJECT_FILE_DIR = examples\batched\CMakeFiles\llama-batched.dir + TARGET_COMPILE_PDB = examples\batched\CMakeFiles\llama-batched.dir\ + TARGET_PDB = bin\llama-batched.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-batched + + +############################################# +# Link the executable bin\llama-batched.exe + +build bin\llama-batched.exe: CXX_EXECUTABLE_LINKER__llama-batched_Debug examples\batched\CMakeFiles\llama-batched.dir\batched.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\batched\CMakeFiles\llama-batched.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\batched\CMakeFiles\llama-batched.dir\ + TARGET_FILE = bin\llama-batched.exe + TARGET_IMPLIB = examples\batched\llama-batched.lib + TARGET_PDB = bin\llama-batched.pdb + + +############################################# +# Utility command for test + +build examples\batched\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\batched" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\batched\test: phony examples\batched\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\batched\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\batched" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\batched\edit_cache: phony examples\batched\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\batched\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\batched" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\batched\rebuild_cache: phony examples\batched\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\batched\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\batched\CMakeFiles\install.util: CUSTOM_COMMAND examples\batched\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\batched" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\batched\install: phony examples\batched\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\batched\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\batched\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\batched" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\batched\install\local: phony examples\batched\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-embedding + + +############################################# +# Order-only phony target for llama-embedding + +build cmake_object_order_depends_target_llama-embedding: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\embedding\CMakeFiles\llama-embedding.dir\embedding.cpp.obj: CXX_COMPILER__llama-embedding_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\embedding\embedding.cpp || cmake_object_order_depends_target_llama-embedding + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\embedding\CMakeFiles\llama-embedding.dir + OBJECT_FILE_DIR = examples\embedding\CMakeFiles\llama-embedding.dir + TARGET_COMPILE_PDB = examples\embedding\CMakeFiles\llama-embedding.dir\ + TARGET_PDB = bin\llama-embedding.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-embedding + + +############################################# +# Link the executable bin\llama-embedding.exe + +build bin\llama-embedding.exe: CXX_EXECUTABLE_LINKER__llama-embedding_Debug examples\embedding\CMakeFiles\llama-embedding.dir\embedding.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\embedding\CMakeFiles\llama-embedding.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\embedding\CMakeFiles\llama-embedding.dir\ + TARGET_FILE = bin\llama-embedding.exe + TARGET_IMPLIB = examples\embedding\llama-embedding.lib + TARGET_PDB = bin\llama-embedding.pdb + + +############################################# +# Utility command for test + +build examples\embedding\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\embedding" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\embedding\test: phony examples\embedding\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\embedding\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\embedding" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\embedding\edit_cache: phony examples\embedding\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\embedding\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\embedding" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\embedding\rebuild_cache: phony examples\embedding\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\embedding\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\embedding\CMakeFiles\install.util: CUSTOM_COMMAND examples\embedding\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\embedding" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\embedding\install: phony examples\embedding\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\embedding\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\embedding\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\embedding" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\embedding\install\local: phony examples\embedding\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-eval-callback + + +############################################# +# Order-only phony target for llama-eval-callback + +build cmake_object_order_depends_target_llama-eval-callback: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\eval-callback\CMakeFiles\llama-eval-callback.dir\eval-callback.cpp.obj: CXX_COMPILER__llama-eval-callback_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\eval-callback\eval-callback.cpp || cmake_object_order_depends_target_llama-eval-callback + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\eval-callback\CMakeFiles\llama-eval-callback.dir + OBJECT_FILE_DIR = examples\eval-callback\CMakeFiles\llama-eval-callback.dir + TARGET_COMPILE_PDB = examples\eval-callback\CMakeFiles\llama-eval-callback.dir\ + TARGET_PDB = bin\llama-eval-callback.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-eval-callback + + +############################################# +# Link the executable bin\llama-eval-callback.exe + +build bin\llama-eval-callback.exe: CXX_EXECUTABLE_LINKER__llama-eval-callback_Debug examples\eval-callback\CMakeFiles\llama-eval-callback.dir\eval-callback.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\eval-callback\CMakeFiles\llama-eval-callback.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\eval-callback\CMakeFiles\llama-eval-callback.dir\ + TARGET_FILE = bin\llama-eval-callback.exe + TARGET_IMPLIB = examples\eval-callback\llama-eval-callback.lib + TARGET_PDB = bin\llama-eval-callback.pdb + + +############################################# +# Utility command for test + +build examples\eval-callback\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\eval-callback" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\eval-callback\test: phony examples\eval-callback\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\eval-callback\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\eval-callback" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\eval-callback\edit_cache: phony examples\eval-callback\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\eval-callback\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\eval-callback" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\eval-callback\rebuild_cache: phony examples\eval-callback\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\eval-callback\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\eval-callback\CMakeFiles\install.util: CUSTOM_COMMAND examples\eval-callback\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\eval-callback" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\eval-callback\install: phony examples\eval-callback\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\eval-callback\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\eval-callback\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\eval-callback" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\eval-callback\install\local: phony examples\eval-callback\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-gguf-hash + + +############################################# +# Order-only phony target for llama-gguf-hash + +build cmake_object_order_depends_target_llama-gguf-hash: phony || cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_sha1 cmake_object_order_depends_target_sha256 cmake_object_order_depends_target_xxhash + +build examples\gguf-hash\CMakeFiles\llama-gguf-hash.dir\gguf-hash.cpp.obj: CXX_COMPILER__llama-gguf-hash_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\gguf-hash\gguf-hash.cpp || cmake_object_order_depends_target_llama-gguf-hash + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\examples\gguf-hash\deps" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\gguf-hash\CMakeFiles\llama-gguf-hash.dir + OBJECT_FILE_DIR = examples\gguf-hash\CMakeFiles\llama-gguf-hash.dir + TARGET_COMPILE_PDB = examples\gguf-hash\CMakeFiles\llama-gguf-hash.dir\ + TARGET_PDB = bin\llama-gguf-hash.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-gguf-hash + + +############################################# +# Link the executable bin\llama-gguf-hash.exe + +build bin\llama-gguf-hash.exe: CXX_EXECUTABLE_LINKER__llama-gguf-hash_Debug examples\gguf-hash\CMakeFiles\xxhash.dir\deps\xxhash\xxhash.c.obj examples\gguf-hash\CMakeFiles\sha1.dir\deps\sha1\sha1.c.obj examples\gguf-hash\CMakeFiles\sha256.dir\deps\sha256\sha256.c.obj examples\gguf-hash\CMakeFiles\llama-gguf-hash.dir\gguf-hash.cpp.obj | ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll examples\gguf-hash\sha1 examples\gguf-hash\sha256 examples\gguf-hash\xxhash + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\gguf-hash\CMakeFiles\llama-gguf-hash.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\gguf-hash\CMakeFiles\llama-gguf-hash.dir\ + TARGET_FILE = bin\llama-gguf-hash.exe + TARGET_IMPLIB = examples\gguf-hash\llama-gguf-hash.lib + TARGET_PDB = bin\llama-gguf-hash.pdb + +# ============================================================================= +# Object build statements for OBJECT_LIBRARY target xxhash + + +############################################# +# Order-only phony target for xxhash + +build cmake_object_order_depends_target_xxhash: phony || . + +build examples\gguf-hash\CMakeFiles\xxhash.dir\deps\xxhash\xxhash.c.obj: C_COMPILER__xxhash_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\gguf-hash\deps\xxhash\xxhash.c || cmake_object_order_depends_target_xxhash + CONFIG = Debug + DEFINES = -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\examples\gguf-hash\deps" + OBJECT_DIR = examples\gguf-hash\CMakeFiles\xxhash.dir + OBJECT_FILE_DIR = examples\gguf-hash\CMakeFiles\xxhash.dir\deps\xxhash + TARGET_COMPILE_PDB = examples\gguf-hash\CMakeFiles\xxhash.dir\ + TARGET_PDB = "" + + + +############################################# +# Object library xxhash + +build examples\gguf-hash\xxhash: phony examples\gguf-hash\CMakeFiles\xxhash.dir\deps\xxhash\xxhash.c.obj + +# ============================================================================= +# Object build statements for OBJECT_LIBRARY target sha1 + + +############################################# +# Order-only phony target for sha1 + +build cmake_object_order_depends_target_sha1: phony || . + +build examples\gguf-hash\CMakeFiles\sha1.dir\deps\sha1\sha1.c.obj: C_COMPILER__sha1_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\gguf-hash\deps\sha1\sha1.c || cmake_object_order_depends_target_sha1 + CONFIG = Debug + DEFINES = -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\examples\gguf-hash\deps" + OBJECT_DIR = examples\gguf-hash\CMakeFiles\sha1.dir + OBJECT_FILE_DIR = examples\gguf-hash\CMakeFiles\sha1.dir\deps\sha1 + TARGET_COMPILE_PDB = examples\gguf-hash\CMakeFiles\sha1.dir\ + TARGET_PDB = "" + + + +############################################# +# Object library sha1 + +build examples\gguf-hash\sha1: phony examples\gguf-hash\CMakeFiles\sha1.dir\deps\sha1\sha1.c.obj + +# ============================================================================= +# Object build statements for OBJECT_LIBRARY target sha256 + + +############################################# +# Order-only phony target for sha256 + +build cmake_object_order_depends_target_sha256: phony || . + +build examples\gguf-hash\CMakeFiles\sha256.dir\deps\sha256\sha256.c.obj: C_COMPILER__sha256_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\gguf-hash\deps\sha256\sha256.c || cmake_object_order_depends_target_sha256 + CONFIG = Debug + DEFINES = -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\examples\gguf-hash\deps" + OBJECT_DIR = examples\gguf-hash\CMakeFiles\sha256.dir + OBJECT_FILE_DIR = examples\gguf-hash\CMakeFiles\sha256.dir\deps\sha256 + TARGET_COMPILE_PDB = examples\gguf-hash\CMakeFiles\sha256.dir\ + TARGET_PDB = "" + + + +############################################# +# Object library sha256 + +build examples\gguf-hash\sha256: phony examples\gguf-hash\CMakeFiles\sha256.dir\deps\sha256\sha256.c.obj + + +############################################# +# Utility command for test + +build examples\gguf-hash\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gguf-hash" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\gguf-hash\test: phony examples\gguf-hash\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\gguf-hash\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gguf-hash" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\gguf-hash\edit_cache: phony examples\gguf-hash\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\gguf-hash\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gguf-hash" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\gguf-hash\rebuild_cache: phony examples\gguf-hash\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\gguf-hash\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\gguf-hash\CMakeFiles\install.util: CUSTOM_COMMAND examples\gguf-hash\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gguf-hash" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\gguf-hash\install: phony examples\gguf-hash\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\gguf-hash\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\gguf-hash\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gguf-hash" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\gguf-hash\install\local: phony examples\gguf-hash\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-gguf + + +############################################# +# Order-only phony target for llama-gguf + +build cmake_object_order_depends_target_llama-gguf: phony || cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu + +build examples\gguf\CMakeFiles\llama-gguf.dir\gguf.cpp.obj: CXX_COMPILER__llama-gguf_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\gguf\gguf.cpp || cmake_object_order_depends_target_llama-gguf + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\gguf\CMakeFiles\llama-gguf.dir + OBJECT_FILE_DIR = examples\gguf\CMakeFiles\llama-gguf.dir + TARGET_COMPILE_PDB = examples\gguf\CMakeFiles\llama-gguf.dir\ + TARGET_PDB = bin\llama-gguf.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-gguf + + +############################################# +# Link the executable bin\llama-gguf.exe + +build bin\llama-gguf.exe: CXX_EXECUTABLE_LINKER__llama-gguf_Debug examples\gguf\CMakeFiles\llama-gguf.dir\gguf.cpp.obj | ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\gguf\CMakeFiles\llama-gguf.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\gguf\CMakeFiles\llama-gguf.dir\ + TARGET_FILE = bin\llama-gguf.exe + TARGET_IMPLIB = examples\gguf\llama-gguf.lib + TARGET_PDB = bin\llama-gguf.pdb + + +############################################# +# Utility command for test + +build examples\gguf\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gguf" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\gguf\test: phony examples\gguf\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\gguf\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gguf" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\gguf\edit_cache: phony examples\gguf\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\gguf\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gguf" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\gguf\rebuild_cache: phony examples\gguf\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\gguf\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\gguf\CMakeFiles\install.util: CUSTOM_COMMAND examples\gguf\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gguf" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\gguf\install: phony examples\gguf\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\gguf\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\gguf\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gguf" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\gguf\install\local: phony examples\gguf\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-gritlm + + +############################################# +# Order-only phony target for llama-gritlm + +build cmake_object_order_depends_target_llama-gritlm: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\gritlm\CMakeFiles\llama-gritlm.dir\gritlm.cpp.obj: CXX_COMPILER__llama-gritlm_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\gritlm\gritlm.cpp || cmake_object_order_depends_target_llama-gritlm + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\gritlm\CMakeFiles\llama-gritlm.dir + OBJECT_FILE_DIR = examples\gritlm\CMakeFiles\llama-gritlm.dir + TARGET_COMPILE_PDB = examples\gritlm\CMakeFiles\llama-gritlm.dir\ + TARGET_PDB = bin\llama-gritlm.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-gritlm + + +############################################# +# Link the executable bin\llama-gritlm.exe + +build bin\llama-gritlm.exe: CXX_EXECUTABLE_LINKER__llama-gritlm_Debug examples\gritlm\CMakeFiles\llama-gritlm.dir\gritlm.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\gritlm\CMakeFiles\llama-gritlm.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\gritlm\CMakeFiles\llama-gritlm.dir\ + TARGET_FILE = bin\llama-gritlm.exe + TARGET_IMPLIB = examples\gritlm\llama-gritlm.lib + TARGET_PDB = bin\llama-gritlm.pdb + + +############################################# +# Utility command for test + +build examples\gritlm\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gritlm" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\gritlm\test: phony examples\gritlm\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\gritlm\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gritlm" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\gritlm\edit_cache: phony examples\gritlm\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\gritlm\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gritlm" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\gritlm\rebuild_cache: phony examples\gritlm\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\gritlm\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\gritlm\CMakeFiles\install.util: CUSTOM_COMMAND examples\gritlm\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gritlm" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\gritlm\install: phony examples\gritlm\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\gritlm\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\gritlm\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gritlm" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\gritlm\install\local: phony examples\gritlm\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-lookahead + + +############################################# +# Order-only phony target for llama-lookahead + +build cmake_object_order_depends_target_llama-lookahead: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\lookahead\CMakeFiles\llama-lookahead.dir\lookahead.cpp.obj: CXX_COMPILER__llama-lookahead_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\lookahead\lookahead.cpp || cmake_object_order_depends_target_llama-lookahead + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\lookahead\CMakeFiles\llama-lookahead.dir + OBJECT_FILE_DIR = examples\lookahead\CMakeFiles\llama-lookahead.dir + TARGET_COMPILE_PDB = examples\lookahead\CMakeFiles\llama-lookahead.dir\ + TARGET_PDB = bin\llama-lookahead.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-lookahead + + +############################################# +# Link the executable bin\llama-lookahead.exe + +build bin\llama-lookahead.exe: CXX_EXECUTABLE_LINKER__llama-lookahead_Debug examples\lookahead\CMakeFiles\llama-lookahead.dir\lookahead.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\lookahead\CMakeFiles\llama-lookahead.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\lookahead\CMakeFiles\llama-lookahead.dir\ + TARGET_FILE = bin\llama-lookahead.exe + TARGET_IMPLIB = examples\lookahead\llama-lookahead.lib + TARGET_PDB = bin\llama-lookahead.pdb + + +############################################# +# Utility command for test + +build examples\lookahead\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\lookahead" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\lookahead\test: phony examples\lookahead\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\lookahead\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\lookahead" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\lookahead\edit_cache: phony examples\lookahead\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\lookahead\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\lookahead" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\lookahead\rebuild_cache: phony examples\lookahead\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\lookahead\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\lookahead\CMakeFiles\install.util: CUSTOM_COMMAND examples\lookahead\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\lookahead" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\lookahead\install: phony examples\lookahead\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\lookahead\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\lookahead\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\lookahead" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\lookahead\install\local: phony examples\lookahead\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-lookup + + +############################################# +# Order-only phony target for llama-lookup + +build cmake_object_order_depends_target_llama-lookup: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\lookup\CMakeFiles\llama-lookup.dir\lookup.cpp.obj: CXX_COMPILER__llama-lookup_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\lookup\lookup.cpp || cmake_object_order_depends_target_llama-lookup + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\lookup\CMakeFiles\llama-lookup.dir + OBJECT_FILE_DIR = examples\lookup\CMakeFiles\llama-lookup.dir + TARGET_COMPILE_PDB = examples\lookup\CMakeFiles\llama-lookup.dir\ + TARGET_PDB = bin\llama-lookup.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-lookup + + +############################################# +# Link the executable bin\llama-lookup.exe + +build bin\llama-lookup.exe: CXX_EXECUTABLE_LINKER__llama-lookup_Debug examples\lookup\CMakeFiles\llama-lookup.dir\lookup.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\lookup\CMakeFiles\llama-lookup.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\lookup\CMakeFiles\llama-lookup.dir\ + TARGET_FILE = bin\llama-lookup.exe + TARGET_IMPLIB = examples\lookup\llama-lookup.lib + TARGET_PDB = bin\llama-lookup.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-lookup-create + + +############################################# +# Order-only phony target for llama-lookup-create + +build cmake_object_order_depends_target_llama-lookup-create: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\lookup\CMakeFiles\llama-lookup-create.dir\lookup-create.cpp.obj: CXX_COMPILER__llama-lookup-create_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\lookup\lookup-create.cpp || cmake_object_order_depends_target_llama-lookup-create + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\lookup\CMakeFiles\llama-lookup-create.dir + OBJECT_FILE_DIR = examples\lookup\CMakeFiles\llama-lookup-create.dir + TARGET_COMPILE_PDB = examples\lookup\CMakeFiles\llama-lookup-create.dir\ + TARGET_PDB = bin\llama-lookup-create.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-lookup-create + + +############################################# +# Link the executable bin\llama-lookup-create.exe + +build bin\llama-lookup-create.exe: CXX_EXECUTABLE_LINKER__llama-lookup-create_Debug examples\lookup\CMakeFiles\llama-lookup-create.dir\lookup-create.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\lookup\CMakeFiles\llama-lookup-create.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\lookup\CMakeFiles\llama-lookup-create.dir\ + TARGET_FILE = bin\llama-lookup-create.exe + TARGET_IMPLIB = examples\lookup\llama-lookup-create.lib + TARGET_PDB = bin\llama-lookup-create.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-lookup-merge + + +############################################# +# Order-only phony target for llama-lookup-merge + +build cmake_object_order_depends_target_llama-lookup-merge: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\lookup\CMakeFiles\llama-lookup-merge.dir\lookup-merge.cpp.obj: CXX_COMPILER__llama-lookup-merge_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\lookup\lookup-merge.cpp || cmake_object_order_depends_target_llama-lookup-merge + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\lookup\CMakeFiles\llama-lookup-merge.dir + OBJECT_FILE_DIR = examples\lookup\CMakeFiles\llama-lookup-merge.dir + TARGET_COMPILE_PDB = examples\lookup\CMakeFiles\llama-lookup-merge.dir\ + TARGET_PDB = bin\llama-lookup-merge.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-lookup-merge + + +############################################# +# Link the executable bin\llama-lookup-merge.exe + +build bin\llama-lookup-merge.exe: CXX_EXECUTABLE_LINKER__llama-lookup-merge_Debug examples\lookup\CMakeFiles\llama-lookup-merge.dir\lookup-merge.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\lookup\CMakeFiles\llama-lookup-merge.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\lookup\CMakeFiles\llama-lookup-merge.dir\ + TARGET_FILE = bin\llama-lookup-merge.exe + TARGET_IMPLIB = examples\lookup\llama-lookup-merge.lib + TARGET_PDB = bin\llama-lookup-merge.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-lookup-stats + + +############################################# +# Order-only phony target for llama-lookup-stats + +build cmake_object_order_depends_target_llama-lookup-stats: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\lookup\CMakeFiles\llama-lookup-stats.dir\lookup-stats.cpp.obj: CXX_COMPILER__llama-lookup-stats_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\lookup\lookup-stats.cpp || cmake_object_order_depends_target_llama-lookup-stats + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\lookup\CMakeFiles\llama-lookup-stats.dir + OBJECT_FILE_DIR = examples\lookup\CMakeFiles\llama-lookup-stats.dir + TARGET_COMPILE_PDB = examples\lookup\CMakeFiles\llama-lookup-stats.dir\ + TARGET_PDB = bin\llama-lookup-stats.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-lookup-stats + + +############################################# +# Link the executable bin\llama-lookup-stats.exe + +build bin\llama-lookup-stats.exe: CXX_EXECUTABLE_LINKER__llama-lookup-stats_Debug examples\lookup\CMakeFiles\llama-lookup-stats.dir\lookup-stats.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\lookup\CMakeFiles\llama-lookup-stats.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\lookup\CMakeFiles\llama-lookup-stats.dir\ + TARGET_FILE = bin\llama-lookup-stats.exe + TARGET_IMPLIB = examples\lookup\llama-lookup-stats.lib + TARGET_PDB = bin\llama-lookup-stats.pdb + + +############################################# +# Utility command for test + +build examples\lookup\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\lookup" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\lookup\test: phony examples\lookup\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\lookup\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\lookup" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\lookup\edit_cache: phony examples\lookup\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\lookup\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\lookup" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\lookup\rebuild_cache: phony examples\lookup\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\lookup\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\lookup\CMakeFiles\install.util: CUSTOM_COMMAND examples\lookup\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\lookup" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\lookup\install: phony examples\lookup\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\lookup\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\lookup\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\lookup" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\lookup\install\local: phony examples\lookup\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-parallel + + +############################################# +# Order-only phony target for llama-parallel + +build cmake_object_order_depends_target_llama-parallel: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\parallel\CMakeFiles\llama-parallel.dir\parallel.cpp.obj: CXX_COMPILER__llama-parallel_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\parallel\parallel.cpp || cmake_object_order_depends_target_llama-parallel + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\parallel\CMakeFiles\llama-parallel.dir + OBJECT_FILE_DIR = examples\parallel\CMakeFiles\llama-parallel.dir + TARGET_COMPILE_PDB = examples\parallel\CMakeFiles\llama-parallel.dir\ + TARGET_PDB = bin\llama-parallel.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-parallel + + +############################################# +# Link the executable bin\llama-parallel.exe + +build bin\llama-parallel.exe: CXX_EXECUTABLE_LINKER__llama-parallel_Debug examples\parallel\CMakeFiles\llama-parallel.dir\parallel.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\parallel\CMakeFiles\llama-parallel.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\parallel\CMakeFiles\llama-parallel.dir\ + TARGET_FILE = bin\llama-parallel.exe + TARGET_IMPLIB = examples\parallel\llama-parallel.lib + TARGET_PDB = bin\llama-parallel.pdb + + +############################################# +# Utility command for test + +build examples\parallel\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\parallel" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\parallel\test: phony examples\parallel\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\parallel\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\parallel" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\parallel\edit_cache: phony examples\parallel\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\parallel\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\parallel" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\parallel\rebuild_cache: phony examples\parallel\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\parallel\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\parallel\CMakeFiles\install.util: CUSTOM_COMMAND examples\parallel\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\parallel" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\parallel\install: phony examples\parallel\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\parallel\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\parallel\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\parallel" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\parallel\install\local: phony examples\parallel\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-passkey + + +############################################# +# Order-only phony target for llama-passkey + +build cmake_object_order_depends_target_llama-passkey: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\passkey\CMakeFiles\llama-passkey.dir\passkey.cpp.obj: CXX_COMPILER__llama-passkey_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\passkey\passkey.cpp || cmake_object_order_depends_target_llama-passkey + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\passkey\CMakeFiles\llama-passkey.dir + OBJECT_FILE_DIR = examples\passkey\CMakeFiles\llama-passkey.dir + TARGET_COMPILE_PDB = examples\passkey\CMakeFiles\llama-passkey.dir\ + TARGET_PDB = bin\llama-passkey.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-passkey + + +############################################# +# Link the executable bin\llama-passkey.exe + +build bin\llama-passkey.exe: CXX_EXECUTABLE_LINKER__llama-passkey_Debug examples\passkey\CMakeFiles\llama-passkey.dir\passkey.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\passkey\CMakeFiles\llama-passkey.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\passkey\CMakeFiles\llama-passkey.dir\ + TARGET_FILE = bin\llama-passkey.exe + TARGET_IMPLIB = examples\passkey\llama-passkey.lib + TARGET_PDB = bin\llama-passkey.pdb + + +############################################# +# Utility command for test + +build examples\passkey\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\passkey" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\passkey\test: phony examples\passkey\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\passkey\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\passkey" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\passkey\edit_cache: phony examples\passkey\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\passkey\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\passkey" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\passkey\rebuild_cache: phony examples\passkey\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\passkey\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\passkey\CMakeFiles\install.util: CUSTOM_COMMAND examples\passkey\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\passkey" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\passkey\install: phony examples\passkey\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\passkey\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\passkey\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\passkey" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\passkey\install\local: phony examples\passkey\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-retrieval + + +############################################# +# Order-only phony target for llama-retrieval + +build cmake_object_order_depends_target_llama-retrieval: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\retrieval\CMakeFiles\llama-retrieval.dir\retrieval.cpp.obj: CXX_COMPILER__llama-retrieval_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\retrieval\retrieval.cpp || cmake_object_order_depends_target_llama-retrieval + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\retrieval\CMakeFiles\llama-retrieval.dir + OBJECT_FILE_DIR = examples\retrieval\CMakeFiles\llama-retrieval.dir + TARGET_COMPILE_PDB = examples\retrieval\CMakeFiles\llama-retrieval.dir\ + TARGET_PDB = bin\llama-retrieval.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-retrieval + + +############################################# +# Link the executable bin\llama-retrieval.exe + +build bin\llama-retrieval.exe: CXX_EXECUTABLE_LINKER__llama-retrieval_Debug examples\retrieval\CMakeFiles\llama-retrieval.dir\retrieval.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\retrieval\CMakeFiles\llama-retrieval.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\retrieval\CMakeFiles\llama-retrieval.dir\ + TARGET_FILE = bin\llama-retrieval.exe + TARGET_IMPLIB = examples\retrieval\llama-retrieval.lib + TARGET_PDB = bin\llama-retrieval.pdb + + +############################################# +# Utility command for test + +build examples\retrieval\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\retrieval" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\retrieval\test: phony examples\retrieval\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\retrieval\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\retrieval" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\retrieval\edit_cache: phony examples\retrieval\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\retrieval\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\retrieval" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\retrieval\rebuild_cache: phony examples\retrieval\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\retrieval\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\retrieval\CMakeFiles\install.util: CUSTOM_COMMAND examples\retrieval\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\retrieval" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\retrieval\install: phony examples\retrieval\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\retrieval\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\retrieval\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\retrieval" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\retrieval\install\local: phony examples\retrieval\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-save-load-state + + +############################################# +# Order-only phony target for llama-save-load-state + +build cmake_object_order_depends_target_llama-save-load-state: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\save-load-state\CMakeFiles\llama-save-load-state.dir\save-load-state.cpp.obj: CXX_COMPILER__llama-save-load-state_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\save-load-state\save-load-state.cpp || cmake_object_order_depends_target_llama-save-load-state + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\save-load-state\CMakeFiles\llama-save-load-state.dir + OBJECT_FILE_DIR = examples\save-load-state\CMakeFiles\llama-save-load-state.dir + TARGET_COMPILE_PDB = examples\save-load-state\CMakeFiles\llama-save-load-state.dir\ + TARGET_PDB = bin\llama-save-load-state.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-save-load-state + + +############################################# +# Link the executable bin\llama-save-load-state.exe + +build bin\llama-save-load-state.exe: CXX_EXECUTABLE_LINKER__llama-save-load-state_Debug examples\save-load-state\CMakeFiles\llama-save-load-state.dir\save-load-state.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\save-load-state\CMakeFiles\llama-save-load-state.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\save-load-state\CMakeFiles\llama-save-load-state.dir\ + TARGET_FILE = bin\llama-save-load-state.exe + TARGET_IMPLIB = examples\save-load-state\llama-save-load-state.lib + TARGET_PDB = bin\llama-save-load-state.pdb + + +############################################# +# Utility command for test + +build examples\save-load-state\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\save-load-state" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\save-load-state\test: phony examples\save-load-state\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\save-load-state\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\save-load-state" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\save-load-state\edit_cache: phony examples\save-load-state\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\save-load-state\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\save-load-state" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\save-load-state\rebuild_cache: phony examples\save-load-state\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\save-load-state\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\save-load-state\CMakeFiles\install.util: CUSTOM_COMMAND examples\save-load-state\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\save-load-state" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\save-load-state\install: phony examples\save-load-state\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\save-load-state\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\save-load-state\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\save-load-state" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\save-load-state\install\local: phony examples\save-load-state\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-simple + + +############################################# +# Order-only phony target for llama-simple + +build cmake_object_order_depends_target_llama-simple: phony || cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\simple\CMakeFiles\llama-simple.dir\simple.cpp.obj: CXX_COMPILER__llama-simple_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\simple\simple.cpp || cmake_object_order_depends_target_llama-simple + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\simple\CMakeFiles\llama-simple.dir + OBJECT_FILE_DIR = examples\simple\CMakeFiles\llama-simple.dir + TARGET_COMPILE_PDB = examples\simple\CMakeFiles\llama-simple.dir\ + TARGET_PDB = bin\llama-simple.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-simple + + +############################################# +# Link the executable bin\llama-simple.exe + +build bin\llama-simple.exe: CXX_EXECUTABLE_LINKER__llama-simple_Debug examples\simple\CMakeFiles\llama-simple.dir\simple.cpp.obj | src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\simple\CMakeFiles\llama-simple.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\simple\CMakeFiles\llama-simple.dir\ + TARGET_FILE = bin\llama-simple.exe + TARGET_IMPLIB = examples\simple\llama-simple.lib + TARGET_PDB = bin\llama-simple.pdb + + +############################################# +# Utility command for test + +build examples\simple\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\simple" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\simple\test: phony examples\simple\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\simple\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\simple" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\simple\edit_cache: phony examples\simple\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\simple\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\simple" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\simple\rebuild_cache: phony examples\simple\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\simple\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\simple\CMakeFiles\install.util: CUSTOM_COMMAND examples\simple\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\simple" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\simple\install: phony examples\simple\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\simple\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\simple\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\simple" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\simple\install\local: phony examples\simple\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-simple-chat + + +############################################# +# Order-only phony target for llama-simple-chat + +build cmake_object_order_depends_target_llama-simple-chat: phony || cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\simple-chat\CMakeFiles\llama-simple-chat.dir\simple-chat.cpp.obj: CXX_COMPILER__llama-simple-chat_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\simple-chat\simple-chat.cpp || cmake_object_order_depends_target_llama-simple-chat + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\simple-chat\CMakeFiles\llama-simple-chat.dir + OBJECT_FILE_DIR = examples\simple-chat\CMakeFiles\llama-simple-chat.dir + TARGET_COMPILE_PDB = examples\simple-chat\CMakeFiles\llama-simple-chat.dir\ + TARGET_PDB = bin\llama-simple-chat.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-simple-chat + + +############################################# +# Link the executable bin\llama-simple-chat.exe + +build bin\llama-simple-chat.exe: CXX_EXECUTABLE_LINKER__llama-simple-chat_Debug examples\simple-chat\CMakeFiles\llama-simple-chat.dir\simple-chat.cpp.obj | src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\simple-chat\CMakeFiles\llama-simple-chat.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\simple-chat\CMakeFiles\llama-simple-chat.dir\ + TARGET_FILE = bin\llama-simple-chat.exe + TARGET_IMPLIB = examples\simple-chat\llama-simple-chat.lib + TARGET_PDB = bin\llama-simple-chat.pdb + + +############################################# +# Utility command for test + +build examples\simple-chat\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\simple-chat" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\simple-chat\test: phony examples\simple-chat\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\simple-chat\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\simple-chat" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\simple-chat\edit_cache: phony examples\simple-chat\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\simple-chat\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\simple-chat" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\simple-chat\rebuild_cache: phony examples\simple-chat\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\simple-chat\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\simple-chat\CMakeFiles\install.util: CUSTOM_COMMAND examples\simple-chat\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\simple-chat" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\simple-chat\install: phony examples\simple-chat\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\simple-chat\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\simple-chat\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\simple-chat" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\simple-chat\install\local: phony examples\simple-chat\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-speculative + + +############################################# +# Order-only phony target for llama-speculative + +build cmake_object_order_depends_target_llama-speculative: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\speculative\CMakeFiles\llama-speculative.dir\speculative.cpp.obj: CXX_COMPILER__llama-speculative_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\speculative\speculative.cpp || cmake_object_order_depends_target_llama-speculative + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\speculative\CMakeFiles\llama-speculative.dir + OBJECT_FILE_DIR = examples\speculative\CMakeFiles\llama-speculative.dir + TARGET_COMPILE_PDB = examples\speculative\CMakeFiles\llama-speculative.dir\ + TARGET_PDB = bin\llama-speculative.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-speculative + + +############################################# +# Link the executable bin\llama-speculative.exe + +build bin\llama-speculative.exe: CXX_EXECUTABLE_LINKER__llama-speculative_Debug examples\speculative\CMakeFiles\llama-speculative.dir\speculative.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\speculative\CMakeFiles\llama-speculative.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\speculative\CMakeFiles\llama-speculative.dir\ + TARGET_FILE = bin\llama-speculative.exe + TARGET_IMPLIB = examples\speculative\llama-speculative.lib + TARGET_PDB = bin\llama-speculative.pdb + + +############################################# +# Utility command for test + +build examples\speculative\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\speculative" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\speculative\test: phony examples\speculative\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\speculative\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\speculative" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\speculative\edit_cache: phony examples\speculative\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\speculative\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\speculative" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\speculative\rebuild_cache: phony examples\speculative\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\speculative\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\speculative\CMakeFiles\install.util: CUSTOM_COMMAND examples\speculative\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\speculative" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\speculative\install: phony examples\speculative\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\speculative\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\speculative\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\speculative" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\speculative\install\local: phony examples\speculative\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-speculative-simple + + +############################################# +# Order-only phony target for llama-speculative-simple + +build cmake_object_order_depends_target_llama-speculative-simple: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\speculative-simple\CMakeFiles\llama-speculative-simple.dir\speculative-simple.cpp.obj: CXX_COMPILER__llama-speculative-simple_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\speculative-simple\speculative-simple.cpp || cmake_object_order_depends_target_llama-speculative-simple + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\speculative-simple\CMakeFiles\llama-speculative-simple.dir + OBJECT_FILE_DIR = examples\speculative-simple\CMakeFiles\llama-speculative-simple.dir + TARGET_COMPILE_PDB = examples\speculative-simple\CMakeFiles\llama-speculative-simple.dir\ + TARGET_PDB = bin\llama-speculative-simple.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-speculative-simple + + +############################################# +# Link the executable bin\llama-speculative-simple.exe + +build bin\llama-speculative-simple.exe: CXX_EXECUTABLE_LINKER__llama-speculative-simple_Debug examples\speculative-simple\CMakeFiles\llama-speculative-simple.dir\speculative-simple.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\speculative-simple\CMakeFiles\llama-speculative-simple.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\speculative-simple\CMakeFiles\llama-speculative-simple.dir\ + TARGET_FILE = bin\llama-speculative-simple.exe + TARGET_IMPLIB = examples\speculative-simple\llama-speculative-simple.lib + TARGET_PDB = bin\llama-speculative-simple.pdb + + +############################################# +# Utility command for test + +build examples\speculative-simple\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\speculative-simple" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\speculative-simple\test: phony examples\speculative-simple\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\speculative-simple\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\speculative-simple" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\speculative-simple\edit_cache: phony examples\speculative-simple\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\speculative-simple\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\speculative-simple" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\speculative-simple\rebuild_cache: phony examples\speculative-simple\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\speculative-simple\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\speculative-simple\CMakeFiles\install.util: CUSTOM_COMMAND examples\speculative-simple\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\speculative-simple" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\speculative-simple\install: phony examples\speculative-simple\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\speculative-simple\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\speculative-simple\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\speculative-simple" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\speculative-simple\install\local: phony examples\speculative-simple\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-gen-docs + + +############################################# +# Order-only phony target for llama-gen-docs + +build cmake_object_order_depends_target_llama-gen-docs: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\gen-docs\CMakeFiles\llama-gen-docs.dir\gen-docs.cpp.obj: CXX_COMPILER__llama-gen-docs_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\gen-docs\gen-docs.cpp || cmake_object_order_depends_target_llama-gen-docs + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\gen-docs\CMakeFiles\llama-gen-docs.dir + OBJECT_FILE_DIR = examples\gen-docs\CMakeFiles\llama-gen-docs.dir + TARGET_COMPILE_PDB = examples\gen-docs\CMakeFiles\llama-gen-docs.dir\ + TARGET_PDB = bin\llama-gen-docs.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-gen-docs + + +############################################# +# Link the executable bin\llama-gen-docs.exe + +build bin\llama-gen-docs.exe: CXX_EXECUTABLE_LINKER__llama-gen-docs_Debug examples\gen-docs\CMakeFiles\llama-gen-docs.dir\gen-docs.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\gen-docs\CMakeFiles\llama-gen-docs.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\gen-docs\CMakeFiles\llama-gen-docs.dir\ + TARGET_FILE = bin\llama-gen-docs.exe + TARGET_IMPLIB = examples\gen-docs\llama-gen-docs.lib + TARGET_PDB = bin\llama-gen-docs.pdb + + +############################################# +# Utility command for test + +build examples\gen-docs\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gen-docs" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\gen-docs\test: phony examples\gen-docs\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\gen-docs\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gen-docs" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\gen-docs\edit_cache: phony examples\gen-docs\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\gen-docs\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gen-docs" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\gen-docs\rebuild_cache: phony examples\gen-docs\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\gen-docs\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\gen-docs\CMakeFiles\install.util: CUSTOM_COMMAND examples\gen-docs\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gen-docs" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\gen-docs\install: phony examples\gen-docs\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\gen-docs\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\gen-docs\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\gen-docs" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\gen-docs\install\local: phony examples\gen-docs\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-finetune + + +############################################# +# Order-only phony target for llama-finetune + +build cmake_object_order_depends_target_llama-finetune: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\training\CMakeFiles\llama-finetune.dir\finetune.cpp.obj: CXX_COMPILER__llama-finetune_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\training\finetune.cpp || cmake_object_order_depends_target_llama-finetune + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\training\CMakeFiles\llama-finetune.dir + OBJECT_FILE_DIR = examples\training\CMakeFiles\llama-finetune.dir + TARGET_COMPILE_PDB = examples\training\CMakeFiles\llama-finetune.dir\ + TARGET_PDB = bin\llama-finetune.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-finetune + + +############################################# +# Link the executable bin\llama-finetune.exe + +build bin\llama-finetune.exe: CXX_EXECUTABLE_LINKER__llama-finetune_Debug examples\training\CMakeFiles\llama-finetune.dir\finetune.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\training\CMakeFiles\llama-finetune.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\training\CMakeFiles\llama-finetune.dir\ + TARGET_FILE = bin\llama-finetune.exe + TARGET_IMPLIB = examples\training\llama-finetune.lib + TARGET_PDB = bin\llama-finetune.pdb + + +############################################# +# Utility command for test + +build examples\training\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\training" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\training\test: phony examples\training\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\training\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\training" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\training\edit_cache: phony examples\training\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\training\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\training" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\training\rebuild_cache: phony examples\training\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\training\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\training\CMakeFiles\install.util: CUSTOM_COMMAND examples\training\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\training" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\training\install: phony examples\training\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\training\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\training\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\training" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\training\install\local: phony examples\training\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-diffusion-cli + + +############################################# +# Order-only phony target for llama-diffusion-cli + +build cmake_object_order_depends_target_llama-diffusion-cli: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\diffusion\CMakeFiles\llama-diffusion-cli.dir\diffusion-cli.cpp.obj: CXX_COMPILER__llama-diffusion-cli_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\diffusion\diffusion-cli.cpp || cmake_object_order_depends_target_llama-diffusion-cli + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" + OBJECT_DIR = examples\diffusion\CMakeFiles\llama-diffusion-cli.dir + OBJECT_FILE_DIR = examples\diffusion\CMakeFiles\llama-diffusion-cli.dir + TARGET_COMPILE_PDB = examples\diffusion\CMakeFiles\llama-diffusion-cli.dir\ + TARGET_PDB = bin\llama-diffusion-cli.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-diffusion-cli + + +############################################# +# Link the executable bin\llama-diffusion-cli.exe + +build bin\llama-diffusion-cli.exe: CXX_EXECUTABLE_LINKER__llama-diffusion-cli_Debug examples\diffusion\CMakeFiles\llama-diffusion-cli.dir\diffusion-cli.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\diffusion\CMakeFiles\llama-diffusion-cli.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\diffusion\CMakeFiles\llama-diffusion-cli.dir\ + TARGET_FILE = bin\llama-diffusion-cli.exe + TARGET_IMPLIB = examples\diffusion\llama-diffusion-cli.lib + TARGET_PDB = bin\llama-diffusion-cli.pdb + + +############################################# +# Utility command for test + +build examples\diffusion\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\diffusion" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\diffusion\test: phony examples\diffusion\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\diffusion\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\diffusion" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\diffusion\edit_cache: phony examples\diffusion\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\diffusion\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\diffusion" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\diffusion\rebuild_cache: phony examples\diffusion\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\diffusion\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\diffusion\CMakeFiles\install.util: CUSTOM_COMMAND examples\diffusion\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\diffusion" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\diffusion\install: phony examples\diffusion\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\diffusion\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\diffusion\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\diffusion" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\diffusion\install\local: phony examples\diffusion\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-convert-llama2c-to-ggml + + +############################################# +# Order-only phony target for llama-convert-llama2c-to-ggml + +build cmake_object_order_depends_target_llama-convert-llama2c-to-ggml: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build examples\convert-llama2c-to-ggml\CMakeFiles\llama-convert-llama2c-to-ggml.dir\convert-llama2c-to-ggml.cpp.obj: CXX_COMPILER__llama-convert-llama2c-to-ggml_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\examples\convert-llama2c-to-ggml\convert-llama2c-to-ggml.cpp || cmake_object_order_depends_target_llama-convert-llama2c-to-ggml + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = examples\convert-llama2c-to-ggml\CMakeFiles\llama-convert-llama2c-to-ggml.dir + OBJECT_FILE_DIR = examples\convert-llama2c-to-ggml\CMakeFiles\llama-convert-llama2c-to-ggml.dir + TARGET_COMPILE_PDB = examples\convert-llama2c-to-ggml\CMakeFiles\llama-convert-llama2c-to-ggml.dir\ + TARGET_PDB = bin\llama-convert-llama2c-to-ggml.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-convert-llama2c-to-ggml + + +############################################# +# Link the executable bin\llama-convert-llama2c-to-ggml.exe + +build bin\llama-convert-llama2c-to-ggml.exe: CXX_EXECUTABLE_LINKER__llama-convert-llama2c-to-ggml_Debug examples\convert-llama2c-to-ggml\CMakeFiles\llama-convert-llama2c-to-ggml.dir\convert-llama2c-to-ggml.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = examples\convert-llama2c-to-ggml\CMakeFiles\llama-convert-llama2c-to-ggml.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = examples\convert-llama2c-to-ggml\CMakeFiles\llama-convert-llama2c-to-ggml.dir\ + TARGET_FILE = bin\llama-convert-llama2c-to-ggml.exe + TARGET_IMPLIB = examples\convert-llama2c-to-ggml\llama-convert-llama2c-to-ggml.lib + TARGET_PDB = bin\llama-convert-llama2c-to-ggml.pdb + + +############################################# +# Utility command for test + +build examples\convert-llama2c-to-ggml\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\convert-llama2c-to-ggml" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build examples\convert-llama2c-to-ggml\test: phony examples\convert-llama2c-to-ggml\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build examples\convert-llama2c-to-ggml\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\convert-llama2c-to-ggml" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build examples\convert-llama2c-to-ggml\edit_cache: phony examples\convert-llama2c-to-ggml\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build examples\convert-llama2c-to-ggml\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\convert-llama2c-to-ggml" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build examples\convert-llama2c-to-ggml\rebuild_cache: phony examples\convert-llama2c-to-ggml\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build examples\convert-llama2c-to-ggml\list_install_components: phony + + +############################################# +# Utility command for install + +build examples\convert-llama2c-to-ggml\CMakeFiles\install.util: CUSTOM_COMMAND examples\convert-llama2c-to-ggml\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\convert-llama2c-to-ggml" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build examples\convert-llama2c-to-ggml\install: phony examples\convert-llama2c-to-ggml\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build examples\convert-llama2c-to-ggml\CMakeFiles\install\local.util: CUSTOM_COMMAND examples\convert-llama2c-to-ggml\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\examples\convert-llama2c-to-ggml" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build examples\convert-llama2c-to-ggml\install\local: phony examples\convert-llama2c-to-ggml\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/CMakeLists.txt +# ============================================================================= + + +############################################# +# Utility command for test + +build pocs\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\pocs" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build pocs\test: phony pocs\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build pocs\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\pocs" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build pocs\edit_cache: phony pocs\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build pocs\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\pocs" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build pocs\rebuild_cache: phony pocs\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build pocs\list_install_components: phony + + +############################################# +# Utility command for install + +build pocs\CMakeFiles\install.util: CUSTOM_COMMAND pocs\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\pocs" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build pocs\install: phony pocs\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build pocs\CMakeFiles\install\local.util: CUSTOM_COMMAND pocs\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\pocs" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build pocs\install\local: phony pocs\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/pocs/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-vdot + + +############################################# +# Order-only phony target for llama-vdot + +build cmake_object_order_depends_target_llama-vdot: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build pocs\vdot\CMakeFiles\llama-vdot.dir\vdot.cpp.obj: CXX_COMPILER__llama-vdot_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\pocs\vdot\vdot.cpp || cmake_object_order_depends_target_llama-vdot + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\pocs" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = pocs\vdot\CMakeFiles\llama-vdot.dir + OBJECT_FILE_DIR = pocs\vdot\CMakeFiles\llama-vdot.dir + TARGET_COMPILE_PDB = pocs\vdot\CMakeFiles\llama-vdot.dir\ + TARGET_PDB = bin\llama-vdot.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-vdot + + +############################################# +# Link the executable bin\llama-vdot.exe + +build bin\llama-vdot.exe: CXX_EXECUTABLE_LINKER__llama-vdot_Debug pocs\vdot\CMakeFiles\llama-vdot.dir\vdot.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = pocs\vdot\CMakeFiles\llama-vdot.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = pocs\vdot\CMakeFiles\llama-vdot.dir\ + TARGET_FILE = bin\llama-vdot.exe + TARGET_IMPLIB = pocs\vdot\llama-vdot.lib + TARGET_PDB = bin\llama-vdot.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-q8dot + + +############################################# +# Order-only phony target for llama-q8dot + +build cmake_object_order_depends_target_llama-q8dot: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build pocs\vdot\CMakeFiles\llama-q8dot.dir\q8dot.cpp.obj: CXX_COMPILER__llama-q8dot_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\pocs\vdot\q8dot.cpp || cmake_object_order_depends_target_llama-q8dot + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\pocs" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = pocs\vdot\CMakeFiles\llama-q8dot.dir + OBJECT_FILE_DIR = pocs\vdot\CMakeFiles\llama-q8dot.dir + TARGET_COMPILE_PDB = pocs\vdot\CMakeFiles\llama-q8dot.dir\ + TARGET_PDB = bin\llama-q8dot.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-q8dot + + +############################################# +# Link the executable bin\llama-q8dot.exe + +build bin\llama-q8dot.exe: CXX_EXECUTABLE_LINKER__llama-q8dot_Debug pocs\vdot\CMakeFiles\llama-q8dot.dir\q8dot.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = pocs\vdot\CMakeFiles\llama-q8dot.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = pocs\vdot\CMakeFiles\llama-q8dot.dir\ + TARGET_FILE = bin\llama-q8dot.exe + TARGET_IMPLIB = pocs\vdot\llama-q8dot.lib + TARGET_PDB = bin\llama-q8dot.pdb + + +############################################# +# Utility command for test + +build pocs\vdot\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\pocs\vdot" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build pocs\vdot\test: phony pocs\vdot\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build pocs\vdot\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\pocs\vdot" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build pocs\vdot\edit_cache: phony pocs\vdot\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build pocs\vdot\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\pocs\vdot" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build pocs\vdot\rebuild_cache: phony pocs\vdot\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build pocs\vdot\list_install_components: phony + + +############################################# +# Utility command for install + +build pocs\vdot\CMakeFiles\install.util: CUSTOM_COMMAND pocs\vdot\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\pocs\vdot" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build pocs\vdot\install: phony pocs\vdot\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build pocs\vdot\CMakeFiles\install\local.util: CUSTOM_COMMAND pocs\vdot\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\pocs\vdot" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build pocs\vdot\install\local: phony pocs\vdot\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/CMakeLists.txt +# ============================================================================= + + +############################################# +# Utility command for test + +build tools\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\test: phony tools\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\edit_cache: phony tools\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\rebuild_cache: phony tools\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\CMakeFiles\install.util: CUSTOM_COMMAND tools\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\install: phony tools\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\install\local: phony tools\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-batched-bench + + +############################################# +# Order-only phony target for llama-batched-bench + +build cmake_object_order_depends_target_llama-batched-bench: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tools\batched-bench\CMakeFiles\llama-batched-bench.dir\batched-bench.cpp.obj: CXX_COMPILER__llama-batched-bench_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\batched-bench\batched-bench.cpp || cmake_object_order_depends_target_llama-batched-bench + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tools\batched-bench\CMakeFiles\llama-batched-bench.dir + OBJECT_FILE_DIR = tools\batched-bench\CMakeFiles\llama-batched-bench.dir + TARGET_COMPILE_PDB = tools\batched-bench\CMakeFiles\llama-batched-bench.dir\ + TARGET_PDB = bin\llama-batched-bench.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-batched-bench + + +############################################# +# Link the executable bin\llama-batched-bench.exe + +build bin\llama-batched-bench.exe: CXX_EXECUTABLE_LINKER__llama-batched-bench_Debug tools\batched-bench\CMakeFiles\llama-batched-bench.dir\batched-bench.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\batched-bench\CMakeFiles\llama-batched-bench.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\batched-bench\CMakeFiles\llama-batched-bench.dir\ + TARGET_FILE = bin\llama-batched-bench.exe + TARGET_IMPLIB = tools\batched-bench\llama-batched-bench.lib + TARGET_PDB = bin\llama-batched-bench.pdb + + +############################################# +# Utility command for test + +build tools\batched-bench\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\batched-bench" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\batched-bench\test: phony tools\batched-bench\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\batched-bench\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\batched-bench" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\batched-bench\edit_cache: phony tools\batched-bench\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\batched-bench\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\batched-bench" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\batched-bench\rebuild_cache: phony tools\batched-bench\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\batched-bench\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\batched-bench\CMakeFiles\install.util: CUSTOM_COMMAND tools\batched-bench\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\batched-bench" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\batched-bench\install: phony tools\batched-bench\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\batched-bench\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\batched-bench\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\batched-bench" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\batched-bench\install\local: phony tools\batched-bench\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-gguf-split + + +############################################# +# Order-only phony target for llama-gguf-split + +build cmake_object_order_depends_target_llama-gguf-split: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tools\gguf-split\CMakeFiles\llama-gguf-split.dir\gguf-split.cpp.obj: CXX_COMPILER__llama-gguf-split_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\gguf-split\gguf-split.cpp || cmake_object_order_depends_target_llama-gguf-split + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tools\gguf-split\CMakeFiles\llama-gguf-split.dir + OBJECT_FILE_DIR = tools\gguf-split\CMakeFiles\llama-gguf-split.dir + TARGET_COMPILE_PDB = tools\gguf-split\CMakeFiles\llama-gguf-split.dir\ + TARGET_PDB = bin\llama-gguf-split.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-gguf-split + + +############################################# +# Link the executable bin\llama-gguf-split.exe + +build bin\llama-gguf-split.exe: CXX_EXECUTABLE_LINKER__llama-gguf-split_Debug tools\gguf-split\CMakeFiles\llama-gguf-split.dir\gguf-split.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\gguf-split\CMakeFiles\llama-gguf-split.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\gguf-split\CMakeFiles\llama-gguf-split.dir\ + TARGET_FILE = bin\llama-gguf-split.exe + TARGET_IMPLIB = tools\gguf-split\llama-gguf-split.lib + TARGET_PDB = bin\llama-gguf-split.pdb + + +############################################# +# Utility command for test + +build tools\gguf-split\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\gguf-split" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\gguf-split\test: phony tools\gguf-split\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\gguf-split\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\gguf-split" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\gguf-split\edit_cache: phony tools\gguf-split\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\gguf-split\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\gguf-split" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\gguf-split\rebuild_cache: phony tools\gguf-split\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\gguf-split\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\gguf-split\CMakeFiles\install.util: CUSTOM_COMMAND tools\gguf-split\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\gguf-split" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\gguf-split\install: phony tools\gguf-split\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\gguf-split\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\gguf-split\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\gguf-split" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\gguf-split\install\local: phony tools\gguf-split\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-imatrix + + +############################################# +# Order-only phony target for llama-imatrix + +build cmake_object_order_depends_target_llama-imatrix: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tools\imatrix\CMakeFiles\llama-imatrix.dir\imatrix.cpp.obj: CXX_COMPILER__llama-imatrix_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\imatrix\imatrix.cpp || cmake_object_order_depends_target_llama-imatrix + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tools\imatrix\CMakeFiles\llama-imatrix.dir + OBJECT_FILE_DIR = tools\imatrix\CMakeFiles\llama-imatrix.dir + TARGET_COMPILE_PDB = tools\imatrix\CMakeFiles\llama-imatrix.dir\ + TARGET_PDB = bin\llama-imatrix.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-imatrix + + +############################################# +# Link the executable bin\llama-imatrix.exe + +build bin\llama-imatrix.exe: CXX_EXECUTABLE_LINKER__llama-imatrix_Debug tools\imatrix\CMakeFiles\llama-imatrix.dir\imatrix.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\imatrix\CMakeFiles\llama-imatrix.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\imatrix\CMakeFiles\llama-imatrix.dir\ + TARGET_FILE = bin\llama-imatrix.exe + TARGET_IMPLIB = tools\imatrix\llama-imatrix.lib + TARGET_PDB = bin\llama-imatrix.pdb + + +############################################# +# Utility command for test + +build tools\imatrix\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\imatrix" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\imatrix\test: phony tools\imatrix\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\imatrix\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\imatrix" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\imatrix\edit_cache: phony tools\imatrix\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\imatrix\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\imatrix" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\imatrix\rebuild_cache: phony tools\imatrix\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\imatrix\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\imatrix\CMakeFiles\install.util: CUSTOM_COMMAND tools\imatrix\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\imatrix" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\imatrix\install: phony tools\imatrix\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\imatrix\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\imatrix\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\imatrix" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\imatrix\install\local: phony tools\imatrix\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-bench + + +############################################# +# Order-only phony target for llama-bench + +build cmake_object_order_depends_target_llama-bench: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tools\llama-bench\CMakeFiles\llama-bench.dir\llama-bench.cpp.obj: CXX_COMPILER__llama-bench_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\llama-bench\llama-bench.cpp || cmake_object_order_depends_target_llama-bench + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tools\llama-bench\CMakeFiles\llama-bench.dir + OBJECT_FILE_DIR = tools\llama-bench\CMakeFiles\llama-bench.dir + TARGET_COMPILE_PDB = tools\llama-bench\CMakeFiles\llama-bench.dir\ + TARGET_PDB = bin\llama-bench.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-bench + + +############################################# +# Link the executable bin\llama-bench.exe + +build bin\llama-bench.exe: CXX_EXECUTABLE_LINKER__llama-bench_Debug tools\llama-bench\CMakeFiles\llama-bench.dir\llama-bench.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\llama-bench\CMakeFiles\llama-bench.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\llama-bench\CMakeFiles\llama-bench.dir\ + TARGET_FILE = bin\llama-bench.exe + TARGET_IMPLIB = tools\llama-bench\llama-bench.lib + TARGET_PDB = bin\llama-bench.pdb + + +############################################# +# Utility command for test + +build tools\llama-bench\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\llama-bench" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\llama-bench\test: phony tools\llama-bench\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\llama-bench\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\llama-bench" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\llama-bench\edit_cache: phony tools\llama-bench\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\llama-bench\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\llama-bench" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\llama-bench\rebuild_cache: phony tools\llama-bench\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\llama-bench\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\llama-bench\CMakeFiles\install.util: CUSTOM_COMMAND tools\llama-bench\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\llama-bench" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\llama-bench\install: phony tools\llama-bench\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\llama-bench\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\llama-bench\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\llama-bench" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\llama-bench\install\local: phony tools\llama-bench\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-cli + + +############################################# +# Order-only phony target for llama-cli + +build cmake_object_order_depends_target_llama-cli: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tools\main\CMakeFiles\llama-cli.dir\main.cpp.obj: CXX_COMPILER__llama-cli_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\main\main.cpp || cmake_object_order_depends_target_llama-cli + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tools\main\CMakeFiles\llama-cli.dir + OBJECT_FILE_DIR = tools\main\CMakeFiles\llama-cli.dir + TARGET_COMPILE_PDB = tools\main\CMakeFiles\llama-cli.dir\ + TARGET_PDB = bin\llama-cli.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-cli + + +############################################# +# Link the executable bin\llama-cli.exe + +build bin\llama-cli.exe: CXX_EXECUTABLE_LINKER__llama-cli_Debug tools\main\CMakeFiles\llama-cli.dir\main.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\main\CMakeFiles\llama-cli.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\main\CMakeFiles\llama-cli.dir\ + TARGET_FILE = bin\llama-cli.exe + TARGET_IMPLIB = tools\main\llama-cli.lib + TARGET_PDB = bin\llama-cli.pdb + + +############################################# +# Utility command for test + +build tools\main\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\main" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\main\test: phony tools\main\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\main\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\main" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\main\edit_cache: phony tools\main\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\main\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\main" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\main\rebuild_cache: phony tools\main\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\main\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\main\CMakeFiles\install.util: CUSTOM_COMMAND tools\main\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\main" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\main\install: phony tools\main\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\main\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\main\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\main" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\main\install\local: phony tools\main\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-perplexity + + +############################################# +# Order-only phony target for llama-perplexity + +build cmake_object_order_depends_target_llama-perplexity: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tools\perplexity\CMakeFiles\llama-perplexity.dir\perplexity.cpp.obj: CXX_COMPILER__llama-perplexity_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\perplexity\perplexity.cpp || cmake_object_order_depends_target_llama-perplexity + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tools\perplexity\CMakeFiles\llama-perplexity.dir + OBJECT_FILE_DIR = tools\perplexity\CMakeFiles\llama-perplexity.dir + TARGET_COMPILE_PDB = tools\perplexity\CMakeFiles\llama-perplexity.dir\ + TARGET_PDB = bin\llama-perplexity.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-perplexity + + +############################################# +# Link the executable bin\llama-perplexity.exe + +build bin\llama-perplexity.exe: CXX_EXECUTABLE_LINKER__llama-perplexity_Debug tools\perplexity\CMakeFiles\llama-perplexity.dir\perplexity.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\perplexity\CMakeFiles\llama-perplexity.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\perplexity\CMakeFiles\llama-perplexity.dir\ + TARGET_FILE = bin\llama-perplexity.exe + TARGET_IMPLIB = tools\perplexity\llama-perplexity.lib + TARGET_PDB = bin\llama-perplexity.pdb + + +############################################# +# Utility command for test + +build tools\perplexity\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\perplexity" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\perplexity\test: phony tools\perplexity\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\perplexity\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\perplexity" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\perplexity\edit_cache: phony tools\perplexity\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\perplexity\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\perplexity" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\perplexity\rebuild_cache: phony tools\perplexity\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\perplexity\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\perplexity\CMakeFiles\install.util: CUSTOM_COMMAND tools\perplexity\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\perplexity" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\perplexity\install: phony tools\perplexity\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\perplexity\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\perplexity\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\perplexity" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\perplexity\install\local: phony tools\perplexity\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-quantize + + +############################################# +# Order-only phony target for llama-quantize + +build cmake_object_order_depends_target_llama-quantize: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tools\quantize\CMakeFiles\llama-quantize.dir\quantize.cpp.obj: CXX_COMPILER__llama-quantize_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\quantize\quantize.cpp || cmake_object_order_depends_target_llama-quantize + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\quantize\..\..\common" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tools\quantize\CMakeFiles\llama-quantize.dir + OBJECT_FILE_DIR = tools\quantize\CMakeFiles\llama-quantize.dir + TARGET_COMPILE_PDB = tools\quantize\CMakeFiles\llama-quantize.dir\ + TARGET_PDB = bin\llama-quantize.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-quantize + + +############################################# +# Link the executable bin\llama-quantize.exe + +build bin\llama-quantize.exe: CXX_EXECUTABLE_LINKER__llama-quantize_Debug tools\quantize\CMakeFiles\llama-quantize.dir\quantize.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\quantize\CMakeFiles\llama-quantize.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\quantize\CMakeFiles\llama-quantize.dir\ + TARGET_FILE = bin\llama-quantize.exe + TARGET_IMPLIB = tools\quantize\llama-quantize.lib + TARGET_PDB = bin\llama-quantize.pdb + + +############################################# +# Utility command for test + +build tools\quantize\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\quantize" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\quantize\test: phony tools\quantize\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\quantize\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\quantize" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\quantize\edit_cache: phony tools\quantize\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\quantize\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\quantize" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\quantize\rebuild_cache: phony tools\quantize\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\quantize\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\quantize\CMakeFiles\install.util: CUSTOM_COMMAND tools\quantize\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\quantize" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\quantize\install: phony tools\quantize\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\quantize\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\quantize\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\quantize" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\quantize\install\local: phony tools\quantize\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-server + + +############################################# +# Order-only phony target for llama-server + +build cmake_object_order_depends_target_llama-server: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama cmake_object_order_depends_target_mtmd tools\server\index.html.gz.hpp tools\server\loading.html.hpp + +build tools\server\CMakeFiles\llama-server.dir\server.cpp.obj: CXX_COMPILER__llama-server_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\server\server.cpp || cmake_object_order_depends_target_llama-server + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\server" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\server" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\server\..\llava" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\." + OBJECT_DIR = tools\server\CMakeFiles\llama-server.dir + OBJECT_FILE_DIR = tools\server\CMakeFiles\llama-server.dir + TARGET_COMPILE_PDB = tools\server\CMakeFiles\llama-server.dir\ + TARGET_PDB = bin\llama-server.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-server + + +############################################# +# Link the executable bin\llama-server.exe + +build bin\llama-server.exe: CXX_EXECUTABLE_LINKER__llama-server_Debug tools\server\CMakeFiles\llama-server.dir\server.cpp.obj | common\common.lib tools\mtmd\mtmd.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll bin\mtmd.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib tools\mtmd\mtmd.lib ws2_32.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\server\CMakeFiles\llama-server.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\server\CMakeFiles\llama-server.dir\ + TARGET_FILE = bin\llama-server.exe + TARGET_IMPLIB = tools\server\llama-server.lib + TARGET_PDB = bin\llama-server.pdb + + +############################################# +# Utility command for test + +build tools\server\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\server" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\server\test: phony tools\server\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\server\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\server" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\server\edit_cache: phony tools\server\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\server\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\server" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\server\rebuild_cache: phony tools\server\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\server\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\server\CMakeFiles\install.util: CUSTOM_COMMAND tools\server\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\server" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\server\install: phony tools\server\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\server\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\server\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\server" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\server\install\local: phony tools\server\CMakeFiles\install\local.util + + +############################################# +# Custom command for tools\server\index.html.gz.hpp + +build tools\server\index.html.gz.hpp | ${cmake_ninja_workdir}tools\server\index.html.gz.hpp: CUSTOM_COMMAND C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\server\public\index.html.gz || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll bin\mtmd.dll common\build_info common\common.lib + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\server" && "C:\Program Files\CMake\bin\cmake.exe" "-DINPUT=C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/server/public/index.html.gz" "-DOUTPUT=C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/server/index.html.gz.hpp" -P "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/scripts/xxd.cmake"" + DESC = Generating index.html.gz.hpp + restat = 1 + + +############################################# +# Custom command for tools\server\loading.html.hpp + +build tools\server\loading.html.hpp | ${cmake_ninja_workdir}tools\server\loading.html.hpp: CUSTOM_COMMAND C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\server\public\loading.html || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll bin\mtmd.dll common\build_info common\common.lib + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\server" && "C:\Program Files\CMake\bin\cmake.exe" "-DINPUT=C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/server/public/loading.html" "-DOUTPUT=C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/server/loading.html.hpp" -P "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/scripts/xxd.cmake"" + DESC = Generating loading.html.hpp + restat = 1 + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-run + + +############################################# +# Order-only phony target for llama-run + +build cmake_object_order_depends_target_llama-run: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tools\run\CMakeFiles\llama-run.dir\run.cpp.obj: CXX_COMPILER__llama-run_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\run\run.cpp || cmake_object_order_depends_target_llama-run + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tools\run\CMakeFiles\llama-run.dir + OBJECT_FILE_DIR = tools\run\CMakeFiles\llama-run.dir + TARGET_COMPILE_PDB = tools\run\CMakeFiles\llama-run.dir\ + TARGET_PDB = bin\llama-run.pdb + +build tools\run\CMakeFiles\llama-run.dir\linenoise.cpp\linenoise.cpp.obj: CXX_COMPILER__llama-run_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\run\linenoise.cpp\linenoise.cpp || cmake_object_order_depends_target_llama-run + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tools\run\CMakeFiles\llama-run.dir + OBJECT_FILE_DIR = tools\run\CMakeFiles\llama-run.dir\linenoise.cpp + TARGET_COMPILE_PDB = tools\run\CMakeFiles\llama-run.dir\ + TARGET_PDB = bin\llama-run.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-run + + +############################################# +# Link the executable bin\llama-run.exe + +build bin\llama-run.exe: CXX_EXECUTABLE_LINKER__llama-run_Debug tools\run\CMakeFiles\llama-run.dir\run.cpp.obj tools\run\CMakeFiles\llama-run.dir\linenoise.cpp\linenoise.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\run\CMakeFiles\llama-run.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\run\CMakeFiles\llama-run.dir\ + TARGET_FILE = bin\llama-run.exe + TARGET_IMPLIB = tools\run\llama-run.lib + TARGET_PDB = bin\llama-run.pdb + + +############################################# +# Utility command for test + +build tools\run\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\run" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\run\test: phony tools\run\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\run\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\run" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\run\edit_cache: phony tools\run\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\run\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\run" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\run\rebuild_cache: phony tools\run\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\run\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\run\CMakeFiles\install.util: CUSTOM_COMMAND tools\run\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\run" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\run\install: phony tools\run\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\run\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\run\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\run" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\run\install\local: phony tools\run\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-tokenize + + +############################################# +# Order-only phony target for llama-tokenize + +build cmake_object_order_depends_target_llama-tokenize: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tools\tokenize\CMakeFiles\llama-tokenize.dir\tokenize.cpp.obj: CXX_COMPILER__llama-tokenize_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\tokenize\tokenize.cpp || cmake_object_order_depends_target_llama-tokenize + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tools\tokenize\CMakeFiles\llama-tokenize.dir + OBJECT_FILE_DIR = tools\tokenize\CMakeFiles\llama-tokenize.dir + TARGET_COMPILE_PDB = tools\tokenize\CMakeFiles\llama-tokenize.dir\ + TARGET_PDB = bin\llama-tokenize.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-tokenize + + +############################################# +# Link the executable bin\llama-tokenize.exe + +build bin\llama-tokenize.exe: CXX_EXECUTABLE_LINKER__llama-tokenize_Debug tools\tokenize\CMakeFiles\llama-tokenize.dir\tokenize.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\tokenize\CMakeFiles\llama-tokenize.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\tokenize\CMakeFiles\llama-tokenize.dir\ + TARGET_FILE = bin\llama-tokenize.exe + TARGET_IMPLIB = tools\tokenize\llama-tokenize.lib + TARGET_PDB = bin\llama-tokenize.pdb + + +############################################# +# Utility command for test + +build tools\tokenize\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\tokenize" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\tokenize\test: phony tools\tokenize\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\tokenize\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\tokenize" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\tokenize\edit_cache: phony tools\tokenize\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\tokenize\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\tokenize" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\tokenize\rebuild_cache: phony tools\tokenize\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\tokenize\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\tokenize\CMakeFiles\install.util: CUSTOM_COMMAND tools\tokenize\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\tokenize" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\tokenize\install: phony tools\tokenize\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\tokenize\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\tokenize\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\tokenize" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\tokenize\install\local: phony tools\tokenize\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-tts + + +############################################# +# Order-only phony target for llama-tts + +build cmake_object_order_depends_target_llama-tts: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tools\tts\CMakeFiles\llama-tts.dir\tts.cpp.obj: CXX_COMPILER__llama-tts_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\tts\tts.cpp || cmake_object_order_depends_target_llama-tts + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" + OBJECT_DIR = tools\tts\CMakeFiles\llama-tts.dir + OBJECT_FILE_DIR = tools\tts\CMakeFiles\llama-tts.dir + TARGET_COMPILE_PDB = tools\tts\CMakeFiles\llama-tts.dir\ + TARGET_PDB = bin\llama-tts.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-tts + + +############################################# +# Link the executable bin\llama-tts.exe + +build bin\llama-tts.exe: CXX_EXECUTABLE_LINKER__llama-tts_Debug tools\tts\CMakeFiles\llama-tts.dir\tts.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\tts\CMakeFiles\llama-tts.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\tts\CMakeFiles\llama-tts.dir\ + TARGET_FILE = bin\llama-tts.exe + TARGET_IMPLIB = tools\tts\llama-tts.lib + TARGET_PDB = bin\llama-tts.pdb + + +############################################# +# Utility command for test + +build tools\tts\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\tts" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\tts\test: phony tools\tts\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\tts\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\tts" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\tts\edit_cache: phony tools\tts\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\tts\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\tts" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\tts\rebuild_cache: phony tools\tts\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\tts\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\tts\CMakeFiles\install.util: CUSTOM_COMMAND tools\tts\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\tts" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\tts\install: phony tools\tts\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\tts\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\tts\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\tts" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\tts\install\local: phony tools\tts\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for SHARED_LIBRARY target mtmd + + +############################################# +# Order-only phony target for mtmd + +build cmake_object_order_depends_target_mtmd: phony || cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tools\mtmd\CMakeFiles\mtmd.dir\mtmd.cpp.obj: CXX_COMPILER__mtmd_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\mtmd\mtmd.cpp || cmake_object_order_depends_target_mtmd + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dmtmd_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\..\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\..\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" + OBJECT_DIR = tools\mtmd\CMakeFiles\mtmd.dir + OBJECT_FILE_DIR = tools\mtmd\CMakeFiles\mtmd.dir + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\mtmd.dir\ + TARGET_PDB = bin\mtmd.pdb + +build tools\mtmd\CMakeFiles\mtmd.dir\mtmd-audio.cpp.obj: CXX_COMPILER__mtmd_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\mtmd\mtmd-audio.cpp || cmake_object_order_depends_target_mtmd + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dmtmd_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\..\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\..\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" + OBJECT_DIR = tools\mtmd\CMakeFiles\mtmd.dir + OBJECT_FILE_DIR = tools\mtmd\CMakeFiles\mtmd.dir + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\mtmd.dir\ + TARGET_PDB = bin\mtmd.pdb + +build tools\mtmd\CMakeFiles\mtmd.dir\clip.cpp.obj: CXX_COMPILER__mtmd_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\mtmd\clip.cpp || cmake_object_order_depends_target_mtmd + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dmtmd_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\..\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\..\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" + OBJECT_DIR = tools\mtmd\CMakeFiles\mtmd.dir + OBJECT_FILE_DIR = tools\mtmd\CMakeFiles\mtmd.dir + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\mtmd.dir\ + TARGET_PDB = bin\mtmd.pdb + +build tools\mtmd\CMakeFiles\mtmd.dir\mtmd-helper.cpp.obj: CXX_COMPILER__mtmd_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\mtmd\mtmd-helper.cpp || cmake_object_order_depends_target_mtmd + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_BUILD -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS -Dmtmd_EXPORTS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\..\.." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\..\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" + OBJECT_DIR = tools\mtmd\CMakeFiles\mtmd.dir + OBJECT_FILE_DIR = tools\mtmd\CMakeFiles\mtmd.dir + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\mtmd.dir\ + TARGET_PDB = bin\mtmd.pdb + + +# ============================================================================= +# Link build statements for SHARED_LIBRARY target mtmd + + +############################################# +# Link the shared library bin\mtmd.dll + +build bin\mtmd.dll tools\mtmd\mtmd.lib: CXX_SHARED_LIBRARY_LINKER__mtmd_Debug tools\mtmd\CMakeFiles\mtmd.dir\mtmd.cpp.obj tools\mtmd\CMakeFiles\mtmd.dir\mtmd-audio.cpp.obj tools\mtmd\CMakeFiles\mtmd.dir\clip.cpp.obj tools\mtmd\CMakeFiles\mtmd.dir\mtmd-helper.cpp.obj | src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll + CONFIG = Debug + LANGUAGE_COMPILE_FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = -shared /machine:x64 /debug /INCREMENTAL + LINK_LIBRARIES = src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\mtmd\CMakeFiles\mtmd.dir + POST_BUILD = cd . + PRE_LINK = cd . + RESTAT = 1 + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\mtmd.dir\ + TARGET_FILE = bin\mtmd.dll + TARGET_IMPLIB = tools\mtmd\mtmd.lib + TARGET_PDB = bin\mtmd.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-llava-cli + + +############################################# +# Order-only phony target for llama-llava-cli + +build cmake_object_order_depends_target_llama-llava-cli: phony || . + +build tools\mtmd\CMakeFiles\llama-llava-cli.dir\deprecation-warning.cpp.obj: CXX_COMPILER__llama-llava-cli_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\mtmd\deprecation-warning.cpp || cmake_object_order_depends_target_llama-llava-cli + CONFIG = Debug + DEFINES = -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 /utf-8 /bigobj + OBJECT_DIR = tools\mtmd\CMakeFiles\llama-llava-cli.dir + OBJECT_FILE_DIR = tools\mtmd\CMakeFiles\llama-llava-cli.dir + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\llama-llava-cli.dir\ + TARGET_PDB = bin\llama-llava-cli.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-llava-cli + + +############################################# +# Link the executable bin\llama-llava-cli.exe + +build bin\llama-llava-cli.exe: CXX_EXECUTABLE_LINKER__llama-llava-cli_Debug tools\mtmd\CMakeFiles\llama-llava-cli.dir\deprecation-warning.cpp.obj + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\mtmd\CMakeFiles\llama-llava-cli.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\llama-llava-cli.dir\ + TARGET_FILE = bin\llama-llava-cli.exe + TARGET_IMPLIB = tools\mtmd\llama-llava-cli.lib + TARGET_PDB = bin\llama-llava-cli.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-gemma3-cli + + +############################################# +# Order-only phony target for llama-gemma3-cli + +build cmake_object_order_depends_target_llama-gemma3-cli: phony || . + +build tools\mtmd\CMakeFiles\llama-gemma3-cli.dir\deprecation-warning.cpp.obj: CXX_COMPILER__llama-gemma3-cli_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\mtmd\deprecation-warning.cpp || cmake_object_order_depends_target_llama-gemma3-cli + CONFIG = Debug + DEFINES = -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 /utf-8 /bigobj + OBJECT_DIR = tools\mtmd\CMakeFiles\llama-gemma3-cli.dir + OBJECT_FILE_DIR = tools\mtmd\CMakeFiles\llama-gemma3-cli.dir + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\llama-gemma3-cli.dir\ + TARGET_PDB = bin\llama-gemma3-cli.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-gemma3-cli + + +############################################# +# Link the executable bin\llama-gemma3-cli.exe + +build bin\llama-gemma3-cli.exe: CXX_EXECUTABLE_LINKER__llama-gemma3-cli_Debug tools\mtmd\CMakeFiles\llama-gemma3-cli.dir\deprecation-warning.cpp.obj + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\mtmd\CMakeFiles\llama-gemma3-cli.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\llama-gemma3-cli.dir\ + TARGET_FILE = bin\llama-gemma3-cli.exe + TARGET_IMPLIB = tools\mtmd\llama-gemma3-cli.lib + TARGET_PDB = bin\llama-gemma3-cli.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-minicpmv-cli + + +############################################# +# Order-only phony target for llama-minicpmv-cli + +build cmake_object_order_depends_target_llama-minicpmv-cli: phony || . + +build tools\mtmd\CMakeFiles\llama-minicpmv-cli.dir\deprecation-warning.cpp.obj: CXX_COMPILER__llama-minicpmv-cli_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\mtmd\deprecation-warning.cpp || cmake_object_order_depends_target_llama-minicpmv-cli + CONFIG = Debug + DEFINES = -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 /utf-8 /bigobj + OBJECT_DIR = tools\mtmd\CMakeFiles\llama-minicpmv-cli.dir + OBJECT_FILE_DIR = tools\mtmd\CMakeFiles\llama-minicpmv-cli.dir + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\llama-minicpmv-cli.dir\ + TARGET_PDB = bin\llama-minicpmv-cli.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-minicpmv-cli + + +############################################# +# Link the executable bin\llama-minicpmv-cli.exe + +build bin\llama-minicpmv-cli.exe: CXX_EXECUTABLE_LINKER__llama-minicpmv-cli_Debug tools\mtmd\CMakeFiles\llama-minicpmv-cli.dir\deprecation-warning.cpp.obj + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\mtmd\CMakeFiles\llama-minicpmv-cli.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\llama-minicpmv-cli.dir\ + TARGET_FILE = bin\llama-minicpmv-cli.exe + TARGET_IMPLIB = tools\mtmd\llama-minicpmv-cli.lib + TARGET_PDB = bin\llama-minicpmv-cli.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-qwen2vl-cli + + +############################################# +# Order-only phony target for llama-qwen2vl-cli + +build cmake_object_order_depends_target_llama-qwen2vl-cli: phony || . + +build tools\mtmd\CMakeFiles\llama-qwen2vl-cli.dir\deprecation-warning.cpp.obj: CXX_COMPILER__llama-qwen2vl-cli_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\mtmd\deprecation-warning.cpp || cmake_object_order_depends_target_llama-qwen2vl-cli + CONFIG = Debug + DEFINES = -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 /utf-8 /bigobj + OBJECT_DIR = tools\mtmd\CMakeFiles\llama-qwen2vl-cli.dir + OBJECT_FILE_DIR = tools\mtmd\CMakeFiles\llama-qwen2vl-cli.dir + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\llama-qwen2vl-cli.dir\ + TARGET_PDB = bin\llama-qwen2vl-cli.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-qwen2vl-cli + + +############################################# +# Link the executable bin\llama-qwen2vl-cli.exe + +build bin\llama-qwen2vl-cli.exe: CXX_EXECUTABLE_LINKER__llama-qwen2vl-cli_Debug tools\mtmd\CMakeFiles\llama-qwen2vl-cli.dir\deprecation-warning.cpp.obj + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\mtmd\CMakeFiles\llama-qwen2vl-cli.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\llama-qwen2vl-cli.dir\ + TARGET_FILE = bin\llama-qwen2vl-cli.exe + TARGET_IMPLIB = tools\mtmd\llama-qwen2vl-cli.lib + TARGET_PDB = bin\llama-qwen2vl-cli.pdb + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-mtmd-cli + + +############################################# +# Order-only phony target for llama-mtmd-cli + +build cmake_object_order_depends_target_llama-mtmd-cli: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama cmake_object_order_depends_target_mtmd + +build tools\mtmd\CMakeFiles\llama-mtmd-cli.dir\mtmd-cli.cpp.obj: CXX_COMPILER__llama-mtmd-cli_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\mtmd\mtmd-cli.cpp || cmake_object_order_depends_target_llama-mtmd-cli + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\tools\mtmd\." + OBJECT_DIR = tools\mtmd\CMakeFiles\llama-mtmd-cli.dir + OBJECT_FILE_DIR = tools\mtmd\CMakeFiles\llama-mtmd-cli.dir + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\llama-mtmd-cli.dir\ + TARGET_PDB = bin\llama-mtmd-cli.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-mtmd-cli + + +############################################# +# Link the executable bin\llama-mtmd-cli.exe + +build bin\llama-mtmd-cli.exe: CXX_EXECUTABLE_LINKER__llama-mtmd-cli_Debug tools\mtmd\CMakeFiles\llama-mtmd-cli.dir\mtmd-cli.cpp.obj | common\common.lib tools\mtmd\mtmd.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll bin\mtmd.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib tools\mtmd\mtmd.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\mtmd\CMakeFiles\llama-mtmd-cli.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\mtmd\CMakeFiles\llama-mtmd-cli.dir\ + TARGET_FILE = bin\llama-mtmd-cli.exe + TARGET_IMPLIB = tools\mtmd\llama-mtmd-cli.lib + TARGET_PDB = bin\llama-mtmd-cli.pdb + + +############################################# +# Utility command for test + +build tools\mtmd\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\mtmd" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\mtmd\test: phony tools\mtmd\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\mtmd\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\mtmd" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\mtmd\edit_cache: phony tools\mtmd\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\mtmd\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\mtmd" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\mtmd\rebuild_cache: phony tools\mtmd\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\mtmd\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\mtmd\CMakeFiles\install.util: CUSTOM_COMMAND tools\mtmd\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\mtmd" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\mtmd\install: phony tools\mtmd\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\mtmd\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\mtmd\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\mtmd" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\mtmd\install\local: phony tools\mtmd\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-cvector-generator + + +############################################# +# Order-only phony target for llama-cvector-generator + +build cmake_object_order_depends_target_llama-cvector-generator: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tools\cvector-generator\CMakeFiles\llama-cvector-generator.dir\cvector-generator.cpp.obj: CXX_COMPILER__llama-cvector-generator_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\cvector-generator\cvector-generator.cpp || cmake_object_order_depends_target_llama-cvector-generator + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tools\cvector-generator\CMakeFiles\llama-cvector-generator.dir + OBJECT_FILE_DIR = tools\cvector-generator\CMakeFiles\llama-cvector-generator.dir + TARGET_COMPILE_PDB = tools\cvector-generator\CMakeFiles\llama-cvector-generator.dir\ + TARGET_PDB = bin\llama-cvector-generator.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-cvector-generator + + +############################################# +# Link the executable bin\llama-cvector-generator.exe + +build bin\llama-cvector-generator.exe: CXX_EXECUTABLE_LINKER__llama-cvector-generator_Debug tools\cvector-generator\CMakeFiles\llama-cvector-generator.dir\cvector-generator.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\cvector-generator\CMakeFiles\llama-cvector-generator.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\cvector-generator\CMakeFiles\llama-cvector-generator.dir\ + TARGET_FILE = bin\llama-cvector-generator.exe + TARGET_IMPLIB = tools\cvector-generator\llama-cvector-generator.lib + TARGET_PDB = bin\llama-cvector-generator.pdb + + +############################################# +# Utility command for test + +build tools\cvector-generator\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\cvector-generator" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\cvector-generator\test: phony tools\cvector-generator\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\cvector-generator\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\cvector-generator" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\cvector-generator\edit_cache: phony tools\cvector-generator\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\cvector-generator\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\cvector-generator" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\cvector-generator\rebuild_cache: phony tools\cvector-generator\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\cvector-generator\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\cvector-generator\CMakeFiles\install.util: CUSTOM_COMMAND tools\cvector-generator\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\cvector-generator" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\cvector-generator\install: phony tools\cvector-generator\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\cvector-generator\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\cvector-generator\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\cvector-generator" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\cvector-generator\install\local: phony tools\cvector-generator\CMakeFiles\install\local.util + +# ============================================================================= +# Write statements declared in CMakeLists.txt: +# C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/CMakeLists.txt +# ============================================================================= + +# ============================================================================= +# Object build statements for EXECUTABLE target llama-export-lora + + +############################################# +# Order-only phony target for llama-export-lora + +build cmake_object_order_depends_target_llama-export-lora: phony || cmake_object_order_depends_target_build_info cmake_object_order_depends_target_common cmake_object_order_depends_target_ggml cmake_object_order_depends_target_ggml-base cmake_object_order_depends_target_ggml-cpu cmake_object_order_depends_target_llama + +build tools\export-lora\CMakeFiles\llama-export-lora.dir\export-lora.cpp.obj: CXX_COMPILER__llama-export-lora_unscanned_Debug C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\tools\export-lora\export-lora.cpp || cmake_object_order_depends_target_llama-export-lora + CONFIG = Debug + DEFINES = -DGGML_BACKEND_SHARED -DGGML_SHARED -DGGML_USE_CPU -DLLAMA_SHARED -D_CRT_SECURE_NO_WARNINGS + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj + INCLUDES = -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\." -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\common\..\vendor" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\src\..\include" -I"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\ggml\src\..\include" + OBJECT_DIR = tools\export-lora\CMakeFiles\llama-export-lora.dir + OBJECT_FILE_DIR = tools\export-lora\CMakeFiles\llama-export-lora.dir + TARGET_COMPILE_PDB = tools\export-lora\CMakeFiles\llama-export-lora.dir\ + TARGET_PDB = bin\llama-export-lora.pdb + + +# ============================================================================= +# Link build statements for EXECUTABLE target llama-export-lora + + +############################################# +# Link the executable bin\llama-export-lora.exe + +build bin\llama-export-lora.exe: CXX_EXECUTABLE_LINKER__llama-export-lora_Debug tools\export-lora\CMakeFiles\llama-export-lora.dir\export-lora.cpp.obj | common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib || bin\ggml-base.dll bin\ggml-cpu.dll bin\ggml.dll bin\llama.dll common\build_info common\common.lib + CONFIG = Debug + FLAGS = /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 + LINK_FLAGS = /machine:x64 /debug /INCREMENTAL /subsystem:console + LINK_LIBRARIES = common\common.lib src\llama.lib ggml\src\ggml.lib ggml\src\ggml-cpu.lib ggml\src\ggml-base.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib + OBJECT_DIR = tools\export-lora\CMakeFiles\llama-export-lora.dir + POST_BUILD = cd . + PRE_LINK = cd . + TARGET_COMPILE_PDB = tools\export-lora\CMakeFiles\llama-export-lora.dir\ + TARGET_FILE = bin\llama-export-lora.exe + TARGET_IMPLIB = tools\export-lora\llama-export-lora.lib + TARGET_PDB = bin\llama-export-lora.pdb + + +############################################# +# Utility command for test + +build tools\export-lora\CMakeFiles\test.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\export-lora" && "C:\Program Files\CMake\bin\ctest.exe" " + DESC = Running tests... + pool = console + restat = 1 + +build tools\export-lora\test: phony tools\export-lora\CMakeFiles\test.util + + +############################################# +# Utility command for edit_cache + +build tools\export-lora\CMakeFiles\edit_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\export-lora" && "C:\Program Files\CMake\bin\cmake-gui.exe" -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake cache editor... + pool = console + restat = 1 + +build tools\export-lora\edit_cache: phony tools\export-lora\CMakeFiles\edit_cache.util + + +############################################# +# Utility command for rebuild_cache + +build tools\export-lora\CMakeFiles\rebuild_cache.util: CUSTOM_COMMAND + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\export-lora" && "C:\Program Files\CMake\bin\cmake.exe" --regenerate-during-build -S"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp" -B"C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build"" + DESC = Running CMake to regenerate build system... + pool = console + restat = 1 + +build tools\export-lora\rebuild_cache: phony tools\export-lora\CMakeFiles\rebuild_cache.util + + +############################################# +# Utility command for list_install_components + +build tools\export-lora\list_install_components: phony + + +############################################# +# Utility command for install + +build tools\export-lora\CMakeFiles\install.util: CUSTOM_COMMAND tools\export-lora\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\export-lora" && "C:\Program Files\CMake\bin\cmake.exe" -P cmake_install.cmake" + DESC = Install the project... + pool = console + restat = 1 + +build tools\export-lora\install: phony tools\export-lora\CMakeFiles\install.util + + +############################################# +# Utility command for install/local + +build tools\export-lora\CMakeFiles\install\local.util: CUSTOM_COMMAND tools\export-lora\all + COMMAND = C:\WINDOWS\system32\cmd.exe /C "cd /D "C:\Users\Kids\Documents\Aryan's Coding Stuff\Aryan's Python Stuff\Actual Python Script\ai-coding-genius-main\llama.cpp\build\tools\export-lora" && "C:\Program Files\CMake\bin\cmake.exe" -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake" + DESC = Installing only the local directory... + pool = console + restat = 1 + +build tools\export-lora\install\local: phony tools\export-lora\CMakeFiles\install\local.util + +# ============================================================================= +# Target aliases. + +build build_info: phony common\build_info + +build common: phony common\common.lib + +build common.lib: phony common\common.lib + +build ggml: phony bin\ggml.dll + +build ggml-base: phony bin\ggml-base.dll + +build ggml-base.dll: phony bin\ggml-base.dll + +build ggml-cpu: phony bin\ggml-cpu.dll + +build ggml-cpu.dll: phony bin\ggml-cpu.dll + +build ggml.dll: phony bin\ggml.dll + +build llama: phony bin\llama.dll + +build llama-batched: phony bin\llama-batched.exe + +build llama-batched-bench: phony bin\llama-batched-bench.exe + +build llama-batched-bench.exe: phony bin\llama-batched-bench.exe + +build llama-batched.exe: phony bin\llama-batched.exe + +build llama-bench: phony bin\llama-bench.exe + +build llama-bench.exe: phony bin\llama-bench.exe + +build llama-cli: phony bin\llama-cli.exe + +build llama-cli.exe: phony bin\llama-cli.exe + +build llama-convert-llama2c-to-ggml: phony bin\llama-convert-llama2c-to-ggml.exe + +build llama-convert-llama2c-to-ggml.exe: phony bin\llama-convert-llama2c-to-ggml.exe + +build llama-cvector-generator: phony bin\llama-cvector-generator.exe + +build llama-cvector-generator.exe: phony bin\llama-cvector-generator.exe + +build llama-diffusion-cli: phony bin\llama-diffusion-cli.exe + +build llama-diffusion-cli.exe: phony bin\llama-diffusion-cli.exe + +build llama-embedding: phony bin\llama-embedding.exe + +build llama-embedding.exe: phony bin\llama-embedding.exe + +build llama-eval-callback: phony bin\llama-eval-callback.exe + +build llama-eval-callback.exe: phony bin\llama-eval-callback.exe + +build llama-export-lora: phony bin\llama-export-lora.exe + +build llama-export-lora.exe: phony bin\llama-export-lora.exe + +build llama-finetune: phony bin\llama-finetune.exe + +build llama-finetune.exe: phony bin\llama-finetune.exe + +build llama-gemma3-cli: phony bin\llama-gemma3-cli.exe + +build llama-gemma3-cli.exe: phony bin\llama-gemma3-cli.exe + +build llama-gen-docs: phony bin\llama-gen-docs.exe + +build llama-gen-docs.exe: phony bin\llama-gen-docs.exe + +build llama-gguf: phony bin\llama-gguf.exe + +build llama-gguf-hash: phony bin\llama-gguf-hash.exe + +build llama-gguf-hash.exe: phony bin\llama-gguf-hash.exe + +build llama-gguf-split: phony bin\llama-gguf-split.exe + +build llama-gguf-split.exe: phony bin\llama-gguf-split.exe + +build llama-gguf.exe: phony bin\llama-gguf.exe + +build llama-gritlm: phony bin\llama-gritlm.exe + +build llama-gritlm.exe: phony bin\llama-gritlm.exe + +build llama-imatrix: phony bin\llama-imatrix.exe + +build llama-imatrix.exe: phony bin\llama-imatrix.exe + +build llama-llava-cli: phony bin\llama-llava-cli.exe + +build llama-llava-cli.exe: phony bin\llama-llava-cli.exe + +build llama-lookahead: phony bin\llama-lookahead.exe + +build llama-lookahead.exe: phony bin\llama-lookahead.exe + +build llama-lookup: phony bin\llama-lookup.exe + +build llama-lookup-create: phony bin\llama-lookup-create.exe + +build llama-lookup-create.exe: phony bin\llama-lookup-create.exe + +build llama-lookup-merge: phony bin\llama-lookup-merge.exe + +build llama-lookup-merge.exe: phony bin\llama-lookup-merge.exe + +build llama-lookup-stats: phony bin\llama-lookup-stats.exe + +build llama-lookup-stats.exe: phony bin\llama-lookup-stats.exe + +build llama-lookup.exe: phony bin\llama-lookup.exe + +build llama-minicpmv-cli: phony bin\llama-minicpmv-cli.exe + +build llama-minicpmv-cli.exe: phony bin\llama-minicpmv-cli.exe + +build llama-mtmd-cli: phony bin\llama-mtmd-cli.exe + +build llama-mtmd-cli.exe: phony bin\llama-mtmd-cli.exe + +build llama-parallel: phony bin\llama-parallel.exe + +build llama-parallel.exe: phony bin\llama-parallel.exe + +build llama-passkey: phony bin\llama-passkey.exe + +build llama-passkey.exe: phony bin\llama-passkey.exe + +build llama-perplexity: phony bin\llama-perplexity.exe + +build llama-perplexity.exe: phony bin\llama-perplexity.exe + +build llama-q8dot: phony bin\llama-q8dot.exe + +build llama-q8dot.exe: phony bin\llama-q8dot.exe + +build llama-quantize: phony bin\llama-quantize.exe + +build llama-quantize.exe: phony bin\llama-quantize.exe + +build llama-qwen2vl-cli: phony bin\llama-qwen2vl-cli.exe + +build llama-qwen2vl-cli.exe: phony bin\llama-qwen2vl-cli.exe + +build llama-retrieval: phony bin\llama-retrieval.exe + +build llama-retrieval.exe: phony bin\llama-retrieval.exe + +build llama-run: phony bin\llama-run.exe + +build llama-run.exe: phony bin\llama-run.exe + +build llama-save-load-state: phony bin\llama-save-load-state.exe + +build llama-save-load-state.exe: phony bin\llama-save-load-state.exe + +build llama-server: phony bin\llama-server.exe + +build llama-server.exe: phony bin\llama-server.exe + +build llama-simple: phony bin\llama-simple.exe + +build llama-simple-chat: phony bin\llama-simple-chat.exe + +build llama-simple-chat.exe: phony bin\llama-simple-chat.exe + +build llama-simple.exe: phony bin\llama-simple.exe + +build llama-speculative: phony bin\llama-speculative.exe + +build llama-speculative-simple: phony bin\llama-speculative-simple.exe + +build llama-speculative-simple.exe: phony bin\llama-speculative-simple.exe + +build llama-speculative.exe: phony bin\llama-speculative.exe + +build llama-tokenize: phony bin\llama-tokenize.exe + +build llama-tokenize.exe: phony bin\llama-tokenize.exe + +build llama-tts: phony bin\llama-tts.exe + +build llama-tts.exe: phony bin\llama-tts.exe + +build llama-vdot: phony bin\llama-vdot.exe + +build llama-vdot.exe: phony bin\llama-vdot.exe + +build llama.dll: phony bin\llama.dll + +build mtmd: phony bin\mtmd.dll + +build mtmd.dll: phony bin\mtmd.dll + +build sha1: phony examples\gguf-hash\sha1 + +build sha256: phony examples\gguf-hash\sha256 + +build test-autorelease: phony bin\test-autorelease.exe + +build test-autorelease.exe: phony bin\test-autorelease.exe + +build test-backend-ops: phony bin\test-backend-ops.exe + +build test-backend-ops.exe: phony bin\test-backend-ops.exe + +build test-barrier: phony bin\test-barrier.exe + +build test-barrier.exe: phony bin\test-barrier.exe + +build test-c: phony bin\test-c.exe + +build test-c.exe: phony bin\test-c.exe + +build test-chat-parser: phony bin\test-chat-parser.exe + +build test-chat-parser.exe: phony bin\test-chat-parser.exe + +build test-chat-template: phony bin\test-chat-template.exe + +build test-chat-template.exe: phony bin\test-chat-template.exe + +build test-gguf: phony bin\test-gguf.exe + +build test-gguf.exe: phony bin\test-gguf.exe + +build test-json-partial: phony bin\test-json-partial.exe + +build test-json-partial.exe: phony bin\test-json-partial.exe + +build test-log: phony bin\test-log.exe + +build test-log.exe: phony bin\test-log.exe + +build test-model-load-cancel: phony bin\test-model-load-cancel.exe + +build test-model-load-cancel.exe: phony bin\test-model-load-cancel.exe + +build test-mtmd-c-api: phony bin\test-mtmd-c-api.exe + +build test-mtmd-c-api.exe: phony bin\test-mtmd-c-api.exe + +build test-quantize-fns: phony bin\test-quantize-fns.exe + +build test-quantize-fns.exe: phony bin\test-quantize-fns.exe + +build test-quantize-perf: phony bin\test-quantize-perf.exe + +build test-quantize-perf.exe: phony bin\test-quantize-perf.exe + +build test-regex-partial: phony bin\test-regex-partial.exe + +build test-regex-partial.exe: phony bin\test-regex-partial.exe + +build test-rope: phony bin\test-rope.exe + +build test-rope.exe: phony bin\test-rope.exe + +build test-thread-safety: phony bin\test-thread-safety.exe + +build test-thread-safety.exe: phony bin\test-thread-safety.exe + +build test-tokenizer-0: phony bin\test-tokenizer-0.exe + +build test-tokenizer-0.exe: phony bin\test-tokenizer-0.exe + +build xxhash: phony examples\gguf-hash\xxhash + +# ============================================================================= +# Folder targets. + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build + +build all: phony ggml\all src\all common\all tests\all examples\all pocs\all tools\all + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/common + +build common\all: phony common\build_info common\common.lib + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples + +build examples\all: phony examples\batched\all examples\embedding\all examples\eval-callback\all examples\gguf-hash\all examples\gguf\all examples\gritlm\all examples\lookahead\all examples\lookup\all examples\parallel\all examples\passkey\all examples\retrieval\all examples\save-load-state\all examples\simple\all examples\simple-chat\all examples\speculative\all examples\speculative-simple\all examples\gen-docs\all examples\training\all examples\diffusion\all examples\convert-llama2c-to-ggml\all + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/batched + +build examples\batched\all: phony bin\llama-batched.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/convert-llama2c-to-ggml + +build examples\convert-llama2c-to-ggml\all: phony bin\llama-convert-llama2c-to-ggml.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/diffusion + +build examples\diffusion\all: phony bin\llama-diffusion-cli.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/embedding + +build examples\embedding\all: phony bin\llama-embedding.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/eval-callback + +build examples\eval-callback\all: phony bin\llama-eval-callback.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gen-docs + +build examples\gen-docs\all: phony bin\llama-gen-docs.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gguf + +build examples\gguf\all: phony bin\llama-gguf.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gguf-hash + +build examples\gguf-hash\all: phony bin\llama-gguf-hash.exe examples\gguf-hash\xxhash examples\gguf-hash\sha1 examples\gguf-hash\sha256 + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gritlm + +build examples\gritlm\all: phony bin\llama-gritlm.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/lookahead + +build examples\lookahead\all: phony bin\llama-lookahead.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/lookup + +build examples\lookup\all: phony bin\llama-lookup.exe bin\llama-lookup-create.exe bin\llama-lookup-merge.exe bin\llama-lookup-stats.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/parallel + +build examples\parallel\all: phony bin\llama-parallel.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/passkey + +build examples\passkey\all: phony bin\llama-passkey.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/retrieval + +build examples\retrieval\all: phony bin\llama-retrieval.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/save-load-state + +build examples\save-load-state\all: phony bin\llama-save-load-state.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/simple + +build examples\simple\all: phony bin\llama-simple.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/simple-chat + +build examples\simple-chat\all: phony bin\llama-simple-chat.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/speculative + +build examples\speculative\all: phony bin\llama-speculative.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/speculative-simple + +build examples\speculative-simple\all: phony bin\llama-speculative-simple.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/training + +build examples\training\all: phony bin\llama-finetune.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml + +build ggml\all: phony ggml\src\all + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src + +build ggml\src\all: phony bin\ggml-base.dll bin\ggml.dll bin\ggml-cpu.dll ggml\src\ggml-cpu\all + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/ggml-cpu + +build ggml\src\ggml-cpu\all: phony + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/pocs + +build pocs\all: phony pocs\vdot\all + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/pocs/vdot + +build pocs\vdot\all: phony bin\llama-vdot.exe bin\llama-q8dot.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/src + +build src\all: phony bin\llama.dll + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tests + +build tests\all: phony bin\test-tokenizer-0.exe bin\test-chat-parser.exe bin\test-chat-template.exe bin\test-json-partial.exe bin\test-log.exe bin\test-regex-partial.exe bin\test-thread-safety.exe bin\test-gguf.exe bin\test-backend-ops.exe bin\test-model-load-cancel.exe bin\test-autorelease.exe bin\test-barrier.exe bin\test-quantize-fns.exe bin\test-quantize-perf.exe bin\test-rope.exe bin\test-mtmd-c-api.exe bin\test-c.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools + +build tools\all: phony tools\batched-bench\all tools\gguf-split\all tools\imatrix\all tools\llama-bench\all tools\main\all tools\perplexity\all tools\quantize\all tools\server\all tools\run\all tools\tokenize\all tools\tts\all tools\mtmd\all tools\cvector-generator\all tools\export-lora\all + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/batched-bench + +build tools\batched-bench\all: phony bin\llama-batched-bench.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/cvector-generator + +build tools\cvector-generator\all: phony bin\llama-cvector-generator.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/export-lora + +build tools\export-lora\all: phony bin\llama-export-lora.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/gguf-split + +build tools\gguf-split\all: phony bin\llama-gguf-split.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/imatrix + +build tools\imatrix\all: phony bin\llama-imatrix.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/llama-bench + +build tools\llama-bench\all: phony bin\llama-bench.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/main + +build tools\main\all: phony bin\llama-cli.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/mtmd + +build tools\mtmd\all: phony bin\mtmd.dll bin\llama-llava-cli.exe bin\llama-gemma3-cli.exe bin\llama-minicpmv-cli.exe bin\llama-qwen2vl-cli.exe bin\llama-mtmd-cli.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/perplexity + +build tools\perplexity\all: phony bin\llama-perplexity.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/quantize + +build tools\quantize\all: phony bin\llama-quantize.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/run + +build tools\run\all: phony bin\llama-run.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/server + +build tools\server\all: phony bin\llama-server.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/tokenize + +build tools\tokenize\all: phony bin\llama-tokenize.exe + +# ============================================================================= + +############################################# +# Folder: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/tts + +build tools\tts\all: phony bin\llama-tts.exe + +# ============================================================================= +# Built-in targets + + +############################################# +# Re-run CMake if any of its inputs changed. + +build build.ninja C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\ggml\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\ggml\src\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\ggml\src\ggml-cpu\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\src\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\common\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\tests\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\batched\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\embedding\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\eval-callback\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\gguf-hash\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\gguf\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\gritlm\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\lookahead\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\lookup\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\parallel\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\passkey\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\retrieval\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ 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C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\training\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\diffusion\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\examples\convert-llama2c-to-ggml\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\pocs\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\build\pocs\vdot\cmake_install.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ 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Files\CMake\share\cmake-4.1\Modules\Platform\WindowsPaths.cmake C$:\Program$ Files\CMake\share\cmake-4.1\Modules\WriteBasicConfigVersionFile.cmake C$:\Program$ Files\CMake\share\cmake-4.1\Templates\CTestScript.cmake.in C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\.git\index C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\CMakeLists.txt C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\cmake\build-info.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\cmake\common.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ Python$ Stuff\Actual$ Python$ Script\ai-coding-genius-main\llama.cpp\cmake\git-vars.cmake C$:\Users\Kids\Documents\Aryan's$ Coding$ Stuff\Aryan's$ 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Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\..\\include\" /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 /Foggml\\src\\CMakeFiles\\ggml-base.dir\\gguf.cpp.obj /Fdggml\\src\\CMakeFiles\\ggml-base.dir\\ /FS -c \"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\gguf.cpp\"", + "file": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/gguf.cpp", + "output": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/CMakeFiles/ggml-base.dir/gguf.cpp.obj" +}, +{ + "directory": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build", + "command": 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-I\"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\" -I\"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\..\\include\" /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 -std:c11 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp /Foggml\\src\\CMakeFiles\\ggml-cpu.dir\\ggml-cpu\\ggml-cpu.c.obj /Fdggml\\src\\CMakeFiles\\ggml-cpu.dir\\ /FS -c \"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\\ggml-cpu.c\"", + "file": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/ggml-cpu/ggml-cpu.c", + "output": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python 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Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\" -I\"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\..\\include\" /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp /Foggml\\src\\CMakeFiles\\ggml-cpu.dir\\ggml-cpu\\ggml-cpu.cpp.obj /Fdggml\\src\\CMakeFiles\\ggml-cpu.dir\\ /FS -c \"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\\ggml-cpu.cpp\"", + "file": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/ggml-cpu/ggml-cpu.cpp", + "output": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python 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Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\" -I\"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\..\\include\" /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp /Foggml\\src\\CMakeFiles\\ggml-cpu.dir\\ggml-cpu\\repack.cpp.obj /Fdggml\\src\\CMakeFiles\\ggml-cpu.dir\\ /FS -c \"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\\repack.cpp\"", + "file": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/ggml-cpu/repack.cpp", + "output": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python 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Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\" -I\"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\..\\include\" /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp /Foggml\\src\\CMakeFiles\\ggml-cpu.dir\\ggml-cpu\\hbm.cpp.obj /Fdggml\\src\\CMakeFiles\\ggml-cpu.dir\\ /FS -c \"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\\hbm.cpp\"", + "file": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/ggml-cpu/hbm.cpp", + "output": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python 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Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\" -I\"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\..\\include\" /DWIN32 /D_WINDOWS /W3 /MDd /Zi /Ob0 /Od /RTC1 -std:c11 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp /Foggml\\src\\CMakeFiles\\ggml-cpu.dir\\ggml-cpu\\quants.c.obj /Fdggml\\src\\CMakeFiles\\ggml-cpu.dir\\ /FS -c \"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\\quants.c\"", + "file": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/ggml-cpu/quants.c", + "output": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/CMakeFiles/ggml-cpu.dir/ggml-cpu/quants.c.obj" +}, 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Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\..\\include\" /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp /Foggml\\src\\CMakeFiles\\ggml-cpu.dir\\ggml-cpu\\traits.cpp.obj /Fdggml\\src\\CMakeFiles\\ggml-cpu.dir\\ /FS -c \"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\\traits.cpp\"", + "file": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/ggml-cpu/traits.cpp", + "output": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/CMakeFiles/ggml-cpu.dir/ggml-cpu/traits.cpp.obj" +}, +{ + "directory": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python 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Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\..\\include\" /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp /Foggml\\src\\CMakeFiles\\ggml-cpu.dir\\ggml-cpu\\amx\\amx.cpp.obj /Fdggml\\src\\CMakeFiles\\ggml-cpu.dir\\ /FS -c \"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\\amx\\amx.cpp\"", + "file": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/ggml-cpu/amx/amx.cpp", + "output": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/CMakeFiles/ggml-cpu.dir/ggml-cpu/amx/amx.cpp.obj" +}, +{ + "directory": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python 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Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\..\\include\" /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp /Foggml\\src\\CMakeFiles\\ggml-cpu.dir\\ggml-cpu\\amx\\mmq.cpp.obj /Fdggml\\src\\CMakeFiles\\ggml-cpu.dir\\ /FS -c \"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\\amx\\mmq.cpp\"", + "file": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/ggml-cpu/amx/mmq.cpp", + "output": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/CMakeFiles/ggml-cpu.dir/ggml-cpu/amx/mmq.cpp.obj" +}, +{ + "directory": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python 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Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\..\\include\" /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp /Foggml\\src\\CMakeFiles\\ggml-cpu.dir\\ggml-cpu\\binary-ops.cpp.obj /Fdggml\\src\\CMakeFiles\\ggml-cpu.dir\\ /FS -c \"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\\binary-ops.cpp\"", + "file": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/ggml-cpu/binary-ops.cpp", + "output": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/CMakeFiles/ggml-cpu.dir/ggml-cpu/binary-ops.cpp.obj" +}, +{ + "directory": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python 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Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\..\\include\" /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp /Foggml\\src\\CMakeFiles\\ggml-cpu.dir\\ggml-cpu\\unary-ops.cpp.obj /Fdggml\\src\\CMakeFiles\\ggml-cpu.dir\\ /FS -c \"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\\unary-ops.cpp\"", + "file": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/ggml-cpu/unary-ops.cpp", + "output": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/CMakeFiles/ggml-cpu.dir/ggml-cpu/unary-ops.cpp.obj" +}, +{ + "directory": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python 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Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\..\\include\" /DWIN32 /D_WINDOWS /W3 /GR /EHsc /MDd /Zi /Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp /Foggml\\src\\CMakeFiles\\ggml-cpu.dir\\ggml-cpu\\vec.cpp.obj /Fdggml\\src\\CMakeFiles\\ggml-cpu.dir\\ /FS -c \"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\\vec.cpp\"", + "file": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/ggml-cpu/vec.cpp", + "output": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/CMakeFiles/ggml-cpu.dir/ggml-cpu/vec.cpp.obj" +}, +{ + "directory": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build", + 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/Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp /Foggml\\src\\CMakeFiles\\ggml-cpu.dir\\ggml-cpu\\llamafile\\sgemm.cpp.obj /Fdggml\\src\\CMakeFiles\\ggml-cpu.dir\\ /FS -c \"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\\llamafile\\sgemm.cpp\"", + "file": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/ggml-cpu/llamafile/sgemm.cpp", + "output": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/CMakeFiles/ggml-cpu.dir/ggml-cpu/llamafile/sgemm.cpp.obj" +}, +{ + "directory": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build", + "command": 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/Ob0 /Od /RTC1 -std:c++17 /utf-8 /bigobj /arch:AVX2 /wd4005 /wd4244 /wd4267 /wd4305 /wd4566 /wd4996 /wd4702 -openmp /Foggml\\src\\CMakeFiles\\ggml-cpu.dir\\ggml-cpu\\arch\\x86\\repack.cpp.obj /Fdggml\\src\\CMakeFiles\\ggml-cpu.dir\\ /FS -c \"C:\\Users\\Kids\\Documents\\Aryan's Coding Stuff\\Aryan's Python Stuff\\Actual Python Script\\ai-coding-genius-main\\llama.cpp\\ggml\\src\\ggml-cpu\\arch\\x86\\repack.cpp\"", + "file": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/src/ggml-cpu/arch/x86/repack.cpp", + "output": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/CMakeFiles/ggml-cpu.dir/ggml-cpu/arch/x86/repack.cpp.obj" +}, +{ + "directory": "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build", + "command": 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Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/export-lora/CMakeFiles/llama-export-lora.dir/export-lora.cpp.obj" +} +] diff --git a/llama.cpp/build/examples/CTestTestfile.cmake b/llama.cpp/build/examples/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..395440d1cf42ae4023ced95ae51e7850e3b22097 --- /dev/null +++ b/llama.cpp/build/examples/CTestTestfile.cmake @@ -0,0 +1,26 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. +subdirs("batched") +subdirs("embedding") +subdirs("eval-callback") +subdirs("gguf-hash") +subdirs("gguf") +subdirs("gritlm") +subdirs("lookahead") +subdirs("lookup") +subdirs("parallel") +subdirs("passkey") +subdirs("retrieval") +subdirs("save-load-state") +subdirs("simple") +subdirs("simple-chat") +subdirs("speculative") +subdirs("speculative-simple") +subdirs("gen-docs") +subdirs("training") +subdirs("diffusion") +subdirs("convert-llama2c-to-ggml") diff --git a/llama.cpp/build/examples/batched/CTestTestfile.cmake b/llama.cpp/build/examples/batched/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..d5afce48ed1412800fb58db1041b8953c8731899 --- /dev/null +++ b/llama.cpp/build/examples/batched/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/batched +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/batched +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/batched/cmake_install.cmake b/llama.cpp/build/examples/batched/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..2baf576b3f453d07a449baa897285672c5526380 --- /dev/null +++ b/llama.cpp/build/examples/batched/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/batched + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-batched.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/batched/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/cmake_install.cmake b/llama.cpp/build/examples/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..76f26c816c328442f6691b99c4dcb3688643dda8 --- /dev/null +++ b/llama.cpp/build/examples/cmake_install.cmake @@ -0,0 +1,140 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/batched/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/embedding/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/eval-callback/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gguf-hash/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gguf/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gritlm/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/lookahead/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/lookup/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/parallel/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/passkey/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/retrieval/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/save-load-state/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/simple/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/simple-chat/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/speculative/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/speculative-simple/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gen-docs/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/training/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/diffusion/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/convert-llama2c-to-ggml/cmake_install.cmake") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/convert-llama2c-to-ggml/CTestTestfile.cmake b/llama.cpp/build/examples/convert-llama2c-to-ggml/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..2a6cbcbf196b1574a8b4263ed188328222a3ddf9 --- /dev/null +++ b/llama.cpp/build/examples/convert-llama2c-to-ggml/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/convert-llama2c-to-ggml +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/convert-llama2c-to-ggml +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/convert-llama2c-to-ggml/cmake_install.cmake b/llama.cpp/build/examples/convert-llama2c-to-ggml/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..8acab5a89f9f974b8feaf14c1fe5f678237f8f5b --- /dev/null +++ b/llama.cpp/build/examples/convert-llama2c-to-ggml/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/convert-llama2c-to-ggml + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-convert-llama2c-to-ggml.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/convert-llama2c-to-ggml/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/diffusion/CTestTestfile.cmake b/llama.cpp/build/examples/diffusion/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..c4a1d46a6231ee5a21dd424db910ad8d2de7de77 --- /dev/null +++ b/llama.cpp/build/examples/diffusion/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/diffusion +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/diffusion +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/diffusion/cmake_install.cmake b/llama.cpp/build/examples/diffusion/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..1b19a7095f2e8507821906d911d730ba4b892462 --- /dev/null +++ b/llama.cpp/build/examples/diffusion/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/diffusion + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-diffusion-cli.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/diffusion/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/embedding/CTestTestfile.cmake b/llama.cpp/build/examples/embedding/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..b7b1f82fb0c0b6d57731ef274dc9301d274400e1 --- /dev/null +++ b/llama.cpp/build/examples/embedding/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/embedding +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/embedding +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/embedding/cmake_install.cmake b/llama.cpp/build/examples/embedding/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..0c21c05126af3a3759034bb3193bacaa97c3f2c2 --- /dev/null +++ b/llama.cpp/build/examples/embedding/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/embedding + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-embedding.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/embedding/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/eval-callback/CTestTestfile.cmake b/llama.cpp/build/examples/eval-callback/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..1c1d4faf7e51486ecddd0428f7246d9865d22f8e --- /dev/null +++ b/llama.cpp/build/examples/eval-callback/CTestTestfile.cmake @@ -0,0 +1,8 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/eval-callback +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/eval-callback +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. +add_test(test-eval-callback "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-eval-callback.exe" "--hf-repo" "ggml-org/models" "--hf-file" "tinyllamas/stories260K.gguf" "--model" "stories260K.gguf" "--prompt" "hello" "--seed" "42" "-ngl" "0") +set_tests_properties(test-eval-callback PROPERTIES LABELS "eval-callback;curl" _BACKTRACE_TRIPLES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/eval-callback/CMakeLists.txt;8;add_test;C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/eval-callback/CMakeLists.txt;0;") diff --git a/llama.cpp/build/examples/eval-callback/cmake_install.cmake b/llama.cpp/build/examples/eval-callback/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..61e9e013c0f5de37b73eedee102f733caf787be6 --- /dev/null +++ b/llama.cpp/build/examples/eval-callback/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/eval-callback + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-eval-callback.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/eval-callback/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/gen-docs/CTestTestfile.cmake b/llama.cpp/build/examples/gen-docs/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..1be09bb8348a49792a73145d6740dc3f4afdb82e --- /dev/null +++ b/llama.cpp/build/examples/gen-docs/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/gen-docs +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gen-docs +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/gen-docs/cmake_install.cmake b/llama.cpp/build/examples/gen-docs/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..ae1569ca6886c8e3bf662f67b5e7052ef379e6d4 --- /dev/null +++ b/llama.cpp/build/examples/gen-docs/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/gen-docs + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-gen-docs.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gen-docs/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/gguf-hash/CTestTestfile.cmake b/llama.cpp/build/examples/gguf-hash/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..cc0606c2feb5f5a608aed0c8f38b5f9820c9dacd --- /dev/null +++ b/llama.cpp/build/examples/gguf-hash/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/gguf-hash +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gguf-hash +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/gguf-hash/cmake_install.cmake b/llama.cpp/build/examples/gguf-hash/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..0af138747c80848875cd8c1f716fc2bd9a71487c --- /dev/null +++ b/llama.cpp/build/examples/gguf-hash/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/gguf-hash + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-gguf-hash.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gguf-hash/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/gguf/CTestTestfile.cmake b/llama.cpp/build/examples/gguf/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..311784cad981f1caac94794a6c6e09f718a67c63 --- /dev/null +++ b/llama.cpp/build/examples/gguf/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/gguf +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gguf +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/gguf/cmake_install.cmake b/llama.cpp/build/examples/gguf/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..913dd5d6bec247edff8ed1925dfa45c1a09f08c9 --- /dev/null +++ b/llama.cpp/build/examples/gguf/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/gguf + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-gguf.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gguf/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/gritlm/CTestTestfile.cmake b/llama.cpp/build/examples/gritlm/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..c71cf8436763d4db1928cc88080eca3b205ec788 --- /dev/null +++ b/llama.cpp/build/examples/gritlm/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/gritlm +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gritlm +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/gritlm/cmake_install.cmake b/llama.cpp/build/examples/gritlm/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..9cbcf296789b9ca343eed7987f697bda52cabbce --- /dev/null +++ b/llama.cpp/build/examples/gritlm/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/gritlm + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-gritlm.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/gritlm/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/lookahead/CTestTestfile.cmake b/llama.cpp/build/examples/lookahead/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..e1ec0de1ef15c7bf9e9eff017b3183854cd5448c --- /dev/null +++ b/llama.cpp/build/examples/lookahead/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/lookahead +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/lookahead +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/lookahead/cmake_install.cmake b/llama.cpp/build/examples/lookahead/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..90fced448c1fa89e5d22fc614a08ca5771fceb45 --- /dev/null +++ b/llama.cpp/build/examples/lookahead/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/lookahead + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-lookahead.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/lookahead/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/lookup/CTestTestfile.cmake b/llama.cpp/build/examples/lookup/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..ea8b99660784de6670b57e136109d30925f9901b --- /dev/null +++ b/llama.cpp/build/examples/lookup/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/lookup +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/lookup +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/lookup/cmake_install.cmake b/llama.cpp/build/examples/lookup/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..b1893201d7ed4d11ba80cef6cbdcbc7fb4c0f2fe --- /dev/null +++ b/llama.cpp/build/examples/lookup/cmake_install.cmake @@ -0,0 +1,56 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/lookup + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-lookup.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-lookup-create.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-lookup-merge.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-lookup-stats.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/lookup/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/parallel/CTestTestfile.cmake b/llama.cpp/build/examples/parallel/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..026526eaa8986920202d3d1065e736b4b8736abd --- /dev/null +++ b/llama.cpp/build/examples/parallel/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/parallel +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/parallel +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/parallel/cmake_install.cmake b/llama.cpp/build/examples/parallel/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..7c73c0a8616656a9b514a17330fd1aa35855a537 --- /dev/null +++ b/llama.cpp/build/examples/parallel/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/parallel + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-parallel.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/parallel/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/passkey/CTestTestfile.cmake b/llama.cpp/build/examples/passkey/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..9d63251f361577874fdd401c017cf929194bc0f8 --- /dev/null +++ b/llama.cpp/build/examples/passkey/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/passkey +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/passkey +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/passkey/cmake_install.cmake b/llama.cpp/build/examples/passkey/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..7d878008efdc8b7a9d08692973bd26ff847b3b79 --- /dev/null +++ b/llama.cpp/build/examples/passkey/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/passkey + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-passkey.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/passkey/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/retrieval/CTestTestfile.cmake b/llama.cpp/build/examples/retrieval/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..6904923934edb99838c4093c6734c141a715afd9 --- /dev/null +++ b/llama.cpp/build/examples/retrieval/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/retrieval +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/retrieval +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/retrieval/cmake_install.cmake b/llama.cpp/build/examples/retrieval/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..0b7251751714f3fd74de1d2147ffa9b1d8fe4097 --- /dev/null +++ b/llama.cpp/build/examples/retrieval/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/retrieval + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-retrieval.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/retrieval/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/save-load-state/CTestTestfile.cmake b/llama.cpp/build/examples/save-load-state/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..2903918c2e5291d26dbf4f01a696edc6ac8bff1d --- /dev/null +++ b/llama.cpp/build/examples/save-load-state/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/save-load-state +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/save-load-state +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/save-load-state/cmake_install.cmake b/llama.cpp/build/examples/save-load-state/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..22fe3686d4accc5edb4328c93ef4a876e35f0c29 --- /dev/null +++ b/llama.cpp/build/examples/save-load-state/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/save-load-state + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-save-load-state.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/save-load-state/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/simple-chat/CTestTestfile.cmake b/llama.cpp/build/examples/simple-chat/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..2495d81728cd964e35997f6a08f42a0f6fc5b67c --- /dev/null +++ b/llama.cpp/build/examples/simple-chat/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/simple-chat +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/simple-chat +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/simple-chat/cmake_install.cmake b/llama.cpp/build/examples/simple-chat/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..4ac04e11195ba9cda6bef73697c2f1a42ec8e9fc --- /dev/null +++ b/llama.cpp/build/examples/simple-chat/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/simple-chat + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-simple-chat.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/simple-chat/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/simple/CTestTestfile.cmake b/llama.cpp/build/examples/simple/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..9e9566d0a591bb2d4d51cbdcc2db06da8e2cb8ac --- /dev/null +++ b/llama.cpp/build/examples/simple/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/simple +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/simple +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/simple/cmake_install.cmake b/llama.cpp/build/examples/simple/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..7c654ddbfbf62688981425cf9439ebe1c41c4d48 --- /dev/null +++ b/llama.cpp/build/examples/simple/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/simple + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-simple.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/simple/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/speculative-simple/CTestTestfile.cmake b/llama.cpp/build/examples/speculative-simple/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..e1d410cc639b3526fdc6e5b4280b52a22b26ab98 --- /dev/null +++ b/llama.cpp/build/examples/speculative-simple/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/speculative-simple +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/speculative-simple +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/speculative-simple/cmake_install.cmake b/llama.cpp/build/examples/speculative-simple/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..1882a3ecbee0bca8a860393b16e8b705ba29c02c --- /dev/null +++ b/llama.cpp/build/examples/speculative-simple/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/speculative-simple + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-speculative-simple.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/speculative-simple/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/speculative/CTestTestfile.cmake b/llama.cpp/build/examples/speculative/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..873d3098a38de7f67759d17617e4f1e28e84652c --- /dev/null +++ b/llama.cpp/build/examples/speculative/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/speculative +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/speculative +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/speculative/cmake_install.cmake b/llama.cpp/build/examples/speculative/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..d4dc89c7db7260d49abb9f3ce7a7e761538e11df --- /dev/null +++ b/llama.cpp/build/examples/speculative/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/speculative + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-speculative.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/speculative/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/examples/training/CTestTestfile.cmake b/llama.cpp/build/examples/training/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..4293e1a8db6cd2f67fb6b33622f1c96c4edfe5b3 --- /dev/null +++ b/llama.cpp/build/examples/training/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/training +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/training +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/examples/training/cmake_install.cmake b/llama.cpp/build/examples/training/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..972518b9848e1cd4178839cb1ca8ce07c193e6da --- /dev/null +++ b/llama.cpp/build/examples/training/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/examples/training + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-finetune.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/examples/training/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/ggml/cmake_install.cmake b/llama.cpp/build/ggml/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..199eb6bf72bef093cb600f26eaf869ea9a0b3668 --- /dev/null +++ b/llama.cpp/build/ggml/cmake_install.cmake @@ -0,0 +1,88 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/cmake_install.cmake") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE STATIC_LIBRARY OPTIONAL FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/ggml.lib") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE SHARED_LIBRARY FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/ggml.dll") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/include" TYPE FILE FILES + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/ggml.h" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/ggml-cpu.h" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/ggml-alloc.h" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/ggml-backend.h" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/ggml-blas.h" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/ggml-cann.h" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/ggml-cpp.h" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/ggml-cuda.h" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/ggml-opt.h" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/ggml-metal.h" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/ggml-rpc.h" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/ggml-sycl.h" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/ggml-vulkan.h" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/ggml-webgpu.h" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/ggml/include/gguf.h" + ) +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE STATIC_LIBRARY OPTIONAL FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/ggml-base.lib") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE SHARED_LIBRARY FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/ggml-base.dll") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib/cmake/ggml" TYPE FILE FILES + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/ggml-config.cmake" + "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/ggml-version.cmake" + ) +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/ggml/ggml-config.cmake b/llama.cpp/build/ggml/ggml-config.cmake new file mode 100644 index 0000000000000000000000000000000000000000..6446a5fe117f5af1e66e83ca93b885139fb771b6 --- /dev/null +++ b/llama.cpp/build/ggml/ggml-config.cmake @@ -0,0 +1,324 @@ + +####### Expanded from @PACKAGE_INIT@ by configure_package_config_file() ####### +####### Any changes to this file will be overwritten by the next CMake run #### +####### The input file was ggml-config.cmake.in ######## + +get_filename_component(PACKAGE_PREFIX_DIR "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE) + +macro(set_and_check _var _file) + set(${_var} "${_file}") + if(NOT EXISTS "${_file}") + message(FATAL_ERROR "File or directory ${_file} referenced by variable ${_var} does not exist !") + endif() +endmacro() + +macro(check_required_components _NAME) + foreach(comp ${${_NAME}_FIND_COMPONENTS}) + if(NOT ${_NAME}_${comp}_FOUND) + if(${_NAME}_FIND_REQUIRED_${comp}) + set(${_NAME}_FOUND FALSE) + endif() + endif() + endforeach() +endmacro() + +#################################################################################### + + +####### Expanded from @GGML_VARIABLES_EXPANED@ by configure_package_config_file() ####### +####### Any changes to this file will be overwritten by the next CMake run ####### + +set(GGML_ACCELERATE "ON") +set(GGML_ALL_WARNINGS "ON") +set(GGML_ALL_WARNINGS_3RD_PARTY "OFF") +set(GGML_AVAILABLE_BACKENDS "ggml-cpu") +set(GGML_AVX "OFF") +set(GGML_AVX2 "OFF") +set(GGML_AVX512 "OFF") +set(GGML_AVX512_BF16 "OFF") +set(GGML_AVX512_VBMI "OFF") +set(GGML_AVX512_VNNI "OFF") +set(GGML_AVX_VNNI "OFF") +set(GGML_BACKEND_DL "OFF") +set(GGML_BLAS "OFF") +set(GGML_BLAS_DEFAULT "OFF") +set(GGML_BLAS_VENDOR "Generic") +set(GGML_BLAS_VENDOR_DEFAULT "Generic") +set(GGML_BMI2 "OFF") +set(GGML_BUILD_COMMIT "03d46982") +set(GGML_BUILD_EXAMPLES "OFF") +set(GGML_BUILD_NUMBER "6074") +set(GGML_BUILD_TESTS "OFF") +set(GGML_CCACHE "ON") +set(GGML_CCACHE_FOUND "GGML_CCACHE_FOUND-NOTFOUND") +set(GGML_CPU "ON") +set(GGML_CPU_ALL_VARIANTS "OFF") +set(GGML_CPU_ARM_ARCH "") +set(GGML_CPU_HBM "OFF") +set(GGML_CPU_KLEIDIAI "OFF") +set(GGML_CPU_POWERPC_CPUTYPE "") +set(GGML_CPU_REPACK "ON") +set(GGML_CUDA "OFF") +set(GGML_CUDA_COMPRESSION_MODE "size") +set(GGML_CUDA_F16 "OFF") +set(GGML_CUDA_FA "ON") +set(GGML_CUDA_FA_ALL_QUANTS "OFF") +set(GGML_CUDA_FORCE_CUBLAS "OFF") +set(GGML_CUDA_FORCE_MMQ "OFF") +set(GGML_CUDA_GRAPHS "ON") +set(GGML_CUDA_GRAPHS_DEFAULT "ON") +set(GGML_CUDA_NO_PEER_COPY "OFF") +set(GGML_CUDA_NO_VMM "OFF") +set(GGML_CUDA_PEER_MAX_BATCH_SIZE "128") +set(GGML_FATAL_WARNINGS "OFF") +set(GGML_GPROF "OFF") +set(GGML_HIP "OFF") +set(GGML_HIP_FORCE_ROCWMMA_FATTN_GFX12 "OFF") +set(GGML_HIP_GRAPHS "OFF") +set(GGML_HIP_MMQ_MFMA "ON") +set(GGML_HIP_NO_VMM "ON") +set(GGML_HIP_ROCWMMA_FATTN "OFF") +set(GGML_LASX "ON") +set(GGML_LLAMAFILE "ON") +set(GGML_LLAMAFILE_DEFAULT "ON") +set(GGML_LSX "ON") +set(GGML_LTO "OFF") +set(GGML_METAL "OFF") +set(GGML_METAL_DEFAULT "OFF") +set(GGML_METAL_EMBED_LIBRARY "OFF") +set(GGML_METAL_MACOSX_VERSION_MIN "") +set(GGML_METAL_NDEBUG "OFF") +set(GGML_METAL_SHADER_DEBUG "OFF") +set(GGML_METAL_STD "") +set(GGML_METAL_USE_BF16 "OFF") +set(GGML_MUSA "OFF") +set(GGML_MUSA_GRAPHS "OFF") +set(GGML_MUSA_MUDNN_COPY "OFF") +set(GGML_NATIVE "ON") +set(GGML_NATIVE_DEFAULT "ON") +set(GGML_NNPA "OFF") +set(GGML_OPENCL "OFF") +set(GGML_OPENCL_EMBED_KERNELS "ON") +set(GGML_OPENCL_PROFILING "OFF") +set(GGML_OPENCL_TARGET_VERSION "300") +set(GGML_OPENCL_USE_ADRENO_KERNELS "ON") +set(GGML_OPENMP "ON") +set(GGML_OPENMP_ENABLED "ON") +set(GGML_PUBLIC_HEADERS "include/ggml.h;include/ggml-cpu.h;include/ggml-alloc.h;include/ggml-backend.h;include/ggml-blas.h;include/ggml-cann.h;include/ggml-cpp.h;include/ggml-cuda.h;include/ggml-opt.h;include/ggml-metal.h;include/ggml-rpc.h;include/ggml-sycl.h;include/ggml-vulkan.h;include/ggml-webgpu.h;include/gguf.h") +set(GGML_RPC "OFF") +set(GGML_RVV "ON") +set(GGML_RV_ZFH "OFF") +set(GGML_SANITIZE_ADDRESS "OFF") +set(GGML_SANITIZE_THREAD "OFF") +set(GGML_SANITIZE_UNDEFINED "OFF") +set(GGML_SCCACHE_FOUND "GGML_SCCACHE_FOUND-NOTFOUND") +set(GGML_SCHED_MAX_COPIES "4") +set(GGML_SHARED_LIB "ON") +set(GGML_SSE42 "OFF") +set(GGML_STANDALONE "OFF") +set(GGML_STATIC "OFF") +set(GGML_SYCL "OFF") +set(GGML_SYCL_DEVICE_ARCH "") +set(GGML_SYCL_DNN "ON") +set(GGML_SYCL_F16 "OFF") +set(GGML_SYCL_GRAPH "ON") +set(GGML_SYCL_TARGET "INTEL") +set(GGML_VULKAN "OFF") +set(GGML_VULKAN_CHECK_RESULTS "OFF") +set(GGML_VULKAN_DEBUG "OFF") +set(GGML_VULKAN_MEMORY_DEBUG "OFF") +set(GGML_VULKAN_RUN_TESTS "OFF") +set(GGML_VULKAN_SHADERS_GEN_TOOLCHAIN "") +set(GGML_VULKAN_SHADER_DEBUG_INFO "OFF") +set(GGML_VULKAN_VALIDATE "OFF") +set(GGML_VXE "ON") +set(GGML_WEBGPU "OFF") +set(GGML_WEBGPU_DEBUG "OFF") +set(GGML_XTHEADVECTOR "OFF") + + +# Find all dependencies before creating any target. +include(CMakeFindDependencyMacro) +find_dependency(Threads) +if (NOT GGML_SHARED_LIB) + set(GGML_CPU_INTERFACE_LINK_LIBRARIES "") + set(GGML_CPU_INTERFACE_LINK_OPTIONS "") + + if (APPLE AND GGML_ACCELERATE) + find_library(ACCELERATE_FRAMEWORK Accelerate) + if(NOT ACCELERATE_FRAMEWORK) + set(${CMAKE_FIND_PACKAGE_NAME}_FOUND 0) + return() + endif() + list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES ${ACCELERATE_FRAMEWORK}) + endif() + + if (GGML_OPENMP_ENABLED) + find_dependency(OpenMP) + list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES OpenMP::OpenMP_C OpenMP::OpenMP_CXX) + endif() + + if (GGML_CPU_HBM) + find_library(memkind memkind) + if(NOT memkind) + set(${CMAKE_FIND_PACKAGE_NAME}_FOUND 0) + return() + endif() + list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES memkind) + endif() + + if (GGML_BLAS) + find_dependency(BLAS) + list(APPEND GGML_BLAS_INTERFACE_LINK_LIBRARIES ${BLAS_LIBRARIES}) + list(APPEND GGML_BLAS_INTERFACE_LINK_OPTIONS ${BLAS_LINKER_FLAGS}) + endif() + + if (GGML_CUDA) + set(GGML_CUDA_INTERFACE_LINK_LIBRARIES "") + find_dependency(CUDAToolkit) + if (GGML_STATIC) + list(APPEND GGML_CUDA_INTERFACE_LINK_LIBRARIES $) + if (WIN32) + list(APPEND GGML_CUDA_INTERFACE_LINK_LIBRARIES $ $) + else() + list(APPEND GGML_CUDA_INTERFACE_LINK_LIBRARIES $ $) + endif() + endif() + if (NOT GGML_CUDA_NO_VMM) + list(APPEND GGML_CUDA_INTERFACE_LINK_LIBRARIES $) + endif() + endif() + + if (GGML_METAL) + find_library(FOUNDATION_LIBRARY Foundation) + find_library(METAL_FRAMEWORK Metal) + find_library(METALKIT_FRAMEWORK MetalKit) + if(NOT FOUNDATION_LIBRARY OR NOT METAL_FRAMEWORK OR NOT METALKIT_FRAMEWORK) + set(${CMAKE_FIND_PACKAGE_NAME}_FOUND 0) + return() + endif() + set(GGML_METAL_INTERFACE_LINK_LIBRARIES + ${FOUNDATION_LIBRARY} ${METAL_FRAMEWORK} ${METALKIT_FRAMEWORK}) + endif() + + if (GGML_OPENCL) + find_dependency(OpenCL) + set(GGML_OPENCL_INTERFACE_LINK_LIBRARIES $) + endif() + + if (GGML_VULKAN) + find_dependency(Vulkan) + set(GGML_VULKAN_INTERFACE_LINK_LIBRARIES $) + endif() + + if (GGML_HIP) + find_dependency(hip) + find_dependency(hipblas) + find_dependency(rocblas) + set(GGML_HIP_INTERFACE_LINK_LIBRARIES hip::host roc::rocblas roc::hipblas) + endif() + + if (GGML_SYCL) + set(GGML_SYCL_INTERFACE_LINK_LIBRARIES "") + find_package(DNNL) + if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL") + list(APPEND GGML_SYCL_INTERFACE_LINK_LIBRARIES DNNL::dnnl) + endif() + if (WIN32) + find_dependency(IntelSYCL) + find_dependency(MKL) + list(APPEND GGML_SYCL_INTERFACE_LINK_LIBRARIES IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL) + endif() + endif() +endif() + +set_and_check(GGML_INCLUDE_DIR "${PACKAGE_PREFIX_DIR}/include") +set_and_check(GGML_LIB_DIR "${PACKAGE_PREFIX_DIR}/lib") +#set_and_check(GGML_BIN_DIR "${PACKAGE_PREFIX_DIR}/bin") + +if(NOT TARGET ggml::ggml) + find_package(Threads REQUIRED) + + find_library(GGML_LIBRARY ggml + REQUIRED + HINTS ${GGML_LIB_DIR} + NO_CMAKE_FIND_ROOT_PATH) + + add_library(ggml::ggml UNKNOWN IMPORTED) + set_target_properties(ggml::ggml + PROPERTIES + IMPORTED_LOCATION "${GGML_LIBRARY}") + + find_library(GGML_BASE_LIBRARY ggml-base + REQUIRED + HINTS ${GGML_LIB_DIR} + NO_CMAKE_FIND_ROOT_PATH) + + add_library(ggml::ggml-base UNKNOWN IMPORTED) + set_target_properties(ggml::ggml-base + PROPERTIES + IMPORTED_LOCATION "${GGML_BASE_LIBRARY}") + + set(_ggml_all_targets "") + foreach(_ggml_backend ${GGML_AVAILABLE_BACKENDS}) + string(REPLACE "-" "_" _ggml_backend_pfx "${_ggml_backend}") + string(TOUPPER "${_ggml_backend_pfx}" _ggml_backend_pfx) + + find_library(${_ggml_backend_pfx}_LIBRARY ${_ggml_backend} + REQUIRED + HINTS ${GGML_LIB_DIR} + NO_CMAKE_FIND_ROOT_PATH) + + message(STATUS "Found ${${_ggml_backend_pfx}_LIBRARY}") + + add_library(ggml::${_ggml_backend} UNKNOWN IMPORTED) + set_target_properties(ggml::${_ggml_backend} + PROPERTIES + INTERFACE_INCLUDE_DIRECTORIES "${GGML_INCLUDE_DIR}" + IMPORTED_LINK_INTERFACE_LANGUAGES "CXX" + IMPORTED_LOCATION "${${_ggml_backend_pfx}_LIBRARY}" + INTERFACE_COMPILE_FEATURES c_std_90 + POSITION_INDEPENDENT_CODE ON) + + string(REGEX MATCH "^ggml-cpu" is_cpu_variant "${_ggml_backend}") + if(is_cpu_variant) + list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES "ggml::ggml-base") + set_target_properties(ggml::${_ggml_backend} + PROPERTIES + INTERFACE_LINK_LIBRARIES "${GGML_CPU_INTERFACE_LINK_LIBRARIES}") + + if(GGML_CPU_INTERFACE_LINK_OPTIONS) + set_target_properties(ggml::${_ggml_backend} + PROPERTIES + INTERFACE_LINK_OPTIONS "${GGML_CPU_INTERFACE_LINK_OPTIONS}") + endif() + + else() + list(APPEND ${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES "ggml::ggml-base") + set_target_properties(ggml::${_ggml_backend} + PROPERTIES + INTERFACE_LINK_LIBRARIES "${${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES}") + + if(${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS) + set_target_properties(ggml::${_ggml_backend} + PROPERTIES + INTERFACE_LINK_OPTIONS "${${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS}") + endif() + endif() + + list(APPEND _ggml_all_targets ggml::${_ggml_backend}) + endforeach() + + list(APPEND GGML_INTERFACE_LINK_LIBRARIES ggml::ggml-base "${_ggml_all_targets}") + set_target_properties(ggml::ggml + PROPERTIES + INTERFACE_LINK_LIBRARIES "${GGML_INTERFACE_LINK_LIBRARIES}") + + add_library(ggml::all INTERFACE IMPORTED) + set_target_properties(ggml::all + PROPERTIES + INTERFACE_LINK_LIBRARIES "${_ggml_all_targets}") + +endif() + +check_required_components(ggml) diff --git a/llama.cpp/build/ggml/ggml-version.cmake b/llama.cpp/build/ggml/ggml-version.cmake new file mode 100644 index 0000000000000000000000000000000000000000..6e292e4868d8f18fb20723e5132d3d572a78d7f8 --- /dev/null +++ b/llama.cpp/build/ggml/ggml-version.cmake @@ -0,0 +1,65 @@ +# This is a basic version file for the Config-mode of find_package(). +# It is used by write_basic_package_version_file() as input file for configure_file() +# to create a version-file which can be installed along a config.cmake file. +# +# The created file sets PACKAGE_VERSION_EXACT if the current version string and +# the requested version string are exactly the same and it sets +# PACKAGE_VERSION_COMPATIBLE if the current version is >= requested version, +# but only if the requested major version is the same as the current one. +# The variable CVF_VERSION must be set before calling configure_file(). + + +set(PACKAGE_VERSION "0.0.6074") + +if(PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION) + set(PACKAGE_VERSION_COMPATIBLE FALSE) +else() + + if("0.0.6074" MATCHES "^([0-9]+)\\.") + set(CVF_VERSION_MAJOR "${CMAKE_MATCH_1}") + if(NOT CVF_VERSION_MAJOR VERSION_EQUAL 0) + string(REGEX REPLACE "^0+" "" CVF_VERSION_MAJOR "${CVF_VERSION_MAJOR}") + endif() + else() + set(CVF_VERSION_MAJOR "0.0.6074") + endif() + + if(PACKAGE_FIND_VERSION_RANGE) + # both endpoints of the range must have the expected major version + math (EXPR CVF_VERSION_MAJOR_NEXT "${CVF_VERSION_MAJOR} + 1") + if (NOT PACKAGE_FIND_VERSION_MIN_MAJOR STREQUAL CVF_VERSION_MAJOR + OR ((PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "INCLUDE" AND NOT PACKAGE_FIND_VERSION_MAX_MAJOR STREQUAL CVF_VERSION_MAJOR) + OR (PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "EXCLUDE" AND NOT PACKAGE_FIND_VERSION_MAX VERSION_LESS_EQUAL CVF_VERSION_MAJOR_NEXT))) + set(PACKAGE_VERSION_COMPATIBLE FALSE) + elseif(PACKAGE_FIND_VERSION_MIN_MAJOR STREQUAL CVF_VERSION_MAJOR + AND ((PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "INCLUDE" 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"C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/ggml-cpu/cmake_install.cmake") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE STATIC_LIBRARY OPTIONAL FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/ggml-cpu.lib") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE SHARED_LIBRARY FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/ggml-cpu.dll") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/ggml/src/ggml-base.exp b/llama.cpp/build/ggml/src/ggml-base.exp new file mode 100644 index 0000000000000000000000000000000000000000..709acff17ef5bbc6ab572e0a65f3ad4d76a3570e Binary files /dev/null and b/llama.cpp/build/ggml/src/ggml-base.exp differ diff --git a/llama.cpp/build/ggml/src/ggml-base.lib b/llama.cpp/build/ggml/src/ggml-base.lib new file mode 100644 index 0000000000000000000000000000000000000000..fe292e9b52b15b3ea1d484b825b28eea80ae9c5a --- /dev/null +++ b/llama.cpp/build/ggml/src/ggml-base.lib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ffd4b689f227f421ebf6e42398ca2eaadb310670a9a001dc6187e294c218308 +size 133100 diff --git a/llama.cpp/build/ggml/src/ggml-cpu.exp b/llama.cpp/build/ggml/src/ggml-cpu.exp new file mode 100644 index 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set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/ggml/src/ggml-cpu/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/ggml/src/ggml.exp b/llama.cpp/build/ggml/src/ggml.exp new file mode 100644 index 0000000000000000000000000000000000000000..38f8eb3394107d990748787813aaf0b59d3dfdca Binary files /dev/null and b/llama.cpp/build/ggml/src/ggml.exp differ diff --git a/llama.cpp/build/ggml/src/ggml.lib b/llama.cpp/build/ggml/src/ggml.lib new file mode 100644 index 0000000000000000000000000000000000000000..04aa621a344bc2d4f3c36abd94d03d46752a0cd5 Binary files /dev/null and b/llama.cpp/build/ggml/src/ggml.lib differ diff --git a/llama.cpp/build/llama-config.cmake b/llama.cpp/build/llama-config.cmake new file mode 100644 index 0000000000000000000000000000000000000000..92ee14a4f3e32d70d800a898c2c0a1b10fc9a7fe --- /dev/null +++ b/llama.cpp/build/llama-config.cmake @@ -0,0 +1,54 @@ +set(LLAMA_VERSION 0.0.6074) +set(LLAMA_BUILD_COMMIT 03d46982) +set(LLAMA_BUILD_NUMBER 6074) +set(LLAMA_SHARED_LIB ON) + + +####### Expanded from @PACKAGE_INIT@ by configure_package_config_file() ####### +####### Any changes to this file will be overwritten by the next CMake run #### +####### The input file was llama-config.cmake.in ######## + +get_filename_component(PACKAGE_PREFIX_DIR "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE) + +macro(set_and_check _var _file) + set(${_var} "${_file}") + if(NOT EXISTS "${_file}") + message(FATAL_ERROR "File or directory ${_file} referenced by variable ${_var} does not exist !") + endif() +endmacro() + +macro(check_required_components _NAME) + foreach(comp ${${_NAME}_FIND_COMPONENTS}) + if(NOT ${_NAME}_${comp}_FOUND) + if(${_NAME}_FIND_REQUIRED_${comp}) + set(${_NAME}_FOUND FALSE) + endif() + endif() + endforeach() +endmacro() + +#################################################################################### + +set_and_check(LLAMA_INCLUDE_DIR "${PACKAGE_PREFIX_DIR}/include") +set_and_check(LLAMA_LIB_DIR "${PACKAGE_PREFIX_DIR}/lib") +set_and_check(LLAMA_BIN_DIR "${PACKAGE_PREFIX_DIR}/bin") + +find_package(ggml REQUIRED HINTS ${LLAMA_LIB_DIR}/cmake) + +find_library(llama_LIBRARY llama + REQUIRED + HINTS ${LLAMA_LIB_DIR} + NO_CMAKE_FIND_ROOT_PATH +) + +add_library(llama UNKNOWN IMPORTED) +set_target_properties(llama + PROPERTIES + INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}" + INTERFACE_LINK_LIBRARIES "ggml::ggml;ggml::ggml-base;" + IMPORTED_LINK_INTERFACE_LANGUAGES "CXX" + IMPORTED_LOCATION "${llama_LIBRARY}" + INTERFACE_COMPILE_FEATURES c_std_90 + POSITION_INDEPENDENT_CODE ON) + +check_required_components(Llama) diff --git a/llama.cpp/build/llama-version.cmake b/llama.cpp/build/llama-version.cmake new file mode 100644 index 0000000000000000000000000000000000000000..6e292e4868d8f18fb20723e5132d3d572a78d7f8 --- /dev/null +++ b/llama.cpp/build/llama-version.cmake @@ -0,0 +1,65 @@ +# This is a basic version file for the Config-mode of find_package(). +# It is used by write_basic_package_version_file() as input file for configure_file() +# to create a version-file which can be installed along a config.cmake file. +# +# The created file sets PACKAGE_VERSION_EXACT if the current version string and +# the requested version string are exactly the same and it sets +# PACKAGE_VERSION_COMPATIBLE if the current version is >= requested version, +# but only if the requested major version is the same as the current one. +# The variable CVF_VERSION must be set before calling configure_file(). + + +set(PACKAGE_VERSION "0.0.6074") + +if(PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION) + set(PACKAGE_VERSION_COMPATIBLE FALSE) +else() + + if("0.0.6074" MATCHES "^([0-9]+)\\.") + set(CVF_VERSION_MAJOR "${CMAKE_MATCH_1}") + if(NOT CVF_VERSION_MAJOR VERSION_EQUAL 0) + string(REGEX REPLACE "^0+" "" CVF_VERSION_MAJOR "${CVF_VERSION_MAJOR}") + endif() + else() + set(CVF_VERSION_MAJOR "0.0.6074") + endif() + + if(PACKAGE_FIND_VERSION_RANGE) + # both endpoints of the range must have the expected major version + math (EXPR CVF_VERSION_MAJOR_NEXT "${CVF_VERSION_MAJOR} + 1") + if (NOT PACKAGE_FIND_VERSION_MIN_MAJOR STREQUAL CVF_VERSION_MAJOR + OR ((PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "INCLUDE" AND NOT PACKAGE_FIND_VERSION_MAX_MAJOR STREQUAL CVF_VERSION_MAJOR) + OR (PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "EXCLUDE" AND NOT PACKAGE_FIND_VERSION_MAX VERSION_LESS_EQUAL CVF_VERSION_MAJOR_NEXT))) + set(PACKAGE_VERSION_COMPATIBLE FALSE) + elseif(PACKAGE_FIND_VERSION_MIN_MAJOR STREQUAL CVF_VERSION_MAJOR + AND ((PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "INCLUDE" AND PACKAGE_VERSION VERSION_LESS_EQUAL PACKAGE_FIND_VERSION_MAX) + OR (PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "EXCLUDE" AND PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION_MAX))) + set(PACKAGE_VERSION_COMPATIBLE TRUE) + else() + set(PACKAGE_VERSION_COMPATIBLE FALSE) + endif() + else() + if(PACKAGE_FIND_VERSION_MAJOR STREQUAL CVF_VERSION_MAJOR) + set(PACKAGE_VERSION_COMPATIBLE TRUE) + else() + set(PACKAGE_VERSION_COMPATIBLE FALSE) + endif() + + if(PACKAGE_FIND_VERSION STREQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT TRUE) + endif() + endif() +endif() + + +# if the installed or the using project don't have CMAKE_SIZEOF_VOID_P set, ignore it: +if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "" OR "8" STREQUAL "") + return() +endif() + +# check that the installed version has the same 32/64bit-ness as the one which is currently searching: +if(NOT CMAKE_SIZEOF_VOID_P STREQUAL "8") + math(EXPR installedBits "8 * 8") + set(PACKAGE_VERSION "${PACKAGE_VERSION} (${installedBits}bit)") + set(PACKAGE_VERSION_UNSUITABLE TRUE) +endif() diff --git a/llama.cpp/build/llama.pc b/llama.cpp/build/llama.pc new file mode 100644 index 0000000000000000000000000000000000000000..d9b51d3aa39e10cc93a8245f70cd281060a9fd04 --- /dev/null +++ b/llama.cpp/build/llama.pc @@ -0,0 +1,10 @@ +prefix=C:/Program Files (x86)/llama.cpp +exec_prefix=C:/Program Files (x86)/llama.cpp +libdir=C:/Program Files (x86)/llama.cpp/lib +includedir=C:/Program Files (x86)/llama.cpp/include + +Name: llama +Description: Port of Facebook's LLaMA model in C/C++ +Version: 0.0.6074 +Libs: -L${libdir} -lggml -lggml-base -lllama +Cflags: -I${includedir} diff --git a/llama.cpp/build/pocs/CTestTestfile.cmake b/llama.cpp/build/pocs/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..853d1c33ca2d8ef069f969d0bfb7272e8088cf76 --- /dev/null +++ b/llama.cpp/build/pocs/CTestTestfile.cmake @@ -0,0 +1,7 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/pocs +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/pocs +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. +subdirs("vdot") diff --git a/llama.cpp/build/pocs/cmake_install.cmake b/llama.cpp/build/pocs/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..cbecc14dcb22bfdc5a3a738d60d107521c678d4b --- /dev/null +++ b/llama.cpp/build/pocs/cmake_install.cmake @@ -0,0 +1,45 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/pocs + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/pocs/vdot/cmake_install.cmake") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/pocs/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/pocs/vdot/CTestTestfile.cmake b/llama.cpp/build/pocs/vdot/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..e4f841c521c0b5706149447ddce4d43fd6dbe814 --- /dev/null +++ b/llama.cpp/build/pocs/vdot/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/pocs/vdot +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/pocs/vdot +# +# This file includes the relevant testing commands required for +# 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+add_test(test-mtmd-c-api "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-mtmd-c-api.exe") +set_tests_properties(test-mtmd-c-api PROPERTIES LABELS "main" WORKING_DIRECTORY "." _BACKTRACE_TRIPLES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tests/CMakeLists.txt;102;add_test;C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tests/CMakeLists.txt;212;llama_build_and_test;C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tests/CMakeLists.txt;0;") diff --git a/llama.cpp/build/tests/cmake_install.cmake b/llama.cpp/build/tests/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..a7ed5c421471254a0f8757edecd613fff6279dc3 --- /dev/null +++ b/llama.cpp/build/tests/cmake_install.cmake @@ -0,0 +1,104 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tests + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-tokenizer-0.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-chat-parser.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-chat-template.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-json-partial.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-log.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-regex-partial.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-thread-safety.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-gguf.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-backend-ops.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-model-load-cancel.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-autorelease.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-barrier.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-quantize-fns.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-quantize-perf.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-rope.exe") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/test-mtmd-c-api.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tests/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/tools/CTestTestfile.cmake b/llama.cpp/build/tools/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..04ff27cc5347534f0ff23bfe380987f2bf2fcc0f --- /dev/null +++ b/llama.cpp/build/tools/CTestTestfile.cmake @@ -0,0 +1,20 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. +subdirs("batched-bench") +subdirs("gguf-split") +subdirs("imatrix") +subdirs("llama-bench") +subdirs("main") +subdirs("perplexity") +subdirs("quantize") +subdirs("server") +subdirs("run") +subdirs("tokenize") +subdirs("tts") +subdirs("mtmd") +subdirs("cvector-generator") +subdirs("export-lora") diff --git a/llama.cpp/build/tools/batched-bench/CTestTestfile.cmake b/llama.cpp/build/tools/batched-bench/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..f92235dc95c79653c51c9dabdfe75cb1a9d3530a --- /dev/null +++ b/llama.cpp/build/tools/batched-bench/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/batched-bench +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/batched-bench +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/tools/batched-bench/cmake_install.cmake b/llama.cpp/build/tools/batched-bench/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..1856eb13b9c7a674fd8e5b5ce24a5009738eb790 --- /dev/null +++ b/llama.cpp/build/tools/batched-bench/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/batched-bench + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-batched-bench.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/batched-bench/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/tools/cmake_install.cmake b/llama.cpp/build/tools/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..bb1ce9d53b14b2e219e68a946e22231ebdacea7a --- /dev/null +++ b/llama.cpp/build/tools/cmake_install.cmake @@ -0,0 +1,110 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/batched-bench/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/gguf-split/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/imatrix/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/llama-bench/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/main/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/perplexity/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/quantize/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/server/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/run/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/tokenize/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/tts/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/mtmd/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/cvector-generator/cmake_install.cmake") +endif() + +if(NOT CMAKE_INSTALL_LOCAL_ONLY) + # Include the install script for the subdirectory. + include("C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/export-lora/cmake_install.cmake") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/tools/cvector-generator/CTestTestfile.cmake b/llama.cpp/build/tools/cvector-generator/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..b3cc3bc0e0213811a6b298a8908a2802d7e63935 --- /dev/null +++ b/llama.cpp/build/tools/cvector-generator/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/cvector-generator +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/cvector-generator +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/tools/cvector-generator/cmake_install.cmake b/llama.cpp/build/tools/cvector-generator/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..413e5e8598496d6be6b7afd107f71d1c1fae011a --- /dev/null +++ b/llama.cpp/build/tools/cvector-generator/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/cvector-generator + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-cvector-generator.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/cvector-generator/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/tools/export-lora/CTestTestfile.cmake b/llama.cpp/build/tools/export-lora/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..9eab47baefafa7cf57a7db48f3ceab76ee6b7dfc --- /dev/null +++ b/llama.cpp/build/tools/export-lora/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/export-lora +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/export-lora +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/tools/export-lora/cmake_install.cmake b/llama.cpp/build/tools/export-lora/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..f8a6fdab5d55472f440e2601d7011f8da5edf4f7 --- /dev/null +++ b/llama.cpp/build/tools/export-lora/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/export-lora + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-export-lora.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/export-lora/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/tools/gguf-split/CTestTestfile.cmake b/llama.cpp/build/tools/gguf-split/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..d9871a88fff83d6eabac3de77f44cdec96a52164 --- /dev/null +++ b/llama.cpp/build/tools/gguf-split/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/gguf-split +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/gguf-split +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/tools/gguf-split/cmake_install.cmake b/llama.cpp/build/tools/gguf-split/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..6cf2d6358bb92892b5aa8265dff9ac9eff24cdc7 --- /dev/null +++ b/llama.cpp/build/tools/gguf-split/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/gguf-split + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-gguf-split.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/gguf-split/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/tools/imatrix/CTestTestfile.cmake b/llama.cpp/build/tools/imatrix/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..9bb11e6995b5addffd02a808e309fbf815acaa5a --- /dev/null +++ b/llama.cpp/build/tools/imatrix/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/imatrix +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/imatrix +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/tools/imatrix/cmake_install.cmake b/llama.cpp/build/tools/imatrix/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..a7b3194d0af6459d78b1d26478d9b482654856c7 --- /dev/null +++ b/llama.cpp/build/tools/imatrix/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/imatrix + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-imatrix.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/imatrix/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/tools/llama-bench/CTestTestfile.cmake b/llama.cpp/build/tools/llama-bench/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..e2669ddeb1ea878ead025d95962cf12763661e34 --- /dev/null +++ b/llama.cpp/build/tools/llama-bench/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/llama-bench +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/llama-bench +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/tools/llama-bench/cmake_install.cmake b/llama.cpp/build/tools/llama-bench/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..4c645f708cd8350414eb004a85b908fa2d7e7908 --- /dev/null +++ b/llama.cpp/build/tools/llama-bench/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/llama-bench + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-bench.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/llama-bench/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/tools/main/CTestTestfile.cmake b/llama.cpp/build/tools/main/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..7d5deeb7512752309de2d1d3afe44866e1f6a84e --- /dev/null +++ b/llama.cpp/build/tools/main/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/main +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/main +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/tools/main/cmake_install.cmake b/llama.cpp/build/tools/main/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..912dd6a095dae36f8ac994975ccce483182b3e6b --- /dev/null +++ b/llama.cpp/build/tools/main/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/main + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + 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Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/mtmd +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/tools/mtmd/cmake_install.cmake b/llama.cpp/build/tools/mtmd/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..22795664786d8a4833bc2722b9f938b435dcac59 --- /dev/null +++ b/llama.cpp/build/tools/mtmd/cmake_install.cmake @@ -0,0 +1,59 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/mtmd + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED 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message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-perplexity.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/perplexity/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git 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set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-server.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/server/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/tools/server/index.html.gz.hpp b/llama.cpp/build/tools/server/index.html.gz.hpp new file mode 100644 index 0000000000000000000000000000000000000000..f4a08d54d992126ca773da1cd9c494e23300a4d4 --- /dev/null +++ b/llama.cpp/build/tools/server/index.html.gz.hpp @@ -0,0 +1,2 @@ +unsigned char index_html_gz[] = 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+unsigned int index_html_gz_len = 1913892; diff --git a/llama.cpp/build/tools/server/loading.html.hpp b/llama.cpp/build/tools/server/loading.html.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7b2d735e8df33692955e24e7e2f52cef8d6b0018 --- /dev/null +++ b/llama.cpp/build/tools/server/loading.html.hpp @@ -0,0 +1,2 @@ +unsigned char loading_html[] = {0x3c,0x21,0x44,0x4f,0x43,0x54,0x59,0x50,0x45,0x20,0x68,0x74,0x6d,0x6c,0x3e,0x0d,0x0a,0x3c,0x68,0x74,0x6d,0x6c,0x3e,0x0d,0x0a,0x20,0x20,0x20,0x20,0x3c,0x68,0x65,0x61,0x64,0x3e,0x0d,0x0a,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x3c,0x6d,0x65,0x74,0x61,0x20,0x68,0x74,0x74,0x70,0x2d,0x65,0x71,0x75,0x69,0x76,0x3d,0x22,0x72,0x65,0x66,0x72,0x65,0x73,0x68,0x22,0x20,0x63,0x6f,0x6e,0x74,0x65,0x6e,0x74,0x3d,0x22,0x35,0x22,0x3e,0x0d,0x0a,0x20,0x20,0x20,0x20,0x3c,0x2f,0x68,0x65,0x61,0x64,0x3e,0x0d,0x0a,0x20,0x20,0x20,0x20,0x3c,0x62,0x6f,0x64,0x79,0x3e,0x0d,0x0a,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x3c,0x64,0x69,0x76,0x20,0x69,0x64,0x3d,0x22,0x6c,0x6f,0x61,0x64,0x69,0x6e,0x67,0x22,0x3e,0x0d,0x0a,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x54,0x68,0x65,0x20,0x6d,0x6f,0x64,0x65,0x6c,0x20,0x69,0x73,0x20,0x6c,0x6f,0x61,0x64,0x69,0x6e,0x67,0x2e,0x20,0x50,0x6c,0x65,0x61,0x73,0x65,0x20,0x77,0x61,0x69,0x74,0x2e,0x3c,0x62,0x72,0x2f,0x3e,0x0d,0x0a,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x54,0x68,0x65,0x20,0x75,0x73,0x65,0x72,0x20,0x69,0x6e,0x74,0x65,0x72,0x66,0x61,0x63,0x65,0x20,0x77,0x69,0x6c,0x6c,0x20,0x61,0x70,0x70,0x65,0x61,0x72,0x20,0x73,0x6f,0x6f,0x6e,0x2e,0x0d,0x0a,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x20,0x3c,0x2f,0x64,0x69,0x76,0x3e,0x0d,0x0a,0x20,0x20,0x20,0x20,0x3c,0x2f,0x62,0x6f,0x64,0x79,0x3e,0x0d,0x0a,0x3c,0x2f,0x68,0x74,0x6d,0x6c,0x3e,0x0d,0x0a,}; +unsigned int loading_html_len = 280; diff --git a/llama.cpp/build/tools/tokenize/CTestTestfile.cmake b/llama.cpp/build/tools/tokenize/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..bf24e89fb3ff876a737d165263ba253fac800ef1 --- /dev/null +++ b/llama.cpp/build/tools/tokenize/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/tokenize +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/tokenize +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/tools/tokenize/cmake_install.cmake b/llama.cpp/build/tools/tokenize/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..c60cd5d6cf26e4abded59995b0bfd4a884d4f815 --- /dev/null +++ b/llama.cpp/build/tools/tokenize/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/tokenize + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-tokenize.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/tokenize/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/build/tools/tts/CTestTestfile.cmake b/llama.cpp/build/tools/tts/CTestTestfile.cmake new file mode 100644 index 0000000000000000000000000000000000000000..bff08907e7c4e7fca2697c6144131fb24043d9ba --- /dev/null +++ b/llama.cpp/build/tools/tts/CTestTestfile.cmake @@ -0,0 +1,6 @@ +# CMake generated Testfile for +# Source directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/tts +# Build directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/tts +# +# This file includes the relevant testing commands required for +# testing this directory and lists subdirectories to be tested as well. diff --git a/llama.cpp/build/tools/tts/cmake_install.cmake b/llama.cpp/build/tools/tts/cmake_install.cmake new file mode 100644 index 0000000000000000000000000000000000000000..5d1e0af6a5ea5afa5d7813fe76c3203363ee0d5b --- /dev/null +++ b/llama.cpp/build/tools/tts/cmake_install.cmake @@ -0,0 +1,44 @@ +# Install script for directory: C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/tools/tts + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "C:/Program Files (x86)/llama.cpp") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "Debug") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE EXECUTABLE FILES "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/bin/llama-tts.exe") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +if(CMAKE_INSTALL_LOCAL_ONLY) + file(WRITE "C:/Users/Kids/Documents/Aryan's Coding Stuff/Aryan's Python Stuff/Actual Python Script/ai-coding-genius-main/llama.cpp/build/tools/tts/install_local_manifest.txt" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") +endif() diff --git a/llama.cpp/ci/README.md b/llama.cpp/ci/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9d42102780242f1b9edb5fa52630d7aa3b092657 --- /dev/null +++ b/llama.cpp/ci/README.md @@ -0,0 +1,68 @@ +# CI + +In addition to [Github Actions](https://github.com/ggml-org/llama.cpp/actions) `llama.cpp` uses a custom CI framework: + +https://github.com/ggml-org/ci + +It monitors the `master` branch for new commits and runs the +[ci/run.sh](https://github.com/ggml-org/llama.cpp/blob/master/ci/run.sh) script on dedicated cloud instances. This allows us +to execute heavier workloads compared to just using Github Actions. Also with time, the cloud instances will be scaled +to cover various hardware architectures, including GPU and Apple Silicon instances. + +Collaborators can optionally trigger the CI run by adding the `ggml-ci` keyword to their commit message. +Only the branches of this repo are monitored for this keyword. + +It is a good practice, before publishing changes to execute the full CI locally on your machine: + +```bash +mkdir tmp + +# CPU-only build +bash ./ci/run.sh ./tmp/results ./tmp/mnt + +# with CUDA support +GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt + +# with SYCL support +source /opt/intel/oneapi/setvars.sh +GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt + +# with MUSA support +GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt +``` + +## Running MUSA CI in a Docker Container + +Assuming `$PWD` is the root of the `llama.cpp` repository, follow these steps to set up and run MUSA CI in a Docker container: + +### 1. Create a local directory to store cached models, configuration files and venv: + +```bash +mkdir -p $HOME/llama.cpp/ci-cache +``` + +### 2. Create a local directory to store CI run results: + +```bash +mkdir -p $HOME/llama.cpp/ci-results +``` + +### 3. Start a Docker container and run the CI: + +```bash +docker run --privileged -it \ + -v $HOME/llama.cpp/ci-cache:/ci-cache \ + -v $HOME/llama.cpp/ci-results:/ci-results \ + -v $PWD:/ws -w /ws \ + mthreads/musa:rc4.2.0-devel-ubuntu22.04-amd64 +``` + +Inside the container, execute the following commands: + +```bash +apt update -y && apt install -y bc cmake ccache git python3.10-venv time unzip wget +git config --global --add safe.directory /ws +GG_BUILD_MUSA=1 bash ./ci/run.sh /ci-results /ci-cache +``` + +This setup ensures that the CI runs within an isolated Docker environment while maintaining cached files and results across runs. diff --git a/llama.cpp/ci/run.sh b/llama.cpp/ci/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..89e84a5e48073eeccbf6e8f2987658f6d682f6ba --- /dev/null +++ b/llama.cpp/ci/run.sh @@ -0,0 +1,894 @@ +#!/usr/bin/env bash +# +# sample usage: +# +# mkdir tmp +# +# # CPU-only build +# bash ./ci/run.sh ./tmp/results ./tmp/mnt +# +# # with CUDA support +# GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt +# +# # with SYCL support +# GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt +# +# # with VULKAN support +# GG_BUILD_VULKAN=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt +# +# # with WebGPU support +# GG_BUILD_WEBGPU=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt +# +# # with MUSA support +# GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt +# + +if [ -z "$2" ]; then + echo "usage: $0 " + exit 1 +fi + +mkdir -p "$1" +mkdir -p "$2" + +OUT=$(realpath "$1") +MNT=$(realpath "$2") + +rm -f "$OUT/*.log" +rm -f "$OUT/*.exit" +rm -f "$OUT/*.md" + +sd=`dirname $0` +cd $sd/../ +SRC=`pwd` + +CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON" + +if [ ! -z ${GG_BUILD_METAL} ]; then + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON -DGGML_METAL_USE_BF16=ON" +fi + +if [ ! -z ${GG_BUILD_CUDA} ]; then + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=ON" + + if command -v nvidia-smi >/dev/null 2>&1; then + CUDA_ARCH=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader,nounits 2>/dev/null | head -1 | tr -d '.') + if [[ -n "$CUDA_ARCH" && "$CUDA_ARCH" =~ ^[0-9]+$ ]]; then + CMAKE_EXTRA="${CMAKE_EXTRA} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH}" + else + echo "Warning: Using fallback CUDA architectures" + CMAKE_EXTRA="${CMAKE_EXTRA} -DCMAKE_CUDA_ARCHITECTURES=61;70;75;80;86;89" + fi + else + echo "Error: nvidia-smi not found, cannot build with CUDA" + exit 1 + fi +fi + +if [ ! -z ${GG_BUILD_SYCL} ]; then + if [ -z ${ONEAPI_ROOT} ]; then + echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:" + echo "source /opt/intel/oneapi/setvars.sh" + exit 1 + fi + # Use only main GPU + export ONEAPI_DEVICE_SELECTOR="level_zero:0" + # Enable sysman for correct memory reporting + export ZES_ENABLE_SYSMAN=1 + # to circumvent precision issues on CPY operations + export SYCL_PROGRAM_COMPILE_OPTIONS="-cl-fp32-correctly-rounded-divide-sqrt" + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON" +fi + +if [ ! -z ${GG_BUILD_VULKAN} ]; then + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_VULKAN=1" +fi + +if [ ! -z ${GG_BUILD_WEBGPU} ]; then + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_WEBGPU=1" +fi + +if [ ! -z ${GG_BUILD_MUSA} ]; then + # Use qy1 by default (MTT S80) + MUSA_ARCH=${MUSA_ARCH:-21} + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_MUSA=ON -DMUSA_ARCHITECTURES=${MUSA_ARCH}" +fi +## helpers + +# download a file if it does not exist or if it is outdated +function gg_wget { + local out=$1 + local url=$2 + + local cwd=`pwd` + + mkdir -p $out + cd $out + + # should not re-download if file is the same + wget -nv -N $url + + cd $cwd +} + +function gg_printf { + printf -- "$@" >> $OUT/README.md +} + +function gg_run { + ci=$1 + + set -o pipefail + set -x + + gg_run_$ci | tee $OUT/$ci.log + cur=$? + echo "$cur" > $OUT/$ci.exit + + set +x + set +o pipefail + + gg_sum_$ci + + ret=$((ret | cur)) +} + +## ci + +# ctest_debug + +function gg_run_ctest_debug { + cd ${SRC} + + rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug + + set -e + + # Check cmake, make and ctest are installed + gg_check_build_requirements + + (time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log + + (time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log + + set +e +} + +function gg_sum_ctest_debug { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs ctest in debug mode\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)" + gg_printf '```\n' + gg_printf '\n' +} + +# ctest_release + +function gg_run_ctest_release { + cd ${SRC} + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + # Check cmake, make and ctest are installed + gg_check_build_requirements + + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log + + if [ -z ${GG_BUILD_LOW_PERF} ]; then + (time ctest --output-on-failure -L main ) 2>&1 | tee -a $OUT/${ci}-ctest.log + else + (time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log + fi + + set +e +} + +function gg_sum_ctest_release { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs ctest in release mode\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)" + gg_printf '```\n' +} + +# test_scripts_debug + +function gg_run_test_scripts_debug { + cd ${SRC} + + set -e + + (cd ./tools/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + (cd ./tools/quantize && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + + set +e +} + +function gg_sum_test_scripts_debug { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs test scripts in debug mode\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)" + gg_printf '```\n' + gg_printf '\n' +} + +# test_scripts_release + +function gg_run_test_scripts_release { + cd ${SRC} + + set -e + + (cd ./tools/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + (cd ./tools/quantize && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + + set +e +} + +function gg_sum_test_scripts_release { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs test scripts in release mode\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)" + gg_printf '```\n' + gg_printf '\n' +} + +function gg_get_model { + local gguf_0="$MNT/models/pythia/1.4B/ggml-model-f16.gguf" + local gguf_1="$MNT/models/pythia/2.8B/ggml-model-f16.gguf" + local gguf_2="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf" + if [[ -s $gguf_0 ]]; then + echo -n "$gguf_0" + elif [[ -s $gguf_1 ]]; then + echo -n "$gguf_1" + elif [[ -s $gguf_2 ]]; then + echo -n "$gguf_2" + else + echo >&2 "No model found. Can't run gg_run_ctest_with_model." + exit 1 + fi +} + +function gg_run_ctest_with_model_debug { + cd ${SRC} + + local model; model=$(gg_get_model) + cd build-ci-debug + set -e + (LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log + set +e + cd .. +} + +function gg_run_ctest_with_model_release { + cd ${SRC} + + local model; model=$(gg_get_model) + cd build-ci-release + set -e + (LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log + set +e + cd .. +} + +function gg_sum_ctest_with_model_debug { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs ctest with model files in debug mode\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)" + gg_printf '```\n' +} + +function gg_sum_ctest_with_model_release { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs ctest with model files in release mode\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)" + gg_printf '```\n' +} + +# open_llama_7b_v2 + +function gg_run_open_llama_7b_v2 { + cd ${SRC} + + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/config.json + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/tokenizer.model + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/tokenizer_config.json + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/special_tokens_map.json + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/pytorch_model.bin.index.json + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00001-of-00002.bin + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00002-of-00002.bin + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/generation_config.json + + gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip + unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ + + path_models="../models-mnt/open-llama/7B-v2" + path_wiki="../models-mnt/wikitext/wikitext-2-raw" + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log + + python3 ../examples/convert_legacy_llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + + model_f16="${path_models}/ggml-model-f16.gguf" + model_q8_0="${path_models}/ggml-model-q8_0.gguf" + model_q4_0="${path_models}/ggml-model-q4_0.gguf" + model_q4_1="${path_models}/ggml-model-q4_1.gguf" + model_q5_0="${path_models}/ggml-model-q5_0.gguf" + model_q5_1="${path_models}/ggml-model-q5_1.gguf" + model_q2_k="${path_models}/ggml-model-q2_k.gguf" + model_q3_k="${path_models}/ggml-model-q3_k.gguf" + model_q4_k="${path_models}/ggml-model-q4_k.gguf" + model_q5_k="${path_models}/ggml-model-q5_k.gguf" + model_q6_k="${path_models}/ggml-model-q6_k.gguf" + + wiki_test="${path_wiki}/wiki.test.raw" + + ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 + ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0 + ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1 + ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0 + ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k + ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k + ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k + ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k + ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k + + (time ./bin/llama-cli -no-cnv --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + + function check_ppl { + qnt="$1" + ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + + if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then + printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl" + return 20 + fi + + printf ' - %s @ %s OK\n' "$qnt" "$ppl" + return 0 + } + + check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + + cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log + + set +e +} + +function gg_sum_open_llama_7b_v2 { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'OpenLLaMA 7B-v2:\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" + gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)" + gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" + gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" + gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" + gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)" + gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)" + gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)" + gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)" + gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)" + gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" + gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" + gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" + gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" +} + +# pythia_1.4b + +function gg_run_pythia_1_4b { + cd ${SRC} + + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/config.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer_config.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/special_tokens_map.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/resolve/main/pytorch_model.bin + + gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip + unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ + head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw + + path_models="../models-mnt/pythia/1.4B" + path_wiki="../models-mnt/wikitext/wikitext-2-raw" + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log + + python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + + model_f16="${path_models}/ggml-model-f16.gguf" + model_q8_0="${path_models}/ggml-model-q8_0.gguf" + model_q4_0="${path_models}/ggml-model-q4_0.gguf" + model_q4_1="${path_models}/ggml-model-q4_1.gguf" + model_q5_0="${path_models}/ggml-model-q5_0.gguf" + model_q5_1="${path_models}/ggml-model-q5_1.gguf" + model_q2_k="${path_models}/ggml-model-q2_k.gguf" + model_q3_k="${path_models}/ggml-model-q3_k.gguf" + model_q4_k="${path_models}/ggml-model-q4_k.gguf" + model_q5_k="${path_models}/ggml-model-q5_k.gguf" + model_q6_k="${path_models}/ggml-model-q6_k.gguf" + + wiki_test_60="${path_wiki}/wiki.test-60.raw" + + ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 + ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0 + ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1 + ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0 + ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k + ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k + ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k + ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k + ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k + + (time ./bin/llama-cli -no-cnv --model ${model_f16} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + + function check_ppl { + qnt="$1" + ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + + if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then + printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl" + return 20 + fi + + printf ' - %s @ %s OK\n' "$qnt" "$ppl" + return 0 + } + + check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model + check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + + cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log + + set +e +} + +function gg_sum_pythia_1_4b { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Pythia 1.4B:\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" + gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)" + gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" + gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" + gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" + gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)" + gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)" + gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)" + gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)" + gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)" + gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" + gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" + gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" + gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" +} + +# pythia_2_8b + +function gg_run_pythia_2_8b { + cd ${SRC} + + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/config.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer_config.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/special_tokens_map.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/resolve/main/pytorch_model.bin + + gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip + unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ + + path_models="../models-mnt/pythia/2.8B" + path_wiki="../models-mnt/wikitext/wikitext-2-raw" + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log + + python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + + model_f16="${path_models}/ggml-model-f16.gguf" + model_q8_0="${path_models}/ggml-model-q8_0.gguf" + model_q4_0="${path_models}/ggml-model-q4_0.gguf" + model_q4_1="${path_models}/ggml-model-q4_1.gguf" + model_q5_0="${path_models}/ggml-model-q5_0.gguf" + model_q5_1="${path_models}/ggml-model-q5_1.gguf" + model_q2_k="${path_models}/ggml-model-q2_k.gguf" + model_q3_k="${path_models}/ggml-model-q3_k.gguf" + model_q4_k="${path_models}/ggml-model-q4_k.gguf" + model_q5_k="${path_models}/ggml-model-q5_k.gguf" + model_q6_k="${path_models}/ggml-model-q6_k.gguf" + + wiki_test="${path_wiki}/wiki.test.raw" + + ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 + ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0 + ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1 + ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0 + ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k + ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k + ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k + ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k + ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k + + (time ./bin/llama-cli -no-cnv --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + + function check_ppl { + qnt="$1" + ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + + if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then + printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl" + return 20 + fi + + printf ' - %s @ %s OK\n' "$qnt" "$ppl" + return 0 + } + + check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model + check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + + cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log + + set +e +} + +function gg_sum_pythia_2_8b { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Pythia 2.8B:\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" + gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)" + gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" + gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" + gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" + gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)" + gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)" + gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)" + gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)" + gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)" + gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" + gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" + gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" + gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" +} + +# bge-small + +function gg_run_embd_bge_small { + cd ${SRC} + + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/config.json + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer.json + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer_config.json + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/special_tokens_map.json + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/pytorch_model.bin + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/sentence_bert_config.json + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/vocab.txt + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/modules.json + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/config.json + + gg_wget models-mnt/bge-small/1_Pooling https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/1_Pooling/config.json + + path_models="../models-mnt/bge-small" + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log + + python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + + model_f16="${path_models}/ggml-model-f16.gguf" + model_q8_0="${path_models}/ggml-model-q8_0.gguf" + + ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 + + (time ./bin/llama-embedding --model ${model_f16} -p "I believe the meaning of life is" -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + + set +e +} + +function gg_sum_embd_bge_small { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'BGE Small (BERT):\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" + gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" +} + +# rerank_tiny + +function gg_run_rerank_tiny { + cd ${SRC} + + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/config.json + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/tokenizer.json + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/tokenizer_config.json + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/special_tokens_map.json + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/resolve/main/pytorch_model.bin + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/sentence_bert_config.json + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/vocab.txt + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/modules.json + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/config.json + + gg_wget models-mnt/rerank-tiny/1_Pooling https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/1_Pooling/config.json + + path_models="../models-mnt/rerank-tiny" + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log + + python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + + model_f16="${path_models}/ggml-model-f16.gguf" + + # for this model, the SEP token is "" + (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?\thi\nwhat is panda?\tit's a bear\nwhat is panda?\tThe giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log + + # sample output + # rerank score 0: 0.029 + # rerank score 1: 0.029 + # rerank score 2: 0.135 + + # check that the score is in the range [$3, $4] + function check_score { + qnt="$1" + score=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + + if [ $(echo "$score < $3" | bc) -eq 1 ] || [ $(echo "$score > $4" | bc) -eq 1 ]; then + printf ' - %s @ %s (FAIL: score not in range [%s, %s])\n' "$qnt" "$score" "$3" "$4" + return 20 + fi + + printf ' - %s @ %s OK\n' "$qnt" "$score" + return 0 + } + + check_score "rerank score 0" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 0")" "0.00" "0.05" | tee -a $OUT/${ci}-rk-f16.log + check_score "rerank score 1" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 1")" "0.00" "0.05" | tee -a $OUT/${ci}-rk-f16.log + check_score "rerank score 2" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 2")" "0.10" "0.30" | tee -a $OUT/${ci}-rk-f16.log + + set +e +} + +function gg_sum_rerank_tiny { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Rerank Tiny (Jina):\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-rk-f16.log)" +} + +function gg_check_build_requirements { + if ! command -v cmake &> /dev/null; then + gg_printf 'cmake not found, please install' + fi + + if ! command -v make &> /dev/null; then + gg_printf 'make not found, please install' + fi + + if ! command -v ctest &> /dev/null; then + gg_printf 'ctest not found, please install' + fi +} + +## main + +export LLAMA_LOG_PREFIX=1 +export LLAMA_LOG_TIMESTAMPS=1 + +if [ -z ${GG_BUILD_LOW_PERF} ]; then + # Create symlink: ./llama.cpp/models-mnt -> $MNT/models + rm -rf ${SRC}/models-mnt + mnt_models=${MNT}/models + mkdir -p ${mnt_models} + ln -sfn ${mnt_models} ${SRC}/models-mnt + + # Create a fresh python3 venv and enter it + if ! python3 -m venv "$MNT/venv"; then + echo "Error: Failed to create Python virtual environment at $MNT/venv." + exit 1 + fi + source "$MNT/venv/bin/activate" + + pip install -r ${SRC}/requirements.txt --disable-pip-version-check + pip install --editable gguf-py --disable-pip-version-check +fi + +ret=0 +if [ -z ${GG_BUILD_SYCL} ]; then + # SYCL build breaks with debug build flags + test $ret -eq 0 && gg_run ctest_debug +fi +test $ret -eq 0 && gg_run ctest_release + +if [ -z ${GG_BUILD_LOW_PERF} ]; then + test $ret -eq 0 && gg_run embd_bge_small + test $ret -eq 0 && gg_run rerank_tiny + + if [ -z ${GG_BUILD_CLOUD} ] || [ ${GG_BUILD_EXTRA_TESTS_0} ]; then + if [ -z ${GG_BUILD_SYCL} ]; then + test $ret -eq 0 && gg_run test_scripts_debug + fi + test $ret -eq 0 && gg_run test_scripts_release + fi + + if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then + if [ -z ${GG_BUILD_CUDA} ] && [ -z ${GG_BUILD_VULKAN} ]; then + test $ret -eq 0 && gg_run pythia_1_4b + else + test $ret -eq 0 && gg_run pythia_2_8b + #test $ret -eq 0 && gg_run open_llama_7b_v2 + fi + if [ -z ${GG_BUILD_SYCL} ]; then + test $ret -eq 0 && gg_run ctest_with_model_debug + fi + test $ret -eq 0 && gg_run ctest_with_model_release + fi +fi + +exit $ret diff --git a/llama.cpp/cmake/arm64-apple-clang.cmake b/llama.cpp/cmake/arm64-apple-clang.cmake new file mode 100644 index 0000000000000000000000000000000000000000..b15b03dcb653bbb900012f5898228233a6fccb4b --- /dev/null +++ b/llama.cpp/cmake/arm64-apple-clang.cmake @@ -0,0 +1,16 @@ +set( CMAKE_SYSTEM_NAME Darwin ) +set( CMAKE_SYSTEM_PROCESSOR arm64 ) + +set( target arm64-apple-darwin-macho ) + +set( CMAKE_C_COMPILER clang ) +set( CMAKE_CXX_COMPILER clang++ ) + +set( CMAKE_C_COMPILER_TARGET ${target} ) +set( CMAKE_CXX_COMPILER_TARGET ${target} ) + +set( arch_c_flags "-march=armv8.4-a -fvectorize -ffp-model=fast -fno-finite-math-only" ) +set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function" ) + +set( CMAKE_C_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) +set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) diff --git a/llama.cpp/cmake/arm64-windows-llvm.cmake b/llama.cpp/cmake/arm64-windows-llvm.cmake new file mode 100644 index 0000000000000000000000000000000000000000..ef1bc4837e74ccee7fdad39766f8aebd17927fcf --- /dev/null +++ b/llama.cpp/cmake/arm64-windows-llvm.cmake @@ -0,0 +1,16 @@ +set( CMAKE_SYSTEM_NAME Windows ) +set( CMAKE_SYSTEM_PROCESSOR arm64 ) + +set( target arm64-pc-windows-msvc ) + +set( CMAKE_C_COMPILER clang ) +set( CMAKE_CXX_COMPILER clang++ ) + +set( CMAKE_C_COMPILER_TARGET ${target} ) +set( CMAKE_CXX_COMPILER_TARGET ${target} ) + +set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast -fno-finite-math-only" ) +set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function -Wno-gnu-zero-variadic-macro-arguments" ) + +set( CMAKE_C_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) +set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) diff --git a/llama.cpp/cmake/build-info.cmake b/llama.cpp/cmake/build-info.cmake new file mode 100644 index 0000000000000000000000000000000000000000..5531d90f70ac8a9c43982b10ff9879b6d32ddc4e --- /dev/null +++ b/llama.cpp/cmake/build-info.cmake @@ -0,0 +1,64 @@ +set(BUILD_NUMBER 0) +set(BUILD_COMMIT "unknown") +set(BUILD_COMPILER "unknown") +set(BUILD_TARGET "unknown") + +# Look for git +find_package(Git) +if(NOT Git_FOUND) + find_program(GIT_EXECUTABLE NAMES git git.exe) + if(GIT_EXECUTABLE) + set(Git_FOUND TRUE) + message(STATUS "Found Git: ${GIT_EXECUTABLE}") + else() + message(WARNING "Git not found. Build info will not be accurate.") + endif() +endif() + +# Get the commit count and hash +if(Git_FOUND) + execute_process( + COMMAND ${GIT_EXECUTABLE} rev-parse --short HEAD + WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} + OUTPUT_VARIABLE HEAD + OUTPUT_STRIP_TRAILING_WHITESPACE + RESULT_VARIABLE RES + ) + if (RES EQUAL 0) + set(BUILD_COMMIT ${HEAD}) + endif() + execute_process( + COMMAND ${GIT_EXECUTABLE} rev-list --count HEAD + WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} + OUTPUT_VARIABLE COUNT + OUTPUT_STRIP_TRAILING_WHITESPACE + RESULT_VARIABLE RES + ) + if (RES EQUAL 0) + set(BUILD_NUMBER ${COUNT}) + endif() +endif() + +if(MSVC) + set(BUILD_COMPILER "${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}") + if (CMAKE_VS_PLATFORM_NAME) + set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME}) + else() + set(BUILD_TARGET "${CMAKE_SYSTEM_NAME} ${CMAKE_SYSTEM_PROCESSOR}") + endif() +else() + execute_process( + COMMAND ${CMAKE_C_COMPILER} --version + OUTPUT_VARIABLE OUT + OUTPUT_STRIP_TRAILING_WHITESPACE + ) + string(REGEX REPLACE " *\n.*" "" OUT "${OUT}") + set(BUILD_COMPILER ${OUT}) + + execute_process( + COMMAND ${CMAKE_C_COMPILER} -dumpmachine + OUTPUT_VARIABLE OUT + OUTPUT_STRIP_TRAILING_WHITESPACE + ) + set(BUILD_TARGET ${OUT}) +endif() diff --git a/llama.cpp/cmake/common.cmake b/llama.cpp/cmake/common.cmake new file mode 100644 index 0000000000000000000000000000000000000000..e92653aec5e5b28cbbdb0977d50075b5e56f6d5e --- /dev/null +++ b/llama.cpp/cmake/common.cmake @@ -0,0 +1,35 @@ +include("ggml/cmake/common.cmake") + +function(llama_add_compile_flags) + if (LLAMA_FATAL_WARNINGS) + if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang") + list(APPEND C_FLAGS -Werror) + list(APPEND CXX_FLAGS -Werror) + elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") + add_compile_options(/WX) + endif() + endif() + + if (LLAMA_ALL_WARNINGS) + if (NOT MSVC) + list(APPEND C_FLAGS -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes + -Werror=implicit-int -Werror=implicit-function-declaration) + + list(APPEND CXX_FLAGS -Wmissing-declarations -Wmissing-noreturn) + + list(APPEND WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function) + + list(APPEND C_FLAGS ${WARNING_FLAGS}) + list(APPEND CXX_FLAGS ${WARNING_FLAGS}) + + ggml_get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION}) + + add_compile_options("$<$:${C_FLAGS};${GF_C_FLAGS}>" + "$<$:${CXX_FLAGS};${GF_CXX_FLAGS}>") + else() + # todo : msvc + set(C_FLAGS "" PARENT_SCOPE) + set(CXX_FLAGS "" PARENT_SCOPE) + endif() + endif() +endfunction() diff --git a/llama.cpp/cmake/git-vars.cmake b/llama.cpp/cmake/git-vars.cmake new file mode 100644 index 0000000000000000000000000000000000000000..4384bf8b6ea25d3fb23726b2299010d3dec6acb2 --- /dev/null +++ b/llama.cpp/cmake/git-vars.cmake @@ -0,0 +1,22 @@ +find_package(Git) + +# the commit's SHA1 +execute_process(COMMAND + "${GIT_EXECUTABLE}" describe --match=NeVeRmAtCh --always --abbrev=8 + WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}" + OUTPUT_VARIABLE GIT_SHA1 + ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) + +# the date of the commit +execute_process(COMMAND + "${GIT_EXECUTABLE}" log -1 --format=%ad --date=local + WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}" + OUTPUT_VARIABLE GIT_DATE + ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) + +# the subject of the commit +execute_process(COMMAND + "${GIT_EXECUTABLE}" log -1 --format=%s + WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}" + OUTPUT_VARIABLE GIT_COMMIT_SUBJECT + ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) diff --git a/llama.cpp/cmake/llama-config.cmake.in b/llama.cpp/cmake/llama-config.cmake.in new file mode 100644 index 0000000000000000000000000000000000000000..f671447af2bea73374322a2562cb60a25dff54db --- /dev/null +++ b/llama.cpp/cmake/llama-config.cmake.in @@ -0,0 +1,30 @@ +set(LLAMA_VERSION @LLAMA_INSTALL_VERSION@) +set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@) +set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@) +set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@) + +@PACKAGE_INIT@ + +set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@") +set_and_check(LLAMA_LIB_DIR "@PACKAGE_LLAMA_LIB_INSTALL_DIR@") +set_and_check(LLAMA_BIN_DIR "@PACKAGE_LLAMA_BIN_INSTALL_DIR@") + +find_package(ggml REQUIRED HINTS ${LLAMA_LIB_DIR}/cmake) + +find_library(llama_LIBRARY llama + REQUIRED + HINTS ${LLAMA_LIB_DIR} + NO_CMAKE_FIND_ROOT_PATH +) + +add_library(llama UNKNOWN IMPORTED) +set_target_properties(llama + PROPERTIES + INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}" + INTERFACE_LINK_LIBRARIES "ggml::ggml;ggml::ggml-base;" + IMPORTED_LINK_INTERFACE_LANGUAGES "CXX" + IMPORTED_LOCATION "${llama_LIBRARY}" + INTERFACE_COMPILE_FEATURES c_std_90 + POSITION_INDEPENDENT_CODE ON) + +check_required_components(Llama) diff --git a/llama.cpp/cmake/llama.pc.in b/llama.cpp/cmake/llama.pc.in new file mode 100644 index 0000000000000000000000000000000000000000..a0e3b5c88297735f2c8e9faca1b656775fe78d9a --- /dev/null +++ b/llama.cpp/cmake/llama.pc.in @@ -0,0 +1,10 @@ +prefix=@CMAKE_INSTALL_PREFIX@ +exec_prefix=@CMAKE_INSTALL_PREFIX@ +libdir=@CMAKE_INSTALL_FULL_LIBDIR@ +includedir=@CMAKE_INSTALL_FULL_INCLUDEDIR@ + +Name: llama +Description: Port of Facebook's LLaMA model in C/C++ +Version: @LLAMA_INSTALL_VERSION@ +Libs: -L${libdir} -lggml -lggml-base -lllama +Cflags: -I${includedir} diff --git a/llama.cpp/cmake/x64-windows-llvm.cmake b/llama.cpp/cmake/x64-windows-llvm.cmake new file mode 100644 index 0000000000000000000000000000000000000000..ddc55538892551bff17a53c0ab6cbdda129c6a8a --- /dev/null +++ b/llama.cpp/cmake/x64-windows-llvm.cmake @@ -0,0 +1,5 @@ +set( CMAKE_SYSTEM_NAME Windows ) +set( CMAKE_SYSTEM_PROCESSOR x86_64 ) + +set( CMAKE_C_COMPILER clang ) +set( CMAKE_CXX_COMPILER clang++ ) diff --git a/llama.cpp/common/CMakeLists.txt b/llama.cpp/common/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..0b0841602f4e7842b6b955bb933222c5d1033d5b --- /dev/null +++ b/llama.cpp/common/CMakeLists.txt @@ -0,0 +1,162 @@ +# common + +find_package(Threads REQUIRED) + +llama_add_compile_flags() + +# Build info header +# + +if(EXISTS "${PROJECT_SOURCE_DIR}/.git") + set(GIT_DIR "${PROJECT_SOURCE_DIR}/.git") + + # Is git submodule + if(NOT IS_DIRECTORY "${GIT_DIR}") + file(READ ${GIT_DIR} REAL_GIT_DIR_LINK) + string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK}) + string(FIND "${REAL_GIT_DIR}" "/" SLASH_POS) + if (SLASH_POS EQUAL 0) + set(GIT_DIR "${REAL_GIT_DIR}") + else() + set(GIT_DIR "${PROJECT_SOURCE_DIR}/${REAL_GIT_DIR}") + endif() + endif() + + if(EXISTS "${GIT_DIR}/index") + # For build-info.cpp below + set_property(DIRECTORY APPEND PROPERTY CMAKE_CONFIGURE_DEPENDS "${GIT_DIR}/index") + else() + message(WARNING "Git index not found in git repository.") + endif() +else() + message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.") +endif() + +set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in") +set(OUTPUT_FILE "${CMAKE_CURRENT_BINARY_DIR}/build-info.cpp") +configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE}) + +set(TARGET build_info) +add_library(${TARGET} OBJECT ${OUTPUT_FILE}) +if (BUILD_SHARED_LIBS) + set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) +endif() + +set(TARGET common) + +add_library(${TARGET} STATIC + arg.cpp + arg.h + base64.hpp + chat-parser.cpp + chat-parser.h + chat.cpp + chat.h + common.cpp + common.h + console.cpp + console.h + json-partial.cpp + json-partial.h + json-schema-to-grammar.cpp + llguidance.cpp + log.cpp + log.h + ngram-cache.cpp + ngram-cache.h + regex-partial.cpp + regex-partial.h + sampling.cpp + sampling.h + speculative.cpp + speculative.h + ) + +if (BUILD_SHARED_LIBS) + set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) +endif() + +set(LLAMA_COMMON_EXTRA_LIBS build_info) + +# Use curl to download model url +if (LLAMA_CURL) + find_package(CURL) + if (NOT CURL_FOUND) + message(FATAL_ERROR "Could NOT find CURL. Hint: to disable this feature, set -DLLAMA_CURL=OFF") + endif() + target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL) + include_directories(${CURL_INCLUDE_DIRS}) + set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARIES}) +endif () + +if (LLAMA_LLGUIDANCE) + include(ExternalProject) + set(LLGUIDANCE_SRC ${CMAKE_BINARY_DIR}/llguidance/source) + set(LLGUIDANCE_PATH ${LLGUIDANCE_SRC}/target/release) + + # Set the correct library file extension based on platform + if (WIN32) + set(LLGUIDANCE_LIB_NAME "llguidance.lib") + # Add Windows-specific libraries + set(LLGUIDANCE_PLATFORM_LIBS + ws2_32 # Windows Sockets API + userenv # For GetUserProfileDirectoryW + ntdll # For NT functions + bcrypt # For BCryptGenRandom + ) + else() + set(LLGUIDANCE_LIB_NAME "libllguidance.a") + set(LLGUIDANCE_PLATFORM_LIBS "") + endif() + + ExternalProject_Add(llguidance_ext + GIT_REPOSITORY https://github.com/guidance-ai/llguidance + # v1.0.1: + GIT_TAG d795912fedc7d393de740177ea9ea761e7905774 + PREFIX ${CMAKE_BINARY_DIR}/llguidance + SOURCE_DIR ${LLGUIDANCE_SRC} + BUILD_IN_SOURCE TRUE + CONFIGURE_COMMAND "" + BUILD_COMMAND cargo build --release --package llguidance + INSTALL_COMMAND "" + BUILD_BYPRODUCTS ${LLGUIDANCE_PATH}/${LLGUIDANCE_LIB_NAME} ${LLGUIDANCE_PATH}/llguidance.h + UPDATE_COMMAND "" + ) + target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_LLGUIDANCE) + + add_library(llguidance STATIC IMPORTED) + set_target_properties(llguidance PROPERTIES IMPORTED_LOCATION ${LLGUIDANCE_PATH}/${LLGUIDANCE_LIB_NAME}) + add_dependencies(llguidance llguidance_ext) + + target_include_directories(${TARGET} PRIVATE ${LLGUIDANCE_PATH}) + # Add platform libraries to the main target + set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} llguidance ${LLGUIDANCE_PLATFORM_LIBS}) +endif () + +target_include_directories(${TARGET} PUBLIC . ../vendor) +target_compile_features (${TARGET} PUBLIC cxx_std_17) +target_link_libraries (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads) + + +# +# copy the license files +# + +# Check if running in GitHub Actions +if (DEFINED ENV{GITHUB_ACTIONS} AND "$ENV{GITHUB_ACTIONS}" STREQUAL "true") + message(STATUS "Running inside GitHub Actions - copying license files") + + # Copy all files from licenses/ to build/bin/ + file(GLOB LICENSE_FILES "${CMAKE_SOURCE_DIR}/licenses/*") + foreach(LICENSE_FILE ${LICENSE_FILES}) + get_filename_component(FILENAME ${LICENSE_FILE} NAME) + add_custom_command( + POST_BUILD + TARGET ${TARGET} + COMMAND ${CMAKE_COMMAND} -E copy_if_different + "${LICENSE_FILE}" + "$/${FILENAME}" + COMMENT "Copying ${FILENAME} to ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}") + message(STATUS "Copying ${LICENSE_FILE} to ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${FILENAME}") + endforeach() +endif() diff --git a/llama.cpp/common/arg.cpp b/llama.cpp/common/arg.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a37e3fdb08f406ace2d52c33e88d9ee1b1d90e7a --- /dev/null +++ b/llama.cpp/common/arg.cpp @@ -0,0 +1,3515 @@ +#include "arg.h" + +#include "chat.h" +#include "common.h" +#include "gguf.h" // for reading GGUF splits +#include "json-schema-to-grammar.h" +#include "log.h" +#include "sampling.h" + +// fix problem with std::min and std::max +#if defined(_WIN32) +#define WIN32_LEAN_AND_MEAN +#ifndef NOMINMAX +# define NOMINMAX +#endif +#include +#endif + +#define JSON_ASSERT GGML_ASSERT +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +//#define LLAMA_USE_CURL + +#if defined(LLAMA_USE_CURL) +#include +#include +#include +#endif + +using json = nlohmann::ordered_json; + +std::initializer_list mmproj_examples = { + LLAMA_EXAMPLE_MTMD, + LLAMA_EXAMPLE_SERVER, +}; + +static std::string read_file(const std::string & fname) { + std::ifstream file(fname); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str())); + } + std::string content((std::istreambuf_iterator(file)), std::istreambuf_iterator()); + file.close(); + return content; +} + +static void write_file(const std::string & fname, const std::string & content) { + std::ofstream file(fname); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str())); + } + file << content; + file.close(); +} + +common_arg & common_arg::set_examples(std::initializer_list examples) { + this->examples = std::move(examples); + return *this; +} + +common_arg & common_arg::set_excludes(std::initializer_list excludes) { + this->excludes = std::move(excludes); + return *this; +} + +common_arg & common_arg::set_env(const char * env) { + help = help + "\n(env: " + env + ")"; + this->env = env; + return *this; +} + +common_arg & common_arg::set_sparam() { + is_sparam = true; + return *this; +} + +bool common_arg::in_example(enum llama_example ex) { + return examples.find(ex) != examples.end(); +} + +bool common_arg::is_exclude(enum llama_example ex) { + return excludes.find(ex) != excludes.end(); +} + +bool common_arg::get_value_from_env(std::string & output) { + if (env == nullptr) return false; + char * value = std::getenv(env); + if (value) { + output = value; + return true; + } + return false; +} + +bool common_arg::has_value_from_env() { + return env != nullptr && std::getenv(env); +} + +static std::vector break_str_into_lines(std::string input, size_t max_char_per_line) { + std::vector result; + std::istringstream iss(input); + std::string line; + auto add_line = [&](const std::string& l) { + if (l.length() <= max_char_per_line) { + result.push_back(l); + } else { + std::istringstream line_stream(l); + std::string word, current_line; + while (line_stream >> word) { + if (current_line.length() + !current_line.empty() + word.length() > max_char_per_line) { + if (!current_line.empty()) result.push_back(current_line); + current_line = word; + } else { + current_line += (!current_line.empty() ? " " : "") + word; + } + } + if (!current_line.empty()) result.push_back(current_line); + } + }; + while (std::getline(iss, line)) { + add_line(line); + } + return result; +} + +std::string common_arg::to_string() { + // params for printing to console + const static int n_leading_spaces = 40; + const static int n_char_per_line_help = 70; // TODO: detect this based on current console + std::string leading_spaces(n_leading_spaces, ' '); + + std::ostringstream ss; + for (const auto arg : args) { + if (arg == args.front()) { + if (args.size() == 1) { + ss << arg; + } else { + // first arg is usually abbreviation, we need padding to make it more beautiful + auto tmp = std::string(arg) + ", "; + auto spaces = std::string(std::max(0, 7 - (int)tmp.size()), ' '); + ss << tmp << spaces; + } + } else { + ss << arg << (arg != args.back() ? ", " : ""); + } + } + if (value_hint) ss << " " << value_hint; + if (value_hint_2) ss << " " << value_hint_2; + if (ss.tellp() > n_leading_spaces - 3) { + // current line is too long, add new line + ss << "\n" << leading_spaces; + } else { + // padding between arg and help, same line + ss << std::string(leading_spaces.size() - ss.tellp(), ' '); + } + const auto help_lines = break_str_into_lines(help, n_char_per_line_help); + for (const auto & line : help_lines) { + ss << (&line == &help_lines.front() ? "" : leading_spaces) << line << "\n"; + } + return ss.str(); +} + +// +// downloader +// + +struct common_hf_file_res { + std::string repo; // repo name with ":tag" removed + std::string ggufFile; + std::string mmprojFile; +}; + +#ifdef LLAMA_USE_CURL + +bool common_has_curl() { + return true; +} + +#ifdef __linux__ +#include +#elif defined(_WIN32) +# if !defined(PATH_MAX) +# define PATH_MAX MAX_PATH +# endif +#elif defined(_AIX) +#include +#else +#include +#endif +#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083 + +// +// CURL utils +// + +using curl_ptr = std::unique_ptr; + +// cannot use unique_ptr for curl_slist, because we cannot update without destroying the old one +struct curl_slist_ptr { + struct curl_slist * ptr = nullptr; + ~curl_slist_ptr() { + if (ptr) { + curl_slist_free_all(ptr); + } + } +}; + +#define CURL_MAX_RETRY 3 +#define CURL_RETRY_DELAY_SECONDS 2 + +static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds, const char * method_name) { + int remaining_attempts = max_attempts; + + while (remaining_attempts > 0) { + LOG_INF("%s: %s %s (attempt %d of %d)...\n", __func__ , method_name, url.c_str(), max_attempts - remaining_attempts + 1, max_attempts); + + CURLcode res = curl_easy_perform(curl); + if (res == CURLE_OK) { + return true; + } + + int exponential_backoff_delay = std::pow(retry_delay_seconds, max_attempts - remaining_attempts) * 1000; + LOG_WRN("%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay); + + remaining_attempts--; + if (remaining_attempts == 0) break; + std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay)); + } + + LOG_ERR("%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts); + + return false; +} + +// download one single file from remote URL to local path +static bool common_download_file_single(const std::string & url, const std::string & path, const std::string & bearer_token, bool offline) { + // Check if the file already exists locally + auto file_exists = std::filesystem::exists(path); + + // If the file exists, check its JSON metadata companion file. + std::string metadata_path = path + ".json"; + nlohmann::json metadata; // TODO @ngxson : get rid of this json, use regex instead + std::string etag; + std::string last_modified; + + if (file_exists) { + if (offline) { + LOG_INF("%s: using cached file (offline mode): %s\n", __func__, path.c_str()); + return true; // skip verification/downloading + } + // Try and read the JSON metadata file (note: stream autoclosed upon exiting this block). + std::ifstream metadata_in(metadata_path); + if (metadata_in.good()) { + try { + metadata_in >> metadata; + LOG_DBG("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str()); + if (metadata.contains("etag") && metadata.at("etag").is_string()) { + etag = metadata.at("etag"); + } + if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) { + last_modified = metadata.at("lastModified"); + } + } catch (const nlohmann::json::exception & e) { + LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what()); + } + } + // if we cannot open the metadata file, we assume that the downloaded file is not valid (etag and last-modified are left empty, so we will download it again) + } else { + if (offline) { + LOG_ERR("%s: required file is not available in cache (offline mode): %s\n", __func__, path.c_str()); + return false; + } + LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str()); + } + + // Send a HEAD request to retrieve the etag and last-modified headers + struct common_load_model_from_url_headers { + std::string etag; + std::string last_modified; + }; + + common_load_model_from_url_headers headers; + bool head_request_ok = false; + bool should_download = !file_exists; // by default, we should download if the file does not exist + + // Initialize libcurl + curl_ptr curl(curl_easy_init(), &curl_easy_cleanup); + curl_slist_ptr http_headers; + if (!curl) { + LOG_ERR("%s: error initializing libcurl\n", __func__); + return false; + } + + // Set the URL, allow to follow http redirection + curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str()); + curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L); + + http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp"); + // Check if hf-token or bearer-token was specified + if (!bearer_token.empty()) { + std::string auth_header = "Authorization: Bearer " + bearer_token; + http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str()); + } + curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr); + +#if defined(_WIN32) + // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of + // operating system. Currently implemented under MS-Windows. + curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA); +#endif + + typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *); + auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t { + common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata; + + static std::regex header_regex("([^:]+): (.*)\r\n"); + static std::regex etag_regex("ETag", std::regex_constants::icase); + static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase); + + std::string header(buffer, n_items); + std::smatch match; + if (std::regex_match(header, match, header_regex)) { + const std::string & key = match[1]; + const std::string & value = match[2]; + if (std::regex_match(key, match, etag_regex)) { + headers->etag = value; + } else if (std::regex_match(key, match, last_modified_regex)) { + headers->last_modified = value; + } + } + return n_items; + }; + + curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb + curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress + curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast(header_callback)); + curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers); + + // we only allow retrying once for HEAD requests + // this is for the use case of using running offline (no internet), retrying can be annoying + bool was_perform_successful = curl_perform_with_retry(url, curl.get(), 1, 0, "HEAD"); + if (!was_perform_successful) { + head_request_ok = false; + } + + long http_code = 0; + curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code); + if (http_code == 200) { + head_request_ok = true; + } else { + LOG_WRN("%s: HEAD invalid http status code received: %ld\n", __func__, http_code); + head_request_ok = false; + } + + // if head_request_ok is false, we don't have the etag or last-modified headers + // we leave should_download as-is, which is true if the file does not exist + if (head_request_ok) { + // check if ETag or Last-Modified headers are different + // if it is, we need to download the file again + if (!etag.empty() && etag != headers.etag) { + LOG_WRN("%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str()); + should_download = true; + } else if (!last_modified.empty() && last_modified != headers.last_modified) { + LOG_WRN("%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str()); + should_download = true; + } + } + + if (should_download) { + std::string path_temporary = path + ".downloadInProgress"; + if (file_exists) { + LOG_WRN("%s: deleting previous downloaded file: %s\n", __func__, path.c_str()); + if (remove(path.c_str()) != 0) { + LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str()); + return false; + } + } + + // Set the output file + + struct FILE_deleter { + void operator()(FILE * f) const { + fclose(f); + } + }; + + std::unique_ptr outfile(fopen(path_temporary.c_str(), "wb")); + if (!outfile) { + LOG_ERR("%s: error opening local file for writing: %s\n", __func__, path.c_str()); + return false; + } + + typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd); + auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t { + return fwrite(data, size, nmemb, (FILE *)fd); + }; + curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L); + curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast(write_callback)); + curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get()); + + // display download progress + curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L); + + // helper function to hide password in URL + auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string { + std::size_t protocol_pos = url.find("://"); + if (protocol_pos == std::string::npos) { + return url; // Malformed URL + } + + std::size_t at_pos = url.find('@', protocol_pos + 3); + if (at_pos == std::string::npos) { + return url; // No password in URL + } + + return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos); + }; + + // start the download + LOG_INF("%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__, + llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str()); + bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS, "GET"); + if (!was_perform_successful) { + return false; + } + + long http_code = 0; + curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code); + if (http_code < 200 || http_code >= 400) { + LOG_ERR("%s: invalid http status code received: %ld\n", __func__, http_code); + return false; + } + + // Causes file to be closed explicitly here before we rename it. + outfile.reset(); + + // Write the updated JSON metadata file. + metadata.update({ + {"url", url}, + {"etag", headers.etag}, + {"lastModified", headers.last_modified} + }); + write_file(metadata_path, metadata.dump(4)); + LOG_DBG("%s: file metadata saved: %s\n", __func__, metadata_path.c_str()); + + if (rename(path_temporary.c_str(), path.c_str()) != 0) { + LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str()); + return false; + } + } else { + LOG_INF("%s: using cached file: %s\n", __func__, path.c_str()); + } + + return true; +} + +// download multiple files from remote URLs to local paths +// the input is a vector of pairs +static bool common_download_file_multiple(const std::vector> & urls, const std::string & bearer_token, bool offline) { + // Prepare download in parallel + std::vector> futures_download; + for (auto const & item : urls) { + futures_download.push_back(std::async(std::launch::async, [bearer_token, offline](const std::pair & it) -> bool { + return common_download_file_single(it.first, it.second, bearer_token, offline); + }, item)); + } + + // Wait for all downloads to complete + for (auto & f : futures_download) { + if (!f.get()) { + return false; + } + } + + return true; +} + +static bool common_download_model( + const common_params_model & model, + const std::string & bearer_token, + bool offline) { + // Basic validation of the model.url + if (model.url.empty()) { + LOG_ERR("%s: invalid model url\n", __func__); + return false; + } + + if (!common_download_file_single(model.url, model.path, bearer_token, offline)) { + return false; + } + + // check for additional GGUFs split to download + int n_split = 0; + { + struct gguf_init_params gguf_params = { + /*.no_alloc = */ true, + /*.ctx = */ NULL, + }; + auto * ctx_gguf = gguf_init_from_file(model.path.c_str(), gguf_params); + if (!ctx_gguf) { + LOG_ERR("\n%s: failed to load input GGUF from %s\n", __func__, model.path.c_str()); + return false; + } + + auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT); + if (key_n_split >= 0) { + n_split = gguf_get_val_u16(ctx_gguf, key_n_split); + } + + gguf_free(ctx_gguf); + } + + if (n_split > 1) { + char split_prefix[PATH_MAX] = {0}; + char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0}; + + // Verify the first split file format + // and extract split URL and PATH prefixes + { + if (!llama_split_prefix(split_prefix, sizeof(split_prefix), model.path.c_str(), 0, n_split)) { + LOG_ERR("\n%s: unexpected model file name: %s n_split=%d\n", __func__, model.path.c_str(), n_split); + return false; + } + + if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model.url.c_str(), 0, n_split)) { + LOG_ERR("\n%s: unexpected model url: %s n_split=%d\n", __func__, model.url.c_str(), n_split); + return false; + } + } + + std::vector> urls; + for (int idx = 1; idx < n_split; idx++) { + char split_path[PATH_MAX] = {0}; + llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split); + + char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0}; + llama_split_path(split_url, sizeof(split_url), split_url_prefix, idx, n_split); + + if (std::string(split_path) == model.path) { + continue; // skip the already downloaded file + } + + urls.push_back({split_url, split_path}); + } + + // Download in parallel + common_download_file_multiple(urls, bearer_token, offline); + } + + return true; +} + +std::pair> common_remote_get_content(const std::string & url, const common_remote_params & params) { + curl_ptr curl(curl_easy_init(), &curl_easy_cleanup); + curl_slist_ptr http_headers; + std::vector res_buffer; + + curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str()); + curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); + curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L); + typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data); + auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t { + auto data_vec = static_cast *>(data); + data_vec->insert(data_vec->end(), (char *)ptr, (char *)ptr + size * nmemb); + return size * nmemb; + }; + curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast(write_callback)); + curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_buffer); +#if defined(_WIN32) + curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA); +#endif + if (params.timeout > 0) { + curl_easy_setopt(curl.get(), CURLOPT_TIMEOUT, params.timeout); + } + if (params.max_size > 0) { + curl_easy_setopt(curl.get(), CURLOPT_MAXFILESIZE, params.max_size); + } + http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp"); + for (const auto & header : params.headers) { + http_headers.ptr = curl_slist_append(http_headers.ptr, header.c_str()); + } + curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr); + + CURLcode res = curl_easy_perform(curl.get()); + + if (res != CURLE_OK) { + std::string error_msg = curl_easy_strerror(res); + throw std::runtime_error("error: cannot make GET request: " + error_msg); + } + + long res_code; + curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code); + + return { res_code, std::move(res_buffer) }; +} + +/** + * Allow getting the HF file from the HF repo with tag (like ollama), for example: + * - bartowski/Llama-3.2-3B-Instruct-GGUF:q4 + * - bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M + * - bartowski/Llama-3.2-3B-Instruct-GGUF:q5_k_s + * Tag is optional, default to "latest" (meaning it checks for Q4_K_M first, then Q4, then if not found, return the first GGUF file in repo) + * + * Return pair of (with "repo" already having tag removed) + * + * Note: we use the Ollama-compatible HF API, but not using the blobId. Instead, we use the special "ggufFile" field which returns the value for "hf_file". This is done to be backward-compatible with existing cache files. + */ +static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & bearer_token, bool offline) { + auto parts = string_split(hf_repo_with_tag, ':'); + std::string tag = parts.size() > 1 ? parts.back() : "latest"; + std::string hf_repo = parts[0]; + if (string_split(hf_repo, '/').size() != 2) { + throw std::invalid_argument("error: invalid HF repo format, expected /[:quant]\n"); + } + + std::string url = get_model_endpoint() + "v2/" + hf_repo + "/manifests/" + tag; + + // headers + std::vector headers; + headers.push_back("Accept: application/json"); + if (!bearer_token.empty()) { + headers.push_back("Authorization: Bearer " + bearer_token); + } + // Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response + // User-Agent header is already set in common_remote_get_content, no need to set it here + + // we use "=" to avoid clashing with other component, while still being allowed on windows + std::string cached_response_fname = "manifest=" + hf_repo + "=" + tag + ".json"; + string_replace_all(cached_response_fname, "/", "_"); + std::string cached_response_path = fs_get_cache_file(cached_response_fname); + + // make the request + common_remote_params params; + params.headers = headers; + long res_code = 0; + std::string res_str; + bool use_cache = false; + if (!offline) { + try { + auto res = common_remote_get_content(url, params); + res_code = res.first; + res_str = std::string(res.second.data(), res.second.size()); + } catch (const std::exception & e) { + LOG_WRN("error: failed to get manifest at %s: %s\n", url.c_str(), e.what()); + } + } + if (res_code == 0) { + if (std::filesystem::exists(cached_response_path)) { + LOG_WRN("trying to read manifest from cache: %s\n", cached_response_path.c_str()); + res_str = read_file(cached_response_path); + res_code = 200; + use_cache = true; + } else { + throw std::runtime_error( + offline ? "error: failed to get manifest (offline mode)" + : "error: failed to get manifest (check your internet connection)"); + } + } + std::string ggufFile; + std::string mmprojFile; + + if (res_code == 200 || res_code == 304) { + // extract ggufFile.rfilename in json, using regex + { + std::regex pattern("\"ggufFile\"[\\s\\S]*?\"rfilename\"\\s*:\\s*\"([^\"]+)\""); + std::smatch match; + if (std::regex_search(res_str, match, pattern)) { + ggufFile = match[1].str(); + } + } + // extract mmprojFile.rfilename in json, using regex + { + std::regex pattern("\"mmprojFile\"[\\s\\S]*?\"rfilename\"\\s*:\\s*\"([^\"]+)\""); + std::smatch match; + if (std::regex_search(res_str, match, pattern)) { + mmprojFile = match[1].str(); + } + } + if (!use_cache) { + // if not using cached response, update the cache file + write_file(cached_response_path, res_str); + } + } else if (res_code == 401) { + throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token"); + } else { + throw std::runtime_error(string_format("error from HF API, response code: %ld, data: %s", res_code, res_str.c_str())); + } + + // check response + if (ggufFile.empty()) { + throw std::runtime_error("error: model does not have ggufFile"); + } + + return { hf_repo, ggufFile, mmprojFile }; +} + +#else + +bool common_has_curl() { + return false; +} + +static bool common_download_file_single(const std::string &, const std::string &, const std::string &, bool) { + LOG_ERR("error: built without CURL, cannot download model from internet\n"); + return false; +} + +static bool common_download_file_multiple(const std::vector> &, const std::string &, bool) { + LOG_ERR("error: built without CURL, cannot download model from the internet\n"); + return false; +} + +static bool common_download_model( + const common_params_model &, + const std::string &, + bool) { + LOG_ERR("error: built without CURL, cannot download model from the internet\n"); + return false; +} + +static struct common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool) { + LOG_ERR("error: built without CURL, cannot download model from the internet\n"); + return {}; +} + +std::pair> common_remote_get_content(const std::string & url, const common_remote_params &) { + if (!url.empty()) { + throw std::runtime_error("error: built without CURL, cannot download model from the internet"); + } + + return {}; +} + +#endif // LLAMA_USE_CURL + +// +// utils +// + +struct handle_model_result { + bool found_mmproj = false; + common_params_model mmproj; +}; + +static handle_model_result common_params_handle_model( + struct common_params_model & model, + const std::string & bearer_token, + const std::string & model_path_default, + bool offline) { + handle_model_result result; + // handle pre-fill default model path and url based on hf_repo and hf_file + { + if (!model.hf_repo.empty()) { + // short-hand to avoid specifying --hf-file -> default it to --model + if (model.hf_file.empty()) { + if (model.path.empty()) { + auto auto_detected = common_get_hf_file(model.hf_repo, bearer_token, offline); + if (auto_detected.repo.empty() || auto_detected.ggufFile.empty()) { + exit(1); // built without CURL, error message already printed + } + model.hf_repo = auto_detected.repo; + model.hf_file = auto_detected.ggufFile; + if (!auto_detected.mmprojFile.empty()) { + result.found_mmproj = true; + result.mmproj.hf_repo = model.hf_repo; + result.mmproj.hf_file = auto_detected.mmprojFile; + } + } else { + model.hf_file = model.path; + } + } + + std::string model_endpoint = get_model_endpoint(); + model.url = model_endpoint + model.hf_repo + "/resolve/main/" + model.hf_file; + // make sure model path is present (for caching purposes) + if (model.path.empty()) { + // this is to avoid different repo having same file name, or same file name in different subdirs + std::string filename = model.hf_repo + "_" + model.hf_file; + // to make sure we don't have any slashes in the filename + string_replace_all(filename, "/", "_"); + model.path = fs_get_cache_file(filename); + } + + } else if (!model.url.empty()) { + if (model.path.empty()) { + auto f = string_split(model.url, '#').front(); + f = string_split(f, '?').front(); + model.path = fs_get_cache_file(string_split(f, '/').back()); + } + + } else if (model.path.empty()) { + model.path = model_path_default; + } + } + + // then, download it if needed + if (!model.url.empty()) { + bool ok = common_download_model(model, bearer_token, offline); + if (!ok) { + LOG_ERR("error: failed to download model from %s\n", model.url.c_str()); + exit(1); + } + } + + return result; +} + +const std::vector kv_cache_types = { + GGML_TYPE_F32, + GGML_TYPE_F16, + GGML_TYPE_BF16, + GGML_TYPE_Q8_0, + GGML_TYPE_Q4_0, + GGML_TYPE_Q4_1, + GGML_TYPE_IQ4_NL, + GGML_TYPE_Q5_0, + GGML_TYPE_Q5_1, +}; + +static ggml_type kv_cache_type_from_str(const std::string & s) { + for (const auto & type : kv_cache_types) { + if (ggml_type_name(type) == s) { + return type; + } + } + throw std::runtime_error("Unsupported cache type: " + s); +} + +static std::string get_all_kv_cache_types() { + std::ostringstream msg; + for (const auto & type : kv_cache_types) { + msg << ggml_type_name(type) << (&type == &kv_cache_types.back() ? "" : ", "); + } + return msg.str(); +} + +// +// CLI argument parsing functions +// + +static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) { + std::string arg; + const std::string arg_prefix = "--"; + common_params & params = ctx_arg.params; + + std::unordered_map arg_to_options; + for (auto & opt : ctx_arg.options) { + for (const auto & arg : opt.args) { + arg_to_options[arg] = &opt; + } + } + + // handle environment variables + for (auto & opt : ctx_arg.options) { + std::string value; + if (opt.get_value_from_env(value)) { + try { + if (opt.handler_void && (value == "1" || value == "true")) { + opt.handler_void(params); + } + if (opt.handler_int) { + opt.handler_int(params, std::stoi(value)); + } + if (opt.handler_string) { + opt.handler_string(params, value); + continue; + } + } catch (std::exception & e) { + throw std::invalid_argument(string_format( + "error while handling environment variable \"%s\": %s\n\n", opt.env, e.what())); + } + } + } + + // handle command line arguments + auto check_arg = [&](int i) { + if (i+1 >= argc) { + throw std::invalid_argument("expected value for argument"); + } + }; + + for (int i = 1; i < argc; i++) { + const std::string arg_prefix = "--"; + + std::string arg = argv[i]; + if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { + std::replace(arg.begin(), arg.end(), '_', '-'); + } + if (arg_to_options.find(arg) == arg_to_options.end()) { + throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str())); + } + auto opt = *arg_to_options[arg]; + if (opt.has_value_from_env()) { + fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str()); + } + try { + if (opt.handler_void) { + opt.handler_void(params); + continue; + } + + // arg with single value + check_arg(i); + std::string val = argv[++i]; + if (opt.handler_int) { + opt.handler_int(params, std::stoi(val)); + continue; + } + if (opt.handler_string) { + opt.handler_string(params, val); + continue; + } + + // arg with 2 values + check_arg(i); + std::string val2 = argv[++i]; + if (opt.handler_str_str) { + opt.handler_str_str(params, val, val2); + continue; + } + } catch (std::exception & e) { + throw std::invalid_argument(string_format( + "error while handling argument \"%s\": %s\n\n" + "usage:\n%s\n\nto show complete usage, run with -h", + arg.c_str(), e.what(), arg_to_options[arg]->to_string().c_str())); + } + } + + postprocess_cpu_params(params.cpuparams, nullptr); + postprocess_cpu_params(params.cpuparams_batch, ¶ms.cpuparams); + + postprocess_cpu_params(params.speculative.cpuparams, ¶ms.cpuparams); + postprocess_cpu_params(params.speculative.cpuparams_batch, ¶ms.cpuparams_batch); + + if (params.prompt_cache_all && (params.interactive || params.interactive_first)) { + throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); + } + + // handle model and download + { + auto res = common_params_handle_model(params.model, params.hf_token, DEFAULT_MODEL_PATH, params.offline); + if (params.no_mmproj) { + params.mmproj = {}; + } else if (res.found_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty()) { + // optionally, handle mmproj model when -hf is specified + params.mmproj = res.mmproj; + } + // only download mmproj if the current example is using it + for (auto & ex : mmproj_examples) { + if (ctx_arg.ex == ex) { + common_params_handle_model(params.mmproj, params.hf_token, "", params.offline); + break; + } + } + common_params_handle_model(params.speculative.model, params.hf_token, "", params.offline); + common_params_handle_model(params.vocoder.model, params.hf_token, "", params.offline); + } + + if (params.escape) { + string_process_escapes(params.prompt); + string_process_escapes(params.input_prefix); + string_process_escapes(params.input_suffix); + for (auto & antiprompt : params.antiprompt) { + string_process_escapes(antiprompt); + } + for (auto & seq_breaker : params.sampling.dry_sequence_breakers) { + string_process_escapes(seq_breaker); + } + for (auto & pair : params.speculative.replacements) { + string_process_escapes(pair.first); + string_process_escapes(pair.second); + } + } + + if (!params.kv_overrides.empty()) { + params.kv_overrides.emplace_back(); + params.kv_overrides.back().key[0] = 0; + } + + if (!params.tensor_buft_overrides.empty()) { + params.tensor_buft_overrides.push_back({nullptr, nullptr}); + } + + if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) { + throw std::runtime_error(string_format( + "error: the supplied chat template is not supported: %s%s\n", + params.chat_template.c_str(), + params.use_jinja ? "" : "\nnote: llama.cpp was started without --jinja, we only support commonly used templates" + )); + } + + return true; +} + +static void common_params_print_usage(common_params_context & ctx_arg) { + auto print_options = [](std::vector & options) { + for (common_arg * opt : options) { + printf("%s", opt->to_string().c_str()); + } + }; + + std::vector common_options; + std::vector sparam_options; + std::vector specific_options; + for (auto & opt : ctx_arg.options) { + // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example + if (opt.is_sparam) { + sparam_options.push_back(&opt); + } else if (opt.in_example(ctx_arg.ex)) { + specific_options.push_back(&opt); + } else { + common_options.push_back(&opt); + } + } + printf("----- common params -----\n\n"); + print_options(common_options); + printf("\n\n----- sampling params -----\n\n"); + print_options(sparam_options); + // TODO: maybe convert enum llama_example to string + printf("\n\n----- example-specific params -----\n\n"); + print_options(specific_options); +} + +static void common_params_print_completion(common_params_context & ctx_arg) { + std::vector common_options; + std::vector sparam_options; + std::vector specific_options; + + for (auto & opt : ctx_arg.options) { + if (opt.is_sparam) { + sparam_options.push_back(&opt); + } else if (opt.in_example(ctx_arg.ex)) { + specific_options.push_back(&opt); + } else { + common_options.push_back(&opt); + } + } + + printf("_llama_completions() {\n"); + printf(" local cur prev opts\n"); + printf(" COMPREPLY=()\n"); + printf(" cur=\"${COMP_WORDS[COMP_CWORD]}\"\n"); + printf(" prev=\"${COMP_WORDS[COMP_CWORD-1]}\"\n\n"); + + printf(" opts=\""); + auto print_options = [](const std::vector & options) { + for (const common_arg * opt : options) { + for (const char * arg : opt->args) { + printf("%s ", arg); + } + } + }; + + print_options(common_options); + print_options(sparam_options); + print_options(specific_options); + printf("\"\n\n"); + + printf(" case \"$prev\" in\n"); + printf(" --model)\n"); + printf(" COMPREPLY=( $(compgen -f -X '!*.gguf' -- \"$cur\") $(compgen -d -- \"$cur\") )\n"); + printf(" return 0\n"); + printf(" ;;\n"); + printf(" --grammar-file)\n"); + printf(" COMPREPLY=( $(compgen -f -X '!*.gbnf' -- \"$cur\") $(compgen -d -- \"$cur\") )\n"); + printf(" return 0\n"); + printf(" ;;\n"); + printf(" --chat-template-file)\n"); + printf(" COMPREPLY=( $(compgen -f -X '!*.jinja' -- \"$cur\") $(compgen -d -- \"$cur\") )\n"); + printf(" return 0\n"); + printf(" ;;\n"); + printf(" *)\n"); + printf(" COMPREPLY=( $(compgen -W \"${opts}\" -- \"$cur\") )\n"); + printf(" return 0\n"); + printf(" ;;\n"); + printf(" esac\n"); + printf("}\n\n"); + + std::set executables = { + "llama-batched", + "llama-batched-bench", + "llama-bench", + "llama-cli", + "llama-convert-llama2c-to-ggml", + "llama-cvector-generator", + "llama-embedding", + "llama-eval-callback", + "llama-export-lora", + "llama-gen-docs", + "llama-gguf", + "llama-gguf-hash", + "llama-gguf-split", + "llama-gritlm", + "llama-imatrix", + "llama-infill", + "llama-mtmd-cli", + "llama-llava-clip-quantize-cli", + "llama-lookahead", + "llama-lookup", + "llama-lookup-create", + "llama-lookup-merge", + "llama-lookup-stats", + "llama-parallel", + "llama-passkey", + "llama-perplexity", + "llama-q8dot", + "llama-quantize", + "llama-qwen2vl-cli", + "llama-retrieval", + "llama-run", + "llama-save-load-state", + "llama-server", + "llama-simple", + "llama-simple-chat", + "llama-speculative", + "llama-speculative-simple", + "llama-tokenize", + "llama-tts", + "llama-vdot" + }; + + for (const auto& exe : executables) { + printf("complete -F _llama_completions %s\n", exe.c_str()); + } +} + +static std::vector parse_device_list(const std::string & value) { + std::vector devices; + auto dev_names = string_split(value, ','); + if (dev_names.empty()) { + throw std::invalid_argument("no devices specified"); + } + if (dev_names.size() == 1 && dev_names[0] == "none") { + devices.push_back(nullptr); + } else { + for (const auto & device : dev_names) { + auto * dev = ggml_backend_dev_by_name(device.c_str()); + if (!dev || ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_GPU) { + throw std::invalid_argument(string_format("invalid device: %s", device.c_str())); + } + devices.push_back(dev); + } + devices.push_back(nullptr); + } + return devices; +} + +static void add_rpc_devices(std::string servers) { + auto rpc_servers = string_split(servers, ','); + if (rpc_servers.empty()) { + throw std::invalid_argument("no RPC servers specified"); + } + ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC"); + if (!rpc_reg) { + throw std::invalid_argument("failed to find RPC backend"); + } + typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint); + ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device"); + if (!ggml_backend_rpc_add_device_fn) { + throw std::invalid_argument("failed to find RPC device add function"); + } + for (const auto & server : rpc_servers) { + ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str()); + if (dev) { + ggml_backend_device_register(dev); + } else { + throw std::invalid_argument("failed to register RPC device"); + } + } +} + +bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) { + auto ctx_arg = common_params_parser_init(params, ex, print_usage); + const common_params params_org = ctx_arg.params; // the example can modify the default params + + try { + if (!common_params_parse_ex(argc, argv, ctx_arg)) { + ctx_arg.params = params_org; + return false; + } + if (ctx_arg.params.usage) { + common_params_print_usage(ctx_arg); + if (ctx_arg.print_usage) { + ctx_arg.print_usage(argc, argv); + } + exit(0); + } + if (ctx_arg.params.completion) { + common_params_print_completion(ctx_arg); + exit(0); + } + } catch (const std::invalid_argument & ex) { + fprintf(stderr, "%s\n", ex.what()); + ctx_arg.params = params_org; + return false; + } catch (std::exception & ex) { + fprintf(stderr, "%s\n", ex.what()); + exit(1); // for other exceptions, we exit with status code 1 + } + + return true; +} + +static std::string list_builtin_chat_templates() { + std::vector supported_tmpl; + int32_t res = llama_chat_builtin_templates(nullptr, 0); + supported_tmpl.resize(res); + res = llama_chat_builtin_templates(supported_tmpl.data(), supported_tmpl.size()); + std::ostringstream msg; + for (auto & tmpl : supported_tmpl) { + msg << tmpl << (&tmpl == &supported_tmpl.back() ? "" : ", "); + } + return msg.str(); +} + +common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) { + // load dynamic backends + ggml_backend_load_all(); + + common_params_context ctx_arg(params); + ctx_arg.print_usage = print_usage; + ctx_arg.ex = ex; + + std::string sampler_type_chars; + std::string sampler_type_names; + for (const auto & sampler : params.sampling.samplers) { + sampler_type_chars += common_sampler_type_to_chr(sampler); + sampler_type_names += common_sampler_type_to_str(sampler) + ";"; + } + sampler_type_names.pop_back(); + + + /** + * filter options by example + * rules: + * - all examples inherit options from LLAMA_EXAMPLE_COMMON + * - if LLAMA_EXAMPLE_* is set (other than COMMON), we only show the option in the corresponding example + * - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example + */ + auto add_opt = [&](common_arg arg) { + if ((arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) && !arg.is_exclude(ex)) { + ctx_arg.options.push_back(std::move(arg)); + } + }; + + + add_opt(common_arg( + {"-h", "--help", "--usage"}, + "print usage and exit", + [](common_params & params) { + params.usage = true; + } + )); + add_opt(common_arg( + {"--version"}, + "show version and build info", + [](common_params &) { + fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT); + fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET); + exit(0); + } + )); + add_opt(common_arg( + {"--completion-bash"}, + "print source-able bash completion script for llama.cpp", + [](common_params & params) { + params.completion = true; + } + )); + add_opt(common_arg( + {"--verbose-prompt"}, + string_format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"), + [](common_params & params) { + params.verbose_prompt = true; + } + )); + add_opt(common_arg( + {"--no-display-prompt"}, + string_format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"), + [](common_params & params) { + params.display_prompt = false; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-co", "--color"}, + string_format("colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false"), + [](common_params & params) { + params.use_color = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP})); + add_opt(common_arg( + {"-t", "--threads"}, "N", + string_format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads), + [](common_params & params, int value) { + params.cpuparams.n_threads = value; + if (params.cpuparams.n_threads <= 0) { + params.cpuparams.n_threads = std::thread::hardware_concurrency(); + } + } + ).set_env("LLAMA_ARG_THREADS")); + add_opt(common_arg( + {"-tb", "--threads-batch"}, "N", + "number of threads to use during batch and prompt processing (default: same as --threads)", + [](common_params & params, int value) { + params.cpuparams_batch.n_threads = value; + if (params.cpuparams_batch.n_threads <= 0) { + params.cpuparams_batch.n_threads = std::thread::hardware_concurrency(); + } + } + )); + add_opt(common_arg( + {"-C", "--cpu-mask"}, "M", + "CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")", + [](common_params & params, const std::string & mask) { + params.cpuparams.mask_valid = true; + if (!parse_cpu_mask(mask, params.cpuparams.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + )); + add_opt(common_arg( + {"-Cr", "--cpu-range"}, "lo-hi", + "range of CPUs for affinity. Complements --cpu-mask", + [](common_params & params, const std::string & range) { + params.cpuparams.mask_valid = true; + if (!parse_cpu_range(range, params.cpuparams.cpumask)) { + throw std::invalid_argument("invalid range"); + } + } + )); + add_opt(common_arg( + {"--cpu-strict"}, "<0|1>", + string_format("use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu), + [](common_params & params, const std::string & value) { + params.cpuparams.strict_cpu = std::stoul(value); + } + )); + add_opt(common_arg( + {"--prio"}, "N", + string_format("set process/thread priority : low(-1), normal(0), medium(1), high(2), realtime(3) (default: %d)\n", params.cpuparams.priority), + [](common_params & params, int prio) { + if (prio < GGML_SCHED_PRIO_LOW || prio > GGML_SCHED_PRIO_REALTIME) { + throw std::invalid_argument("invalid value"); + } + params.cpuparams.priority = (enum ggml_sched_priority) prio; + } + )); + add_opt(common_arg( + {"--poll"}, "<0...100>", + string_format("use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll), + [](common_params & params, const std::string & value) { + params.cpuparams.poll = std::stoul(value); + } + )); + add_opt(common_arg( + {"-Cb", "--cpu-mask-batch"}, "M", + "CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask)", + [](common_params & params, const std::string & mask) { + params.cpuparams_batch.mask_valid = true; + if (!parse_cpu_mask(mask, params.cpuparams_batch.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + )); + add_opt(common_arg( + {"-Crb", "--cpu-range-batch"}, "lo-hi", + "ranges of CPUs for affinity. Complements --cpu-mask-batch", + [](common_params & params, const std::string & range) { + params.cpuparams_batch.mask_valid = true; + if (!parse_cpu_range(range, params.cpuparams_batch.cpumask)) { + throw std::invalid_argument("invalid range"); + } + } + )); + add_opt(common_arg( + {"--cpu-strict-batch"}, "<0|1>", + "use strict CPU placement (default: same as --cpu-strict)", + [](common_params & params, int value) { + params.cpuparams_batch.strict_cpu = value; + } + )); + add_opt(common_arg( + {"--prio-batch"}, "N", + string_format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams_batch.priority), + [](common_params & params, int prio) { + if (prio < 0 || prio > 3) { + throw std::invalid_argument("invalid value"); + } + params.cpuparams_batch.priority = (enum ggml_sched_priority) prio; + } + )); + add_opt(common_arg( + {"--poll-batch"}, "<0|1>", + "use polling to wait for work (default: same as --poll)", + [](common_params & params, int value) { + params.cpuparams_batch.poll = value; + } + )); + add_opt(common_arg( + {"-lcs", "--lookup-cache-static"}, "FNAME", + "path to static lookup cache to use for lookup decoding (not updated by generation)", + [](common_params & params, const std::string & value) { + params.lookup_cache_static = value; + } + ).set_examples({LLAMA_EXAMPLE_LOOKUP})); + add_opt(common_arg( + {"-lcd", "--lookup-cache-dynamic"}, "FNAME", + "path to dynamic lookup cache to use for lookup decoding (updated by generation)", + [](common_params & params, const std::string & value) { + params.lookup_cache_dynamic = value; + } + ).set_examples({LLAMA_EXAMPLE_LOOKUP})); + add_opt(common_arg( + {"-c", "--ctx-size"}, "N", + string_format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx), + [](common_params & params, int value) { + params.n_ctx = value; + } + ).set_env("LLAMA_ARG_CTX_SIZE")); + add_opt(common_arg( + {"-n", "--predict", "--n-predict"}, "N", + string_format( + ex == LLAMA_EXAMPLE_MAIN + ? "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)" + : "number of tokens to predict (default: %d, -1 = infinity)", + params.n_predict), + [](common_params & params, int value) { + params.n_predict = value; + } + ).set_env("LLAMA_ARG_N_PREDICT")); + add_opt(common_arg( + {"-b", "--batch-size"}, "N", + string_format("logical maximum batch size (default: %d)", params.n_batch), + [](common_params & params, int value) { + params.n_batch = value; + } + ).set_env("LLAMA_ARG_BATCH")); + add_opt(common_arg( + {"-ub", "--ubatch-size"}, "N", + string_format("physical maximum batch size (default: %d)", params.n_ubatch), + [](common_params & params, int value) { + params.n_ubatch = value; + } + ).set_env("LLAMA_ARG_UBATCH")); + add_opt(common_arg( + {"--keep"}, "N", + string_format("number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep), + [](common_params & params, int value) { + params.n_keep = value; + } + )); + add_opt(common_arg( + {"--swa-full"}, + string_format("use full-size SWA cache (default: %s)\n" + "[(more info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)", params.swa_full ? "true" : "false"), + [](common_params & params) { + params.swa_full = true; + } + ).set_env("LLAMA_ARG_SWA_FULL")); + add_opt(common_arg( + {"--kv-unified", "-kvu"}, + string_format("use single unified KV buffer for the KV cache of all sequences (default: %s)\n" + "[(more info)](https://github.com/ggml-org/llama.cpp/pull/14363)", params.kv_unified ? "true" : "false"), + [](common_params & params) { + params.kv_unified = true; + } + ).set_env("LLAMA_ARG_KV_SPLIT")); + add_opt(common_arg( + {"--no-context-shift"}, + string_format("disables context shift on infinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"), + [](common_params & params) { + params.ctx_shift = false; + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT")); + add_opt(common_arg( + {"--chunks"}, "N", + string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks), + [](common_params & params, int value) { + params.n_chunks = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_RETRIEVAL})); + add_opt(common_arg( + {"-fa", "--flash-attn"}, + string_format("enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled"), + [](common_params & params) { + params.flash_attn = true; + } + ).set_env("LLAMA_ARG_FLASH_ATTN")); + add_opt(common_arg( + {"-p", "--prompt"}, "PROMPT", + "prompt to start generation with; for system message, use -sys", + [](common_params & params, const std::string & value) { + params.prompt = value; + } + ).set_excludes({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-sys", "--system-prompt"}, "PROMPT", + "system prompt to use with model (if applicable, depending on chat template)", + [](common_params & params, const std::string & value) { + params.system_prompt = value; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--no-perf"}, + string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"), + [](common_params & params) { + params.no_perf = true; + params.sampling.no_perf = true; + } + ).set_env("LLAMA_ARG_NO_PERF")); + add_opt(common_arg( + {"-f", "--file"}, "FNAME", + "a file containing the prompt (default: none)", + [](common_params & params, const std::string & value) { + params.prompt = read_file(value); + // store the external file name in params + params.prompt_file = value; + if (!params.prompt.empty() && params.prompt.back() == '\n') { + params.prompt.pop_back(); + } + } + ).set_excludes({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-sysf", "--system-prompt-file"}, "FNAME", + "a file containing the system prompt (default: none)", + [](common_params & params, const std::string & value) { + params.system_prompt = read_file(value); + if (!params.system_prompt.empty() && params.system_prompt.back() == '\n') { + params.system_prompt.pop_back(); + } + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--in-file"}, "FNAME", + "an input file (repeat to specify multiple files)", + [](common_params & params, const std::string & value) { + std::ifstream file(value); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + params.in_files.push_back(value); + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"-bf", "--binary-file"}, "FNAME", + "binary file containing the prompt (default: none)", + [](common_params & params, const std::string & value) { + std::ifstream file(value, std::ios::binary); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + // store the external file name in params + params.prompt_file = value; + std::ostringstream ss; + ss << file.rdbuf(); + params.prompt = ss.str(); + fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), value.c_str()); + } + ).set_excludes({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-e", "--escape"}, + string_format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"), + [](common_params & params) { + params.escape = true; + } + )); + add_opt(common_arg( + {"--no-escape"}, + "do not process escape sequences", + [](common_params & params) { + params.escape = false; + } + )); + add_opt(common_arg( + {"-ptc", "--print-token-count"}, "N", + string_format("print token count every N tokens (default: %d)", params.n_print), + [](common_params & params, int value) { + params.n_print = value; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--prompt-cache"}, "FNAME", + "file to cache prompt state for faster startup (default: none)", + [](common_params & params, const std::string & value) { + params.path_prompt_cache = value; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--prompt-cache-all"}, + "if specified, saves user input and generations to cache as well\n", + [](common_params & params) { + params.prompt_cache_all = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--prompt-cache-ro"}, + "if specified, uses the prompt cache but does not update it", + [](common_params & params) { + params.prompt_cache_ro = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-r", "--reverse-prompt"}, "PROMPT", + "halt generation at PROMPT, return control in interactive mode\n", + [](common_params & params, const std::string & value) { + params.antiprompt.emplace_back(value); + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-sp", "--special"}, + string_format("special tokens output enabled (default: %s)", params.special ? "true" : "false"), + [](common_params & params) { + params.special = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-cnv", "--conversation"}, + "run in conversation mode:\n" + "- does not print special tokens and suffix/prefix\n" + "- interactive mode is also enabled\n" + "(default: auto enabled if chat template is available)", + [](common_params & params) { + params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-no-cnv", "--no-conversation"}, + "force disable conversation mode (default: false)", + [](common_params & params) { + params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-st", "--single-turn"}, + "run conversation for a single turn only, then exit when done\n" + "will not be interactive if first turn is predefined with --prompt\n" + "(default: false)", + [](common_params & params) { + params.single_turn = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-i", "--interactive"}, + string_format("run in interactive mode (default: %s)", params.interactive ? "true" : "false"), + [](common_params & params) { + params.interactive = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-if", "--interactive-first"}, + string_format("run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false"), + [](common_params & params) { + params.interactive_first = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-mli", "--multiline-input"}, + "allows you to write or paste multiple lines without ending each in '\\'", + [](common_params & params) { + params.multiline_input = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--in-prefix-bos"}, + "prefix BOS to user inputs, preceding the `--in-prefix` string", + [](common_params & params) { + params.input_prefix_bos = true; + params.enable_chat_template = false; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--in-prefix"}, "STRING", + "string to prefix user inputs with (default: empty)", + [](common_params & params, const std::string & value) { + params.input_prefix = value; + params.enable_chat_template = false; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--in-suffix"}, "STRING", + "string to suffix after user inputs with (default: empty)", + [](common_params & params, const std::string & value) { + params.input_suffix = value; + params.enable_chat_template = false; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--no-warmup"}, + "skip warming up the model with an empty run", + [](common_params & params) { + params.warmup = false; + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL})); + add_opt(common_arg( + {"--spm-infill"}, + string_format( + "use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)", + params.spm_infill ? "enabled" : "disabled" + ), + [](common_params & params) { + params.spm_infill = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--samplers"}, "SAMPLERS", + string_format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()), + [](common_params & params, const std::string & value) { + const auto sampler_names = string_split(value, ';'); + params.sampling.samplers = common_sampler_types_from_names(sampler_names, true); + } + ).set_sparam()); + add_opt(common_arg( + {"-s", "--seed"}, "SEED", + string_format("RNG seed (default: %d, use random seed for %d)", params.sampling.seed, LLAMA_DEFAULT_SEED), + [](common_params & params, const std::string & value) { + params.sampling.seed = std::stoul(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--sampling-seq", "--sampler-seq"}, "SEQUENCE", + string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()), + [](common_params & params, const std::string & value) { + params.sampling.samplers = common_sampler_types_from_chars(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--ignore-eos"}, + "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)", + [](common_params & params) { + params.sampling.ignore_eos = true; + } + ).set_sparam()); + add_opt(common_arg( + {"--temp"}, "N", + string_format("temperature (default: %.1f)", (double)params.sampling.temp), + [](common_params & params, const std::string & value) { + params.sampling.temp = std::stof(value); + params.sampling.temp = std::max(params.sampling.temp, 0.0f); + } + ).set_sparam()); + add_opt(common_arg( + {"--top-k"}, "N", + string_format("top-k sampling (default: %d, 0 = disabled)", params.sampling.top_k), + [](common_params & params, int value) { + params.sampling.top_k = value; + } + ).set_sparam()); + add_opt(common_arg( + {"--top-p"}, "N", + string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sampling.top_p), + [](common_params & params, const std::string & value) { + params.sampling.top_p = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--min-p"}, "N", + string_format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sampling.min_p), + [](common_params & params, const std::string & value) { + params.sampling.min_p = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--top-nsigma"}, "N", + string_format("top-n-sigma sampling (default: %.1f, -1.0 = disabled)", params.sampling.top_n_sigma), + [](common_params & params, const std::string & value) { + params.sampling.top_n_sigma = std::stof(value); + } + ).set_examples({LLAMA_EXAMPLE_MAIN}).set_sparam()); + add_opt(common_arg( + {"--xtc-probability"}, "N", + string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sampling.xtc_probability), + [](common_params & params, const std::string & value) { + params.sampling.xtc_probability = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--xtc-threshold"}, "N", + string_format("xtc threshold (default: %.1f, 1.0 = disabled)", (double)params.sampling.xtc_threshold), + [](common_params & params, const std::string & value) { + params.sampling.xtc_threshold = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--typical"}, "N", + string_format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sampling.typ_p), + [](common_params & params, const std::string & value) { + params.sampling.typ_p = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--repeat-last-n"}, "N", + string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n), + [](common_params & params, int value) { + if (value < -1) { + throw std::runtime_error(string_format("error: invalid repeat-last-n = %d\n", value)); + } + params.sampling.penalty_last_n = value; + params.sampling.n_prev = std::max(params.sampling.n_prev, params.sampling.penalty_last_n); + } + ).set_sparam()); + add_opt(common_arg( + {"--repeat-penalty"}, "N", + string_format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sampling.penalty_repeat), + [](common_params & params, const std::string & value) { + params.sampling.penalty_repeat = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--presence-penalty"}, "N", + string_format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sampling.penalty_present), + [](common_params & params, const std::string & value) { + params.sampling.penalty_present = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--frequency-penalty"}, "N", + string_format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sampling.penalty_freq), + [](common_params & params, const std::string & value) { + params.sampling.penalty_freq = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-multiplier"}, "N", + string_format("set DRY sampling multiplier (default: %.1f, 0.0 = disabled)", (double)params.sampling.dry_multiplier), + [](common_params & params, const std::string & value) { + params.sampling.dry_multiplier = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-base"}, "N", + string_format("set DRY sampling base value (default: %.2f)", (double)params.sampling.dry_base), + [](common_params & params, const std::string & value) { + float potential_base = std::stof(value); + if (potential_base >= 1.0f) + { + params.sampling.dry_base = potential_base; + } + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-allowed-length"}, "N", + string_format("set allowed length for DRY sampling (default: %d)", params.sampling.dry_allowed_length), + [](common_params & params, int value) { + params.sampling.dry_allowed_length = value; + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-penalty-last-n"}, "N", + string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n), + [](common_params & params, int value) { + if (value < -1) { + throw std::runtime_error(string_format("error: invalid dry-penalty-last-n = %d\n", value)); + } + params.sampling.dry_penalty_last_n = value; + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-sequence-breaker"}, "STRING", + string_format("add sequence breaker for DRY sampling, clearing out default breakers (%s) in the process; use \"none\" to not use any sequence breakers\n", + params.sampling.dry_sequence_breakers.empty() ? "none" : + std::accumulate(std::next(params.sampling.dry_sequence_breakers.begin()), + params.sampling.dry_sequence_breakers.end(), + std::string("'") + (params.sampling.dry_sequence_breakers[0] == "\n" ? "\\n" : params.sampling.dry_sequence_breakers[0]) + "'", + [](const std::string& a, const std::string& b) { + std::string formatted_b = (b == "\n") ? "\\n" : b; + return a + ", '" + formatted_b + "'"; + }).c_str()), + [](common_params & params, const std::string & value) { + static bool defaults_cleared = false; + + if (!defaults_cleared) { + params.sampling.dry_sequence_breakers.clear(); + defaults_cleared = true; + } + + if (value == "none") { + params.sampling.dry_sequence_breakers.clear(); + } else { + params.sampling.dry_sequence_breakers.emplace_back(value); + } + } + ).set_sparam()); + add_opt(common_arg( + {"--dynatemp-range"}, "N", + string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sampling.dynatemp_range), + [](common_params & params, const std::string & value) { + params.sampling.dynatemp_range = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--dynatemp-exp"}, "N", + string_format("dynamic temperature exponent (default: %.1f)", (double)params.sampling.dynatemp_exponent), + [](common_params & params, const std::string & value) { + params.sampling.dynatemp_exponent = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--mirostat"}, "N", + string_format("use Mirostat sampling.\nTop K, Nucleus and Locally Typical samplers are ignored if used.\n" + "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sampling.mirostat), + [](common_params & params, int value) { + params.sampling.mirostat = value; + } + ).set_sparam()); + add_opt(common_arg( + {"--mirostat-lr"}, "N", + string_format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sampling.mirostat_eta), + [](common_params & params, const std::string & value) { + params.sampling.mirostat_eta = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--mirostat-ent"}, "N", + string_format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sampling.mirostat_tau), + [](common_params & params, const std::string & value) { + params.sampling.mirostat_tau = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"-l", "--logit-bias"}, "TOKEN_ID(+/-)BIAS", + "modifies the likelihood of token appearing in the completion,\n" + "i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n" + "or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'", + [](common_params & params, const std::string & value) { + std::stringstream ss(value); + llama_token key; + char sign; + std::string value_str; + try { + if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) { + const float bias = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f); + params.sampling.logit_bias.push_back({key, bias}); + } else { + throw std::invalid_argument("invalid input format"); + } + } catch (const std::exception&) { + throw std::invalid_argument("invalid input format"); + } + } + ).set_sparam()); + add_opt(common_arg( + {"--grammar"}, "GRAMMAR", + string_format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sampling.grammar.c_str()), + [](common_params & params, const std::string & value) { + params.sampling.grammar = value; + } + ).set_sparam()); + add_opt(common_arg( + {"--grammar-file"}, "FNAME", + "file to read grammar from", + [](common_params & params, const std::string & value) { + params.sampling.grammar = read_file(value); + } + ).set_sparam()); + add_opt(common_arg( + {"-j", "--json-schema"}, "SCHEMA", + "JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead", + [](common_params & params, const std::string & value) { + params.sampling.grammar = json_schema_to_grammar(json::parse(value)); + } + ).set_sparam()); + add_opt(common_arg( + {"-jf", "--json-schema-file"}, "FILE", + "File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead", + [](common_params & params, const std::string & value) { + std::ifstream file(value); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + std::string schema; + std::copy( + std::istreambuf_iterator(file), + std::istreambuf_iterator(), + std::back_inserter(schema) + ); + params.sampling.grammar = json_schema_to_grammar(json::parse(schema)); + } + ).set_sparam()); + add_opt(common_arg( + {"--pooling"}, "{none,mean,cls,last,rank}", + "pooling type for embeddings, use model default if unspecified", + [](common_params & params, const std::string & value) { + /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; } + else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } + else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } + else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; } + else if (value == "rank") { params.pooling_type = LLAMA_POOLING_TYPE_RANK; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_POOLING")); + add_opt(common_arg( + {"--attention"}, "{causal,non-causal}", + "attention type for embeddings, use model default if unspecified", + [](common_params & params, const std::string & value) { + /**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; } + else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--rope-scaling"}, "{none,linear,yarn}", + "RoPE frequency scaling method, defaults to linear unless specified by the model", + [](common_params & params, const std::string & value) { + /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; } + else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } + else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_env("LLAMA_ARG_ROPE_SCALING_TYPE")); + add_opt(common_arg( + {"--rope-scale"}, "N", + "RoPE context scaling factor, expands context by a factor of N", + [](common_params & params, const std::string & value) { + params.rope_freq_scale = 1.0f / std::stof(value); + } + ).set_env("LLAMA_ARG_ROPE_SCALE")); + add_opt(common_arg( + {"--rope-freq-base"}, "N", + "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)", + [](common_params & params, const std::string & value) { + params.rope_freq_base = std::stof(value); + } + ).set_env("LLAMA_ARG_ROPE_FREQ_BASE")); + add_opt(common_arg( + {"--rope-freq-scale"}, "N", + "RoPE frequency scaling factor, expands context by a factor of 1/N", + [](common_params & params, const std::string & value) { + params.rope_freq_scale = std::stof(value); + } + ).set_env("LLAMA_ARG_ROPE_FREQ_SCALE")); + add_opt(common_arg( + {"--yarn-orig-ctx"}, "N", + string_format("YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx), + [](common_params & params, int value) { + params.yarn_orig_ctx = value; + } + ).set_env("LLAMA_ARG_YARN_ORIG_CTX")); + add_opt(common_arg( + {"--yarn-ext-factor"}, "N", + string_format("YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor), + [](common_params & params, const std::string & value) { + params.yarn_ext_factor = std::stof(value); + } + ).set_env("LLAMA_ARG_YARN_EXT_FACTOR")); + add_opt(common_arg( + {"--yarn-attn-factor"}, "N", + string_format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor), + [](common_params & params, const std::string & value) { + params.yarn_attn_factor = std::stof(value); + } + ).set_env("LLAMA_ARG_YARN_ATTN_FACTOR")); + add_opt(common_arg( + {"--yarn-beta-slow"}, "N", + string_format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow), + [](common_params & params, const std::string & value) { + params.yarn_beta_slow = std::stof(value); + } + ).set_env("LLAMA_ARG_YARN_BETA_SLOW")); + add_opt(common_arg( + {"--yarn-beta-fast"}, "N", + string_format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast), + [](common_params & params, const std::string & value) { + params.yarn_beta_fast = std::stof(value); + } + ).set_env("LLAMA_ARG_YARN_BETA_FAST")); + add_opt(common_arg( + {"-gan", "--grp-attn-n"}, "N", + string_format("group-attention factor (default: %d)", params.grp_attn_n), + [](common_params & params, int value) { + params.grp_attn_n = value; + } + ).set_env("LLAMA_ARG_GRP_ATTN_N").set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_PASSKEY})); + add_opt(common_arg( + {"-gaw", "--grp-attn-w"}, "N", + string_format("group-attention width (default: %d)", params.grp_attn_w), + [](common_params & params, int value) { + params.grp_attn_w = value; + } + ).set_env("LLAMA_ARG_GRP_ATTN_W").set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-nkvo", "--no-kv-offload"}, + "disable KV offload", + [](common_params & params) { + params.no_kv_offload = true; + } + ).set_env("LLAMA_ARG_NO_KV_OFFLOAD")); + add_opt(common_arg( + {"-nr", "--no-repack"}, + "disable weight repacking", + [](common_params & params) { + params.no_extra_bufts = true; + } + ).set_env("LLAMA_ARG_NO_REPACK")); + add_opt(common_arg( + {"-ctk", "--cache-type-k"}, "TYPE", + string_format( + "KV cache data type for K\n" + "allowed values: %s\n" + "(default: %s)", + get_all_kv_cache_types().c_str(), + ggml_type_name(params.cache_type_k) + ), + [](common_params & params, const std::string & value) { + params.cache_type_k = kv_cache_type_from_str(value); + } + ).set_env("LLAMA_ARG_CACHE_TYPE_K")); + add_opt(common_arg( + {"-ctv", "--cache-type-v"}, "TYPE", + string_format( + "KV cache data type for V\n" + "allowed values: %s\n" + "(default: %s)", + get_all_kv_cache_types().c_str(), + ggml_type_name(params.cache_type_v) + ), + [](common_params & params, const std::string & value) { + params.cache_type_v = kv_cache_type_from_str(value); + } + ).set_env("LLAMA_ARG_CACHE_TYPE_V")); + add_opt(common_arg( + {"--hellaswag"}, + "compute HellaSwag score over random tasks from datafile supplied with -f", + [](common_params & params) { + params.hellaswag = true; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--hellaswag-tasks"}, "N", + string_format("number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks), + [](common_params & params, int value) { + params.hellaswag_tasks = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--winogrande"}, + "compute Winogrande score over random tasks from datafile supplied with -f", + [](common_params & params) { + params.winogrande = true; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--winogrande-tasks"}, "N", + string_format("number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks), + [](common_params & params, int value) { + params.winogrande_tasks = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--multiple-choice"}, + "compute multiple choice score over random tasks from datafile supplied with -f", + [](common_params & params) { + params.multiple_choice = true; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--multiple-choice-tasks"}, "N", + string_format("number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks), + [](common_params & params, int value) { + params.multiple_choice_tasks = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--kl-divergence"}, + "computes KL-divergence to logits provided via --kl-divergence-base", + [](common_params & params) { + params.kl_divergence = true; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--save-all-logits", "--kl-divergence-base"}, "FNAME", + "set logits file", + [](common_params & params, const std::string & value) { + params.logits_file = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--ppl-stride"}, "N", + string_format("stride for perplexity calculation (default: %d)", params.ppl_stride), + [](common_params & params, int value) { + params.ppl_stride = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--ppl-output-type"}, "<0|1>", + string_format("output type for perplexity calculation (default: %d)", params.ppl_output_type), + [](common_params & params, int value) { + params.ppl_output_type = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"-dt", "--defrag-thold"}, "N", + string_format("KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold), + [](common_params & params, const std::string & value) { + params.defrag_thold = std::stof(value); + } + ).set_env("LLAMA_ARG_DEFRAG_THOLD")); + add_opt(common_arg( + {"-np", "--parallel"}, "N", + string_format("number of parallel sequences to decode (default: %d)", params.n_parallel), + [](common_params & params, int value) { + params.n_parallel = value; + } + ).set_env("LLAMA_ARG_N_PARALLEL")); + add_opt(common_arg( + {"-ns", "--sequences"}, "N", + string_format("number of sequences to decode (default: %d)", params.n_sequences), + [](common_params & params, int value) { + params.n_sequences = value; + } + ).set_examples({LLAMA_EXAMPLE_PARALLEL})); + add_opt(common_arg( + {"-cb", "--cont-batching"}, + string_format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"), + [](common_params & params) { + params.cont_batching = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CONT_BATCHING")); + add_opt(common_arg( + {"-nocb", "--no-cont-batching"}, + "disable continuous batching", + [](common_params & params) { + params.cont_batching = false; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING")); + add_opt(common_arg( + {"--mmproj"}, "FILE", + "path to a multimodal projector file. see tools/mtmd/README.md\n" + "note: if -hf is used, this argument can be omitted", + [](common_params & params, const std::string & value) { + params.mmproj.path = value; + } + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ")); + add_opt(common_arg( + {"--mmproj-url"}, "URL", + "URL to a multimodal projector file. see tools/mtmd/README.md", + [](common_params & params, const std::string & value) { + params.mmproj.url = value; + } + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_URL")); + add_opt(common_arg( + {"--no-mmproj"}, + "explicitly disable multimodal projector, useful when using -hf", + [](common_params & params) { + params.no_mmproj = true; + } + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ")); + add_opt(common_arg( + {"--no-mmproj-offload"}, + "do not offload multimodal projector to GPU", + [](common_params & params) { + params.mmproj_use_gpu = false; + } + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ_OFFLOAD")); + add_opt(common_arg( + {"--image", "--audio"}, "FILE", + "path to an image or audio file. use with multimodal models, can be repeated if you have multiple files\n", + [](common_params & params, const std::string & value) { + params.image.emplace_back(value); + } + ).set_examples({LLAMA_EXAMPLE_MTMD})); + if (llama_supports_rpc()) { + add_opt(common_arg( + {"--rpc"}, "SERVERS", + "comma separated list of RPC servers", + [](common_params & params, const std::string & value) { + add_rpc_devices(value); + GGML_UNUSED(params); + } + ).set_env("LLAMA_ARG_RPC")); + } + add_opt(common_arg( + {"--mlock"}, + "force system to keep model in RAM rather than swapping or compressing", + [](common_params & params) { + params.use_mlock = true; + } + ).set_env("LLAMA_ARG_MLOCK")); + add_opt(common_arg( + {"--no-mmap"}, + "do not memory-map model (slower load but may reduce pageouts if not using mlock)", + [](common_params & params) { + params.use_mmap = false; + } + ).set_env("LLAMA_ARG_NO_MMAP")); + add_opt(common_arg( + {"--numa"}, "TYPE", + "attempt optimizations that help on some NUMA systems\n" + "- distribute: spread execution evenly over all nodes\n" + "- isolate: only spawn threads on CPUs on the node that execution started on\n" + "- numactl: use the CPU map provided by numactl\n" + "if run without this previously, it is recommended to drop the system page cache before using this\n" + "see https://github.com/ggml-org/llama.cpp/issues/1437", + [](common_params & params, const std::string & value) { + /**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } + else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } + else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_env("LLAMA_ARG_NUMA")); + add_opt(common_arg( + {"-dev", "--device"}, "", + "comma-separated list of devices to use for offloading (none = don't offload)\n" + "use --list-devices to see a list of available devices", + [](common_params & params, const std::string & value) { + params.devices = parse_device_list(value); + } + ).set_env("LLAMA_ARG_DEVICE")); + add_opt(common_arg( + {"--list-devices"}, + "print list of available devices and exit", + [](common_params &) { + std::vector rpc_devices; + std::vector all_devices; + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + auto * dev = ggml_backend_dev_get(i); + if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) { + ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev); + if (ggml_backend_reg_name(reg) == std::string("RPC")) { + rpc_devices.push_back(dev); + } else { + all_devices.push_back(dev); + } + } + } + // insert RPC devices in front + all_devices.insert(all_devices.begin(), rpc_devices.begin(), rpc_devices.end()); + printf("Available devices:\n"); + for (size_t i = 0; i < all_devices.size(); ++i) { + auto * dev = all_devices[i]; + size_t free, total; + ggml_backend_dev_memory(dev, &free, &total); + printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024); + } + exit(0); + } + )); + add_opt(common_arg( + {"--override-tensor", "-ot"}, "=,...", + "override tensor buffer type", [](common_params & params, const std::string & value) { + /* static */ std::map buft_list; + if (buft_list.empty()) { + // enumerate all the devices and add their buffer types to the list + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + auto * dev = ggml_backend_dev_get(i); + auto * buft = ggml_backend_dev_buffer_type(dev); + if (buft) { + buft_list[ggml_backend_buft_name(buft)] = buft; + } + } + } + + for (const auto & override : string_split(value, ',')) { + std::string::size_type pos = override.find('='); + if (pos == std::string::npos) { + throw std::invalid_argument("invalid value"); + } + std::string tensor_name = override.substr(0, pos); + std::string buffer_type = override.substr(pos + 1); + + if (buft_list.find(buffer_type) == buft_list.end()) { + printf("Available buffer types:\n"); + for (const auto & it : buft_list) { + printf(" %s\n", ggml_backend_buft_name(it.second)); + } + throw std::invalid_argument("unknown buffer type"); + } + // FIXME: this leaks memory + params.tensor_buft_overrides.push_back({strdup(tensor_name.c_str()), buft_list.at(buffer_type)}); + } + } + )); + add_opt(common_arg( + {"--cpu-moe"}, + "use CPU for Mixture of Experts (MoE) weights", + [](common_params & params) { + params.tensor_buft_overrides.push_back({"\\.ffn_up_exps\\.weight$", ggml_backend_cpu_buffer_type()}); + params.tensor_buft_overrides.push_back({"\\.ffn_down_exps\\.weight$", ggml_backend_cpu_buffer_type()}); + params.tensor_buft_overrides.push_back({"\\.ffn_gate_exps\\.weight$", ggml_backend_cpu_buffer_type()}); + } + ).set_env("LLAMA_ARG_CPU_MOE")); + add_opt(common_arg( + {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N", + "number of layers to store in VRAM", + [](common_params & params, int value) { + params.n_gpu_layers = value; + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: no usable GPU found, --gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n"); + fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n"); + } + } + ).set_env("LLAMA_ARG_N_GPU_LAYERS")); + add_opt(common_arg( + {"-sm", "--split-mode"}, "{none,layer,row}", + "how to split the model across multiple GPUs, one of:\n" + "- none: use one GPU only\n" + "- layer (default): split layers and KV across GPUs\n" + "- row: split rows across GPUs", + [](common_params & params, const std::string & value) { + std::string arg_next = value; + if (arg_next == "none") { + params.split_mode = LLAMA_SPLIT_MODE_NONE; + } else if (arg_next == "layer") { + params.split_mode = LLAMA_SPLIT_MODE_LAYER; + } else if (arg_next == "row") { + params.split_mode = LLAMA_SPLIT_MODE_ROW; + } else { + throw std::invalid_argument("invalid value"); + } + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting the split mode has no effect.\n"); + } + } + ).set_env("LLAMA_ARG_SPLIT_MODE")); + add_opt(common_arg( + {"-ts", "--tensor-split"}, "N0,N1,N2,...", + "fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1", + [](common_params & params, const std::string & value) { + std::string arg_next = value; + + // split string by , and / + const std::regex regex{ R"([,/]+)" }; + std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 }; + std::vector split_arg{ it, {} }; + if (split_arg.size() >= llama_max_devices()) { + throw std::invalid_argument( + string_format("got %d input configs, but system only has %d devices", (int)split_arg.size(), (int)llama_max_devices()) + ); + } + for (size_t i = 0; i < llama_max_devices(); ++i) { + if (i < split_arg.size()) { + params.tensor_split[i] = std::stof(split_arg[i]); + } else { + params.tensor_split[i] = 0.0f; + } + } + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting a tensor split has no effect.\n"); + } + } + ).set_env("LLAMA_ARG_TENSOR_SPLIT")); + add_opt(common_arg( + {"-mg", "--main-gpu"}, "INDEX", + string_format("the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu), + [](common_params & params, int value) { + params.main_gpu = value; + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting the main GPU has no effect.\n"); + } + } + ).set_env("LLAMA_ARG_MAIN_GPU")); + add_opt(common_arg( + {"--check-tensors"}, + string_format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"), + [](common_params & params) { + params.check_tensors = true; + } + )); + add_opt(common_arg( + {"--override-kv"}, "KEY=TYPE:VALUE", + "advanced option to override model metadata by key. may be specified multiple times.\n" + "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false", + [](common_params & params, const std::string & value) { + if (!string_parse_kv_override(value.c_str(), params.kv_overrides)) { + throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", value.c_str())); + } + } + )); + add_opt(common_arg( + {"--no-op-offload"}, + string_format("disable offloading host tensor operations to device (default: %s)", params.no_op_offload ? "true" : "false"), + [](common_params & params) { + params.no_op_offload = true; + } + )); + add_opt(common_arg( + {"--lora"}, "FNAME", + "path to LoRA adapter (can be repeated to use multiple adapters)", + [](common_params & params, const std::string & value) { + params.lora_adapters.push_back({ std::string(value), 1.0, nullptr }); + } + // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg + ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA})); + add_opt(common_arg( + {"--lora-scaled"}, "FNAME", "SCALE", + "path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)", + [](common_params & params, const std::string & fname, const std::string & scale) { + params.lora_adapters.push_back({ fname, std::stof(scale), nullptr }); + } + // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg + ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA})); + add_opt(common_arg( + {"--control-vector"}, "FNAME", + "add a control vector\nnote: this argument can be repeated to add multiple control vectors", + [](common_params & params, const std::string & value) { + params.control_vectors.push_back({ 1.0f, value, }); + } + )); + add_opt(common_arg( + {"--control-vector-scaled"}, "FNAME", "SCALE", + "add a control vector with user defined scaling SCALE\n" + "note: this argument can be repeated to add multiple scaled control vectors", + [](common_params & params, const std::string & fname, const std::string & scale) { + params.control_vectors.push_back({ std::stof(scale), fname }); + } + )); + add_opt(common_arg( + {"--control-vector-layer-range"}, "START", "END", + "layer range to apply the control vector(s) to, start and end inclusive", + [](common_params & params, const std::string & start, const std::string & end) { + params.control_vector_layer_start = std::stoi(start); + params.control_vector_layer_end = std::stoi(end); + } + )); + add_opt(common_arg( + {"-a", "--alias"}, "STRING", + "set alias for model name (to be used by REST API)", + [](common_params & params, const std::string & value) { + params.model_alias = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ALIAS")); + add_opt(common_arg( + {"-m", "--model"}, "FNAME", + ex == LLAMA_EXAMPLE_EXPORT_LORA + ? std::string("model path from which to load base model") + : string_format( + "model path (default: `models/$filename` with filename from `--hf-file` " + "or `--model-url` if set, otherwise %s)", DEFAULT_MODEL_PATH + ), + [](common_params & params, const std::string & value) { + params.model.path = value; + } + ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}).set_env("LLAMA_ARG_MODEL")); + add_opt(common_arg( + {"-mu", "--model-url"}, "MODEL_URL", + "model download url (default: unused)", + [](common_params & params, const std::string & value) { + params.model.url = value; + } + ).set_env("LLAMA_ARG_MODEL_URL")); + add_opt(common_arg( + {"-hf", "-hfr", "--hf-repo"}, "/[:quant]", + "Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n" + "mmproj is also downloaded automatically if available. to disable, add --no-mmproj\n" + "example: unsloth/phi-4-GGUF:q4_k_m\n" + "(default: unused)", + [](common_params & params, const std::string & value) { + params.model.hf_repo = value; + } + ).set_env("LLAMA_ARG_HF_REPO")); + add_opt(common_arg( + {"-hfd", "-hfrd", "--hf-repo-draft"}, "/[:quant]", + "Same as --hf-repo, but for the draft model (default: unused)", + [](common_params & params, const std::string & value) { + params.speculative.model.hf_repo = value; + } + ).set_env("LLAMA_ARG_HFD_REPO")); + add_opt(common_arg( + {"-hff", "--hf-file"}, "FILE", + "Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)", + [](common_params & params, const std::string & value) { + params.model.hf_file = value; + } + ).set_env("LLAMA_ARG_HF_FILE")); + add_opt(common_arg( + {"-hfv", "-hfrv", "--hf-repo-v"}, "/[:quant]", + "Hugging Face model repository for the vocoder model (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.model.hf_repo = value; + } + ).set_env("LLAMA_ARG_HF_REPO_V")); + add_opt(common_arg( + {"-hffv", "--hf-file-v"}, "FILE", + "Hugging Face model file for the vocoder model (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.model.hf_file = value; + } + ).set_env("LLAMA_ARG_HF_FILE_V")); + add_opt(common_arg( + {"-hft", "--hf-token"}, "TOKEN", + "Hugging Face access token (default: value from HF_TOKEN environment variable)", + [](common_params & params, const std::string & value) { + params.hf_token = value; + } + ).set_env("HF_TOKEN")); + add_opt(common_arg( + {"--context-file"}, "FNAME", + "file to load context from (repeat to specify multiple files)", + [](common_params & params, const std::string & value) { + std::ifstream file(value, std::ios::binary); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + params.context_files.push_back(value); + } + ).set_examples({LLAMA_EXAMPLE_RETRIEVAL})); + add_opt(common_arg( + {"--chunk-size"}, "N", + string_format("minimum length of embedded text chunks (default: %d)", params.chunk_size), + [](common_params & params, int value) { + params.chunk_size = value; + } + ).set_examples({LLAMA_EXAMPLE_RETRIEVAL})); + add_opt(common_arg( + {"--chunk-separator"}, "STRING", + string_format("separator between chunks (default: '%s')", params.chunk_separator.c_str()), + [](common_params & params, const std::string & value) { + params.chunk_separator = value; + } + ).set_examples({LLAMA_EXAMPLE_RETRIEVAL})); + add_opt(common_arg( + {"--junk"}, "N", + string_format("number of times to repeat the junk text (default: %d)", params.n_junk), + [](common_params & params, int value) { + params.n_junk = value; + } + ).set_examples({LLAMA_EXAMPLE_PASSKEY, LLAMA_EXAMPLE_PARALLEL})); + add_opt(common_arg( + {"--pos"}, "N", + string_format("position of the passkey in the junk text (default: %d)", params.i_pos), + [](common_params & params, int value) { + params.i_pos = value; + } + ).set_examples({LLAMA_EXAMPLE_PASSKEY})); + add_opt(common_arg( + {"-o", "--output", "--output-file"}, "FNAME", + string_format("output file (default: '%s')", params.out_file.c_str()), + [](common_params & params, const std::string & value) { + params.out_file = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA, LLAMA_EXAMPLE_TTS})); + add_opt(common_arg( + {"-ofreq", "--output-frequency"}, "N", + string_format("output the imatrix every N iterations (default: %d)", params.n_out_freq), + [](common_params & params, int value) { + params.n_out_freq = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--save-frequency"}, "N", + string_format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq), + [](common_params & params, int value) { + params.n_save_freq = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--process-output"}, + string_format("collect data for the output tensor (default: %s)", params.process_output ? "true" : "false"), + [](common_params & params) { + params.process_output = true; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--no-ppl"}, + string_format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"), + [](common_params & params) { + params.compute_ppl = false; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--chunk", "--from-chunk"}, "N", + string_format("start processing the input from chunk N (default: %d)", params.i_chunk), + [](common_params & params, int value) { + params.i_chunk = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--show-statistics"}, + string_format("show imatrix statistics and then exit (default: %s)", params.show_statistics ? "true" : "false"), + [](common_params & params) { + params.show_statistics = true; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--parse-special"}, + string_format("prase special tokens (chat, tool, etc) (default: %s)", params.parse_special ? "true" : "false"), + [](common_params & params) { + params.parse_special = true; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"-pps"}, + string_format("is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false"), + [](common_params & params) { + params.is_pp_shared = true; + } + ).set_examples({LLAMA_EXAMPLE_BENCH, LLAMA_EXAMPLE_PARALLEL})); + add_opt(common_arg( + {"-npp"}, "n0,n1,...", + "number of prompt tokens", + [](common_params & params, const std::string & value) { + auto p = string_split(value, ','); + params.n_pp.insert(params.n_pp.end(), p.begin(), p.end()); + } + ).set_examples({LLAMA_EXAMPLE_BENCH})); + add_opt(common_arg( + {"-ntg"}, "n0,n1,...", + "number of text generation tokens", + [](common_params & params, const std::string & value) { + auto p = string_split(value, ','); + params.n_tg.insert(params.n_tg.end(), p.begin(), p.end()); + } + ).set_examples({LLAMA_EXAMPLE_BENCH})); + add_opt(common_arg( + {"-npl"}, "n0,n1,...", + "number of parallel prompts", + [](common_params & params, const std::string & value) { + auto p = string_split(value, ','); + params.n_pl.insert(params.n_pl.end(), p.begin(), p.end()); + } + ).set_examples({LLAMA_EXAMPLE_BENCH})); + add_opt(common_arg( + {"--embd-normalize"}, "N", + string_format("normalisation for embeddings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize), + [](common_params & params, int value) { + params.embd_normalize = value; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--embd-output-format"}, "FORMAT", + "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix", + [](common_params & params, const std::string & value) { + params.embd_out = value; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--embd-separator"}, "STRING", + "separator of embeddings (default \\n) for example \"<#sep#>\"", + [](common_params & params, const std::string & value) { + params.embd_sep = value; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--cls-separator"}, "STRING", + "separator of classification sequences (default \\t) for example \"<#seq#>\"", + [](common_params & params, const std::string & value) { + params.cls_sep = value; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--host"}, "HOST", + string_format("ip address to listen, or bind to an UNIX socket if the address ends with .sock (default: %s)", params.hostname.c_str()), + [](common_params & params, const std::string & value) { + params.hostname = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_HOST")); + add_opt(common_arg( + {"--port"}, "PORT", + string_format("port to listen (default: %d)", params.port), + [](common_params & params, int value) { + params.port = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PORT")); + add_opt(common_arg( + {"--path"}, "PATH", + string_format("path to serve static files from (default: %s)", params.public_path.c_str()), + [](common_params & params, const std::string & value) { + params.public_path = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_STATIC_PATH")); + add_opt(common_arg( + {"--api-prefix"}, "PREFIX", + string_format("prefix path the server serves from, without the trailing slash (default: %s)", params.api_prefix.c_str()), + [](common_params & params, const std::string & value) { + params.api_prefix = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_API_PREFIX")); + add_opt(common_arg( + {"--no-webui"}, + string_format("Disable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"), + [](common_params & params) { + params.webui = false; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_WEBUI")); + add_opt(common_arg( + {"--embedding", "--embeddings"}, + string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"), + [](common_params & params) { + params.embedding = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS")); + add_opt(common_arg( + {"--reranking", "--rerank"}, + string_format("enable reranking endpoint on server (default: %s)", "disabled"), + [](common_params & params) { + params.embedding = true; + params.pooling_type = LLAMA_POOLING_TYPE_RANK; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_RERANKING")); + add_opt(common_arg( + {"--api-key"}, "KEY", + "API key to use for authentication (default: none)", + [](common_params & params, const std::string & value) { + params.api_keys.push_back(value); + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_API_KEY")); + add_opt(common_arg( + {"--api-key-file"}, "FNAME", + "path to file containing API keys (default: none)", + [](common_params & params, const std::string & value) { + std::ifstream key_file(value); + if (!key_file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + std::string key; + while (std::getline(key_file, key)) { + if (!key.empty()) { + params.api_keys.push_back(key); + } + } + key_file.close(); + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--ssl-key-file"}, "FNAME", + "path to file a PEM-encoded SSL private key", + [](common_params & params, const std::string & value) { + params.ssl_file_key = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_KEY_FILE")); + add_opt(common_arg( + {"--ssl-cert-file"}, "FNAME", + "path to file a PEM-encoded SSL certificate", + [](common_params & params, const std::string & value) { + params.ssl_file_cert = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_CERT_FILE")); + add_opt(common_arg( + {"--chat-template-kwargs"}, "STRING", + string_format("sets additional params for the json template parser"), + [](common_params & params, const std::string & value) { + auto parsed = json::parse(value); + for (const auto & item : parsed.items()) { + params.default_template_kwargs[item.key()] = item.value().dump(); + } + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_CHAT_TEMPLATE_KWARGS")); + add_opt(common_arg( + {"-to", "--timeout"}, "N", + string_format("server read/write timeout in seconds (default: %d)", params.timeout_read), + [](common_params & params, int value) { + params.timeout_read = value; + params.timeout_write = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TIMEOUT")); + add_opt(common_arg( + {"--threads-http"}, "N", + string_format("number of threads used to process HTTP requests (default: %d)", params.n_threads_http), + [](common_params & params, int value) { + params.n_threads_http = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP")); + add_opt(common_arg( + {"--cache-reuse"}, "N", + string_format( + "min chunk size to attempt reusing from the cache via KV shifting (default: %d)\n" + "[(card)](https://ggml.ai/f0.png)", params.n_cache_reuse + ), + [](common_params & params, int value) { + params.n_cache_reuse = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CACHE_REUSE")); + add_opt(common_arg( + {"--metrics"}, + string_format("enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled"), + [](common_params & params) { + params.endpoint_metrics = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_METRICS")); + add_opt(common_arg( + {"--slots"}, + string_format("enable slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"), + [](common_params & params) { + params.endpoint_slots = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_SLOTS")); + add_opt(common_arg( + {"--props"}, + string_format("enable changing global properties via POST /props (default: %s)", params.endpoint_props ? "enabled" : "disabled"), + [](common_params & params) { + params.endpoint_props = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_PROPS")); + add_opt(common_arg( + {"--no-slots"}, + "disables slots monitoring endpoint", + [](common_params & params) { + params.endpoint_slots = false; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_ENDPOINT_SLOTS")); + add_opt(common_arg( + {"--slot-save-path"}, "PATH", + "path to save slot kv cache (default: disabled)", + [](common_params & params, const std::string & value) { + params.slot_save_path = value; + // if doesn't end with DIRECTORY_SEPARATOR, add it + if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) { + params.slot_save_path += DIRECTORY_SEPARATOR; + } + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--jinja"}, + "use jinja template for chat (default: disabled)", + [](common_params & params) { + params.use_jinja = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_JINJA")); + add_opt(common_arg( + {"--reasoning-format"}, "FORMAT", + "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n" + "- none: leaves thoughts unparsed in `message.content`\n" + "- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)\n" + "(default: deepseek)", + [](common_params & params, const std::string & value) { + /**/ if (value == "deepseek") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; } + else if (value == "deepseek-legacy") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY; } + else if (value == "none") { params.reasoning_format = COMMON_REASONING_FORMAT_NONE; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK")); + add_opt(common_arg( + {"--reasoning-budget"}, "N", + "controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)", + [](common_params & params, int value) { + if (value != 0 && value != -1) { throw std::invalid_argument("invalid value"); } + params.reasoning_budget = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK_BUDGET")); + add_opt(common_arg( + {"--chat-template"}, "JINJA_TEMPLATE", + string_format( + "set custom jinja chat template (default: template taken from model's metadata)\n" + "if suffix/prefix are specified, template will be disabled\n" + "only commonly used templates are accepted (unless --jinja is set before this flag):\n" + "list of built-in templates:\n%s", list_builtin_chat_templates().c_str() + ), + [](common_params & params, const std::string & value) { + params.chat_template = value; + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_CHAT_TEMPLATE")); + add_opt(common_arg( + {"--chat-template-file"}, "JINJA_TEMPLATE_FILE", + string_format( + "set custom jinja chat template file (default: template taken from model's metadata)\n" + "if suffix/prefix are specified, template will be disabled\n" + "only commonly used templates are accepted (unless --jinja is set before this flag):\n" + "list of built-in templates:\n%s", list_builtin_chat_templates().c_str() + ), + [](common_params & params, const std::string & value) { + params.chat_template = read_file(value); + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE")); + add_opt(common_arg( + {"--no-prefill-assistant"}, + string_format( + "whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)\n" + "when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled\n" + ), + [](common_params & params) { + params.prefill_assistant = false; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_PREFILL_ASSISTANT")); + add_opt(common_arg( + {"-sps", "--slot-prompt-similarity"}, "SIMILARITY", + string_format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity), + [](common_params & params, const std::string & value) { + params.slot_prompt_similarity = std::stof(value); + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--lora-init-without-apply"}, + string_format("load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"), + [](common_params & params) { + params.lora_init_without_apply = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--simple-io"}, + "use basic IO for better compatibility in subprocesses and limited consoles", + [](common_params & params) { + params.simple_io = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--positive-file"}, "FNAME", + string_format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()), + [](common_params & params, const std::string & value) { + params.cvector_positive_file = value; + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--negative-file"}, "FNAME", + string_format("negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str()), + [](common_params & params, const std::string & value) { + params.cvector_negative_file = value; + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--pca-batch"}, "N", + string_format("batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch), + [](common_params & params, int value) { + params.n_pca_batch = value; + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--pca-iter"}, "N", + string_format("number of iterations used for PCA (default: %d)", params.n_pca_iterations), + [](common_params & params, int value) { + params.n_pca_iterations = value; + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--method"}, "{pca, mean}", + "dimensionality reduction method to be used (default: pca)", + [](common_params & params, const std::string & value) { + /**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; } + else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--output-format"}, "{md,jsonl}", + "output format for batched-bench results (default: md)", + [](common_params & params, const std::string & value) { + /**/ if (value == "jsonl") { params.batched_bench_output_jsonl = true; } + else if (value == "md") { params.batched_bench_output_jsonl = false; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_examples({LLAMA_EXAMPLE_BENCH})); + add_opt(common_arg( + {"--log-disable"}, + "Log disable", + [](common_params &) { + common_log_pause(common_log_main()); + } + )); + add_opt(common_arg( + {"--log-file"}, "FNAME", + "Log to file", + [](common_params &, const std::string & value) { + common_log_set_file(common_log_main(), value.c_str()); + } + )); + add_opt(common_arg( + {"--log-colors"}, + "Enable colored logging", + [](common_params &) { + common_log_set_colors(common_log_main(), true); + } + ).set_env("LLAMA_LOG_COLORS")); + add_opt(common_arg( + {"-v", "--verbose", "--log-verbose"}, + "Set verbosity level to infinity (i.e. log all messages, useful for debugging)", + [](common_params & params) { + params.verbosity = INT_MAX; + common_log_set_verbosity_thold(INT_MAX); + } + )); + add_opt(common_arg( + {"--offline"}, + "Offline mode: forces use of cache, prevents network access", + [](common_params & params) { + params.offline = true; + } + ).set_env("LLAMA_OFFLINE")); + add_opt(common_arg( + {"-lv", "--verbosity", "--log-verbosity"}, "N", + "Set the verbosity threshold. Messages with a higher verbosity will be ignored.", + [](common_params & params, int value) { + params.verbosity = value; + common_log_set_verbosity_thold(value); + } + ).set_env("LLAMA_LOG_VERBOSITY")); + add_opt(common_arg( + {"--log-prefix"}, + "Enable prefix in log messages", + [](common_params &) { + common_log_set_prefix(common_log_main(), true); + } + ).set_env("LLAMA_LOG_PREFIX")); + add_opt(common_arg( + {"--log-timestamps"}, + "Enable timestamps in log messages", + [](common_params &) { + common_log_set_timestamps(common_log_main(), true); + } + ).set_env("LLAMA_LOG_TIMESTAMPS")); + + // speculative parameters + add_opt(common_arg( + {"-td", "--threads-draft"}, "N", + "number of threads to use during generation (default: same as --threads)", + [](common_params & params, int value) { + params.speculative.cpuparams.n_threads = value; + if (params.speculative.cpuparams.n_threads <= 0) { + params.speculative.cpuparams.n_threads = std::thread::hardware_concurrency(); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"-tbd", "--threads-batch-draft"}, "N", + "number of threads to use during batch and prompt processing (default: same as --threads-draft)", + [](common_params & params, int value) { + params.speculative.cpuparams_batch.n_threads = value; + if (params.speculative.cpuparams_batch.n_threads <= 0) { + params.speculative.cpuparams_batch.n_threads = std::thread::hardware_concurrency(); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"-Cd", "--cpu-mask-draft"}, "M", + "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)", + [](common_params & params, const std::string & mask) { + params.speculative.cpuparams.mask_valid = true; + if (!parse_cpu_mask(mask, params.speculative.cpuparams.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"-Crd", "--cpu-range-draft"}, "lo-hi", + "Ranges of CPUs for affinity. Complements --cpu-mask-draft", + [](common_params & params, const std::string & range) { + params.speculative.cpuparams.mask_valid = true; + if (!parse_cpu_range(range, params.speculative.cpuparams.cpumask)) { + throw std::invalid_argument("invalid range"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--cpu-strict-draft"}, "<0|1>", + "Use strict CPU placement for draft model (default: same as --cpu-strict)", + [](common_params & params, int value) { + params.speculative.cpuparams.strict_cpu = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--prio-draft"}, "N", + string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.speculative.cpuparams.priority), + [](common_params & params, int prio) { + if (prio < 0 || prio > 3) { + throw std::invalid_argument("invalid value"); + } + params.speculative.cpuparams.priority = (enum ggml_sched_priority) prio; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--poll-draft"}, "<0|1>", + "Use polling to wait for draft model work (default: same as --poll])", + [](common_params & params, int value) { + params.speculative.cpuparams.poll = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"-Cbd", "--cpu-mask-batch-draft"}, "M", + "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)", + [](common_params & params, const std::string & mask) { + params.speculative.cpuparams_batch.mask_valid = true; + if (!parse_cpu_mask(mask, params.speculative.cpuparams_batch.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"-Crbd", "--cpu-range-batch-draft"}, "lo-hi", + "Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)", + [](common_params & params, const std::string & range) { + params.speculative.cpuparams_batch.mask_valid = true; + if (!parse_cpu_range(range, params.speculative.cpuparams_batch.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--cpu-strict-batch-draft"}, "<0|1>", + "Use strict CPU placement for draft model (default: --cpu-strict-draft)", + [](common_params & params, int value) { + params.speculative.cpuparams_batch.strict_cpu = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--prio-batch-draft"}, "N", + string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.speculative.cpuparams_batch.priority), + [](common_params & params, int prio) { + if (prio < 0 || prio > 3) { + throw std::invalid_argument("invalid value"); + } + params.speculative.cpuparams_batch.priority = (enum ggml_sched_priority) prio; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--poll-batch-draft"}, "<0|1>", + "Use polling to wait for draft model work (default: --poll-draft)", + [](common_params & params, int value) { + params.speculative.cpuparams_batch.poll = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--draft-max", "--draft", "--draft-n"}, "N", + string_format("number of tokens to draft for speculative decoding (default: %d)", params.speculative.n_max), + [](common_params & params, int value) { + params.speculative.n_max = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MAX")); + add_opt(common_arg( + {"--draft-min", "--draft-n-min"}, "N", + string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.speculative.n_min), + [](common_params & params, int value) { + params.speculative.n_min = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MIN")); + add_opt(common_arg( + {"--draft-p-split"}, "P", + string_format("speculative decoding split probability (default: %.1f)", (double)params.speculative.p_split), + [](common_params & params, const std::string & value) { + params.speculative.p_split = std::stof(value); + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}).set_env("LLAMA_ARG_DRAFT_P_SPLIT")); + add_opt(common_arg( + {"--draft-p-min"}, "P", + string_format("minimum speculative decoding probability (greedy) (default: %.1f)", (double)params.speculative.p_min), + [](common_params & params, const std::string & value) { + params.speculative.p_min = std::stof(value); + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_P_MIN")); + add_opt(common_arg( + {"-cd", "--ctx-size-draft"}, "N", + string_format("size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx), + [](common_params & params, int value) { + params.speculative.n_ctx = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CTX_SIZE_DRAFT")); + add_opt(common_arg( + {"-devd", "--device-draft"}, "", + "comma-separated list of devices to use for offloading the draft model (none = don't offload)\n" + "use --list-devices to see a list of available devices", + [](common_params & params, const std::string & value) { + params.speculative.devices = parse_device_list(value); + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N", + "number of layers to store in VRAM for the draft model", + [](common_params & params, int value) { + params.speculative.n_gpu_layers = value; + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: no usable GPU found, --gpu-layers-draft option will be ignored\n"); + fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n"); + fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_N_GPU_LAYERS_DRAFT")); + add_opt(common_arg( + {"-md", "--model-draft"}, "FNAME", + "draft model for speculative decoding (default: unused)", + [](common_params & params, const std::string & value) { + params.speculative.model.path = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT")); + add_opt(common_arg( + {"--spec-replace"}, "TARGET", "DRAFT", + "translate the string in TARGET into DRAFT if the draft model and main model are not compatible", + [](common_params & params, const std::string & tgt, const std::string & dft) { + params.speculative.replacements.push_back({ tgt, dft }); + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-ctkd", "--cache-type-k-draft"}, "TYPE", + string_format( + "KV cache data type for K for the draft model\n" + "allowed values: %s\n" + "(default: %s)", + get_all_kv_cache_types().c_str(), + ggml_type_name(params.speculative.cache_type_k) + ), + [](common_params & params, const std::string & value) { + params.speculative.cache_type_k = kv_cache_type_from_str(value); + } + ).set_env("LLAMA_ARG_CACHE_TYPE_K_DRAFT")); + add_opt(common_arg( + {"-ctvd", "--cache-type-v-draft"}, "TYPE", + string_format( + "KV cache data type for V for the draft model\n" + "allowed values: %s\n" + "(default: %s)", + get_all_kv_cache_types().c_str(), + ggml_type_name(params.speculative.cache_type_v) + ), + [](common_params & params, const std::string & value) { + params.speculative.cache_type_v = kv_cache_type_from_str(value); + } + ).set_env("LLAMA_ARG_CACHE_TYPE_V_DRAFT")); + + add_opt(common_arg( + {"-mv", "--model-vocoder"}, "FNAME", + "vocoder model for audio generation (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.model.path = value; + } + ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--tts-use-guide-tokens"}, + "Use guide tokens to improve TTS word recall", + [](common_params & params) { + params.vocoder.use_guide_tokens = true; + } + ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--tts-speaker-file"}, "FNAME", + "speaker file path for audio generation", + [](common_params & params, const std::string & value) { + params.vocoder.speaker_file = value; + } + ).set_examples({LLAMA_EXAMPLE_TTS})); + + // model-specific + add_opt(common_arg( + {"--tts-oute-default"}, + string_format("use default OuteTTS models (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "OuteAI/OuteTTS-0.2-500M-GGUF"; + params.model.hf_file = "OuteTTS-0.2-500M-Q8_0.gguf"; + params.vocoder.model.hf_repo = "ggml-org/WavTokenizer"; + params.vocoder.model.hf_file = "WavTokenizer-Large-75-F16.gguf"; + } + ).set_examples({LLAMA_EXAMPLE_TTS})); + + add_opt(common_arg( + {"--embd-bge-small-en-default"}, + string_format("use default bge-small-en-v1.5 model (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/bge-small-en-v1.5-Q8_0-GGUF"; + params.model.hf_file = "bge-small-en-v1.5-q8_0.gguf"; + params.pooling_type = LLAMA_POOLING_TYPE_NONE; + params.embd_normalize = 2; + params.n_ctx = 512; + params.verbose_prompt = true; + params.embedding = true; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--embd-e5-small-en-default"}, + string_format("use default e5-small-v2 model (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/e5-small-v2-Q8_0-GGUF"; + params.model.hf_file = "e5-small-v2-q8_0.gguf"; + params.pooling_type = LLAMA_POOLING_TYPE_NONE; + params.embd_normalize = 2; + params.n_ctx = 512; + params.verbose_prompt = true; + params.embedding = true; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--embd-gte-small-default"}, + string_format("use default gte-small model (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/gte-small-Q8_0-GGUF"; + params.model.hf_file = "gte-small-q8_0.gguf"; + params.pooling_type = LLAMA_POOLING_TYPE_NONE; + params.embd_normalize = 2; + params.n_ctx = 512; + params.verbose_prompt = true; + params.embedding = true; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--fim-qwen-1.5b-default"}, + string_format("use default Qwen 2.5 Coder 1.5B (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/Qwen2.5-Coder-1.5B-Q8_0-GGUF"; + params.model.hf_file = "qwen2.5-coder-1.5b-q8_0.gguf"; + params.port = 8012; + params.n_gpu_layers = 99; + params.flash_attn = true; + params.n_ubatch = 1024; + params.n_batch = 1024; + params.n_ctx = 0; + params.n_cache_reuse = 256; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--fim-qwen-3b-default"}, + string_format("use default Qwen 2.5 Coder 3B (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/Qwen2.5-Coder-3B-Q8_0-GGUF"; + params.model.hf_file = "qwen2.5-coder-3b-q8_0.gguf"; + params.port = 8012; + params.n_gpu_layers = 99; + params.flash_attn = true; + params.n_ubatch = 1024; + params.n_batch = 1024; + params.n_ctx = 0; + params.n_cache_reuse = 256; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--fim-qwen-7b-default"}, + string_format("use default Qwen 2.5 Coder 7B (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF"; + params.model.hf_file = "qwen2.5-coder-7b-q8_0.gguf"; + params.port = 8012; + params.n_gpu_layers = 99; + params.flash_attn = true; + params.n_ubatch = 1024; + params.n_batch = 1024; + params.n_ctx = 0; + params.n_cache_reuse = 256; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--fim-qwen-7b-spec"}, + string_format("use Qwen 2.5 Coder 7B + 0.5B draft for speculative decoding (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF"; + params.model.hf_file = "qwen2.5-coder-7b-q8_0.gguf"; + params.speculative.model.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF"; + params.speculative.model.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf"; + params.speculative.n_gpu_layers = 99; + params.port = 8012; + params.n_gpu_layers = 99; + params.flash_attn = true; + params.n_ubatch = 1024; + params.n_batch = 1024; + params.n_ctx = 0; + params.n_cache_reuse = 256; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--fim-qwen-14b-spec"}, + string_format("use Qwen 2.5 Coder 14B + 0.5B draft for speculative decoding (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/Qwen2.5-Coder-14B-Q8_0-GGUF"; + params.model.hf_file = "qwen2.5-coder-14b-q8_0.gguf"; + params.speculative.model.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF"; + params.speculative.model.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf"; + params.speculative.n_gpu_layers = 99; + params.port = 8012; + params.n_gpu_layers = 99; + params.flash_attn = true; + params.n_ubatch = 1024; + params.n_batch = 1024; + params.n_ctx = 0; + params.n_cache_reuse = 256; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + { "--diffusion-steps" }, "N", + string_format("number of diffusion steps (default: %d)", params.diffusion.steps), + [](common_params & params, int value) { params.diffusion.steps = value; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + { "--diffusion-visual" }, + string_format("enable visual diffusion mode (show progressive generation) (default: %s)", + params.diffusion.visual_mode ? "true" : "false"), + [](common_params & params) { params.diffusion.visual_mode = true; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + + add_opt(common_arg( + { "--diffusion-eps" }, "F", + string_format("epsilon for timesteps (default: %.6f)", (double) params.diffusion.eps), + [](common_params & params, const std::string & value) { params.diffusion.eps = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + { "--diffusion-algorithm" }, "N", + string_format("diffusion algorithm: 0=ORIGIN, 1=ENTROPY_BASED, 2=MARGIN_BASED, 3=RANDOM, 4=LOW_CONFIDENCE (default: %d)", + params.diffusion.algorithm), + [](common_params & params, int value) { params.diffusion.algorithm = value; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + { "--diffusion-alg-temp" }, "F", + string_format("dream algorithm temperature (default: %.3f)", (double) params.diffusion.alg_temp), + [](common_params & params, const std::string & value) { params.diffusion.alg_temp = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + + add_opt(common_arg( + { "--diffusion-block-length" }, "N", + string_format("llada block length for generation (default: %d)", params.diffusion.block_length), + [](common_params & params, int value) { params.diffusion.block_length = value; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + { "--diffusion-cfg-scale" }, "F", + string_format("llada classifier-free guidance scale (default: %.3f)", (double) params.diffusion.cfg_scale), + [](common_params & params, const std::string & value) { params.diffusion.cfg_scale = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + { "--diffusion-add-gumbel-noise" }, "F", + string_format("add gumbel noise to the logits if temp > 0.0 (default: %s)", params.diffusion.add_gumbel_noise ? "true" : "false"), + [](common_params & params, const std::string & value) { params.diffusion.add_gumbel_noise = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + + + return ctx_arg; +} diff --git a/llama.cpp/common/arg.h b/llama.cpp/common/arg.h new file mode 100644 index 0000000000000000000000000000000000000000..4efeb6616d0684c38e60237132ed6bafd6fcd5a5 --- /dev/null +++ b/llama.cpp/common/arg.h @@ -0,0 +1,89 @@ +#pragma once + +#include "common.h" + +#include +#include +#include + +// +// CLI argument parsing +// + +struct common_arg { + std::set examples = {LLAMA_EXAMPLE_COMMON}; + std::set excludes = {}; + std::vector args; + const char * value_hint = nullptr; // help text or example for arg value + const char * value_hint_2 = nullptr; // for second arg value + const char * env = nullptr; + std::string help; + bool is_sparam = false; // is current arg a sampling param? + void (*handler_void) (common_params & params) = nullptr; + void (*handler_string) (common_params & params, const std::string &) = nullptr; + void (*handler_str_str)(common_params & params, const std::string &, const std::string &) = nullptr; + void (*handler_int) (common_params & params, int) = nullptr; + + common_arg( + const std::initializer_list & args, + const char * value_hint, + const std::string & help, + void (*handler)(common_params & params, const std::string &) + ) : args(args), value_hint(value_hint), help(help), handler_string(handler) {} + + common_arg( + const std::initializer_list & args, + const char * value_hint, + const std::string & help, + void (*handler)(common_params & params, int) + ) : args(args), value_hint(value_hint), help(help), handler_int(handler) {} + + common_arg( + const std::initializer_list & args, + const std::string & help, + void (*handler)(common_params & params) + ) : args(args), help(help), handler_void(handler) {} + + // support 2 values for arg + common_arg( + const std::initializer_list & args, + const char * value_hint, + const char * value_hint_2, + const std::string & help, + void (*handler)(common_params & params, const std::string &, const std::string &) + ) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {} + + common_arg & set_examples(std::initializer_list examples); + common_arg & set_excludes(std::initializer_list excludes); + common_arg & set_env(const char * env); + common_arg & set_sparam(); + bool in_example(enum llama_example ex); + bool is_exclude(enum llama_example ex); + bool get_value_from_env(std::string & output); + bool has_value_from_env(); + std::string to_string(); +}; + +struct common_params_context { + enum llama_example ex = LLAMA_EXAMPLE_COMMON; + common_params & params; + std::vector options; + void(*print_usage)(int, char **) = nullptr; + common_params_context(common_params & params) : params(params) {} +}; + +// parse input arguments from CLI +// if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message) +bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr); + +// function to be used by test-arg-parser +common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr); +bool common_has_curl(); + +struct common_remote_params { + std::vector headers; + long timeout = 0; // CURLOPT_TIMEOUT, in seconds ; 0 means no timeout + long max_size = 0; // max size of the response ; unlimited if 0 ; max is 2GB +}; +// get remote file content, returns +std::pair> common_remote_get_content(const std::string & url, const common_remote_params & params); diff --git a/llama.cpp/common/base64.hpp b/llama.cpp/common/base64.hpp new file mode 100644 index 0000000000000000000000000000000000000000..04df58e82e2e33a89e40ed1c6149be53bf02d096 --- /dev/null +++ b/llama.cpp/common/base64.hpp @@ -0,0 +1,392 @@ +/* +This is free and unencumbered software released into the public domain. + +Anyone is free to copy, modify, publish, use, compile, sell, or +distribute this software, either in source code form or as a compiled +binary, for any purpose, commercial or non-commercial, and by any +means. + +In jurisdictions that recognize copyright laws, the author or authors +of this software dedicate any and all copyright interest in the +software to the public domain. We make this dedication for the benefit +of the public at large and to the detriment of our heirs and +successors. We intend this dedication to be an overt act of +relinquishment in perpetuity of all present and future rights to this +software under copyright law. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR +OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, +ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR +OTHER DEALINGS IN THE SOFTWARE. + +For more information, please refer to +*/ + +#ifndef PUBLIC_DOMAIN_BASE64_HPP_ +#define PUBLIC_DOMAIN_BASE64_HPP_ + +#include +#include +#include +#include + +class base64_error : public std::runtime_error +{ +public: + using std::runtime_error::runtime_error; +}; + +class base64 +{ +public: + enum class alphabet + { + /** the alphabet is detected automatically */ + auto_, + /** the standard base64 alphabet is used */ + standard, + /** like `standard` except that the characters `+` and `/` are replaced by `-` and `_` respectively*/ + url_filename_safe + }; + + enum class decoding_behavior + { + /** if the input is not padded, the remaining bits are ignored */ + moderate, + /** if a padding character is encounter decoding is finished */ + loose + }; + + /** + Encodes all the elements from `in_begin` to `in_end` to `out`. + + @warning The source and destination cannot overlap. The destination must be able to hold at least + `required_encode_size(std::distance(in_begin, in_end))`, otherwise the behavior depends on the output iterator. + + @tparam Input_iterator the source; the returned elements are cast to `std::uint8_t` and should not be greater than + 8 bits + @tparam Output_iterator the destination; the elements written to it are from the type `char` + @param in_begin the beginning of the source + @param in_end the ending of the source + @param out the destination iterator + @param alphabet which alphabet should be used + @returns the iterator to the next element past the last element copied + @throws see `Input_iterator` and `Output_iterator` + */ + template + static Output_iterator encode(Input_iterator in_begin, Input_iterator in_end, Output_iterator out, + alphabet alphabet = alphabet::standard) + { + constexpr auto pad = '='; + const char* alpha = alphabet == alphabet::url_filename_safe + ? "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_" + : "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"; + + while (in_begin != in_end) { + std::uint8_t i0 = 0, i1 = 0, i2 = 0; + + // first character + i0 = static_cast(*in_begin); + ++in_begin; + + *out = alpha[i0 >> 2 & 0x3f]; + ++out; + + // part of first character and second + if (in_begin != in_end) { + i1 = static_cast(*in_begin); + ++in_begin; + + *out = alpha[((i0 & 0x3) << 4) | (i1 >> 4 & 0x0f)]; + ++out; + } else { + *out = alpha[(i0 & 0x3) << 4]; + ++out; + + // last padding + *out = pad; + ++out; + + // last padding + *out = pad; + ++out; + + break; + } + + // part of second character and third + if (in_begin != in_end) { + i2 = static_cast(*in_begin); + ++in_begin; + + *out = alpha[((i1 & 0xf) << 2) | (i2 >> 6 & 0x03)]; + ++out; + } else { + *out = alpha[(i1 & 0xf) << 2]; + ++out; + + // last padding + *out = pad; + ++out; + + break; + } + + // rest of third + *out = alpha[i2 & 0x3f]; + ++out; + } + + return out; + } + /** + Encodes a string. + + @param str the string that should be encoded + @param alphabet which alphabet should be used + @returns the encoded base64 string + @throws see base64::encode() + */ + static std::string encode(const std::string& str, alphabet alphabet = alphabet::standard) + { + std::string result; + + result.reserve(required_encode_size(str.length()) + 1); + + encode(str.begin(), str.end(), std::back_inserter(result), alphabet); + + return result; + } + /** + Encodes a char array. + + @param buffer the char array + @param size the size of the array + @param alphabet which alphabet should be used + @returns the encoded string + */ + static std::string encode(const char* buffer, std::size_t size, alphabet alphabet = alphabet::standard) + { + std::string result; + + result.reserve(required_encode_size(size) + 1); + + encode(buffer, buffer + size, std::back_inserter(result), alphabet); + + return result; + } + /** + Decodes all the elements from `in_begin` to `in_end` to `out`. `in_begin` may point to the same location as `out`, + in other words: inplace decoding is possible. + + @warning The destination must be able to hold at least `required_decode_size(std::distance(in_begin, in_end))`, + otherwise the behavior depends on the output iterator. + + @tparam Input_iterator the source; the returned elements are cast to `char` + @tparam Output_iterator the destination; the elements written to it are from the type `std::uint8_t` + @param in_begin the beginning of the source + @param in_end the ending of the source + @param out the destination iterator + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @returns the iterator to the next element past the last element copied + @throws base64_error depending on the set behavior + @throws see `Input_iterator` and `Output_iterator` + */ + template + static Output_iterator decode(Input_iterator in_begin, Input_iterator in_end, Output_iterator out, + alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + //constexpr auto pad = '='; + std::uint8_t last = 0; + auto bits = 0; + + while (in_begin != in_end) { + auto c = *in_begin; + ++in_begin; + + if (c == '=') { + break; + } + + auto part = _base64_value(alphabet, c); + + // enough bits for one byte + if (bits + 6 >= 8) { + *out = (last << (8 - bits)) | (part >> (bits - 2)); + ++out; + + bits -= 2; + } else { + bits += 6; + } + + last = part; + } + + // check padding + if (behavior != decoding_behavior::loose) { + while (in_begin != in_end) { + auto c = *in_begin; + ++in_begin; + + if (c != '=') { + throw base64_error("invalid base64 character."); + } + } + } + + return out; + } + /** + Decodes a string. + + @param str the base64 encoded string + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @returns the decoded string + @throws see base64::decode() + */ + static std::string decode(const std::string& str, alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + std::string result; + + result.reserve(max_decode_size(str.length())); + + decode(str.begin(), str.end(), std::back_inserter(result), alphabet, behavior); + + return result; + } + /** + Decodes a string. + + @param buffer the base64 encoded buffer + @param size the size of the buffer + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @returns the decoded string + @throws see base64::decode() + */ + static std::string decode(const char* buffer, std::size_t size, alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + std::string result; + + result.reserve(max_decode_size(size)); + + decode(buffer, buffer + size, std::back_inserter(result), alphabet, behavior); + + return result; + } + /** + Decodes a string inplace. + + @param[in,out] str the base64 encoded string + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @throws base64::decode_inplace() + */ + static void decode_inplace(std::string& str, alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + str.resize(decode(str.begin(), str.end(), str.begin(), alphabet, behavior) - str.begin()); + } + /** + Decodes a char array inplace. + + @param[in,out] str the string array + @param size the length of the array + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @returns the pointer to the next element past the last element decoded + @throws base64::decode_inplace() + */ + static char* decode_inplace(char* str, std::size_t size, alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + return decode(str, str + size, str, alphabet, behavior); + } + /** + Returns the required decoding size for a given size. The value is calculated with the following formula: + + $$ + \lceil \frac{size}{4} \rceil \cdot 3 + $$ + + @param size the size of the encoded input + @returns the size of the resulting decoded buffer; this the absolute maximum + */ + static std::size_t max_decode_size(std::size_t size) noexcept + { + return (size / 4 + (size % 4 ? 1 : 0)) * 3; + } + /** + Returns the required encoding size for a given size. The value is calculated with the following formula: + + $$ + \lceil \frac{size}{3} \rceil \cdot 4 + $$ + + @param size the size of the decoded input + @returns the size of the resulting encoded buffer + */ + static std::size_t required_encode_size(std::size_t size) noexcept + { + return (size / 3 + (size % 3 ? 1 : 0)) * 4; + } + +private: + static std::uint8_t _base64_value(alphabet& alphabet, char c) + { + if (c >= 'A' && c <= 'Z') { + return c - 'A'; + } else if (c >= 'a' && c <= 'z') { + return c - 'a' + 26; + } else if (c >= '0' && c <= '9') { + return c - '0' + 52; + } + + // comes down to alphabet + if (alphabet == alphabet::standard) { + if (c == '+') { + return 62; + } else if (c == '/') { + return 63; + } + } else if (alphabet == alphabet::url_filename_safe) { + if (c == '-') { + return 62; + } else if (c == '_') { + return 63; + } + } // auto detect + else { + if (c == '+') { + alphabet = alphabet::standard; + + return 62; + } else if (c == '/') { + alphabet = alphabet::standard; + + return 63; + } else if (c == '-') { + alphabet = alphabet::url_filename_safe; + + return 62; + } else if (c == '_') { + alphabet = alphabet::url_filename_safe; + + return 63; + } + } + + throw base64_error("invalid base64 character."); + } +}; + +#endif // !PUBLIC_DOMAIN_BASE64_HPP_ diff --git a/llama.cpp/common/build-info.cpp.in b/llama.cpp/common/build-info.cpp.in new file mode 100644 index 0000000000000000000000000000000000000000..1ce3522747e54ae5982fbf0bd3e789b291af2048 --- /dev/null +++ b/llama.cpp/common/build-info.cpp.in @@ -0,0 +1,4 @@ +int LLAMA_BUILD_NUMBER = @LLAMA_BUILD_NUMBER@; +char const *LLAMA_COMMIT = "@LLAMA_BUILD_COMMIT@"; +char const *LLAMA_COMPILER = "@BUILD_COMPILER@"; +char const *LLAMA_BUILD_TARGET = "@BUILD_TARGET@"; diff --git a/llama.cpp/common/chat-parser.cpp b/llama.cpp/common/chat-parser.cpp new file mode 100644 index 0000000000000000000000000000000000000000..461a17aee2cc15f6a2d957f31bccefff27661163 --- /dev/null +++ b/llama.cpp/common/chat-parser.cpp @@ -0,0 +1,385 @@ +#include "chat-parser.h" +#include "common.h" +#include "log.h" +#include "regex-partial.h" + +#include +#include +#include +#include + +using json = nlohmann::ordered_json; + +common_chat_msg_parser::common_chat_msg_parser(const std::string & input, bool is_partial, const common_chat_syntax & syntax) + : input_(input), is_partial_(is_partial), syntax_(syntax) +{ + result_.role = "assistant"; + + while (true) { + std::string id = std::to_string(std::rand()); + if (input.find(id) == std::string::npos) { + healing_marker_ = id; + break; + } + } +} + +std::string common_chat_msg_parser::str(const common_string_range & rng) const { + GGML_ASSERT(rng.begin <= rng.end); + return input_.substr(rng.begin, rng.end - rng.begin); +} + +void common_chat_msg_parser::add_content(const std::string &content) { + result_.content += content; +} + +void common_chat_msg_parser::add_reasoning_content(const std::string &reasoning_content) { + result_.reasoning_content += reasoning_content; +} + +bool common_chat_msg_parser::add_tool_call(const std::string & name, const std::string & id, const std::string & arguments) { + if (name.empty()) { + return false; + } + + common_chat_tool_call tool_call; + tool_call.name = name; + tool_call.arguments = arguments; + tool_call.id = id; + + // LOG_DBG("Tool call arguments:\n\traw: %s\n\tresult: %s\n", arguments.c_str(), tool_call.arguments.c_str()); + result_.tool_calls.emplace_back(tool_call); + + return true; +} +bool common_chat_msg_parser::add_tool_call(const json & tool_call) { + std::string name = tool_call.contains("name") ? tool_call.at("name") : ""; + std::string id = tool_call.contains("id") ? tool_call.at("id") : ""; + std::string arguments = tool_call.contains("arguments") ? tool_call.at("arguments") : ""; + return add_tool_call(name, id, arguments); +} + +bool common_chat_msg_parser::add_tool_calls(const json & arr) { + for (const auto & item : arr) { + if (!add_tool_call(item)) { + return false; + } + } + return true; +} +void common_chat_msg_parser::finish() { + if (!is_partial_ && pos_ != input_.size()) { + throw std::runtime_error("Unexpected content at end of input");// + input_.substr(pos_)); + } +} + +bool common_chat_msg_parser::consume_spaces() { + const auto length = input_.size(); + auto consumed = false; + while (pos_ < length && std::isspace(input_[pos_])) { + ++pos_; + consumed = true; + } + return consumed; +} + +bool common_chat_msg_parser::try_consume_literal(const std::string & literal) { + auto pos = pos_; + for (auto i = 0u; i < literal.size(); ++i) { + if (pos >= input_.size()) { + return false; + } + if (input_[pos] != literal[i]) { + return false; + } + ++pos; + } + pos_ = pos; + return true; +} + +std::optional common_chat_msg_parser::try_find_literal(const std::string & literal) { + auto idx = input_.find(literal, pos_); + if (idx != std::string::npos) { + find_regex_result res; + res.prelude = input_.substr(pos_, idx - pos_); + auto end = idx + literal.size(); + res.groups.emplace_back(common_string_range{idx, end}); + move_to(end); + return res; + } + if (is_partial_) { + idx = string_find_partial_stop(input_, literal); + if (idx != std::string::npos && idx >= pos_) { + find_regex_result res; + res.prelude = input_.substr(pos_, idx - pos_); + auto end = input_.size(); + res.groups.emplace_back(common_string_range{idx, end}); + move_to(end); + return res; + } + } + return std::nullopt; +} + +void common_chat_msg_parser::consume_literal(const std::string & literal) { + if (!try_consume_literal(literal)) { + throw common_chat_msg_partial_exception(literal); + } +} + +bool common_chat_msg_parser::try_parse_reasoning(const std::string & start_think, const std::string & end_think) { + auto handle_reasoning = [&](const std::string & reasoning, bool closed) { + auto stripped_reasoning = string_strip(reasoning); + if (stripped_reasoning.empty()) { + return; + } + if (syntax_.reasoning_in_content) { + add_content(syntax_.reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK ? "" : start_think); + add_content(stripped_reasoning); + if (closed) { + add_content(syntax_.reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK ? "" : end_think); + } + } else { + add_reasoning_content(stripped_reasoning); + } + }; + if (syntax_.reasoning_format != COMMON_REASONING_FORMAT_NONE) { + if (syntax_.thinking_forced_open || try_consume_literal(start_think)) { + if (auto res = try_find_literal(end_think)) { + handle_reasoning(res->prelude, /* closed */ true); + consume_spaces(); + return true; + } + auto rest = consume_rest(); + if (!rest.empty()) { + handle_reasoning(rest, /* closed */ !is_partial()); + } + // Allow unclosed thinking tags, for now (https://github.com/ggml-org/llama.cpp/issues/13812, https://github.com/ggml-org/llama.cpp/issues/13877) + // if (!syntax_.thinking_forced_open) { + // throw common_chat_msg_partial_exception(end_think); + // } + return true; + } + } + return false; +} + +std::string common_chat_msg_parser::consume_rest() { + auto rest = input_.substr(pos_); + pos_ = input_.size(); + return rest; +} + +// Tries to find the regex, consumes it (pos right after it) and gives the prelude (right before it) and the groups to the callback. +std::optional common_chat_msg_parser::try_find_regex(const common_regex & regex, size_t from, bool add_prelude_to_content) { + auto m = regex.search(input_, from == std::string::npos ? pos_ : from); + if (m.type == COMMON_REGEX_MATCH_TYPE_NONE) { + return std::nullopt; + } + auto prelude = input_.substr(pos_, m.groups[0].begin - pos_); + pos_ = m.groups[0].end; + + if (add_prelude_to_content) { + add_content(prelude); + } + if (m.type == COMMON_REGEX_MATCH_TYPE_PARTIAL) { + if (is_partial()) { + throw common_chat_msg_partial_exception(regex.str()); + } + return std::nullopt; + } + return find_regex_result{prelude, m.groups}; +} + +common_chat_msg_parser::find_regex_result common_chat_msg_parser::consume_regex(const common_regex & regex) { + if (auto result = try_consume_regex(regex)) { + return *result; + } + throw common_chat_msg_partial_exception(regex.str()); +} + +std::optional common_chat_msg_parser::try_consume_regex(const common_regex & regex) { + auto m = regex.search(input_, pos_); + if (m.type == COMMON_REGEX_MATCH_TYPE_NONE) { + return std::nullopt; + } + if (m.type == COMMON_REGEX_MATCH_TYPE_PARTIAL) { + if (is_partial()) { + throw common_chat_msg_partial_exception(regex.str()); + } + return std::nullopt; + } + if (m.groups[0].begin != pos_) { + // Didn't match at the current position. + return std::nullopt; + } + pos_ = m.groups[0].end; + + return find_regex_result { + /* .prelude = */ "", + m.groups, + }; +} + +std::optional common_chat_msg_parser::try_consume_json() { + auto it = input_.cbegin() + pos_; + const auto end = input_.cend(); + common_json result; + if (!common_json_parse(it, end, healing_marker_, result)) { + return std::nullopt; + } + pos_ = std::distance(input_.cbegin(), it); + if (result.healing_marker.marker.empty()) { + // No healing marker, just return the parsed json + return result; + } + if (!is_partial()) { + throw common_chat_msg_partial_exception("JSON"); + } + return result; +} + +common_json common_chat_msg_parser::consume_json() { + if (auto result = try_consume_json()) { + return *result; + } + throw common_chat_msg_partial_exception("JSON"); +} + +common_chat_msg_parser::consume_json_result common_chat_msg_parser::consume_json_with_dumped_args( + const std::vector> & args_paths, + const std::vector> & content_paths +) { + if (auto result = try_consume_json_with_dumped_args(args_paths, content_paths)) { + return *result; + } + throw common_chat_msg_partial_exception("JSON"); +} + +std::optional common_chat_msg_parser::try_consume_json_with_dumped_args( + const std::vector> & args_paths, + const std::vector> & content_paths +) { + auto partial = try_consume_json(); + if (!partial) { + return std::nullopt; + } + auto is_arguments_path = [&](const std::vector & path) { + return std::find(args_paths.begin(), args_paths.end(), path) != args_paths.end(); + }; + auto is_content_path = [&](const std::vector & path) { + return std::find(content_paths.begin(), content_paths.end(), path) != content_paths.end(); + }; + + if (partial->healing_marker.marker.empty()) { + if (args_paths.empty()) { + // No arguments to dump, and JSON was parsed fully. + return consume_json_result { + partial->json, + /* .is_partial = */ false, + }; + } + if (is_arguments_path({})) { + // Entire JSON is the arguments and was parsed fully. + return consume_json_result { + partial->json.dump(), + /* .is_partial = */ false, + }; + } + } + + LOG_DBG("Parsed partial JSON: %s (json_healing_marker: %s)\n", partial->json.dump().c_str(), partial->healing_marker.json_dump_marker.c_str()); + + auto found_healing_marker = false; + std::vector path; + std::function remove_unsupported_healings_and_dump_args = [&](const json & j) -> json { + if (is_arguments_path(path)) { + auto arguments = j.dump(); + if (is_partial() && !partial->healing_marker.marker.empty()) { + auto idx = arguments.find(partial->healing_marker.json_dump_marker); + if (idx != std::string::npos) { + arguments.resize(idx); + found_healing_marker = true; + } + if (arguments == "\"") { + // This happens because of completing `:"$magic` after `"arguments"` + arguments = ""; + } + } + return arguments; + } + if (is_content_path(path)) { + if (!j.is_string()) { + throw std::runtime_error("Content path must be a string"); + } + std::string str = j; + auto idx = str.find(partial->healing_marker.marker); // not using json_dump_marker as we're inside a string + if (idx != std::string::npos) { + str.resize(idx); + found_healing_marker = true; + } + return str; + } + if (j.is_object()) { + auto obj = json::object(); + for (const auto & p : j.items()) { + const auto & key = p.key(); + const auto & value = p.value(); + const std::string key_str = key; // NOLINT + auto idx = key_str.find(healing_marker_); + if (idx != std::string::npos) { + found_healing_marker = true; + break; + } + path.push_back(key_str); + if (value.is_string()) { + const std::string value_str = value; + if (value_str.find(healing_marker_) != std::string::npos) { + found_healing_marker = true; + if (is_content_path(path)) { + if (partial->healing_marker.marker == partial->healing_marker.json_dump_marker) { + // The healing occurred inside the string: good. Otherwise we just ditch the entire key/value pair. + obj[key] = remove_unsupported_healings_and_dump_args(value); + } + } + break; + } + obj[key] = value; + } else { + obj[key] = remove_unsupported_healings_and_dump_args(value); + } + path.pop_back(); + } + return obj; + } + if (j.is_array()) { + auto arr = json::array(); + for (const auto & value : j) { + if (value.is_string()) { + std::string str = value; + auto idx = str.find(healing_marker_); + if (idx != std::string::npos) { + // Don't heal array values that aren't in the arguments. + found_healing_marker = true; + break; + } + } + arr.push_back(remove_unsupported_healings_and_dump_args(value)); + } + return arr; + } + return j; + }; + + auto cleaned = remove_unsupported_healings_and_dump_args(partial->json); + LOG_DBG("Cleaned up JSON %s to %s (json_healing_marker : '%s')\n", partial->json.dump().c_str(), cleaned.dump().c_str(), partial->healing_marker.json_dump_marker.c_str()); + return consume_json_result { + cleaned, + /* .is_partial = */ found_healing_marker, + }; +} + +void common_chat_msg_parser::clear_tools() { + result_.tool_calls.clear(); +} diff --git a/llama.cpp/common/chat-parser.h b/llama.cpp/common/chat-parser.h new file mode 100644 index 0000000000000000000000000000000000000000..a4dca4fa1812001268c0823f20f95716ceda20fc --- /dev/null +++ b/llama.cpp/common/chat-parser.h @@ -0,0 +1,120 @@ +#pragma once + +#include "chat.h" +#include "json-partial.h" +#include "regex-partial.h" + +#include + +#include +#include +#include + +class common_chat_msg_partial_exception : public std::runtime_error { + public: + common_chat_msg_partial_exception(const std::string & message) : std::runtime_error(message) {} +}; + +class common_chat_msg_parser { + std::string input_; + bool is_partial_; + common_chat_syntax syntax_; + std::string healing_marker_; + + size_t pos_ = 0; + common_chat_msg result_; + + public: + common_chat_msg_parser(const std::string & input, bool is_partial, const common_chat_syntax & syntax); + const std::string & input() const { return input_; } + size_t pos() const { return pos_; } + const std::string & healing_marker() const { return healing_marker_; } + const bool & is_partial() const { return is_partial_; } + const common_chat_msg & result() const { return result_; } + const common_chat_syntax & syntax() const { return syntax_; } + + void move_to(size_t pos) { + if (pos > input_.size()) { + throw std::runtime_error("Invalid position!"); + } + pos_ = pos; + } + void move_back(size_t n) { + if (pos_ < n) { + throw std::runtime_error("Can't move back that far!"); + } + pos_ -= n; + } + + // Get the substring of the input at the given range + std::string str(const common_string_range & rng) const; + + // Appends to the result.content field + void add_content(const std::string & content); + + // Appends to the result.reasoning_content field + void add_reasoning_content(const std::string & reasoning_content); + + // Adds a tool call to the result. If the tool call is too incomplete (e.g. name empty), it won't add anything. + bool add_tool_call(const std::string & name, const std::string & id, const std::string & arguments); + + // Adds a tool call using the "name", "id" and "arguments" fields of the json object + bool add_tool_call(const nlohmann::ordered_json & tool_call); + + // Adds an array of tool calls using their "name", "id" and "arguments" fields. + bool add_tool_calls(const nlohmann::ordered_json & arr); + + void finish(); + + bool consume_spaces(); + + void consume_literal(const std::string & literal); + + bool try_parse_reasoning(const std::string & start_think, const std::string & end_think); + + std::string consume_rest(); + + struct find_regex_result { + std::string prelude; + std::vector groups; + }; + + std::optional try_find_regex(const common_regex & regex, size_t from = std::string::npos, bool add_prelude_to_content = true); + + bool try_consume_literal(const std::string & literal); + + std::optional try_find_literal(const std::string & literal); + + find_regex_result consume_regex(const common_regex & regex); + + std::optional try_consume_regex(const common_regex & regex); + + std::optional try_consume_json(); + common_json consume_json(); + + struct consume_json_result { + nlohmann::ordered_json value; + bool is_partial; + }; + + /* + Consume (possibly partial) json and converts specific subtrees to (possibly truncated) JSON strings. + + By default, object keys can't be truncated, nor can string values (their corresponding key is removed, + e.g. `{"foo": "bar", "baz": "b` -> `{"foo": "bar"}` + + But one can allow subpaths to be kept truncated, and possibly json-dumped to truncated json strings + - with `content_paths={{"foo"}}` -> `{"foo": "b` -> {"foo": "b"}` + - with `args_paths={{"foo"}}` -> `{"foo": {"b` -> `{"foo": "{b"}` + */ + consume_json_result consume_json_with_dumped_args( + const std::vector> & args_paths = {}, + const std::vector> & content_paths = {} + ); + std::optional try_consume_json_with_dumped_args( + const std::vector> & args_paths = {}, + const std::vector> & content_paths = {} + ); + + void clear_tools(); +}; diff --git a/llama.cpp/common/chat.cpp b/llama.cpp/common/chat.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a0beb12f30a7b0858eb240767fdcc7741ea05d09 --- /dev/null +++ b/llama.cpp/common/chat.cpp @@ -0,0 +1,1949 @@ +#include "chat.h" +#include "chat-parser.h" +#include "common.h" +#include "json-partial.h" +#include "json-schema-to-grammar.h" +#include "log.h" +#include "regex-partial.h" + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +using json = nlohmann::ordered_json; + +static std::string format_time(const std::chrono::system_clock::time_point & now, const std::string & format) { + auto time = std::chrono::system_clock::to_time_t(now); + auto local_time = *std::localtime(&time); + std::ostringstream ss; + ss << std::put_time(&local_time, format.c_str()); + auto res = ss.str(); + return res; +} + +static std::string string_diff(const std::string & last, const std::string & current) { + if (last.empty()) { + return current; + } + if (!string_starts_with(current, last)) { + if (string_starts_with(last, current)) { + // This happens if the last generation ended on a partial stop word (not erased), + // and the current ended on a stop word (erased). + return ""; + } + throw std::runtime_error("Invalid diff: '" + last + "' not found at start of '" + current + "'"); + } + return current.substr(last.size()); +} + +static bool has_content_or_tool_calls(const common_chat_msg & msg) { + return !msg.content.empty() || !msg.tool_calls.empty(); +} + +template <> +json common_chat_msg::to_json_oaicompat() const +{ + json message { + {"role", "assistant"}, + }; + if (!reasoning_content.empty()) { + message["reasoning_content"] = reasoning_content; + } + if (content.empty() && !tool_calls.empty()) { + message["content"] = json(); + } else { + message["content"] = content; + } + if (!tool_calls.empty()) { + auto arr = json::array(); + for (const auto & tc : tool_calls) { + arr.push_back({ + {"type", "function"}, + {"function", { + {"name", tc.name}, + {"arguments", tc.arguments}, + }}, + {"id", tc.id}, + // // Some templates generate and require an id (sometimes in a very specific format, e.g. Mistral Nemo). + // // We only generate a random id for the ones that don't generate one by themselves + // // (they also won't get to see it as their template likely doesn't use it, so it's all for the client) + // {"id", tc.id.empty() ? gen_tool_call_id() : tc.id}, + }); + } + message["tool_calls"] = arr; + } + return message; +} + +std::vector common_chat_msg_diff::compute_diffs(const common_chat_msg & previous_msg, const common_chat_msg & new_msg) { + std::vector diffs; + if (previous_msg.reasoning_content != new_msg.reasoning_content) { + auto & diff = diffs.emplace_back(); + diff.reasoning_content_delta = string_diff(previous_msg.reasoning_content, new_msg.reasoning_content); + } + if (previous_msg.content != new_msg.content) { + auto & diff = diffs.emplace_back(); + diff.content_delta = string_diff(previous_msg.content, new_msg.content); + } + + if (new_msg.tool_calls.size() < previous_msg.tool_calls.size()) { + throw std::runtime_error("Invalid diff: now finding less tool calls!"); + } + + if (!previous_msg.tool_calls.empty()) { + auto idx = previous_msg.tool_calls.size() - 1; + const auto & pref = previous_msg.tool_calls[idx]; + const auto & newf = new_msg.tool_calls[idx]; + if (pref.name != newf.name) { + throw std::runtime_error("Invalid diff: tool call mismatch!"); + } + auto args_diff = string_diff(pref.arguments, newf.arguments); + if (!args_diff.empty() || pref.id != newf.id) { + auto & diff = diffs.emplace_back(); + diff.tool_call_index = idx; + if (pref.id != newf.id) { + diff.tool_call_delta.id = newf.id; + diff.tool_call_delta.name = newf.name; + } + diff.tool_call_delta.arguments = args_diff; + } + } + for (size_t idx = previous_msg.tool_calls.size(); idx < new_msg.tool_calls.size(); ++idx) { + auto & diff = diffs.emplace_back(); + diff.tool_call_index = idx; + diff.tool_call_delta = new_msg.tool_calls[idx]; + } + return diffs; +} + +typedef minja::chat_template common_chat_template; + +struct common_chat_templates { + bool has_explicit_template; // Model had builtin template or template overridde was specified. + std::unique_ptr template_default; // always set (defaults to chatml) + std::unique_ptr template_tool_use; +}; + +struct templates_params { + json messages; + json tools; + common_chat_tool_choice tool_choice; + json json_schema; + bool parallel_tool_calls; + bool stream; + std::string grammar; + bool add_generation_prompt = true; + bool enable_thinking = true; + std::chrono::system_clock::time_point now = std::chrono::system_clock::now(); + json extra_context; +}; + +common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice) { + if (tool_choice == "auto") { + return COMMON_CHAT_TOOL_CHOICE_AUTO; + } + if (tool_choice == "none") { + return COMMON_CHAT_TOOL_CHOICE_NONE; + } + if (tool_choice == "required") { + return COMMON_CHAT_TOOL_CHOICE_REQUIRED; + } + throw std::runtime_error("Invalid tool_choice: " + tool_choice); +} + +template <> +std::vector common_chat_msgs_parse_oaicompat(const json & messages) { + std::vector msgs; + + try { + + if (!messages.is_array()) { + throw std::runtime_error("Expected 'messages' to be an array, got " + messages.dump()); + } + + for (const auto & message : messages) { + if (!message.is_object()) { + throw std::runtime_error("Expected 'message' to be an object, got " + message.dump()); + } + + common_chat_msg msg; + if (!message.contains("role")) { + throw std::runtime_error("Missing 'role' in message: " + message.dump()); + } + msg.role = message.at("role"); + + auto has_content = message.contains("content"); + auto has_tool_calls = message.contains("tool_calls"); + if (has_content) { + const auto & content = message.at("content"); + if (content.is_string()) { + msg.content = content; + } else if (content.is_array()) { + for (const auto & part : content) { + if (!part.contains("type")) { + throw std::runtime_error("Missing content part type: " + part.dump()); + } + const auto & type = part.at("type"); + if (type != "text") { + throw std::runtime_error("Unsupported content part type: " + type.dump()); + } + common_chat_msg_content_part msg_part; + msg_part.type = type; + msg_part.text = part.at("text"); + msg.content_parts.push_back(msg_part); + } + } else if (!content.is_null()) { + throw std::runtime_error("Invalid 'content' type: expected string or array, got " + content.dump() + " (ref: https://github.com/ggml-org/llama.cpp/issues/8367)"); + } + } + if (has_tool_calls) { + for (const auto & tool_call : message.at("tool_calls")) { + common_chat_tool_call tc; + if (!tool_call.contains("type")) { + throw std::runtime_error("Missing tool call type: " + tool_call.dump()); + } + const auto & type = tool_call.at("type"); + if (type != "function") { + throw std::runtime_error("Unsupported tool call type: " + tool_call.dump()); + } + if (!tool_call.contains("function")) { + throw std::runtime_error("Missing tool call function: " + tool_call.dump()); + } + const auto & fc = tool_call.at("function"); + if (!fc.contains("name")) { + throw std::runtime_error("Missing tool call name: " + tool_call.dump()); + } + tc.name = fc.at("name"); + tc.arguments = fc.at("arguments"); + if (tool_call.contains("id")) { + tc.id = tool_call.at("id"); + } + msg.tool_calls.push_back(tc); + } + } + if (!has_content && !has_tool_calls) { + throw std::runtime_error("Expected 'content' or 'tool_calls' (ref: https://github.com/ggml-org/llama.cpp/issues/8367 & https://github.com/ggml-org/llama.cpp/issues/12279)"); + } + if (message.contains("reasoning_content")) { + msg.reasoning_content = message.at("reasoning_content"); + } + if (message.contains("name")) { + msg.tool_name = message.at("name"); + } + if (message.contains("tool_call_id")) { + msg.tool_call_id = message.at("tool_call_id"); + } + + msgs.push_back(msg); + } + } catch (const std::exception & e) { + // @ngxson : disable otherwise it's bloating the API response + // printf("%s\n", std::string("; messages = ") + messages.dump(2)); + throw std::runtime_error("Failed to parse messages: " + std::string(e.what())); + } + + return msgs; +} + +template <> +json common_chat_msgs_to_json_oaicompat(const std::vector & msgs, bool concat_typed_text) { + json messages = json::array(); + for (const auto & msg : msgs) { + if (!msg.content.empty() && !msg.content_parts.empty()) { + throw std::runtime_error("Cannot specify both content and content_parts"); + } + json jmsg { + {"role", msg.role}, + }; + if (!msg.content.empty()) { + jmsg["content"] = msg.content; + } else if (!msg.content_parts.empty()) { + if (concat_typed_text) { + std::string text; + for (const auto & part : msg.content_parts) { + if (part.type != "text") { + LOG_WRN("Ignoring content part type: %s\n", part.type.c_str()); + continue; + } + if (!text.empty()) { + text += '\n'; + } + text += part.text; + } + jmsg["content"] = text; + } else { + auto & parts = jmsg["content"] = json::array(); + for (const auto & part : msg.content_parts) { + parts.push_back({ + {"type", part.type}, + {"text", part.text}, + }); + } + } + } else { + jmsg["content"] = json(); // null + } + if (!msg.reasoning_content.empty()) { + jmsg["reasoning_content"] = msg.reasoning_content; + } + if (!msg.tool_name.empty()) { + jmsg["name"] = msg.tool_name; + } + if (!msg.tool_call_id.empty()) { + jmsg["tool_call_id"] = msg.tool_call_id; + } + if (!msg.tool_calls.empty()) { + auto & tool_calls = jmsg["tool_calls"] = json::array(); + for (const auto & tool_call : msg.tool_calls) { + json tc { + {"type", "function"}, + {"function", { + {"name", tool_call.name}, + {"arguments", tool_call.arguments}, + }}, + }; + if (!tool_call.id.empty()) { + tc["id"] = tool_call.id; + } + tool_calls.push_back(tc); + } + } + messages.push_back(jmsg); + } + return messages; +} + +template <> +std::vector common_chat_msgs_parse_oaicompat(const std::string & messages) { + return common_chat_msgs_parse_oaicompat(json::parse(messages)); +} + +template <> +std::vector common_chat_tools_parse_oaicompat(const json & tools) { + std::vector result; + + try { + if (!tools.is_null()) { + if (!tools.is_array()) { + throw std::runtime_error("Expected 'tools' to be an array, got " + tools.dump()); + } + for (const auto & tool : tools) { + if (!tool.contains("type")) { + throw std::runtime_error("Missing tool type: " + tool.dump()); + } + const auto & type = tool.at("type"); + if (!type.is_string() || type != "function") { + throw std::runtime_error("Unsupported tool type: " + tool.dump()); + } + if (!tool.contains("function")) { + throw std::runtime_error("Missing tool function: " + tool.dump()); + } + + const auto & function = tool.at("function"); + result.push_back({ + /* .name = */ function.at("name"), + /* .description = */ function.at("description"), + /* .parameters = */ function.at("parameters").dump(), + }); + } + } + } catch (const std::exception & e) { + throw std::runtime_error("Failed to parse tools: " + std::string(e.what()) + "; tools = " + tools.dump(2)); + } + + return result; +} + +template <> +std::vector common_chat_tools_parse_oaicompat(const std::string & tools) { + return common_chat_tools_parse_oaicompat(json::parse(tools)); +} + +template <> +json common_chat_tools_to_json_oaicompat(const std::vector & tools) { + if (tools.empty()) { + return json(); + } + + auto result = json::array(); + for (const auto & tool : tools) { + result.push_back({ + {"type", "function"}, + {"function", { + {"name", tool.name}, + {"description", tool.description}, + {"parameters", json::parse(tool.parameters)}, + }}, + }); + } + return result; +} + +template <> json common_chat_msg_diff_to_json_oaicompat(const common_chat_msg_diff & diff) { + json delta = json::object(); + if (!diff.reasoning_content_delta.empty()) { + delta["reasoning_content"] = diff.reasoning_content_delta; + } + if (!diff.content_delta.empty()) { + delta["content"] = diff.content_delta; + } + if (diff.tool_call_index != std::string::npos) { + json tool_call; + tool_call["index"] = diff.tool_call_index; + if (!diff.tool_call_delta.id.empty()) { + tool_call["id"] = diff.tool_call_delta.id; + tool_call["type"] = "function"; + } + json function = json::object(); + if (!diff.tool_call_delta.name.empty()) { + function["name"] = diff.tool_call_delta.name; + } + function["arguments"] = diff.tool_call_delta.arguments; + tool_call["function"] = function; + delta["tool_calls"] = json::array({tool_call}); + } + return delta; +} + +bool common_chat_verify_template(const std::string & tmpl, bool use_jinja) { + if (use_jinja) { + try { + common_chat_msg msg; + msg.role = "user"; + msg.content = "test"; + + auto tmpls = common_chat_templates_init(/* model= */ nullptr, tmpl); + + common_chat_templates_inputs inputs; + inputs.messages = {msg}; + + common_chat_templates_apply(tmpls.get(), inputs); + return true; + } catch (const std::exception & e) { + LOG_ERR("%s: failed to apply template: %s\n", __func__, e.what()); + return false; + } + } + llama_chat_message chat[] = {{"user", "test"}}; + const int res = llama_chat_apply_template(tmpl.c_str(), chat, 1, true, nullptr, 0); + return res >= 0; +} + +std::string common_chat_format_single( + const struct common_chat_templates * tmpls, + const std::vector & past_msg, + const common_chat_msg & new_msg, + bool add_ass, + bool use_jinja) { + + common_chat_templates_inputs inputs; + inputs.use_jinja = use_jinja; + + std::string fmt_past_msg; + if (!past_msg.empty()) { + inputs.messages = past_msg; + inputs.add_generation_prompt = false; + fmt_past_msg = common_chat_templates_apply(tmpls, inputs).prompt; + } + std::ostringstream ss; + // if the past_msg ends with a newline, we must preserve it in the formatted version + if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') { + ss << "\n"; + }; + // format chat with new_msg + inputs.messages.push_back(new_msg); + inputs.add_generation_prompt = add_ass; + auto fmt_new_msg = common_chat_templates_apply(tmpls, inputs).prompt; + // get the diff part + ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size()); + return ss.str(); +} + +std::string common_chat_format_example(const struct common_chat_templates * tmpls, bool use_jinja) { + common_chat_templates_inputs inputs; + inputs.use_jinja = use_jinja; + auto add_simple_msg = [&](auto role, auto content) { + common_chat_msg msg; + msg.role = role; + msg.content = content; + inputs.messages.push_back(msg); + }; + add_simple_msg("system", "You are a helpful assistant"); + add_simple_msg("user", "Hello"); + add_simple_msg("assistant", "Hi there"); + add_simple_msg("user", "How are you?"); + return common_chat_templates_apply(tmpls, inputs).prompt; +} + +#define CHATML_TEMPLATE_SRC \ + "{%- for message in messages -%}\n" \ + " {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' -}}\n" \ + "{%- endfor -%}\n" \ + "{%- if add_generation_prompt -%}\n" \ + " {{- '<|im_start|>assistant\n' -}}\n" \ + "{%- endif -%}" + +void common_chat_templates_free(struct common_chat_templates * tmpls) { + delete tmpls; +} + +bool common_chat_templates_was_explicit(const struct common_chat_templates * tmpls) { + return tmpls->has_explicit_template; +} + +const char * common_chat_templates_source(const struct common_chat_templates * tmpls, const char * variant) { + if (variant != nullptr) { + if (strcmp(variant, "tool_use") == 0) { + if (tmpls->template_tool_use) { + return tmpls->template_tool_use->source().c_str(); + } + return nullptr; + } else { + LOG_DBG("%s: unknown template variant: %s\n", __func__, variant); + } + } + return tmpls->template_default->source().c_str(); +} + +common_chat_templates_ptr common_chat_templates_init( + const struct llama_model * model, + const std::string & chat_template_override, + const std::string & bos_token_override, + const std::string & eos_token_override) +{ + std::string default_template_src; + std::string template_tool_use_src; + + bool has_explicit_template = !chat_template_override.empty(); + if (chat_template_override.empty()) { + GGML_ASSERT(model != nullptr); + const auto * str = llama_model_chat_template(model, /* name */ nullptr); + if (str) { + default_template_src = str; + has_explicit_template = true; + } + str = llama_model_chat_template(model, /* name */ "tool_use"); + if (str) { + template_tool_use_src = str; + has_explicit_template = true; + } + } else { + default_template_src = chat_template_override; + } + if (default_template_src.empty() || default_template_src == "chatml") { + if (!template_tool_use_src.empty()) { + default_template_src = template_tool_use_src; + } else { + default_template_src = CHATML_TEMPLATE_SRC; + } + } + std::string token_bos = bos_token_override; + std::string token_eos = eos_token_override; + if (model) { + const auto * vocab = llama_model_get_vocab(model); + const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) { + if (token == LLAMA_TOKEN_NULL) { + if (default_template_src.find(jinja_variable_name) != std::string::npos + || template_tool_use_src.find(jinja_variable_name) != std::string::npos) { + LOG_WRN("common_chat_templates_init: warning: vocab does not have a %s token, jinja template won't work as intended.\n", name); + } + return std::string(); + } + return common_token_to_piece(vocab, token, true); + }; + token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token"); + token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token"); + } + common_chat_templates_ptr tmpls(new common_chat_templates()); + tmpls->has_explicit_template = has_explicit_template; + try { + tmpls->template_default = std::make_unique(default_template_src, token_bos, token_eos); + } catch (const std::exception & e) { + LOG_ERR("%s: failed to parse chat template (defaulting to chatml): %s \n", __func__, e.what()); + tmpls->template_default = std::make_unique(CHATML_TEMPLATE_SRC, token_bos, token_eos); + } + if (!template_tool_use_src.empty()) { + try { + tmpls->template_tool_use = std::make_unique(template_tool_use_src, token_bos, token_eos); + } catch (const std::exception & e) { + LOG_ERR("%s: failed to parse tool use chat template (ignoring it): %s\n", __func__, e.what()); + } + } + return tmpls; +} + +const char * common_chat_format_name(common_chat_format format) { + switch (format) { + case COMMON_CHAT_FORMAT_CONTENT_ONLY: return "Content-only"; + case COMMON_CHAT_FORMAT_GENERIC: return "Generic"; + case COMMON_CHAT_FORMAT_MISTRAL_NEMO: return "Mistral Nemo"; + case COMMON_CHAT_FORMAT_LLAMA_3_X: return "Llama 3.x"; + case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS: return "Llama 3.x with builtin tools"; + case COMMON_CHAT_FORMAT_DEEPSEEK_R1: return "DeepSeek R1"; + case COMMON_CHAT_FORMAT_FIREFUNCTION_V2: return "FireFunction v2"; + case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: return "Functionary v3.2"; + case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1"; + case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro"; + case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B"; + default: + throw std::runtime_error("Unknown chat format"); + } +} + +const char * common_reasoning_format_name(common_reasoning_format format) { + switch (format) { + case COMMON_REASONING_FORMAT_NONE: return "none"; + case COMMON_REASONING_FORMAT_DEEPSEEK: return "deepseek"; + case COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY: return "deepseek-legacy"; + default: + throw std::runtime_error("Unknown reasoning format"); + } +} + +static std::string wrap_code_as_arguments(common_chat_msg_parser & builder, const std::string & code) { + std::string arguments; + if (builder.is_partial()) { + arguments = (json {{"code", code + builder.healing_marker()}}).dump(); + auto idx = arguments.find(builder.healing_marker()); + if (idx != std::string::npos) { + arguments.resize(idx); + } + } else { + arguments = (json {{"code", code}}).dump(); + } + return arguments; +} + +/** + * Takes a prefix regex that must have 1 group to capture the function name, a closing suffix, and expects json parameters in between. + * Aggregates the prefix, suffix and in-between text into the content. + */ +static void parse_json_tool_calls( + common_chat_msg_parser & builder, + const std::optional & block_open, + const std::optional & function_regex_start_only, + const std::optional & function_regex, + const common_regex & close_regex, + const std::optional & block_close, + bool allow_raw_python = false, + const std::function & get_function_name = nullptr) { + + auto parse_tool_calls = [&]() { + size_t from = std::string::npos; + auto first = true; + while (true) { + auto res = function_regex_start_only && first + ? builder.try_consume_regex(*function_regex_start_only) + : function_regex + ? builder.try_find_regex(*function_regex, from) + : std::nullopt; + if (res) { + std::string name; + if (get_function_name) { + name = get_function_name(*res); + } else { + GGML_ASSERT(res->groups.size() == 2); + name = builder.str(res->groups[1]); + } + first = false; + if (name.empty()) { + // get_function_name signalled us that we should skip this match and treat it as content. + from = res->groups[0].begin + 1; + continue; + } + from = std::string::npos; + + auto maybe_raw_python = name == "python" && allow_raw_python; + if (builder.input()[builder.pos()] == '{' || !maybe_raw_python) { + if (auto arguments = builder.try_consume_json_with_dumped_args({{}})) { + if (!builder.add_tool_call(name, "", arguments->value) || arguments->is_partial) { + throw common_chat_msg_partial_exception("incomplete tool call"); + } + builder.consume_regex(close_regex); + } + continue; + } + if (maybe_raw_python) { + auto arguments = wrap_code_as_arguments(builder, builder.consume_rest()); + if (!builder.add_tool_call(name, "", arguments)) { + throw common_chat_msg_partial_exception("incomplete tool call"); + } + return; + } + throw common_chat_msg_partial_exception("incomplete tool call"); + } + break; + } + if (block_close) { + builder.consume_regex(*block_close); + } + builder.consume_spaces(); + builder.add_content(builder.consume_rest()); + }; + if (block_open) { + if (auto res = builder.try_find_regex(*block_open)) { + parse_tool_calls(); + } else { + builder.add_content(builder.consume_rest()); + } + } else { + parse_tool_calls(); + } +} + +static void parse_prefixed_json_tool_call_array(common_chat_msg_parser & builder, const common_regex & prefix, size_t rstrip_prefix = 0) { + static const std::vector> args_paths = {{"arguments"}}; + if (auto res = builder.try_find_regex(prefix)) { + builder.move_back(rstrip_prefix); + auto tool_calls = builder.consume_json_with_dumped_args(args_paths); + if (!builder.add_tool_calls(tool_calls.value) || tool_calls.is_partial) { + throw common_chat_msg_partial_exception("incomplete tool call array"); + } + } else { + builder.add_content(builder.consume_rest()); + } +} + +static void foreach_function(const json & tools, const std::function & fn) { + for (const auto & tool : tools) { + if (!tool.contains("type") || tool.at("type") != "function" || !tool.contains("function")) { + LOG_INF("Skipping tool without function: %s", tool.dump(2).c_str()); + continue; + } + fn(tool); + } +} + +static std::string apply( + const common_chat_template & tmpl, + const struct templates_params & inputs, + const std::optional & messages_override = std::nullopt, + const std::optional & tools_override = std::nullopt, + const std::optional & additional_context = std::nullopt) +{ + minja::chat_template_inputs tmpl_inputs; + tmpl_inputs.messages = messages_override ? *messages_override : inputs.messages; + if (tools_override) { + tmpl_inputs.tools = *tools_override; + } else { + tmpl_inputs.tools = inputs.tools.empty() ? json() : inputs.tools; + } + tmpl_inputs.add_generation_prompt = inputs.add_generation_prompt; + tmpl_inputs.extra_context = inputs.extra_context; + if (additional_context) { + tmpl_inputs.extra_context.merge_patch(*additional_context); + } + // TODO: add flag to control date/time, if only for testing purposes. + // tmpl_inputs.now = std::chrono::system_clock::now(); + + minja::chat_template_options tmpl_opts; + // To avoid double BOS / EOS tokens, we're manually removing begining / trailing tokens + // instead of using `chat_template_options.use_bos_token = false`, since these tokens + // may be needed inside the template / between messages too. + auto result = tmpl.apply(tmpl_inputs, tmpl_opts); + if (string_starts_with(result, tmpl.bos_token())) { + result = result.substr(tmpl.bos_token().size()); + } + if (string_ends_with(result, tmpl.eos_token())) { + result = result.substr(0, result.size() - tmpl.eos_token().size()); + } + return result; +} + +static common_chat_params common_chat_params_init_generic(const common_chat_template & tmpl, const struct templates_params & inputs) { + common_chat_params data; + + auto tool_call_schemas = json::array(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + auto tool_schema = json { + {"type", "object"}, + {"properties", { + {"name", { + {"type", "string"}, + {"const", function.at("name")}, + }}, + {"arguments", function.at("parameters")}, + }}, + {"required", json::array({"name", "arguments"})}, + }; + if (function.contains("description")) { + tool_schema["description"] = function.at("description"); + } + if (inputs.parallel_tool_calls) { + tool_schema.at("properties")["id"] = { + {"type", "string"}, + {"minLength", 4}, + }; + tool_schema.at("required").push_back("id"); + } + tool_call_schemas.emplace_back(tool_schema); + }); + const auto tool_call = + inputs.parallel_tool_calls + ? json { + {"type", "object"}, + {"properties", { + {"tool_calls", { + {"type", "array"}, + {"items", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json { + {"anyOf", tool_call_schemas}, + }}, + {"minItems", 1}, + }}, + }}, + {"required", json::array({"tool_calls"})}, + } + : json { + {"type", "object"}, + {"properties", { + {"tool_call", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json { + {"anyOf", tool_call_schemas}, + }}, + }}, + {"required", json::array({"tool_call"})}, + }; + const auto schema = + inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED + ? json { + {"anyOf", json::array({ + tool_call, + { + {"type", "object"}, + {"properties", { + {"response", inputs.json_schema.is_null() + ? json {{"type", "string"}} + : inputs.json_schema + }, + }}, + {"required", json::array({"response"})}, + }, + })} + } + : tool_call; + + data.grammar_lazy = false; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + builder.add_schema("root", schema); + }); + + auto tweaked_messages = common_chat_template::add_system( + inputs.messages, + "Respond in JSON format, either with `tool_call` (a request to call tools) or with `response` reply to the user's request"); + + data.prompt = apply(tmpl, inputs, /* messages_override= */ tweaked_messages); + data.format = COMMON_CHAT_FORMAT_GENERIC; + return data; +} +static void common_chat_parse_generic(common_chat_msg_parser & builder) { + if (!builder.syntax().parse_tool_calls) { + builder.add_content(builder.consume_rest()); + return; + } + static const std::vector> content_paths = { + {"response"}, + }; + static const std::vector> args_paths = { + {"tool_call", "arguments"}, + {"tool_calls", "arguments"}, + }; + auto data = builder.consume_json_with_dumped_args(args_paths, content_paths); + if (data.value.contains("tool_calls")) { + if (!builder.add_tool_calls(data.value.at("tool_calls")) || data.is_partial) { + throw common_chat_msg_partial_exception("incomplete tool calls"); + } + } else if (data.value.contains("tool_call")) { + if (!builder.add_tool_call(data.value.at("tool_call")) || data.is_partial) { + throw common_chat_msg_partial_exception("incomplete tool call"); + } + } else if (data.value.contains("response")) { + const auto & response = data.value.at("response"); + builder.add_content(response.is_string() ? response.template get() : response.dump(2)); + if (data.is_partial) { + throw common_chat_msg_partial_exception("incomplete response"); + } + } else { + throw common_chat_msg_partial_exception("Expected 'tool_call', 'tool_calls' or 'response' in JSON"); + } +} + +static common_chat_params common_chat_params_init_mistral_nemo(const common_chat_template & tmpl, const struct templates_params & inputs) { + common_chat_params data; + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + auto schemas = json::array(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + schemas.push_back({ + {"type", "object"}, + {"properties", { + // Important note: the model is probably trained to take a JSON stringified arguments value. + // It's hard to constrain that for now (while reusing the JSON schema conversion), so we're just expecting a plain object. + {"name", { + {"type", "string"}, + {"const", function.at("name")}, + }}, + {"arguments", function.at("parameters")}, + {"id", { + {"type", "string"}, + // Nemo's template expects a 9-character alphanumeric ID. + {"pattern", "^[a-zA-Z0-9]{9}$"}, + }}, + }}, + {"required", json::array({"name", "arguments", "id"})}, + }); + }); + auto schema = json { + {"type", "array"}, + {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}}, + {"minItems", 1}, + }; + if (!inputs.parallel_tool_calls) { + schema["maxItems"] = 1; + } + builder.add_rule("root", "\"[TOOL_CALLS]\" " + builder.add_schema("tool_calls", schema)); + }); + data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "[TOOL_CALLS]"}); + data.preserved_tokens = { + "[TOOL_CALLS]", + }; + data.prompt = apply(tmpl, inputs); + data.format = COMMON_CHAT_FORMAT_MISTRAL_NEMO; + return data; +} +static void common_chat_parse_mistral_nemo(common_chat_msg_parser & builder) { + if (!builder.syntax().parse_tool_calls) { + builder.add_content(builder.consume_rest()); + return; + } + + static const common_regex prefix(regex_escape("[TOOL_CALLS]")); + parse_prefixed_json_tool_call_array(builder, prefix); +} + +static common_chat_params common_chat_params_init_command_r7b(const common_chat_template & tmpl, const struct templates_params & inputs) { + common_chat_params data; + + auto adjusted_messages = json::array(); + for (const auto & msg : inputs.messages) { + auto has_reasoning_content = msg.contains("reasoning_content") && msg.at("reasoning_content").is_string(); + auto has_tool_calls = msg.contains("tool_calls") && msg.at("tool_calls").is_array(); + if (has_reasoning_content && has_tool_calls) { + auto adjusted_message = msg; + adjusted_message["tool_plan"] = msg.at("reasoning_content"); + adjusted_message.erase("reasoning_content"); + adjusted_messages.push_back(adjusted_message); + } else { + adjusted_messages.push_back(msg); + } + } + data.prompt = apply(tmpl, inputs, /* messages_override= */ adjusted_messages); + data.format = COMMON_CHAT_FORMAT_COMMAND_R7B; + if (string_ends_with(data.prompt, "<|START_THINKING|>")) { + if (!inputs.enable_thinking) { + data.prompt += "<|END_THINKING|>"; + } else { + data.thinking_forced_open = true; + } + } else if (!inputs.enable_thinking && string_ends_with(data.prompt, "<|CHATBOT_TOKEN|>")) { + data.prompt += "<|START_THINKING|><|END_THINKING|>"; + } + + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + auto schemas = json::array(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + schemas.push_back({ + {"type", "object"}, + {"properties", { + {"tool_call_id", { + {"type", "string"}, + // Command-R's template expects an integer string. + {"pattern", "^[0-9]{1,10}$"}, + }}, + {"tool_name", { + {"type", "string"}, + {"const", function.at("name")}, + }}, + {"parameters", function.at("parameters")}, + }}, + {"required", json::array({"tool_call_id", "tool_name", "parameters"})}, + }); + }); + auto schema = json { + {"type", "array"}, + {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}}, + {"minItems", 1}, + }; + if (!inputs.parallel_tool_calls) { + schema["maxItems"] = 1; + } + builder.add_rule("root", + std::string(data.thinking_forced_open ? "( \"<|END_THINKING|>\" space )? " : "") + + "\"<|START_ACTION|>\" " + builder.add_schema("tool_calls", schema) + " \"<|END_ACTION|>\""); + }); + data.grammar_triggers.push_back({ + COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, + // If thinking_forced_open, then we capture the tag in the grammar, + // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar) + std::string(data.thinking_forced_open ? "[\\s\\S]*?(<\\|END_THINKING\\|>\\s*)" : "(?:<\\|START_THINKING\\|>[\\s\\S]*?<\\|END_THINKING\\|>\\s*)?") + + "(<\\|START_ACTION\\|>)[\\s\\S]*" + }); + data.preserved_tokens = { + "<|START_ACTION|>", + "<|END_ACTION|>", + "<|START_RESPONSE|>", + "<|END_RESPONSE|>", + "<|START_THINKING|>", + "<|END_THINKING|>", + }; + return data; +} + +static void common_chat_parse_command_r7b(common_chat_msg_parser & builder) { + builder.try_parse_reasoning("<|START_THINKING|>", "<|END_THINKING|>"); + + static const common_regex start_action_regex("<\\|START_ACTION\\|>"); + static const common_regex end_action_regex("<\\|END_ACTION\\|>"); + static const common_regex start_response_regex("<\\|START_RESPONSE\\|>"); + static const common_regex end_response_regex("<\\|END_RESPONSE\\|>"); + + if (auto res = builder.try_find_regex(start_action_regex)) { + // If we didn't extract thoughts, prelude includes them. + auto tool_calls = builder.consume_json_with_dumped_args({{"parameters"}}); + for (const auto & tool_call : tool_calls.value) { + std::string name = tool_call.contains("tool_name") ? tool_call.at("tool_name") : ""; + std::string id = tool_call.contains("tool_call_id") ? tool_call.at("tool_call_id") : ""; + std::string arguments = tool_call.contains("parameters") ? tool_call.at("parameters") : ""; + if (!builder.add_tool_call(name, id, arguments) || tool_calls.is_partial) { + throw common_chat_msg_partial_exception("incomplete tool call"); + } + } + if (tool_calls.is_partial) { + throw common_chat_msg_partial_exception("incomplete tool call"); + } + builder.consume_regex(end_action_regex); + } else if (auto res = builder.try_find_regex(start_response_regex)) { + if (!builder.try_find_regex(end_response_regex)) { + builder.add_content(builder.consume_rest()); + throw common_chat_msg_partial_exception(end_response_regex.str()); + } + } else { + builder.add_content(builder.consume_rest()); + } +} + +static void expect_tool_parameters(const std::string & name, const json & parameters, const std::vector & expected_properties) { + if (!parameters.is_object() || !parameters.contains("type") || parameters.at("type") != "object" || !parameters.contains("properties") || !parameters.contains("required")) { + throw std::runtime_error("Parameters of tool " + name + " must be an object w/ required properties"); + } + const auto & parameters_properties = parameters.at("properties"); + const auto & parameters_required = parameters.at("required"); + for (const auto & prop : expected_properties) { + if (!parameters_properties.contains(prop)) { + throw std::runtime_error("Parameters of tool " + name + " is missing property: " + prop); // NOLINT + } + if (std::find(parameters_required.begin(), parameters_required.end(), json(prop)) == parameters_required.end()) { + throw std::runtime_error("Parameters of tool " + name + " must have property marked as required: " + prop); // NOLINT + } + } + if (parameters_properties.size() != expected_properties.size()) { + throw std::runtime_error("Parameters of tool " + name + " must only have these properties:" + string_join(expected_properties, ", ")); + } +} + +static common_chat_params common_chat_params_init_llama_3_x(const common_chat_template & tmpl, const struct templates_params & inputs, bool allow_python_tag_builtin_tools) { + auto builtin_tools = json::array(); + common_chat_params data; + if (!inputs.tools.is_null()) { + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector tool_rules; + + auto handle_builtin_tool = [&](const std::string & name, const json & parameters) { + if (name == "wolfram_alpha" || name == "web_search" || name == "brave_search") { + // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py + // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py + expect_tool_parameters(name, parameters, {"query"}); + } else if (name == "python" || name == "code_interpreter") { + // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py + expect_tool_parameters(name, parameters, {"code"}); + } else { + return false; + } + + std::vector kvs; + for (const auto & [key, value] : parameters.at("properties").items()) { + kvs.push_back("\"" + key + "=\" " + builder.add_schema(name + "-args-" + key, value)); // NOLINT + } + + tool_rules.push_back( + builder.add_rule( + name + "-call", + "\"<|python_tag|>" + name + ".call(\" " + string_join(kvs, " \", \" ") + " \")\"")); + builtin_tools.push_back(name); + + return true; + }; + + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + auto parameters = function.at("parameters"); + builder.resolve_refs(parameters); + + // https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/tool_runtime + if (allow_python_tag_builtin_tools) { + handle_builtin_tool(name, parameters); + } + tool_rules.push_back( + builder.add_rule( + name + "-call", + "\"{\" space " + "( \"\\\"type\\\"\" space \":\" space \"\\\"function\\\"\" space \",\" space )? " + " \"\\\"name\\\"\" space \":\" space \"\\\"" + name + "\\\"\" space \",\" space " + " \"\\\"parameters\\\"\" space \":\" space " + builder.add_schema(name + "-args", parameters) + " " + "\"}\" space")); + }); + // Small models may hallucinate function names so we match anything (*at the start*) that looks like the JSON of a function call, regardless of the name. + data.grammar_triggers.push_back({ + COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, + "(\\{\\s*(?:\"type\"\\s*:\\s*\"function\"\\s*,\\s*)?\"name\"\\s*:\\s*\")[\\s\\S]*", // + name + "\"[\\s\\S]*", + }); + if (!builtin_tools.empty()) { + data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|python_tag|>"}); + data.preserved_tokens.push_back("<|python_tag|>"); + } + // Allow a few empty lines on top of the usual constrained json schema space rule. + builder.add_rule("root", string_join(tool_rules, " | ")); + data.additional_stops.push_back("<|eom_id|>"); + }); + data.format = allow_python_tag_builtin_tools && !builtin_tools.empty() + ? COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS + : COMMON_CHAT_FORMAT_LLAMA_3_X; + } else { + data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + } + data.prompt = apply(tmpl, inputs, /* messages_override =*/ std::nullopt, /* tools_override= */ std::nullopt, json { + {"date_string", format_time(inputs.now, "%d %b %Y")}, + {"tools_in_user_message", false}, + {"builtin_tools", builtin_tools.empty() ? json() : builtin_tools}, + }); + return data; +} +static void common_chat_parse_llama_3_1(common_chat_msg_parser & builder, bool with_builtin_tools = false) { + if (!builder.syntax().parse_tool_calls) { + builder.add_content(builder.consume_rest()); + return; + } + + static const common_regex function_regex( + "\\s*\\{\\s*(?:\"type\"\\s*:\\s*\"function\"\\s*,\\s*)?\"name\"\\s*:\\s*\"([^\"]+)\"\\s*,\\s*\"parameters\"\\s*: "); + static const common_regex close_regex("\\}\\s*"); + + static const common_regex function_name_regex("\\s*(\\w+)\\s*\\.\\s*call\\("); + static const common_regex arg_name_regex("\\s*(\\w+)\\s*=\\s*"); + + if (with_builtin_tools) { + static const common_regex builtin_call_regex("<\\|python_tag\\|>"); + if (auto res = builder.try_find_regex(builtin_call_regex)) { + auto fun_res = builder.consume_regex(function_name_regex); + auto function_name = builder.str(fun_res.groups[1]); + + common_healing_marker healing_marker; + json args = json::object(); + while (true) { + if (auto arg_res = builder.try_consume_regex(arg_name_regex)) { + auto arg_name = builder.str(arg_res->groups[1]); + auto partial = builder.consume_json(); + args[arg_name] = partial.json; + healing_marker.marker = partial.healing_marker.marker; + healing_marker.json_dump_marker = partial.healing_marker.json_dump_marker; + builder.consume_spaces(); + if (!builder.try_consume_literal(",")) { + break; + } + } else { + break; + } + } + builder.consume_literal(")"); + builder.consume_spaces(); + + auto arguments = args.dump(); + if (!builder.add_tool_call(function_name, "", arguments)) { + throw common_chat_msg_partial_exception("Incomplete tool call"); + } + return; + } + } + parse_json_tool_calls( + builder, + /* block_open= */ std::nullopt, + /* function_regex_start_only= */ function_regex, + /* function_regex= */ std::nullopt, + close_regex, + std::nullopt); + +} + +static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_template & tmpl, const struct templates_params & inputs) { + common_chat_params data; + auto prompt = apply(tmpl, inputs); + + // Hacks to fix the official (broken) prompt. + // It is advisable to use --chat-template-file models/templates/llama-cpp-deepseek-r1.jinja instead, + // until the official template is fixed. + if (tmpl.source().find("{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}") != std::string::npos) { + // Don't leave the chat dangling after tool results + if (string_ends_with(prompt, "<｜tool▁outputs▁end｜>")) { + prompt += "<｜end▁of▁sentence｜>"; + if (inputs.add_generation_prompt) { + prompt += "<｜Assistant｜>"; + } + } + // Fix up tool call delta example added by Minja + prompt = std::regex_replace( + prompt, + std::regex("(<｜tool▁call▁end｜>)[\\s\\r\\n]*(<｜tool▁outputs▁begin｜>|<｜User｜>)"), + "$1<｜tool▁calls▁end｜><｜end▁of▁sentence｜>$2"); + } + data.prompt = prompt; + data.format = COMMON_CHAT_FORMAT_DEEPSEEK_R1; + if (string_ends_with(data.prompt, "\n")) { + if (!inputs.enable_thinking) { + data.prompt += ""; + } else { + data.thinking_forced_open = true; + } + } + + if (inputs.tools.is_array() && !inputs.tools.empty()) { + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED && inputs.json_schema.is_null(); + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector tool_rules; + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + auto parameters = function.at("parameters"); + builder.resolve_refs(parameters); + tool_rules.push_back(builder.add_rule(name + "-call", + "( \"<｜tool▁call▁begin｜>\" )? \"function<｜tool▁sep｜>" + name + "\\n" + "```json\\n\" " + builder.add_schema(name + "-args", parameters) + " " + "\"```<｜tool▁call▁end｜>\"")); + }); + // Distill Qwen 7B & 32B models seem confused re/ syntax of their tool call opening tag, + // so we accept common variants (then it's all constrained) + builder.add_rule("root", + std::string(data.thinking_forced_open ? "( \"\" space )? " : "") + + "( \"<｜tool▁calls▁begin｜>\" | \"<｜tool_calls_begin｜>\" | \"<｜tool calls begin｜>\" | \"<｜tool\\\\_calls\\\\_begin｜>\" | \"<｜tool▁calls｜>\" ) " + "(" + string_join(tool_rules, " | ") + ")" + (inputs.parallel_tool_calls ? "*" : "") + " " + "\"<｜tool▁calls▁end｜>\"" + " space"); + data.grammar_triggers.push_back({ + COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, + // If thinking_forced_open, then we capture the tag in the grammar, + // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar) + std::string(data.thinking_forced_open ? "[\\s\\S]*?(\\s*)" : "(?:[\\s\\S]*?\\s*)?") + + "(<｜tool▁calls▁begin｜>|<｜tool_calls_begin｜>|<｜tool calls begin｜>|<｜tool\\\\_calls\\\\_begin｜>|<｜tool▁calls｜>)[\\s\\S]*" + }); + data.preserved_tokens = { + "", + "", + "<｜tool▁calls▁begin｜>", + "<｜tool▁call▁begin｜>", + "<｜tool▁sep｜>", + "<｜tool▁call▁end｜>", + "<｜tool▁calls▁end｜", + }; + }); + } + return data; +} +static void common_chat_parse_deepseek_r1(common_chat_msg_parser & builder) { + builder.try_parse_reasoning("", ""); + if (!builder.syntax().parse_tool_calls) { + builder.add_content(builder.consume_rest()); + return; + } + + static const common_regex tool_calls_begin("(?:<｜tool▁calls▁begin｜>|<｜tool_calls_begin｜>|<｜tool calls begin｜>|<｜tool\\\\_calls\\\\_begin｜>|<｜tool▁calls｜>)"); + static const common_regex tool_calls_end("<｜tool▁calls▁end｜>"); + static const common_regex function_regex("(?:<｜tool▁call▁begin｜>)?function<｜tool▁sep｜>([^\n]+)\n```json\n"); + static const common_regex close_regex("```[\\s\\r\\n]*<｜tool▁call▁end｜>"); + + parse_json_tool_calls( + builder, + /* block_open= */ tool_calls_begin, + /* function_regex_start_only= */ std::nullopt, + function_regex, + close_regex, + tool_calls_end); +} + +static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct templates_params & inputs) { + LOG_DBG("%s\n", __func__); + common_chat_params data; + data.prompt = apply(tmpl, inputs, /* messages_override =*/ std::nullopt, /* tools_override= */ json(), json { + {"datetime", format_time(inputs.now, "%b %d %Y %H:%M:%S GMT")}, + {"functions", json(inputs.tools.empty() ? "" : inputs.tools.dump(2))}, + }); + if (inputs.tools.is_array() && !inputs.tools.empty()) { + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + auto schemas = json::array(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + schemas.push_back({ + {"type", "object"}, + {"properties", { + {"name", { + {"type", "string"}, + {"const", function.at("name")}, + }}, + {"arguments", function.at("parameters")}, + }}, + {"required", json::array({"name", "arguments", "id"})}, + }); + }); + auto schema = json { + {"type", "array"}, + {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}}, + {"minItems", 1}, + }; + if (!inputs.parallel_tool_calls) { + schema["maxItems"] = 1; + } + builder.add_rule("root", "\" functools\"? " + builder.add_schema("tool_calls", schema)); + }); + data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, " functools["}); + data.preserved_tokens = { + " functools[", + }; + data.format = COMMON_CHAT_FORMAT_FIREFUNCTION_V2; + } else { + data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + } + return data; +} +static void common_chat_parse_firefunction_v2(common_chat_msg_parser & builder) { + if (!builder.syntax().parse_tool_calls) { + builder.add_content(builder.consume_rest()); + return; + } + static const common_regex prefix(regex_escape(" functools[")); + parse_prefixed_json_tool_call_array(builder, prefix, /* rstrip_prefix= */ 1); +} + +static common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template & tmpl, const struct templates_params & inputs) { + // >>>all\nlet's call functions>>>fn1\n{"arg1": 1...}\n>>>fn2\n{"arg1": 1...}... + // Using ">>>f1\n", ">>>f2\n"... as trigger words for the grammar + // If the function is python, we also allow raw python code (if the line after `python\n` doesn't start w/ opening `{`), which the model seems to prefer for multiline code. + common_chat_params data; + data.prompt = apply(tmpl, inputs); + data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2; + if (inputs.tools.is_array() && !inputs.tools.empty()) { + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector first_tool_rules; + std::vector subsequent_tool_rules; + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + auto parameters = function.at("parameters"); + builder.resolve_refs(parameters); + std::string args_pattern = "[\\s\\S]*"; + auto args_rule = builder.add_schema(name + "-args", parameters); + if (name == "python") { + args_rule = builder.add_rule(name + "-maybe-raw-args", args_rule + " | [^{] .*"); + } else { + args_pattern = "\\{" + args_pattern; + } + auto call_rule = builder.add_rule(name + "-call", "\"" + name + "\\n\" " + args_rule); + first_tool_rules.push_back(call_rule); + if (inputs.parallel_tool_calls) { + subsequent_tool_rules.push_back(builder.add_rule(name + "-call2", "\">>>\" " + call_rule)); + } + data.grammar_triggers.push_back({ + COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, + "((?:[\\s\\S]+?>>>)?" + regex_escape(name) + "\n)" + args_pattern, + }); + }); + data.preserved_tokens = { + "<|end_header_id|>", + }; + auto first_rule = first_tool_rules.empty() ? "" : builder.add_rule("first_tool_call", string_join(first_tool_rules, " | ")) + " space"; + if (inputs.parallel_tool_calls) { + auto subsequent_rule = builder.add_rule("subsequent_tool_call", string_join(subsequent_tool_rules, " | ")) + " space"; + builder.add_rule("root", first_rule + " (" + subsequent_rule + ")*"); + } else { + builder.add_rule("root", first_rule); + } + + }); + } + return data; +} +static void common_chat_parse_functionary_v3_2(common_chat_msg_parser & builder) { + static const common_regex function_regex_start_only(R"((\w+\n\{|python\n|all\n))"); + static const common_regex function_regex(R"(>>>(\w+\n\{|python\n|all\n))"); + static const common_regex close_regex(R"(\s*)"); + + parse_json_tool_calls( + builder, + std::nullopt, + function_regex_start_only, + function_regex, + close_regex, + std::nullopt, + /* allow_raw_python= */ true, + /* get_function_name= */ [&](const auto & res) -> std::string { + auto at_start = res.groups[0].begin == 0; + auto name = builder.str(res.groups[1]); + if (!name.empty() && name.back() == '{') { + // Unconsume the opening brace '{' to ensure the JSON parsing goes well. + builder.move_back(1); + } + auto idx = name.find_last_not_of("\n{"); + name = name.substr(0, idx + 1); + if (at_start && name == "all") { + return ""; + } + return name; + }); +} + +static common_chat_params common_chat_params_init_functionary_v3_1_llama_3_1(const common_chat_template & tmpl, const struct templates_params & inputs) { + // https://github.com/MeetKai/functionary/blob/main/tests/prompt_test_v3-llama3.1.txt + common_chat_params data; + + if (!inputs.tools.is_null()) { + std::string python_code_argument_name; + auto has_raw_python = false; + + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector tool_rules; + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + const auto & parameters = function.at("parameters"); + std::string name = function.at("name"); + if (name == "python" || name == "ipython") { + if (!parameters.contains("type")) { + throw std::runtime_error("Missing type in python tool"); + } + has_raw_python = true; + const auto & type = parameters.at("type"); + if (type == "object") { + auto properties = parameters.at("properties"); + for (auto it = properties.begin(); it != properties.end(); ++it) { + if (it.value().at("type") == "string") { + if (!python_code_argument_name.empty()) { + throw std::runtime_error("Multiple string arguments found in python tool"); + } + python_code_argument_name = it.key(); + } + } + if (python_code_argument_name.empty()) { + throw std::runtime_error("No string argument found in python tool"); + } + } else if (type != "string") { + throw std::runtime_error("Invalid type in python tool: " + type.dump()); + } + } + tool_rules.push_back(builder.add_rule(name + "-call", "\"\" " + builder.add_schema(name + "-args", parameters) + " \"\" space")); + }); + if (has_raw_python) { + tool_rules.push_back(builder.add_rule("python-call", "\"<|python_tag|>\" .*")); + data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|python_tag|>"}); + data.preserved_tokens.push_back("<|python_tag|>"); + } + auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " space"; + builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call); + data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "")); + + static const common_regex function_regex(R"()"); + static const common_regex close_regex(R"()"); + + parse_json_tool_calls( + builder, + /* block_open= */ std::nullopt, + /* function_regex_start_only= */ std::nullopt, + function_regex, + close_regex, + std::nullopt); + + if (auto res = builder.try_find_regex(python_tag_regex)) { + auto arguments = wrap_code_as_arguments(builder, builder.consume_rest()); + builder.add_tool_call("python", "", arguments); + return; + } +} + +static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat_template & tmpl, const struct templates_params & inputs) { + common_chat_params data; + + json extra_context = json { + {"enable_thinking", inputs.enable_thinking}, + }; + extra_context.update(inputs.extra_context); + + data.prompt = apply(tmpl, inputs, /* messages_override =*/ std::nullopt, /* tools_override= */ std::nullopt, extra_context); + data.format = COMMON_CHAT_FORMAT_HERMES_2_PRO; + if (string_ends_with(data.prompt, "\n")) { + if (!extra_context["enable_thinking"]) { + data.prompt += ""; + } else { + data.thinking_forced_open = true; + } + } + + if (!inputs.tools.is_null()) { + // (content)?({"name": "foo", "arguments": {"a": 1}})* + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector tool_rules; + std::vector tool_call_alts; + std::vector escaped_names; + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + auto parameters = function.at("parameters"); + builder.resolve_refs(parameters); + tool_rules.push_back(builder.add_schema(name + "-call", { + {"type", "object"}, + {"properties", json { + {"name", json {{"const", name}}}, + {"arguments", parameters}, + }}, + {"required", json::array({"name", "arguments"})}, + })); + tool_call_alts.push_back(builder.add_rule( + name + "-function-tag", + "\"\" space " + + builder.add_schema(name + "-args", parameters) + " " + "\"\" space")); + + data.grammar_triggers.push_back({ + COMMON_GRAMMAR_TRIGGER_TYPE_WORD, + "", + }); + auto escaped_name = regex_escape(name); + data.grammar_triggers.push_back({ + COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, + " alt_tags { + any_tool_call, + "\"\" space " + any_tool_call + " \"\"", + // The rest is just to accommodate common "good bad" outputs. + "\"\" space " + any_tool_call + " \"\"", + "\"\" space " + any_tool_call + " \"\"", + "\"\" space " + any_tool_call + " \"\"", + "\"\" space " + any_tool_call + " \"\"", + "\"\" space " + any_tool_call + " \"\"", + "\"\" space " + any_tool_call + " \"\"", + }; + auto wrappable_tool_call = builder.add_rule("wrappable_tool_call", "( " + string_join(alt_tags, " | ") + " ) space"); + tool_call_alts.push_back(wrappable_tool_call); + tool_call_alts.push_back( + "( \"```\\n\" | \"```json\\n\" | \"```xml\\n\" ) space " + wrappable_tool_call + " space \"```\" space "); + auto tool_call = builder.add_rule("tool_call", string_join(tool_call_alts, " | ")); + builder.add_rule("root", + std::string(data.thinking_forced_open ? "( \"\" space )? " : "") + + (inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call)); + // Trigger on some common known "good bad" outputs (only from the start and with a json that's about a specific argument name to avoid false positives) + data.grammar_triggers.push_back({ + COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, + // If thinking_forced_open, then we capture the tag in the grammar, + // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar) + std::string(data.thinking_forced_open ? "[\\s\\S]*?(\\s*)" : "(?:[\\s\\S]*?\\s*)?") + ( + "(\\s*" + "(?:" + "||||)?" + "\\s*\\{\\s*\"name\"\\s*:\\s*\"(?:" + string_join(escaped_names, "|") + ")\"" + ")" + ")[\\s\\S]*" + ), + }); + data.preserved_tokens = { + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "```", + "```json", + "```xml", + }; + }); + } + + return data; +} +static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) { + builder.try_parse_reasoning("", ""); + if (!builder.syntax().parse_tool_calls) { + builder.add_content(builder.consume_rest()); + return; + } + + static const common_regex open_regex( + "(?:" + "(```(?:xml|json)?\\n\\s*)?" // match 1 (block_start) + "(" // match 2 (open_tag) + "" + "|" + "|" + "|" + "|" + "|" + "|" + "|" + ")?" + "(\\s*\\{\\s*\"name\")" // match 3 (named tool call) + ")" + "|]+)>" // match 4 (function name) + "|" // match 5 (function name again) + ); + + while (auto res = builder.try_find_regex(open_regex)) { + const auto & block_start = res->groups[1]; + std::string block_end = block_start.empty() ? "" : "```"; + + const auto & open_tag = res->groups[2]; + std::string close_tag; + + if (!res->groups[3].empty()) { + builder.move_to(res->groups[3].begin); + close_tag = open_tag.empty() ? "" : "value) || tool_call->is_partial) { + throw common_chat_msg_partial_exception("incomplete tool call"); + } + builder.consume_spaces(); + builder.consume_literal(close_tag); + builder.consume_spaces(); + if (!block_end.empty()) { + builder.consume_literal(block_end); + builder.consume_spaces(); + } + } else { + throw common_chat_msg_partial_exception("failed to parse tool call"); + } + } else { + auto function_name = builder.str(res->groups[4]); + if (function_name.empty()) { + function_name = builder.str(res->groups[5]); + } + GGML_ASSERT(!function_name.empty()); + + close_tag = ""; + + if (auto arguments = builder.try_consume_json_with_dumped_args({{}})) { + if (!builder.add_tool_call(function_name, "", arguments->value) || arguments->is_partial) { + throw common_chat_msg_partial_exception("incomplete tool call"); + } + builder.consume_spaces(); + builder.consume_literal(close_tag); + builder.consume_spaces(); + if (!block_end.empty()) { + builder.consume_literal(block_end); + builder.consume_spaces(); + } + } + } + } + + builder.add_content(builder.consume_rest()); +} + +static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct templates_params & inputs) { + common_chat_params data; + data.prompt = apply(tmpl, inputs); + data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + data.grammar_lazy = false; + if (!inputs.json_schema.is_null()) { + if (!inputs.grammar.empty()) { + throw std::runtime_error("Either \"json_schema\" or \"grammar\" can be specified, but not both"); + } + data.grammar = json_schema_to_grammar(inputs.json_schema); + } else { + data.grammar = inputs.grammar; + } + return data; +} + +static common_chat_params common_chat_templates_apply_jinja( + const struct common_chat_templates * tmpls, + const struct common_chat_templates_inputs & inputs) +{ + templates_params params; + params.tools = common_chat_tools_to_json_oaicompat(inputs.tools); + const auto & tmpl = params.tools.is_array() && tmpls->template_tool_use + ? *tmpls->template_tool_use + : *tmpls->template_default; + const auto & src = tmpl.source(); + const auto & caps = tmpl.original_caps(); + params.messages = common_chat_msgs_to_json_oaicompat(inputs.messages, /* concat_text= */ !tmpl.original_caps().requires_typed_content); + params.add_generation_prompt = inputs.add_generation_prompt; + params.tool_choice = inputs.tool_choice; + params.enable_thinking = inputs.enable_thinking; + params.grammar = inputs.grammar; + params.now = inputs.now; + + params.extra_context = json::object(); + for (auto el : inputs.chat_template_kwargs) { + params.extra_context[el.first] = json::parse(el.second); + } + + if (!inputs.json_schema.empty()) { + params.json_schema = json::parse(inputs.json_schema); + } + + if (inputs.parallel_tool_calls && !tmpl.original_caps().supports_parallel_tool_calls) { + LOG_DBG("Disabling parallel_tool_calls because the template does not support it\n"); + params.parallel_tool_calls = false; + } else { + params.parallel_tool_calls = inputs.parallel_tool_calls; + } + + if (params.tools.is_array()) { + if (params.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && !params.grammar.empty()) { + throw std::runtime_error("Cannot specify grammar with tools"); + } + if (caps.supports_tool_calls && !caps.supports_tools) { + LOG_WRN("Template supports tool calls but does not natively describe tools. The fallback behaviour used may produce bad results, inspect prompt w/ --verbose & consider overriding the template.\n"); + } + } + + // DeepSeek R1: use handler in all cases except json schema (thinking / tools). + if (src.find("<｜tool▁calls▁begin｜>") != std::string::npos && params.json_schema.is_null()) { + return common_chat_params_init_deepseek_r1(tmpl, params); + } + + // Command R7B: : use handler in all cases except json schema (thinking / tools). + if (src.find("<|END_THINKING|><|START_ACTION|>") != std::string::npos && params.json_schema.is_null()) { + return common_chat_params_init_command_r7b(tmpl, params); + } + + // Hermes 2/3 Pro, Qwen 2.5 Instruct (w/ tools) + if (src.find("") != std::string::npos && params.json_schema.is_null()) { + return common_chat_params_init_hermes_2_pro(tmpl, params); + } + + // Use generic handler when mixing tools + JSON schema. + // TODO: support that mix in handlers below. + if ((params.tools.is_array() && params.json_schema.is_object())) { + return common_chat_params_init_generic(tmpl, params); + } + + // Functionary prepends "all\n" to plain content outputs, so we use its handler in all cases. + if (src.find(">>>all") != std::string::npos) { + return common_chat_params_init_functionary_v3_2(tmpl, params); + } + + // Firefunction v2 requires datetime and functions in the context even w/o tools, so we also use its handler in all cases. + if (src.find(" functools[") != std::string::npos) { + return common_chat_params_init_firefunction_v2(tmpl, params); + } + + // Functionary v3.1 (w/ tools) + if (src.find("<|start_header_id|>") != std::string::npos + && src.find("ipython<|end_header_id|>") != std::string::npos) { + auto allow_python_tag_builtin_tools = src.find("<|python_tag|>") != std::string::npos; + return common_chat_params_init_llama_3_x(tmpl, params, allow_python_tag_builtin_tools); + } + + // Plain handler (no tools) + if (params.tools.is_null() || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { + return common_chat_params_init_without_tools(tmpl, params); + } + + // Mistral Nemo (w/ tools) + if (src.find("[TOOL_CALLS]") != std::string::npos) { + return common_chat_params_init_mistral_nemo(tmpl, params); + } + + // Generic fallback + return common_chat_params_init_generic(tmpl, params); +} + +// Legacy template route (adhoc C++ implementation of known templates), forward to llama_chat_apply_template. +static common_chat_params common_chat_templates_apply_legacy( + const struct common_chat_templates * tmpls, + const struct common_chat_templates_inputs & inputs) +{ + int alloc_size = 0; + std::vector chat; + std::vector contents; + for (const auto & msg : inputs.messages) { + auto content = msg.content; + for (const auto & part : msg.content_parts) { + if (part.type != "text") { + LOG_WRN("Ignoring non-text content part: %s\n", part.type.c_str()); + continue; + } + if (!content.empty()) { + content += "\n";; + } + content += part.text; + } + contents.emplace_back(std::move(content)); + } + for (size_t i = 0; i < contents.size(); ++i) { + const auto & msg = inputs.messages[i]; + const auto & content = contents[i]; + chat.push_back({msg.role.c_str(), content.c_str()}); + alloc_size += (msg.role.size() + content.size()) * 1.25; + } + + std::vector buf(alloc_size); + + // run the first time to get the total output length + const auto & src = tmpls->template_default->source(); + int32_t res = llama_chat_apply_template(src.c_str(), chat.data(), chat.size(), inputs.add_generation_prompt, buf.data(), buf.size()); + + // error: chat template is not supported + if (res < 0) { + // if the custom "tmpl" is not supported, we throw an error + // this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template() + throw std::runtime_error("this custom template is not supported, try using --jinja"); + } + + // if it turns out that our buffer is too small, we resize it + if ((size_t) res > buf.size()) { + buf.resize(res); + res = llama_chat_apply_template(src.c_str(), chat.data(), chat.size(), inputs.add_generation_prompt, buf.data(), buf.size()); + } + + common_chat_params params; + params.prompt = std::string(buf.data(), res); + if (!inputs.json_schema.empty()) { + params.grammar = json_schema_to_grammar(json::parse(inputs.json_schema)); + } else { + params.grammar = inputs.grammar; + } + return params; +} + +common_chat_params common_chat_templates_apply( + const struct common_chat_templates * tmpls, + const struct common_chat_templates_inputs & inputs) +{ + GGML_ASSERT(tmpls != nullptr); + return inputs.use_jinja + ? common_chat_templates_apply_jinja(tmpls, inputs) + : common_chat_templates_apply_legacy(tmpls, inputs); +} + +static void common_chat_parse_content_only(common_chat_msg_parser & builder) { + builder.add_content(builder.consume_rest()); +} + +static void common_chat_parse(common_chat_msg_parser & builder) { + LOG_DBG("Parsing input with format %s: %s\n", common_chat_format_name(builder.syntax().format), builder.input().c_str()); + + switch (builder.syntax().format) { + case COMMON_CHAT_FORMAT_CONTENT_ONLY: + common_chat_parse_content_only(builder); + break; + case COMMON_CHAT_FORMAT_GENERIC: + common_chat_parse_generic(builder); + break; + case COMMON_CHAT_FORMAT_MISTRAL_NEMO: + common_chat_parse_mistral_nemo(builder); + break; + case COMMON_CHAT_FORMAT_LLAMA_3_X: + common_chat_parse_llama_3_1(builder); + break; + case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS: + common_chat_parse_llama_3_1(builder, /* with_builtin_tools= */ true); + break; + case COMMON_CHAT_FORMAT_DEEPSEEK_R1: + common_chat_parse_deepseek_r1(builder); + break; + case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: + common_chat_parse_functionary_v3_2(builder); + break; + case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: + common_chat_parse_functionary_v3_1_llama_3_1(builder); + break; + case COMMON_CHAT_FORMAT_HERMES_2_PRO: + common_chat_parse_hermes_2_pro(builder); + break; + case COMMON_CHAT_FORMAT_FIREFUNCTION_V2: + common_chat_parse_firefunction_v2(builder); + break; + case COMMON_CHAT_FORMAT_COMMAND_R7B: + common_chat_parse_command_r7b(builder); + break; + default: + throw std::runtime_error(std::string("Unsupported format: ") + common_chat_format_name(builder.syntax().format)); + } + builder.finish(); +} + +common_chat_msg common_chat_parse(const std::string & input, bool is_partial, const common_chat_syntax & syntax) { + common_chat_msg_parser builder(input, is_partial, syntax); + try { + common_chat_parse(builder); + } catch (const common_chat_msg_partial_exception & ex) { + LOG_DBG("Partial parse: %s\n", ex.what()); + if (!is_partial) { + builder.clear_tools(); + builder.move_to(0); + common_chat_parse_content_only(builder); + } + } + auto msg = builder.result(); + if (!is_partial) { + LOG_DBG("Parsed message: %s\n", common_chat_msgs_to_json_oaicompat({msg}).at(0).dump().c_str()); + } + return msg; +} diff --git a/llama.cpp/common/chat.h b/llama.cpp/common/chat.h new file mode 100644 index 0000000000000000000000000000000000000000..9c43c5f9510ba3d0ed13d2f5b95769e287fd7a4e --- /dev/null +++ b/llama.cpp/common/chat.h @@ -0,0 +1,204 @@ +// Chat support (incl. tool call grammar constraining & output parsing) w/ generic & custom template handlers. + +#pragma once + +#include "common.h" +#include +#include +#include +#include +#include + +struct common_chat_templates; + +struct common_chat_tool_call { + std::string name; + std::string arguments; + std::string id; + + bool operator==(const common_chat_tool_call & other) const { + return name == other.name && arguments == other.arguments && id == other.id; + } +}; + +struct common_chat_msg_content_part { + std::string type; + std::string text; + + bool operator==(const common_chat_msg_content_part & other) const { + return type == other.type && text == other.text; + } +}; + +struct common_chat_msg { + std::string role; + std::string content; + std::vector content_parts = {}; + std::vector tool_calls = {}; + std::string reasoning_content; + std::string tool_name; + std::string tool_call_id; + + template T to_json_oaicompat() const; + + bool empty() const { + return content.empty() && content_parts.empty() && tool_calls.empty() && reasoning_content.empty() && tool_name.empty() && tool_call_id.empty(); + } + void ensure_tool_call_ids_set(std::vector & ids_cache, const std::function & gen_tool_call_id) { + for (auto i = 0u; i < tool_calls.size(); i++) { + if (ids_cache.size() <= i) { + auto id = tool_calls[i].id; + if (id.empty()) { + id = gen_tool_call_id(); + } + ids_cache.push_back(id); + } + tool_calls[i].id = ids_cache[i]; + } + } + bool operator==(const common_chat_msg & other) const { + return role == other.role + && content == other.content + && content_parts == other.content_parts + && tool_calls == other.tool_calls + && reasoning_content == other.reasoning_content + && tool_name == other.tool_name + && tool_call_id == other.tool_call_id; + } + bool operator!=(const common_chat_msg & other) const { + return !(*this == other); + } +}; + +struct common_chat_msg_diff { + std::string reasoning_content_delta; + std::string content_delta; + size_t tool_call_index = std::string::npos; + common_chat_tool_call tool_call_delta; + + static std::vector compute_diffs(const common_chat_msg & previous_msg, const common_chat_msg & new_msg); + + bool operator==(const common_chat_msg_diff & other) const { + return content_delta == other.content_delta + && tool_call_index == other.tool_call_index + && tool_call_delta == other.tool_call_delta; + } +}; + +struct common_chat_tool { + std::string name; + std::string description; + std::string parameters; +}; + +enum common_chat_tool_choice { + COMMON_CHAT_TOOL_CHOICE_AUTO, + COMMON_CHAT_TOOL_CHOICE_REQUIRED, + COMMON_CHAT_TOOL_CHOICE_NONE, +}; + +enum common_chat_format { + COMMON_CHAT_FORMAT_CONTENT_ONLY, + COMMON_CHAT_FORMAT_GENERIC, + COMMON_CHAT_FORMAT_MISTRAL_NEMO, + COMMON_CHAT_FORMAT_LLAMA_3_X, + COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS, + COMMON_CHAT_FORMAT_DEEPSEEK_R1, + COMMON_CHAT_FORMAT_FIREFUNCTION_V2, + COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2, + COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1, + COMMON_CHAT_FORMAT_HERMES_2_PRO, + COMMON_CHAT_FORMAT_COMMAND_R7B, + + COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats +}; + +struct common_chat_templates_inputs { + std::vector messages; + std::string grammar; + std::string json_schema; + bool add_generation_prompt = true; + bool use_jinja = true; + // Parameters below only supported when use_jinja is true + std::vector tools; + common_chat_tool_choice tool_choice = COMMON_CHAT_TOOL_CHOICE_AUTO; + bool parallel_tool_calls = false; + common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE; + bool enable_thinking = true; + std::chrono::system_clock::time_point now = std::chrono::system_clock::now(); + std::map chat_template_kwargs; +}; + +struct common_chat_params { + common_chat_format format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + std::string prompt; + std::string grammar; + bool grammar_lazy = false; + bool thinking_forced_open = false; + std::vector grammar_triggers; + std::vector preserved_tokens; + std::vector additional_stops; +}; + +struct common_chat_syntax { + common_chat_format format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE; + // Whether reasoning_content should be inlined in the content (e.g. for reasoning_format=deepseek in stream mode) + bool reasoning_in_content = false; + bool thinking_forced_open = false; + bool parse_tool_calls = true; +}; + +// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid +bool common_chat_verify_template(const std::string & tmpl, bool use_jinja); + +void common_chat_templates_free(struct common_chat_templates * tmpls); + +struct common_chat_templates_deleter { void operator()(common_chat_templates * tmpls) { common_chat_templates_free(tmpls); } }; + +typedef std::unique_ptr common_chat_templates_ptr; + +common_chat_templates_ptr common_chat_templates_init( + const struct llama_model * model, + const std::string & chat_template_override, + const std::string & bos_token_override = "", + const std::string & eos_token_override = ""); + +bool common_chat_templates_was_explicit(const struct common_chat_templates * tmpls); +const char * common_chat_templates_source(const struct common_chat_templates * tmpls, const char * variant = nullptr); + + +struct common_chat_params common_chat_templates_apply( + const struct common_chat_templates * tmpls, + const struct common_chat_templates_inputs & inputs); + +// Format single message, while taking into account the position of that message in chat history +std::string common_chat_format_single( + const struct common_chat_templates * tmpls, + const std::vector & past_msg, + const common_chat_msg & new_msg, + bool add_ass, + bool use_jinja); + +// Returns an example of formatted chat +std::string common_chat_format_example( + const struct common_chat_templates * tmpls, + bool use_jinja); + +const char* common_chat_format_name(common_chat_format format); +const char* common_reasoning_format_name(common_reasoning_format format); +common_chat_msg common_chat_parse(const std::string & input, bool is_partial, const common_chat_syntax & syntax); + +common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice); + +// Parses a JSON array of messages in OpenAI's chat completion API format. +// T can be std::string containing JSON or nlohmann::ordered_json +template std::vector common_chat_msgs_parse_oaicompat(const T & messages); +template T common_chat_msgs_to_json_oaicompat(const std::vector & msgs, bool concat_typed_text = false); + +// Parses a JSON array of tools in OpenAI's chat completion tool call API format. +// T can be std::string containing JSON or nlohmann::ordered_json +template std::vector common_chat_tools_parse_oaicompat(const T & tools); +template T common_chat_tools_to_json_oaicompat(const std::vector & tools); + +template T common_chat_msg_diff_to_json_oaicompat(const common_chat_msg_diff & diff); diff --git a/llama.cpp/common/common.cpp b/llama.cpp/common/common.cpp new file mode 100644 index 0000000000000000000000000000000000000000..40edeeb1687a053b562c06b4c0e8d56f224ffa1a --- /dev/null +++ b/llama.cpp/common/common.cpp @@ -0,0 +1,1567 @@ +#if defined(_MSC_VER) +#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING +#endif + +#include "ggml.h" +#include "gguf.h" + +#include "common.h" +#include "log.h" +#include "llama.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#if defined(__APPLE__) && defined(__MACH__) +#include +#include +#endif + +#if defined(_WIN32) +#define WIN32_LEAN_AND_MEAN +#ifndef NOMINMAX +# define NOMINMAX +#endif +#include +#include +#include +#include +#else +#include +#include +#include +#endif + +#if defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + +// +// CPU utils +// + +int32_t cpu_get_num_physical_cores() { +#ifdef __linux__ + // enumerate the set of thread siblings, num entries is num cores + std::unordered_set siblings; + for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) { + std::ifstream thread_siblings("/sys/devices/system/cpu/cpu" + + std::to_string(cpu) + "/topology/thread_siblings"); + if (!thread_siblings.is_open()) { + break; // no more cpus + } + std::string line; + if (std::getline(thread_siblings, line)) { + siblings.insert(line); + } + } + if (!siblings.empty()) { + return static_cast(siblings.size()); + } +#elif defined(__APPLE__) && defined(__MACH__) + int32_t num_physical_cores; + size_t len = sizeof(num_physical_cores); + int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0); + if (result == 0) { + return num_physical_cores; + } + result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0); + if (result == 0) { + return num_physical_cores; + } +#elif defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later + // TODO: windows + arm64 + mingw64 + unsigned int n_threads_win = std::thread::hardware_concurrency(); + unsigned int default_threads = n_threads_win > 0 ? (n_threads_win <= 4 ? n_threads_win : n_threads_win / 2) : 4; + + DWORD buffer_size = 0; + if (!GetLogicalProcessorInformationEx(RelationProcessorCore, nullptr, &buffer_size)) { + if (GetLastError() != ERROR_INSUFFICIENT_BUFFER) { + return default_threads; + } + } + + std::vector buffer(buffer_size); + if (!GetLogicalProcessorInformationEx(RelationProcessorCore, reinterpret_cast(buffer.data()), &buffer_size)) { + return default_threads; + } + + int32_t num_physical_cores = 0; + PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX info = reinterpret_cast(buffer.data()); + while (buffer_size > 0) { + if (info->Relationship == RelationProcessorCore) { + num_physical_cores += info->Processor.GroupCount; + } + buffer_size -= info->Size; + info = reinterpret_cast(reinterpret_cast(info) + info->Size); + } + + return num_physical_cores > 0 ? num_physical_cores : default_threads; +#endif + unsigned int n_threads = std::thread::hardware_concurrency(); + return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4; +} + +#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__) +#include + +static void cpuid(unsigned leaf, unsigned subleaf, + unsigned *eax, unsigned *ebx, unsigned *ecx, unsigned *edx) { + __asm__("movq\t%%rbx,%%rsi\n\t" + "cpuid\n\t" + "xchgq\t%%rbx,%%rsi" + : "=a"(*eax), "=S"(*ebx), "=c"(*ecx), "=d"(*edx) + : "0"(leaf), "2"(subleaf)); +} + +static int pin_cpu(int cpu) { + cpu_set_t mask; + CPU_ZERO(&mask); + CPU_SET(cpu, &mask); + return pthread_setaffinity_np(pthread_self(), sizeof(mask), &mask); +} + +static bool is_hybrid_cpu(void) { + unsigned eax, ebx, ecx, edx; + cpuid(7, 0, &eax, &ebx, &ecx, &edx); + return !!(edx & (1u << 15)); +} + +static bool is_running_on_efficiency_core(void) { + unsigned eax, ebx, ecx, edx; + cpuid(0x1a, 0, &eax, &ebx, &ecx, &edx); + int intel_atom = 0x20; + int core_type = (eax & 0xff000000u) >> 24; + return core_type == intel_atom; +} + +static int cpu_count_math_cpus(int n_cpu) { + int result = 0; + for (int cpu = 0; cpu < n_cpu; ++cpu) { + if (pin_cpu(cpu)) { + return -1; + } + if (is_running_on_efficiency_core()) { + continue; // efficiency cores harm lockstep threading + } + ++cpu; // hyperthreading isn't useful for linear algebra + ++result; + } + return result; +} + +#endif // __x86_64__ && __linux__ + +/** + * Returns number of CPUs on system that are useful for math. + */ +int32_t cpu_get_num_math() { +#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__) + int n_cpu = sysconf(_SC_NPROCESSORS_ONLN); + if (n_cpu < 1) { + return cpu_get_num_physical_cores(); + } + if (is_hybrid_cpu()) { + cpu_set_t affinity; + if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) { + int result = cpu_count_math_cpus(n_cpu); + pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity); + if (result > 0) { + return result; + } + } + } +#endif + return cpu_get_num_physical_cores(); +} + +// Helper for setting process priority + +#if defined(_WIN32) + +bool set_process_priority(enum ggml_sched_priority prio) { + if (prio == GGML_SCHED_PRIO_NORMAL) { + return true; + } + + DWORD p = NORMAL_PRIORITY_CLASS; + switch (prio) { + case GGML_SCHED_PRIO_LOW: p = BELOW_NORMAL_PRIORITY_CLASS; break; + case GGML_SCHED_PRIO_NORMAL: p = NORMAL_PRIORITY_CLASS; break; + case GGML_SCHED_PRIO_MEDIUM: p = ABOVE_NORMAL_PRIORITY_CLASS; break; + case GGML_SCHED_PRIO_HIGH: p = HIGH_PRIORITY_CLASS; break; + case GGML_SCHED_PRIO_REALTIME: p = REALTIME_PRIORITY_CLASS; break; + } + + if (!SetPriorityClass(GetCurrentProcess(), p)) { + LOG_WRN("failed to set process priority class %d : (%d)\n", prio, (int) GetLastError()); + return false; + } + + return true; +} + +#else // MacOS and POSIX +#include +#include + +bool set_process_priority(enum ggml_sched_priority prio) { + if (prio == GGML_SCHED_PRIO_NORMAL) { + return true; + } + + int p = 0; + switch (prio) { + case GGML_SCHED_PRIO_LOW: p = 5; break; + case GGML_SCHED_PRIO_NORMAL: p = 0; break; + case GGML_SCHED_PRIO_MEDIUM: p = -5; break; + case GGML_SCHED_PRIO_HIGH: p = -10; break; + case GGML_SCHED_PRIO_REALTIME: p = -20; break; + } + + if (!setpriority(PRIO_PROCESS, 0, p)) { + LOG_WRN("failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno); + return false; + } + return true; +} + +#endif + +// +// CLI argument parsing +// + + +void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model) { + int32_t n_set = 0; + + if (cpuparams.n_threads < 0) { + // Assuming everything about cpuparams is invalid + if (role_model != nullptr) { + cpuparams = *role_model; + } else { + cpuparams.n_threads = cpu_get_num_math(); + } + } + + for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) { + if (cpuparams.cpumask[i]) { + n_set++; + } + } + + if (n_set && n_set < cpuparams.n_threads) { + // Not enough set bits, may experience performance issues. + LOG_WRN("Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads); + } +} + +bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) { + size_t dash_loc = range.find('-'); + if (dash_loc == std::string::npos) { + LOG_ERR("Format of CPU range is invalid! Expected []-[].\n"); + return false; + } + + size_t start_i; + size_t end_i; + + if (dash_loc == 0) { + start_i = 0; + } else { + start_i = std::stoull(range.substr(0, dash_loc)); + if (start_i >= GGML_MAX_N_THREADS) { + LOG_ERR("Start index out of bounds!\n"); + return false; + } + } + + if (dash_loc == range.length() - 1) { + end_i = GGML_MAX_N_THREADS - 1; + } else { + end_i = std::stoull(range.substr(dash_loc + 1)); + if (end_i >= GGML_MAX_N_THREADS) { + LOG_ERR("End index out of bounds!\n"); + return false; + } + } + + for (size_t i = start_i; i <= end_i; i++) { + boolmask[i] = true; + } + + return true; +} + +bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREADS]) { + // Discard potential 0x prefix + size_t start_i = 0; + if (mask.length() >= 2 && mask.substr(0, 2) == "0x") { + start_i = 2; + } + + size_t num_digits = mask.length() - start_i; + if (num_digits > 128) num_digits = 128; + + size_t end_i = num_digits + start_i; + + for (size_t i = start_i, n = (num_digits*4 - 1); i < end_i; i++, n-=4) { + char c = mask.at(i); + int8_t id = c; + + if ((c >= '0' && c <= '9')) { + id -= '0'; + } else if (c >= 'a' && c <= 'f') { + id -= 'a' - 10; + } else if (c >= 'A' && c <= 'F') { + id -= 'A' - 10; + } else { + LOG_ERR("Invalid hex character '%c' at position %d\n", c, int32_t(i)); + return false; + } + + boolmask[ n ] = boolmask[ n ] || ((id & 8) != 0); + boolmask[n - 1] = boolmask[n - 1] || ((id & 4) != 0); + boolmask[n - 2] = boolmask[n - 2] || ((id & 2) != 0); + boolmask[n - 3] = boolmask[n - 3] || ((id & 1) != 0); + } + + return true; +} + +void common_init() { + llama_log_set([](ggml_log_level level, const char * text, void * /*user_data*/) { + if (LOG_DEFAULT_LLAMA <= common_log_verbosity_thold) { + common_log_add(common_log_main(), level, "%s", text); + } + }, NULL); + +#ifdef NDEBUG + const char * build_type = ""; +#else + const char * build_type = " (debug)"; +#endif + + LOG_INF("build: %d (%s) with %s for %s%s\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT, LLAMA_COMPILER, LLAMA_BUILD_TARGET, build_type); +} + +std::string common_params_get_system_info(const common_params & params) { + std::ostringstream os; + + os << "system_info: n_threads = " << params.cpuparams.n_threads; + if (params.cpuparams_batch.n_threads != -1) { + os << " (n_threads_batch = " << params.cpuparams_batch.n_threads << ")"; + } +#if defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later + // TODO: windows + arm64 + mingw64 + DWORD logicalProcessorCount = GetActiveProcessorCount(ALL_PROCESSOR_GROUPS); + os << " / " << logicalProcessorCount << " | " << llama_print_system_info(); +#else + os << " / " << std::thread::hardware_concurrency() << " | " << llama_print_system_info(); +#endif + + return os.str(); +} + +// +// String utils +// + +std::string string_format(const char * fmt, ...) { + va_list ap; + va_list ap2; + va_start(ap, fmt); + va_copy(ap2, ap); + int size = vsnprintf(NULL, 0, fmt, ap); + GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT + std::vector buf(size + 1); + int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2); + GGML_ASSERT(size2 == size); + va_end(ap2); + va_end(ap); + return std::string(buf.data(), size); +} + +std::string string_strip(const std::string & str) { + size_t start = 0; + size_t end = str.size(); + while (start < end && std::isspace(str[start])) { + start++; + } + while (end > start && std::isspace(str[end - 1])) { + end--; + } + return str.substr(start, end - start); +} + +std::string string_get_sortable_timestamp() { + using clock = std::chrono::system_clock; + + const clock::time_point current_time = clock::now(); + const time_t as_time_t = clock::to_time_t(current_time); + char timestamp_no_ns[100]; + std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t)); + + const int64_t ns = std::chrono::duration_cast( + current_time.time_since_epoch() % 1000000000).count(); + char timestamp_ns[11]; + snprintf(timestamp_ns, 11, "%09" PRId64, ns); + + return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns); +} + +void string_replace_all(std::string & s, const std::string & search, const std::string & replace) { + if (search.empty()) { + return; + } + std::string builder; + builder.reserve(s.length()); + size_t pos = 0; + size_t last_pos = 0; + while ((pos = s.find(search, last_pos)) != std::string::npos) { + builder.append(s, last_pos, pos - last_pos); + builder.append(replace); + last_pos = pos + search.length(); + } + builder.append(s, last_pos, std::string::npos); + s = std::move(builder); +} + +bool string_ends_with(const std::string_view & str, const std::string_view & suffix) { + return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0; +} + +bool string_remove_suffix(std::string & str, const std::string_view & suffix) { + bool has_suffix = string_ends_with(str, suffix); + if (has_suffix) { + str = str.substr(0, str.size() - suffix.size()); + } + return has_suffix; +} + +size_t string_find_partial_stop(const std::string_view & str, const std::string_view & stop) { + if (!str.empty() && !stop.empty()) { + const char text_last_char = str.back(); + for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) { + if (stop[char_index] == text_last_char) { + const auto current_partial = stop.substr(0, char_index + 1); + if (string_ends_with(str, current_partial)) { + return str.size() - char_index - 1; + } + } + } + } + + return std::string::npos; +} + +std::string regex_escape(const std::string & s) { + static const std::regex special_chars("[.^$|()*+?\\[\\]{}\\\\]"); + return std::regex_replace(s, special_chars, "\\$&"); +} + +std::string string_join(const std::vector & values, const std::string & separator) { + std::ostringstream result; + for (size_t i = 0; i < values.size(); ++i) { + if (i > 0) { + result << separator; + } + result << values[i]; + } + return result.str(); +} + +std::vector string_split(const std::string & str, const std::string & delimiter) { + std::vector parts; + size_t start = 0; + size_t end = str.find(delimiter); + + while (end != std::string::npos) { + parts.push_back(str.substr(start, end - start)); + start = end + delimiter.length(); + end = str.find(delimiter, start); + } + + parts.push_back(str.substr(start)); + + return parts; +} + +std::string string_repeat(const std::string & str, size_t n) { + if (n == 0) { + return ""; + } + + std::string result; + result.reserve(str.length() * n); + + for (size_t i = 0; i < n; ++i) { + result += str; + } + + return result; +} + +std::string string_from(bool value) { + return value ? "true" : "false"; +} + +std::string string_from(const std::vector & values) { + std::stringstream buf; + + buf << "[ "; + bool first = true; + for (auto e : values) { + if (first) { + first = false; + } else { + buf << ", "; + } + buf << std::to_string(e); + } + buf << " ]"; + + return buf.str(); +} + +std::string string_from(const struct llama_context * ctx, const std::vector & tokens) { + std::stringstream buf; + + buf << "[ "; + + bool first = true; + for (const auto & token : tokens) { + if (!first) { + buf << ", "; + } else { + first = false; + } + + auto detokenized = common_token_to_piece(ctx, token); + + detokenized.erase( + std::remove_if( + detokenized.begin(), + detokenized.end(), + [](const unsigned char c) { return !std::isprint(c); }), + detokenized.end()); + + buf << "'" << detokenized << "'" + << ":" << std::to_string(token); + } + + buf << " ]"; + + return buf.str(); +} + +std::string string_from(const struct llama_context * ctx, const struct llama_batch & batch) { + std::stringstream buf; + + buf << "[ "; + + bool first = true; + for (int i = 0; i < batch.n_tokens; ++i) { + if (!first) { + buf << ", "; + } else { + first = false; + } + + auto detokenized = common_token_to_piece(ctx, batch.token[i]); + + detokenized.erase( + std::remove_if( + detokenized.begin(), + detokenized.end(), + [](const unsigned char c) { return !std::isprint(c); }), + detokenized.end()); + + buf << "\n" << std::to_string(i) + << ", token '" << detokenized << "'" + << ", pos " << std::to_string(batch.pos[i]) + << ", n_seq_id " << std::to_string(batch.n_seq_id[i]) + << ", seq_id " << std::to_string(batch.seq_id[i][0]) + << ", logits " << std::to_string(batch.logits[i]); + } + + buf << " ]"; + + return buf.str(); +} + +void string_process_escapes(std::string & input) { + std::size_t input_len = input.length(); + std::size_t output_idx = 0; + + for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) { + if (input[input_idx] == '\\' && input_idx + 1 < input_len) { + switch (input[++input_idx]) { + case 'n': input[output_idx++] = '\n'; break; + case 'r': input[output_idx++] = '\r'; break; + case 't': input[output_idx++] = '\t'; break; + case '\'': input[output_idx++] = '\''; break; + case '\"': input[output_idx++] = '\"'; break; + case '\\': input[output_idx++] = '\\'; break; + case 'x': + // Handle \x12, etc + if (input_idx + 2 < input_len) { + const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 }; + char *err_p = nullptr; + const long val = std::strtol(x, &err_p, 16); + if (err_p == x + 2) { + input_idx += 2; + input[output_idx++] = char(val); + break; + } + } + // fall through + default: input[output_idx++] = '\\'; + input[output_idx++] = input[input_idx]; break; + } + } else { + input[output_idx++] = input[input_idx]; + } + } + + input.resize(output_idx); +} + +bool string_parse_kv_override(const char * data, std::vector & overrides) { + const char * sep = strchr(data, '='); + if (sep == nullptr || sep - data >= 128) { + LOG_ERR("%s: malformed KV override '%s'\n", __func__, data); + return false; + } + llama_model_kv_override kvo; + std::strncpy(kvo.key, data, sep - data); + kvo.key[sep - data] = 0; + sep++; + if (strncmp(sep, "int:", 4) == 0) { + sep += 4; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; + kvo.val_i64 = std::atol(sep); + } else if (strncmp(sep, "float:", 6) == 0) { + sep += 6; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; + kvo.val_f64 = std::atof(sep); + } else if (strncmp(sep, "bool:", 5) == 0) { + sep += 5; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; + if (std::strcmp(sep, "true") == 0) { + kvo.val_bool = true; + } else if (std::strcmp(sep, "false") == 0) { + kvo.val_bool = false; + } else { + LOG_ERR("%s: invalid boolean value for KV override '%s'\n", __func__, data); + return false; + } + } else if (strncmp(sep, "str:", 4) == 0) { + sep += 4; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; + if (strlen(sep) > 127) { + LOG_ERR("%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data); + return false; + } + strncpy(kvo.val_str, sep, 127); + kvo.val_str[127] = '\0'; + } else { + LOG_ERR("%s: invalid type for KV override '%s'\n", __func__, data); + return false; + } + overrides.emplace_back(std::move(kvo)); + return true; +} + +// +// Filesystem utils +// + +// Validate if a filename is safe to use +// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function +bool fs_validate_filename(const std::string & filename) { + if (!filename.length()) { + // Empty filename invalid + return false; + } + if (filename.length() > 255) { + // Limit at common largest possible filename on Linux filesystems + // to avoid unnecessary further validation + // (On systems with smaller limits it will be caught by the OS) + return false; + } + + std::u32string filename_utf32; + try { +#if defined(__clang__) + // disable C++17 deprecation warning for std::codecvt_utf8 +# pragma clang diagnostic push +# pragma clang diagnostic ignored "-Wdeprecated-declarations" +#elif defined(__GNUC__) +# pragma GCC diagnostic push +# pragma GCC diagnostic ignored "-Wdeprecated-declarations" +#endif + + std::wstring_convert, char32_t> converter; + +#if defined(__clang__) +# pragma clang diagnostic pop +#elif defined(__GNUC__) +# pragma GCC diagnostic pop +#endif + + filename_utf32 = converter.from_bytes(filename); + + // If the reverse conversion mismatches, it means overlong UTF-8 sequences were used, + // or invalid encodings were encountered. Reject such attempts + std::string filename_reencoded = converter.to_bytes(filename_utf32); + if (filename_reencoded != filename) { + return false; + } + } catch (const std::exception &) { + return false; + } + + // Check for forbidden codepoints: + // - Control characters + // - Unicode equivalents of illegal characters + // - UTF-16 surrogate pairs + // - UTF-8 replacement character + // - Byte order mark (BOM) + // - Illegal characters: / \ : * ? " < > | + for (char32_t c : filename_utf32) { + if (c <= 0x1F // Control characters (C0) + || c == 0x7F // Control characters (DEL) + || (c >= 0x80 && c <= 0x9F) // Control characters (C1) + || c == 0xFF0E // Fullwidth Full Stop (period equivalent) + || c == 0x2215 // Division Slash (forward slash equivalent) + || c == 0x2216 // Set Minus (backslash equivalent) + || (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs + || c == 0xFFFD // Replacement Character (UTF-8) + || c == 0xFEFF // Byte Order Mark (BOM) + || c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters + || c == '?' || c == '"' || c == '<' || c == '>' || c == '|') { + return false; + } + } + + // Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename + // Unicode and other whitespace is not affected, only 0x20 space + if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') { + return false; + } + + // Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead) + if (filename.find("..") != std::string::npos) { + return false; + } + + // Reject "." + if (filename == ".") { + return false; + } + + return true; +} + +#include + + +// returns true if successful, false otherwise +bool fs_create_directory_with_parents(const std::string & path) { +#ifdef _WIN32 + std::wstring_convert> converter; + std::wstring wpath = converter.from_bytes(path); + + // if the path already exists, check whether it's a directory + const DWORD attributes = GetFileAttributesW(wpath.c_str()); + if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) { + return true; + } + + size_t pos_slash = 0; + + // process path from front to back, procedurally creating directories + while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) { + const std::wstring subpath = wpath.substr(0, pos_slash); + + pos_slash += 1; + + // skip the drive letter, in some systems it can return an access denied error + if (subpath.length() == 2 && subpath[1] == ':') { + continue; + } + + const bool success = CreateDirectoryW(subpath.c_str(), NULL); + + if (!success) { + const DWORD error = GetLastError(); + + // if the path already exists, ensure that it's a directory + if (error == ERROR_ALREADY_EXISTS) { + const DWORD attributes = GetFileAttributesW(subpath.c_str()); + if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) { + return false; + } + } else { + return false; + } + } + } + + return true; +#else + // if the path already exists, check whether it's a directory + struct stat info; + if (stat(path.c_str(), &info) == 0) { + return S_ISDIR(info.st_mode); + } + + size_t pos_slash = 1; // skip leading slashes for directory creation + + // process path from front to back, procedurally creating directories + while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) { + const std::string subpath = path.substr(0, pos_slash); + struct stat info; + + // if the path already exists, ensure that it's a directory + if (stat(subpath.c_str(), &info) == 0) { + if (!S_ISDIR(info.st_mode)) { + return false; + } + } else { + // create parent directories + const int ret = mkdir(subpath.c_str(), 0755); + if (ret != 0) { + return false; + } + } + + pos_slash += 1; + } + + return true; +#endif // _WIN32 +} + +std::string fs_get_cache_directory() { + std::string cache_directory = ""; + auto ensure_trailing_slash = [](std::string p) { + // Make sure to add trailing slash + if (p.back() != DIRECTORY_SEPARATOR) { + p += DIRECTORY_SEPARATOR; + } + return p; + }; + if (getenv("LLAMA_CACHE")) { + cache_directory = std::getenv("LLAMA_CACHE"); + } else { +#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || defined(__OpenBSD__) + if (std::getenv("XDG_CACHE_HOME")) { + cache_directory = std::getenv("XDG_CACHE_HOME"); + } else { + cache_directory = std::getenv("HOME") + std::string("/.cache/"); + } +#elif defined(__APPLE__) + cache_directory = std::getenv("HOME") + std::string("/Library/Caches/"); +#elif defined(_WIN32) + cache_directory = std::getenv("LOCALAPPDATA"); +#else +# error Unknown architecture +#endif + cache_directory = ensure_trailing_slash(cache_directory); + cache_directory += "llama.cpp"; + } + return ensure_trailing_slash(cache_directory); +} + +std::string fs_get_cache_file(const std::string & filename) { + GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos); + std::string cache_directory = fs_get_cache_directory(); + const bool success = fs_create_directory_with_parents(cache_directory); + if (!success) { + throw std::runtime_error("failed to create cache directory: " + cache_directory); + } + return cache_directory + filename; +} + + +// +// Model utils +// + +struct common_init_result common_init_from_params(common_params & params) { + common_init_result iparams; + auto mparams = common_model_params_to_llama(params); + + llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams); + if (model == NULL) { + LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str()); + return iparams; + } + + const llama_vocab * vocab = llama_model_get_vocab(model); + + auto cparams = common_context_params_to_llama(params); + + llama_context * lctx = llama_init_from_model(model, cparams); + if (lctx == NULL) { + LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str()); + llama_model_free(model); + return iparams; + } + + if (params.ctx_shift && !llama_memory_can_shift(llama_get_memory(lctx))) { + LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__); + params.ctx_shift = false; + } + + if (!params.control_vectors.empty()) { + if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; + if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_model_n_layer(model); + + const auto cvec = common_control_vector_load(params.control_vectors); + if (cvec.n_embd == -1) { + llama_free(lctx); + llama_model_free(model); + + return iparams; + } + + int err = llama_apply_adapter_cvec( + lctx, + cvec.data.data(), + cvec.data.size(), + cvec.n_embd, + params.control_vector_layer_start, + params.control_vector_layer_end); + if (err) { + llama_free(lctx); + llama_model_free(model); + + return iparams; + } + } + + if (llama_pooling_type(lctx) == LLAMA_POOLING_TYPE_RANK) { + bool ok = true; + + if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) { + LOG_WRN("%s: warning: vocab does not have a BOS token, reranking will not work\n", __func__); + ok = false; + } + + bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL; + bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL; + + if (!has_eos && !has_sep) { + LOG_WRN("%s: warning: vocab does not have an EOS token or SEP token, reranking will not work\n", __func__); + ok = false; + } else if (!has_eos) { + LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__); + } else if (!has_sep) { + LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__); + ok = false; + } + + if (!ok) { + llama_free(lctx); + llama_model_free(model); + + return iparams; + } + } + + // load and optionally apply lora adapters + for (auto & la : params.lora_adapters) { + llama_adapter_lora_ptr lora; + lora.reset(llama_adapter_lora_init(model, la.path.c_str())); + if (lora == nullptr) { + LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str()); + llama_free(lctx); + llama_model_free(model); + return iparams; + } + + la.ptr = lora.get(); + iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters + } + + if (!params.lora_init_without_apply) { + common_set_adapter_lora(lctx, params.lora_adapters); + } + + if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) { + LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__); + params.sampling.ignore_eos = false; + } + + // initialize once + for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) { + if (llama_vocab_is_eog(vocab, i)) { + LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY); + params.sampling.logit_bias_eog.push_back({i, -INFINITY}); + } + } + + if (params.sampling.ignore_eos) { + // add EOG biases to the active set of logit biases + params.sampling.logit_bias.insert( + params.sampling.logit_bias.end(), + params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end()); + } + + if (params.sampling.penalty_last_n == -1) { + LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); + params.sampling.penalty_last_n = llama_n_ctx(lctx); + } + + if (params.sampling.dry_penalty_last_n == -1) { + LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); + params.sampling.dry_penalty_last_n = llama_n_ctx(lctx); + } + + if (params.warmup) { + LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__); + + llama_set_warmup(lctx, true); + + std::vector tmp; + llama_token bos = llama_vocab_bos(vocab); + llama_token eos = llama_vocab_eos(vocab); + + // some models (e.g. T5) don't have a BOS token + if (bos != LLAMA_TOKEN_NULL) { + tmp.push_back(bos); + } + if (eos != LLAMA_TOKEN_NULL) { + tmp.push_back(eos); + } + if (tmp.empty()) { + tmp.push_back(0); + } + + if (llama_model_has_encoder(model)) { + llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size())); + llama_token decoder_start_token_id = llama_model_decoder_start_token(model); + if (decoder_start_token_id == LLAMA_TOKEN_NULL) { + decoder_start_token_id = bos; + } + tmp.clear(); + tmp.push_back(decoder_start_token_id); + } + if (llama_model_has_decoder(model)) { + llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch))); + } + llama_memory_clear(llama_get_memory(lctx), true); + llama_synchronize(lctx); + llama_perf_context_reset(lctx); + llama_set_warmup(lctx, false); + } + + iparams.model.reset(model); + iparams.context.reset(lctx); + + return iparams; +} + +std::string get_model_endpoint() { + const char * model_endpoint_env = getenv("MODEL_ENDPOINT"); + // We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility. + const char * hf_endpoint_env = getenv("HF_ENDPOINT"); + const char * endpoint_env = model_endpoint_env ? model_endpoint_env : hf_endpoint_env; + std::string model_endpoint = "https://huggingface.co/"; + if (endpoint_env) { + model_endpoint = endpoint_env; + if (model_endpoint.back() != '/') model_endpoint += '/'; + } + return model_endpoint; +} + +void common_set_adapter_lora(struct llama_context * ctx, std::vector & lora) { + llama_clear_adapter_lora(ctx); + for (auto & la : lora) { + if (la.scale != 0.0f) { + llama_set_adapter_lora(ctx, la.ptr, la.scale); + } + } +} + +struct llama_model_params common_model_params_to_llama(common_params & params) { + auto mparams = llama_model_default_params(); + + if (!params.devices.empty()) { + mparams.devices = params.devices.data(); + } + + if (params.n_gpu_layers != -1) { + mparams.n_gpu_layers = params.n_gpu_layers; + } + + mparams.main_gpu = params.main_gpu; + mparams.split_mode = params.split_mode; + mparams.tensor_split = params.tensor_split; + mparams.use_mmap = params.use_mmap; + mparams.use_mlock = params.use_mlock; + mparams.check_tensors = params.check_tensors; + mparams.use_extra_bufts = !params.no_extra_bufts; + + if (params.kv_overrides.empty()) { + mparams.kv_overrides = NULL; + } else { + GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key"); + mparams.kv_overrides = params.kv_overrides.data(); + } + + if (params.tensor_buft_overrides.empty()) { + mparams.tensor_buft_overrides = NULL; + } else { + GGML_ASSERT(params.tensor_buft_overrides.back().pattern == nullptr && "Tensor buffer overrides not terminated with empty pattern"); + mparams.tensor_buft_overrides = params.tensor_buft_overrides.data(); + } + + mparams.progress_callback = params.load_progress_callback; + mparams.progress_callback_user_data = params.load_progress_callback_user_data; + + return mparams; +} + +struct llama_context_params common_context_params_to_llama(const common_params & params) { + auto cparams = llama_context_default_params(); + + cparams.n_ctx = params.n_ctx; + cparams.n_seq_max = params.n_parallel; + cparams.n_batch = params.n_batch; + cparams.n_ubatch = params.n_ubatch; + cparams.n_threads = params.cpuparams.n_threads; + cparams.n_threads_batch = params.cpuparams_batch.n_threads == -1 ? + params.cpuparams.n_threads : params.cpuparams_batch.n_threads; + cparams.embeddings = params.embedding; + cparams.rope_scaling_type = params.rope_scaling_type; + cparams.rope_freq_base = params.rope_freq_base; + cparams.rope_freq_scale = params.rope_freq_scale; + cparams.yarn_ext_factor = params.yarn_ext_factor; + cparams.yarn_attn_factor = params.yarn_attn_factor; + cparams.yarn_beta_fast = params.yarn_beta_fast; + cparams.yarn_beta_slow = params.yarn_beta_slow; + cparams.yarn_orig_ctx = params.yarn_orig_ctx; + cparams.pooling_type = params.pooling_type; + cparams.attention_type = params.attention_type; + cparams.defrag_thold = params.defrag_thold; + cparams.cb_eval = params.cb_eval; + cparams.cb_eval_user_data = params.cb_eval_user_data; + cparams.offload_kqv = !params.no_kv_offload; + cparams.flash_attn = params.flash_attn; + cparams.no_perf = params.no_perf; + cparams.op_offload = !params.no_op_offload; + cparams.swa_full = params.swa_full; + cparams.kv_unified = params.kv_unified; + + cparams.type_k = params.cache_type_k; + cparams.type_v = params.cache_type_v; + + return cparams; +} + +struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params) { + struct ggml_threadpool_params tpp; + + ggml_threadpool_params_init(&tpp, params.n_threads); // setup the defaults + + if (params.mask_valid) { + std::memcpy(&tpp.cpumask, ¶ms.cpumask, GGML_MAX_N_THREADS); + } + + tpp.prio = params.priority; + tpp.poll = params.poll; + tpp.strict_cpu = params.strict_cpu; + + return tpp; +} + +// +// Batch utils +// + +void common_batch_clear(struct llama_batch & batch) { + batch.n_tokens = 0; +} + +void common_batch_add( + struct llama_batch & batch, + llama_token id, + llama_pos pos, + const std::vector & seq_ids, + bool logits) { + GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded"); + + batch.token [batch.n_tokens] = id; + batch.pos [batch.n_tokens] = pos; + batch.n_seq_id[batch.n_tokens] = seq_ids.size(); + for (size_t i = 0; i < seq_ids.size(); ++i) { + batch.seq_id[batch.n_tokens][i] = seq_ids[i]; + } + batch.logits [batch.n_tokens] = logits; + + batch.n_tokens++; +} + +// +// Token utils +// + +size_t common_lcp(const llama_tokens & a, const llama_tokens & b) { + size_t i; + for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} + + return i; +} + +size_t common_lcs(const llama_tokens & a, const llama_tokens & b) { + // check for empty sequences + if (a.empty() || b.empty()) { + return 0; + } + + // get the lengths of the input sequences + size_t a_len = a.size(); + size_t b_len = b.size(); + + // initialize the maximum length of the longest common subsequence (LCS) + size_t max_length = 0; + + // use two rows instead of a 2D matrix to optimize space + std::vector prev_row(b_len + 1, 0); + std::vector curr_row(b_len + 1, 0); + + // iterate through the elements of a + for (size_t i = 1; i <= a_len; i++) { + // iterate through the elements of b + for (size_t j = 1; j <= b_len; j++) { + // if elements at the current positions match + if (a[i - 1] == b[j - 1]) { + // if it's the first element of either sequences, set LCS length to 1 + if (i == 1 || j == 1) { + curr_row[j] = 1; + } else { + // increment LCS length by 1 compared to the previous element + curr_row[j] = prev_row[j - 1] + 1; + } + + // update max_length if necessary + if (curr_row[j] > max_length) { + max_length = curr_row[j]; + } + } else { + // reset LCS length if elements don't match + curr_row[j] = 0; + } + } + + // update the previous row for the next iteration + prev_row = curr_row; + } + + // return the maximum length of the LCS + return max_length; +} + +// +// Vocab utils +// + +std::vector common_tokenize( + const struct llama_context * ctx, + const std::string & text, + bool add_special, + bool parse_special) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + return common_tokenize(vocab, text, add_special, parse_special); +} + +std::vector common_tokenize( + const struct llama_vocab * vocab, + const std::string & text, + bool add_special, + bool parse_special) { + // upper limit for the number of tokens + int n_tokens = text.length() + 2 * add_special; + std::vector result(n_tokens); + n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special); + if (n_tokens == std::numeric_limits::min()) { + throw std::runtime_error("Tokenization failed: input text too large, tokenization result exceeds int32_t limit"); + } + if (n_tokens < 0) { + result.resize(-n_tokens); + int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special); + GGML_ASSERT(check == -n_tokens); + } else { + result.resize(n_tokens); + } + return result; +} + +std::string common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + return common_token_to_piece(vocab, token, special); +} + +std::string common_token_to_piece(const struct llama_vocab * vocab, llama_token token, bool special) { + std::string piece; + piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n' + const int n_chars = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special); + if (n_chars < 0) { + piece.resize(-n_chars); + int check = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special); + GGML_ASSERT(check == -n_chars); + } + else { + piece.resize(n_chars); + } + + return piece; +} + +std::string common_detokenize(const struct llama_context * ctx, const std::vector & tokens, bool special) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + return common_detokenize(vocab, tokens, special); +} + +std::string common_detokenize(const struct llama_vocab * vocab, const std::vector & tokens, bool special) { + std::string text; + text.resize(std::max(text.capacity(), tokens.size())); + int32_t n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); + if (n_chars < 0) { + text.resize(-n_chars); + n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); + GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization + } + + text.resize(n_chars); + + // NOTE: the original tokenizer decodes bytes after collecting the pieces. + return text; +} + +// +// Embedding utils +// + +void common_embd_normalize(const float * inp, float * out, int n, int embd_norm) { + double sum = 0.0; + + switch (embd_norm) { + case -1: // no normalisation + sum = 1.0; + break; + case 0: // max absolute + for (int i = 0; i < n; i++) { + if (sum < std::abs(inp[i])) { + sum = std::abs(inp[i]); + } + } + sum /= 32760.0; // make an int16 range + break; + case 2: // euclidean + for (int i = 0; i < n; i++) { + sum += inp[i] * inp[i]; + } + sum = std::sqrt(sum); + break; + default: // p-norm (euclidean is p-norm p=2) + for (int i = 0; i < n; i++) { + sum += std::pow(std::abs(inp[i]), embd_norm); + } + sum = std::pow(sum, 1.0 / embd_norm); + break; + } + + const float norm = sum > 0.0 ? 1.0 / sum : 0.0f; + + for (int i = 0; i < n; i++) { + out[i] = inp[i] * norm; + } +} + +float common_embd_similarity_cos(const float * embd1, const float * embd2, int n){ + double sum = 0.0; + double sum1 = 0.0; + double sum2 = 0.0; + + for (int i = 0; i < n; i++) { + sum += embd1[i] * embd2[i]; + sum1 += embd1[i] * embd1[i]; + sum2 += embd2[i] * embd2[i]; + } + + // Handle the case where one or both vectors are zero vectors + if (sum1 == 0.0 || sum2 == 0.0) { + if (sum1 == 0.0 && sum2 == 0.0) { + return 1.0f; // two zero vectors are similar + } + return 0.0f; + } + + return sum / (sqrt(sum1) * sqrt(sum2)); +} + +// +// Control vector utils +// + +static common_control_vector_data common_control_vector_load_one(const common_control_vector_load_info & load_info) { + common_control_vector_data result = { -1, {} }; + + ggml_context * ctx = nullptr; + struct gguf_init_params meta_gguf_params = { + /* .no_alloc = */ false, + /* .ctx = */ &ctx, + }; + struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params); + if (!ctx_gguf) { + LOG_ERR("%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str()); + return result; + } + + int32_t n_tensors = gguf_get_n_tensors(ctx_gguf); + if (n_tensors == 0) { + LOG_WRN("%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str()); + } + + for (int i = 0; i < n_tensors; i++) { + std::string name = gguf_get_tensor_name(ctx_gguf, i); + + int layer_idx = -1; + + // split on '.' + size_t dotpos = name.find('.'); + if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") { + try { + layer_idx = std::stoi(name.substr(dotpos + 1)); + } catch (...) { + layer_idx = -1; + } + } + if (layer_idx < 0) { + LOG_ERR("%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str()); + result.n_embd = -1; + break; + } else if (layer_idx == 0) { + LOG_ERR("%s: invalid (zero) direction tensor layer index in %s\n", __func__, load_info.fname.c_str()); + result.n_embd = -1; + break; + } + + struct ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str()); + if (tensor->type != GGML_TYPE_F32) { + LOG_ERR("%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str()); + result.n_embd = -1; + break; + } + if (ggml_n_dims(tensor) != 1) { + LOG_ERR("%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str()); + result.n_embd = -1; + break; + } + + if (result.n_embd == -1) { + result.n_embd = ggml_nelements(tensor); + } else if (ggml_nelements(tensor) != result.n_embd) { + LOG_ERR("%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str()); + result.n_embd = -1; + break; + } + + // extend if necessary - do not store data for layer 0 (it's not used) + result.data.resize(std::max(result.data.size(), static_cast(result.n_embd * layer_idx)), 0.0f); + + const float * src = (const float *) tensor->data; + float * dst = result.data.data() + result.n_embd * (layer_idx - 1); // layer 1 at [0] + for (int j = 0; j < result.n_embd; j++) { + dst[j] += src[j] * load_info.strength; // allows multiple directions for same layer in same file + } + + } + + if (result.n_embd == -1) { + LOG_WRN("%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str()); + result.data.clear(); + } + + gguf_free(ctx_gguf); + ggml_free(ctx); + + return result; +} + +common_control_vector_data common_control_vector_load(const std::vector & load_infos) { + common_control_vector_data result = { -1, {} }; + + for (const auto & info : load_infos) { + auto cur = common_control_vector_load_one(info); + + if (cur.n_embd == -1) { + result.n_embd = -1; + break; + } + if (result.n_embd != -1 && result.n_embd != cur.n_embd) { + LOG_ERR("%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str()); + result.n_embd = -1; + break; + } + + if (result.n_embd == -1) { + result = std::move(cur); + } else { + result.data.resize(std::max(result.data.size(), cur.data.size()), 0.0f); // extend if necessary + for (size_t i = 0; i < cur.data.size(); i++) { + result.data[i] += cur.data[i]; + } + } + } + + if (result.n_embd == -1) { + LOG_ERR("%s: no valid control vector files passed\n", __func__); + result.data.clear(); + } + + return result; +} + +ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector & tokens, int64_t stride) { + const int64_t ne_datapoint = llama_n_ctx(ctx); + const int64_t ndata = (tokens.size() - ne_datapoint - 1) / stride; + ggml_opt_dataset_t result = ggml_opt_dataset_init( + GGML_TYPE_I32, GGML_TYPE_I32, ne_datapoint, ne_datapoint, ndata, /*ndata_shard =*/ 1); + + llama_token * data = (llama_token *) ggml_opt_dataset_data(result)->data; + llama_token * labels = (llama_token *) ggml_opt_dataset_labels(result)->data; + + for (int64_t idata = 0; idata < ndata; ++idata) { + memcpy(data + idata*ne_datapoint, tokens.data() + idata*stride + 0, ne_datapoint*sizeof(llama_token)); + memcpy(labels + idata*ne_datapoint, tokens.data() + idata*stride + 1, ne_datapoint*sizeof(llama_token)); + } + + return result; +} diff --git a/llama.cpp/common/common.h b/llama.cpp/common/common.h new file mode 100644 index 0000000000000000000000000000000000000000..87db73148dab144b6f5e6fa2dfbdddf1d45765b8 --- /dev/null +++ b/llama.cpp/common/common.h @@ -0,0 +1,702 @@ +// Various helper functions and utilities + +#pragma once + +#include "llama-cpp.h" + +#include +#include +#include +#include +#include +#include + +#ifdef _WIN32 +#define DIRECTORY_SEPARATOR '\\' +#else +#define DIRECTORY_SEPARATOR '/' +#endif // _WIN32 + +#define die(msg) do { fputs("error: " msg "\n", stderr); exit(1); } while (0) +#define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0) + +#define print_build_info() do { \ + fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \ + fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \ +} while(0) + +#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf" + +struct common_adapter_lora_info { + std::string path; + float scale; + + struct llama_adapter_lora * ptr; +}; + +using llama_tokens = std::vector; + +// build info +extern int LLAMA_BUILD_NUMBER; +extern const char * LLAMA_COMMIT; +extern const char * LLAMA_COMPILER; +extern const char * LLAMA_BUILD_TARGET; + +struct common_control_vector_load_info; + +// +// CPU utils +// + +struct cpu_params { + int n_threads = -1; + bool cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask. + bool mask_valid = false; // Default: any CPU + enum ggml_sched_priority priority = GGML_SCHED_PRIO_NORMAL; // Scheduling prio : (0 - normal, 1 - medium, 2 - high, 3 - realtime) + bool strict_cpu = false; // Use strict CPU placement + uint32_t poll = 50; // Polling (busywait) level (0 - no polling, 100 - mostly polling) +}; + +int32_t cpu_get_num_physical_cores(); +int32_t cpu_get_num_math(); + +// +// Common params +// + +enum llama_example { + LLAMA_EXAMPLE_COMMON, + LLAMA_EXAMPLE_SPECULATIVE, + LLAMA_EXAMPLE_MAIN, + LLAMA_EXAMPLE_EMBEDDING, + LLAMA_EXAMPLE_PERPLEXITY, + LLAMA_EXAMPLE_RETRIEVAL, + LLAMA_EXAMPLE_PASSKEY, + LLAMA_EXAMPLE_IMATRIX, + LLAMA_EXAMPLE_BENCH, + LLAMA_EXAMPLE_SERVER, + LLAMA_EXAMPLE_CVECTOR_GENERATOR, + LLAMA_EXAMPLE_EXPORT_LORA, + LLAMA_EXAMPLE_MTMD, + LLAMA_EXAMPLE_LOOKUP, + LLAMA_EXAMPLE_PARALLEL, + LLAMA_EXAMPLE_TTS, + LLAMA_EXAMPLE_DIFFUSION, + + LLAMA_EXAMPLE_COUNT, +}; + +enum common_sampler_type { + COMMON_SAMPLER_TYPE_NONE = 0, + COMMON_SAMPLER_TYPE_DRY = 1, + COMMON_SAMPLER_TYPE_TOP_K = 2, + COMMON_SAMPLER_TYPE_TOP_P = 3, + COMMON_SAMPLER_TYPE_MIN_P = 4, + //COMMON_SAMPLER_TYPE_TFS_Z = 5, + COMMON_SAMPLER_TYPE_TYPICAL_P = 6, + COMMON_SAMPLER_TYPE_TEMPERATURE = 7, + COMMON_SAMPLER_TYPE_XTC = 8, + COMMON_SAMPLER_TYPE_INFILL = 9, + COMMON_SAMPLER_TYPE_PENALTIES = 10, + COMMON_SAMPLER_TYPE_TOP_N_SIGMA = 11, +}; + +// dimensionality reduction methods, used by cvector-generator +enum dimre_method { + DIMRE_METHOD_PCA, + DIMRE_METHOD_MEAN, +}; + +enum common_conversation_mode { + COMMON_CONVERSATION_MODE_DISABLED = 0, + COMMON_CONVERSATION_MODE_ENABLED = 1, + COMMON_CONVERSATION_MODE_AUTO = 2, +}; + +enum common_grammar_trigger_type { + COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN, + COMMON_GRAMMAR_TRIGGER_TYPE_WORD, + COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, + COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, +}; + +struct common_grammar_trigger { + common_grammar_trigger_type type; + std::string value; + llama_token token = LLAMA_TOKEN_NULL; +}; + +// sampling parameters +struct common_params_sampling { + uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler + + int32_t n_prev = 64; // number of previous tokens to remember + int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. + int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens + int32_t top_k = 40; // <= 0 to use vocab size + float top_p = 0.95f; // 1.0 = disabled + float min_p = 0.05f; // 0.0 = disabled + float xtc_probability = 0.00f; // 0.0 = disabled + float xtc_threshold = 0.10f; // > 0.5 disables XTC + float typ_p = 1.00f; // typical_p, 1.0 = disabled + float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities + float dynatemp_range = 0.00f; // 0.0 = disabled + float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler + int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) + float penalty_repeat = 1.00f; // 1.0 = disabled + float penalty_freq = 0.00f; // 0.0 = disabled + float penalty_present = 0.00f; // 0.0 = disabled + float dry_multiplier = 0.0f; // 0.0 = disabled; DRY repetition penalty for tokens extending repetition: + float dry_base = 1.75f; // 0.0 = disabled; multiplier * base ^ (length of sequence before token - allowed length) + int32_t dry_allowed_length = 2; // tokens extending repetitions beyond this receive penalty + int32_t dry_penalty_last_n = -1; // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size) + int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 + float top_n_sigma = -1.00f;// -1.0 = disabled + float mirostat_tau = 5.00f; // target entropy + float mirostat_eta = 0.10f; // learning rate + bool ignore_eos = false; + bool no_perf = false; // disable performance metrics + bool timing_per_token = false; + + std::vector dry_sequence_breakers = {"\n", ":", "\"", "*"}; // default sequence breakers for DRY + + + std::vector samplers = { + COMMON_SAMPLER_TYPE_PENALTIES, + COMMON_SAMPLER_TYPE_DRY, + COMMON_SAMPLER_TYPE_TOP_N_SIGMA, + COMMON_SAMPLER_TYPE_TOP_K, + COMMON_SAMPLER_TYPE_TYPICAL_P, + COMMON_SAMPLER_TYPE_TOP_P, + COMMON_SAMPLER_TYPE_MIN_P, + COMMON_SAMPLER_TYPE_XTC, + COMMON_SAMPLER_TYPE_TEMPERATURE, + }; + + std::string grammar; // optional BNF-like grammar to constrain sampling + bool grammar_lazy = false; + std::vector grammar_triggers; // optional triggers (for lazy grammars) + std::set preserved_tokens; + + std::vector logit_bias; // logit biases to apply + std::vector logit_bias_eog; // pre-calculated logit biases for EOG tokens + + // print the parameters into a string + std::string print() const; +}; + +struct common_params_model { + std::string path = ""; // model local path // NOLINT + std::string url = ""; // model url to download // NOLINT + std::string hf_repo = ""; // HF repo // NOLINT + std::string hf_file = ""; // HF file // NOLINT +}; + +struct common_params_speculative { + std::vector devices; // devices to use for offloading + + int32_t n_ctx = 0; // draft context size + int32_t n_max = 16; // maximum number of tokens to draft during speculative decoding + int32_t n_min = 0; // minimum number of draft tokens to use for speculative decoding + int32_t n_gpu_layers = -1; // number of layers to store in VRAM for the draft model (-1 - use default) + float p_split = 0.1f; // speculative decoding split probability + float p_min = 0.75f; // minimum speculative decoding probability (greedy) + std::vector> replacements; // main to speculative model replacements + + ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K + ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V + + struct cpu_params cpuparams; + struct cpu_params cpuparams_batch; + + struct common_params_model model; +}; + +struct common_params_vocoder { + struct common_params_model model; + + std::string speaker_file = ""; // speaker file path // NOLINT + + bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy // NOLINT +}; + +struct common_params_diffusion { + int32_t steps = 128; + bool visual_mode = false; + + float eps = 0; // epsilon for timesteps + int32_t block_length = 0; // block length for generation + + int32_t algorithm = 4; // default algorithm: low-confidence + float alg_temp = 0.0f; // algorithm temperature + + float cfg_scale = 0; // classifier-free guidance scale + bool add_gumbel_noise = false; // add gumbel noise to the logits if temp > 0.0 +}; + +enum common_reasoning_format { + COMMON_REASONING_FORMAT_NONE, + COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY, // Extract thinking tag contents and return as `message.reasoning_content`, or leave inline in tags in stream mode + COMMON_REASONING_FORMAT_DEEPSEEK, // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas. +}; + +struct common_params { + int32_t n_predict = -1; // new tokens to predict + int32_t n_ctx = 4096; // context size + int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS) + int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS) + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) + int32_t n_parallel = 1; // number of parallel sequences to decode + int32_t n_sequences = 1; // number of sequences to decode + int32_t grp_attn_n = 1; // group-attention factor + int32_t grp_attn_w = 512; // group-attention width + int32_t n_print = -1; // print token count every n tokens (-1 = disabled) + float rope_freq_base = 0.0f; // RoPE base frequency + float rope_freq_scale = 0.0f; // RoPE frequency scaling factor + float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor + float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor + float yarn_beta_fast = 32.0f; // YaRN low correction dim + float yarn_beta_slow = 1.0f; // YaRN high correction dim + int32_t yarn_orig_ctx = 0; // YaRN original context length + float defrag_thold = 0.1f; // KV cache defragmentation threshold + + // offload params + std::vector devices; // devices to use for offloading + + int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) + int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors + float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs + + enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs + + struct cpu_params cpuparams; + struct cpu_params cpuparams_batch; + + ggml_backend_sched_eval_callback cb_eval = nullptr; + void * cb_eval_user_data = nullptr; + + ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; + + enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; + enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings + enum llama_attention_type attention_type = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings + + struct common_params_sampling sampling; + struct common_params_speculative speculative; + struct common_params_vocoder vocoder; + struct common_params_diffusion diffusion; + + struct common_params_model model; + + std::string model_alias = ""; // model alias // NOLINT + std::string hf_token = ""; // HF token // NOLINT + std::string prompt = ""; // NOLINT + std::string system_prompt = ""; // NOLINT + std::string prompt_file = ""; // store the external prompt file name // NOLINT + std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state // NOLINT + std::string input_prefix = ""; // string to prefix user inputs with // NOLINT + std::string input_suffix = ""; // string to suffix user inputs with // NOLINT + std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding // NOLINT + std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding // NOLINT + std::string logits_file = ""; // file for saving *all* logits // NOLINT + + std::vector in_files; // all input files + std::vector antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts) + std::vector kv_overrides; + std::vector tensor_buft_overrides; + + bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_adapter_lora_apply) + std::vector lora_adapters; // lora adapter path with user defined scale + + std::vector control_vectors; // control vector with user defined scale + + int32_t verbosity = 0; + int32_t control_vector_layer_start = -1; // layer range for control vector + int32_t control_vector_layer_end = -1; // layer range for control vector + bool offline = false; + + int32_t ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used. + int32_t ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line + // (which is more convenient to use for plotting) + // + bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt + size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score + + bool winogrande = false; // compute Winogrande score over random tasks from datafile supplied in prompt + size_t winogrande_tasks = 0; // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed + + bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt + size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed + + bool kl_divergence = false; // compute KL divergence + + bool usage = false; // print usage + bool completion = false; // print source-able completion script + bool use_color = false; // use color to distinguish generations and inputs + bool special = false; // enable special token output + bool interactive = false; // interactive mode + bool interactive_first = false; // wait for user input immediately + bool prompt_cache_all = false; // save user input and generations to prompt cache + bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it + + bool escape = true; // escape "\n", "\r", "\t", "\'", "\"", and "\\" + bool multiline_input = false; // reverse the usage of `\` + bool simple_io = false; // improves compatibility with subprocesses and limited consoles + bool cont_batching = true; // insert new sequences for decoding on-the-fly + bool flash_attn = false; // flash attention + bool no_perf = false; // disable performance metrics + bool ctx_shift = true; // context shift on inifinite text generation + bool swa_full = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055) + bool kv_unified = false; // enable unified KV cache + + bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix + bool use_mmap = true; // use mmap for faster loads + bool use_mlock = false; // use mlock to keep model in memory + bool verbose_prompt = false; // print prompt tokens before generation + bool display_prompt = true; // print prompt before generation + bool no_kv_offload = false; // disable KV offloading + bool warmup = true; // warmup run + bool check_tensors = false; // validate tensor data + bool no_op_offload = false; // globally disable offload host tensor operations to device + bool no_extra_bufts = false; // disable extra buffer types (used for weight repacking) + + bool single_turn = false; // single turn chat conversation + + ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K + ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V + + common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO; + + // multimodal models (see tools/mtmd) + struct common_params_model mmproj; + bool mmproj_use_gpu = true; // use GPU for multimodal model + bool no_mmproj = false; // explicitly disable multimodal model + std::vector image; // path to image file(s) + + // embedding + bool embedding = false; // get only sentence embedding + int32_t embd_normalize = 2; // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm) + std::string embd_out = ""; // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix + std::string embd_sep = "\n"; // separator of embeddings + std::string cls_sep = "\t"; // separator of classification sequences + + // server params + int32_t port = 8080; // server listens on this network port + int32_t timeout_read = 600; // http read timeout in seconds + int32_t timeout_write = timeout_read; // http write timeout in seconds + int32_t n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool) + int32_t n_cache_reuse = 0; // min chunk size to reuse from the cache via KV shifting + + std::string hostname = "127.0.0.1"; + std::string public_path = ""; // NOLINT + std::string api_prefix = ""; // NOLINT + std::string chat_template = ""; // NOLINT + bool use_jinja = false; // NOLINT + bool enable_chat_template = true; + common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; + int reasoning_budget = -1; + bool prefill_assistant = true; // if true, any trailing assistant message will be prefilled into the response + + std::vector api_keys; + + std::string ssl_file_key = ""; // NOLINT + std::string ssl_file_cert = ""; // NOLINT + + std::map default_template_kwargs; + + // "advanced" endpoints are disabled by default for better security + bool webui = true; + bool endpoint_slots = false; + bool endpoint_props = false; // only control POST requests, not GET + bool endpoint_metrics = false; + + bool log_json = false; + + std::string slot_save_path; + + float slot_prompt_similarity = 0.5f; + + // batched-bench params + bool is_pp_shared = false; + + std::vector n_pp; + std::vector n_tg; + std::vector n_pl; + + // retrieval params + std::vector context_files; // context files to embed + + int32_t chunk_size = 64; // chunk size for context embedding + + std::string chunk_separator = "\n"; // chunk separator for context embedding + + // passkey params + int32_t n_junk = 250; // number of times to repeat the junk text + int32_t i_pos = -1; // position of the passkey in the junk text + + // imatrix params + int32_t n_out_freq = 10; // output the imatrix every n_out_freq iterations + int32_t n_save_freq = 0; // save the imatrix every n_save_freq iterations + int32_t i_chunk = 0; // start processing from this chunk + + bool process_output = false; // collect data for the output tensor + bool compute_ppl = true; // whether to compute perplexity + bool show_statistics = false; // show imatrix statistics per tensor + bool parse_special = false; // whether to parse special tokens during imatrix tokenization + + // cvector-generator params + int n_pca_batch = 100; + int n_pca_iterations = 1000; + dimre_method cvector_dimre_method = DIMRE_METHOD_PCA; + std::string cvector_positive_file = "tools/cvector-generator/positive.txt"; + std::string cvector_negative_file = "tools/cvector-generator/negative.txt"; + + bool spm_infill = false; // suffix/prefix/middle pattern for infill + + // batched-bench params + bool batched_bench_output_jsonl = false; + + // common params + std::string out_file; // output filename for all example programs + // optional callback for model loading progress and cancellation: + // called with a progress value between 0.0 and 1.0. + // return false from callback to abort model loading or true to continue + llama_progress_callback load_progress_callback = NULL; + void * load_progress_callback_user_data = NULL; +}; + +// call once at the start of a program if it uses libcommon +// initializes the logging system and prints info about the build +void common_init(); + +std::string common_params_get_system_info(const common_params & params); + +bool parse_cpu_range(const std::string & range, bool(&boolmask)[GGML_MAX_N_THREADS]); +bool parse_cpu_mask(const std::string & mask, bool(&boolmask)[GGML_MAX_N_THREADS]); +void postprocess_cpu_params(cpu_params & cpuparams, const cpu_params * role_model = nullptr); +bool set_process_priority(enum ggml_sched_priority prio); + +// +// String utils +// + +#ifdef __GNUC__ +# if defined(__MINGW32__) && !defined(__clang__) +# define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__))) +# else +# define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__))) +# endif +#else +# define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) +#endif + +LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2) +std::string string_format(const char * fmt, ...); + +std::string string_strip(const std::string & str); +std::string string_get_sortable_timestamp(); + +std::string string_join(const std::vector & values, const std::string & separator); +std::vector string_split(const std::string & str, const std::string & delimiter); +std::string string_repeat(const std::string & str, size_t n); + +void string_replace_all(std::string & s, const std::string & search, const std::string & replace); + +std::string regex_escape(const std::string & s); + +template +static std::vector string_split(const std::string & str, char delim) { + static_assert(!std::is_same::value, "Please use the specialized version for std::string"); + std::vector values; + std::istringstream str_stream(str); + std::string token; + while (std::getline(str_stream, token, delim)) { + T value; + std::istringstream token_stream(token); + token_stream >> value; + values.push_back(value); + } + return values; +} + +template<> +std::vector string_split(const std::string & input, char separator) +{ + std::vector parts; + size_t begin_pos = 0; + size_t separator_pos = input.find(separator); + while (separator_pos != std::string::npos) { + std::string part = input.substr(begin_pos, separator_pos - begin_pos); + parts.emplace_back(part); + begin_pos = separator_pos + 1; + separator_pos = input.find(separator, begin_pos); + } + parts.emplace_back(input.substr(begin_pos, separator_pos - begin_pos)); + return parts; +} + +static bool string_starts_with(const std::string & str, + const std::string & prefix) { // While we wait for C++20's std::string::starts_with... + return str.rfind(prefix, 0) == 0; +} + +// While we wait for C++20's std::string::ends_with... +bool string_ends_with(const std::string_view & str, const std::string_view & suffix); +bool string_remove_suffix(std::string & str, const std::string_view & suffix); +size_t string_find_partial_stop(const std::string_view & str, const std::string_view & stop); + +bool string_parse_kv_override(const char * data, std::vector & overrides); +void string_process_escapes(std::string & input); + +std::string string_from(bool value); +std::string string_from(const std::vector & values); +std::string string_from(const struct llama_context * ctx, const std::vector & tokens); +std::string string_from(const struct llama_context * ctx, const struct llama_batch & batch); + +// +// Filesystem utils +// + +bool fs_validate_filename(const std::string & filename); +bool fs_create_directory_with_parents(const std::string & path); + +std::string fs_get_cache_directory(); +std::string fs_get_cache_file(const std::string & filename); + +// +// Model utils +// + +// note: defines object's lifetime +struct common_init_result { + llama_model_ptr model; + llama_context_ptr context; + + std::vector lora; +}; + +struct common_init_result common_init_from_params(common_params & params); + +struct llama_model_params common_model_params_to_llama ( common_params & params); +struct llama_context_params common_context_params_to_llama(const common_params & params); +struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params); + +// clear LoRA adapters from context, then apply new list of adapters +void common_set_adapter_lora(struct llama_context * ctx, std::vector & lora); + +std::string get_model_endpoint(); + +// +// Batch utils +// + +void common_batch_clear(struct llama_batch & batch); + +void common_batch_add( + struct llama_batch & batch, + llama_token id, + llama_pos pos, + const std::vector & seq_ids, + bool logits); + +// +// Token utils +// + +// longest common prefix +size_t common_lcp(const llama_tokens & a, const llama_tokens & b); + +// longet common subsequence +size_t common_lcs(const llama_tokens & a, const llama_tokens & b); + +// +// Vocab utils +// + +// tokenizes a string into a vector of tokens +// should work similar to Python's `tokenizer.encode` +std::vector common_tokenize( + const struct llama_context * ctx, + const std::string & text, + bool add_special, + bool parse_special = false); + +std::vector common_tokenize( + const struct llama_vocab * vocab, + const std::string & text, + bool add_special, + bool parse_special = false); + +// tokenizes a token into a piece, optionally renders special/control tokens +// should work similar to Python's `tokenizer.id_to_piece` +std::string common_token_to_piece( + const struct llama_context * ctx, + llama_token token, + bool special = true); + +std::string common_token_to_piece( + const struct llama_vocab * vocab, + llama_token token, + bool special = true); + +// detokenizes a vector of tokens into a string +// should work similar to Python's `tokenizer.decode` +// optionally renders special/control tokens +std::string common_detokenize( + const struct llama_context * ctx, + const std::vector & tokens, + bool special = true); + +std::string common_detokenize( + const struct llama_vocab * vocab, + const std::vector & tokens, + bool special = true); + +// +// Embedding utils +// + +// TODO: repace embd_norm with an enum +void common_embd_normalize(const float * inp, float * out, int n, int embd_norm); + +float common_embd_similarity_cos(const float * embd1, const float * embd2, int n); + +// +// Control vector utils +// + +struct common_control_vector_data { + int n_embd; + + // stores data for layers [1, n_layer] where n_layer = data.size() / n_embd + std::vector data; +}; + +struct common_control_vector_load_info { + float strength; + + std::string fname; +}; + +// Load control vectors, scale each by strength, and add them together. +// On error, returns {-1, empty} +common_control_vector_data common_control_vector_load(const std::vector & load_infos); + +// +// Split utils +// + +namespace { + +const char * const LLM_KV_SPLIT_NO = "split.no"; +const char * const LLM_KV_SPLIT_COUNT = "split.count"; +const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count"; + +} + +// +// training utils +// + +ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector & tokens, int64_t stride); diff --git a/llama.cpp/common/console.cpp b/llama.cpp/common/console.cpp new file mode 100644 index 0000000000000000000000000000000000000000..fddaa0f22b14f1ca82a8b37a064536ffc3ba3c81 --- /dev/null +++ b/llama.cpp/common/console.cpp @@ -0,0 +1,504 @@ +#include "console.h" +#include +#include + +#if defined(_WIN32) +#define WIN32_LEAN_AND_MEAN +#ifndef NOMINMAX +#define NOMINMAX +#endif +#include +#include +#include +#ifndef ENABLE_VIRTUAL_TERMINAL_PROCESSING +#define ENABLE_VIRTUAL_TERMINAL_PROCESSING 0x0004 +#endif +#else +#include +#include +#include +#include +#include +#include +#include +#include +#endif + +#define ANSI_COLOR_RED "\x1b[31m" +#define ANSI_COLOR_GREEN "\x1b[32m" +#define ANSI_COLOR_YELLOW "\x1b[33m" +#define ANSI_COLOR_BLUE "\x1b[34m" +#define ANSI_COLOR_MAGENTA "\x1b[35m" +#define ANSI_COLOR_CYAN "\x1b[36m" +#define ANSI_COLOR_RESET "\x1b[0m" +#define ANSI_BOLD "\x1b[1m" + +namespace console { + + // + // Console state + // + + static bool advanced_display = false; + static bool simple_io = true; + static display_t current_display = reset; + + static FILE* out = stdout; + +#if defined (_WIN32) + static void* hConsole; +#else + static FILE* tty = nullptr; + static termios initial_state; +#endif + + // + // Init and cleanup + // + + void init(bool use_simple_io, bool use_advanced_display) { + advanced_display = use_advanced_display; + simple_io = use_simple_io; +#if defined(_WIN32) + // Windows-specific console initialization + DWORD dwMode = 0; + hConsole = GetStdHandle(STD_OUTPUT_HANDLE); + if (hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(hConsole, &dwMode)) { + hConsole = GetStdHandle(STD_ERROR_HANDLE); + if (hConsole != INVALID_HANDLE_VALUE && (!GetConsoleMode(hConsole, &dwMode))) { + hConsole = nullptr; + simple_io = true; + } + } + if (hConsole) { + // Check conditions combined to reduce nesting + if (advanced_display && !(dwMode & ENABLE_VIRTUAL_TERMINAL_PROCESSING) && + !SetConsoleMode(hConsole, dwMode | ENABLE_VIRTUAL_TERMINAL_PROCESSING)) { + advanced_display = false; + } + // Set console output codepage to UTF8 + SetConsoleOutputCP(CP_UTF8); + } + HANDLE hConIn = GetStdHandle(STD_INPUT_HANDLE); + if (hConIn != INVALID_HANDLE_VALUE && GetConsoleMode(hConIn, &dwMode)) { + // Set console input codepage to UTF16 + _setmode(_fileno(stdin), _O_WTEXT); + + // Set ICANON (ENABLE_LINE_INPUT) and ECHO (ENABLE_ECHO_INPUT) + if (simple_io) { + dwMode |= ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT; + } else { + dwMode &= ~(ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT); + } + if (!SetConsoleMode(hConIn, dwMode)) { + simple_io = true; + } + } + if (simple_io) { + _setmode(_fileno(stdin), _O_U8TEXT); + } +#else + // POSIX-specific console initialization + if (!simple_io) { + struct termios new_termios; + tcgetattr(STDIN_FILENO, &initial_state); + new_termios = initial_state; + new_termios.c_lflag &= ~(ICANON | ECHO); + new_termios.c_cc[VMIN] = 1; + new_termios.c_cc[VTIME] = 0; + tcsetattr(STDIN_FILENO, TCSANOW, &new_termios); + + tty = fopen("/dev/tty", "w+"); + if (tty != nullptr) { + out = tty; + } + } + + setlocale(LC_ALL, ""); +#endif + } + + void cleanup() { + // Reset console display + set_display(reset); + +#if !defined(_WIN32) + // Restore settings on POSIX systems + if (!simple_io) { + if (tty != nullptr) { + out = stdout; + fclose(tty); + tty = nullptr; + } + tcsetattr(STDIN_FILENO, TCSANOW, &initial_state); + } +#endif + } + + // + // Display and IO + // + + // Keep track of current display and only emit ANSI code if it changes + void set_display(display_t display) { + if (advanced_display && current_display != display) { + fflush(stdout); + switch(display) { + case reset: + fprintf(out, ANSI_COLOR_RESET); + break; + case prompt: + fprintf(out, ANSI_COLOR_YELLOW); + break; + case user_input: + fprintf(out, ANSI_BOLD ANSI_COLOR_GREEN); + break; + case error: + fprintf(out, ANSI_BOLD ANSI_COLOR_RED); + } + current_display = display; + fflush(out); + } + } + + static char32_t getchar32() { +#if defined(_WIN32) + HANDLE hConsole = GetStdHandle(STD_INPUT_HANDLE); + wchar_t high_surrogate = 0; + + while (true) { + INPUT_RECORD record; + DWORD count; + if (!ReadConsoleInputW(hConsole, &record, 1, &count) || count == 0) { + return WEOF; + } + + if (record.EventType == KEY_EVENT && record.Event.KeyEvent.bKeyDown) { + wchar_t wc = record.Event.KeyEvent.uChar.UnicodeChar; + if (wc == 0) { + continue; + } + + if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate + high_surrogate = wc; + continue; + } + if ((wc >= 0xDC00) && (wc <= 0xDFFF)) { // Check if wc is a low surrogate + if (high_surrogate != 0) { // Check if we have a high surrogate + return ((high_surrogate - 0xD800) << 10) + (wc - 0xDC00) + 0x10000; + } + } + + high_surrogate = 0; // Reset the high surrogate + return static_cast(wc); + } + } +#else + wchar_t wc = getwchar(); + if (static_cast(wc) == WEOF) { + return WEOF; + } + +#if WCHAR_MAX == 0xFFFF + if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate + wchar_t low_surrogate = getwchar(); + if ((low_surrogate >= 0xDC00) && (low_surrogate <= 0xDFFF)) { // Check if the next wchar is a low surrogate + return (static_cast(wc & 0x03FF) << 10) + (low_surrogate & 0x03FF) + 0x10000; + } + } + if ((wc >= 0xD800) && (wc <= 0xDFFF)) { // Invalid surrogate pair + return 0xFFFD; // Return the replacement character U+FFFD + } +#endif + + return static_cast(wc); +#endif + } + + static void pop_cursor() { +#if defined(_WIN32) + if (hConsole != NULL) { + CONSOLE_SCREEN_BUFFER_INFO bufferInfo; + GetConsoleScreenBufferInfo(hConsole, &bufferInfo); + + COORD newCursorPosition = bufferInfo.dwCursorPosition; + if (newCursorPosition.X == 0) { + newCursorPosition.X = bufferInfo.dwSize.X - 1; + newCursorPosition.Y -= 1; + } else { + newCursorPosition.X -= 1; + } + + SetConsoleCursorPosition(hConsole, newCursorPosition); + return; + } +#endif + putc('\b', out); + } + + static int estimateWidth(char32_t codepoint) { +#if defined(_WIN32) + (void)codepoint; + return 1; +#else + return wcwidth(codepoint); +#endif + } + + static int put_codepoint(const char* utf8_codepoint, size_t length, int expectedWidth) { +#if defined(_WIN32) + CONSOLE_SCREEN_BUFFER_INFO bufferInfo; + if (!GetConsoleScreenBufferInfo(hConsole, &bufferInfo)) { + // go with the default + return expectedWidth; + } + COORD initialPosition = bufferInfo.dwCursorPosition; + DWORD nNumberOfChars = length; + WriteConsole(hConsole, utf8_codepoint, nNumberOfChars, &nNumberOfChars, NULL); + + CONSOLE_SCREEN_BUFFER_INFO newBufferInfo; + GetConsoleScreenBufferInfo(hConsole, &newBufferInfo); + + // Figure out our real position if we're in the last column + if (utf8_codepoint[0] != 0x09 && initialPosition.X == newBufferInfo.dwSize.X - 1) { + DWORD nNumberOfChars; + WriteConsole(hConsole, &" \b", 2, &nNumberOfChars, NULL); + GetConsoleScreenBufferInfo(hConsole, &newBufferInfo); + } + + int width = newBufferInfo.dwCursorPosition.X - initialPosition.X; + if (width < 0) { + width += newBufferInfo.dwSize.X; + } + return width; +#else + // We can trust expectedWidth if we've got one + if (expectedWidth >= 0 || tty == nullptr) { + fwrite(utf8_codepoint, length, 1, out); + return expectedWidth; + } + + fputs("\033[6n", tty); // Query cursor position + int x1; + int y1; + int x2; + int y2; + int results = 0; + results = fscanf(tty, "\033[%d;%dR", &y1, &x1); + + fwrite(utf8_codepoint, length, 1, tty); + + fputs("\033[6n", tty); // Query cursor position + results += fscanf(tty, "\033[%d;%dR", &y2, &x2); + + if (results != 4) { + return expectedWidth; + } + + int width = x2 - x1; + if (width < 0) { + // Calculate the width considering text wrapping + struct winsize w; + ioctl(STDOUT_FILENO, TIOCGWINSZ, &w); + width += w.ws_col; + } + return width; +#endif + } + + static void replace_last(char ch) { +#if defined(_WIN32) + pop_cursor(); + put_codepoint(&ch, 1, 1); +#else + fprintf(out, "\b%c", ch); +#endif + } + + static void append_utf8(char32_t ch, std::string & out) { + if (ch <= 0x7F) { + out.push_back(static_cast(ch)); + } else if (ch <= 0x7FF) { + out.push_back(static_cast(0xC0 | ((ch >> 6) & 0x1F))); + out.push_back(static_cast(0x80 | (ch & 0x3F))); + } else if (ch <= 0xFFFF) { + out.push_back(static_cast(0xE0 | ((ch >> 12) & 0x0F))); + out.push_back(static_cast(0x80 | ((ch >> 6) & 0x3F))); + out.push_back(static_cast(0x80 | (ch & 0x3F))); + } else if (ch <= 0x10FFFF) { + out.push_back(static_cast(0xF0 | ((ch >> 18) & 0x07))); + out.push_back(static_cast(0x80 | ((ch >> 12) & 0x3F))); + out.push_back(static_cast(0x80 | ((ch >> 6) & 0x3F))); + out.push_back(static_cast(0x80 | (ch & 0x3F))); + } else { + // Invalid Unicode code point + } + } + + // Helper function to remove the last UTF-8 character from a string + static void pop_back_utf8_char(std::string & line) { + if (line.empty()) { + return; + } + + size_t pos = line.length() - 1; + + // Find the start of the last UTF-8 character (checking up to 4 bytes back) + for (size_t i = 0; i < 3 && pos > 0; ++i, --pos) { + if ((line[pos] & 0xC0) != 0x80) { + break; // Found the start of the character + } + } + line.erase(pos); + } + + static bool readline_advanced(std::string & line, bool multiline_input) { + if (out != stdout) { + fflush(stdout); + } + + line.clear(); + std::vector widths; + bool is_special_char = false; + bool end_of_stream = false; + + char32_t input_char; + while (true) { + fflush(out); // Ensure all output is displayed before waiting for input + input_char = getchar32(); + + if (input_char == '\r' || input_char == '\n') { + break; + } + + if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D*/) { + end_of_stream = true; + break; + } + + if (is_special_char) { + set_display(user_input); + replace_last(line.back()); + is_special_char = false; + } + + if (input_char == '\033') { // Escape sequence + char32_t code = getchar32(); + if (code == '[' || code == 0x1B) { + // Discard the rest of the escape sequence + while ((code = getchar32()) != (char32_t) WEOF) { + if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~') { + break; + } + } + } + } else if (input_char == 0x08 || input_char == 0x7F) { // Backspace + if (!widths.empty()) { + int count; + do { + count = widths.back(); + widths.pop_back(); + // Move cursor back, print space, and move cursor back again + for (int i = 0; i < count; i++) { + replace_last(' '); + pop_cursor(); + } + pop_back_utf8_char(line); + } while (count == 0 && !widths.empty()); + } + } else { + int offset = line.length(); + append_utf8(input_char, line); + int width = put_codepoint(line.c_str() + offset, line.length() - offset, estimateWidth(input_char)); + if (width < 0) { + width = 0; + } + widths.push_back(width); + } + + if (!line.empty() && (line.back() == '\\' || line.back() == '/')) { + set_display(prompt); + replace_last(line.back()); + is_special_char = true; + } + } + + bool has_more = multiline_input; + if (is_special_char) { + replace_last(' '); + pop_cursor(); + + char last = line.back(); + line.pop_back(); + if (last == '\\') { + line += '\n'; + fputc('\n', out); + has_more = !has_more; + } else { + // llama will just eat the single space, it won't act as a space + if (line.length() == 1 && line.back() == ' ') { + line.clear(); + pop_cursor(); + } + has_more = false; + } + } else { + if (end_of_stream) { + has_more = false; + } else { + line += '\n'; + fputc('\n', out); + } + } + + fflush(out); + return has_more; + } + + static bool readline_simple(std::string & line, bool multiline_input) { +#if defined(_WIN32) + std::wstring wline; + if (!std::getline(std::wcin, wline)) { + // Input stream is bad or EOF received + line.clear(); + GenerateConsoleCtrlEvent(CTRL_C_EVENT, 0); + return false; + } + + int size_needed = WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), NULL, 0, NULL, NULL); + line.resize(size_needed); + WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), &line[0], size_needed, NULL, NULL); +#else + if (!std::getline(std::cin, line)) { + // Input stream is bad or EOF received + line.clear(); + return false; + } +#endif + if (!line.empty()) { + char last = line.back(); + if (last == '/') { // Always return control on '/' symbol + line.pop_back(); + return false; + } + if (last == '\\') { // '\\' changes the default action + line.pop_back(); + multiline_input = !multiline_input; + } + } + line += '\n'; + + // By default, continue input if multiline_input is set + return multiline_input; + } + + bool readline(std::string & line, bool multiline_input) { + set_display(user_input); + + if (simple_io) { + return readline_simple(line, multiline_input); + } + return readline_advanced(line, multiline_input); + } + +} diff --git a/llama.cpp/common/console.h b/llama.cpp/common/console.h new file mode 100644 index 0000000000000000000000000000000000000000..eced3c68369ae970dffc973c554c32105d347ef2 --- /dev/null +++ b/llama.cpp/common/console.h @@ -0,0 +1,19 @@ +// Console functions + +#pragma once + +#include + +namespace console { + enum display_t { + reset = 0, + prompt, + user_input, + error + }; + + void init(bool use_simple_io, bool use_advanced_display); + void cleanup(); + void set_display(display_t display); + bool readline(std::string & line, bool multiline_input); +} diff --git a/llama.cpp/common/json-partial.cpp b/llama.cpp/common/json-partial.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d16a6cc262835dcef68c97ecc9793d1f6f86754c --- /dev/null +++ b/llama.cpp/common/json-partial.cpp @@ -0,0 +1,256 @@ +#include "json-partial.h" + +#include "log.h" + +#include + +#include + +using json = nlohmann::ordered_json; + +enum common_json_stack_element_type { + COMMON_JSON_STACK_ELEMENT_OBJECT, + COMMON_JSON_STACK_ELEMENT_KEY, + COMMON_JSON_STACK_ELEMENT_ARRAY, +}; + +struct common_json_stack_element { + common_json_stack_element_type type; + std::string key; +}; + +bool common_json_parse( + const std::string & input, + const std::string & healing_marker, + common_json & out) +{ + std::string::const_iterator it = input.begin(); + const auto end = input.end(); + return common_json_parse(it, end, healing_marker, out); +} + +bool common_json_parse( + std::string::const_iterator & it, + const std::string::const_iterator & end, + const std::string & healing_marker, + common_json & out) +{ + // // https://json.nlohmann.me/features/parsing/sax_interface/ + struct json_error_locator : public nlohmann::json_sax { + std::size_t position; + bool found_error; + std::string last_token; + std::string exception_message; + std::vector stack; + + json_error_locator() : position(0), found_error(false) {} + + bool parse_error(std::size_t position, const std::string & last_token, const json::exception & ex) override { // NOLINT + this->position = position - 1; + this->found_error = true; + this->last_token = last_token; + this->exception_message = ex.what(); + return false; + } + void close_value() { + if (!stack.empty() && (stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY)) { + stack.pop_back(); + } + } + bool null() override { // NOLINT + close_value(); + return true; + } + bool boolean(bool) override { // NOLINT + close_value(); + return true; + } + bool number_integer(number_integer_t) override { // NOLINT + close_value(); + return true; + } + bool number_unsigned(number_unsigned_t) override { // NOLINT + close_value(); + return true; + } + bool number_float(number_float_t, const string_t &) override { // NOLINT + close_value(); + return true; + } + bool string(string_t &) override { // NOLINT + close_value(); + return true; + } + bool binary(binary_t &) override { // NOLINT + close_value(); + return true; + } + bool start_object(std::size_t) override { // NOLINT + stack.push_back({COMMON_JSON_STACK_ELEMENT_OBJECT, ""}); + return true; + } + bool end_object() override { + GGML_ASSERT(!stack.empty() && stack.back().type == COMMON_JSON_STACK_ELEMENT_OBJECT); + stack.pop_back(); + close_value(); + return true; + } + bool key(string_t & key) override { // NOLINT + stack.push_back({COMMON_JSON_STACK_ELEMENT_KEY, key}); + return true; + } + bool start_array(std::size_t) override { // NOLINT + stack.push_back({COMMON_JSON_STACK_ELEMENT_ARRAY, ""}); + return true; + } + bool end_array() override { + GGML_ASSERT(!stack.empty() && stack.back().type == COMMON_JSON_STACK_ELEMENT_ARRAY); + stack.pop_back(); + close_value(); + return true; + } + }; + json_error_locator err_loc; + auto start = it; + json::sax_parse(it, end, &err_loc); + + if (err_loc.found_error) { + it = start; + auto temptative_end = it + err_loc.position; + // LOG_DBG("Error at position %zu (is_end = %s): %s\n", err_loc.position, temptative_end == end ? "true" : "false", err_loc.exception_message.c_str()); + + auto input = std::string(it, temptative_end); + try { + out.json = json::parse(input); + // out.json = json::parse(it, temptative_end); + it = temptative_end; + return true; + } catch (const std::exception & ex) { + // No, needs healing. + LOG_DBG("Failed to parse up to error: %s: <<<%s>>>\n", ex.what(), std::string(it, temptative_end).c_str()); + } + auto can_parse = [](const std::string & str) { + try { + auto _ = json::parse(str); // NOLINT + return true; + } catch (const std::exception &) { + return false; + } + }; + if (!healing_marker.empty() && !err_loc.stack.empty()) { + std::string str(it, temptative_end); + auto last_non_sp_pos = str.find_last_not_of(" \n\r\t"); + if (last_non_sp_pos == std::string::npos) { + throw std::runtime_error("Cannot heal a truncated JSON that stopped in an unknown location"); + } + auto last_non_sp_char = str[last_non_sp_pos]; + // Used to detect stops on a number, which may not be complete. + auto was_maybe_number = [&]() { + if (!str.empty() && std::isspace(str.back())) { + return false; + } + return std::isdigit(last_non_sp_char) || + last_non_sp_char == '.' || + last_non_sp_char == 'e' || + last_non_sp_char == 'E' || + last_non_sp_char == '-'; + }; + + std::string closing; + for (size_t i = err_loc.stack.size(); i > 0; i--) { + auto & el = err_loc.stack[i - 1]; + if (el.type == COMMON_JSON_STACK_ELEMENT_OBJECT) { + closing += "}"; + } else if (el.type == COMMON_JSON_STACK_ELEMENT_ARRAY) { + closing += "]"; + } else if (el.type != COMMON_JSON_STACK_ELEMENT_KEY) { + throw std::runtime_error("Unexpected stack element type"); + } + } + + const auto & magic_seed = out.healing_marker.marker = healing_marker;//"$llama.cpp.json$"; + + if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY) { + // We're inside an object value + if (last_non_sp_char == ':' && can_parse(str + "1" + closing)) { + // Was about to create an object value + str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing; + } else if (can_parse(str + ": 1" + closing)) { + str += (out.healing_marker.json_dump_marker = ":\"" + magic_seed) + "\"" + closing; + } else if (last_non_sp_char == '{' && can_parse(str + closing)) { + // Was about to create an object + str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\": 1" + closing; + } else if (can_parse(str + "\"" + closing)) { + // Was inside an object value string + str += (out.healing_marker.json_dump_marker = magic_seed) + "\"" + closing; + } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) { + // Was inside an object value string after an escape + str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing; + } else { + // find last : + auto last_pos = str.find_last_of(':'); + if (last_pos == std::string::npos) { + throw std::runtime_error("Cannot heal a truncated JSON that stopped in an unknown location"); + } + // Cutting back to opening : for object value + str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing; + } + } else if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_ARRAY) { + if ((last_non_sp_char == ',' || last_non_sp_char == '[') && can_parse(str + "1" + closing)) { + // Was about to create an array value + str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing; + } else if (can_parse(str + "\"" + closing)) { + // Was inside an array value string + str += (out.healing_marker.json_dump_marker = magic_seed) + "\"" + closing; + } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) { + // Was inside an array value string after an escape + str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing; + } else if (!was_maybe_number() && can_parse(str + ", 1" + closing)) { + // Had just finished a value + str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\"" + closing; + } else { + auto last_pos = str.find_last_of("[,"); + if (last_pos == std::string::npos) { + throw std::runtime_error("Cannot heal a truncated JSON array stopped in an unknown location"); + } + // Cutting back to last [ or , for array value + str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing; + } + } else if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_OBJECT) { + if ((last_non_sp_char == '{' && can_parse(str + closing)) || + (last_non_sp_char == ',' && can_parse(str + "\"\": 1" + closing))) { + // Was about to create an object key+value + str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\": 1" + closing; + } else if (!was_maybe_number() && can_parse(str + ",\"\": 1" + closing)) { + // Was about to create an object key+value + str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\": 1" + closing; + } else if (can_parse(str + "\": 1" + closing)) { + // Was inside an object key string + str += (out.healing_marker.json_dump_marker = magic_seed) + "\": 1" + closing; + } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\": 1" + closing)) { + // Was inside an object key string after an escape + str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\": 1" + closing; + } else { + auto last_pos = str.find_last_of(':'); + if (last_pos == std::string::npos) { + throw std::runtime_error("Cannot heal a truncated JSON object stopped in an unknown location"); + } + // fprintf(stderr, "Cutting back to last : for object key+value\n"); + str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing; + } + } else { + throw std::runtime_error("Cannot heal a truncated JSON object stopped in an unknown location"); + } + // fprintf(stderr, "HEALED:\nSTRING <<<\n%s\n>>>\n\nmagic_cut: <<<\n%s\n>>>\n\n", str.c_str(), out.healing_marker.json_dump_marker.c_str()); + out.json = json::parse(str); + it = temptative_end; + return true; + } + // TODO: handle unclosed top-level primitive if the stack was empty but we got an error (e.g. "tru", "\"", etc...) + // fprintf(stderr, "Closing: TODO\n"); + return false; + } + out.json = json::parse(it, end); + it = end; + return true; +} diff --git a/llama.cpp/common/json-partial.h b/llama.cpp/common/json-partial.h new file mode 100644 index 0000000000000000000000000000000000000000..3ca24afd59e4c6961a6b39bab83e8957df831e95 --- /dev/null +++ b/llama.cpp/common/json-partial.h @@ -0,0 +1,38 @@ +#pragma once + +#include + +// Healing marker (empty if the JSON was fully parsed / wasn't healed). +struct common_healing_marker { + // Raw marker. + std::string marker; + + // Cutting the `common_json.json.dump()` string at the (only) occurrence of this marker should yield the original partial JSON string (modulo spaces / if it had the same dump format). + std::string json_dump_marker; +}; + +// Represents a parsed JSON object, with its optional healing marker (a JSON dump fragment that can be used to find the position of healing in the JSON dump string) +struct common_json { + nlohmann::ordered_json json; + + common_healing_marker healing_marker; +}; + +// Parse the JSON string, healing (closing) any partial JSON if `healing_marker` is not empty. +// +// Healing completes partial JSON strings by adding a (possibly modified) healing marker, then whatever is needed to close the JSON. +// This allows to parse the resulting healed JSON string, yet be able to cut it again if needed at the healing marker. +// (this is used when parsing JSON outputs from the models, then crafting partial JSONs for the partial tool calls in OAI format). +// +// For instance, parsing `{` with a healing marker `foo` will produce a healed JSON `{"foo":1}`, w/ json_dump_marker = `"foo"` (which can be used to break the JSON again). +bool common_json_parse( + const std::string & input, + const std::string & healing_marker, + common_json & out); + +// Parse the JSON string (see overload above), but advancing an iterator to the end of the input when the (potentially partial) parsing succeeds. +bool common_json_parse( + std::string::const_iterator & it, + const std::string::const_iterator & end, + const std::string & healing_marker, + common_json & out); diff --git a/llama.cpp/common/json-schema-to-grammar.cpp b/llama.cpp/common/json-schema-to-grammar.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f25dd57b9f7355b401c641a57343c18c0f3457ed --- /dev/null +++ b/llama.cpp/common/json-schema-to-grammar.cpp @@ -0,0 +1,985 @@ +#include "json-schema-to-grammar.h" +#include "common.h" + +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +using json = nlohmann::ordered_json; + +static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") { + auto has_max = max_items != std::numeric_limits::max(); + + if (max_items == 0) { + return ""; + } + if (min_items == 0 && max_items == 1) { + return item_rule + "?"; + } + + if (separator_rule.empty()) { + if (min_items == 1 && !has_max) { + return item_rule + "+"; + } else if (min_items == 0 && !has_max) { + return item_rule + "*"; + } else { + return item_rule + "{" + std::to_string(min_items) + "," + (has_max ? std::to_string(max_items) : "") + "}"; + } + } + + auto result = item_rule + " " + build_repetition("(" + separator_rule + " " + item_rule + ")", min_items == 0 ? 0 : min_items - 1, has_max ? max_items - 1 : max_items); + if (min_items == 0) { + result = "(" + result + ")?"; + } + return result; +} + +static void _build_min_max_int(int min_value, int max_value, std::stringstream & out, int decimals_left = 16, bool top_level = true) { + auto has_min = min_value != std::numeric_limits::min(); + auto has_max = max_value != std::numeric_limits::max(); + + auto digit_range = [&](char from, char to) { + out << "["; + if (from == to) { + out << from; + } else { + out << from << "-" << to; + } + out << "]"; + }; + auto more_digits = [&](int min_digits, int max_digits) { + out << "[0-9]"; + if (min_digits == max_digits && min_digits == 1) { + return; + } + out << "{"; + out << min_digits; + if (max_digits != min_digits) { + out << ","; + if (max_digits != std::numeric_limits::max()) { + out << max_digits; + } + } + out << "}"; + }; + std::function uniform_range = + [&](const std::string_view & from, const std::string_view & to) { + size_t i = 0; + while (i < from.length() && i < to.length() && from[i] == to[i]) { + i++; + } + if (i > 0) { + out << "\"" << from.substr(0, i) << "\""; + } + if (i < from.length() && i < to.length()) { + if (i > 0) { + out << " "; + } + auto sub_len = from.length() - i - 1; + if (sub_len > 0) { + auto from_sub = from.substr(i + 1); + auto to_sub = to.substr(i + 1); + auto sub_zeros = string_repeat("0", sub_len); + auto sub_nines = string_repeat("9", sub_len); + + auto to_reached = false; + out << "("; + if (from_sub == sub_zeros) { + digit_range(from[i], to[i] - 1); + out << " "; + more_digits(sub_len, sub_len); + } else { + out << "[" << from[i] << "] "; + out << "("; + uniform_range(from_sub, sub_nines); + out << ")"; + if (from[i] < to[i] - 1) { + out << " | "; + if (to_sub == sub_nines) { + digit_range(from[i] + 1, to[i]); + to_reached = true; + } else { + digit_range(from[i] + 1, to[i] - 1); + } + out << " "; + more_digits(sub_len, sub_len); + } + } + if (!to_reached) { + out << " | "; + digit_range(to[i], to[i]); + out << " "; + uniform_range(sub_zeros, to_sub); + } + out << ")"; + } else { + out << "[" << from[i] << "-" << to[i] << "]"; + } + } + }; + + if (has_min && has_max) { + if (min_value < 0 && max_value < 0) { + out << "\"-\" ("; + _build_min_max_int(-max_value, -min_value, out, decimals_left, /* top_level= */ true); + out << ")"; + return; + } + + if (min_value < 0) { + out << "\"-\" ("; + _build_min_max_int(0, -min_value, out, decimals_left, /* top_level= */ true); + out << ") | "; + min_value = 0; + } + + auto min_s = std::to_string(min_value); + auto max_s = std::to_string(max_value); + auto min_digits = min_s.length(); + auto max_digits = max_s.length(); + + for (auto digits = min_digits; digits < max_digits; digits++) { + uniform_range(min_s, string_repeat("9", digits)); + min_s = "1" + string_repeat("0", digits); + out << " | "; + } + uniform_range(min_s, max_s); + return; + } + + auto less_decimals = std::max(decimals_left - 1, 1); + + if (has_min) { + if (min_value < 0) { + out << "\"-\" ("; + _build_min_max_int(std::numeric_limits::min(), -min_value, out, decimals_left, /* top_level= */ false); + out << ") | [0] | [1-9] "; + more_digits(0, decimals_left - 1); + } else if (min_value == 0) { + if (top_level) { + out << "[0] | [1-9] "; + more_digits(0, less_decimals); + } else { + more_digits(1, decimals_left); + } + } else if (min_value <= 9) { + char c = '0' + min_value; + auto range_start = top_level ? '1' : '0'; + if (c > range_start) { + digit_range(range_start, c - 1); + out << " "; + more_digits(1, less_decimals); + out << " | "; + } + digit_range(c, '9'); + out << " "; + more_digits(0, less_decimals); + } else { + auto min_s = std::to_string(min_value); + auto len = min_s.length(); + auto c = min_s[0]; + + if (c > '1') { + digit_range(top_level ? '1' : '0', c - 1); + out << " "; + more_digits(len, less_decimals); + out << " | "; + } + digit_range(c, c); + out << " ("; + _build_min_max_int(std::stoi(min_s.substr(1)), std::numeric_limits::max(), out, less_decimals, /* top_level= */ false); + out << ")"; + if (c < '9') { + out << " | "; + digit_range(c + 1, '9'); + out << " "; + more_digits(len - 1, less_decimals); + } + } + return; + } + + if (has_max) { + if (max_value >= 0) { + if (top_level) { + out << "\"-\" [1-9] "; + more_digits(0, less_decimals); + out << " | "; + } + _build_min_max_int(0, max_value, out, decimals_left, /* top_level= */ true); + } else { + out << "\"-\" ("; + _build_min_max_int(-max_value, std::numeric_limits::max(), out, decimals_left, /* top_level= */ false); + out << ")"; + } + return; + } + + throw std::runtime_error("At least one of min_value or max_value must be set"); +} + +const std::string SPACE_RULE = "| \" \" | \"\\n\"{1,2} [ \\t]{0,20}"; + +struct BuiltinRule { + std::string content; + std::vector deps; +}; + +std::unordered_map PRIMITIVE_RULES = { + {"boolean", {"(\"true\" | \"false\") space", {}}}, + {"decimal-part", {"[0-9]{1,16}", {}}}, + {"integral-part", {"[0] | [1-9] [0-9]{0,15}", {}}}, + {"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)? space", {"integral-part", "decimal-part"}}}, + {"integer", {"(\"-\"? integral-part) space", {"integral-part"}}}, + {"value", {"object | array | string | number | boolean | null", {"object", "array", "string", "number", "boolean", "null"}}}, + {"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}}, + {"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}}, + {"uuid", {"\"\\\"\" [0-9a-fA-F]{8} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{12} \"\\\"\" space", {}}}, + {"char", {"[^\"\\\\\\x7F\\x00-\\x1F] | [\\\\] ([\"\\\\bfnrt] | \"u\" [0-9a-fA-F]{4})", {}}}, + {"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}}, + {"null", {"\"null\" space", {}}}, +}; + +std::unordered_map STRING_FORMAT_RULES = { + {"date", {"[0-9]{4} \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}}, + {"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9]{3} )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}}, + {"date-time", {"date \"T\" time", {"date", "time"}}}, + {"date-string", {"\"\\\"\" date \"\\\"\" space", {"date"}}}, + {"time-string", {"\"\\\"\" time \"\\\"\" space", {"time"}}}, + {"date-time-string", {"\"\\\"\" date-time \"\\\"\" space", {"date-time"}}} +}; + +static bool is_reserved_name(const std::string & name) { + static std::unordered_set RESERVED_NAMES; + if (RESERVED_NAMES.empty()) { + RESERVED_NAMES.insert("root"); + for (const auto &p : PRIMITIVE_RULES) RESERVED_NAMES.insert(p.first); + for (const auto &p : STRING_FORMAT_RULES) RESERVED_NAMES.insert(p.first); + } + return RESERVED_NAMES.find(name) != RESERVED_NAMES.end(); +} + +std::regex INVALID_RULE_CHARS_RE("[^a-zA-Z0-9-]+"); +std::regex GRAMMAR_LITERAL_ESCAPE_RE("[\r\n\"]"); +std::regex GRAMMAR_RANGE_LITERAL_ESCAPE_RE("[\r\n\"\\]\\-\\\\]"); +std::unordered_map GRAMMAR_LITERAL_ESCAPES = { + {'\r', "\\r"}, {'\n', "\\n"}, {'"', "\\\""}, {'-', "\\-"}, {']', "\\]"} +}; + +std::unordered_set NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'}; +std::unordered_set ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'^', '$', '.', '[', ']', '(', ')', '|', '{', '}', '*', '+', '?'}; + +static std::string replacePattern(const std::string & input, const std::regex & regex, const std::function & replacement) { + std::smatch match; + std::string result; + + std::string::const_iterator searchStart(input.cbegin()); + std::string::const_iterator searchEnd(input.cend()); + + while (std::regex_search(searchStart, searchEnd, match, regex)) { + result.append(searchStart, searchStart + match.position()); + result.append(replacement(match)); + searchStart = match.suffix().first; + } + + result.append(searchStart, searchEnd); + + return result; +} + +static std::string format_literal(const std::string & literal) { + std::string escaped = replacePattern(literal, GRAMMAR_LITERAL_ESCAPE_RE, [&](const std::smatch & match) { + char c = match.str()[0]; + return GRAMMAR_LITERAL_ESCAPES.at(c); + }); + return "\"" + escaped + "\""; +} + +class SchemaConverter { +private: + friend std::string build_grammar(const std::function & cb, const common_grammar_options & options); + std::function _fetch_json; + bool _dotall; + std::map _rules; + std::unordered_map _refs; + std::unordered_set _refs_being_resolved; + std::vector _errors; + std::vector _warnings; + + std::string _add_rule(const std::string & name, const std::string & rule) { + std::string esc_name = regex_replace(name, INVALID_RULE_CHARS_RE, "-"); + if (_rules.find(esc_name) == _rules.end() || _rules[esc_name] == rule) { + _rules[esc_name] = rule; + return esc_name; + } else { + int i = 0; + while (_rules.find(esc_name + std::to_string(i)) != _rules.end() && _rules[esc_name + std::to_string(i)] != rule) { + i++; + } + std::string key = esc_name + std::to_string(i); + _rules[key] = rule; + return key; + } + } + + std::string _generate_union_rule(const std::string & name, const std::vector & alt_schemas) { + std::vector rules; + for (size_t i = 0; i < alt_schemas.size(); i++) { + rules.push_back(visit(alt_schemas[i], name + (name.empty() ? "alternative-" : "-") + std::to_string(i))); + } + return string_join(rules, " | "); + } + + std::string _visit_pattern(const std::string & pattern, const std::string & name) { + if (!(pattern.front() == '^' && pattern.back() == '$')) { + _errors.push_back("Pattern must start with '^' and end with '$'"); + return ""; + } + std::string sub_pattern = pattern.substr(1, pattern.length() - 2); + std::unordered_map sub_rule_ids; + + size_t i = 0; + size_t length = sub_pattern.length(); + + using literal_or_rule = std::pair; + auto to_rule = [&](const literal_or_rule & ls) { + auto is_literal = ls.second; + auto s = ls.first; + return is_literal ? "\"" + s + "\"" : s; + }; + std::function transform = [&]() -> literal_or_rule { + size_t start = i; + std::vector seq; + + auto get_dot = [&]() { + std::string rule; + if (_dotall) { + rule = "[\\U00000000-\\U0010FFFF]"; + } else { + rule = "[^\\x0A\\x0D]"; + } + return _add_rule("dot", rule); + }; + + // Joins the sequence, merging consecutive literals together. + auto join_seq = [&]() { + std::vector ret; + + std::string literal; + auto flush_literal = [&]() { + if (literal.empty()) { + return false; + } + ret.emplace_back(literal, true); + literal.clear(); + return true; + }; + + for (const auto & item : seq) { + auto is_literal = item.second; + if (is_literal) { + literal += item.first; + } else { + flush_literal(); + ret.push_back(item); + } + } + flush_literal(); + + std::vector results; + for (const auto & item : ret) { + results.push_back(to_rule(item)); + } + return std::make_pair(string_join(results, " "), false); + }; + + while (i < length) { + char c = sub_pattern[i]; + if (c == '.') { + seq.emplace_back(get_dot(), false); + i++; + } else if (c == '(') { + i++; + if (i < length) { + if (sub_pattern[i] == '?') { + _warnings.push_back("Unsupported pattern syntax"); + } + } + seq.emplace_back("(" + to_rule(transform()) + ")", false); + } else if (c == ')') { + i++; + if (start > 0 && sub_pattern[start - 1] != '(') { + _errors.push_back("Unbalanced parentheses"); + } + return join_seq(); + } else if (c == '[') { + std::string square_brackets = std::string(1, c); + i++; + while (i < length && sub_pattern[i] != ']') { + if (sub_pattern[i] == '\\') { + square_brackets += sub_pattern.substr(i, 2); + i += 2; + } else { + square_brackets += sub_pattern[i]; + i++; + } + } + if (i >= length) { + _errors.push_back("Unbalanced square brackets"); + } + square_brackets += ']'; + i++; + seq.emplace_back(square_brackets, false); + } else if (c == '|') { + seq.emplace_back("|", false); + i++; + } else if (c == '*' || c == '+' || c == '?') { + seq.back() = std::make_pair(to_rule(seq.back()) + c, false); + i++; + } else if (c == '{') { + std::string curly_brackets = std::string(1, c); + i++; + while (i < length && sub_pattern[i] != '}') { + curly_brackets += sub_pattern[i]; + i++; + } + if (i >= length) { + _errors.push_back("Unbalanced curly brackets"); + } + curly_brackets += '}'; + i++; + auto nums = string_split(curly_brackets.substr(1, curly_brackets.length() - 2), ","); + int min_times = 0; + int max_times = std::numeric_limits::max(); + try { + if (nums.size() == 1) { + min_times = max_times = std::stoi(nums[0]); + } else if (nums.size() != 2) { + _errors.push_back("Wrong number of values in curly brackets"); + } else { + if (!nums[0].empty()) { + min_times = std::stoi(nums[0]); + } + if (!nums[1].empty()) { + max_times = std::stoi(nums[1]); + } + } + } catch (const std::invalid_argument & e) { + _errors.push_back("Invalid number in curly brackets"); + return std::make_pair("", false); + } + auto &last = seq.back(); + auto &sub = last.first; + auto sub_is_literal = last.second; + + if (!sub_is_literal) { + std::string & sub_id = sub_rule_ids[sub]; + if (sub_id.empty()) { + sub_id = _add_rule(name + "-" + std::to_string(sub_rule_ids.size()), sub); + } + sub = sub_id; + } + seq.back().first = build_repetition( + sub_is_literal ? "\"" + sub + "\"" : sub, + min_times, + max_times, + "" + ); + seq.back().second = false; + } else { + std::string literal; + auto is_non_literal = [&](char c) { + return NON_LITERAL_SET.find(c) != NON_LITERAL_SET.end(); + }; + while (i < length) { + if (sub_pattern[i] == '\\' && i < length - 1) { + char next = sub_pattern[i + 1]; + if (ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.find(next) != ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.end()) { + i++; + literal += sub_pattern[i]; + i++; + } else { + literal += sub_pattern.substr(i, 2); + i += 2; + } + } else if (sub_pattern[i] == '"') { + literal += "\\\""; + i++; + } else if (!is_non_literal(sub_pattern[i]) && + (i == length - 1 || literal.empty() || sub_pattern[i + 1] == '.' || !is_non_literal(sub_pattern[i + 1]))) { + literal += sub_pattern[i]; + i++; + } else { + break; + } + } + if (!literal.empty()) { + seq.emplace_back(literal, true); + } + } + } + return join_seq(); + }; + return _add_rule(name, "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space"); + } + + /* + Returns a rule that matches a JSON string that is none of the provided strings + + not_strings({"a"}) + -> ["] ( [a] char+ | [^"a] char* )? ["] space + not_strings({"and", "also"}) + -> ["] ( [a] ([l] ([s] ([o] char+ | [^"o] char*) | [^"s] char*) | [n] ([d] char+ | [^"d] char*) | [^"ln] char*) | [^"a] char* )? ["] space + */ + std::string _not_strings(const std::vector & strings) { + + struct TrieNode { + std::map children; + bool is_end_of_string; + + TrieNode() : is_end_of_string(false) {} + + void insert(const std::string & string) { + auto node = this; + for (char c : string) { + node = &node->children[c]; + } + node->is_end_of_string = true; + } + }; + + TrieNode trie; + for (const auto & s : strings) { + trie.insert(s); + } + + std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char")); + std::ostringstream out; + out << "[\"] ( "; + std::function visit = [&](const TrieNode & node) { + std::ostringstream rejects; + auto first = true; + for (const auto & kv : node.children) { + rejects << kv.first; + if (first) { + first = false; + } else { + out << " | "; + } + out << "[" << kv.first << "]"; + if (!kv.second.children.empty()) { + out << " ("; + visit(kv.second); + out << ")"; + } else if (kv.second.is_end_of_string) { + out << " " << char_rule << "+"; + } + } + if (!node.children.empty()) { + if (!first) { + out << " | "; + } + out << "[^\"" << rejects.str() << "] " << char_rule << "*"; + } + }; + visit(trie); + + out << " )"; + if (!trie.is_end_of_string) { + out << "?"; + } + out << " [\"] space"; + return out.str(); + } + + std::string _resolve_ref(const std::string & ref) { + std::string ref_name = ref.substr(ref.find_last_of('/') + 1); + if (_rules.find(ref_name) == _rules.end() && _refs_being_resolved.find(ref) == _refs_being_resolved.end()) { + _refs_being_resolved.insert(ref); + json resolved = _refs[ref]; + ref_name = visit(resolved, ref_name); + _refs_being_resolved.erase(ref); + } + return ref_name; + } + + std::string _build_object_rule( + const std::vector> & properties, + const std::unordered_set & required, + const std::string & name, + const json & additional_properties) + { + std::vector required_props; + std::vector optional_props; + std::unordered_map prop_kv_rule_names; + std::vector prop_names; + for (const auto & kv : properties) { + const auto &prop_name = kv.first; + const auto &prop_schema = kv.second; + + std::string prop_rule_name = visit(prop_schema, name + (name.empty() ? "" : "-") + prop_name); + prop_kv_rule_names[prop_name] = _add_rule( + name + (name.empty() ? "" : "-") + prop_name + "-kv", + format_literal(json(prop_name).dump()) + " space \":\" space " + prop_rule_name + ); + if (required.find(prop_name) != required.end()) { + required_props.push_back(prop_name); + } else { + optional_props.push_back(prop_name); + } + prop_names.push_back(prop_name); + } + if ((additional_properties.is_boolean() && additional_properties.get()) || additional_properties.is_object()) { + std::string sub_name = name + (name.empty() ? "" : "-") + "additional"; + std::string value_rule = + additional_properties.is_object() ? visit(additional_properties, sub_name + "-value") + : _add_primitive("value", PRIMITIVE_RULES.at("value")); + + auto key_rule = + prop_names.empty() ? _add_primitive("string", PRIMITIVE_RULES.at("string")) + : _add_rule(sub_name + "-k", _not_strings(prop_names)); + std::string kv_rule = _add_rule(sub_name + "-kv", key_rule + " \":\" space " + value_rule); + prop_kv_rule_names["*"] = kv_rule; + optional_props.push_back("*"); + } + + std::string rule = "\"{\" space "; + for (size_t i = 0; i < required_props.size(); i++) { + if (i > 0) { + rule += " \",\" space "; + } + rule += prop_kv_rule_names[required_props[i]]; + } + + if (!optional_props.empty()) { + rule += " ("; + if (!required_props.empty()) { + rule += " \",\" space ( "; + } + + std::function &, bool)> get_recursive_refs = [&](const std::vector & ks, bool first_is_optional) { + std::string res; + if (ks.empty()) { + return res; + } + std::string k = ks[0]; + std::string kv_rule_name = prop_kv_rule_names[k]; + std::string comma_ref = "( \",\" space " + kv_rule_name + " )"; + if (first_is_optional) { + res = comma_ref + (k == "*" ? "*" : "?"); + } else { + res = kv_rule_name + (k == "*" ? " " + comma_ref + "*" : ""); + } + if (ks.size() > 1) { + res += " " + _add_rule( + name + (name.empty() ? "" : "-") + k + "-rest", + get_recursive_refs(std::vector(ks.begin() + 1, ks.end()), true) + ); + } + return res; + }; + + for (size_t i = 0; i < optional_props.size(); i++) { + if (i > 0) { + rule += " | "; + } + rule += get_recursive_refs(std::vector(optional_props.begin() + i, optional_props.end()), false); + } + if (!required_props.empty()) { + rule += " )"; + } + rule += " )?"; + } + + rule += " \"}\" space"; + + return rule; + } + + std::string _add_primitive(const std::string & name, const BuiltinRule & rule) { + auto n = _add_rule(name, rule.content); + for (const auto & dep : rule.deps) { + BuiltinRule dep_rule; + auto it = PRIMITIVE_RULES.find(dep); + if (it == PRIMITIVE_RULES.end()) { + it = STRING_FORMAT_RULES.find(dep); + if (it == STRING_FORMAT_RULES.end()) { + _errors.push_back("Rule " + dep + " not known"); + continue; + } + } + if (_rules.find(dep) == _rules.end()) { + _add_primitive(dep, it->second); + } + } + return n; + } + +public: + SchemaConverter( + const std::function & fetch_json, + bool dotall) + : _fetch_json(fetch_json), _dotall(dotall) + { + _rules["space"] = SPACE_RULE; + } + + void resolve_refs(json & schema, const std::string & url) { + /* + * Resolves all $ref fields in the given schema, fetching any remote schemas, + * replacing each $ref with absolute reference URL and populates _refs with the + * respective referenced (sub)schema dictionaries. + */ + std::function visit_refs = [&](json & n) { + if (n.is_array()) { + for (auto & x : n) { + visit_refs(x); + } + } else if (n.is_object()) { + if (n.contains("$ref")) { + std::string ref = n["$ref"]; + if (_refs.find(ref) == _refs.end()) { + json target; + if (ref.find("https://") == 0) { + std::string base_url = ref.substr(0, ref.find('#')); + auto it = _refs.find(base_url); + if (it != _refs.end()) { + target = it->second; + } else { + // Fetch the referenced schema and resolve its refs + auto referenced = _fetch_json(ref); + resolve_refs(referenced, base_url); + _refs[base_url] = referenced; + } + if (ref.find('#') == std::string::npos || ref.substr(ref.find('#') + 1).empty()) { + return; + } + } else if (ref.find("#/") == 0) { + target = schema; + n["$ref"] = url + ref; + ref = url + ref; + } else { + _errors.push_back("Unsupported ref: " + ref); + return; + } + std::string pointer = ref.substr(ref.find('#') + 1); + std::vector tokens = string_split(pointer, "/"); + for (size_t i = 1; i < tokens.size(); ++i) { + std::string sel = tokens[i]; + if (target.is_null() || !target.contains(sel)) { + _errors.push_back("Error resolving ref " + ref + ": " + sel + " not in " + target.dump()); + return; + } + target = target[sel]; + } + _refs[ref] = target; + } + } else { + for (auto & kv : n.items()) { + visit_refs(kv.value()); + } + } + } + }; + + visit_refs(schema); + } + + std::string _generate_constant_rule(const json & value) { + return format_literal(value.dump()); + } + + std::string visit(const json & schema, const std::string & name) { + json schema_type = schema.contains("type") ? schema["type"] : json(); + std::string schema_format = schema.contains("format") ? schema["format"].get() : ""; + std::string rule_name = is_reserved_name(name) ? name + "-" : name.empty() ? "root" : name; + + if (schema.contains("$ref")) { + return _add_rule(rule_name, _resolve_ref(schema["$ref"])); + } else if (schema.contains("oneOf") || schema.contains("anyOf")) { + std::vector alt_schemas = schema.contains("oneOf") ? schema["oneOf"].get>() : schema["anyOf"].get>(); + return _add_rule(rule_name, _generate_union_rule(name, alt_schemas)); + } else if (schema_type.is_array()) { + std::vector schema_types; + for (const auto & t : schema_type) { + json schema_copy(schema); + schema_copy["type"] = t; + schema_types.push_back(schema_copy); + } + return _add_rule(rule_name, _generate_union_rule(name, schema_types)); + } else if (schema.contains("const")) { + return _add_rule(rule_name, _generate_constant_rule(schema["const"]) + " space"); + } else if (schema.contains("enum")) { + std::vector enum_values; + for (const auto & v : schema["enum"]) { + enum_values.push_back(_generate_constant_rule(v)); + } + return _add_rule(rule_name, "(" + string_join(enum_values, " | ") + ") space"); + } else if ((schema_type.is_null() || schema_type == "object") + && (schema.contains("properties") || + (schema.contains("additionalProperties") && schema["additionalProperties"] != true))) { + std::unordered_set required; + if (schema.contains("required") && schema["required"].is_array()) { + for (const auto & item : schema["required"]) { + if (item.is_string()) { + required.insert(item.get()); + } + } + } + std::vector> properties; + if (schema.contains("properties")) { + for (const auto & prop : schema["properties"].items()) { + properties.emplace_back(prop.key(), prop.value()); + } + } + return _add_rule(rule_name, + _build_object_rule( + properties, required, name, + schema.contains("additionalProperties") ? schema["additionalProperties"] : json())); + } else if ((schema_type.is_null() || schema_type == "object") && schema.contains("allOf")) { + std::unordered_set required; + std::vector> properties; + std::string hybrid_name = name; + std::function add_component = [&](const json & comp_schema, bool is_required) { + if (comp_schema.contains("$ref")) { + add_component(_refs[comp_schema["$ref"]], is_required); + } else if (comp_schema.contains("properties")) { + for (const auto & prop : comp_schema["properties"].items()) { + properties.emplace_back(prop.key(), prop.value()); + if (is_required) { + required.insert(prop.key()); + } + } + } else { + // todo warning + } + }; + for (auto & t : schema["allOf"]) { + if (t.contains("anyOf")) { + for (auto & tt : t["anyOf"]) { + add_component(tt, false); + } + } else { + add_component(t, true); + } + } + return _add_rule(rule_name, _build_object_rule(properties, required, hybrid_name, json())); + } else if ((schema_type.is_null() || schema_type == "array") && (schema.contains("items") || schema.contains("prefixItems"))) { + json items = schema.contains("items") ? schema["items"] : schema["prefixItems"]; + if (items.is_array()) { + std::string rule = "\"[\" space "; + for (size_t i = 0; i < items.size(); i++) { + if (i > 0) { + rule += " \",\" space "; + } + rule += visit(items[i], name + (name.empty() ? "" : "-") + "tuple-" + std::to_string(i)); + } + rule += " \"]\" space"; + return _add_rule(rule_name, rule); + } else { + std::string item_rule_name = visit(items, name + (name.empty() ? "" : "-") + "item"); + int min_items = schema.contains("minItems") ? schema["minItems"].get() : 0; + json max_items_json = schema.contains("maxItems") ? schema["maxItems"] : json(); + int max_items = max_items_json.is_number_integer() ? max_items_json.get() : std::numeric_limits::max(); + + return _add_rule(rule_name, "\"[\" space " + build_repetition(item_rule_name, min_items, max_items, "\",\" space") + " \"]\" space"); + } + } else if ((schema_type.is_null() || schema_type == "string") && schema.contains("pattern")) { + return _visit_pattern(schema["pattern"], rule_name); + } else if ((schema_type.is_null() || schema_type == "string") && std::regex_match(schema_format, std::regex("^uuid[1-5]?$"))) { + return _add_primitive(rule_name == "root" ? "root" : schema_format, PRIMITIVE_RULES.at("uuid")); + } else if ((schema_type.is_null() || schema_type == "string") && STRING_FORMAT_RULES.find(schema_format + "-string") != STRING_FORMAT_RULES.end()) { + auto prim_name = schema_format + "-string"; + return _add_rule(rule_name, _add_primitive(prim_name, STRING_FORMAT_RULES.at(prim_name))); + } else if (schema_type == "string" && (schema.contains("minLength") || schema.contains("maxLength"))) { + std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char")); + int min_len = schema.contains("minLength") ? schema["minLength"].get() : 0; + int max_len = schema.contains("maxLength") ? schema["maxLength"].get() : std::numeric_limits::max(); + return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\" space"); + } else if (schema_type == "integer" && (schema.contains("minimum") || schema.contains("exclusiveMinimum") || schema.contains("maximum") || schema.contains("exclusiveMaximum"))) { + int min_value = std::numeric_limits::min(); + int max_value = std::numeric_limits::max(); + if (schema.contains("minimum")) { + min_value = schema["minimum"].get(); + } else if (schema.contains("exclusiveMinimum")) { + min_value = schema["exclusiveMinimum"].get() + 1; + } + if (schema.contains("maximum")) { + max_value = schema["maximum"].get(); + } else if (schema.contains("exclusiveMaximum")) { + max_value = schema["exclusiveMaximum"].get() - 1; + } + std::stringstream out; + out << "("; + _build_min_max_int(min_value, max_value, out); + out << ") space"; + return _add_rule(rule_name, out.str()); + } else if (schema.empty() || schema_type == "object") { + return _add_rule(rule_name, _add_primitive("object", PRIMITIVE_RULES.at("object"))); + } else { + if (!schema_type.is_string() || PRIMITIVE_RULES.find(schema_type.get()) == PRIMITIVE_RULES.end()) { + _errors.push_back("Unrecognized schema: " + schema.dump()); + return ""; + } + // TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero + return _add_primitive(rule_name == "root" ? "root" : schema_type.get(), PRIMITIVE_RULES.at(schema_type.get())); + } + } + + void check_errors() { + if (!_errors.empty()) { + throw std::runtime_error("JSON schema conversion failed:\n" + string_join(_errors, "\n")); + } + if (!_warnings.empty()) { + fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", string_join(_warnings, "; ").c_str()); + } + } + + std::string format_grammar() { + std::stringstream ss; + for (const auto & kv : _rules) { + ss << kv.first << " ::= " << kv.second << std::endl; + } + return ss.str(); + } +}; + +std::string json_schema_to_grammar(const json & schema, bool force_gbnf) { +#ifdef LLAMA_USE_LLGUIDANCE + if (!force_gbnf) { + return "%llguidance {}\nstart: %json " + schema.dump(); + } +#else + (void)force_gbnf; +#endif // LLAMA_USE_LLGUIDANCE + return build_grammar([&](const common_grammar_builder & callbacks) { + auto copy = schema; + callbacks.resolve_refs(copy); + callbacks.add_schema("", copy); + }); +} + +std::string build_grammar(const std::function & cb, const common_grammar_options & options) { + SchemaConverter converter([&](const std::string &) { return json(); }, options.dotall); + common_grammar_builder builder { + /* .add_rule = */ [&](const std::string & name, const std::string & rule) { + return converter._add_rule(name, rule); + }, + /* .add_schema = */ [&](const std::string & name, const nlohmann::ordered_json & schema) { + return converter.visit(schema, name == "root" ? "" : name); + }, + /* .resolve_refs = */ [&](nlohmann::ordered_json & schema) { + converter.resolve_refs(schema, ""); + } + }; + cb(builder); + converter.check_errors(); + return converter.format_grammar(); +} diff --git a/llama.cpp/common/json-schema-to-grammar.h b/llama.cpp/common/json-schema-to-grammar.h new file mode 100644 index 0000000000000000000000000000000000000000..25a57906d926796406a0b9bf7af660d479c7974b --- /dev/null +++ b/llama.cpp/common/json-schema-to-grammar.h @@ -0,0 +1,21 @@ +#pragma once + +#include + +#include +#include + +std::string json_schema_to_grammar(const nlohmann::ordered_json & schema, + bool force_gbnf = false); + +struct common_grammar_builder { + std::function add_rule; + std::function add_schema; + std::function resolve_refs; +}; + +struct common_grammar_options { + bool dotall = false; +}; + +std::string build_grammar(const std::function & cb, const common_grammar_options & options = {}); diff --git a/llama.cpp/common/llguidance.cpp b/llama.cpp/common/llguidance.cpp new file mode 100644 index 0000000000000000000000000000000000000000..56b97c0030ae5f7ac47098c6140ec7481a0cad0c --- /dev/null +++ b/llama.cpp/common/llguidance.cpp @@ -0,0 +1,254 @@ +#include "sampling.h" +#include "log.h" + +#ifdef LLAMA_USE_LLGUIDANCE + +# include "llguidance.h" +# include + +struct llama_sampler_llg { + const llama_vocab * vocab; + std::string grammar_kind; + std::string grammar_data; + LlgTokenizer * tokenizer; + LlgMatcher * grammar; +}; + +static LlgMatcher * llama_sampler_llg_new(LlgTokenizer * tokenizer, const char * grammar_kind, + const char * grammar_data) { + LlgConstraintInit cinit; + llg_constraint_init_set_defaults(&cinit, tokenizer); + const char * log_level = getenv("LLGUIDANCE_LOG_LEVEL"); + if (log_level && *log_level) { + cinit.log_stderr_level = atoi(log_level); + } + auto c = llg_new_matcher(&cinit, grammar_kind, grammar_data); + if (llg_matcher_get_error(c)) { + LOG_ERR("llg error: %s\n", llg_matcher_get_error(c)); + llg_free_matcher(c); + return nullptr; + } + + return c; +} + +static const char * llama_sampler_llg_name(const llama_sampler * /*smpl*/) { + return "llguidance"; +} + +static void llama_sampler_llg_accept_impl(llama_sampler * smpl, llama_token token) { + auto * ctx = (llama_sampler_llg *) smpl->ctx; + if (ctx->grammar) { + llg_matcher_consume_token(ctx->grammar, token); + } +} + +static void llama_sampler_llg_apply(llama_sampler * smpl, llama_token_data_array * cur_p) { + auto * ctx = (llama_sampler_llg *) smpl->ctx; + if (ctx->grammar) { + const uint32_t * mask = llg_matcher_get_mask(ctx->grammar); + if (mask == nullptr) { + if (llg_matcher_compute_mask(ctx->grammar) == 0) { + mask = llg_matcher_get_mask(ctx->grammar); + } else { + LOG_ERR("llg error: %s\n", llg_matcher_get_error(ctx->grammar)); + llg_free_matcher(ctx->grammar); + ctx->grammar = nullptr; + return; + } + } + + for (size_t i = 0; i < cur_p->size; ++i) { + auto token = cur_p->data[i].id; + if ((mask[token / 32] & (1 << (token % 32))) == 0) { + cur_p->data[i].logit = -INFINITY; + } + } + } +} + +static void llama_sampler_llg_reset(llama_sampler * smpl) { + auto * ctx = (llama_sampler_llg *) smpl->ctx; + if (ctx->grammar) { + llg_matcher_reset(ctx->grammar); + } +} + +static llama_sampler * llama_sampler_llg_clone(const llama_sampler * smpl) { + const auto * ctx = (const llama_sampler_llg *) smpl->ctx; + + auto * result = llama_sampler_init_llg(ctx->vocab, nullptr, nullptr); + + // copy the state + { + auto * result_ctx = (llama_sampler_llg *) result->ctx; + + if (ctx->grammar) { + result_ctx->grammar_kind = ctx->grammar_kind; + result_ctx->grammar_data = ctx->grammar_data; + result_ctx->grammar = llg_clone_matcher(ctx->grammar); + result_ctx->tokenizer = llg_clone_tokenizer(ctx->tokenizer); + } + } + + return result; +} + +static void llama_sampler_llg_free(llama_sampler * smpl) { + const auto * ctx = (llama_sampler_llg *) smpl->ctx; + + if (ctx->grammar) { + llg_free_matcher(ctx->grammar); + llg_free_tokenizer(ctx->tokenizer); + } + + delete ctx; +} + +static llama_sampler_i llama_sampler_llg_i = { + /* .name = */ llama_sampler_llg_name, + /* .accept = */ llama_sampler_llg_accept_impl, + /* .apply = */ llama_sampler_llg_apply, + /* .reset = */ llama_sampler_llg_reset, + /* .clone = */ llama_sampler_llg_clone, + /* .free = */ llama_sampler_llg_free, +}; + +static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len, + uint32_t * output_tokens, size_t output_tokens_len) { + const llama_vocab * vocab = (const llama_vocab *) user_data; + int r = 0; + try { + r = llama_tokenize(vocab, (const char *) bytes, bytes_len, (int32_t *) output_tokens, output_tokens_len, false, + true); + } catch (const std::exception & e) { + GGML_ABORT("llama_tokenize failed: %s\n", e.what()); + } + if (r < 0) { + return -r; + } + return r; +} + +static LlgTokenizer * llama_sampler_llg_new_tokenizer(const llama_vocab * vocab) { + // TODO store the tokenizer in the vocab somehow + static const llama_vocab * vocab_cache; + static LlgTokenizer * tokenizer_cache; + + if (vocab_cache == vocab) { + return llg_clone_tokenizer(tokenizer_cache); + } + + auto tok_eos = llama_vocab_eot(vocab); + if (tok_eos == LLAMA_TOKEN_NULL) { + tok_eos = llama_vocab_eos(vocab); + } + + size_t vocab_size = llama_vocab_n_tokens(vocab); + + auto token_lens = new uint32_t[vocab_size]; + // we typically have ~7 bytes per token; let's go on the safe side here + auto token_bytes_size = vocab_size * 16 + 1024 * 1024; + auto token_bytes = new uint8_t[token_bytes_size]; + + size_t offset = 0; + for (size_t i = 0; i < vocab_size; i++) { + size_t max_token = 1024; + if (token_bytes_size - offset < max_token) { + GGML_ABORT("token_bytes buffer too small\n"); + } + + llama_token token = i; + auto dp = (char *) token_bytes + offset; + auto size = llama_detokenize(vocab, &token, 1, dp, max_token, false, false); + if (size < 0) { + GGML_ABORT("llama_detokenize failed\n"); + } + if (size == 0) { + size = llama_detokenize(vocab, &token, 1, dp + 1, max_token - 1, false, true); + if (size < 0) { + GGML_ABORT("llama_detokenize failed\n"); + } + if (size != 0) { + *dp = '\xff'; // special token prefix marker + size += 1; + } + } + + token_lens[i] = size; + offset += size; + } + + LlgTokenizerInit tinit = { + /* .vocab_size = */ (uint32_t) vocab_size, + /* .tok_eos = */ (uint32_t) tok_eos, + /* .token_lens = */ token_lens, + /* .token_bytes = */ token_bytes, + /* .tokenizer_json = */ nullptr, + /* .tokenize_assumes_string = */ true, + /* .tokenize_fn = */ llama_sampler_llg_tokenize_fn, + /* .use_approximate_greedy_tokenize_fn = */ false, + /* .tokenize_user_data = */ vocab, + /* .slices = */ nullptr, + }; + + char error_buffer[1024]; + LlgTokenizer * tokenizer = llg_new_tokenizer(&tinit, error_buffer, sizeof(error_buffer)); + + delete[] token_bytes; + delete[] token_lens; + + if (tokenizer == nullptr) { + LOG_ERR("llg tokenizer error: %s\n", error_buffer); + return tokenizer; + } + + if (tokenizer_cache) { + llg_free_tokenizer(tokenizer_cache); + } + vocab_cache = vocab; + tokenizer_cache = tokenizer; + + return llg_clone_tokenizer(tokenizer_cache); +} + +llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * grammar_kind, + const char * grammar_data) { + auto * ctx = new llama_sampler_llg; + + if (grammar_kind != nullptr && grammar_kind[0] != '\0') { + auto tokenizer = llama_sampler_llg_new_tokenizer(vocab); + *ctx = { + /* .vocab = */ vocab, + /* .grammar_kind = */ grammar_kind, + /* .grammar_data = */ grammar_data, + /* .tokenizer = */ tokenizer, + /* .grammar = */ llama_sampler_llg_new(tokenizer, grammar_kind, grammar_data), + }; + if (ctx->grammar) { + GGML_ASSERT(((size_t) llama_vocab_n_tokens(vocab) + 31) / 32 * 4 == + llg_matcher_get_mask_byte_size(ctx->grammar)); + } + } else { + *ctx = { + /* .vocab = */ vocab, + /* .grammar_kind = */ {}, + /* .grammar_data = */ {}, + /* .tokenizer = */ nullptr, + /* .grammar = */ nullptr, + }; + } + + return llama_sampler_init( + /* .iface = */ &llama_sampler_llg_i, + /* .ctx = */ ctx); +} + +#else + +llama_sampler * llama_sampler_init_llg(const llama_vocab *, const char *, const char *) { + LOG_WRN("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled"); + return nullptr; +} + +#endif // LLAMA_USE_LLGUIDANCE diff --git a/llama.cpp/common/log.cpp b/llama.cpp/common/log.cpp new file mode 100644 index 0000000000000000000000000000000000000000..69eda7513e9fbef1ab4f696ef36615830d322ad9 --- /dev/null +++ b/llama.cpp/common/log.cpp @@ -0,0 +1,393 @@ +#include "log.h" + +#include +#include +#include +#include +#include +#include +#include +#include + +int common_log_verbosity_thold = LOG_DEFAULT_LLAMA; + +void common_log_set_verbosity_thold(int verbosity) { + common_log_verbosity_thold = verbosity; +} + +static int64_t t_us() { + return std::chrono::duration_cast(std::chrono::system_clock::now().time_since_epoch()).count(); +} + +// colors +enum common_log_col : int { + COMMON_LOG_COL_DEFAULT = 0, + COMMON_LOG_COL_BOLD, + COMMON_LOG_COL_RED, + COMMON_LOG_COL_GREEN, + COMMON_LOG_COL_YELLOW, + COMMON_LOG_COL_BLUE, + COMMON_LOG_COL_MAGENTA, + COMMON_LOG_COL_CYAN, + COMMON_LOG_COL_WHITE, +}; + +// disable colors by default +static std::vector g_col = { + "", + "", + "", + "", + "", + "", + "", + "", + "", +}; + +struct common_log_entry { + enum ggml_log_level level; + + bool prefix; + + int64_t timestamp; + + std::vector msg; + + // signals the worker thread to stop + bool is_end; + + void print(FILE * file = nullptr) const { + FILE * fcur = file; + if (!fcur) { + // stderr displays DBG messages only when their verbosity level is not higher than the threshold + // these messages will still be logged to a file + if (level == GGML_LOG_LEVEL_DEBUG && common_log_verbosity_thold < LOG_DEFAULT_DEBUG) { + return; + } + + fcur = stdout; + + if (level != GGML_LOG_LEVEL_NONE) { + fcur = stderr; + } + } + + if (level != GGML_LOG_LEVEL_NONE && level != GGML_LOG_LEVEL_CONT && prefix) { + if (timestamp) { + // [M.s.ms.us] + fprintf(fcur, "%s%d.%02d.%03d.%03d%s ", + g_col[COMMON_LOG_COL_BLUE], + (int) (timestamp / 1000000 / 60), + (int) (timestamp / 1000000 % 60), + (int) (timestamp / 1000 % 1000), + (int) (timestamp % 1000), + g_col[COMMON_LOG_COL_DEFAULT]); + } + + switch (level) { + case GGML_LOG_LEVEL_INFO: fprintf(fcur, "%sI %s", g_col[COMMON_LOG_COL_GREEN], g_col[COMMON_LOG_COL_DEFAULT]); break; + case GGML_LOG_LEVEL_WARN: fprintf(fcur, "%sW %s", g_col[COMMON_LOG_COL_MAGENTA], "" ); break; + case GGML_LOG_LEVEL_ERROR: fprintf(fcur, "%sE %s", g_col[COMMON_LOG_COL_RED], "" ); break; + case GGML_LOG_LEVEL_DEBUG: fprintf(fcur, "%sD %s", g_col[COMMON_LOG_COL_YELLOW], "" ); break; + default: + break; + } + } + + fprintf(fcur, "%s", msg.data()); + + if (level == GGML_LOG_LEVEL_WARN || level == GGML_LOG_LEVEL_ERROR || level == GGML_LOG_LEVEL_DEBUG) { + fprintf(fcur, "%s", g_col[COMMON_LOG_COL_DEFAULT]); + } + + fflush(fcur); + } +}; + +struct common_log { + // default capacity - will be expanded if needed + common_log() : common_log(256) {} + + common_log(size_t capacity) { + file = nullptr; + prefix = false; + timestamps = false; + running = false; + t_start = t_us(); + + // initial message size - will be expanded if longer messages arrive + entries.resize(capacity); + for (auto & entry : entries) { + entry.msg.resize(256); + } + + head = 0; + tail = 0; + + resume(); + } + + ~common_log() { + pause(); + if (file) { + fclose(file); + } + } + +private: + std::mutex mtx; + std::thread thrd; + std::condition_variable cv; + + FILE * file; + + bool prefix; + bool timestamps; + bool running; + + int64_t t_start; + + // ring buffer of entries + std::vector entries; + size_t head; + size_t tail; + + // worker thread copies into this + common_log_entry cur; + +public: + void add(enum ggml_log_level level, const char * fmt, va_list args) { + std::lock_guard lock(mtx); + + if (!running) { + // discard messages while the worker thread is paused + return; + } + + auto & entry = entries[tail]; + + { + // cannot use args twice, so make a copy in case we need to expand the buffer + va_list args_copy; + va_copy(args_copy, args); + +#if 1 + const size_t n = vsnprintf(entry.msg.data(), entry.msg.size(), fmt, args); + if (n >= entry.msg.size()) { + entry.msg.resize(n + 1); + vsnprintf(entry.msg.data(), entry.msg.size(), fmt, args_copy); + } +#else + // hack for bolding arguments + + std::stringstream ss; + for (int i = 0; fmt[i] != 0; i++) { + if (fmt[i] == '%') { + ss << LOG_COL_BOLD; + while (fmt[i] != ' ' && fmt[i] != ')' && fmt[i] != ']' && fmt[i] != 0) ss << fmt[i++]; + ss << LOG_COL_DEFAULT; + if (fmt[i] == 0) break; + } + ss << fmt[i]; + } + const size_t n = vsnprintf(entry.msg.data(), entry.msg.size(), ss.str().c_str(), args); + if (n >= entry.msg.size()) { + entry.msg.resize(n + 1); + vsnprintf(entry.msg.data(), entry.msg.size(), ss.str().c_str(), args_copy); + } +#endif + va_end(args_copy); + } + + entry.level = level; + entry.prefix = prefix; + entry.timestamp = 0; + if (timestamps) { + entry.timestamp = t_us() - t_start; + } + entry.is_end = false; + + tail = (tail + 1) % entries.size(); + if (tail == head) { + // expand the buffer + std::vector new_entries(2*entries.size()); + + size_t new_tail = 0; + + do { + new_entries[new_tail] = std::move(entries[head]); + + head = (head + 1) % entries.size(); + new_tail = (new_tail + 1); + } while (head != tail); + + head = 0; + tail = new_tail; + + for (size_t i = tail; i < new_entries.size(); i++) { + new_entries[i].msg.resize(256); + } + + entries = std::move(new_entries); + } + + cv.notify_one(); + } + + void resume() { + std::lock_guard lock(mtx); + + if (running) { + return; + } + + running = true; + + thrd = std::thread([this]() { + while (true) { + { + std::unique_lock lock(mtx); + cv.wait(lock, [this]() { return head != tail; }); + + cur = entries[head]; + + head = (head + 1) % entries.size(); + } + + if (cur.is_end) { + break; + } + + cur.print(); // stdout and stderr + + if (file) { + cur.print(file); + } + } + }); + } + + void pause() { + { + std::lock_guard lock(mtx); + + if (!running) { + return; + } + + running = false; + + // push an entry to signal the worker thread to stop + { + auto & entry = entries[tail]; + entry.is_end = true; + + tail = (tail + 1) % entries.size(); + } + + cv.notify_one(); + } + + thrd.join(); + } + + void set_file(const char * path) { + pause(); + + if (file) { + fclose(file); + } + + if (path) { + file = fopen(path, "w"); + } else { + file = nullptr; + } + + resume(); + } + + void set_colors(bool colors) { + pause(); + + if (colors) { + g_col[COMMON_LOG_COL_DEFAULT] = LOG_COL_DEFAULT; + g_col[COMMON_LOG_COL_BOLD] = LOG_COL_BOLD; + g_col[COMMON_LOG_COL_RED] = LOG_COL_RED; + g_col[COMMON_LOG_COL_GREEN] = LOG_COL_GREEN; + g_col[COMMON_LOG_COL_YELLOW] = LOG_COL_YELLOW; + g_col[COMMON_LOG_COL_BLUE] = LOG_COL_BLUE; + g_col[COMMON_LOG_COL_MAGENTA] = LOG_COL_MAGENTA; + g_col[COMMON_LOG_COL_CYAN] = LOG_COL_CYAN; + g_col[COMMON_LOG_COL_WHITE] = LOG_COL_WHITE; + } else { + for (size_t i = 0; i < g_col.size(); i++) { + g_col[i] = ""; + } + } + + resume(); + } + + void set_prefix(bool prefix) { + std::lock_guard lock(mtx); + + this->prefix = prefix; + } + + void set_timestamps(bool timestamps) { + std::lock_guard lock(mtx); + + this->timestamps = timestamps; + } +}; + +// +// public API +// + +struct common_log * common_log_init() { + return new common_log; +} + +struct common_log * common_log_main() { + static struct common_log log; + + return &log; +} + +void common_log_pause(struct common_log * log) { + log->pause(); +} + +void common_log_resume(struct common_log * log) { + log->resume(); +} + +void common_log_free(struct common_log * log) { + delete log; +} + +void common_log_add(struct common_log * log, enum ggml_log_level level, const char * fmt, ...) { + va_list args; + va_start(args, fmt); + log->add(level, fmt, args); + va_end(args); +} + +void common_log_set_file(struct common_log * log, const char * file) { + log->set_file(file); +} + +void common_log_set_colors(struct common_log * log, bool colors) { + log->set_colors(colors); +} + +void common_log_set_prefix(struct common_log * log, bool prefix) { + log->set_prefix(prefix); +} + +void common_log_set_timestamps(struct common_log * log, bool timestamps) { + log->set_timestamps(timestamps); +} diff --git a/llama.cpp/common/log.h b/llama.cpp/common/log.h new file mode 100644 index 0000000000000000000000000000000000000000..96cb7c9fca9dbb862078cc266ab8f53657e70643 --- /dev/null +++ b/llama.cpp/common/log.h @@ -0,0 +1,103 @@ +#pragma once + +#include "ggml.h" // for ggml_log_level + +#define LOG_CLR_TO_EOL "\033[K\r" +#define LOG_COL_DEFAULT "\033[0m" +#define LOG_COL_BOLD "\033[1m" +#define LOG_COL_RED "\033[31m" +#define LOG_COL_GREEN "\033[32m" +#define LOG_COL_YELLOW "\033[33m" +#define LOG_COL_BLUE "\033[34m" +#define LOG_COL_MAGENTA "\033[35m" +#define LOG_COL_CYAN "\033[36m" +#define LOG_COL_WHITE "\033[37m" + +#ifndef __GNUC__ +# define LOG_ATTRIBUTE_FORMAT(...) +#elif defined(__MINGW32__) && !defined(__clang__) +# define LOG_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__))) +#else +# define LOG_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__))) +#endif + +#define LOG_DEFAULT_DEBUG 1 +#define LOG_DEFAULT_LLAMA 0 + +// needed by the LOG_TMPL macro to avoid computing log arguments if the verbosity lower +// set via common_log_set_verbosity() +extern int common_log_verbosity_thold; + +void common_log_set_verbosity_thold(int verbosity); // not thread-safe + +// the common_log uses an internal worker thread to print/write log messages +// when the worker thread is paused, incoming log messages are discarded +struct common_log; + +struct common_log * common_log_init(); +struct common_log * common_log_main(); // singleton, automatically destroys itself on exit +void common_log_pause (struct common_log * log); // pause the worker thread, not thread-safe +void common_log_resume(struct common_log * log); // resume the worker thread, not thread-safe +void common_log_free (struct common_log * log); + +LOG_ATTRIBUTE_FORMAT(3, 4) +void common_log_add(struct common_log * log, enum ggml_log_level level, const char * fmt, ...); + +// defaults: file = NULL, colors = false, prefix = false, timestamps = false +// +// regular log output: +// +// ggml_backend_metal_log_allocated_size: allocated buffer, size = 6695.84 MiB, ( 6695.91 / 21845.34) +// llm_load_tensors: ggml ctx size = 0.27 MiB +// llm_load_tensors: offloading 32 repeating layers to GPU +// llm_load_tensors: offloading non-repeating layers to GPU +// +// with prefix = true, timestamps = true, the log output will look like this: +// +// 0.00.035.060 D ggml_backend_metal_log_allocated_size: allocated buffer, size = 6695.84 MiB, ( 6695.91 / 21845.34) +// 0.00.035.064 I llm_load_tensors: ggml ctx size = 0.27 MiB +// 0.00.090.578 I llm_load_tensors: offloading 32 repeating layers to GPU +// 0.00.090.579 I llm_load_tensors: offloading non-repeating layers to GPU +// +// I - info (stdout, V = 0) +// W - warning (stderr, V = 0) +// E - error (stderr, V = 0) +// D - debug (stderr, V = LOG_DEFAULT_DEBUG) +// + +void common_log_set_file (struct common_log * log, const char * file); // not thread-safe +void common_log_set_colors (struct common_log * log, bool colors); // not thread-safe +void common_log_set_prefix (struct common_log * log, bool prefix); // whether to output prefix to each log +void common_log_set_timestamps(struct common_log * log, bool timestamps); // whether to output timestamps in the prefix + +// helper macros for logging +// use these to avoid computing log arguments if the verbosity of the log is higher than the threshold +// +// for example: +// +// LOG_DBG("this is a debug message: %d\n", expensive_function()); +// +// this will avoid calling expensive_function() if LOG_DEFAULT_DEBUG > common_log_verbosity_thold +// + +#define LOG_TMPL(level, verbosity, ...) \ + do { \ + if ((verbosity) <= common_log_verbosity_thold) { \ + common_log_add(common_log_main(), (level), __VA_ARGS__); \ + } \ + } while (0) + +#define LOG(...) LOG_TMPL(GGML_LOG_LEVEL_NONE, 0, __VA_ARGS__) +#define LOGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_NONE, verbosity, __VA_ARGS__) + +#define LOG_INF(...) LOG_TMPL(GGML_LOG_LEVEL_INFO, 0, __VA_ARGS__) +#define LOG_WRN(...) LOG_TMPL(GGML_LOG_LEVEL_WARN, 0, __VA_ARGS__) +#define LOG_ERR(...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, 0, __VA_ARGS__) +#define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, LOG_DEFAULT_DEBUG, __VA_ARGS__) +#define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT, 0, __VA_ARGS__) + +#define LOG_INFV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_INFO, verbosity, __VA_ARGS__) +#define LOG_WRNV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_WARN, verbosity, __VA_ARGS__) +#define LOG_ERRV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, verbosity, __VA_ARGS__) +#define LOG_DBGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, verbosity, __VA_ARGS__) +#define LOG_CNTV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_CONT, verbosity, __VA_ARGS__) diff --git a/llama.cpp/common/ngram-cache.cpp b/llama.cpp/common/ngram-cache.cpp new file mode 100644 index 0000000000000000000000000000000000000000..992d330e19662a84de0f51edca4bf97a2754fddb --- /dev/null +++ b/llama.cpp/common/ngram-cache.cpp @@ -0,0 +1,286 @@ +#include "ngram-cache.h" +#include "common.h" +#include "log.h" + +#include +#include +#include +#include +#include +#include + +void common_ngram_cache_update(common_ngram_cache & ngram_cache, int ngram_min, int ngram_max, + std::vector & inp, int nnew, bool print_progress) { + const int64_t t_start_ms = ggml_time_ms(); + const int64_t inp_size = inp.size(); + + const int64_t n_todo = inp_size * (ngram_max - ngram_min + 1); + int64_t n_done = 0; + + for (int64_t ngram_size = ngram_min; ngram_size <= ngram_max; ++ngram_size) { + const int64_t i_start = std::max(inp_size - nnew, ngram_size); + for (int64_t i = i_start; i < inp_size; ++i) { + const int64_t ngram_start = i - ngram_size; + common_ngram ngram(&inp[ngram_start], ngram_size); + const llama_token token = inp[i]; + + common_ngram_cache::iterator part_it = ngram_cache.find(ngram); + if (part_it == ngram_cache.end()) { + common_ngram_cache_part part; + part.emplace(token, 1); + ngram_cache.emplace(ngram, part); + } else { + common_ngram_cache_part::iterator token_count_it = part_it->second.find(token); + if (token_count_it == part_it->second.end()) { + part_it->second.emplace(token, 1); + } else { + token_count_it->second++; + } + } + ++n_done; + + if (print_progress && n_done % 10000000 == 0) { + const int64_t t_now_ms = ggml_time_ms(); + const int64_t eta_ms = (inp_size*(ngram_max-ngram_min+1) - n_done) * (t_now_ms - t_start_ms) / n_done; + const int64_t eta_min = eta_ms / (60*1000); + const int64_t eta_s = (eta_ms - 60*1000*eta_min) / 1000; + + fprintf(stderr, "%s: %" PRId64 "/%" PRId64 " done, ETA: %02" PRId64 ":%02" PRId64 "\n", __func__, n_done, n_todo, eta_min, eta_s); + } + } + } +} + +// Helper function to get a token from the combined, speculative sequence of inp and draft. +static llama_token get_token(const std::vector & inp, const std::vector & draft, const size_t i) { + return i < inp.size() ? inp[i] : draft[1 + i - inp.size()]; +} + +// If sample size or percentage are below these thresholds the draft is aborted early: +constexpr int draft_min_sample_size_lax[LLAMA_NGRAM_MAX] = { 2, 2, 1, 1}; +constexpr int draft_min_percent_lax[LLAMA_NGRAM_MAX] = {66, 50, 50, 50}; +constexpr int draft_min_sample_size_strict[LLAMA_NGRAM_MAX] = { 4, 3, 2, 2}; +constexpr int draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66}; + +// Helper function that tries to draft a token from only the static ngram cache: +static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram ngram_static) { + common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static); + if (part_static_it == nc_static.end()) { + return LLAMA_TOKEN_NULL; + } + const common_ngram_cache_part part_static = part_static_it->second; + + int max_count_static = 0; + int sum_count_static = 0; + llama_token max_token = LLAMA_TOKEN_NULL; + + for (std::pair token_count_static : part_static) { + const llama_token token = token_count_static.first; + const int32_t count_static = token_count_static.second; + + if (count_static > max_count_static) { + max_token = token; + max_count_static = count_static; + } + sum_count_static += count_static; + } + + if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) { + return LLAMA_TOKEN_NULL; + } + if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) { + return LLAMA_TOKEN_NULL; + } + return max_token; +} + +// Try to draft a token from primary cache (context/dynamic), validate with static cache: +static llama_token try_draft( + common_ngram_cache & nc_primary, const std::vector & ngrams_primary, common_ngram_cache_part & part_static, + const int * min_sample_size, const int * min_percent) { + + llama_token drafted_token = LLAMA_TOKEN_NULL; + + for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == LLAMA_TOKEN_NULL; --i) { + const common_ngram ngram_primary = ngrams_primary[i]; + + common_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary); + if (part_primary_it == nc_primary.end()) { + continue; + } + const common_ngram_cache_part part_primary = part_primary_it->second; + + int max_count_primary = 0; + int max_count_static = 0; + int sum_count_primary = 0; + llama_token max_token = LLAMA_TOKEN_NULL; + + for (std::pair token_count_primary : part_primary) { + const llama_token token = token_count_primary.first; + + common_ngram_cache_part::iterator token_count_static_it = part_static.find(token); + + const int32_t count_primary = token_count_primary.second; + const int32_t count_static = token_count_static_it != part_static.end() ? 100*token_count_static_it->second : 1; + + if (count_primary*count_static > max_count_primary*max_count_static) { + max_token = token; + max_count_primary = count_primary; + max_count_static = count_static; + } + sum_count_primary += count_primary; + } + + if (sum_count_primary < min_sample_size[i]) { + continue; + } + if (100*max_count_primary < min_percent[i]*sum_count_primary) { + continue;; + } + drafted_token = max_token; + } + + return drafted_token; +} + +void common_ngram_cache_draft( + std::vector & inp, std::vector & draft, int n_draft, int ngram_min, int ngram_max, + common_ngram_cache & nc_context, common_ngram_cache & nc_dynamic, common_ngram_cache & nc_static +) { + GGML_ASSERT(draft.size() == 1); + const int inp_size = inp.size(); + + if (inp_size < LLAMA_NGRAM_STATIC) { + return; + } + + while ((int) draft.size()-1 < n_draft) { + llama_token drafted_token = LLAMA_TOKEN_NULL; + + const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1; + common_ngram ngram_static; + for (int j = ngram_start_static; j < ngram_start_static + LLAMA_NGRAM_STATIC; ++j) { + ngram_static.tokens[j-ngram_start_static] = get_token(inp, draft, j); + } + common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static); + common_ngram_cache_part part_static; + if (part_static_it != nc_static.end()) { + part_static = part_static_it->second; + } + + // cd = context + dynamic + std::vector ngrams_cd; + for (int ngram_size_cd = ngram_min; ngram_size_cd <= ngram_max; ++ngram_size_cd) { + const int ngram_start_cd = inp_size-ngram_size_cd + draft.size()-1; + common_ngram ngram_cd; + for (int j = ngram_start_cd; j < ngram_start_cd + ngram_size_cd; ++j) { + ngram_cd.tokens[j-ngram_start_cd] = get_token(inp, draft, j); + } + ngrams_cd.push_back(ngram_cd); + } + if (drafted_token == LLAMA_TOKEN_NULL) { + drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax); + } + if (drafted_token == LLAMA_TOKEN_NULL) { + drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict); + } + if (drafted_token == LLAMA_TOKEN_NULL) { + drafted_token = try_draft(nc_static, ngram_static); + } + + if (drafted_token == LLAMA_TOKEN_NULL) { + break; + } + + LOG(" - draft candidate: token=%d\n", drafted_token); + draft.push_back(drafted_token); + } +} + +void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & filename) { + std::ofstream file_out(filename, std::ios::binary); + for (std::pair item : ngram_cache) { + const common_ngram ngram = item.first; + common_ngram_cache_part token_counts = item.second; + GGML_ASSERT(!token_counts.empty()); + const int32_t ntokens = token_counts.size(); + GGML_ASSERT(ntokens > 0); + + file_out.write(reinterpret_cast(&ngram), sizeof(common_ngram)); + file_out.write(reinterpret_cast(&ntokens), sizeof(int32_t)); + for (std::pair item2 : token_counts) { + const llama_token token = item2.first; + const int32_t count = item2.second; + GGML_ASSERT(count > 0); + + file_out.write(reinterpret_cast(&token), sizeof(llama_token)); + file_out.write(reinterpret_cast(&count), sizeof(int32_t)); + } + } + +} + +common_ngram_cache common_ngram_cache_load(std::string & filename) { + std::ifstream hashmap_file(filename, std::ios::binary); + if (!hashmap_file) { + throw std::ifstream::failure("Unable to open file " + filename); + } + common_ngram_cache ngram_cache; + + common_ngram ngram; + int32_t ntokens; + llama_token token; + int32_t count; + + char * ngramc = reinterpret_cast(&ngram); + char * ntokensc = reinterpret_cast(&ntokens); + char * tokenc = reinterpret_cast(&token); + char * countc = reinterpret_cast(&count); + while(hashmap_file.read(ngramc, sizeof(common_ngram))) { + GGML_ASSERT(!hashmap_file.eof()); + GGML_ASSERT(hashmap_file.read(ntokensc, sizeof(int32_t))); + GGML_ASSERT(ntokens > 0); + common_ngram_cache_part token_counts; + + for (int i = 0; i < ntokens; ++i) { + GGML_ASSERT(!hashmap_file.eof()); + GGML_ASSERT(hashmap_file.read(tokenc, sizeof(llama_token))); + GGML_ASSERT(!hashmap_file.eof()); + GGML_ASSERT(hashmap_file.read(countc, sizeof(int32_t))); + GGML_ASSERT(count > 0); + token_counts.emplace(token, count); + } + + ngram_cache.emplace(ngram, token_counts); + } + GGML_ASSERT(hashmap_file.eof()); + + return ngram_cache; +} + +void common_ngram_cache_merge(common_ngram_cache & ngram_cache_target, common_ngram_cache & ngram_cache_add) { + for (std::pair ngram_part : ngram_cache_add) { + const common_ngram ngram = ngram_part.first; + common_ngram_cache_part part = ngram_part.second; + + common_ngram_cache::iterator part_merged_it = ngram_cache_target.find(ngram); + if (part_merged_it == ngram_cache_target.end()) { + ngram_cache_target.emplace(ngram, part); + continue; + } + + for (std::pair token_count : part) { + const llama_token token = token_count.first; + const int32_t count = token_count.second; + GGML_ASSERT(count > 0); + + common_ngram_cache_part::iterator token_count_merged_it = part_merged_it->second.find(token); + if (token_count_merged_it == part_merged_it->second.end()) { + part_merged_it->second.emplace(token, count); + continue; + } + + token_count_merged_it->second += count; + } + } +} diff --git a/llama.cpp/common/ngram-cache.h b/llama.cpp/common/ngram-cache.h new file mode 100644 index 0000000000000000000000000000000000000000..b2e64aca670b709e3c963ceac7c00a625d29896d --- /dev/null +++ b/llama.cpp/common/ngram-cache.h @@ -0,0 +1,101 @@ +#pragma once + +#include "llama.h" + +#include +#include +#include + +#define LLAMA_NGRAM_MIN 1 +#define LLAMA_NGRAM_MAX 4 +#define LLAMA_NGRAM_STATIC 2 + +// Data structures to map n-grams to empirical token probabilities: + +struct common_ngram { + llama_token tokens[LLAMA_NGRAM_MAX]; + + common_ngram() { + for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { + tokens[i] = LLAMA_TOKEN_NULL; + } + } + + common_ngram(const llama_token * input, const int ngram_size) { + for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { + tokens[i] = i < ngram_size ? input[i] : LLAMA_TOKEN_NULL; + } + } + + bool operator==(const common_ngram & other) const { + for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { + if (tokens[i] != other.tokens[i]) { + return false; + } + } + return true; + } +}; + +struct common_token_hash_function { + size_t operator()(const llama_token token) const { + // see https://probablydance.com/2018/06/16/fibonacci-hashing-the-optimization-that-the-world-forgot-or-a-better-alternative-to-integer-modulo/ + return token * 11400714819323198485llu; + } +}; + +struct common_ngram_hash_function { + size_t operator()(const common_ngram & ngram) const { + size_t hash = common_token_hash_function{}(ngram.tokens[0]); + for (int i = 1; i < LLAMA_NGRAM_MAX; ++i) { + hash ^= common_token_hash_function{}(ngram.tokens[i]); + } + return hash; + } +}; + +// token -> number of times token has been seen +typedef std::unordered_map common_ngram_cache_part; + +// n-gram -> empirical distribution of following tokens +typedef std::unordered_map common_ngram_cache; + + +// Update an ngram cache with tokens. +// ngram_cache: the cache to modify. +// ngram_min/ngram_max: the min/max size of the ngrams to extract from inp_data. +// inp_data: the token sequence with which to update ngram_cache. +// nnew: how many new tokens have been appended to inp_data since the last call to this function. +// print_progress: whether to print progress to stderr. +// +// In order to get correct results inp_data can ONLY BE APPENDED TO. +// Changes in the middle need a complete rebuild. +void common_ngram_cache_update( + common_ngram_cache & ngram_cache, int ngram_min, int ngram_max, std::vector & inp_data, int nnew, bool print_progress); + +// Try to draft tokens from ngram caches. +// inp: the tokens generated so far. +// draft: the token sequence to draft. Expected to initially contain the previously sampled token. +// n_draft: maximum number of tokens to add to draft. +// ngram_min/gram_max: the min/max size of the ngrams in nc_context and nc_dynamic. +// nc_context: ngram cache based on current context. +// nc_dynamic: ngram cache based on previous user generations. +// nc_static: ngram cache generated from a large text corpus, used for validation. +void common_ngram_cache_draft( + std::vector & inp, std::vector & draft, int n_draft, int ngram_min, int ngram_max, + common_ngram_cache & nc_context, common_ngram_cache & nc_dynamic, common_ngram_cache & nc_static); + +// Save an ngram cache to a file. +// ngram_cache: the ngram cache to save. +// filename: the path under which to save the ngram cache. +void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & filename); + +// Load an ngram cache saved with common_ngram_cache_save. +// filename: the path from which to load the ngram cache. +// returns: an ngram cache containing the information saved to filename. +common_ngram_cache common_ngram_cache_load(std::string & filename); + +// Merge two ngram caches. +// ngram_cache_target: the ngram cache to which to add the information from ngram_cache_add. +// ngram_cache_add: the ngram cache to add to ngram_cache_target. +void common_ngram_cache_merge(common_ngram_cache & ngram_cache_target, common_ngram_cache & ngram_cache_add); diff --git a/llama.cpp/common/regex-partial.cpp b/llama.cpp/common/regex-partial.cpp new file mode 100644 index 0000000000000000000000000000000000000000..2d362fbd7834bf4c8c11c943377e463f81183081 --- /dev/null +++ b/llama.cpp/common/regex-partial.cpp @@ -0,0 +1,204 @@ +#include "regex-partial.h" +#include "common.h" +#include +#include + +common_regex::common_regex(const std::string & pattern) : + pattern(pattern), + rx(pattern), + rx_reversed_partial(regex_to_reversed_partial_regex(pattern)) {} + +common_regex_match common_regex::search(const std::string & input, size_t pos, bool as_match) const { + std::smatch match; + if (pos > input.size()) { + throw std::runtime_error("Position out of bounds"); + } + auto start = input.begin() + pos; + auto found = as_match + ? std::regex_match(start, input.end(), match, rx) + : std::regex_search(start, input.end(), match, rx); + if (found) { + common_regex_match res; + res.type = COMMON_REGEX_MATCH_TYPE_FULL; + for (size_t i = 0; i < match.size(); ++i) { + auto begin = pos + match.position(i); + res.groups.emplace_back(begin, begin + match.length(i)); + } + return res; + } + std::match_results srmatch; + if (std::regex_match(input.rbegin(), input.rend() - pos, srmatch, rx_reversed_partial)) { + auto group = srmatch[1].str(); + if (group.length() != 0) { + auto it = srmatch[1].second.base(); + // auto position = static_cast(std::distance(input.begin(), it)); + if ((!as_match) || it == input.begin()) { + common_regex_match res; + res.type = COMMON_REGEX_MATCH_TYPE_PARTIAL; + const size_t begin = std::distance(input.begin(), it); + const size_t end = input.size(); + if (begin == std::string::npos || end == std::string::npos || begin > end) { + throw std::runtime_error("Invalid range"); + } + res.groups.push_back({begin, end}); + return res; + } + } + } + return {}; +} + +/* + Transforms a regex pattern to a partial match pattern that operates on a reversed input string to find partial final matches of the original pattern. + + Ideally we'd like to use boost::match_partial (https://beta.boost.org/doc/libs/1_59_0/libs/regex/doc/html/boost_regex/partial_matches.html) + to see if a string ends with a partial regex match, but but it's not in std::regex yet. + Instead, we'll the regex into a partial match regex operating as a full match on the reverse iterators of the input. + + - /abcd/ -> (dcba|cba|ba|a).* -> ((?:(?:(?:(?:d)?c)?b)?a).* + - /a|b/ -> (a|b).* + - /a*?/ -> error, could match "" + - /a*b/ -> ((?:b)?a*+).* (final repetitions become eager) + - /.*?ab/ -> ((?:b)?a).* (merge .*) + - /a.*?b/ -> ((?:b)?.*?a).* (keep reluctant matches) + - /a(bc)d/ -> ((?:(?:d)?(?:(?:c)?b))?a).* + - /a(bc|de)/ -> ((?:(?:(?:e)?d)?|(?:(?:c)?b)?)?a).* + - /ab{2,4}c/ -> abbb?b?c -> ((?:(?:(?:(?:(?:c)?b)?b)?b?)?b?)?a).* + + The regex will match a reversed string fully, and the end of the first (And only) capturing group will indicate the reversed start of the original partial pattern + (i.e. just where the final .* starts in the inverted pattern; all other groups are turned into non-capturing groups, and reluctant quantifiers are ignored) +*/ +std::string regex_to_reversed_partial_regex(const std::string & pattern) { + auto it = pattern.begin(); + const auto end = pattern.end(); + + std::function process = [&]() { + std::vector> alternatives(1); + std::vector * sequence = &alternatives.back(); + + while (it != end) { + if (*it == '[') { + auto start = it; + ++it; + while (it != end) { + if ((*it == '\\') && (++it != end)) { + ++it; + } else if ((it != end) && (*it == ']')) { + break; + } else { + ++it; + } + } + if (it == end) { + throw std::runtime_error("Unmatched '[' in pattern"); + } + ++it; + sequence->push_back(std::string(start, it)); + } else if (*it == '*' || *it == '?' || *it == '+') { + if (sequence->empty()) { + throw std::runtime_error("Quantifier without preceding element"); + } + sequence->back() += *it; + auto is_star = *it == '*'; + ++it; + if (is_star) { + if (*it == '?') { + ++it; + } + } + } else if (*it == '{') { + if (sequence->empty()) { + throw std::runtime_error("Repetition without preceding element"); + } + ++it; + auto start = it; + while (it != end && *it != '}') { + ++it; + } + if (it == end) { + throw std::runtime_error("Unmatched '{' in pattern"); + } + auto parts = string_split(std::string(start, it), ","); + ++it; + if (parts.size() > 2) { + throw std::runtime_error("Invalid repetition range in pattern"); + } + + auto parseOptInt = [&](const std::string & s, const std::optional & def = std::nullopt) -> std::optional { + if (s.empty()) { + return def; + } + return std::stoi(s); + }; + auto min = parseOptInt(parts[0], 0); + auto max = parts.size() == 1 ? min : parseOptInt(parts[1]); + if (min && max && *max < *min) { + throw std::runtime_error("Invalid repetition range in pattern"); + } + // Brutal but... let's repeat at least min times, then ? for the delta between min & max (or * for unbounded) + auto part = sequence->back(); + sequence->pop_back(); + for (int i = 0; i < *min; i++) { + sequence->push_back(part); + } + if (max) { + for (int i = *min; i < *max; i++) { + sequence->push_back(part + "?"); + } + } else { + sequence->push_back(part + "*"); + } + } else if (*it == '(') { + ++it; + if (it != end && *it == '?' && (it + 1 != end) && *(it + 1) == ':') { + it += 2; + } + auto sub = process(); + if (*it != ')') { + throw std::runtime_error("Unmatched '(' in pattern"); + } + ++it; + auto & part = sequence->emplace_back("(?:"); + part += sub; + part += ")"; + } else if (*it == ')') { + break; + } else if (*it == '|') { + ++it; + alternatives.emplace_back(); + sequence = &alternatives.back(); + } else if (*it == '\\' && (++it != end)) { + auto str = std::string("\\") + *it; + sequence->push_back(str); + ++it; + } else if (it != end) { + sequence->push_back(std::string(1, *it)); + ++it; + } + } + + // /abcd/ -> (dcba|cba|ba|a).* -> ((?:(?:(?:d)?c)?b)?a).* + // if n(=4) parts, opening n-1(=3) non-capturing groups after the 1 capturing group + // We'll do the outermost capturing group and final .* in the enclosing function. + std::vector res_alts; + for (const auto & parts : alternatives) { + auto & res = res_alts.emplace_back(); + for (size_t i = 0; i < parts.size() - 1; i++) { + res += "(?:"; + } + for (auto it = parts.rbegin(); it != parts.rend(); ++it) { + res += *it; + if (it != parts.rend() - 1) { + res += ")?"; + } + } + } + return string_join(res_alts, "|"); + }; + auto res = process(); + if (it != end) { + throw std::runtime_error("Unmatched '(' in pattern"); + } + + return "(" + res + ")[\\s\\S]*"; +} diff --git a/llama.cpp/common/regex-partial.h b/llama.cpp/common/regex-partial.h new file mode 100644 index 0000000000000000000000000000000000000000..c0b3f5148e308e7fb212adf2e46d88c2e887a7f4 --- /dev/null +++ b/llama.cpp/common/regex-partial.h @@ -0,0 +1,56 @@ +#pragma once + +#include +#include + +enum common_regex_match_type { + COMMON_REGEX_MATCH_TYPE_NONE, + COMMON_REGEX_MATCH_TYPE_PARTIAL, + COMMON_REGEX_MATCH_TYPE_FULL, +}; + +struct common_string_range { + size_t begin; + size_t end; + common_string_range(size_t begin, size_t end) : begin(begin), end(end) { + if (begin > end) { + throw std::runtime_error("Invalid range"); + } + } + // prevent default ctor + common_string_range() = delete; + bool empty() const { + return begin == end; + } + bool operator==(const common_string_range & other) const { + return begin == other.begin && end == other.end; + } +}; + +struct common_regex_match { + common_regex_match_type type = COMMON_REGEX_MATCH_TYPE_NONE; + std::vector groups; + + bool operator==(const common_regex_match & other) const { + return type == other.type && groups == other.groups; + } + bool operator!=(const common_regex_match & other) const { + return !(*this == other); + } +}; + +class common_regex { + std::string pattern; + std::regex rx; + std::regex rx_reversed_partial; + + public: + explicit common_regex(const std::string & pattern); + + common_regex_match search(const std::string & input, size_t pos, bool as_match = false) const; + + const std::string & str() const { return pattern; } +}; + +// For testing only (pretty print of failures). +std::string regex_to_reversed_partial_regex(const std::string & pattern); diff --git a/llama.cpp/common/sampling.cpp b/llama.cpp/common/sampling.cpp new file mode 100644 index 0000000000000000000000000000000000000000..000ed178d14a69c95d05f2423d5f2db2eff6821c --- /dev/null +++ b/llama.cpp/common/sampling.cpp @@ -0,0 +1,579 @@ +#include "sampling.h" + +#include "common.h" +#include "log.h" + +#include +#include +#include + +// the ring buffer works similarly to std::deque, but with a fixed capacity +// TODO: deduplicate with llama-impl.h +template +struct ring_buffer { + ring_buffer(size_t cap) : capacity(cap), data(cap) {} + + T & front() { + if (sz == 0) { + throw std::runtime_error("ring buffer is empty"); + } + return data[first]; + } + + const T & front() const { + if (sz == 0) { + throw std::runtime_error("ring buffer is empty"); + } + return data[first]; + } + + T & back() { + if (sz == 0) { + throw std::runtime_error("ring buffer is empty"); + } + return data[pos]; + } + + const T & back() const { + if (sz == 0) { + throw std::runtime_error("ring buffer is empty"); + } + return data[pos]; + } + + void push_back(const T & value) { + if (sz == capacity) { + // advance the start when buffer is full + first = (first + 1) % capacity; + } else { + sz++; + } + data[pos] = value; + pos = (pos + 1) % capacity; + } + + T pop_front() { + if (sz == 0) { + throw std::runtime_error("ring buffer is empty"); + } + T value = data[first]; + first = (first + 1) % capacity; + sz--; + return value; + } + + const T & rat(size_t i) const { + if (i >= sz) { + throw std::runtime_error("ring buffer: index out of bounds"); + } + return data[(first + sz - i - 1) % capacity]; + } + + std::vector to_vector() const { + std::vector result; + result.reserve(sz); + for (size_t i = 0; i < sz; i++) { + result.push_back(data[(first + i) % capacity]); + } + return result; + } + + void clear() { + // here only reset the status of the buffer + sz = 0; + first = 0; + pos = 0; + } + + bool empty() const { + return sz == 0; + } + + size_t size() const { + return sz; + } + + size_t capacity = 0; + size_t sz = 0; + size_t first = 0; + size_t pos = 0; + std::vector data; +}; + +struct common_sampler { + common_params_sampling params; + + struct llama_sampler * grmr; + struct llama_sampler * chain; + + ring_buffer prev; + + std::vector cur; + + llama_token_data_array cur_p; + + void set_logits(struct llama_context * ctx, int idx) { + const auto * logits = llama_get_logits_ith(ctx, idx); + + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const int n_vocab = llama_vocab_n_tokens(vocab); + + cur.resize(n_vocab); + + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f}; + } + + cur_p = { cur.data(), cur.size(), -1, false }; + } +}; + +std::string common_params_sampling::print() const { + char result[1024]; + + snprintf(result, sizeof(result), + "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n" + "\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n" + "\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, top_n_sigma = %.3f, temp = %.3f\n" + "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f", + penalty_last_n, penalty_repeat, penalty_freq, penalty_present, + dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, + top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, top_n_sigma, temp, + mirostat, mirostat_eta, mirostat_tau); + + return std::string(result); +} + +struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) { + const llama_vocab * vocab = llama_model_get_vocab(model); + + llama_sampler_chain_params lparams = llama_sampler_chain_default_params(); + + lparams.no_perf = params.no_perf; + + struct llama_sampler * grmr; + if (params.grammar.compare(0, 11, "%llguidance") == 0) { +#ifdef LLAMA_USE_LLGUIDANCE + grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str()); +#else + GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled"); +#endif // LLAMA_USE_LLGUIDANCE + } else { + std::vector trigger_patterns; + std::vector patterns_anywhere; + std::vector trigger_tokens; + for (const auto & trigger : params.grammar_triggers) { + switch (trigger.type) { + case COMMON_GRAMMAR_TRIGGER_TYPE_WORD: + { + const auto & word = trigger.value; + patterns_anywhere.push_back(regex_escape(word)); + break; + } + case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN: + { + patterns_anywhere.push_back(trigger.value); + break; + } + case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL: + { + trigger_patterns.push_back(trigger.value); + break; + } + case COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN: + { + const auto token = trigger.token; + trigger_tokens.push_back(token); + break; + } + default: + GGML_ASSERT(false && "unknown trigger type"); + } + } + + if (!patterns_anywhere.empty()) { + trigger_patterns.push_back("^[\\s\\S]*?(" + string_join(patterns_anywhere, "|") + ")[\\s\\S]*"); + } + + std::vector trigger_patterns_c; + trigger_patterns_c.reserve(trigger_patterns.size()); + for (const auto & regex : trigger_patterns) { + trigger_patterns_c.push_back(regex.c_str()); + } + + grmr = params.grammar_lazy + ? llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root", + trigger_patterns_c.data(), trigger_patterns_c.size(), + trigger_tokens.data(), trigger_tokens.size()) + : llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"); + if (!grmr) { + return nullptr; + } + } + + auto * result = new common_sampler { + /* .params = */ params, + /* .grmr = */ grmr, + /* .chain = */ llama_sampler_chain_init(lparams), + /* .prev = */ ring_buffer(std::max(32, params.n_prev)), + /* .cur = */ {}, + /* .cur_p = */ {}, + }; + + llama_sampler_chain_add(result->chain, + llama_sampler_init_logit_bias( + llama_vocab_n_tokens(vocab), + params.logit_bias.size(), + params.logit_bias.data())); + + if (params.mirostat == 0) { + for (const auto & cnstr : params.samplers) { + switch (cnstr) { + case COMMON_SAMPLER_TYPE_DRY: + { + std::vector c_breakers; + c_breakers.reserve(params.dry_sequence_breakers.size()); + for (const auto & str : params.dry_sequence_breakers) { + c_breakers.push_back(str.c_str()); + } + + llama_sampler_chain_add(result->chain, llama_sampler_init_dry (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size())); + } + break; + case COMMON_SAMPLER_TYPE_TOP_K: + llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); + break; + case COMMON_SAMPLER_TYPE_TOP_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: + llama_sampler_chain_add(result->chain, llama_sampler_init_top_n_sigma (params.top_n_sigma)); + break; + case COMMON_SAMPLER_TYPE_MIN_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_XTC: + llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); + break; + case COMMON_SAMPLER_TYPE_TYPICAL_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_TEMPERATURE: + llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); + break; + case COMMON_SAMPLER_TYPE_INFILL: + llama_sampler_chain_add(result->chain, llama_sampler_init_infill (vocab)); + break; + case COMMON_SAMPLER_TYPE_PENALTIES: + llama_sampler_chain_add(result->chain, llama_sampler_init_penalties (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present)); + break; + default: + GGML_ASSERT(false && "unknown sampler type"); + } + } + llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed)); + } else if (params.mirostat == 1) { + llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); + llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100)); + } else if (params.mirostat == 2) { + llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); + llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta)); + } else { + GGML_ASSERT(false && "unknown mirostat version"); + } + + return result; +} + +void common_sampler_free(struct common_sampler * gsmpl) { + if (gsmpl) { + llama_sampler_free(gsmpl->grmr); + + llama_sampler_free(gsmpl->chain); + + delete gsmpl; + } +} + +void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) { + if (accept_grammar) { + llama_sampler_accept(gsmpl->grmr, token); + } + + llama_sampler_accept(gsmpl->chain, token); + + gsmpl->prev.push_back(token); +} + +void common_sampler_reset(struct common_sampler * gsmpl) { + llama_sampler_reset(gsmpl->grmr); + + llama_sampler_reset(gsmpl->chain); +} + +struct common_sampler * common_sampler_clone(common_sampler * gsmpl) { + return new common_sampler { + /* .params = */ gsmpl->params, + /* .grmr = */ llama_sampler_clone(gsmpl->grmr), + /* .chain = */ llama_sampler_clone(gsmpl->chain), + /* .prev = */ gsmpl->prev, + /* .cur = */ gsmpl->cur, + /* .cur_p = */ gsmpl->cur_p, + }; +} + +void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl) { + // TODO: measure grammar performance + + if (gsmpl) { + llama_perf_sampler_print(gsmpl->chain); + } + if (ctx) { + llama_perf_context_print(ctx); + } +} + +llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) { + gsmpl->set_logits(ctx, idx); + + auto & grmr = gsmpl->grmr; + auto & chain = gsmpl->chain; + auto & cur_p = gsmpl->cur_p; // initialized by set_logits + + if (grammar_first) { + llama_sampler_apply(grmr, &cur_p); + } + + llama_sampler_apply(chain, &cur_p); + + GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration"); + + const llama_token id = cur_p.data[cur_p.selected].id; + + if (grammar_first) { + return id; + } + + // check if it the sampled token fits the grammar + { + llama_token_data single_token_data = { id, 1.0f, 0.0f }; + llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false }; + + llama_sampler_apply(grmr, &single_token_data_array); + + const bool is_valid = single_token_data_array.data[0].logit != -INFINITY; + if (is_valid) { + return id; + } + } + + // resampling: + // if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain + gsmpl->set_logits(ctx, idx); + + llama_sampler_apply(grmr, &cur_p); + llama_sampler_apply(chain, &cur_p); + + GGML_ASSERT(cur_p.selected != -1 && "no selected token during re-sampling - check your sampling configuration"); + + return cur_p.data[cur_p.selected].id; +} + +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector & idxs, const llama_tokens & draft, bool grammar_first) { + GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1"); + + std::vector result; + result.reserve(idxs.size()); + + size_t i = 0; + for (; i < draft.size(); i++) { + const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first); + + common_sampler_accept(gsmpl, id, true); + + result.push_back(id); + + if (draft[i] != id) { + break; + } + } + + if (i == draft.size()) { + const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first); + + common_sampler_accept(gsmpl, id, true); + + result.push_back(id); + } + + return result; +} + +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) { + std::vector idxs(draft.size() + 1); + for (size_t i = 0; i < idxs.size(); ++i) { + idxs[i] = i; + } + + return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first); +} + +uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) { + return llama_sampler_get_seed(gsmpl->chain); +} + +// helpers + +llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl) { + return &gsmpl->cur_p; +} + +llama_token common_sampler_last(const struct common_sampler * gsmpl) { + return gsmpl->prev.rat(0); +} + +std::string common_sampler_print(const struct common_sampler * gsmpl) { + std::string result = "logits "; + + for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) { + const auto * smpl = llama_sampler_chain_get(gsmpl->chain, i); + result += std::string("-> ") + llama_sampler_name(smpl) + " "; + } + + return result; +} + +std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx_main, int n) { + n = std::min(n, (int) gsmpl->prev.size()); + + if (n <= 0) { + return ""; + } + + std::string result; + result.reserve(8*n); // 8 is the average length of a token [citation needed], TODO: compute this from the vocab + + for (int i = n - 1; i >= 0; i--) { + const llama_token id = gsmpl->prev.rat(i); + + GGML_ASSERT(id != LLAMA_TOKEN_NULL && "null token in the sampling history - should not happen"); + + result += common_token_to_piece(ctx_main, id); + } + + return result; +} + +char common_sampler_type_to_chr(enum common_sampler_type cnstr) { + switch (cnstr) { + case COMMON_SAMPLER_TYPE_DRY: return 'd'; + case COMMON_SAMPLER_TYPE_TOP_K: return 'k'; + case COMMON_SAMPLER_TYPE_TYPICAL_P: return 'y'; + case COMMON_SAMPLER_TYPE_TOP_P: return 'p'; + case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: return 's'; + case COMMON_SAMPLER_TYPE_MIN_P: return 'm'; + case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't'; + case COMMON_SAMPLER_TYPE_XTC: return 'x'; + case COMMON_SAMPLER_TYPE_INFILL: return 'i'; + case COMMON_SAMPLER_TYPE_PENALTIES: return 'e'; + default : return '?'; + } +} + +std::string common_sampler_type_to_str(enum common_sampler_type cnstr) { + switch (cnstr) { + case COMMON_SAMPLER_TYPE_DRY: return "dry"; + case COMMON_SAMPLER_TYPE_TOP_K: return "top_k"; + case COMMON_SAMPLER_TYPE_TYPICAL_P: return "typ_p"; + case COMMON_SAMPLER_TYPE_TOP_P: return "top_p"; + case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: return "top_n_sigma"; + case COMMON_SAMPLER_TYPE_MIN_P: return "min_p"; + case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature"; + case COMMON_SAMPLER_TYPE_XTC: return "xtc"; + case COMMON_SAMPLER_TYPE_INFILL: return "infill"; + case COMMON_SAMPLER_TYPE_PENALTIES: return "penalties"; + default : return ""; + } +} + +std::vector common_sampler_types_from_names(const std::vector & names, bool allow_alt_names) { + std::unordered_map sampler_canonical_name_map { + { "dry", COMMON_SAMPLER_TYPE_DRY }, + { "top_k", COMMON_SAMPLER_TYPE_TOP_K }, + { "top_p", COMMON_SAMPLER_TYPE_TOP_P }, + { "top_n_sigma", COMMON_SAMPLER_TYPE_TOP_N_SIGMA }, + { "typ_p", COMMON_SAMPLER_TYPE_TYPICAL_P }, + { "min_p", COMMON_SAMPLER_TYPE_MIN_P }, + { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE }, + { "xtc", COMMON_SAMPLER_TYPE_XTC }, + { "infill", COMMON_SAMPLER_TYPE_INFILL }, + { "penalties", COMMON_SAMPLER_TYPE_PENALTIES }, + }; + + // since samplers names are written multiple ways + // make it ready for both system names and input names + std::unordered_map sampler_alt_name_map { + { "top-k", COMMON_SAMPLER_TYPE_TOP_K }, + { "top-p", COMMON_SAMPLER_TYPE_TOP_P }, + { "top-n-sigma", COMMON_SAMPLER_TYPE_TOP_N_SIGMA }, + { "nucleus", COMMON_SAMPLER_TYPE_TOP_P }, + { "typical-p", COMMON_SAMPLER_TYPE_TYPICAL_P }, + { "typical", COMMON_SAMPLER_TYPE_TYPICAL_P }, + { "typ-p", COMMON_SAMPLER_TYPE_TYPICAL_P }, + { "typ", COMMON_SAMPLER_TYPE_TYPICAL_P }, + { "min-p", COMMON_SAMPLER_TYPE_MIN_P }, + { "temp", COMMON_SAMPLER_TYPE_TEMPERATURE }, + }; + + std::vector samplers; + samplers.reserve(names.size()); + + for (const auto & name : names) { + auto sampler = sampler_canonical_name_map.find(name); + if (sampler != sampler_canonical_name_map.end()) { + samplers.push_back(sampler->second); + continue; + } + if (allow_alt_names) { + sampler = sampler_alt_name_map.find(name); + if (sampler != sampler_alt_name_map.end()) { + samplers.push_back(sampler->second); + continue; + } + } + LOG_WRN("%s: unable to match sampler by name '%s'\n", __func__, name.c_str()); + } + + return samplers; +} + +std::vector common_sampler_types_from_chars(const std::string & chars) { + std::unordered_map sampler_name_map = { + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_DRY), COMMON_SAMPLER_TYPE_DRY }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K), COMMON_SAMPLER_TYPE_TOP_K }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P), COMMON_SAMPLER_TYPE_TYPICAL_P }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P), COMMON_SAMPLER_TYPE_TOP_P }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_N_SIGMA), COMMON_SAMPLER_TYPE_TOP_N_SIGMA }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P), COMMON_SAMPLER_TYPE_MIN_P }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL), COMMON_SAMPLER_TYPE_INFILL }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES), COMMON_SAMPLER_TYPE_PENALTIES }, + }; + + std::vector samplers; + samplers.reserve(chars.size()); + + for (const auto & c : chars) { + const auto sampler = sampler_name_map.find(c); + if (sampler != sampler_name_map.end()) { + samplers.push_back(sampler->second); + } else { + LOG_WRN("%s: unable to match sampler by char '%c'\n", __func__, c); + } + } + + return samplers; +} diff --git a/llama.cpp/common/sampling.h b/llama.cpp/common/sampling.h new file mode 100644 index 0000000000000000000000000000000000000000..03c5952a147d1385f90ca66b626cd11ecfa2427e --- /dev/null +++ b/llama.cpp/common/sampling.h @@ -0,0 +1,107 @@ +#pragma once + +#include "llama.h" + +#include "common.h" + +#include +#include + +// common_sampler extends llama_sampler with additional functionality: +// +// - grammar support +// - custom sampler logic based on the parameters +// - history of the last accepted tokens +// - performance metrics +// +// This goal is to have a common implementation of the sampling logic shared across the examples. +// For example, depending on the temperature, the sampling chain can be very simple (greedy) or more +// complex (top-k, top-p, etc). +// +// Another example is related to the grammar. In general, the grammar constraints applied on the full +// vocabulary can be very taxing. To improve performance, the grammar can be applied only to the sampled +// token in order to verify if it fits the grammar. And only if the token doesn't fit the grammar, the +// grammar constraints are applied to the full vocabulary and the token is resampled. +// +// The common_sampler also maintains a container with the last accepted tokens. In the future, this can +// be moved into the core llama library. +// +// For convenience, the common_sampler also maintains a container with the current candidate tokens. +// This can be used to access the probabilities of the rest of the non-sampled tokens. +// +// TODO: measure grammar performance +// + +struct common_sampler; + +// llama_sampler API overloads + +struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params); + +void common_sampler_free(struct common_sampler * gsmpl); + +// if accept_grammar is true, the token is accepted both by the sampling chain and the grammar +void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar); +void common_sampler_reset (struct common_sampler * gsmpl); +struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl); + +// arguments can be nullptr to skip printing +void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl); + +// extended sampling implementation: +// +// - set logits +// - apply the configured sampler chain +// - check if the token fits the grammar (if any) +// - if not: resample by first applying the grammar constraints and then sampling again (slower path) +// +// if grammar_first is true, the grammar is applied before the samplers (slower) +// useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar +// +llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false); + +// generalized version of common_sampler_sample +// +// will cross-reference the sampled tokens with a batch of draft tokens and accept those that match +// if the sampler disagrees at some point, we stop and return the accepted tokens up to now +// +// common_sampler_sample_n(gsmpl, ctx, { idx }, {}); +// +// is equivalent to +// +// common_sampler_sample(gsmpl, ctx, idx); +// common_sampler_accept(gsmpl, token, true); +// +// requires: idxs.size() == draft.size() + 1 +// +// returns at least 1 token, up to idxs.size() +// +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector & idxs, const llama_tokens & draft, bool grammar_first = false); + +// assume idxs == [ 0, 1, 2, ..., draft.size() ] +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false); + +uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl); + +// helpers + +// access the internal list of current candidate tokens +llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl); + +// get the last accepted token +llama_token common_sampler_last(const struct common_sampler * gsmpl); + +// print the sampler chain into a string +std::string common_sampler_print(const struct common_sampler * gsmpl); + +// get a string representation of the last accepted tokens +std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx, int n); + +char common_sampler_type_to_chr(enum common_sampler_type cnstr); +std::string common_sampler_type_to_str(enum common_sampler_type cnstr); + +std::vector common_sampler_types_from_names(const std::vector & names, bool allow_alt_names); +std::vector common_sampler_types_from_chars(const std::string & chars); + +llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, + const char * grammar_kind, const char * grammar_data); diff --git a/llama.cpp/common/speculative.cpp b/llama.cpp/common/speculative.cpp new file mode 100644 index 0000000000000000000000000000000000000000..edc6ef132deae116f68c48bffb83eb53361d7052 --- /dev/null +++ b/llama.cpp/common/speculative.cpp @@ -0,0 +1,361 @@ +#include "speculative.h" + +#include "ggml.h" +#include "llama.h" +#include "log.h" +#include "common.h" +#include "sampling.h" + +#include +#include +#include + +#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 128 +#define SPEC_VOCAB_CHECK_START_TOKEN_ID 5 + +struct common_speculative { + struct llama_context * ctx_tgt; // only used for retokenizing from ctx_dft + struct llama_context * ctx_dft; + struct common_sampler * smpl; + + llama_batch batch; + llama_tokens prompt_dft; + bool vocab_dft_compatible = true; // whether retokenization is needed + std::map tgt_dft_replacements = {}; +}; + +struct common_speculative * common_speculative_init( + struct llama_context * ctx_tgt, + struct llama_context * ctx_dft) { + auto * result = new common_speculative { + /* .ctx_tgt = */ ctx_tgt, + /* .ctx_dft = */ ctx_dft, + /* .smpl = */ nullptr, + /* .batch = */ llama_batch_init(llama_n_batch(ctx_dft), 0, 1), + /* .prompt_dft = */ {}, + /* .vocab_dft_compatible = */ false, + }; + + // TODO: optimize or pass from outside? +#if 0 + { + common_params_sampling params; + params.no_perf = false; + + params.top_k = 40; + params.top_p = 0.9; + + params.samplers = { + COMMON_SAMPLER_TYPE_TOP_K, + COMMON_SAMPLER_TYPE_TOP_P, + COMMON_SAMPLER_TYPE_INFILL, + }; + + result->smpl = common_sampler_init(llama_get_model(ctx_dft), params); + } +#else + { + common_params_sampling params; + params.no_perf = false; + + params.top_k = 10; + + params.samplers = { + COMMON_SAMPLER_TYPE_TOP_K, + }; + + result->smpl = common_sampler_init(llama_get_model(ctx_dft), params); + } +#endif + + result->vocab_dft_compatible = common_speculative_are_compatible(ctx_tgt, ctx_dft); + LOG_DBG("vocab_dft_compatible = %d\n", result->vocab_dft_compatible); + + return result; +} + +void common_speculative_free(struct common_speculative * spec) { + if (spec == nullptr) { + return; + } + + common_sampler_free(spec->smpl); + + llama_batch_free(spec->batch); + + delete spec; +} + +bool common_speculative_are_compatible( + const struct llama_context * ctx_tgt, + const struct llama_context * ctx_dft) { + const struct llama_model * model_tgt = llama_get_model(ctx_tgt); + const struct llama_model * model_dft = llama_get_model(ctx_dft); + + const struct llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt); + const struct llama_vocab * vocab_dft = llama_model_get_vocab(model_dft); + + const bool vocab_type_tgt = llama_vocab_type(vocab_tgt); + LOG_DBG("%s: vocab_type tgt: %d\n", __func__, vocab_type_tgt); + + const bool vocab_type_dft = llama_vocab_type(vocab_dft); + LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft); + + if (vocab_type_tgt != vocab_type_dft) { + LOG_DBG("%s: draft model vocab type must match target model to use speculation but ", __func__); + LOG_DBG("vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt); + return false; + } + + if ( + llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) || + llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) || + llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) || + llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft) + ) { + LOG_DBG("%s: draft model special tokens must match target model to use speculation\n", __func__); + return false; + } + + { + const int n_vocab_tgt = llama_vocab_n_tokens(vocab_tgt); + const int n_vocab_dft = llama_vocab_n_tokens(vocab_dft); + const int vocab_diff = n_vocab_tgt > n_vocab_dft + ? n_vocab_tgt - n_vocab_dft + : n_vocab_dft - n_vocab_tgt; + + if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) { + LOG_DBG("%s: draft model vocab must closely match target model to use speculation but ", __func__); + LOG_DBG("target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n", + n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE); + return false; + } + + for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) { + const char * token_text_tgt = llama_vocab_get_text(vocab_tgt, i); + const char * token_text_dft = llama_vocab_get_text(vocab_dft, i); + if (std::strcmp(token_text_tgt, token_text_dft) != 0) { + LOG_DBG("%s: draft model vocab must match target model to use speculation but ", __func__); + LOG_DBG("token %d content differs - target '%s', draft '%s'\n", i, + common_token_to_piece(ctx_tgt, i).c_str(), + common_token_to_piece(ctx_dft, i).c_str()); + return false; + } + } + } + + return true; +} + +void common_speculative_add_replacement_tgt_dft( + struct common_speculative * spec, + const char *source, const char *dest) { + spec->tgt_dft_replacements[source] = dest; +} + +static std::string replace_to_dft( + struct common_speculative * spec, + const std::string& input) { + std::string result = input; + for (const auto & pair : spec->tgt_dft_replacements) { + size_t pos = result.find(pair.first); + while (pos != std::string::npos) { + result.replace(pos, pair.first.length(), pair.second); + pos = result.find(pair.first, pos + pair.second.length()); + } + } + return result; +} + +static std::string replace_to_tgt( + struct common_speculative * spec, + const std::string& input) { + std::string result = input; + for (const auto& pair : spec->tgt_dft_replacements) { + size_t pos = result.find(pair.second); + while (pos != std::string::npos) { + result.replace(pos, pair.second.length(), pair.first); + pos = result.find(pair.second, pos + pair.first.length()); + } + } + return result; +} + + +llama_tokens common_speculative_gen_draft( + struct common_speculative * spec, + struct common_speculative_params params, + const llama_tokens & prompt_tgt_main_model, // specified in target model vocab + llama_token id_last) { + auto & batch = spec->batch; + auto & ctx_tgt = spec->ctx_tgt; + auto & ctx_dft = spec->ctx_dft; + auto & smpl = spec->smpl; + auto & prompt_dft = spec->prompt_dft; + + auto * mem_dft = llama_get_memory(ctx_dft); + + int reuse_i = 0; + int reuse_n = 0; + + const int n_ctx = llama_n_ctx(ctx_dft) - params.n_draft; + + llama_tokens prompt_tgt_draft_model; + if (!spec->vocab_dft_compatible) { + std::string text; + text = common_detokenize(ctx_tgt, prompt_tgt_main_model, true); + text = replace_to_dft(spec, text); + LOG_DBG("%s: main->draft detokenized string: '%s'\n", __func__, text.c_str()); + prompt_tgt_draft_model = common_tokenize(ctx_dft, text, false, true); + + // convert id_last to draft vocab. llama_detokenize is called directly to avoid an allocation + const auto * model_tgt = llama_get_model(ctx_tgt); + const auto * vocab_tgt = llama_model_get_vocab(model_tgt); + + int32_t n_chars = llama_detokenize(vocab_tgt, &id_last, 1, nullptr, 0, false, false); + GGML_ASSERT(n_chars < 0 && "failed to detokenize id_last"); + text.resize(-n_chars); + llama_detokenize(vocab_tgt, &id_last, 1, text.data(), text.size(), false, false); + text = replace_to_dft(spec, text); + + LOG_DBG("main->draft detokenized id_last(%d): '%s'\n", id_last, text.c_str()); + id_last = common_tokenize(ctx_dft, text, false, true)[0]; + } + // prompt_tgt's tokens will always be compatible with ctx_dft + const llama_tokens &prompt_tgt = + spec->vocab_dft_compatible ? prompt_tgt_main_model : prompt_tgt_draft_model; + + const int i_start = std::max(0, (int) prompt_tgt.size() - n_ctx); + + // reuse as much as possible from the old draft context + // ideally, the draft context should be as big as the target context and we will always reuse the entire prompt + for (int i = 0; i < (int) prompt_dft.size(); ++i) { + int cur = 0; + while (i_start + cur < (int) prompt_tgt.size() && + i + cur < (int) prompt_dft.size() && + prompt_tgt[i_start + cur] == prompt_dft[i + cur]) { + cur++; + } + + if ((cur >= params.n_reuse || n_ctx >= (int) prompt_tgt.size()) && cur > reuse_n) { + reuse_i = i; + reuse_n = cur; + } + } + + LOG_DBG("%s: reuse_i = %d, reuse_n = %d, prompt = %d\n", __func__, reuse_i, reuse_n, (int) prompt_dft.size()); + + llama_tokens result; + result.reserve(params.n_draft); + + if (reuse_n == 0) { + llama_memory_clear(mem_dft, false); + prompt_dft.clear(); + } else { + // this happens when a previous draft has been discarded (for example, due to being too small), but the + // target model agreed with it. in this case, we simply pass back the previous results to save compute + if (reuse_i + reuse_n < (int) prompt_dft.size() && prompt_dft[reuse_i + reuse_n] == id_last) { + for (int i = reuse_i + reuse_n + 1; i < (int) prompt_dft.size(); ++i) { + result.push_back(prompt_dft[i]); + + if (params.n_draft <= (int) result.size()) { + break; + } + } + + return result; + } + + if (reuse_i > 0) { + llama_memory_seq_rm (mem_dft, 0, 0, reuse_i); + llama_memory_seq_add(mem_dft, 0, reuse_i, -1, -reuse_i); + + prompt_dft.erase(prompt_dft.begin(), prompt_dft.begin() + reuse_i); + } + + if (reuse_n < (int) prompt_dft.size()) { + llama_memory_seq_rm (mem_dft, 0, reuse_n, -1); + prompt_dft.erase(prompt_dft.begin() + reuse_n, prompt_dft.end()); + } + } + + // prepare a batch to evaluate any new tokens in the prompt + common_batch_clear(batch); + + for (size_t i = i_start + reuse_n; i < prompt_tgt.size(); ++i) { + //LOG_DBG("i = %d, i_start = %d, reuse_n = %d, i - i_start = %d, id = %6d\n", i, i_start, reuse_n, i - i_start, prompt_tgt[i]); + common_batch_add(batch, prompt_tgt[i], i - i_start, { 0 }, false); + + prompt_dft.push_back(prompt_tgt[i]); + } + + // we should rarely end-up here during normal decoding + if (batch.n_tokens > 0) { + //LOG_DBG("%s: draft prompt batch: %s\n", __func__, string_from(ctx, batch).c_str()); + + llama_decode(ctx_dft, batch); + } + + const llama_pos n_past = prompt_dft.size(); + + LOG_DBG("%s: n_past = %d\n", __func__, n_past); + + common_batch_clear(batch); + common_batch_add (batch, id_last, n_past, { 0 }, true); + + prompt_dft.push_back(id_last); + + LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx_dft, prompt_dft).c_str()); + + llama_decode(ctx_dft, batch); + + common_sampler_reset(smpl); + + // sample n_draft tokens from the draft model + for (int i = 0; i < params.n_draft; ++i) { + common_batch_clear(batch); + + common_sampler_sample(smpl, ctx_dft, 0, true); + + const auto * cur_p = common_sampler_get_candidates(smpl); + + for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) { + LOG_DBG(" - draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n", + k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str()); + } + + // add drafted token for each sequence + const llama_token id = cur_p->data[0].id; + + common_sampler_accept(smpl, id, true); + + result.push_back(id); + + if (params.n_draft <= (int) result.size()) { + break; + } + + // only collect very high-confidence draft tokens + if (cur_p->data[0].p < params.p_min) { + break; + } + + common_batch_add(batch, id, n_past + i + 1, { 0 }, true); + + // evaluate the drafted tokens on the draft model + llama_decode(ctx_dft, batch); + + prompt_dft.push_back(id); + } + + if (!spec->vocab_dft_compatible) { + std::string detokenized = common_detokenize(ctx_dft, result, true); + detokenized = replace_to_tgt(spec, detokenized); + LOG_DBG("draft->main detokenized string: '%s'\n", detokenized.c_str()); + result = common_tokenize(ctx_tgt, detokenized, false, true); + if (result.size() > (size_t)params.n_draft) { + result.resize(params.n_draft); + } + } + return result; +} diff --git a/llama.cpp/common/speculative.h b/llama.cpp/common/speculative.h new file mode 100644 index 0000000000000000000000000000000000000000..ede615f51ee568e28201f7fd59f6d7c7da870863 --- /dev/null +++ b/llama.cpp/common/speculative.h @@ -0,0 +1,35 @@ +#pragma once + +#include "llama.h" +#include "common.h" + +struct common_speculative; + +struct common_speculative_params { + int n_draft = 16; // max drafted tokens + int n_reuse = 256; + + float p_min = 0.75f; // min probability required to accept a token in the draft +}; + +struct common_speculative * common_speculative_init( + struct llama_context * ctx_tgt, + struct llama_context * ctx_dft +); + +void common_speculative_free(struct common_speculative * spec); + +bool common_speculative_are_compatible( + const struct llama_context * ctx_tgt, + const struct llama_context * ctx_dft); + +void common_speculative_add_replacement_tgt_dft( + struct common_speculative * spec, + const char *source, const char *dest); + +// sample up to n_draft tokens and add them to the batch using the draft model +llama_tokens common_speculative_gen_draft( + struct common_speculative * spec, + struct common_speculative_params params, + const llama_tokens & prompt, + llama_token id_last); diff --git a/llama.cpp/convert_hf_to_gguf.py b/llama.cpp/convert_hf_to_gguf.py new file mode 100644 index 0000000000000000000000000000000000000000..99b7463c85d783df5e58d3696639d9e4d9cd9672 --- /dev/null +++ b/llama.cpp/convert_hf_to_gguf.py @@ -0,0 +1,8218 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +from __future__ import annotations + +import ast +import logging +import argparse +import contextlib +import json +import os +import re +import sys +from enum import IntEnum +from pathlib import Path +from hashlib import sha256 +from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast +from itertools import chain +from transformers import AutoConfig + +import math +import numpy as np +import torch + +if TYPE_CHECKING: + from torch import Tensor + +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) +import gguf + +logger = logging.getLogger("hf-to-gguf") + + +###### MODEL DEFINITIONS ###### + +class SentencePieceTokenTypes(IntEnum): + NORMAL = 1 + UNKNOWN = 2 + CONTROL = 3 + USER_DEFINED = 4 + UNUSED = 5 + BYTE = 6 + + +class ModelType(IntEnum): + TEXT = 1 + MMPROJ = 2 + + +AnyModel = TypeVar("AnyModel", bound="type[ModelBase]") + + +class ModelBase: + _model_classes: dict[ModelType, dict[str, type[ModelBase]]] = { + ModelType.TEXT: {}, + ModelType.MMPROJ: {}, + } + + dir_model: Path + ftype: gguf.LlamaFileType + fname_out: Path + is_big_endian: bool + endianess: gguf.GGUFEndian + use_temp_file: bool + lazy: bool + part_names: list[str] + is_safetensors: bool + hparams: dict[str, Any] + tensor_names: set[str] | None + gguf_writer: gguf.GGUFWriter + model_name: str | None + metadata_override: Path | None + dir_model_card: Path + remote_hf_model_id: str | None + + # subclasses should define this! + model_arch: gguf.MODEL_ARCH + + # subclasses should initialize this! + block_count: int + tensor_map: gguf.TensorNameMap + + def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, *, is_big_endian: bool = False, + use_temp_file: bool = False, eager: bool = False, + metadata_override: Path | None = None, model_name: str | None = None, + split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, + small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None): + if type(self) is ModelBase or \ + type(self) is TextModel or \ + type(self) is MmprojModel: + raise TypeError(f"{type(self).__name__!r} should not be directly instantiated") + + self.dir_model = dir_model + self.ftype = ftype + self.fname_out = fname_out + self.is_big_endian = is_big_endian + self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE + self.use_temp_file = use_temp_file + self.lazy = not eager or (remote_hf_model_id is not None) + self.remote_hf_model_id = remote_hf_model_id + if remote_hf_model_id is not None: + self.is_safetensors = True + + def get_remote_tensors() -> Iterator[tuple[str, Tensor]]: + logger.info(f"Using remote model with HuggingFace id: {remote_hf_model_id}") + remote_tensors = gguf.utility.SafetensorRemote.get_list_tensors_hf_model(remote_hf_model_id) + self.tensor_names = set(name for name in remote_tensors.keys()) + for name, remote_tensor in gguf.utility.SafetensorRemote.get_list_tensors_hf_model(remote_hf_model_id).items(): + yield (name, LazyTorchTensor.from_remote_tensor(remote_tensor)) + + self.get_tensors = get_remote_tensors + else: + self.part_names = ModelBase.get_model_part_names(self.dir_model, "model", ".safetensors") + self.is_safetensors = len(self.part_names) > 0 + if not self.is_safetensors: + self.part_names = ModelBase.get_model_part_names(self.dir_model, "pytorch_model", ".bin") + self.hparams = ModelBase.load_hparams(self.dir_model) if hparams is None else hparams + self.tensor_names = None + self.metadata_override = metadata_override + self.model_name = model_name + self.dir_model_card = dir_model # overridden in convert_lora_to_gguf.py + + # Apply heuristics to figure out typical tensor encoding based on first layer tensor encoding type + if self.ftype == gguf.LlamaFileType.GUESSED: + # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie. + _, first_tensor = next(self.get_tensors()) + if first_tensor.dtype == torch.float16: + logger.info(f"choosing --outtype f16 from first tensor type ({first_tensor.dtype})") + self.ftype = gguf.LlamaFileType.MOSTLY_F16 + else: + logger.info(f"choosing --outtype bf16 from first tensor type ({first_tensor.dtype})") + self.ftype = gguf.LlamaFileType.MOSTLY_BF16 + + # Configure GGUF Writer + self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file, + split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard) + + @classmethod + def add_prefix_to_filename(cls, path: Path, prefix: str) -> Path: + stem, suffix = path.stem, path.suffix + new_name = f"{prefix}{stem}{suffix}" + return path.with_name(new_name) + + def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any: + key = next((k for k in keys if k in self.hparams), None) + if key is not None: + return self.hparams[key] + if optional: + return None + raise KeyError(f"could not find any of: {keys}") + + def get_tensors(self) -> Iterator[tuple[str, Tensor]]: + tensor_names_from_parts: set[str] = set() + + index_name = "model.safetensors" if self.is_safetensors else "pytorch_model.bin" + index_name += ".index.json" + index_file = self.dir_model / index_name + + if index_file.is_file(): + self.tensor_names = set() + logger.info(f"gguf: loading model weight map from '{index_name}'") + with open(index_file, "r", encoding="utf-8") as f: + index: dict[str, Any] = json.load(f) + weight_map = index.get("weight_map") + if weight_map is None or not isinstance(weight_map, dict): + raise ValueError(f"Can't load 'weight_map' from {index_name!r}") + self.tensor_names.update(weight_map.keys()) + else: + self.tensor_names = tensor_names_from_parts + weight_map = {} + + for part_name in self.part_names: + logger.info(f"gguf: loading model part '{part_name}'") + ctx: ContextManager[Any] + if self.is_safetensors: + from safetensors import safe_open + ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu")) + else: + ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True)) + + with ctx as model_part: + tensor_names_from_parts.update(model_part.keys()) + + for name in model_part.keys(): + if self.is_safetensors: + if self.lazy: + data = model_part.get_slice(name) + data = LazyTorchTensor.from_safetensors_slice(data) + else: + data = model_part.get_tensor(name) + else: + data = model_part[name] + if self.lazy: + data = LazyTorchTensor.from_eager(data) + yield name, data + + # verify tensor name presence and identify potentially missing files + if len(tensor_names_from_parts.symmetric_difference(self.tensor_names)) > 0: + missing = sorted(self.tensor_names.difference(tensor_names_from_parts)) + extra = sorted(tensor_names_from_parts.difference(self.tensor_names)) + missing_files = sorted(set(weight_map[n] for n in missing if n in weight_map)) + if len(extra) == 0 and len(missing_files) > 0: + raise ValueError(f"Missing or incomplete model files: {missing_files}\n" + f"Missing tensors: {missing}") + else: + raise ValueError("Mismatch between weight map and model parts for tensor names:\n" + f"Missing tensors: {missing}\n" + f"Extra tensors: {extra}") + + def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str: + if key not in gguf.MODEL_TENSORS[self.model_arch]: + raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {self.model_arch!r}") + name: str = gguf.TENSOR_NAMES[key] + if "{bid}" in name: + assert bid is not None + name = name.format(bid=bid) + return name + suffix + + def match_model_tensor_name(self, name: str, key: gguf.MODEL_TENSOR, bid: int | None, suffix: str = ".weight") -> bool: + if key not in gguf.MODEL_TENSORS[self.model_arch]: + return False + key_name: str = gguf.TENSOR_NAMES[key] + if "{bid}" in key_name: + if bid is None: + return False + key_name = key_name.format(bid=bid) + else: + if bid is not None: + return False + return name == (key_name + suffix) + + def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str: + new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes) + if new_name is None: + raise ValueError(f"Can not map tensor {name!r}") + return new_name + + def set_gguf_parameters(self): + raise NotImplementedError("set_gguf_parameters() must be implemented in subclasses") + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + return [(self.map_tensor_name(name), data_torch)] + + def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool: + del name, new_name, bid, n_dims # unused + + return False + + # some models need extra generated tensors (like rope_freqs) + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + return () + + def prepare_tensors(self): + max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,") + + for name, data_torch in chain(self.generate_extra_tensors(), self.get_tensors()): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")): + continue + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + # use the first number-like part of the tensor name as the block id + bid = None + for part in name.split("."): + if part.isdecimal(): + bid = int(part) + break + + for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)): + # TODO: why do we squeeze here? + # data = data_torch.squeeze().numpy() + data = data_torch.numpy() + + # if data ends up empty, it means data_torch was a scalar tensor -> restore + if len(data.shape) == 0: + data = data_torch.numpy() + + n_dims = len(data.shape) + data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims) + + # Most of the codebase that takes in 1D tensors or norms only handles F32 tensors + if n_dims <= 1 or new_name.endswith("_norm.weight"): + data_qtype = gguf.GGMLQuantizationType.F32 + + # Conditions should closely match those in llama_model_quantize_internal in llama.cpp + # Some tensor types are always in float32 + if data_qtype is False and ( + any( + self.match_model_tensor_name(new_name, key, bid) + for key in ( + gguf.MODEL_TENSOR.FFN_GATE_INP, + gguf.MODEL_TENSOR.POS_EMBD, + gguf.MODEL_TENSOR.TOKEN_TYPES, + gguf.MODEL_TENSOR.SSM_CONV1D, + gguf.MODEL_TENSOR.SHORTCONV_CONV, + gguf.MODEL_TENSOR.TIME_MIX_FIRST, + gguf.MODEL_TENSOR.TIME_MIX_W1, + gguf.MODEL_TENSOR.TIME_MIX_W2, + gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1, + gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2, + gguf.MODEL_TENSOR.TIME_MIX_LERP_FUSED, + gguf.MODEL_TENSOR.POSNET_NORM1, + gguf.MODEL_TENSOR.POSNET_NORM2, + gguf.MODEL_TENSOR.V_ENC_EMBD_POS, + gguf.MODEL_TENSOR.A_ENC_EMBD_POS, + gguf.MODEL_TENSOR.ALTUP_CORRECT_COEF, + gguf.MODEL_TENSOR.ALTUP_PREDICT_COEF, + ) + ) + or not new_name.endswith(".weight") + ): + data_qtype = gguf.GGMLQuantizationType.F32 + + if data_qtype is False and any( + self.match_model_tensor_name(new_name, key, bid) + for key in ( + gguf.MODEL_TENSOR.TOKEN_EMBD, + gguf.MODEL_TENSOR.PER_LAYER_TOKEN_EMBD, + gguf.MODEL_TENSOR.OUTPUT, + gguf.MODEL_TENSOR.ALTUP_ROUTER, + gguf.MODEL_TENSOR.LAUREL_L, + gguf.MODEL_TENSOR.LAUREL_R, + ) + ): + if self.ftype in ( + gguf.LlamaFileType.MOSTLY_TQ1_0, + gguf.LlamaFileType.MOSTLY_TQ2_0, + ): + # TODO: use Q4_K and Q6_K + data_qtype = gguf.GGMLQuantizationType.F16 + + # No override (data_qtype is False), or wants to be quantized (data_qtype is True) + if isinstance(data_qtype, bool): + if self.ftype == gguf.LlamaFileType.ALL_F32: + data_qtype = gguf.GGMLQuantizationType.F32 + elif self.ftype == gguf.LlamaFileType.MOSTLY_F16: + data_qtype = gguf.GGMLQuantizationType.F16 + elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16: + data_qtype = gguf.GGMLQuantizationType.BF16 + elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0: + data_qtype = gguf.GGMLQuantizationType.Q8_0 + elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ1_0: + data_qtype = gguf.GGMLQuantizationType.TQ1_0 + elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ2_0: + data_qtype = gguf.GGMLQuantizationType.TQ2_0 + else: + raise ValueError(f"Unknown file type: {self.ftype.name}") + + try: + data = gguf.quants.quantize(data, data_qtype) + except gguf.QuantError as e: + logger.warning("%s, %s", e, "falling back to F16") + data_qtype = gguf.GGMLQuantizationType.F16 + data = gguf.quants.quantize(data, data_qtype) + + shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape + + # reverse shape to make it similar to the internal ggml dimension order + shape_str = f"{{{', '.join(str(n) for n in reversed(shape))}}}" + + # n_dims is implicit in the shape + logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}") + + self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype) + + def set_type(self): + self.gguf_writer.add_type(gguf.GGUFType.MODEL) + + def prepare_metadata(self, vocab_only: bool): + + total_params, shared_params, expert_params, expert_count = self.gguf_writer.get_total_parameter_count() + + self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model_card, self.model_name, total_params) + + # If we are using HF model id, set the metadata name to the model id + if self.remote_hf_model_id: + self.metadata.name = self.remote_hf_model_id + + # Fallback to model directory name if metadata name is still missing + if self.metadata.name is None: + self.metadata.name = self.dir_model.name + + # Generate parameter weight class (useful for leader boards) if not yet determined + if self.metadata.size_label is None and total_params > 0: + self.metadata.size_label = gguf.size_label(total_params, shared_params, expert_params, expert_count) + + self.set_type() + + logger.info("Set meta model") + self.metadata.set_gguf_meta_model(self.gguf_writer) + + logger.info("Set model parameters") + self.set_gguf_parameters() + + logger.info("Set model quantization version") + self.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION) + + def write_vocab(self): + raise NotImplementedError("write_vocab() must be implemented in subclasses") + + def write(self): + self.prepare_tensors() + self.prepare_metadata(vocab_only=False) + self.gguf_writer.write_header_to_file(path=self.fname_out) + self.gguf_writer.write_kv_data_to_file() + self.gguf_writer.write_tensors_to_file(progress=True) + self.gguf_writer.close() + + @staticmethod + def get_model_part_names(dir_model: Path, prefix: str, suffix: str) -> list[str]: + part_names: list[str] = [] + for filename in os.listdir(dir_model): + if filename.startswith(prefix) and filename.endswith(suffix): + part_names.append(filename) + + part_names.sort() + + return part_names + + @staticmethod + def load_hparams(dir_model: Path): + try: + # for security reason, we don't allow loading remote code by default + # if a model need remote code, we will fallback to config.json + config = AutoConfig.from_pretrained(dir_model, trust_remote_code=False).to_dict() + except Exception as e: + logger.warning(f"Failed to load model config from {dir_model}: {e}") + logger.warning("Trying to load config.json instead") + with open(dir_model / "config.json", "r", encoding="utf-8") as f: + config = json.load(f) + if "llm_config" in config: + # rename for InternVL + config["text_config"] = config["llm_config"] + if "thinker_config" in config: + # rename for Qwen2.5-Omni + config["text_config"] = config["thinker_config"]["text_config"] + return config + + @classmethod + def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]: + assert names + + def func(modelcls: AnyModel) -> AnyModel: + model_type = ModelType.MMPROJ if modelcls.model_arch == gguf.MODEL_ARCH.MMPROJ else ModelType.TEXT + for name in names: + cls._model_classes[model_type][name] = modelcls + return modelcls + return func + + @classmethod + def print_registered_models(cls): + for model_type, model_classes in cls._model_classes.items(): + logger.error(f"{model_type.name} models:") + for name in sorted(model_classes.keys()): + logger.error(f" - {name}") + + @classmethod + def from_model_architecture(cls, arch: str, model_type = ModelType.TEXT) -> type[ModelBase]: + try: + return cls._model_classes[model_type][arch] + except KeyError: + raise NotImplementedError(f'Architecture {arch!r} not supported!') from None + + +class TextModel(ModelBase): + model_type = ModelType.TEXT + hf_arch: str + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.hf_arch = get_model_architecture(self.hparams, self.model_type) + + if "text_config" in self.hparams: + # move the text_config to the root level + self.hparams = {**self.hparams, **self.hparams["text_config"]} + + self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"]) + self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) + + @classmethod + def __init_subclass__(cls): + # can't use an abstract property, because overriding it without type errors + # would require using decorated functions instead of simply defining the property + if "model_arch" not in cls.__dict__: + raise TypeError(f"Missing property 'model_arch' for {cls.__name__!r}") + + def set_vocab(self): + self._set_vocab_gpt2() + + def prepare_metadata(self, vocab_only: bool): + super().prepare_metadata(vocab_only=vocab_only) + + total_params = self.gguf_writer.get_total_parameter_count()[0] + # Extract the encoding scheme from the file type name. e.g. 'gguf.LlamaFileType.MOSTLY_Q8_0' --> 'Q8_0' + output_type: str = self.ftype.name.partition("_")[2] + + # Filename Output + if self.fname_out.is_dir(): + # Generate default filename based on model specification and available metadata + if not vocab_only: + fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, self.metadata.size_label, output_type, model_type="LoRA" if total_params < 0 else None) + else: + fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, size_label=None, output_type=None, model_type="vocab") + + # Use the default filename + self.fname_out = self.fname_out / f"{fname_default}.gguf" + else: + # Output path is a custom defined templated filename + # Note: `not is_dir()` is used because `.is_file()` will not detect + # file template strings as it doesn't actually exist as a file + + # Process templated file name with the output ftype, useful with the "auto" ftype + self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type) + + logger.info("Set model tokenizer") + self.set_vocab() + + def set_gguf_parameters(self): + self.gguf_writer.add_block_count(self.block_count) + + if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions", "max_length"], optional=True)) is not None: + self.gguf_writer.add_context_length(n_ctx) + logger.info(f"gguf: context length = {n_ctx}") + + if (n_embd := self.find_hparam(["hidden_size", "n_embd", "dim"], optional=True)) is not None: + self.gguf_writer.add_embedding_length(n_embd) + logger.info(f"gguf: embedding length = {n_embd}") + + if (n_ff := self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None: + self.gguf_writer.add_feed_forward_length(n_ff) + logger.info(f"gguf: feed forward length = {n_ff}") + + if (n_head := self.find_hparam(["num_attention_heads", "n_head", "n_heads"], optional=True)) is not None: + self.gguf_writer.add_head_count(n_head) + logger.info(f"gguf: head count = {n_head}") + + if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None: + self.gguf_writer.add_head_count_kv(n_head_kv) + logger.info(f"gguf: key-value head count = {n_head_kv}") + + if (rope_theta := self.hparams.get("rope_theta")) is not None: + self.gguf_writer.add_rope_freq_base(rope_theta) + logger.info(f"gguf: rope theta = {rope_theta}") + if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None: + self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps) + logger.info(f"gguf: rms norm epsilon = {f_rms_eps}") + if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None: + self.gguf_writer.add_layer_norm_eps(f_norm_eps) + logger.info(f"gguf: layer norm epsilon = {f_norm_eps}") + if (n_experts := self.hparams.get("num_local_experts")) is not None: + self.gguf_writer.add_expert_count(n_experts) + logger.info(f"gguf: expert count = {n_experts}") + if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None: + self.gguf_writer.add_expert_used_count(n_experts_used) + logger.info(f"gguf: experts used count = {n_experts_used}") + + if (head_dim := self.hparams.get("head_dim")) is not None: + self.gguf_writer.add_key_length(head_dim) + self.gguf_writer.add_value_length(head_dim) + + self.gguf_writer.add_file_type(self.ftype) + logger.info(f"gguf: file type = {self.ftype}") + + def write_vocab(self): + if len(self.gguf_writer.tensors) != 1: + raise ValueError('Splitting the vocabulary is not supported') + + self.prepare_metadata(vocab_only=True) + self.gguf_writer.write_header_to_file(path=self.fname_out) + self.gguf_writer.write_kv_data_to_file() + self.gguf_writer.close() + + def does_token_look_special(self, token: str | bytes) -> bool: + if isinstance(token, (bytes, bytearray)): + token_text = token.decode(encoding="utf-8") + elif isinstance(token, memoryview): + token_text = token.tobytes().decode(encoding="utf-8") + else: + token_text = token + + # Some models mark some added tokens which ought to be control tokens as not special. + # (e.g. command-r, command-r-plus, deepseek-coder, gemma{,-2}) + seems_special = token_text in ( + "", # deepseek-coder + "", "<2mass>", "[@BOS@]", # gemma{,-2} + ) + + seems_special = seems_special or (token_text.startswith("<|") and token_text.endswith("|>")) + seems_special = seems_special or (token_text.startswith("<｜") and token_text.endswith("｜>")) # deepseek-coder + + # TODO: should these be marked as UNUSED instead? (maybe not) + seems_special = seems_special or (token_text.startswith("")) # gemma{,-2} + + return seems_special + + # used for GPT-2 BPE and WordPiece vocabs + def get_vocab_base(self) -> tuple[list[str], list[int], str]: + tokens: list[str] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model) + vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab)) + assert max(tokenizer.vocab.values()) < vocab_size + + tokpre = self.get_vocab_base_pre(tokenizer) + + reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()} + added_vocab = tokenizer.get_added_vocab() + + added_tokens_decoder = tokenizer.added_tokens_decoder + + for i in range(vocab_size): + if i not in reverse_vocab: + tokens.append(f"[PAD{i}]") + toktypes.append(gguf.TokenType.UNUSED) + else: + token: str = reverse_vocab[i] + if token in added_vocab: + # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized. + # To avoid unexpected issues - we make sure to normalize non-normalized tokens + if not added_tokens_decoder[i].normalized: + previous_token = token + token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False)) + if previous_token != token: + logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer") + + if added_tokens_decoder[i].special or self.does_token_look_special(token): + toktypes.append(gguf.TokenType.CONTROL) + else: + # NOTE: this was added for Gemma. + # Encoding and decoding the tokens above isn't sufficient for this case. + token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces + toktypes.append(gguf.TokenType.USER_DEFINED) + else: + toktypes.append(gguf.TokenType.NORMAL) + tokens.append(token) + + return tokens, toktypes, tokpre + + # NOTE: this function is generated by convert_hf_to_gguf_update.py + # do not modify it manually! + # ref: https://github.com/ggml-org/llama.cpp/pull/6920 + # Marker: Start get_vocab_base_pre + def get_vocab_base_pre(self, tokenizer) -> str: + # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that + # is specific for the BPE pre-tokenizer used by the model + # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can + # use in llama.cpp to implement the same pre-tokenizer + + chktxt = '\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶\u200d🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````""""......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL' + + chktok = tokenizer.encode(chktxt) + chkhsh = sha256(str(chktok).encode()).hexdigest() + + logger.debug(f"chktok: {chktok}") + logger.debug(f"chkhsh: {chkhsh}") + + res = None + + # NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script + # or pull the latest version of the model from Huggingface + # don't edit the hashes manually! + if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b": + # ref: https://huggingface.co/THUDM/glm-4-9b-chat + res = "chatglm-bpe" + if chkhsh == "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516": + # ref: https://huggingface.co/THUDM/glm-4-9b-chat + res = "chatglm-bpe" + if chkhsh == "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2": + # ref: https://huggingface.co/THUDM/glm-4-9b-hf + res = "glm4" + if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35": + # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0 + res = "minerva-7b" + if chkhsh == "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664": + # ref: https://huggingface.co/tencent/Hunyuan-A13B-Instruct + res = "hunyuan" + if chkhsh == "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6": + # ref: https://huggingface.co/tencent/Hunyuan-4B-Instruct + res = "hunyuan-dense" + if chkhsh == "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6": + # ref: https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base + res = "falcon-h1" + if chkhsh == "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86": + # ref: https://huggingface.co/tiiuae/Falcon-H1-1B-Base + res = "falcon-h1" + if chkhsh == "3eda48b4c4dc7de733d1a8b3e3b4a85243dbbf704da2ee9d42c6beced8897896": + # ref: https://huggingface.co/tiiuae/Falcon-H1-7B-Base + res = "falcon-h1" + if chkhsh == "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b": + # ref: https://huggingface.co/tiiuae/Falcon-H1-34B-Base + res = "falcon-h1" + if chkhsh == "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890": + # ref: https://huggingface.co/moonshotai/Kimi-K2-Base + res = "kimi-k2" + if chkhsh == "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c": + # ref: https://huggingface.co/Qwen/Qwen3-Embedding-0.6B + res = "qwen2" + if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5": + # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B + res = "llama-bpe" + if chkhsh == "049ecf7629871e3041641907f3de7c733e4dbfdc736f57d882ba0b0845599754": + # ref: https://huggingface.co/deepseek-ai/deepseek-llm-7b-base + res = "deepseek-llm" + if chkhsh == "347715f544604f9118bb75ed199f68779f423cabb20db6de6f31b908d04d7821": + # ref: https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base + res = "deepseek-coder" + if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed": + # ref: https://huggingface.co/tiiuae/falcon-7b + res = "falcon" + if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f": + # ref: https://huggingface.co/BAAI/bge-small-en-v1.5 + res = "bert-bge" + if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e": + # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base + res = "falcon3" + if chkhsh == "8e62295832751ca1e8f92f2226f403dea30dc5165e448b5bfa05af5340c64ec7": + # ref: https://huggingface.co/BAAI/bge-large-zh-v1.5 + res = "bert-bge-large" + if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166": + # ref: https://huggingface.co/mosaicml/mpt-7b + res = "mpt" + if chkhsh == "35d91631860c815f952d711435f48d356ebac988362536bed955d43bfa436e34": + # ref: https://huggingface.co/bigcode/starcoder2-3b + res = "starcoder" + if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454": + # ref: https://huggingface.co/openai-community/gpt2 + res = "gpt-2" + if chkhsh == "32d85c31273f8019248f2559fed492d929ea28b17e51d81d3bb36fff23ca72b3": + # ref: https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b + res = "stablelm2" + if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff": + # ref: https://huggingface.co/smallcloudai/Refact-1_6-base + res = "refact" + if chkhsh == "9c2227e4dd922002fb81bde4fc02b0483ca4f12911410dee2255e4987644e3f8": + # ref: https://huggingface.co/CohereForAI/c4ai-command-r-v01 + res = "command-r" + if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea": + # ref: https://huggingface.co/Qwen/Qwen1.5-7B + res = "qwen2" + if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166": + # ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf + res = "olmo" + if chkhsh == "a8594e3edff7c29c003940395316294b2c623e09894deebbc65f33f1515df79e": + # ref: https://huggingface.co/databricks/dbrx-base + res = "dbrx" + if chkhsh == "c7699093ba4255a91e702aa38a596aa81669f3525dae06c2953267dde580f448": + # ref: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en + res = "jina-v1-en" + if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-en + res = "jina-v2-en" + if chkhsh == "171aeeedd6fb548d418a7461d053f11b6f1f1fc9b387bd66640d28a4b9f5c643": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-es + res = "jina-v2-es" + if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de + res = "jina-v2-de" + if chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d": + # ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct + res = "smaug-bpe" + if chkhsh == "c7ea5862a53e4272c035c8238367063e2b270d51faa48c0f09e9d5b54746c360": + # ref: https://huggingface.co/LumiOpen/Poro-34B-chat + res = "poro-chat" + if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code + res = "jina-v2-code" + if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee": + # ref: https://huggingface.co/LumiOpen/Viking-7B + res = "viking" + if chkhsh == "b53802fb28e26d645c3a310b34bfe07da813026ec7c7716883404d5e0f8b1901": + # ref: https://huggingface.co/core42/jais-13b + res = "jais" + if chkhsh == "7b3e7548e4308f52a76e8229e4e6cc831195d0d1df43aed21ac6c93da05fec5f": + # ref: https://huggingface.co/WisdomShell/CodeShell-7B + res = "codeshell" + if chkhsh == "63b97e4253352e6f357cc59ea5b583e3a680eaeaf2632188c2b952de2588485e": + # ref: https://huggingface.co/mistralai/Mistral-Nemo-Base-2407 + res = "tekken" + if chkhsh == "855059429035d75a914d1eda9f10a876752e281a054a7a3d421ef0533e5b6249": + # ref: https://huggingface.co/HuggingFaceTB/SmolLM-135M + res = "smollm" + if chkhsh == "3c30d3ad1d6b64202cd222813e7736c2db6e1bd6d67197090fc1211fbc612ae7": + # ref: https://huggingface.co/bigscience/bloom + res = "bloom" + if chkhsh == "bc01ce58980e1db43859146dc51b1758b3b88729b217a74792e9f8d43e479d21": + # ref: https://huggingface.co/TurkuNLP/gpt3-finnish-small + res = "gpt3-finnish" + if chkhsh == "4e2b24cc4770243d65a2c9ec19770a72f08cffc161adbb73fcbb6b7dd45a0aae": + # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct + res = "exaone" + if chkhsh == "fcace8b9cac38ce847670c970cd5892031a753a1ef381abd1d9af00f713da085": + # ref: https://huggingface.co/microsoft/phi-2 + res = "phi-2" + if chkhsh == "60824e3c0d9401f89943cbb2fff727f0e2d4c545ba4df2d6e4f09a6db0f5b450": + # ref: https://huggingface.co/facebook/chameleon-7b + res = "chameleon" + if chkhsh == "8b5a93ed704057481f240da0be7e7dca721d7f8f4755263b6807227a2cbeae65": + # ref: https://huggingface.co/sentence-transformers/stsb-roberta-base + res = "roberta-bpe" + if chkhsh == "ad851be1dba641f2e3711822f816db2c265f788b37c63b4e1aeacb9ee92de8eb": + # ref: https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct + res = "gigachat" + if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1": + # ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct + res = "megrez" + if chkhsh == "877081d19cf6996e2c4ff0e1236341e9b7bde288f5311a56a937f0afbbb3aeb5": + # ref: https://huggingface.co/deepseek-ai/DeepSeek-V3 + res = "deepseek-v3" + if chkhsh == "b3f499bb4255f8ca19fccd664443283318f2fd2414d5e0b040fbdd0cc195d6c5": + # ref: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B + res = "deepseek-r1-qwen" + if chkhsh == "ccc2ef013c104be7bae2965776d611e1d7a8a2a9c547dd93a682c9a9fc80352e": + # ref: https://huggingface.co/Xenova/gpt-4o + res = "gpt-4o" + if chkhsh == "7dec86086fcc38b66b7bc1575a160ae21cf705be7718b9d5598190d7c12db76f": + # ref: https://huggingface.co/UW/OLMo2-8B-SuperBPE-t180k + res = "superbpe" + if chkhsh == "1994ffd01900cfb37395608534236ecd63f2bd5995d6cb1004dda1af50240f15": + # ref: https://huggingface.co/trillionlabs/Trillion-7B-preview + res = "trillion" + if chkhsh == "96a5f08be6259352137b512d4157e333e21df7edd3fcd152990608735a65b224": + # ref: https://huggingface.co/inclusionAI/Ling-lite + res = "bailingmoe" + if chkhsh == "d353350c764d8c3b39c763113960e4fb4919bea5fbf208a0e3b22e8469dc7406": + # ref: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct + res = "llama4" + if chkhsh == "0e9433cbbb161f89e264eb32e8e64bfe69e834973ffca5d41d3948a604a3e2a3": + # ref: https://huggingface.co/mistral-community/pixtral-12b + res = "pixtral" + if chkhsh == "d5f1dd6f980fec569fb218a81a7658ac45fc56b38c5a0adeb1c232fbe04ef5ec": + # ref: https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base + res = "seed-coder" + if chkhsh == "b0a6b1c0bd5998ebd9df08611efde34a4ff03faed45ae09c43e6b31ebd4b94cf": + # ref: https://huggingface.co/skt/A.X-4.0 + res = "a.x-4.0" + if chkhsh == "f6791d196f87ce6b56a7d234be618e0d58f8cda3549416635b2bebcd22cd95c4": + # ref: https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct + res = "midm-2.0" + if chkhsh == "169bf0296a13c4d9b7672313f749eb36501d931022de052aad6e36f2bf34dd51": + # ref: https://huggingface.co/LiquidAI/LFM2-Tokenizer + res = "lfm2" + if chkhsh == "2085e1638f6c377a0aa4ead21b27bb4cb941bf800df86ed391011769c1758dfb": + # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B + res = "exaone4" + + if res is None: + logger.warning("\n") + logger.warning("**************************************************************************************") + logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!") + logger.warning("** There are 2 possible reasons for this:") + logger.warning("** - the model has not been added to convert_hf_to_gguf_update.py yet") + logger.warning("** - the pre-tokenization config has changed upstream") + logger.warning("** Check your model files and convert_hf_to_gguf_update.py and update them accordingly.") + logger.warning("** ref: https://github.com/ggml-org/llama.cpp/pull/6920") + logger.warning("**") + logger.warning(f"** chkhsh: {chkhsh}") + logger.warning("**************************************************************************************") + logger.warning("\n") + raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()") + + logger.debug(f"tokenizer.ggml.pre: {repr(res)}") + logger.debug(f"chkhsh: {chkhsh}") + + return res + # Marker: End get_vocab_base_pre + + def _set_vocab_none(self) -> None: + self.gguf_writer.add_tokenizer_model("none") + + def _set_vocab_gpt2(self) -> None: + tokens, toktypes, tokpre = self.get_vocab_base() + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + special_vocab.add_to_gguf(self.gguf_writer) + + def _set_vocab_qwen(self): + dir_model = self.dir_model + hparams = self.hparams + tokens: list[str] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) + vocab_size = hparams["vocab_size"] + assert max(tokenizer.get_vocab().values()) < vocab_size + + tokpre = self.get_vocab_base_pre(tokenizer) + + merges = [] + vocab = {} + mergeable_ranks = tokenizer.mergeable_ranks + for token, rank in mergeable_ranks.items(): + vocab[QwenModel.token_bytes_to_string(token)] = rank + if len(token) == 1: + continue + merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank) + assert len(merged) == 2 + merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged))) + + # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined + added_vocab = tokenizer.special_tokens + reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()} + + for i in range(vocab_size): + if i not in reverse_vocab: + tokens.append(f"[PAD{i}]") + toktypes.append(gguf.TokenType.UNUSED) + elif reverse_vocab[i] in added_vocab: + tokens.append(reverse_vocab[i]) + toktypes.append(gguf.TokenType.CONTROL) + else: + tokens.append(reverse_vocab[i]) + toktypes.append(gguf.TokenType.NORMAL) + + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(dir_model, load_merges=False) + special_vocab.merges = merges + # only add special tokens when they were not already loaded from config.json + if len(special_vocab.special_token_ids) == 0: + special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"]) + special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"]) + # this one is usually not in config.json anyway + special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"]) + special_vocab.add_to_gguf(self.gguf_writer) + + def _set_vocab_sentencepiece(self, add_to_gguf=True): + tokens, scores, toktypes = self._create_vocab_sentencepiece() + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def _create_vocab_sentencepiece(self): + from sentencepiece import SentencePieceProcessor + + tokenizer_path = self.dir_model / 'tokenizer.model' + + if not tokenizer_path.is_file(): + raise FileNotFoundError(f"File not found: {tokenizer_path}") + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.find_hparam([ + "vocab_size_per_layer_input", # gemma3n + "vocab_size", + ], optional=True) or tokenizer.vocab_size() + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + if token_id >= vocab_size: + logger.warning(f'ignore tokens from {token_id}: id is out of range, max={vocab_size - 1}') + break + + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + for key in added_tokens_json: + token_id = added_tokens_json[key] + if token_id >= vocab_size: + logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + tokens[token_id] = key.encode("utf-8") + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {}) + for token_id, token_data in added_tokens_decoder.items(): + token_id = int(token_id) + token: str = token_data["content"] + if token_id >= vocab_size: + logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: + if tokens[token_id] != token.encode("utf-8"): + logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token!r}') + if token_data.get("special") or self.does_token_look_special(token): + toktypes[token_id] = SentencePieceTokenTypes.CONTROL + else: + token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + + scores[token_id] = -1000.0 + tokens[token_id] = token.encode("utf-8") + + if vocab_size > len(tokens): + pad_count = vocab_size - len(tokens) + logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]") + for i in range(1, pad_count + 1): + tokens.append(bytes(f"[PAD{i}]", encoding="utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.UNUSED) + + return tokens, scores, toktypes + + def _set_vocab_llama_hf(self): + vocab = gguf.LlamaHfVocab(self.dir_model) + tokens = [] + scores = [] + toktypes = [] + + for text, score, toktype in vocab.all_tokens(): + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + assert len(tokens) == vocab.vocab_size + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def _set_vocab_rwkv_world(self): + assert (self.dir_model / "rwkv_vocab_v20230424.txt").is_file() + vocab_size = self.hparams.get("vocab_size", 65536) + + tokens: list[bytes] = [''.encode("utf-8")] + toktypes: list[int] = [gguf.TokenType.CONTROL] + + with open(self.dir_model / "rwkv_vocab_v20230424.txt", "r", encoding="utf-8") as f: + lines = f.readlines() + for line in lines: + parts = line.split(' ') + assert len(parts) >= 3 + token, token_len = ast.literal_eval(' '.join(parts[1:-1])), int(parts[-1]) + token = token.encode("utf-8") if isinstance(token, str) else token + assert isinstance(token, bytes) + assert len(token) == token_len + token_text: str = repr(token)[2:-1] # "b'\xff'" -> "\xff" + tokens.append(token_text.encode("utf-8")) + toktypes.append(gguf.TokenType.NORMAL) + remainder = vocab_size - len(tokens) + assert remainder >= 0 + for i in range(len(tokens), vocab_size): + tokens.append(f"[PAD{i}]".encode("utf-8")) + toktypes.append(gguf.TokenType.UNUSED) + + self.gguf_writer.add_tokenizer_model("rwkv") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False) + if special_vocab.chat_template is None: + template_path = Path(__file__).parent / "models" / "templates" / "llama-cpp-rwkv-world.jinja" + if template_path.is_file(): + with open(template_path, "r", encoding="utf-8") as f: + template = f.read() + else: + template = "rwkv-world" + special_vocab.chat_template = template + # hack: Add '\n\n' as the EOT token to make it chat normally + special_vocab._set_special_token("eot", 261) + # hack: Override these as they have already been set (incorrectly) + special_vocab.special_token_ids["bos"] = 0 + special_vocab.special_token_ids["eos"] = 0 + + special_vocab.add_to_gguf(self.gguf_writer) + + def _set_vocab_builtin(self, model_name: Literal["gpt-neox", "llama-spm"], vocab_size: int): + tokenizer_path = Path(sys.path[0]) / "models" / f"ggml-vocab-{model_name}.gguf" + logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'") + vocab_reader = gguf.GGUFReader(tokenizer_path, "r") + + default_pre = "mpt" if model_name == "gpt-neox" else "default" + + field = vocab_reader.get_field(gguf.Keys.Tokenizer.MODEL) + assert field # tokenizer model + self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8")) + + field = vocab_reader.get_field(gguf.Keys.Tokenizer.PRE) + self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else default_pre) + + field = vocab_reader.get_field(gguf.Keys.Tokenizer.LIST) + assert field # token list + self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size]) + + if model_name == "llama-spm": + field = vocab_reader.get_field(gguf.Keys.Tokenizer.SCORES) + assert field # token scores + self.gguf_writer.add_token_scores([field.parts[i].tolist()[0] for i in field.data][:vocab_size]) + + field = vocab_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE) + assert field # token types + self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size]) + + if model_name != "llama-spm": + field = vocab_reader.get_field(gguf.Keys.Tokenizer.MERGES) + assert field # token merges + self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data]) + + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)) is not None: + self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)) is not None: + self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)) is not None: + self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)) is not None: + self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_BOS)) is not None: + self.gguf_writer.add_add_bos_token(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_EOS)) is not None: + self.gguf_writer.add_add_eos_token(field.parts[-1].tolist()[0]) + + def _try_set_pooling_type(self) -> None: + # get pooling path + pooling_path = None + module_path = self.dir_model / "modules.json" + if module_path.is_file(): + with open(module_path, encoding="utf-8") as f: + modules = json.load(f) + for mod in modules: + if mod["type"] == "sentence_transformers.models.Pooling": + pooling_path = mod["path"] + break + + # get pooling type + if pooling_path is not None: + with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f: + pooling = json.load(f) + if pooling["pooling_mode_mean_tokens"]: + pooling_type = gguf.PoolingType.MEAN + elif pooling["pooling_mode_cls_token"]: + pooling_type = gguf.PoolingType.CLS + elif pooling["pooling_mode_lasttoken"]: + pooling_type = gguf.PoolingType.LAST + else: + raise NotImplementedError("Only MEAN, CLS, and LAST pooling types supported") + self.gguf_writer.add_pooling_type(pooling_type) + + +class MmprojModel(ModelBase): + model_type = ModelType.MMPROJ + model_arch = gguf.MODEL_ARCH.MMPROJ + preprocessor_config: dict[str, Any] + global_config: dict[str, Any] + + n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth"] + + has_vision_encoder: bool = True # by default + has_audio_encoder: bool = False + + # for models having multiple encoders, we need to separate their hparams + hparams_vision: dict[str, Any] | None = None + hparams_audio: dict[str, Any] | None = None + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + if self.model_arch != gguf.MODEL_ARCH.MMPROJ: + raise TypeError("MmprojModel must be subclassed with model_arch = gguf.MODEL_ARCH.MMPROJ") + + # get n_embd of the text model + if "text_config" not in self.hparams: + self.hparams["text_config"] = {} + if "audio_config" not in self.hparams: + self.hparams["audio_config"] = {} + text_config = {**self.hparams, **self.hparams["text_config"]} + self.n_embd_text = text_config.get("hidden_size", text_config.get("n_embd", 0)) + assert self.n_embd_text > 0, "n_embd not found in hparams" + + # move vision config to the top level, while preserving the original hparams in global_config + import copy + self.global_config = copy.deepcopy(self.hparams) + self.hparams_vision = self.get_vision_config() + self.hparams_audio = self.get_audio_config() + + if self.hparams_vision is None and self.hparams_audio is None: + raise ValueError("vision_config / audio_config not found in hparams") + + # for compat with vision-only models + self.hparams = self.hparams_vision or self.hparams_audio or self.hparams + + # TODO @ngxson : this is a hack to support both vision and audio encoders + have_multiple_encoders = self.has_audio_encoder and self.has_vision_encoder + self.block_count = 128 if have_multiple_encoders else self.find_hparam(self.n_block_keys, True) + self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.MMPROJ, self.block_count) + + # load preprocessor config + with open(self.dir_model / "preprocessor_config.json", "r", encoding="utf-8") as f: + self.preprocessor_config = json.load(f) + + def get_vision_config(self) -> dict[str, Any] | None: + return self.global_config.get("vision_config") + + def get_audio_config(self) -> dict[str, Any] | None: + return self.global_config.get("audio_config") + + def set_type(self): + self.gguf_writer.add_type(gguf.GGUFType.MMPROJ) + + def set_gguf_parameters(self): + self.gguf_writer.add_file_type(self.ftype) + + if self.has_vision_encoder: + self.gguf_writer.add_clip_has_vision_encoder(True) + self.gguf_writer.add_vision_projection_dim(self.n_embd_text) + + # vision config + self.gguf_writer.add_vision_image_size(self.find_vparam(["image_size"])) + self.gguf_writer.add_vision_patch_size(self.find_vparam(["patch_size"])) + self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size"])) + self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size"])) + self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys)) + self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads"])) + + # preprocessor config + self.gguf_writer.add_vision_image_mean(self.preprocessor_config["image_mean"]) + self.gguf_writer.add_vision_image_std(self.preprocessor_config["image_std"]) + + if self.has_audio_encoder: + self.gguf_writer.add_clip_has_audio_encoder(True) + self.gguf_writer.add_audio_projection_dim(self.n_embd_text) + + # audio config + self.gguf_writer.add_audio_embedding_length(self.find_aparam(["hidden_size"])) + self.gguf_writer.add_audio_feed_forward_length(self.find_aparam(["intermediate_size"])) + self.gguf_writer.add_audio_block_count(self.find_aparam(self.n_block_keys)) + self.gguf_writer.add_audio_head_count(self.find_aparam(["num_attention_heads"])) + + if not self.has_vision_encoder and not self.has_audio_encoder: + raise ValueError("MmprojModel must have either vision or audio encoder") + + def write_vocab(self): + raise ValueError("MmprojModel does not support vocab writing") + + def find_vparam(self, keys: Iterable[str], optional: bool = False) -> Any: + assert self.hparams_vision is not None + return self._find_param(self.hparams_vision, keys, optional) + + def find_aparam(self, keys: Iterable[str], optional: bool = False) -> Any: + assert self.hparams_audio is not None + return self._find_param(self.hparams_audio, keys, optional) + + def _find_param(self, obj: dict[str, Any], keys: Iterable[str], optional: bool = False) -> Any: + key = next((k for k in keys if k in obj), None) + if key is not None: + return obj[key] + if optional: + return None + raise KeyError(f"could not find any of: {keys}") + + +@ModelBase.register("GPTNeoXForCausalLM") +class GPTNeoXModel(TextModel): + model_arch = gguf.MODEL_ARCH.GPTNEOX + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_dimension_count( + int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])), + ) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True)) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + + tensors: list[tuple[str, Tensor]] = [] + + if re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.weight", name): + # Map bloom-style qkv_linear to gpt-style qkv_linear + # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa + # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa + qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed)) + data_torch = torch.cat( + ( + qkv_weights[:, 0, :, :].reshape((-1, n_embed)), + qkv_weights[:, 1, :, :].reshape((-1, n_embed)), + qkv_weights[:, 2, :, :].reshape((-1, n_embed)), + ), + dim=0, + ) + logger.info("re-format attention.linear_qkv.weight") + elif re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.bias", name): + qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head)) + data_torch = torch.cat( + ( + qkv_bias[:, 0, :].reshape((n_embed,)), + qkv_bias[:, 1, :].reshape((n_embed,)), + qkv_bias[:, 2, :].reshape((n_embed,)), + ), + dim=0, + ) + logger.info("re-format attention.linear_qkv.bias") + + tensors.append((self.map_tensor_name(name), data_torch)) + + return tensors + + +@ModelBase.register("BloomForCausalLM", "BloomModel") +class BloomModel(TextModel): + model_arch = gguf.MODEL_ARCH.BLOOM + + def set_gguf_parameters(self): + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed)) + self.gguf_writer.add_embedding_length(n_embed) + self.gguf_writer.add_feed_forward_length(4 * n_embed) + self.gguf_writer.add_block_count(self.hparams["n_layer"]) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + + name = re.sub(r'transformer\.', '', name) + + tensors: list[tuple[str, Tensor]] = [] + + if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name): + # Map bloom-style qkv_linear to gpt-style qkv_linear + # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa + # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa + qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed)) + data_torch = torch.cat( + ( + qkv_weights[:, 0, :, :].reshape((-1, n_embed)), + qkv_weights[:, 1, :, :].reshape((-1, n_embed)), + qkv_weights[:, 2, :, :].reshape((-1, n_embed)), + ), + dim=0, + ) + logger.info("re-format attention.linear_qkv.weight") + elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name): + qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head)) + data_torch = torch.cat( + ( + qkv_bias[:, 0, :].reshape((n_embed,)), + qkv_bias[:, 1, :].reshape((n_embed,)), + qkv_bias[:, 2, :].reshape((n_embed,)), + ), + dim=0, + ) + logger.info("re-format attention.linear_qkv.bias") + + tensors.append((self.map_tensor_name(name), data_torch)) + + return tensors + + +@ModelBase.register("MPTForCausalLM") +class MPTModel(TextModel): + model_arch = gguf.MODEL_ARCH.MPT + + def set_vocab(self): + try: + self._set_vocab_gpt2() + except Exception: + # Fallback for SEA-LION model + self._set_vocab_sentencepiece() + self.gguf_writer.add_add_bos_token(False) + self.gguf_writer.add_pad_token_id(3) + self.gguf_writer.add_eos_token_id(1) + self.gguf_writer.add_unk_token_id(0) + + def set_gguf_parameters(self): + block_count = self.hparams["n_layers"] + self.gguf_writer.add_context_length(self.hparams["max_seq_len"]) + self.gguf_writer.add_embedding_length(self.hparams["d_model"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"]) + self.gguf_writer.add_head_count(self.hparams["n_heads"]) + if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"): + self.gguf_writer.add_head_count_kv(kv_n_heads) + self.gguf_writer.add_layer_norm_eps(1e-5) + if self.hparams["attn_config"]["clip_qkv"] is not None: + self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"]) + if self.hparams["attn_config"]["alibi"]: + self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"]) + else: + self.gguf_writer.add_max_alibi_bias(0.0) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if "scales" in name: + new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias", ".scales")) + new_name = new_name.replace("scales", "act.scales") + else: + new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias")) + + return [(new_name, data_torch)] + + +@ModelBase.register("OrionForCausalLM") +class OrionModel(TextModel): + model_arch = gguf.MODEL_ARCH.ORION + + def set_vocab(self): + self._set_vocab_sentencepiece() + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + head_count = self.hparams["num_attention_heads"] + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + + ctx_length = 0 + if "max_sequence_length" in self.hparams: + ctx_length = self.hparams["max_sequence_length"] + elif "max_position_embeddings" in self.hparams: + ctx_length = self.hparams["max_position_embeddings"] + elif "model_max_length" in self.hparams: + ctx_length = self.hparams["model_max_length"] + else: + raise ValueError("gguf: can not find ctx length parameter.") + + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_tensor_data_layout("Meta AI original pth") + self.gguf_writer.add_context_length(ctx_length) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_head_count(head_count) + self.gguf_writer.add_head_count_kv(head_count_kv) + # note: config provides rms norm but it is actually layer norm + # ref: https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/276a17221ce42beb45f66fac657a41540e71f4f5/modeling_orion.py#L570-L571 + self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"]) + + +@ModelBase.register("BaichuanForCausalLM", "BaiChuanForCausalLM") +class BaichuanModel(TextModel): + model_arch = gguf.MODEL_ARCH.BAICHUAN + + def set_vocab(self): + self._set_vocab_sentencepiece() + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + head_count = self.hparams["num_attention_heads"] + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + + ctx_length = 0 + if "max_sequence_length" in self.hparams: + ctx_length = self.hparams["max_sequence_length"] + elif "max_position_embeddings" in self.hparams: + ctx_length = self.hparams["max_position_embeddings"] + elif "model_max_length" in self.hparams: + ctx_length = self.hparams["model_max_length"] + else: + raise ValueError("gguf: can not find ctx length parameter.") + + self.gguf_writer.add_tensor_data_layout("Meta AI original pth") + self.gguf_writer.add_context_length(ctx_length) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + self.gguf_writer.add_head_count(head_count) + self.gguf_writer.add_head_count_kv(head_count_kv) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_file_type(self.ftype) + + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + head_count = self.hparams["num_attention_heads"] + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + + tensors: list[tuple[str, Tensor]] = [] + + if bid is not None and name == f"model.layers.{bid}.self_attn.W_pack.weight": + logger.info(f"Unpacking and permuting layer {bid}") + tensors = [ + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), + self._reverse_hf_permute_part(data_torch, 0, head_count, head_count)), + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), + self._reverse_hf_permute_part(data_torch, 1, head_count, head_count_kv)), + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), + self._reverse_hf_part(data_torch, 2)), + ] + else: + tensors = [(self.map_tensor_name(name), data_torch)] + + return tensors + + def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor: + if n_kv_head is not None and n_head != n_kv_head: + n_head //= n_kv_head + + return ( + weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape) + ) + + def _reverse_hf_permute_part( + self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None, + ) -> Tensor: + r = weights.shape[0] // 3 + return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv) + + def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor: + r = weights.shape[0] // 3 + return weights[r * n_part:r * n_part + r, ...] + + +@ModelBase.register("XverseForCausalLM") +class XverseModel(TextModel): + model_arch = gguf.MODEL_ARCH.XVERSE + + def set_vocab(self): + assert (self.dir_model / "tokenizer.json").is_file() + dir_model = self.dir_model + hparams = self.hparams + + tokens: list[bytes] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(dir_model) + vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) + # Since we are checking the maximum index, we need to ensure it's strictly less than vocab_size, + # because vocab_size is the count of items, and indexes start at 0. + max_vocab_index = max(tokenizer.get_vocab().values()) + if max_vocab_index >= vocab_size: + raise ValueError("Vocabulary size exceeds expected maximum size.") + + reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()} + added_vocab = tokenizer.get_added_vocab() + + for token_id in range(vocab_size): + token_text = reverse_vocab[token_id].encode('utf-8') + # replace "\x00" to string with length > 0 + if token_text == b"\x00": + toktype = gguf.TokenType.BYTE # special + token_text = f"<{token_text}>".encode('utf-8') + elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text): + toktype = gguf.TokenType.BYTE # special + elif reverse_vocab[token_id] in added_vocab: + if tokenizer.added_tokens_decoder[token_id].special: + toktype = gguf.TokenType.CONTROL + else: + toktype = gguf.TokenType.USER_DEFINED + else: + toktype = gguf.TokenType.NORMAL + + tokens.append(token_text) + toktypes.append(toktype) + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + head_count = self.hparams["num_attention_heads"] + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + + ctx_length = 0 + if "max_sequence_length" in self.hparams: + ctx_length = self.hparams["max_sequence_length"] + elif "max_position_embeddings" in self.hparams: + ctx_length = self.hparams["max_position_embeddings"] + elif "model_max_length" in self.hparams: + ctx_length = self.hparams["model_max_length"] + else: + raise ValueError("gguf: can not find ctx length parameter.") + + self.gguf_writer.add_tensor_data_layout("Meta AI original pth") + self.gguf_writer.add_context_length(ctx_length) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + self.gguf_writer.add_head_count(head_count) + self.gguf_writer.add_head_count_kv(head_count_kv) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_file_type(self.ftype) + + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + head_count = self.hparams["num_attention_heads"] + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + + # HF models permute some of the tensors, so we need to undo that + if name.endswith("q_proj.weight"): + data_torch = self._reverse_hf_permute(data_torch, head_count, head_count) + if name.endswith("k_proj.weight"): + data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv) + + return [(self.map_tensor_name(name), data_torch)] + + def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor: + if n_kv_head is not None and n_head != n_kv_head: + n_head //= n_kv_head + + return ( + weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape) + ) + + +@ModelBase.register("FalconForCausalLM", "RWForCausalLM") +class FalconModel(TextModel): + model_arch = gguf.MODEL_ARCH.FALCON + + def set_gguf_parameters(self): + block_count = self.hparams.get("num_hidden_layers") + if block_count is None: + block_count = self.hparams["n_layer"] # old name + + n_head = self.hparams.get("num_attention_heads") + if n_head is None: + n_head = self.hparams["n_head"] # old name + + n_head_kv = self.hparams.get("num_kv_heads") + if n_head_kv is None: + n_head_kv = self.hparams.get("n_head_kv", 1) # old name + + self.gguf_writer.add_context_length(2048) # not in config.json + self.gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head_kv) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + # QKV tensor transform + # The original query_key_value tensor contains n_head_kv "kv groups", + # each consisting of n_head/n_head_kv query weights followed by one key + # and one value weight (shared by all query heads in the kv group). + # This layout makes it a big pain to work with in GGML. + # So we rearrange them here,, so that we have n_head query weights + # followed by n_head_kv key weights followed by n_head_kv value weights, + # in contiguous fashion. + # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py + + if "query_key_value" in name: + n_head = self.find_hparam(["num_attention_heads", "n_head"]) + n_head_kv = self.find_hparam(["num_kv_heads", "n_head_kv"], optional=True) or 1 + head_dim = self.hparams["hidden_size"] // n_head + + qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) + q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head) + k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) + v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) + data_torch = torch.cat((q, k, v)).reshape_as(data_torch) + + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("GPTBigCodeForCausalLM") +class StarCoderModel(TextModel): + model_arch = gguf.MODEL_ARCH.STARCODER + + def set_gguf_parameters(self): + block_count = self.hparams["n_layer"] + + self.gguf_writer.add_context_length(self.hparams["n_positions"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_head_count_kv(1) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + +@ModelBase.register("GPTRefactForCausalLM") +class RefactModel(TextModel): + model_arch = gguf.MODEL_ARCH.REFACT + + def set_vocab(self): + super().set_vocab() + + # TODO: how to determine special FIM tokens automatically? + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False, + special_token_types = ['prefix', 'suffix', 'middle', 'eot']) + special_vocab._set_special_token("prefix", 1) + special_vocab._set_special_token("suffix", 3) + special_vocab._set_special_token("middle", 2) + special_vocab.chat_template = None # do not add it twice + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + hidden_dim = self.hparams["n_embd"] + inner_dim = 4 * hidden_dim + hidden_dim = int(2 * inner_dim / 3) + multiple_of = 256 + ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + block_count = self.hparams["n_layer"] + + # refact uses Alibi. So this is from config.json which might be used by training. + self.gguf_writer.add_context_length(self.hparams["n_positions"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + + self.gguf_writer.add_feed_forward_length(ff_dim) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_head_count_kv(1) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + hidden_dim = self.hparams["n_embd"] + inner_dim = 4 * hidden_dim + hidden_dim = int(2 * inner_dim / 3) + multiple_of = 256 + ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + n_head = self.hparams["n_head"] + n_head_kv = 1 + head_dim = self.hparams["n_embd"] // n_head + + tensors: list[tuple[str, Tensor]] = [] + + if bid is not None: + if name == f"transformer.h.{bid}.attn.kv.weight": + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), data_torch[:n_head_kv * head_dim])) + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), data_torch[n_head_kv * head_dim:])) + elif name == f"transformer.h.{bid}.attn.q.weight": + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), data_torch)) + elif name == f"transformer.h.{bid}.mlp.gate_up_proj.weight": + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim])) + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:])) + + if len(tensors) == 0: + tensors.append((self.map_tensor_name(name), data_torch)) + + return tensors + + +@ModelBase.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM") +class StableLMModel(TextModel): + model_arch = gguf.MODEL_ARCH.STABLELM + + def set_vocab(self): + if (self.dir_model / "tokenizer.json").is_file(): + self._set_vocab_gpt2() + else: + # StableLM 2 1.6B used to have a vocab in a similar format to Qwen's vocab + self._set_vocab_qwen() + + def set_gguf_parameters(self): + hparams = self.hparams + block_count = hparams["num_hidden_layers"] + + self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"]) + self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"]))) + self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"]) + self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) + self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"])) + self.gguf_writer.add_file_type(self.ftype) + + _q_norms: list[dict[str, Tensor]] | None = None + _k_norms: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams["num_key_value_heads"] + + if name.find("q_layernorm.norms") != -1: + assert bid is not None + + if self._q_norms is None: + self._q_norms = [{} for _ in range(self.block_count)] + + self._q_norms[bid][name] = data_torch + + if len(self._q_norms[bid]) >= n_head: + return self._stack_qk_norm(bid, n_head, self._q_norms[bid], "q_layernorm") + else: + return [] + + if name.find("k_layernorm.norms") != -1: + assert bid is not None + + if self._k_norms is None: + self._k_norms = [{} for _ in range(self.block_count)] + + self._k_norms[bid][name] = data_torch + + if len(self._k_norms[bid]) >= n_kv_head: + return self._stack_qk_norm(bid, n_kv_head, self._k_norms[bid], "k_layernorm") + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def _stack_qk_norm(self, bid: int, n_head: int, norms: dict[str, Tensor], layer_name: str = "q_layernorm"): + datas: list[Tensor] = [] + # extract the norms in order + for xid in range(n_head): + ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight" + datas.append(norms[ename]) + del norms[ename] + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight" + new_name = self.map_tensor_name(merged_name) + + return [(new_name, data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._q_norms is not None or self._k_norms is not None: + # flatten two `list[dict[str, Tensor]]` into a single `list[str]` + norms = ( + [k for d in self._q_norms for k in d.keys()] if self._q_norms is not None else [] + ) + ( + [k for d in self._k_norms for k in d.keys()] if self._k_norms is not None else [] + ) + if len(norms) > 0: + raise ValueError(f"Unprocessed norms: {norms}") + + +@ModelBase.register( + "LLaMAForCausalLM", + "LlamaForCausalLM", + "MistralForCausalLM", + "MixtralForCausalLM", + "VLlama3ForCausalLM", + "LlavaForConditionalGeneration", + "VoxtralForConditionalGeneration", + "LlamaModel") +class LlamaModel(TextModel): + model_arch = gguf.MODEL_ARCH.LLAMA + undo_permute = True + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + # fix for SmolVLM2, missing `num_attention_heads` in config.json + if self.hf_arch == "VLlama3ForCausalLM": + self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32) + + def set_vocab(self): + path_tekken_json = self.dir_model / "tekken.json" + path_tokenizer_json = self.dir_model / "tokenizer.json" + if path_tekken_json.is_file() and not path_tokenizer_json.is_file(): + return self.set_vocab_tekken() + + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + try: + self._set_vocab_llama_hf() + except (FileNotFoundError, TypeError): + # Llama 3 + self._set_vocab_gpt2() + + # Apply to CodeLlama only (and ignore for Llama 3 with a vocab size of 128256) + if self.hparams.get("vocab_size", 32000) == 32016: + special_vocab = gguf.SpecialVocab( + self.dir_model, load_merges=False, + special_token_types = ['prefix', 'suffix', 'middle', 'eot'] + ) + special_vocab._set_special_token("prefix", 32007) + special_vocab._set_special_token("suffix", 32008) + special_vocab._set_special_token("middle", 32009) + special_vocab._set_special_token("eot", 32010) + special_vocab.add_to_gguf(self.gguf_writer) + + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + if "add_prefix_space" in tokenizer_config_json: + self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"]) + + # Apply to granite small models only + if self.hparams.get("vocab_size", 32000) == 49152: + self.gguf_writer.add_add_bos_token(False) + + def set_vocab_tekken(self): + vocab = gguf.vocab.MistralVocab(self.dir_model) + self.gguf_writer.add_tokenizer_model(vocab.gguf_tokenizer_model) + + tokens = [] + scores = [] + toktypes = [] + + for text, score, toktype in vocab.all_tokens(): + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + assert len(tokens) == vocab.vocab_size, ( + f"token count ({len(tokens)}) != vocab size ({vocab.vocab_size})" + ) + + if vocab.tokenizer_type == gguf.vocab.MistralTokenizerType.tekken: + self.gguf_writer.add_tokenizer_pre("tekken") + self.gguf_writer.add_token_merges( + vocab.extract_vocab_merges_from_model() + ) + + logger.info( + f"Setting bos, eos, unk and pad token IDs to {vocab.bos_id}, {vocab.eos_id}, {vocab.unk_id}, {vocab.pad_id}." + ) + + self.gguf_writer.add_bos_token_id(vocab.bos_id) + self.gguf_writer.add_eos_token_id(vocab.eos_id) + self.gguf_writer.add_unk_token_id(vocab.unk_id) + self.gguf_writer.add_pad_token_id(vocab.pad_id) + + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_vocab_size(vocab.vocab_size) + + self.gguf_writer.add_add_bos_token(True) + self.gguf_writer.add_add_eos_token(False) + + script_dir = Path(__file__).parent + template_path = script_dir / "models/templates/unsloth-mistral-Devstral-Small-2507.jinja" + with open(template_path, "r", encoding="utf-8") as f: + template = f.read() + self.gguf_writer.add_chat_template(template) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + if (rope_dim := hparams.get("head_dim")) is None: + rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(rope_dim) + + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + + @staticmethod + def permute(weights: Tensor, n_head: int, n_head_kv: int | None): + if n_head_kv is not None and n_head != n_head_kv: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + is_multimodal_tensor = "vision_tower" in name \ + or "vision_model" in name \ + or "audio_tower" in name \ + or "model.connector" in name \ + or "multi_modal_projector" in name + + if is_multimodal_tensor: + return [] # skip vision tensors + elif self.hf_arch == "LlamaModel": + name = "model." + name + elif name.startswith("model.text_model"): + name = name.replace("text_model.", "") # for SmolVLM + elif name.startswith("language_model."): + name = name.replace("language_model.", "") # for the rest + + if self.undo_permute: + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + + # process the experts separately + if name.find("block_sparse_moe.experts") != -1: + n_experts = self.hparams["num_local_experts"] + + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for wid in ["w1", "w2", "w3"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): + if rope_scaling.get("rope_type", '').lower() == "llama3": + base = self.hparams.get("rope_theta", 10000.0) + if (dim := self.hparams.get("head_dim")) is None: + dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] + freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + + factor = rope_scaling.get("factor", 8.0) + low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) + high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + old_context_len = self.hparams.get("original_max_position_embeddings", 8192) + + low_freq_wavelen = old_context_len / low_freq_factor + high_freq_wavelen = old_context_len / high_freq_factor + # assert low_freq_wavelen != high_freq_wavelen # Errors for Llama4 + + rope_factors = [] + for freq in freqs: + wavelen = 2 * math.pi / freq + if wavelen < high_freq_wavelen: + rope_factors.append(1) + elif wavelen > low_freq_wavelen: + rope_factors.append(factor) + else: + smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) + rope_factors.append(1 / ((1 - smooth) / factor + smooth)) + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32)) + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@ModelBase.register("ArceeForCausalLM") +class ArceeModel(LlamaModel): + model_arch = gguf.MODEL_ARCH.ARCEE + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self._try_set_pooling_type() + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) + + +@ModelBase.register( + "LlavaForConditionalGeneration", # pixtral + "Mistral3ForConditionalGeneration", # mistral small 3.1 +) +class LlavaVisionModel(MmprojModel): + img_break_tok_id = -1 + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + if self.hparams["model_type"] == "pixtral": + # layer_norm_eps is not in config.json, it is hard-coded in modeling_pixtral.py + self.hparams["layer_norm_eps"] = self.hparams.get("layer_norm_eps", 1e-5) + self.img_break_tok_id = self.get_token_id("[IMG_BREAK]") + logger.info(f"Image break token id: {self.img_break_tok_id}") + else: + raise ValueError(f"Unsupported model type: {self.hparams['model_type']}") + + def get_token_id(self, token: str) -> int: + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + added_tokens_decoder = json.load(f)['added_tokens_decoder'] + for id_, token_data in added_tokens_decoder.items(): + if token_data["content"] == token: + return int(id_) + raise ValueError(f"Token '{token}' not found in tokenizer config.") + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + if hparams["model_type"] == "pixtral": + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PIXTRAL) + self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"]) + + # hidden_act + if hparams["hidden_act"] == "silu": + self.gguf_writer.add_vision_use_silu(True) + elif hparams["hidden_act"] == "gelu": + self.gguf_writer.add_vision_use_gelu(True) + else: + raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}") + + # spatial_merge_size + if "spatial_merge_size" in self.global_config: + self.gguf_writer.add_vision_spatial_merge_size(self.global_config["spatial_merge_size"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + n_head = self.hparams["num_attention_heads"] + n_kv_head = n_head + + if name.startswith("multi_modal_projector.") or name.startswith("vision_tower."): + # process vision tensors + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + return [(self.map_tensor_name(name), data_torch)] + + if self.img_break_tok_id > 0 and "embed_tokens.weight" in name: + logger.info(f"Extracting [IMG_BREAK] token embedding from {name}") + # for pixtral model, we need to extract the [IMG_BREAK] token embedding + img_break_embd = data_torch[self.img_break_tok_id] + name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK] + return [(self.map_tensor_name(name), img_break_embd)] + + return [] # skip other tensors + + +@ModelBase.register("Idefics3ForConditionalGeneration", "SmolVLMForConditionalGeneration") +class SmolVLMModel(MmprojModel): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + if self.hparams["model_type"] == "smolvlm_vision": + # fix for SmolVLM2, missing some keys in config.json + # default values are taken from transformers code + self.hparams["hidden_size"] = self.hparams.get("hidden_size", 1152) + self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 16) + self.hparams["intermediate_size"] = self.hparams.get("intermediate_size", 3072) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.IDEFICS3) + self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5)) + self.gguf_writer.add_vision_projector_scale_factor(self.global_config.get("scale_factor", 2)) + self.gguf_writer.add_vision_use_gelu(True) + + def tensor_force_quant(self, name, new_name, bid, n_dims): + del bid, new_name, n_dims # unused + if ".embeddings." in name: + return gguf.GGMLQuantizationType.F32 + return False + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + is_vision_tensor = "vision_tower" in name or "vision_model" in name or "model.connector" in name + + if is_vision_tensor: + return [(self.map_tensor_name(name), data_torch)] + + return [] # skip other tensors + + +@ModelBase.register("Llama4ForConditionalGeneration") +class Llama4Model(LlamaModel): + model_arch = gguf.MODEL_ARCH.LLAMA4 + undo_permute = False + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + # IMPORTANT: the normal "intermediate_size" is renamed to "intermediate_size_mlp", we need to undo this + self.hparams["intermediate_size_moe"] = self.hparams["intermediate_size"] + self.hparams["intermediate_size"] = self.hparams["intermediate_size_mlp"] + + def set_vocab(self): + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_interleave_moe_layer_step(self.hparams["interleave_moe_layer_step"]) + self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size_moe"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): + if name.startswith("language_model."): + name = name.replace("language_model.", "") + + # split the gate_up into gate and up + if "gate_up_proj" in name: + name_up = name.replace("gate_up_proj", "up_proj.weight") + name_gate = name.replace("gate_up_proj", "gate_proj.weight") + dim_half = data_torch.shape[-1] // 2 + gate_proj_weight, up_proj_weight = data_torch.transpose(-1, -2).split(dim_half, dim=-2) + return [ + (self.map_tensor_name(name_gate), gate_proj_weight), + (self.map_tensor_name(name_up), up_proj_weight) + ] + + if name.endswith("down_proj"): + name += ".weight" + data_torch = data_torch.transpose(-1, -2) + + if "multi_modal_projector" in name or "vision_model" in name: + return [] + return super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("Llama4ForConditionalGeneration") +class Llama4VisionModel(MmprojModel): + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LLAMA4) + self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams["norm_eps"]) + self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / self.hparams["pixel_shuffle_ratio"])) + assert self.hparams["hidden_act"] == "gelu" + self.gguf_writer.add_vision_use_gelu(True) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + if "multi_modal_projector" in name or "vision_model" in name: + # process vision tensors + if "positional_embedding_vlm" in name and ".weight" not in name: + name += ".weight" + if "multi_modal_projector.linear_1" in name: + # despite the name with number postfix, this is a single fully connected layer + return [(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_MMPROJ_FC] + '.weight', data_torch)] + return [(self.map_tensor_name(name), data_torch)] + return [] + + +@ModelBase.register("Mistral3ForConditionalGeneration") +class Mistral3Model(LlamaModel): + model_arch = gguf.MODEL_ARCH.LLAMA + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): + name = name.replace("language_model.", "") + if "multi_modal_projector" in name or "vision_tower" in name: + return [] + return super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("DeciLMForCausalLM") +class DeciModel(TextModel): + model_arch = gguf.MODEL_ARCH.DECI + + @staticmethod + def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int: + # DeciLM-specific code + intermediate_size = int(2 * ffn_mult * n_embd / 3) + return DeciModel._find_multiple(intermediate_size, 256) + + @staticmethod + def _find_multiple(n: int, k: int) -> int: + # DeciLM-specific code + if n % k == 0: + return n + return n + k - (n % k) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B + _block_configs: list[dict[str,Any]] = self.hparams["block_configs"] + assert self.block_count == len(_block_configs) + self._num_kv_heads = list() + self._num_heads = list() + _ffn_multipliers = list() + # ***linear attention layer*** + # if n_heads_in_group is None and replace_with_linear is True + # then _num_kv_heads[il] is 0 and _num_heads[il] is num_attention_heads + # ***attention-free layer*** + # if n_heads_in_group is None and replace_with_linear is False + # then _num_kv_heads[il] is 0 and _num_heads[il] is 0 + # ***normal attention-layer*** + # if n_heads_in_group is not None, then + # _num_kv_heads[il] is num_attention_head // n_heads_in_group and + # _num_heads[il] is num_attention_head + # ***dummy layer*** for nemotron 253B + # if n_heads_in_group is None and ffn_mult is None + # then _num_kv_heads[il] is 0 and _num_heads[il] is 0 and _ffn_dims is 0 + for il in range(len(_block_configs)): + if _block_configs[il]["attention"]["n_heads_in_group"] is None: + if _block_configs[il]["attention"]["replace_with_linear"] is True: + self._num_kv_heads.append(0) + self._num_heads.append(self.hparams["num_attention_heads"]) + else: + self._num_kv_heads.append(0) + self._num_heads.append(0) + else: + self._num_kv_heads.append(self.hparams["num_attention_heads"] // _block_configs[il]["attention"]["n_heads_in_group"]) + self._num_heads.append(self.hparams["num_attention_heads"]) + if _block_configs[il]["ffn"]["ffn_mult"] is None: # dummy layer + _ffn_multipliers.append(0.0) + else: + _ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"]) + assert self.block_count == len(self._num_kv_heads) + assert self.block_count == len(self._num_heads) + assert self.block_count == len(_ffn_multipliers) + assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int) + assert isinstance(self._num_heads, list) and isinstance(self._num_heads[0], int) + assert isinstance(_ffn_multipliers, list) and isinstance(_ffn_multipliers[0], float) + self._ffn_dims: list[int] = [ + DeciModel._ffn_mult_to_intermediate_size(multiplier, self.hparams["hidden_size"]) + for multiplier in _ffn_multipliers + ] + + def set_vocab(self): + # Please change tokenizer_config.json of Llama-3_1-Nemotron-51B's + # eos_token from '|eot_id|' to '|end_of_text|' + if self.hparams.get("vocab_size", 128256) == 128256: + tokens, toktypes, tokpre = self.get_vocab_base() + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + special_vocab.add_to_gguf(self.gguf_writer) + else: + # DeciLM-7B + self._set_vocab_llama_hf() + + def set_gguf_parameters(self): + if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B + assert self.block_count == len(self._num_kv_heads) + assert self.block_count == len(self._num_heads) + assert self.block_count == len(self._ffn_dims) + if (rope_theta := self.hparams.get("rope_theta")) is not None: + self.gguf_writer.add_rope_freq_base(rope_theta) + self.gguf_writer.add_head_count_kv(self._num_kv_heads) + self.gguf_writer.add_head_count(self._num_heads) + self.gguf_writer.add_feed_forward_length(self._ffn_dims) + self.gguf_writer.add_block_count(self.block_count) + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_key_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + self.gguf_writer.add_value_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + self.gguf_writer.add_file_type(self.ftype) + else: # DeciLM-7B + super().set_gguf_parameters() + if "num_key_value_heads_per_layer" in self.hparams: # DeciLM-7B + self._num_kv_heads: list[int] = self.hparams["num_key_value_heads_per_layer"] + assert self.block_count == len(self._num_kv_heads) + self.gguf_writer.add_head_count_kv(self._num_kv_heads) + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + if (rope_dim := hparams.get("head_dim")) is None: + rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(rope_dim) + + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + + @staticmethod + def permute(weights: Tensor, n_head: int, n_head_kv: int | None): + if n_head_kv is not None and n_head != n_head_kv: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + if bid is not None: + if "num_key_value_heads_per_layer" in self.hparams: + n_kv_head = self.hparams["num_key_value_heads_per_layer"][bid] + elif "block_configs" in self.hparams: + n_kv_head = self._num_kv_heads[bid] + n_head = self._num_heads[bid] + else: + n_kv_head = self.hparams.get("num_key_value_heads") + else: + n_kv_head = self.hparams.get("num_key_value_heads") + + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = DeciModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = DeciModel.permute(data_torch, n_head, n_kv_head) + return [(self.map_tensor_name(name), data_torch)] + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): + if rope_scaling.get("rope_type", '').lower() == "llama3": + base = self.hparams.get("rope_theta", 10000.0) + if (dim := self.hparams.get("head_dim")) is None: + dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] + freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + + factor = rope_scaling.get("factor", 8.0) + low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) + high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + old_context_len = self.hparams.get("original_max_position_embeddings", 8192) + + low_freq_wavelen = old_context_len / low_freq_factor + high_freq_wavelen = old_context_len / high_freq_factor + assert low_freq_wavelen != high_freq_wavelen + + rope_factors = [] + for freq in freqs: + wavelen = 2 * math.pi / freq + if wavelen < high_freq_wavelen: + rope_factors.append(1) + elif wavelen > low_freq_wavelen: + rope_factors.append(factor) + else: + smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) + rope_factors.append(1 / ((1 - smooth) / factor + smooth)) + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32)) + + def prepare_tensors(self): + super().prepare_tensors() + + +@ModelBase.register("BitnetForCausalLM") +class BitnetModel(TextModel): + model_arch = gguf.MODEL_ARCH.BITNET + + def set_vocab(self): + self._set_vocab_sentencepiece() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(1.0) + + def weight_quant(self, weight: Tensor) -> Tensor: + dtype = weight.dtype + weight = weight.float() + scale = weight.abs().mean().clamp(min=1e-5) + iscale = 1 / scale + # TODO: multiply by the scale directly instead of inverting it twice + # (this is also unnecessarily doubly inverted upstream) + # ref: https://huggingface.co/1bitLLM/bitnet_b1_58-3B/blob/af89e318d78a70802061246bf037199d2fb97020/utils_quant.py#L10 + result = (weight * iscale).round().clamp(-1, 1) / iscale + return result.type(dtype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + new_name = self.map_tensor_name(name) + + if any(self.match_model_tensor_name(new_name, key, bid) for key in [ + gguf.MODEL_TENSOR.ATTN_Q, + gguf.MODEL_TENSOR.ATTN_K, + gguf.MODEL_TENSOR.ATTN_V, + gguf.MODEL_TENSOR.ATTN_OUT, + gguf.MODEL_TENSOR.FFN_UP, + gguf.MODEL_TENSOR.FFN_DOWN, + gguf.MODEL_TENSOR.FFN_GATE, + ]): + # transform weight into 1/0/-1 (in fp32) + data_torch = self.weight_quant(data_torch) + + yield (new_name, data_torch) + + +@ModelBase.register("GrokForCausalLM") +class GrokModel(TextModel): + model_arch = gguf.MODEL_ARCH.GROK + + def set_vocab(self): + self._set_vocab_sentencepiece() + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # process the experts separately + if name.find(".moe.") != -1: + n_experts = self.hparams["num_local_experts"] + + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for wid in ["linear", "linear_1", "linear_v"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("DbrxForCausalLM") +class DbrxModel(TextModel): + model_arch = gguf.MODEL_ARCH.DBRX + + def set_gguf_parameters(self): + ffn_config = self.hparams["ffn_config"] + attn_config = self.hparams["attn_config"] + self.gguf_writer.add_block_count(self.hparams["n_layers"]) + + self.gguf_writer.add_context_length(self.hparams["max_seq_len"]) + self.gguf_writer.add_embedding_length(self.hparams["d_model"]) + self.gguf_writer.add_feed_forward_length(ffn_config["ffn_hidden_size"]) + + self.gguf_writer.add_head_count(self.hparams["n_heads"]) + self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"]) + + self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"]) + + self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"]) + + self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"]) + self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"]) + + self.gguf_writer.add_layer_norm_eps(1e-5) + + self.gguf_writer.add_file_type(self.ftype) + logger.info(f"gguf: file type = {self.ftype}") + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + n_expert = self.hparams["ffn_config"]["moe_num_experts"] + n_ff = self.hparams["ffn_config"]["ffn_hidden_size"] + n_embd = self.hparams["d_model"] + + # Specific behavior for experts tensors: suffix .weight, view as 3D and transpose + # original implementation expects (n_expert, n_ff, n_embd) for all experts weights + # But llama.cpp moe graph works differently + # AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions + # so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor + exp_tensor_names = {"ffn.experts.mlp.w1": None, # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert} + "ffn.experts.mlp.w2": (0, 2, 1), # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff, n_embd, n_expert} + "ffn.experts.mlp.v1": None} # LLM_TENSOR_FFN_UP_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert} + experts = False + + for exp_tensor_name in exp_tensor_names.keys(): + if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1: + experts = True + data_torch = data_torch.view(n_expert, n_ff, n_embd) + if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None: + data_torch = data_torch.permute(*permute_tensor) + break + + # map tensor names + # In MoE models the ffn tensors are typically most of the model weights, + # and need to be quantizable. Quantize expects tensor names to be suffixed by .weight. + # Every other model has the weight names ending in .weight, + # let's assume that is the convention which is not the case for dbrx: + # https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15 + new_name = self.map_tensor_name(name if not experts else name + ".weight", try_suffixes=(".weight",)) + + return [(new_name, data_torch)] + + def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool: + del name, new_name, bid # unused + + return n_dims > 1 + + +@ModelBase.register("MiniCPMForCausalLM") +class MiniCPMModel(TextModel): + model_arch = gguf.MODEL_ARCH.MINICPM + + def set_gguf_parameters(self): + super().set_gguf_parameters() + embedding_scale = float(self.hparams["scale_emb"]) + self.gguf_writer.add_embedding_scale(embedding_scale) + logger.info(f"gguf: (minicpm) embedding_scale = {embedding_scale}") + residual_scale = self.hparams["scale_depth"] / self.hparams["num_hidden_layers"] ** 0.5 + self.gguf_writer.add_residual_scale(residual_scale) + logger.info(f"gguf: (minicpm) residual_scale = {residual_scale}") + logit_scale = self.hparams["hidden_size"] / self.hparams["dim_model_base"] + self.gguf_writer.add_logit_scale(logit_scale) + logger.info(f"gguf: (minicpm) logit_scale = {logit_scale}") + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "longrope": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LONGROPE) + logger.info(f"gguf: (minicpm) rope_scaling_type = {gguf.RopeScalingType.LONGROPE}") + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + rope_dims = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] + + rope_scaling = self.find_hparam(['rope_scaling'], True) + if rope_scaling is not None: + long_factors = rope_scaling.get('long_factor', None) + short_factors = rope_scaling.get('short_factor', None) + + if long_factors is None or short_factors is None: + raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor') + + if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2: + raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}') + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32)) + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32)) + + def set_vocab(self): + self._set_vocab_sentencepiece() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + + # HF models permute some of the tensors, so we need to undo that + if name.endswith(("q_proj.weight")): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight")): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("MiniCPM3ForCausalLM") +class MiniCPM3Model(TextModel): + model_arch = gguf.MODEL_ARCH.MINICPM3 + + def set_gguf_parameters(self): + hparams = self.hparams + + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_block_count(self.block_count) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None: + self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"]) + self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"]) + self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"]) + self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + rope_scaling = self.find_hparam(['rope_scaling'], True) + if rope_scaling is not None: + rope_dims = self.hparams["qk_rope_head_dim"] + + long_factors = rope_scaling.get('long_factor', None) + short_factors = rope_scaling.get('short_factor', None) + + if long_factors is None or short_factors is None: + raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor') + + if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2: + raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}') + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32)) + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32)) + + def set_vocab(self): + self._set_vocab_sentencepiece() + + def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor: + if n_kv_head is not None and n_head != n_kv_head: + n_head //= n_kv_head + + return ( + weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape) + ) + + +@ModelBase.register("QWenLMHeadModel") +class QwenModel(TextModel): + model_arch = gguf.MODEL_ARCH.QWEN + + @staticmethod + def token_bytes_to_string(b): + from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode + byte_encoder = bytes_to_unicode() + return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')]) + + @staticmethod + def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]: + parts = [bytes([b]) for b in token] + while True: + min_idx = None + min_rank = None + for i, pair in enumerate(zip(parts[:-1], parts[1:])): + rank = mergeable_ranks.get(pair[0] + pair[1]) + if rank is not None and (min_rank is None or rank < min_rank): + min_idx = i + min_rank = rank + if min_rank is None or (max_rank is not None and min_rank >= max_rank): + break + assert min_idx is not None + parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:] + return parts + + def set_vocab(self): + self._set_vocab_qwen() + + def set_gguf_parameters(self): + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) + self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + +@ModelBase.register("Qwen2Model", "Qwen2ForCausalLM", "Qwen2AudioForConditionalGeneration") +class Qwen2Model(TextModel): + model_arch = gguf.MODEL_ARCH.QWEN2 + + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self._try_set_pooling_type() + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if self.hf_arch == "Qwen2Model": + name = f"model.{name}" # map to Qwen2ForCausalLM tensors + if "language_model." in name: + name = name.replace("language_model.", "") # for InternVL + if name.startswith("mlp") or name.startswith("multi_modal_projector") \ + or name.startswith("vision_model") or name.startswith("audio_tower"): + # skip vision and audio tensors + return [] + yield from super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("DreamModel") +class DreamModel(TextModel): + model_arch = gguf.MODEL_ARCH.DREAM + + def get_vocab_base(self) -> tuple[list[str], list[int], str]: + tokens: list[str] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True) + + vocab_dict = tokenizer.get_vocab() + vocab_size = self.hparams.get("vocab_size", len(vocab_dict)) + assert max(vocab_dict.values()) < vocab_size + + tokpre = self.get_vocab_base_pre(tokenizer) + + reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab_dict.items()} + added_vocab = tokenizer.get_added_vocab() + + for i in range(vocab_size): + if i not in reverse_vocab: + tokens.append(f"[PAD{i}]") + toktypes.append(gguf.TokenType.UNUSED) + elif reverse_vocab[i] in added_vocab: + tokens.append(reverse_vocab[i]) + # Check if it's a special token - treat special tokens as CONTROL tokens + if hasattr(tokenizer, 'added_tokens_decoder') and i in tokenizer.added_tokens_decoder: + if tokenizer.added_tokens_decoder[i].special: + toktypes.append(gguf.TokenType.CONTROL) + else: + toktypes.append(gguf.TokenType.USER_DEFINED) + else: + # Fallback: treat all added vocab as control tokens for special tokens like <|im_start|> + toktypes.append(gguf.TokenType.CONTROL) + else: + tokens.append(reverse_vocab[i]) + toktypes.append(gguf.TokenType.NORMAL) + + return tokens, toktypes, tokpre + + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self._try_set_pooling_type() + + # Dream models use non-causal attention for diffusion + self.gguf_writer.add_causal_attention(False) + # Handle RoPE scaling similar to Qwen2 + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) + + # Add Dream-specific parameters + mask_token_id = self.hparams.get("mask_token_id") + if mask_token_id is not None: + self.gguf_writer.add_mask_token_id(mask_token_id) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # Dream model tensors should be mapped directly since it's the base model + yield from super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("LLaDAModelLM") +class LLaDAModel(TextModel): + model_arch = gguf.MODEL_ARCH.LLADA + undo_permute = True + + def get_vocab_base(self) -> tuple[list[str], list[int], str]: + tokens: list[str] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True) + + vocab_dict = tokenizer.get_vocab() + vocab_size = self.hparams.get("vocab_size", len(vocab_dict)) + assert max(vocab_dict.values()) < vocab_size + + tokpre = self.get_vocab_base_pre(tokenizer) + + reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab_dict.items()} + added_vocab = tokenizer.get_added_vocab() + + for i in range(vocab_size): + if i not in reverse_vocab: + tokens.append(f"[PAD{i}]") + toktypes.append(gguf.TokenType.UNUSED) + elif reverse_vocab[i] in added_vocab: + tokens.append(reverse_vocab[i]) + # Check if it's a special token - treat special tokens as CONTROL tokens + if hasattr(tokenizer, 'added_tokens_decoder') and i in tokenizer.added_tokens_decoder: + if tokenizer.added_tokens_decoder[i].special: + toktypes.append(gguf.TokenType.CONTROL) + else: + toktypes.append(gguf.TokenType.USER_DEFINED) + else: + # Fallback: treat all added vocab as control tokens for special tokens like <|im_start|> + toktypes.append(gguf.TokenType.CONTROL) + else: + tokens.append(reverse_vocab[i]) + toktypes.append(gguf.TokenType.NORMAL) + + return tokens, toktypes, tokpre + + def set_vocab(self): + self._set_vocab_gpt2() + + # LLaDA specific parameters + self.gguf_writer.add_add_bos_token(True) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self._try_set_pooling_type() + + # Add parameters similar to LlamaModel + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + if (rope_dim := hparams.get("head_dim")) is None: + n_heads = hparams.get("num_attention_heads", hparams.get("n_heads")) + rope_dim = hparams.get("hidden_size", hparams.get("d_model")) // n_heads + self.gguf_writer.add_rope_dimension_count(rope_dim) + + # Set context length for LLaDA + context_length = self.hparams.get("max_sequence_length", 4096) + self.gguf_writer.add_context_length(context_length) + + # Set embedding length (dimension size) + embedding_length = self.hparams.get("d_model", 4096) + self.gguf_writer.add_embedding_length(embedding_length) + + # Set feed forward length (MLP hidden size) + feed_forward_length = self.hparams.get("mlp_hidden_size", 12288) + self.gguf_writer.add_feed_forward_length(feed_forward_length) + + # LLaDA models use non-causal attention for diffusion, similar to Dream + self.gguf_writer.add_causal_attention(False) + + # LLaDA models don't shift their logits + self.gguf_writer.add_diffusion_shift_logits(False) + + @staticmethod + def permute(weights: Tensor, n_head: int, n_head_kv: int | None): + if n_head_kv is not None and n_head != n_head_kv: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams.get("num_attention_heads", self.hparams.get("n_heads")) + n_kv_head = self.hparams.get("num_key_value_heads", self.hparams.get("n_kv_heads")) + + if self.undo_permute: + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = LLaDAModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = LLaDAModel.permute(data_torch, n_head, n_kv_head) + + # LLaDA model tensors should be mapped directly since it's the base model + yield from super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("Ernie4_5_ForCausalLM") +class Ernie4_5Model(TextModel): + model_arch = gguf.MODEL_ARCH.ERNIE4_5 + + def set_vocab(self): + self._set_vocab_sentencepiece() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + num_heads = self.hparams["num_attention_heads"] + num_kv_heads = self.hparams["num_key_value_heads"] + if (head_dim := self.hparams.get("head_dim")) is None: + head_dim = self.hparams["hidden_size"] // num_heads + + if "ernie." in name: + name = name.replace("ernie.", "model.") + # split the qkv weights + # qkv_proj shape: [(num_heads + 2 * num_kv_heads) * head_dim, hidden_size] + if "qkv_proj" in name: + name_q = name.replace("qkv_proj.weight", "q_proj.weight") + name_k = name.replace("qkv_proj.weight", "k_proj.weight") + name_v = name.replace("qkv_proj.weight", "v_proj.weight") + total_q_dim = num_heads * head_dim + total_k_dim = num_kv_heads * head_dim + total_v_dim = num_kv_heads * head_dim + q_proj_weight, k_proj_weight, v_proj_weight = data_torch.split([total_q_dim, total_k_dim, total_v_dim], dim=0) + return [ + (self.map_tensor_name(name_q), q_proj_weight), + (self.map_tensor_name(name_k), k_proj_weight), + (self.map_tensor_name(name_v), v_proj_weight) + ] + # split the up_gate_proj into gate and up + # up_gate_proj shape: [2 * intermediate_size, hidden_size] + if "up_gate_proj" in name: + name_up = name.replace("up_gate_proj.weight", "up_proj.weight") + name_gate = name.replace("up_gate_proj.weight", "gate_proj.weight") + dim_half = data_torch.shape[0] // 2 + gate_proj_weight, up_proj_weight = data_torch.split(dim_half, dim=0) + return [ + (self.map_tensor_name(name_gate), gate_proj_weight), + (self.map_tensor_name(name_up), up_proj_weight) + ] + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("Ernie4_5_MoeForCausalLM") +class Ernie4_5MoeModel(Ernie4_5Model): + model_arch = gguf.MODEL_ARCH.ERNIE4_5_MOE + _experts: list[dict[str, Tensor]] | None = None + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._experts = [{} for _ in range(self.block_count)] + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"]) + self.gguf_writer.add_expert_used_count(self.hparams["moe_k"]) + self.gguf_writer.add_interleave_moe_layer_step(self.hparams["moe_layer_interval"]) + self.gguf_writer.add_leading_dense_block_count(self.hparams["moe_layer_start_index"]) + if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None: + self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size) + if (shared_expert_count := self.hparams.get('moe_num_shared_experts')) is not None: + self.gguf_writer.add_expert_shared_count(shared_expert_count) + if shared_expert_count > 0 and (shared_expert_intermediate_size := self.hparams.get('intermediate_size')) is not None and (num_key_value_heads := self.hparams.get('num_key_value_heads')) is not None: + self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size // num_key_value_heads) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # Modify correction bias name as in DeepseekV2 + if name.endswith("e_score_correction_bias"): + name = name.replace("e_score_correction_bias", "e_score_correction.bias") + + # skip Multi-Token Prediction (MTP) layers (again, same as DeepseekV2) + match = re.match(r"model.mtp_block.(\d+)", name) + if match: + return [] + + # skip all other MTP tensors for now + match = re.match(r"model.mtp_emb_norm.(\d+)", name) + if match: + return [] + + match = re.match(r"model.mtp_hidden_norm.(\d+)", name) + if match: + return [] + + match = re.match(r"model.mtp_linear_proj.(\d+)", name) + if match: + return [] + + # process the experts separately + if name.find("mlp.experts") != -1: + n_experts = self.hparams["moe_num_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["gate_proj", "up_proj", "down_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename_to_retrieve]) + del self._experts[bid][ename_to_retrieve] + + data_torch = torch.stack(datas, dim=0) + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + new_name = self.map_tensor_name(merged_name) + tensors.append((new_name, data_torch)) + + return tensors + else: + return [] + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@ModelBase.register( + "Qwen2VLModel", + "Qwen2VLForConditionalGeneration", + "Qwen2_5_VLForConditionalGeneration", + "Qwen2_5OmniModel", +) +class Qwen2VLModel(TextModel): + model_arch = gguf.MODEL_ARCH.QWEN2VL + + def set_gguf_parameters(self): + super().set_gguf_parameters() + mrope_section = self.hparams["rope_scaling"]["mrope_section"] + mrope_section += [0] * max(0, 4 - len(mrope_section)) + self.gguf_writer.add_rope_dimension_sections(mrope_section) + + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + if name.startswith("thinker."): + name = name.replace("thinker.", "") + if name.startswith("visual") or name.startswith("audio") or \ + name.startswith("talker") or name.startswith("token2wav"): + # skip multimodal tensors + return [] + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("Qwen2VLModel", "Qwen2VLForConditionalGeneration", "Qwen2_5_VLForConditionalGeneration") +class Qwen2VLVisionModel(MmprojModel): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + assert self.hparams_vision is not None + self.hparams_vision["image_size"] = self.hparams_vision.get("image_size", 560) + # rename config.json values + self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_heads") + self.hparams_vision["num_hidden_layers"] = self.hparams_vision.get("depth") + if "embed_dim" in self.hparams_vision: # qwen2vl + self.hparams_vision["intermediate_size"] = self.hparams_vision.get("hidden_size") + self.hparams_vision["hidden_size"] = self.hparams_vision.get("embed_dim") + + def set_gguf_parameters(self): + super().set_gguf_parameters() + assert self.hparams_vision is not None + hparams = self.hparams_vision + model_type = self.global_config['model_type'] + if model_type == 'qwen2_vl': + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN2VL) + elif model_type == 'qwen2_5_vl' or model_type == 'qwen2_5_omni': + if model_type == 'qwen2_5_omni': + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25O) + else: + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25VL) + self.gguf_writer.add_vision_use_silu(True) + # find n_wa_pattern (window attention pattern) + fullatt_block_indexes = hparams.get("fullatt_block_indexes") + assert fullatt_block_indexes is not None, "fullatt_block_indexes is required for qwen2_5_vl" + n_wa_pattern = fullatt_block_indexes[0] + 1 + # validate n_wa_pattern + for i in range(1, len(fullatt_block_indexes)): + if fullatt_block_indexes[i] - fullatt_block_indexes[i - 1] != n_wa_pattern: + raise ValueError(f"Invalid fullatt_block_indexes: {fullatt_block_indexes}") + self.gguf_writer.add_vision_n_wa_pattern(n_wa_pattern) + else: + raise ValueError(f"Unknown QwenVL model type: {self.global_config['model_type']}") + # default values below are taken from HF tranformers code + self.gguf_writer.add_vision_attention_layernorm_eps(self.global_config.get("rms_norm_eps", 1e-6)) + + def tensor_force_quant(self, name, new_name, bid, n_dims): + del bid, name, n_dims # unused + if ".patch_embd." in new_name: + return gguf.GGMLQuantizationType.F16 + if ".position_embd." in new_name: + return gguf.GGMLQuantizationType.F32 + return False + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + if name.startswith("visual."): + # process visual tensors + # split QKV tensors if needed + if ".qkv." in name: + if data_torch.ndim == 2: # weight + c3, _ = data_torch.shape + else: # bias + c3 = data_torch.shape[0] + assert c3 % 3 == 0 + c = c3 // 3 + wq = data_torch[:c] + wk = data_torch[c: c * 2] + wv = data_torch[c * 2:] + return [ + (self.map_tensor_name(name.replace("qkv", "q")), wq), + (self.map_tensor_name(name.replace("qkv", "k")), wk), + (self.map_tensor_name(name.replace("qkv", "v")), wv), + ] + elif 'patch_embed.proj.weight' in name: + # split Conv3D into Conv2Ds + c1, c2, kt, kh, kw = data_torch.shape + del c1, c2, kh, kw # unused + assert kt == 2, "Current implmentation only support temporal_patch_size of 2" + return [ + (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight" , data_torch[:, :, 0, ...]), + (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight.1", data_torch[:, :, 1, ...]), + ] + else: + return [(self.map_tensor_name(name), data_torch)] + return [] # skip other tensors + + +@ModelBase.register("Qwen2_5OmniModel") +class Qwen25OmniModel(Qwen2VLVisionModel): + has_vision_encoder = True + has_audio_encoder = True + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + assert self.hparams_audio is not None + self.hparams_audio["hidden_size"] = self.hparams_audio["d_model"] + self.hparams_audio["intermediate_size"] = self.hparams_audio["encoder_ffn_dim"] + self.hparams_audio["num_attention_heads"] = self.hparams_audio["encoder_attention_heads"] + + def set_gguf_parameters(self): + super().set_gguf_parameters() + assert self.hparams_audio is not None + self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["num_mel_bins"]) + self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams_audio.get("layer_norm_eps", 1e-5)) + + def get_vision_config(self) -> dict[str, Any] | None: + return self.global_config["thinker_config"].get("vision_config") + + def get_audio_config(self) -> dict[str, Any] | None: + return self.global_config["thinker_config"].get("audio_config") + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + # SinusoidsPositionEmbedding + assert self.hparams_audio is not None + max_timescale = 10000 + length = 1500 + channels = self.hparams_audio["hidden_size"] + log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2).float()) + scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] + pos_embd = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1).to(dtype=torch.float32) + yield ("audio_tower.embed_positions.weight", pos_embd) + + def tensor_force_quant(self, name, new_name, bid, n_dims): + del bid, new_name, n_dims # unused + if ".conv" in name and ".weight" in name: + return gguf.GGMLQuantizationType.F16 + return False + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if name.startswith("thinker."): + name = name.replace("thinker.", "") + + if name.startswith("audio_tower"): + # process audio tensors + if "conv1.bias" in name or "conv2.bias" in name: + # transpose conv1 and conv2 bias + data_torch = data_torch.unsqueeze(-1) + if "audio_bos_eos_token" in name: + # this tensor is left unused in transformers code + # https://github.com/huggingface/transformers/blob/6e3063422c4b1c014aa60c32b9254fd2902f0f28/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py#L1809 + return [] + return [(self.map_tensor_name(name), data_torch)] + + return super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("InternVisionModel") +class InternVisionModel(MmprojModel): + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.INTERNVL) + self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"]) + # hidden_act + if hparams["hidden_act"] == "silu": + self.gguf_writer.add_vision_use_silu(True) + elif hparams["hidden_act"] == "gelu": + self.gguf_writer.add_vision_use_gelu(True) + else: + raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}") + # downsample_ratio + downsample_ratio = self.global_config.get("downsample_ratio") + assert downsample_ratio is not None + self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio)) + + def tensor_force_quant(self, name, new_name, bid, n_dims): + del bid, name, n_dims # unused + if ".patch_embd." in new_name: + return gguf.GGMLQuantizationType.F16 + if ".position_embd." in new_name: + return gguf.GGMLQuantizationType.F32 + return False + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + if name.startswith("vision_model") or name.startswith("mlp"): + # process visual tensors + # correct name + if name.startswith("vision_model"): + name = "vision_tower." + name + if (".ls" in name or "position_embedding" in name) and not name.endswith(".weight"): + name += ".weight" + # split QKV tensors if needed + if ".qkv." in name: + if data_torch.ndim == 2: # weight + c3, _ = data_torch.shape + else: # bias + c3 = data_torch.shape[0] + assert c3 % 3 == 0 + c = c3 // 3 + wq = data_torch[:c] + wk = data_torch[c: c * 2] + wv = data_torch[c * 2:] + return [ + (self.map_tensor_name(name.replace("attn.qkv", "self_attn.q_proj")), wq), + (self.map_tensor_name(name.replace("attn.qkv", "self_attn.k_proj")), wk), + (self.map_tensor_name(name.replace("attn.qkv", "self_attn.v_proj")), wv), + ] + return [(self.map_tensor_name(name), data_torch)] + return [] # skip other tensors + + +@ModelBase.register("WavTokenizerDec") +class WavTokenizerDecModel(TextModel): + model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if \ + name.endswith("codebook.cluster_size") or \ + name.endswith("codebook.embed_avg") or \ + name.endswith("codebook.inited"): + logger.debug(f"Skipping {name!r}") + return [] + + logger.info(f"{self.map_tensor_name(name)} -> {data_torch.shape}") + + return [(self.map_tensor_name(name), data_torch)] + + def set_vocab(self): + self._set_vocab_none() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_vocab_size (self.hparams["vocab_size"]) + self.gguf_writer.add_features_length (self.hparams["n_embd_features"]) + self.gguf_writer.add_feed_forward_length(self.hparams["n_ff"]) + self.gguf_writer.add_group_norm_eps (self.hparams["group_norm_epsilon"]) + self.gguf_writer.add_group_norm_groups (self.hparams["group_norm_groups"]) + + self.gguf_writer.add_posnet_embedding_length(self.hparams["posnet"]["n_embd"]) + self.gguf_writer.add_posnet_block_count (self.hparams["posnet"]["n_layer"]) + + self.gguf_writer.add_convnext_embedding_length(self.hparams["convnext"]["n_embd"]) + self.gguf_writer.add_convnext_block_count (self.hparams["convnext"]["n_layer"]) + + self.gguf_writer.add_causal_attention(False) + + +@ModelBase.register("Qwen2MoeForCausalLM") +class Qwen2MoeModel(TextModel): + model_arch = gguf.MODEL_ARCH.QWEN2MOE + + def set_gguf_parameters(self): + super().set_gguf_parameters() + if (n_experts := self.hparams.get("num_experts")) is not None: + self.gguf_writer.add_expert_count(n_experts) + if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None: + self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size) + logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}") + if (shared_expert_intermediate_size := self.hparams.get('shared_expert_intermediate_size')) is not None: + self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size) + logger.info(f"gguf: expert shared feed forward length = {shared_expert_intermediate_size}") + # YaRN is not enabled by default + # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # process the experts separately + if name.find("experts") != -1: + n_experts = self.hparams["num_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@ModelBase.register("Qwen3ForCausalLM") +class Qwen3Model(Qwen2Model): + model_arch = gguf.MODEL_ARCH.QWEN3 + + +@ModelBase.register("Qwen3MoeForCausalLM") +class Qwen3MoeModel(Qwen2MoeModel): + model_arch = gguf.MODEL_ARCH.QWEN3MOE + + +@ModelBase.register("GPT2LMHeadModel") +class GPT2Model(TextModel): + model_arch = gguf.MODEL_ARCH.GPT2 + + def set_gguf_parameters(self): + self.gguf_writer.add_block_count(self.hparams["n_layer"]) + self.gguf_writer.add_context_length(self.hparams["n_ctx"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + tensors: list[tuple[str, Tensor]] = [] + + # we don't need these + if name.endswith((".attn.bias", ".attn.masked_bias")): + return tensors + + if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")): + data_torch = data_torch.transpose(1, 0) + + new_name = self.map_tensor_name(name) + + tensors.append((new_name, data_torch)) + + return tensors + + +@ModelBase.register("PhiForCausalLM") +class Phi2Model(TextModel): + model_arch = gguf.MODEL_ARCH.PHI2 + + def set_gguf_parameters(self): + block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) + + rot_pct = self.find_hparam(["partial_rotary_factor"]) + n_embd = self.find_hparam(["hidden_size", "n_embd"]) + n_head = self.find_hparam(["num_attention_heads", "n_head"]) + + self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"])) + + self.gguf_writer.add_embedding_length(n_embd) + self.gguf_writer.add_feed_forward_length(4 * n_embd) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head) + self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"])) + self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_add_bos_token(False) + + +@ModelBase.register("Phi3ForCausalLM") +class Phi3MiniModel(TextModel): + model_arch = gguf.MODEL_ARCH.PHI3 + + def set_vocab(self): + # Phi-4 model uses GPT2Tokenizer + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + tokenizer_class = tokenizer_config_json['tokenizer_class'] + if tokenizer_class == 'GPT2Tokenizer': + return self._set_vocab_gpt2() + + from sentencepiece import SentencePieceProcessor + + tokenizer_path = self.dir_model / 'tokenizer.model' + + if not tokenizer_path.is_file(): + raise ValueError(f'Error: Missing {tokenizer_path}') + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + + for key in added_tokens_json: + token_id = added_tokens_json[key] + if token_id >= vocab_size: + logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + tokens[token_id] = key.encode("utf-8") + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {}) + for token_id, foken_data in added_tokens_decoder.items(): + token_id = int(token_id) + token = foken_data["content"].encode("utf-8") + if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: + if tokens[token_id] != token: + logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}') + tokens[token_id] = token + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + if foken_data.get("special"): + toktypes[token_id] = SentencePieceTokenTypes.CONTROL + + tokenizer_file = self.dir_model / 'tokenizer.json' + if tokenizer_file.is_file(): + with open(tokenizer_file, "r", encoding="utf-8") as f: + tokenizer_json = json.load(f) + added_tokens = tokenizer_json.get("added_tokens", []) + for foken_data in added_tokens: + token_id = int(foken_data["id"]) + token = foken_data["content"].encode("utf-8") + if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: + if tokens[token_id] != token: + logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}') + tokens[token_id] = token + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + if foken_data.get("special"): + toktypes[token_id] = SentencePieceTokenTypes.CONTROL + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) + + n_embd = self.find_hparam(["hidden_size", "n_embd"]) + n_head = self.find_hparam(["num_attention_heads", "n_head"]) + n_head_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"]) + rms_eps = self.find_hparam(["rms_norm_eps"]) + max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"]) + orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"]) + rot_pct = self.hparams.get("partial_rotary_factor", 1.0) + rope_dims = int(rot_pct * n_embd) // n_head + + self.gguf_writer.add_context_length(max_pos_embds) + self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds) + self.gguf_writer.add_embedding_length(n_embd) + self.gguf_writer.add_feed_forward_length(self.find_hparam(["intermediate_size"])) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head_kv) + self.gguf_writer.add_layer_norm_rms_eps(rms_eps) + self.gguf_writer.add_rope_dimension_count(rope_dims) + self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) + self.gguf_writer.add_file_type(self.ftype) + sliding_window = self.hparams.get("sliding_window") + # use zero value of sliding_window to distinguish Phi-4 from other PHI3 models + if sliding_window is None: + sliding_window = 0 + self.gguf_writer.add_sliding_window(sliding_window) + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + n_embd = self.find_hparam(["hidden_size", "n_embd"]) + n_head = self.find_hparam(["num_attention_heads", "n_head"]) + max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"]) + orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"]) + rot_pct = self.hparams.get("partial_rotary_factor", 1.0) + rope_dims = int(rot_pct * n_embd) // n_head + + # write rope scaling for long context (128k) model + rope_scaling = self.find_hparam(['rope_scaling'], True) + if rope_scaling is None: + return + + scale = max_pos_embds / orig_max_pos_embds + + rope_scaling_type = rope_scaling.get('rope_type', rope_scaling.get('type', '')).lower() + if len(rope_scaling_type) == 0: + raise KeyError('Missing the required key rope_scaling.type') + + if rope_scaling_type == 'su' or rope_scaling_type == 'longrope': + attn_factor = math.sqrt(1 + math.log(scale) / math.log(orig_max_pos_embds)) if scale > 1.0 else 1.0 + elif rope_scaling_type == 'yarn': + attn_factor = 0.1 * math.log(scale) + 1.0 if scale > 1.0 else 1.0 + else: + raise NotImplementedError(f'The rope scaling type {rope_scaling_type} is not supported yet') + + self.gguf_writer.add_rope_scaling_attn_factors(attn_factor) + + long_factors = rope_scaling.get('long_factor', None) + short_factors = rope_scaling.get('short_factor', None) + + if long_factors is None or short_factors is None: + raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor') + + if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2: + raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}. long_factors = {len(long_factors)}, short_factors = {len(short_factors)}.') + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32)) + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32)) + + +@ModelBase.register("PhiMoEForCausalLM") +class PhiMoeModel(Phi3MiniModel): + model_arch = gguf.MODEL_ARCH.PHIMOE + + _experts: list[dict[str, Tensor]] | None = None + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_expert_used_count(self.hparams["num_experts_per_tok"]) + self.gguf_writer.add_expert_count(self.hparams["num_local_experts"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # process the experts separately + if name.find("block_sparse_moe.experts") != -1: + n_experts = self.hparams["num_local_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["w1", "w2", "w3"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@ModelBase.register("PlamoForCausalLM") +class PlamoModel(TextModel): + model_arch = gguf.MODEL_ARCH.PLAMO + + def set_vocab(self): + self._set_vocab_sentencepiece() + + def set_gguf_parameters(self): + hparams = self.hparams + block_count = hparams["num_hidden_layers"] + + self.gguf_writer.add_context_length(4096) # not in config.json + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(5) # hparams["num_key_value_heads"]) is wrong + self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) + self.gguf_writer.add_file_type(self.ftype) + + def shuffle_attn_q_weight(self, data_torch): + assert data_torch.size() == (5120, 5120) + data_torch = data_torch.reshape(8, 5, 128, 5120) + data_torch = torch.permute(data_torch, (1, 0, 2, 3)) + data_torch = torch.reshape(data_torch, (5120, 5120)) + return data_torch + + def shuffle_attn_output_weight(self, data_torch): + assert data_torch.size() == (5120, 5120) + data_torch = data_torch.reshape(5120, 8, 5, 128) + data_torch = torch.permute(data_torch, (0, 2, 1, 3)) + data_torch = torch.reshape(data_torch, (5120, 5120)) + return data_torch + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + new_name = self.map_tensor_name(name) + + # shuffle for broadcasting of gqa in ggml_mul_mat + if new_name.endswith("attn_q.weight"): + data_torch = self.shuffle_attn_q_weight(data_torch) + elif new_name.endswith("attn_output.weight"): + data_torch = self.shuffle_attn_output_weight(data_torch) + + return [(new_name, data_torch)] + + +@ModelBase.register("Plamo2ForCausalLM", "PLaMo2ForCausalLM") +class Plamo2Model(TextModel): + model_arch = gguf.MODEL_ARCH.PLAMO2 + + def set_vocab(self): + # PLaMo 2 uses a custom tokenizer with a .jsonl file + # We need to handle this specially + tokenizer_jsonl_path = self.dir_model / "tokenizer.jsonl" + tokenizer_config_path = self.dir_model / "tokenizer_config.json" + + if not tokenizer_jsonl_path.is_file(): + raise FileNotFoundError(f"PLaMo 2 tokenizer file not found: {tokenizer_jsonl_path}") + + # Load tokenizer config + with open(tokenizer_config_path, 'r', encoding='utf-8') as f: + tokenizer_config = json.load(f) + + # Load tokens from JSONL file (actually a list format) + tokens = [] + scores = [] + toktypes = [] + + with open(tokenizer_jsonl_path, 'r', encoding='utf-8') as f: + for line_num, line in enumerate(f): + if line.strip(): + token_data = json.loads(line) + # Format: [token, score, type, ?, ?, ?, ?] + token = token_data[0].encode("utf-8") + score = float(token_data[1]) + token_type_str = token_data[2] if len(token_data) > 2 else "NORMAL" + + tokens.append(token) + scores.append(score) + + # Map token type strings to GGUF token types + if token_type_str == "UNKNOWN": + toktypes.append(gguf.TokenType.UNKNOWN) + elif token_type_str == "CONTROL": + toktypes.append(gguf.TokenType.CONTROL) + elif token_type_str == "BYTE": + toktypes.append(gguf.TokenType.BYTE) + else: + # Check for PLaMo-2 special tokens + token_str = token_data[0] + if token_str.startswith("<|plamo:") and token_str.endswith("|>"): + toktypes.append(gguf.TokenType.CONTROL) + else: + toktypes.append(gguf.TokenType.NORMAL) + + vocab_size = self.hparams["vocab_size"] + if vocab_size > len(tokens): + pad_count = vocab_size - len(tokens) + logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]") + for i in range(1, pad_count + 1): + tokens.append(bytes(f"[PAD{i}]", encoding="utf-8")) + scores.append(-1000.0) + toktypes.append(gguf.TokenType.UNUSED) + + # Use "plamo2" tokenizer type for PLaMo-2's custom Aho-Corasick tokenizer + self.gguf_writer.add_tokenizer_model("plamo2") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + # Add special tokens from config + if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] is not None: + token_id = tokens.index(tokenizer_config["bos_token"].encode("utf-8")) + self.gguf_writer.add_bos_token_id(token_id) + if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] is not None: + token_id = tokens.index(tokenizer_config["eos_token"].encode("utf-8")) + self.gguf_writer.add_eos_token_id(token_id) + if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] is not None: + token_id = tokens.index(tokenizer_config["pad_token"].encode("utf-8")) + self.gguf_writer.add_pad_token_id(token_id) + if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] is not None: + token_id = tokens.index(tokenizer_config["sep_token"].encode("utf-8")) + self.gguf_writer.add_sep_token_id(token_id) + if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] is not None: + token_id = tokens.index(tokenizer_config["unk_token"].encode("utf-8")) + self.gguf_writer.add_unk_token_id(token_id) + + # Add <|plamo:op|> as EOT to ensure appropriate end of generation + self.gguf_writer.add_eot_token_id(4) + + self.gguf_writer.add_add_space_prefix(False) + + def set_gguf_parameters(self): + hparams = self.hparams + block_count = hparams["num_hidden_layers"] + self.gguf_writer.add_vocab_size(self.hparams["vocab_size"]) + + # Which layers are Mamba layers + # PLaMo 2 uses mamba_step to indicate the pattern (e.g., 2 means every other layer) + # This logic matches modeling_plamo.py's is_mamba function + mamba_step = hparams.get("mamba_step", 2) + mamba_enabled = hparams.get("mamba_enabled", True) + mamba_layers = [] + + if mamba_enabled: + for i in range(block_count): + if block_count <= (mamba_step // 2): + # use attention in last layer + is_mamba = (i != block_count - 1) + else: + is_mamba = (i % mamba_step) != (mamba_step // 2) + if is_mamba: + mamba_layers.append(0) + else: + mamba_layers.append(hparams.get("num_key_value_heads", 4)) + + if mamba_layers: + self.gguf_writer.add_head_count_kv(mamba_layers) + + self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 2048)) + self.gguf_writer.add_embedding_length(hparams.get("hidden_size", 4096)) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(hparams.get("num_attention_heads", 32)) + self.gguf_writer.add_layer_norm_rms_eps(hparams.get("rms_norm_eps", 1e-06)) + self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 10000)) + + # Mamba parameters + self.gguf_writer.add_ssm_state_size(hparams.get("mamba_d_state", 64)) + self.gguf_writer.add_ssm_conv_kernel(hparams.get("mamba_d_conv", 4)) + self.gguf_writer.add_ssm_time_step_rank(hparams.get("mamba_num_heads", 64)) + intermediate_size = hparams.get("mamba_num_heads", 64) * hparams.get("hidden_size_per_head", 128) + self.gguf_writer.add_ssm_inner_size(intermediate_size) + self.gguf_writer.add_ssm_group_count(0) + + # MLP feed forward parameters (for attention layers) + self.gguf_writer.add_feed_forward_length(hparams.get("intermediate_size", 13312)) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if name.endswith(".A_log"): + data_torch = -torch.exp(data_torch) + elif name.endswith(".dt_bias"): + name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias" + elif name.endswith(".dt_norm_weight"): + name = name.rpartition(".dt_norm_weight")[0] + ".dt_norm.weight" + elif name.endswith(".B_norm_weight"): + name = name.rpartition(".B_norm_weight")[0] + ".B_norm.weight" + elif name.endswith(".C_norm_weight"): + name = name.rpartition(".C_norm_weight")[0] + ".C_norm.weight" + elif name.endswith(".k_weight"): + name = name.rpartition(".k_weight")[0] + ".k.weight" + elif name.endswith(".q_weight"): + name = name.rpartition(".q_weight")[0] + ".q.weight" + elif name.endswith(".conv1d.weight"): + data_torch = torch.squeeze(data_torch) # remove (, 1, ) + assert data_torch.ndim == 2 + elif name.endswith(".pre_mixer_norm.weight"): + data_torch += 1.0 + elif name.endswith(".post_mixer_norm.weight"): + data_torch += 1.0 / 5 + elif name.endswith(".pre_mlp_norm.weight"): + data_torch += 1.0 + elif name.endswith(".post_mlp_norm.weight"): + data_torch += 1.0 / (5**1.5) + elif name.endswith(".norm.weight"): + data_torch += 1.0 + + new_name = self.map_tensor_name(name) + + return [(new_name, data_torch)] + + +@ModelBase.register("CodeShellForCausalLM") +class CodeShellModel(TextModel): + model_arch = gguf.MODEL_ARCH.CODESHELL + + def set_gguf_parameters(self): + block_count = self.hparams["n_layer"] + + self.gguf_writer.add_context_length(self.hparams["n_positions"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_query_groups"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_rope_freq_base(10000.0) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(1.0) + + _has_tok_embd = False + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT) + tok_embd_name = self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD) + + new_name = self.map_tensor_name(name) + + # assuming token_embd.weight is seen before output.weight + if not self._has_tok_embd and new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT): + # even though the tensor file(s) does not contain the word embeddings they are still in the weight map + if self.tensor_names and "transformer.wte.weight" in self.tensor_names: + logger.debug(f"{tok_embd_name} not found before {output_name}, assuming they are tied") + self.tensor_names.remove("transformer.wte.weight") + elif new_name == tok_embd_name: + self._has_tok_embd = True + + return [(new_name, data_torch)] + + +@ModelBase.register("InternLM2ForCausalLM") +class InternLM2Model(TextModel): + model_arch = gguf.MODEL_ARCH.INTERNLM2 + + def set_vocab(self): + # (TODO): Is there a better way? + # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character + # \x00 specially and convert it into an emoji character to prevent it from being mistakenly + # recognized as an empty string in C++. + from sentencepiece import SentencePieceProcessor + from sentencepiece import sentencepiece_model_pb2 as model + + tokenizer_path = self.dir_model / 'tokenizer.model' + + tokens: list[bytes] = [] + scores: list[float] = [] + toktypes: list[int] = [] + + if not tokenizer_path.is_file(): + logger.error(f'Error: Missing {tokenizer_path}') + sys.exit(1) + + sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] + sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + for token_id in range(vocab_size): + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + if text == b"\x00": + # (TODO): fixme + # Hack here and replace the \x00 characters. + logger.warning(f"InternLM2 convert token '{text}' to '🐉'!") + text = "🐉".encode("utf-8") + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + # take care of ununsed raw token + if piece.startswith('[UNUSED'): + toktype = SentencePieceTokenTypes.UNUSED + + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + + for key in added_tokens_json: + tokens.append(key.encode("utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.USER_DEFINED) + + chat_eos_token = '<|im_end|>' + chat_eos_token_id = None + + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {}) + for token_id, foken_data in added_tokens_decoder.items(): + token_id = int(token_id) + token = foken_data["content"] + if token == chat_eos_token: + chat_eos_token_id = token_id + token = token.encode("utf-8") + if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: + if tokens[token_id] != token: + logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}') + tokens[token_id] = token + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + if foken_data.get("special"): + toktypes[token_id] = SentencePieceTokenTypes.CONTROL + + tokenizer_file = self.dir_model / 'tokenizer.json' + if tokenizer_file.is_file(): + with open(tokenizer_file, "r", encoding="utf-8") as f: + tokenizer_json = json.load(f) + added_tokens = tokenizer_json.get("added_tokens", []) + for foken_data in added_tokens: + token_id = int(foken_data["id"]) + token = foken_data["content"] + if token == chat_eos_token: + chat_eos_token_id = token_id + token = token.encode("utf-8") + if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: + if tokens[token_id] != token: + logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}') + tokens[token_id] = token + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + if foken_data.get("special"): + toktypes[token_id] = SentencePieceTokenTypes.CONTROL + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_add_space_prefix(add_prefix) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + old_eos = special_vocab.special_token_ids["eos"] + if chat_eos_token_id is not None: + # For the chat model, we replace the eos with '<|im_end|>'. + # TODO: this is a hack, should be fixed + # https://github.com/ggml-org/llama.cpp/pull/6745#issuecomment-2067687048 + special_vocab.special_token_ids["eos"] = chat_eos_token_id + logger.warning(f"Replace eos:{old_eos} with a special token:{chat_eos_token_id}" + " in chat mode so that the conversation can end normally.") + + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) + self.gguf_writer.add_file_type(self.ftype) + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + num_heads = self.hparams["num_attention_heads"] + num_kv_heads = self.hparams["num_key_value_heads"] + n_embd = self.hparams["hidden_size"] + q_per_kv = num_heads // num_kv_heads + head_dim = n_embd // num_heads + num_groups = num_heads // q_per_kv + + name = name.replace("language_model.", "") # InternVL + if name.startswith("mlp") or name.startswith("vision_model"): + # skip visual tensors + return [] + + if bid is not None and f"model.layers.{bid}.attention.wqkv" in name: + qkv = data_torch + + qkv = qkv.reshape((num_groups, q_per_kv + 2, head_dim, n_embd)) + q, k, v = qkv[:, : q_per_kv], qkv[:, -2], qkv[:, -1] + + # The model weights of q and k equire additional reshape. + q = LlamaModel.permute(q.reshape((-1, q.shape[-1])), num_heads, num_heads) + k = LlamaModel.permute(k.reshape((-1, k.shape[-1])), num_heads, num_kv_heads) + v = v.reshape((-1, v.shape[-1])) + + return [ + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), q), + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), k), + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), v), + ] + else: + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("InternLM3ForCausalLM") +class InternLM3Model(TextModel): + model_arch = gguf.MODEL_ARCH.LLAMA + + def set_vocab(self): + tokens, scores, toktypes = self._create_vocab_sentencepiece() + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + if "add_prefix_space" in tokenizer_config_json: + self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"]) + + if "added_tokens_decoder" in tokenizer_config_json: + for token_id, token_data in tokenizer_config_json["added_tokens_decoder"].items(): + if token_data.get("special"): + token_id = int(token_id) + token = token_data["content"] + special_vocab._set_special_token(token, token_id) + # update eos token + if token == '<|im_end|>' and "eos" in special_vocab.special_token_ids: + special_vocab.special_token_ids["eos"] = token_id + + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + if (rope_dim := hparams.get("head_dim")) is None: + rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(rope_dim) + + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + name = name.replace("language_model.", "") # InternVL + if name.startswith("mlp") or name.startswith("vision_model"): + # skip visual tensors + return [] + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("BertModel", "BertForMaskedLM", "CamembertModel", "BertForSequenceClassification") +class BertModel(TextModel): + model_arch = gguf.MODEL_ARCH.BERT + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.vocab_size = None + + if cls_out_labels := self.hparams.get("id2label"): + if len(cls_out_labels) == 2 and cls_out_labels[0] == "LABEL_0": + # Remove dummy labels added by AutoConfig + cls_out_labels = None + self.cls_out_labels = cls_out_labels + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_causal_attention(False) + self._try_set_pooling_type() + + if self.cls_out_labels: + self.gguf_writer.add_classifier_output_labels([v for k, v in sorted(self.cls_out_labels.items())]) + + def set_vocab(self): + tokens, toktypes, tokpre = self.get_vocab_base() + self.vocab_size = len(tokens) + + # we need this to validate the size of the token_type embeddings + # though currently we are passing all zeros to the token_type embeddings + # "Sequence A" or "Sequence B" + self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1)) + + # convert to phantom space vocab + def phantom(tok): + if tok.startswith("[") and tok.endswith("]"): + return tok + if tok.startswith("##"): + return tok[2:] + return "\u2581" + tok + tokens = list(map(phantom, tokens)) + + # add vocab to gguf + self.gguf_writer.add_tokenizer_model("bert") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + # handle special tokens + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if name.startswith("bert."): + name = name[5:] + + if name.endswith(".gamma"): + name = name[:-6] + ".weight" + + if name.endswith(".beta"): + name = name[:-5] + ".bias" + + # we are only using BERT for embeddings so we don't need the pooling layer + if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"): + return [] # we don't need these + + if name.startswith("cls.predictions"): + return [] + + if name.startswith("cls.seq_relationship"): + return [] + + if self.cls_out_labels: + # For BertForSequenceClassification (direct projection layer) + if name == "classifier.weight": + name = "classifier.out_proj.weight" + + if name == "classifier.bias": + name = "classifier.out_proj.bias" + + return [(self.map_tensor_name(name), data_torch)] + + def _xlmroberta_tokenizer_init(self) -> None: + # we need the pad_token_id to know how to chop down position_embd matrix + if (pad_token_id := self.hparams.get("pad_token_id")) is not None: + self._position_offset = 1 + pad_token_id + if "max_position_embeddings" in self.hparams: + self.hparams["max_position_embeddings"] -= self._position_offset + else: + self._position_offset = None + + def _xlmroberta_set_vocab(self) -> None: + # to avoid TypeError: Descriptors cannot be created directly + # exception when importing sentencepiece_model_pb2 + os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" + from sentencepiece import SentencePieceProcessor + from sentencepiece import sentencepiece_model_pb2 as model + + tokenizer_path = self.dir_model / 'sentencepiece.bpe.model' + + tokenizer_json = {} + tokenizer_config_json = {} + if not tokenizer_path.is_file(): + tokenizer_path = self.dir_model / 'tokenizer.json' + tokenizer_config_path = self.dir_model / 'tokenizer_config.json' + + if not tokenizer_path.is_file(): + raise FileNotFoundError(f"File not found: {tokenizer_path}") + + from base64 import b64decode + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model) + + with open(tokenizer_path, "r", encoding="utf-8") as fp: + tokenizer_json = json.load(fp) + + if tokenizer_config_path.is_file(): + with open(tokenizer_config_path, "r", encoding="utf-8") as fp: + tokenizer_config_json = json.load(fp) + + add_prefix = tokenizer.add_prefix_space + remove_whitespaces = tokenizer.clean_up_tokenization_spaces + precompiled_charsmap = b64decode(tokenizer_json["normalizer"]["precompiled_charsmap"]) + + vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size) + else: + sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] + sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM + + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix + remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces + precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + if isinstance(tokenizer, SentencePieceProcessor): + for token_id in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + else: + added_vocab = tokenizer.get_added_vocab() + unk_token = tokenizer_config_json.get("unk_token") + unk_token_id = added_vocab.get(unk_token, tokenizer_json["model"].get("unk_id", 3)) + + for token_id in range(tokenizer.vocab_size): + piece = tokenizer._convert_id_to_token(token_id) + if (piece := tokenizer._convert_id_to_token(token_id)) is not None: + text = piece.encode("utf-8") + score = tokenizer_json["model"]["vocab"][token_id][1] + + toktype = SentencePieceTokenTypes.NORMAL + if token_id == unk_token_id: + toktype = SentencePieceTokenTypes.UNKNOWN + elif token_id in tokenizer.all_special_ids: + toktype = SentencePieceTokenTypes.CONTROL + elif token_id in added_vocab.values(): + toktype = SentencePieceTokenTypes.USER_DEFINED + # No reliable way to detect this, but jina doesn't have any + # elif tokenizer.IsByte(token_id): + # toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + if isinstance(tokenizer, SentencePieceProcessor): + # realign tokens (see HF tokenizer code) + tokens = [b'', b'', b'', b''] + tokens[3:-1] + scores = [0.0, 0.0, 0.0, 0.0] + scores[3:-1] + toktypes = [ + SentencePieceTokenTypes.CONTROL, + SentencePieceTokenTypes.CONTROL, + SentencePieceTokenTypes.CONTROL, + SentencePieceTokenTypes.UNKNOWN, + ] + toktypes[3:-1] + + if self.model_arch == gguf.MODEL_ARCH.NOMIC_BERT_MOE: + # Add mask token missing from sentencepiece.bpe.model + tokens[250001] = b'' + scores[250001] = 0.0 + toktypes[250001] = SentencePieceTokenTypes.CONTROL + + self.gguf_writer.add_tokenizer_model("t5") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_add_space_prefix(add_prefix) + self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1)) + self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces) + if precompiled_charsmap: + self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + +@ModelBase.register("DistilBertModel", "DistilBertForMaskedLM", "DistilBertForSequenceClassification") +class DistilBertModel(BertModel): + model_arch = gguf.MODEL_ARCH.BERT + + def set_gguf_parameters(self): + self.gguf_writer.add_layer_norm_eps(1e-12) + logger.info("gguf: layer norm epsilon = 1e-12") + super().set_gguf_parameters() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if name.startswith("distilbert."): + name = name[11:] + + # These layers act as MLM head, so we don't need them + if name.startswith("vocab_"): + return [] + + return super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("RobertaModel", "RobertaForSequenceClassification") +class RobertaModel(BertModel): + model_arch = gguf.MODEL_ARCH.BERT + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # we need the pad_token_id to know how to chop down position_embd matrix + if (pad_token_id := self.hparams.get("pad_token_id")) is not None: + self._position_offset = 1 + pad_token_id + if "max_position_embeddings" in self.hparams: + self.hparams["max_position_embeddings"] -= self._position_offset + else: + self._position_offset = None + + def set_vocab(self): + """Support BPE tokenizers for roberta models""" + bpe_tok_path = self.dir_model / "tokenizer.json" + if bpe_tok_path.exists(): + self._set_vocab_gpt2() + + # we need this to validate the size of the token_type embeddings + # though currently we are passing all zeros to the token_type embeddings + # "Sequence A" or "Sequence B" + self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1)) + + else: + return super().set_vocab() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # if name starts with "roberta.", remove the prefix + # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main + if name.startswith("roberta."): + name = name[8:] + + # position embeddings start at pad_token_id + 1, so just chop down the weight tensor + if name == "embeddings.position_embeddings.weight": + if self._position_offset is not None: + data_torch = data_torch[self._position_offset:,:] + + return super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("NomicBertModel") +class NomicBertModel(BertModel): + model_arch = gguf.MODEL_ARCH.BERT + + def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, **kwargs: Any): + hparams = kwargs.pop("hparams", None) + if hparams is None: + hparams = ModelBase.load_hparams(dir_model) + + self.is_moe = bool(hparams.get("moe_every_n_layers")) + self.model_arch = gguf.MODEL_ARCH.NOMIC_BERT_MOE if self.is_moe else gguf.MODEL_ARCH.NOMIC_BERT + + super().__init__(dir_model, ftype, fname_out, hparams=hparams, **kwargs) + + self._tokenizer_is_xlmroberta = self._is_tokenizer_xlmroberta() + if self._tokenizer_is_xlmroberta: + self._xlmroberta_tokenizer_init() + + npos, mtp = self.hparams["n_positions"], self.hparams.get("max_trained_positions", 2048) + if npos == 8192 and mtp == 2048: + self.hparams["n_positions"] = 2048 # nomic-embed-text v1 and v1.5 are trained for 2048 tokens. + elif npos == 2048 and mtp == 2048: + self.hparams["n_positions"] = 512 # nomic-embed-text-v2-moe is trained for 512 tokens. + else: + raise ValueError(f"unrecognized parameters: n_positions={npos}, max_trained_positions={mtp}") + + assert self.hparams["activation_function"] == "gelu" if self.is_moe else "swiglu" + + # this doesn't do anything in the HF version + assert self.hparams["causal"] is False + # no bias tensors unless MoE + assert self.hparams["qkv_proj_bias"] == self.is_moe + assert self.hparams["mlp_fc1_bias"] == self.is_moe + assert self.hparams["mlp_fc2_bias"] == self.is_moe + + # norm at end of layer + assert self.hparams["prenorm"] is False + # standard RoPE + assert self.hparams["rotary_emb_fraction"] == 1.0 + assert self.hparams["rotary_emb_interleaved"] is False + assert self.hparams["rotary_emb_scale_base"] is None + + def set_vocab(self) -> None: + if self._tokenizer_is_xlmroberta: + return self._xlmroberta_set_vocab() + return super().set_vocab() + + def modify_tensors(self, data_torch: torch.Tensor, name: str, bid: int | None) -> Iterable[tuple[str, torch.Tensor]]: + # If the tensor is an experts bias tensor, skip it by returning an empty list. + if "mlp.experts.bias" in name: + return [] # Explicitly return an empty list. + + if "mlp.experts.mlp.w1" in name: + data_torch = data_torch.view(self.hparams["num_experts"], self.hparams["n_inner"], self.hparams["n_embd"]) + name += ".weight" + + if "mlp.experts.mlp.w2" in name: + data_torch = data_torch.view(self.hparams["num_experts"], self.hparams["n_inner"], self.hparams["n_embd"]) + data_torch = data_torch.transpose(1, 2) + name += ".weight" + + return [(self.map_tensor_name(name), data_torch)] + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) + if self.is_moe: + self.gguf_writer.add_moe_every_n_layers(self.hparams["moe_every_n_layers"]) + self.gguf_writer.add_expert_count(self.hparams["num_experts"]) + self.gguf_writer.add_expert_used_count(self.hparams["moe_top_k"]) + + def _is_tokenizer_xlmroberta(self) -> bool: + with open(self.dir_model / "tokenizer.json") as f: + tokenizer_json = json.load(f) + toktyp = tokenizer_json["model"]["type"] + if toktyp == "Unigram": + return True + if toktyp == "WordPiece": + return False + raise ValueError(f"unknown tokenizer: {toktyp}") + + +@ModelBase.register("NeoBERT", "NeoBERTLMHead", "NeoBERTForSequenceClassification") +class NeoBert(BertModel): + model_arch = gguf.MODEL_ARCH.NEO_BERT + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + # NeoBERT uses 2/3 of the intermediate size as feed forward length + self.gguf_writer.add_feed_forward_length(int(2 * self.hparams["intermediate_size"] / 3)) + self.gguf_writer.add_rope_freq_base(10000.0) # default value for NeoBERT + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + + f_rms_eps = self.hparams.get("norm_eps", 1e-6) # default value for NeoBERT + self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps) + logger.info(f"gguf: rms norm epsilon = {f_rms_eps}") + + self.gguf_writer.add_pooling_type(gguf.PoolingType.CLS) # https://huggingface.co/chandar-lab/NeoBERT#how-to-use + + def modify_tensors(self, data_torch, name, bid): + if name.startswith("decoder."): + return [] + + if name.startswith("model."): + name = name[6:] + + return super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification") +class XLMRobertaModel(BertModel): + model_arch = gguf.MODEL_ARCH.BERT + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._xlmroberta_tokenizer_init() + + def set_vocab(self): + self._xlmroberta_set_vocab() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # if name starts with "roberta.", remove the prefix + # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main + if name.startswith("roberta."): + name = name[8:] + + # position embeddings start at pad_token_id + 1, so just chop down the weight tensor + if name == "embeddings.position_embeddings.weight": + if self._position_offset is not None: + data_torch = data_torch[self._position_offset:,:] + + return super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("GemmaForCausalLM") +class GemmaModel(TextModel): + model_arch = gguf.MODEL_ARCH.GEMMA + + def set_vocab(self): + self._set_vocab_sentencepiece() + + # TODO: these special tokens should be exported only for the CodeGemma family + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False, + special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot']) + special_vocab._set_special_token("prefix", 67) + special_vocab._set_special_token("suffix", 69) + special_vocab._set_special_token("middle", 68) + special_vocab._set_special_token("fsep", 70) + special_vocab._set_special_token("eot", 107) + special_vocab.chat_template = None # do not add it twice + special_vocab.add_to_gguf(self.gguf_writer) + + self.gguf_writer.add_add_space_prefix(False) + + def set_gguf_parameters(self): + hparams = self.hparams + block_count = hparams["num_hidden_layers"] + + self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_key_length(hparams["head_dim"]) + self.gguf_writer.add_value_length(hparams["head_dim"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + # lm_head is not used in llama.cpp, while autoawq will include this tensor in model + # To prevent errors, skip loading lm_head.weight. + if name == "lm_head.weight": + logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.") + return [] + + # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 + if name.endswith("norm.weight"): + data_torch = data_torch + 1 + + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("Gemma2ForCausalLM") +class Gemma2Model(TextModel): + model_arch = gguf.MODEL_ARCH.GEMMA2 + + def set_vocab(self): + self._set_vocab_sentencepiece() + + self.gguf_writer.add_add_space_prefix(False) + + def set_gguf_parameters(self): + hparams = self.hparams + block_count = hparams["num_hidden_layers"] + + self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_key_length(hparams["head_dim"]) + self.gguf_writer.add_value_length(hparams["head_dim"]) + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_attn_logit_softcapping( + self.hparams["attn_logit_softcapping"] + ) + self.gguf_writer.add_final_logit_softcapping( + self.hparams["final_logit_softcapping"] + ) + self.gguf_writer.add_sliding_window(self.hparams["sliding_window"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + # lm_head is not used in llama.cpp, while autoawq will include this tensor in model + # To prevent errors, skip loading lm_head.weight. + if name == "lm_head.weight": + logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.") + return [] + + # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 + if name.endswith("norm.weight"): + data_torch = data_torch + 1 + + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("Gemma3ForCausalLM", "Gemma3ForConditionalGeneration") +class Gemma3Model(TextModel): + model_arch = gguf.MODEL_ARCH.GEMMA3 + norm_shift = 1.0 # Gemma3RMSNorm adds 1.0 to the norm value + + def set_vocab(self): + self._set_vocab_sentencepiece() + + self.gguf_writer.add_add_space_prefix(False) + + def set_gguf_parameters(self): + hparams = self.hparams + block_count = hparams["num_hidden_layers"] + + # some default values are not specified in the hparams + self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 131072)) + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + self.gguf_writer.add_head_count(hparams.get("num_attention_heads", 8)) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-6)) + self.gguf_writer.add_key_length(hparams.get("head_dim", 256)) + self.gguf_writer.add_value_length(hparams.get("head_dim", 256)) + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 1_000_000.0)) # for global layers + # attn_logit_softcapping is removed in Gemma3 + assert hparams.get("attn_logit_softcapping") is None + self.gguf_writer.add_sliding_window(hparams["sliding_window"]) + self.gguf_writer.add_head_count_kv(hparams.get("num_key_value_heads", 4)) + if hparams.get("rope_scaling") is not None: + assert hparams["rope_scaling"]["rope_type"] == "linear" + # important: this rope_scaling is only applied for global layers, and not used by 1B model + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(hparams["rope_scaling"]["factor"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if "language_model." in name: + name = name.replace("language_model.", "") + + elif name.startswith("multi_modal_projector.") or name.startswith("vision_tower.") \ + or name.startswith("multimodal_projector.") or name.startswith("vision_model."): + return [] # skip vision tensors + + # remove OOV (out-of-vocabulary) rows in token_embd + if "embed_tokens.weight" in name: + vocab = self._create_vocab_sentencepiece() + tokens = vocab[0] + data_torch = data_torch[:len(tokens)] + + # ref code in Gemma3RMSNorm + # output = output * (1.0 + self.weight.float()) + # note: this is not the case on gemma3n + if name.endswith("norm.weight"): + data_torch = data_torch + self.norm_shift + + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("Gemma3ForConditionalGeneration") +class Gemma3VisionModel(MmprojModel): + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GEMMA3) + # default values below are taken from HF tranformers code + self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6)) + self.gguf_writer.add_vision_use_gelu(True) + # calculate proj_scale_factor (used by tinygemma3 test model) + image_seq_length = self.preprocessor_config.get("image_seq_length", 256) + n_per_side = int(image_seq_length ** 0.5) + image_size = self.hparams["image_size"] + patch_size = self.hparams["patch_size"] + proj_scale_factor = (image_size // patch_size) // n_per_side + if proj_scale_factor > 0 and proj_scale_factor != 4: + # we only need to write this if it's not the default value + # in this case, we are converting a test model + self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor) + + def tensor_force_quant(self, name, new_name, bid, n_dims): + del bid, new_name, n_dims # unused + # related to https://github.com/ggml-org/llama.cpp/issues/13025 + if "input_projection" in name: + return gguf.GGMLQuantizationType.F16 + if ".embeddings." in name: + return gguf.GGMLQuantizationType.F32 + return False + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if "vision_model.head." in name: + return [] # skip redundant tensors for tinygemma3 + + if name.startswith("multi_modal_projector.") or name.startswith("vision_tower.") \ + or name.startswith("multimodal_projector.") or name.startswith("vision_model."): + # process vision tensors + name = name.replace("_weight", ".weight") + + # correct norm value ; only this "soft_emb_norm" need to be corrected as it's part of Gemma projector + # the other norm values are part of SigLIP model, and they are already correct + # ref code: Gemma3RMSNorm + if "soft_emb_norm.weight" in name: + logger.info(f"Correcting norm value for '{name}'") + data_torch = data_torch + 1 + + return [(self.map_tensor_name(name), data_torch)] + + return [] # skip other tensors + + +@ModelBase.register("Gemma3nForConditionalGeneration") +class Gemma3NModel(Gemma3Model): + model_arch = gguf.MODEL_ARCH.GEMMA3N + norm_shift = 0.0 # same value with Gemma3p5RMSNorm scale_shift on python code + + _altup_proj: list[Tensor] = [] + _altup_unembd: list[Tensor] = [] + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + assert self.hparams["altup_num_inputs"] == 4, "Current conversion only supports 4 altup inputs" + self._altup_proj = [ + torch.Tensor(), # to be replaced + torch.Tensor(), # to be replaced + torch.Tensor(), # to be replaced + ] + self._altup_unembd = [ + torch.Tensor(), # to be replaced + torch.Tensor(), # to be replaced + torch.Tensor(), # to be replaced + ] + + def set_vocab(self): + super().set_vocab() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_altup_active_idx(self.hparams["altup_active_idx"]) + self.gguf_writer.add_altup_num_inputs(self.hparams["altup_num_inputs"]) + self.gguf_writer.add_embedding_length_per_layer_input(self.hparams["hidden_size_per_layer_input"]) + self.gguf_writer.add_shared_kv_layers(self.hparams["num_kv_shared_layers"]) + + activation_sparsity_scale = [] + for s in self.hparams["activation_sparsity_pattern"]: + normal_dist = torch.distributions.normal.Normal(0, 1) + std_multiplier = normal_dist.icdf(torch.tensor(s, dtype=torch.float32)) + activation_sparsity_scale.append(std_multiplier.item()) + self.gguf_writer.add_activation_sparsity_scale(activation_sparsity_scale) + + sliding_window_pattern = [] + for t in self.hparams["layer_types"]: + sliding_window_pattern.append(t == "sliding_attention") + self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern) + + def _stack_matrices(self, matrices: list[Tensor]) -> Tensor | None: + has_all = all(m.numel() > 0 for m in matrices) + if not has_all: + return None + else: + return torch.stack(matrices, dim=0) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if name.endswith("_scale"): + name = name + ".weight" + + # TODO: implement self.prediction_coefs.weight.clamp_(...) + + if "language_model." not in name: + return [] # skip non-language model tensors + + if "altup_unembed_projections" in name: + data_torch = data_torch.to(device="cpu") + if ".0." in name: + self._altup_unembd[0] = data_torch + elif ".1." in name: + self._altup_unembd[1] = data_torch + elif ".2." in name: + self._altup_unembd[2] = data_torch + else: + raise ValueError(f"Unknown name: {name}") + out = self._stack_matrices(self._altup_unembd) + if out is not None: + return [(self.map_tensor_name("model.altup_unembed_projections.weight"), out)] + else: + return [] + + if "altup_projections" in name: + data_torch = data_torch.to(device="cpu") + if ".0." in name: + self._altup_proj[0] = data_torch + elif ".1." in name: + self._altup_proj[1] = data_torch + elif ".2." in name: + self._altup_proj[2] = data_torch + else: + raise ValueError(f"Unknown name: {name}") + out = self._stack_matrices(self._altup_proj) + if out is not None: + return [(self.map_tensor_name("model.altup_projections.weight"), out)] + else: + return [] + + return super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("Starcoder2ForCausalLM") +class StarCoder2Model(TextModel): + model_arch = gguf.MODEL_ARCH.STARCODER2 + + +@ModelBase.register("Rwkv6ForCausalLM") +class Rwkv6Model(TextModel): + model_arch = gguf.MODEL_ARCH.RWKV6 + + def set_vocab(self): + self._set_vocab_rwkv_world() + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + head_size = self.hparams["head_size"] + hidden_size = self.hparams["hidden_size"] + layer_norm_eps = self.hparams["layer_norm_epsilon"] + rescale_every_n_layers = self.hparams["rescale_every"] + intermediate_size = self.hparams["intermediate_size"] if self.hparams["intermediate_size"] is not None else int((hidden_size * 3.5) // 32 * 32) + time_mix_extra_dim = 64 if hidden_size == 4096 else 32 + time_decay_extra_dim = 128 if hidden_size == 4096 else 64 + + # RWKV isn't context limited + self.gguf_writer.add_context_length(1048576) + self.gguf_writer.add_embedding_length(hidden_size) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_layer_norm_eps(layer_norm_eps) + self.gguf_writer.add_rescale_every_n_layers(rescale_every_n_layers) + self.gguf_writer.add_wkv_head_size(head_size) + self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim) + self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim) + self.gguf_writer.add_feed_forward_length(intermediate_size) + self.gguf_writer.add_file_type(self.ftype) + + # required by llama.cpp, unused + self.gguf_writer.add_head_count(0) + + lerp_weights: dict[int, dict[str, Tensor]] = {} + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + new_name = self.map_tensor_name(name) + + if not (new_name.endswith(".weight") or new_name.endswith(".bias")): + new_name += ".weight" + + if new_name.endswith("time_mix_w1.weight") or new_name.endswith("time_mix_decay_w1.weight") or new_name.endswith("time_mix_decay_w2.weight"): + data_torch = data_torch.transpose(0, 1) + + if new_name.endswith("time_mix_w2.weight"): + data_torch = data_torch.permute(0, 2, 1) + + if new_name.endswith("time_mix_decay.weight") or "lerp" in new_name: + data_torch = data_torch.squeeze() + + try: + rescale_every_n_layers = self.hparams["rescale_every"] + if rescale_every_n_layers > 0: + if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"): + data_torch = data_torch.div_(2 ** int(bid // rescale_every_n_layers)) + except KeyError: + pass + + # concat time_mix_lerp weights to reduce some cpu overhead + # also reduces the number of tensors in the model + if bid is not None and "time_mix_lerp" in new_name and "time_mix_lerp_x" not in new_name: + try: + self.lerp_weights[bid][new_name] = data_torch + except KeyError: + self.lerp_weights[bid] = {new_name: data_torch} + if all(f"blk.{bid}.time_mix_lerp_{i}.weight" in self.lerp_weights[bid].keys() for i in ["w", "k", "v", "r", "g"]): + new_name = f"blk.{bid}.time_mix_lerp_fused.weight" + data = torch.stack([self.lerp_weights[bid][f"blk.{bid}.time_mix_lerp_{i}.weight"].unsqueeze(0) for i in ["w", "k", "v", "r", "g"]], dim=0).unsqueeze(1) + yield (new_name, data) + return + + yield (new_name, data_torch) + + +@ModelBase.register("RWKV6Qwen2ForCausalLM") +class RWKV6Qwen2Model(Rwkv6Model): + model_arch = gguf.MODEL_ARCH.RWKV6QWEN2 + + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + num_attention_heads = self.hparams["num_attention_heads"] + num_key_value_heads = self.hparams["num_key_value_heads"] + hidden_size = self.hparams["hidden_size"] + head_size = hidden_size // num_attention_heads + rms_norm_eps = self.hparams["rms_norm_eps"] + intermediate_size = self.hparams["intermediate_size"] + time_mix_extra_dim = self.hparams.get("lora_rank_tokenshift", 64 if hidden_size >= 4096 else 32) + time_decay_extra_dim = self.hparams.get("lora_rank_decay", 128 if hidden_size >= 4096 else 64) + + # RWKV isn't context limited + self.gguf_writer.add_context_length(1048576) + self.gguf_writer.add_embedding_length(hidden_size) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_wkv_head_size(head_size) + self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim) + self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim) + self.gguf_writer.add_feed_forward_length(intermediate_size) + self.gguf_writer.add_file_type(self.ftype) + + # special parameters for time_mixing in RWKV6QWEN2 + self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps) + self.gguf_writer.add_token_shift_count(1) + # RWKV6QWEN2 use grouped key/value like GQA + self.gguf_writer.add_head_count_kv(num_key_value_heads) + + # required by llama.cpp, unused + self.gguf_writer.add_head_count(0) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + for new_name, data in super().modify_tensors(data_torch, name, bid): + if "time_mix_w1" in new_name or "time_mix_w2" in new_name: + data = data.view(5, -1, data.shape[-1]) + # rwkv6qwen2 has a different order of rkvwg instead of the original wkvrg + # permute them here to avoid code changes + data = torch.stack([data[3], data[1], data[2], data[0], data[4]], dim=0).view(-1, data.shape[-1]) + if "w2" in new_name: + data = data.view(5, -1, data.shape[-1]) + yield (new_name, data) + continue + yield (new_name, data) + + +@ModelBase.register("Rwkv7ForCausalLM", "RWKV7ForCausalLM") +class Rwkv7Model(TextModel): + model_arch = gguf.MODEL_ARCH.RWKV7 + + def set_vocab(self): + self._set_vocab_rwkv_world() + + def calc_lora_rank(self, hidden_size, exponent, multiplier): + return max(1, round(hidden_size ** exponent * multiplier / 32)) * 32 + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + try: + head_size = self.hparams["head_size"] + layer_norm_eps = self.hparams["layer_norm_epsilon"] + except KeyError: + head_size = self.hparams["head_dim"] + layer_norm_eps = self.hparams["norm_eps"] + hidden_size = self.hparams["hidden_size"] + intermediate_size = self.hparams["intermediate_size"] if self.hparams["intermediate_size"] is not None else (hidden_size * 4) + + # ICLR: In-Context-Learning-Rate + try: + lora_rank_decay = self.hparams["lora_rank_decay"] if self.hparams["lora_rank_decay"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8) + lora_rank_iclr = self.hparams["lora_rank_iclr"] if self.hparams["lora_rank_iclr"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8) + lora_rank_value_residual_mix = self.hparams["lora_rank_value_residual_mix"] if self.hparams["lora_rank_value_residual_mix"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.3) + lora_rank_gate = self.hparams["lora_rank_gate"] if self.hparams["lora_rank_gate"] is not None else self.calc_lora_rank(hidden_size, 0.8, 0.6) + except KeyError: + lora_rank_decay = self.hparams["decay_low_rank_dim"] if self.hparams["decay_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8) + lora_rank_iclr = self.hparams["a_low_rank_dim"] if self.hparams["a_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8) + lora_rank_value_residual_mix = self.hparams["v_low_rank_dim"] if self.hparams["v_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.3) + lora_rank_gate = self.hparams["gate_low_rank_dim"] if self.hparams["gate_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.8, 0.6) + + # RWKV isn't context limited + self.gguf_writer.add_context_length(1048576) + self.gguf_writer.add_embedding_length(hidden_size) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_layer_norm_eps(layer_norm_eps) + self.gguf_writer.add_wkv_head_size(head_size) + self.gguf_writer.add_decay_lora_rank(lora_rank_decay) + self.gguf_writer.add_iclr_lora_rank(lora_rank_iclr) + self.gguf_writer.add_value_residual_mix_lora_rank(lora_rank_value_residual_mix) + self.gguf_writer.add_gate_lora_rank(lora_rank_gate) + self.gguf_writer.add_feed_forward_length(intermediate_size) + self.gguf_writer.add_file_type(self.ftype) + + # required by llama.cpp, unused + self.gguf_writer.add_head_count(0) + + lerp_weights: dict[int, dict[str, Tensor]] = {} + lora_needs_transpose: bool = True + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # unify tensor names here to make life easier + name = name.replace("blocks", "layers").replace("ffn", "feed_forward") + name = name.replace("self_attn", "attention").replace("attn", "attention") + name = name.replace("time_mixer.", "") + # lora layer names in fla-hub's impl + if "_lora.lora" in name: + self.lora_needs_transpose = False + name = name.replace("_lora.lora.0.weight", "1.weight") + name = name.replace("_lora.lora.2.weight", "2.weight") + name = name.replace("_lora.lora.2.bias", "0.weight") + + name = name.replace("feed_forward_norm", "ln2") + name = name.replace("g_norm", "ln_x") + + if "attention.v" in name and "value" not in self.map_tensor_name(name) and bid == 0: + # some models have dummy v0/v1/v2 on first layer while others don't + # ignore them all since they are not used + return + + wkv_has_gate = self.hparams.get("wkv_has_gate", True) + lerp_list = ["r", "w", "k", "v", "a", "g"] if wkv_has_gate else ["r", "w", "k", "v", "a"] + + if bid is not None and "attention.x_" in name: + if "attention.x_x" in name: + # already concatenated + new_name = f"blk.{bid}.time_mix_lerp_fused.weight" + data = data_torch.reshape(len(lerp_list), 1, 1, -1) + yield (new_name, data) + else: + try: + self.lerp_weights[bid][name] = data_torch + except KeyError: + self.lerp_weights[bid] = {name: data_torch} + if all(f"model.layers.{bid}.attention.x_{i}" in self.lerp_weights[bid].keys() for i in lerp_list): + new_name = f"blk.{bid}.time_mix_lerp_fused.weight" + data = torch.stack([self.lerp_weights[bid][f"model.layers.{bid}.attention.x_{i}"] for i in lerp_list], dim=0) + yield (new_name, data) + return + else: + data_torch = data_torch.squeeze() + new_name = self.map_tensor_name(name) + + if not (new_name.endswith(".weight") or new_name.endswith(".bias")): + new_name += ".weight" + + if self.lora_needs_transpose and any( + new_name.endswith(t) for t in [ + "time_mix_w1.weight", "time_mix_w2.weight", + "time_mix_a1.weight", "time_mix_a2.weight", + "time_mix_v1.weight", "time_mix_v2.weight", + "time_mix_g1.weight", "time_mix_g2.weight", + ] + ): + data_torch = data_torch.transpose(0, 1) + + if 'r_k' in new_name: + data_torch = data_torch.flatten() + + if bid == 0 and "time_mix_a" in new_name: + # dummy v0/v1/v2 on first layer + # easist way to make llama happy + yield (new_name.replace("time_mix_a", "time_mix_v"), data_torch) + + yield (new_name, data_torch) + + +@ModelBase.register("RwkvHybridForCausalLM") +class ARwkv7Model(Rwkv7Model): + model_arch = gguf.MODEL_ARCH.ARWKV7 + + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + hidden_size = self.hparams["hidden_size"] + head_size = self.hparams["head_size"] + rms_norm_eps = self.hparams["rms_norm_eps"] + intermediate_size = self.hparams["intermediate_size"] + wkv_has_gate = self.hparams["wkv_has_gate"] + assert self.hparams["wkv_version"] == 7 + + # ICLR: In-Context-Learning-Rate + lora_rank_decay = 64 + lora_rank_iclr = 64 + lora_rank_value_residual_mix = 32 + lora_rank_gate = 128 if wkv_has_gate else 0 + + # RWKV isn't context limited + self.gguf_writer.add_context_length(1048576) + self.gguf_writer.add_embedding_length(hidden_size) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps) + self.gguf_writer.add_wkv_head_size(head_size) + self.gguf_writer.add_decay_lora_rank(lora_rank_decay) + self.gguf_writer.add_iclr_lora_rank(lora_rank_iclr) + self.gguf_writer.add_value_residual_mix_lora_rank(lora_rank_value_residual_mix) + self.gguf_writer.add_gate_lora_rank(lora_rank_gate) + self.gguf_writer.add_feed_forward_length(intermediate_size) + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_token_shift_count(1) + + # required by llama.cpp, unused + self.gguf_writer.add_head_count(0) + + +@ModelBase.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM") +class MambaModel(TextModel): + model_arch = gguf.MODEL_ARCH.MAMBA + + def __init__(self, dir_model: Path, *args, **kwargs): + # Avoid using AutoConfig for hparams + hparams = kwargs.pop("hparams", None) + if hparams is None: + with open(dir_model / "config.json", "r", encoding="utf-8") as f: + hparams = json.load(f) + super().__init__(dir_model, *args, hparams=hparams, **kwargs) + + def set_vocab(self): + vocab_size = self.hparams["vocab_size"] + # Round vocab size to next multiple of 8 + pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8) + # pad using ceiling division + # ref: https://stackoverflow.com/a/17511341/22827863 + vocab_size = -(vocab_size // -pad_vocab) * pad_vocab + self.hparams["vocab_size"] = vocab_size + + if (self.dir_model / "tokenizer.json").is_file(): + self._set_vocab_gpt2() + elif (self.dir_model / "tokenizer.model").is_file(): + self._set_vocab_sentencepiece() + else: + # Use the GPT-NeoX tokenizer when no tokenizer files are present + self._set_vocab_builtin("gpt-neox", vocab_size) + + def set_gguf_parameters(self): + d_model = self.find_hparam(["hidden_size", "d_model"]) + d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4 + d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model + d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16 + # ceiling division + # ref: https://stackoverflow.com/a/17511341/22827863 + # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58 + dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16) + rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5 + use_dt_b_c_norm = False + # For falconmamba we do apply RMS norm on B / DT and C layers + if self.find_hparam(["model_type"], optional=True) in ("falcon_mamba",): + use_dt_b_c_norm = True + # Fail early for models which don't have a block expansion factor of 2 + assert d_inner == 2 * d_model + + self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default + self.gguf_writer.add_embedding_length(d_model) + self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading + self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading + self.gguf_writer.add_block_count(self.block_count) + self.gguf_writer.add_ssm_conv_kernel(d_conv) + self.gguf_writer.add_ssm_inner_size(d_inner) + self.gguf_writer.add_ssm_state_size(d_state) + self.gguf_writer.add_ssm_time_step_rank(dt_rank) + self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps) + self.gguf_writer.add_ssm_dt_b_c_rms(use_dt_b_c_norm) # For classic Mamba we don't apply rms norm on B / DT layers + self.gguf_writer.add_file_type(self.ftype) + + _tok_embd = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT) + tok_embd_name = self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD) + + new_name = self.map_tensor_name(name) + + if name.endswith(".A_log"): + logger.debug("A_log --> A ==> " + new_name) + data_torch = -torch.exp(data_torch) + + # [4 1 8192 1] -> [4 8192 1 1] + if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid): + data_torch = data_torch.squeeze() + + # assuming token_embd.weight is seen before output.weight + if self._tok_embd is not None and new_name == output_name: + if torch.equal(self._tok_embd, data_torch): + logger.debug(f"{output_name} is equivalent to {tok_embd_name}, omitting") + return [] + elif new_name == tok_embd_name: + self._tok_embd = data_torch + + return [(new_name, data_torch)] + + +@ModelBase.register("Mamba2ForCausalLM") +class Mamba2Model(TextModel): + model_arch = gguf.MODEL_ARCH.MAMBA2 + + def __init__(self, dir_model: Path, *args, **kwargs): + # Avoid using AutoConfig for hparams + # It wrongly assumes all Mamba2 models are Mamba-Codestral-7B-v0.1 + hparams = kwargs.pop("hparams", None) + if hparams is None: + with open(dir_model / "config.json", "r", encoding="utf-8") as f: + hparams = json.load(f) + super().__init__(dir_model, *args, hparams=hparams, **kwargs) + self.d_model = self.find_hparam(["hidden_size", "d_model", "dim"]) + self.d_inner = self.find_hparam(["mamba_d_ssm", "intermediate_size", "d_inner"], optional=True) or 2 * self.d_model + self.n_group = self.find_hparam(["n_groups"], optional=True) or 1 + + def set_vocab(self): + vocab_size = self.hparams["vocab_size"] + # Round vocab size to next multiple of 16 + pad_vocab = self.hparams.get("pad_vocab_size_multiple", 16) + # pad using ceiling division + # ref: https://stackoverflow.com/a/17511341/22827863 + vocab_size = -(vocab_size // -pad_vocab) * pad_vocab + self.hparams["vocab_size"] = vocab_size + + if (self.dir_model / "tokenizer.model").is_file(): + self._set_vocab_sentencepiece() + elif (self.dir_model / "tokenizer.model.v3").is_file(): + # mamba-codestral + raise NotImplementedError(f"Please rename {self.dir_model / 'tokenizer.model.v3'} to {self.dir_model / 'tokenizer.model'}") + elif (self.dir_model / "tokenizer.json").is_file(): + self._set_vocab_gpt2() + else: + # Use the GPT-NeoX tokenizer when no tokenizer files are present + self._set_vocab_builtin("gpt-neox", vocab_size) + + def set_gguf_parameters(self): + d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4 + d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 128 + head_dim = self.find_hparam(["mamba_d_head", "head_dim"], optional=True) or 64 + + rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5 + + # Fail early for models which don't have a block expansion factor of 2 + # TODO: does this really matter? + # skip the assertion for FalconH1 Model + if self.model_arch != gguf.MODEL_ARCH.FALCON_H1: + assert self.d_inner == 2 * self.d_model + assert self.d_inner % head_dim == 0 + + self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default + self.gguf_writer.add_embedding_length(self.d_model) + self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading + self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading + self.gguf_writer.add_block_count(self.block_count) + self.gguf_writer.add_ssm_conv_kernel(d_conv) + self.gguf_writer.add_ssm_inner_size(self.d_inner) + self.gguf_writer.add_ssm_state_size(d_state) + self.gguf_writer.add_ssm_time_step_rank(self.d_inner // head_dim) + self.gguf_writer.add_ssm_group_count(self.n_group) + self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + + if name.startswith("model.backbone") or name.startswith("model.lm_head"): + # map Mamba-Codestral-7B-v0.1 tensor names to the names used by Mamba-2 + name = name.removeprefix("model.") + + if name.endswith(".dt_bias"): + name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias" + + new_name = self.map_tensor_name(name) + + if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid): + data_torch = data_torch.squeeze() + elif any(self.match_model_tensor_name(new_name, t, bid, suffix="") for t in [ + gguf.MODEL_TENSOR.SSM_A, + gguf.MODEL_TENSOR.SSM_D, + ]): + # unsqueeze A to use similar shape semantics as Mamba-1 + # (D is also unsqueezed, but for more straightforward broadcast internally) + data_torch = data_torch.reshape((*data_torch.shape, 1)) + elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_NORM, bid): + data_torch = data_torch.reshape((self.n_group, self.d_inner // self.n_group)) + + if name.endswith(".A_log"): + logger.debug("A_log --> A ==> " + new_name) + data_torch = -torch.exp(data_torch) + + yield (new_name, data_torch) + + +@ModelBase.register("JambaForCausalLM") +class JambaModel(TextModel): + model_arch = gguf.MODEL_ARCH.JAMBA + + def get_vocab_base_pre(self, tokenizer) -> str: + del tokenizer # unused + + return "gpt-2" + + def set_vocab(self): + if (self.dir_model / "tokenizer.model").is_file(): + # Using Jamba's tokenizer.json causes errors on model load + # (something about "byte not found in vocab"), + # but there's a working tokenizer.model + self._set_vocab_sentencepiece() + else: + # Some Jamba models only have a tokenizer.json, which works. + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + d_model = self.find_hparam(["hidden_size", "mamba_d_model"]) + d_conv = self.find_hparam(["mamba_d_conv"], optional=True) or 4 + d_inner = self.hparams["mamba_expand"] * d_model + d_state = self.find_hparam(["mamba_d_state"], optional=True) or 16 + # ceiling division + # ref: https://stackoverflow.com/a/17511341/22827863 + # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58 + dt_rank = self.find_hparam(["mamba_dt_rank"], optional=True) or -(d_model // -16) + rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-6 + n_kv_head = self.hparams["num_key_value_heads"] + attn_offset = self.hparams["attn_layer_offset"] + attn_period = self.hparams["attn_layer_period"] + n_kv_vec = [0 for _ in range(attn_offset)] + [ + n_kv_head if (i - attn_offset) % attn_period == 0 else 0 for i in range(attn_offset, self.block_count) + ] + + self.gguf_writer.add_block_count(self.block_count) + self.gguf_writer.add_context_length(self.find_hparam(["max_position_embeddings", "n_ctx"])) + self.gguf_writer.add_embedding_length(d_model) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(n_kv_vec) + self.gguf_writer.add_ssm_conv_kernel(d_conv) + self.gguf_writer.add_ssm_inner_size(d_inner) + self.gguf_writer.add_ssm_state_size(d_state) + self.gguf_writer.add_ssm_time_step_rank(dt_rank) + self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps) + self.gguf_writer.add_expert_count(self.hparams["num_experts"]) + self.gguf_writer.add_expert_used_count(self.hparams["num_experts_per_tok"]) + self.gguf_writer.add_file_type(self.ftype) + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + + # Mini-Jamba + name = name.replace(".moe.", ".feed_forward.") + if bid is not None: + moe_offset = self.hparams["expert_layer_offset"] + moe_period = self.hparams["expert_layer_period"] + + if not (bid >= moe_offset and (bid - moe_offset) % moe_period == 0): + name = name.replace(".experts.0.", ".") + + # process the experts separately + if ".feed_forward.experts." in name: + n_experts = self.hparams["num_experts"] + + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + + # merge the experts into a single 3d tensor + for wid in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.feed_forward.experts.{xid}.{wid}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + # using the same merged name as qwen2moe + merged_name = f"model.layers.{bid}.mlp.experts.{wid}.weight" + + new_name = self.map_tensor_name(merged_name) + + yield new_name, data_torch + return + + new_name = self.map_tensor_name(name) + + if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid): + data_torch = data_torch.squeeze() + + if name.endswith(".A_log"): + logger.debug("A_log --> A ==> " + new_name) + data_torch = -torch.exp(data_torch) + + yield (new_name, data_torch) + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@ModelBase.register("CohereForCausalLM") +class CommandR2Model(TextModel): + model_arch = gguf.MODEL_ARCH.COMMAND_R + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # max_position_embeddings = 8192 in config.json but model was actually + # trained on 128k context length + # aya-23 models don't have model_max_length specified + self.hparams["max_position_embeddings"] = self.find_hparam(["model_max_length", "max_position_embeddings"]) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_logit_scale(self.hparams["logit_scale"]) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + + +@ModelBase.register("Cohere2ForCausalLM") +class Cohere2Model(TextModel): + model_arch = gguf.MODEL_ARCH.COHERE2 + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + self.gguf_writer.add_logit_scale(self.hparams["logit_scale"]) + self.gguf_writer.add_sliding_window(self.hparams["sliding_window"]) + self.gguf_writer.add_vocab_size(self.hparams["vocab_size"]) + + rotary_pct = self.hparams["rotary_pct"] + hidden_size = self.hparams["hidden_size"] + num_attention_heads = self.hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(int(rotary_pct * (hidden_size // num_attention_heads))) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + + +@ModelBase.register("OlmoForCausalLM") +@ModelBase.register("OLMoForCausalLM") +class OlmoModel(TextModel): + model_arch = gguf.MODEL_ARCH.OLMO + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_layer_norm_eps(1e-5) + clip_qkv = self.hparams.get("clip_qkv") + if clip_qkv is not None: + self.gguf_writer.add_clamp_kqv(clip_qkv) + + # Same as super class, but permuting q_proj, k_proj + # Copied from: LlamaModel + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + + if name.endswith("q_proj.weight"): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith("k_proj.weight"): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("Olmo2ForCausalLM") +class Olmo2Model(TextModel): + model_arch = gguf.MODEL_ARCH.OLMO2 + + +@ModelBase.register("OlmoeForCausalLM") +class OlmoeModel(TextModel): + model_arch = gguf.MODEL_ARCH.OLMOE + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_layer_norm_rms_eps(1e-5) + if (n_experts := self.hparams.get("num_experts")) is not None: + self.gguf_writer.add_expert_count(n_experts) + + _experts: list[dict[str, Tensor]] | None = None + + # Copied from: Qwen2MoeModel + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # process the experts separately + if name.find("experts") != -1: + n_experts = self.hparams["num_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + # Copied from: Qwen2MoeModel + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@ModelBase.register("JinaBertModel", "JinaBertForMaskedLM") +class JinaBertV2Model(BertModel): + model_arch = gguf.MODEL_ARCH.JINA_BERT_V2 + + def set_vocab(self): + tokenizer_class = 'BertTokenizer' + with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f: + tokenizer_class = json.load(f)['tokenizer_class'] + + if tokenizer_class == 'BertTokenizer': + super().set_vocab() + elif tokenizer_class == 'RobertaTokenizer': + self._set_vocab_gpt2() + self.gguf_writer.add_token_type_count(2) + else: + raise NotImplementedError(f'Tokenizer {tokenizer_class} is not supported for JinaBertModel') + + +@ModelBase.register("OpenELMForCausalLM") +class OpenELMModel(TextModel): + model_arch = gguf.MODEL_ARCH.OPENELM + + @staticmethod + def _make_divisible(v: float | int, divisor: int) -> int: + # ref: https://huggingface.co/apple/OpenELM-270M-Instruct/blob/eb111ff2e6724348e5b905984063d4064d4bc579/configuration_openelm.py#L34-L38 + new_v = max(divisor, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < 0.9 * v: + new_v += divisor + return new_v + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + ffn_multipliers: list[float] = self.hparams["ffn_multipliers"] + ffn_dim_divisor: int = self.hparams["ffn_dim_divisor"] + self._n_embd: int = self.hparams["model_dim"] + self._num_kv_heads: list[int] = self.hparams["num_kv_heads"] + self._num_query_heads: list[int] = self.hparams["num_query_heads"] + self._ffn_dims: list[int] = [ + OpenELMModel._make_divisible(multiplier * self._n_embd, ffn_dim_divisor) + for multiplier in ffn_multipliers + ] + assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int) + assert isinstance(self._num_query_heads, list) and isinstance(self._num_query_heads[0], int) + + # Uses the tokenizer from meta-llama/Llama-2-7b-hf + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_builtin("llama-spm", self.hparams["vocab_size"]) + + def set_gguf_parameters(self): + n_embd = self._n_embd + head_dim = self.hparams["head_dim"] + rot_pct = 1.0 + assert self.block_count == len(self._num_kv_heads) + assert self.block_count == len(self._num_query_heads) + assert self.block_count == len(self._ffn_dims) + + self.gguf_writer.add_block_count(self.block_count) + self.gguf_writer.add_context_length(self.hparams["max_context_length"]) + self.gguf_writer.add_embedding_length(n_embd) + self.gguf_writer.add_feed_forward_length(self._ffn_dims) + self.gguf_writer.add_head_count(self._num_query_heads) + self.gguf_writer.add_head_count_kv(self._num_kv_heads) + self.gguf_writer.add_rope_freq_base(self.hparams["rope_freq_constant"]) + # https://huggingface.co/apple/OpenELM-270M-Instruct/blob/c401df2/modeling_openelm.py#L30 + self.gguf_writer.add_layer_norm_rms_eps(1e-6) + self.gguf_writer.add_rope_dimension_count(int(rot_pct * head_dim)) + self.gguf_writer.add_key_length(head_dim) + self.gguf_writer.add_value_length(head_dim) + self.gguf_writer.add_file_type(self.ftype) + + def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any: + if "n_layers" in keys: + return self.hparams["num_transformer_layers"] + + return super().find_hparam(keys, optional) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + + # split ff + if bid is not None and name == f"transformer.layers.{bid}.ffn.proj_1.weight": + ff_dim = self._ffn_dims[bid] + yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim]) + yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:]) + return + + yield (self.map_tensor_name(name), data_torch) + + +@ModelBase.register("ArcticForCausalLM") +class ArcticModel(TextModel): + model_arch = gguf.MODEL_ARCH.ARCTIC + + def set_vocab(self): + # The reason for using a custom implementation here is that the + # snowflake-arctic-instruct model redefined tokens 31998 and 31999 from + # tokenizer.model and used them as BOS and EOS instead of adding new tokens. + from sentencepiece import SentencePieceProcessor + + tokenizer_path = self.dir_model / 'tokenizer.model' + + if not tokenizer_path.is_file(): + logger.error(f'Error: Missing {tokenizer_path}') + sys.exit(1) + + # Read the whole vocabulary from the tokenizer.model file + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + # Use the added_tokens_decoder field from tokeniser_config.json as the source + # of information about added/redefined tokens and modify them accordingly. + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + + if "added_tokens_decoder" in tokenizer_config_json: + added_tokens_decoder = tokenizer_config_json["added_tokens_decoder"] + for token_id, token_json in added_tokens_decoder.items(): + token_id = int(token_id) + if token_id >= vocab_size: + logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + token_content = token_json["content"] + token_type = SentencePieceTokenTypes.USER_DEFINED + token_score = -10000.0 + + # Map unk_token to UNKNOWN, other special tokens to CONTROL + # Set the score to 0.0 as in the original tokenizer.model + if ("special" in token_json) and token_json["special"]: + if token_content == tokenizer_config_json["unk_token"]: + token_type = SentencePieceTokenTypes.UNKNOWN + else: + token_type = SentencePieceTokenTypes.CONTROL + token_score = 0.0 + + logger.info(f"Setting added token {token_id} to '{token_content}' (type: {token_type}, score: {token_score:.2f})") + tokens[token_id] = token_content.encode("utf-8") + toktypes[token_id] = token_type + scores[token_id] = token_score + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + + if name.endswith("q_proj.weight"): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith("k_proj.weight"): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + + # process the experts separately + if name.find("block_sparse_moe.experts") != -1: + n_experts = self.hparams["num_local_experts"] + + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for wid in ["w1", "w2", "w3"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@ModelBase.register("DeepseekForCausalLM") +class DeepseekModel(TextModel): + model_arch = gguf.MODEL_ARCH.DEEPSEEK + + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + if (rope_dim := hparams.get("head_dim")) is None: + rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] + + self.gguf_writer.add_rope_dimension_count(rope_dim) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) + self.gguf_writer.add_expert_weights_scale(1.0) + self.gguf_writer.add_expert_count(hparams["n_routed_experts"]) + self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"]) + + _experts: list[dict[str, Tensor]] | None = None + + @staticmethod + def permute(weights: Tensor, n_head: int, n_head_kv: int | None): + if n_head_kv is not None and n_head != n_head_kv: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = DeepseekModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = DeepseekModel.permute(data_torch, n_head, n_kv_head) + + # process the experts separately + if name.find("mlp.experts") != -1: + n_experts = self.hparams["n_routed_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@ModelBase.register("DeepseekV2ForCausalLM") +@ModelBase.register("DeepseekV3ForCausalLM") +class DeepseekV2Model(TextModel): + model_arch = gguf.MODEL_ARCH.DEEPSEEK2 + + def set_vocab(self): + try: + self._set_vocab_gpt2() + return + except Exception: + pass + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True) + tokpre = self.get_vocab_base_pre(tokenizer) + + if tokpre == "kimi-k2": + # Build merges list using the approach similar to HunYuanMoE + merges = [] + vocab = {} + mergeable_ranks = tokenizer.model._mergeable_ranks + for token, rank in mergeable_ranks.items(): + vocab[QwenModel.token_bytes_to_string(token)] = rank + if len(token) == 1: + continue + merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank) + if len(merged) == 2: + merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged))) + + # Build token list + vocab_size = self.hparams["vocab_size"] + special_tokens = tokenizer.special_tokens + reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()} + tokens: list[str] = [] + toktypes: list[int] = [] + + for i in range(vocab_size): + if i not in reverse_vocab: + tokens.append(f"[PAD{i}]") + toktypes.append(gguf.TokenType.UNUSED) + else: + token = reverse_vocab[i] + tokens.append(token) + if i in special_tokens.values(): + toktypes.append(gguf.TokenType.CONTROL) + else: + toktypes.append(gguf.TokenType.NORMAL) + + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_token_merges(merges) + + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False) + special_vocab.add_to_gguf(self.gguf_writer) + else: + raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!") + + def set_gguf_parameters(self): + + # note: deepseek2 using MLA converts into MQA (ie: GQA with 1 group) + self.hparams["num_key_value_heads"] = 1 + + super().set_gguf_parameters() + hparams = self.hparams + + self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None: + self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"]) + self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"]) + + # note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA + self.gguf_writer.add_key_length(hparams["kv_lora_rank"] + hparams["qk_rope_head_dim"]) + self.gguf_writer.add_value_length(hparams["kv_lora_rank"]) + self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"]) + self.gguf_writer.add_value_length_mla(hparams["v_head_dim"]) + + self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) + self.gguf_writer.add_expert_count(hparams["n_routed_experts"]) + self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"]) + self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"]) + self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"]) + + if hparams["scoring_func"] == "sigmoid": + self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID) + elif hparams["scoring_func"] == "softmax": + self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX) + else: + raise ValueError(f"Unsupported scoring_func value: {hparams['scoring_func']}") + + self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) + + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) + self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_scaling["mscale_all_dim"]) + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # rename e_score_correction_bias tensors + if name.endswith("e_score_correction_bias"): + name = name.replace("e_score_correction_bias", "e_score_correction.bias") + + # skip Multi-Token Prediction (MTP) layers + block_count = self.hparams["num_hidden_layers"] + match = re.match(r"model.layers.(\d+)", name) + if match and int(match.group(1)) >= block_count: + return [] + + # process the experts separately + if name.find("mlp.experts") != -1: + n_experts = self.hparams["n_routed_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + # note: MLA with the absorption optimization, needs these two split and k_b_proj transposed + if name.endswith("kv_b_proj.weight"): + name_kb = name.replace("kv_b_proj", "k_b_proj") + name_vb = name.replace("kv_b_proj", "v_b_proj") + + n_head_kv = self.hparams["num_key_value_heads"] + v_head_dim = self.hparams["v_head_dim"] + qk_nope_head_dim = self.hparams["qk_nope_head_dim"] + + assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim) + + kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1]) + k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1) + k_b = k_b.transpose(1, 2) + + return [ + (self.map_tensor_name(name_kb), k_b), + (self.map_tensor_name(name_vb), v_b) + ] + + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@ModelBase.register("Dots1ForCausalLM") +class Dots1Model(Qwen2MoeModel): + model_arch = gguf.MODEL_ARCH.DOTS1 + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.hparams["num_experts"] = self.hparams["n_routed_experts"] + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"]) + self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"]) + self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"]) + self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"]) + + if self.hparams["scoring_func"] == "noaux_tc": + self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID) + else: + raise ValueError(f"Unsupported scoring_func value: {self.hparams['scoring_func']}") + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): + if name.endswith("e_score_correction_bias"): + name = name.replace("e_score_correction_bias", "e_score_correction.bias") + if "shared_experts" in name: + return [(self.map_tensor_name(name), data_torch)] + return super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("PLMForCausalLM") +class PLMModel(TextModel): + model_arch = gguf.MODEL_ARCH.PLM + + def set_vocab(self): + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"]) + self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"]) + self.gguf_writer.add_value_length(hparams["v_head_dim"]) + self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + +@ModelBase.register("T5WithLMHeadModel") +@ModelBase.register("T5ForConditionalGeneration") +@ModelBase.register("MT5ForConditionalGeneration") +@ModelBase.register("UMT5ForConditionalGeneration") +class T5Model(TextModel): + model_arch = gguf.MODEL_ARCH.T5 + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.shared_token_embeddings_found = False + + def set_vocab(self): + # to avoid TypeError: Descriptors cannot be created directly + # exception when importing sentencepiece_model_pb2 + os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" + from sentencepiece import SentencePieceProcessor + from sentencepiece import sentencepiece_model_pb2 as model + + tokenizer_path = self.dir_model / 'tokenizer.model' + + # many older models use spiece.model tokenizer model filename + if not tokenizer_path.is_file(): + tokenizer_path = self.dir_model / 'spiece.model' + + if not tokenizer_path.is_file(): + raise FileNotFoundError(f"File not found: {tokenizer_path}") + + sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] + sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + + # some models like Pile-T5 family use BPE tokenizer instead of Unigram + if sentencepiece_model.trainer_spec.model_type == 2: # BPE + # assure the tokenizer model file name is correct + assert tokenizer_path.name == 'tokenizer.model' + return self._set_vocab_sentencepiece() + else: + assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM + + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix + remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces + precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + for key in added_tokens_json: + token_id = added_tokens_json[key] + if token_id >= vocab_size: + logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + tokens[token_id] = key.encode("utf-8") + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + + if vocab_size > len(tokens): + pad_count = vocab_size - len(tokens) + logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]") + for i in range(1, pad_count + 1): + tokens.append(bytes(f"[PAD{i}]", encoding="utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.UNUSED) + + self.gguf_writer.add_tokenizer_model("t5") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_add_space_prefix(add_prefix) + self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces) + if precompiled_charsmap: + self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None: + logger.warning("Couldn't find context length in config.json, assuming default value of 512") + n_ctx = 512 + self.gguf_writer.add_context_length(n_ctx) + self.gguf_writer.add_embedding_length(self.hparams["d_model"]) + self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"]) + self.gguf_writer.add_block_count(self.hparams["num_layers"]) + self.gguf_writer.add_head_count(self.hparams["num_heads"]) + self.gguf_writer.add_key_length(self.hparams["d_kv"]) + self.gguf_writer.add_value_length(self.hparams["d_kv"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_decoder_start_token_id(self.hparams["decoder_start_token_id"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight", + # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored + # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder + # and decoder and ignore the remaining ones. + if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]: + if not self.shared_token_embeddings_found: + name = "shared.weight" + self.shared_token_embeddings_found = True + else: + logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.") + return [] + + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("T5EncoderModel") +class T5EncoderModel(TextModel): + model_arch = gguf.MODEL_ARCH.T5ENCODER + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.shared_token_embeddings_found = False + + def set_vocab(self): + # to avoid TypeError: Descriptors cannot be created directly + # exception when importing sentencepiece_model_pb2 + os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" + from sentencepiece import SentencePieceProcessor + from sentencepiece import sentencepiece_model_pb2 as model + + tokenizer_path = self.dir_model / 'tokenizer.model' + + # many older models use spiece.model tokenizer model filename + if not tokenizer_path.is_file(): + tokenizer_path = self.dir_model / 'spiece.model' + + if not tokenizer_path.is_file(): + raise FileNotFoundError(f"File not found: {tokenizer_path}") + + sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] + sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + + # some models like Pile-T5 family use BPE tokenizer instead of Unigram + if sentencepiece_model.trainer_spec.model_type == 2: # BPE + # assure the tokenizer model file name is correct + assert tokenizer_path.name == 'tokenizer.model' + return self._set_vocab_sentencepiece() + else: + assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM + + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix + remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces + precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + for key in added_tokens_json: + token_id = added_tokens_json[key] + if token_id >= vocab_size: + logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + tokens[token_id] = key.encode("utf-8") + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + + if vocab_size > len(tokens): + pad_count = vocab_size - len(tokens) + logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]") + for i in range(1, pad_count + 1): + tokens.append(bytes(f"[PAD{i}]", encoding="utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.UNUSED) + + self.gguf_writer.add_tokenizer_model("t5") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_add_space_prefix(add_prefix) + self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces) + if precompiled_charsmap: + self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None: + logger.warning("Couldn't find context length in config.json, assuming default value of 512") + n_ctx = 512 + self.gguf_writer.add_context_length(n_ctx) + self.gguf_writer.add_embedding_length(self.hparams["d_model"]) + self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"]) + self.gguf_writer.add_block_count(self.hparams["num_layers"]) + self.gguf_writer.add_head_count(self.hparams["num_heads"]) + self.gguf_writer.add_key_length(self.hparams["d_kv"]) + self.gguf_writer.add_value_length(self.hparams["d_kv"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight", + # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored + # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder + # and decoder and ignore the remaining ones. + if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]: + if not self.shared_token_embeddings_found: + name = "shared.weight" + self.shared_token_embeddings_found = True + else: + logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.") + return [] + + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("JAISLMHeadModel") +class JaisModel(TextModel): + model_arch = gguf.MODEL_ARCH.JAIS + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # SwigLU activation + assert self.hparams["activation_function"] == "swiglu" + # ALiBi position embedding + assert self.hparams["position_embedding_type"] == "alibi" + + # Embeddings scale + self.embeddings_scale = 1.0 + if 'mup_embeddings_scale' in self.hparams: + self.embeddings_scale = self.hparams['mup_embeddings_scale'] + elif 'embeddings_scale' in self.hparams: + self.embeddings_scale = self.hparams['embeddings_scale'] + else: + assert False + + self.width_scale = 1.0 + if 'mup_output_alpha' in self.hparams: + assert 'mup_width_scale' in self.hparams + self.width_scale = self.hparams['mup_output_alpha'] * self.hparams['mup_width_scale'] + elif 'width_scale' in self.hparams: + self.width_scale = self.hparams['width_scale'] + else: + assert False + + self.max_alibi_bias = 8.0 + + def set_vocab(self): + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + self.gguf_writer.add_block_count(self.hparams["n_layer"]) + self.gguf_writer.add_context_length(self.hparams["n_positions"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + self.gguf_writer.add_feed_forward_length(self.hparams["n_inner"]) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + tensors: list[tuple[str, Tensor]] = [] + + # we don't need these + if name.endswith((".attn.bias")): + return tensors + + if name.endswith(("relative_pe.slopes")): + # Calculate max ALiBi bias (this is the inverse of the ALiBi calculation) + # Some other models has max_alibi_bias spelled out explicitly in the hyperparams, + # but Jais's PyTorch model simply precalculates the slope values and places them + # in relative_pes.slopes + n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"])) + first_val = float(data_torch[0].item()) + self.max_alibi_bias = -round(math.log2(first_val) * n_head_closest_log2) + + return tensors + + if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_fc2.weight")): + data_torch = data_torch.transpose(1, 0) + + new_name = self.map_tensor_name(name) + + if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD): + tensors.append((new_name, data_torch * self.embeddings_scale)) + elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT): + tensors.append((new_name, data_torch * self.width_scale)) + else: + tensors.append((new_name, data_torch)) + + return tensors + + def prepare_tensors(self): + super().prepare_tensors() + self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias) + + +@ModelBase.register("Glm4ForCausalLM", "Glm4vForConditionalGeneration") +class Glm4Model(TextModel): + model_arch = gguf.MODEL_ARCH.GLM4 + + def set_vocab(self): + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True) + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + tokens, toktypes, tokpre = self.get_vocab_base() + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) + special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + if (rope_dim := self.hparams.get("head_dim")) is None: + rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))) + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if name.startswith("model.visual."): # ignore visual part of Glm4v + return [] + elif name.startswith("model.language_model."): + name = name.replace("language_model.", "") # for Glm4v + return super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration") +class ChatGLMModel(TextModel): + model_arch = gguf.MODEL_ARCH.CHATGLM + + def set_vocab_chatglm3(self): + dir_model = self.dir_model + hparams = self.hparams + tokens: list[bytes] = [] + toktypes: list[int] = [] + scores: list[float] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) + vocab_size = hparams.get("padded_vocab_size", len(tokenizer.get_vocab())) + assert max(tokenizer.get_vocab().values()) < vocab_size + role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"] + special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens + for token_id in range(vocab_size): + piece = tokenizer._convert_id_to_token(token_id) + if token_id == 0: + piece = "" + elif token_id == 1: + piece = "" + elif token_id == 2: + piece = "" + + text = piece.encode("utf-8") + score = 0.0 + # Referencing the tokenizer Python implementation(https://huggingface.co/THUDM/chatglm3-6b/blob/main/tokenization_chatglm.py), + # it is only valid if it is less than tokenizer.tokenizer.sp_model.vocab_size() + if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size(): + score = tokenizer.tokenizer.sp_model.get_score(token_id) + + if token_id >= tokenizer.tokenizer.sp_model.vocab_size(): + if piece in special_tokens: + toktype = SentencePieceTokenTypes.CONTROL + elif len(piece) == 0: + text = f"[PAD{token_id}]".encode("utf-8") + toktype = SentencePieceTokenTypes.UNUSED + else: + toktype = SentencePieceTokenTypes.USER_DEFINED + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + continue + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.tokenizer.sp_model.is_unknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.tokenizer.sp_model.is_control(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.tokenizer.sp_model.is_unused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.tokenizer.sp_model.is_byte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + self.gguf_writer.add_tokenizer_model("llama") + # glm3 needs prefix and suffix formatted as: + # prompt = "[gMASK]sop<|user|>\n" + prompt + "<|assistant|>" + self.gguf_writer.add_tokenizer_pre("chatglm-spm") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + @staticmethod + def token_bytes_to_string(b): + from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode + byte_encoder = bytes_to_unicode() + return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')]) + + @staticmethod + def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]: + parts = [bytes([b]) for b in token] + while True: + min_idx = None + min_rank = None + for i, pair in enumerate(zip(parts[:-1], parts[1:])): + rank = mergeable_ranks.get(pair[0] + pair[1]) + if rank is not None and (min_rank is None or rank < min_rank): + min_idx = i + min_rank = rank + if min_rank is None or (max_rank is not None and min_rank >= max_rank): + break + assert min_idx is not None + parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:] + return parts + + def set_vocab(self): + if "THUDM/chatglm3-6b" in self.hparams.get("_name_or_path", ""): + self.set_vocab_chatglm3() + return + + dir_model = self.dir_model + hparams = self.hparams + tokens: list[str] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) + vocab_size = hparams.get("padded_vocab_size",hparams["vocab_size"]) + assert max(tokenizer.get_vocab().values()) < vocab_size + + tokens, toktypes, tokpre = self.get_vocab_base() + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + # only add special tokens when they were not already loaded from config.json + special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) + # this one is usually not in config.json anyway + special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + n_head_kv = self.hparams.get("multi_query_group_num", self.hparams.get("num_key_value_heads", n_head)) + self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed)) + self.gguf_writer.add_embedding_length(n_embed) + self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", self.hparams.get("intermediate_size", 4 * n_embed))) + self.gguf_writer.add_block_count(self.hparams.get("num_layers", self.hparams["num_hidden_layers"])) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head_kv) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon",1e-5)) + self.gguf_writer.add_file_type(self.ftype) + if "attention_dim" in self.hparams: + rope_dim = self.hparams["attention_dim"] + else: + rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))) + self.gguf_writer.add_add_bos_token(False) + rope_freq = 10000 + if "rope_ratio" in self.hparams: + rope_freq = rope_freq * self.hparams["rope_ratio"] + self.gguf_writer.add_rope_freq_base(rope_freq) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if name.endswith(".rotary_pos_emb.inv_freq") or name.startswith("model.vision."): + return [] + + name = name.removeprefix("transformer.") + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("NemotronForCausalLM") +class NemotronModel(TextModel): + model_arch = gguf.MODEL_ARCH.NEMOTRON + + def set_vocab(self): + self._set_vocab_sentencepiece() + self.gguf_writer.add_pad_token_id(0) + self.gguf_writer.add_unk_token_id(1) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + f_norm_eps = self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon", "norm_eps"]) + self.gguf_writer.add_layer_norm_eps(f_norm_eps) + + # * Partial RoPE + rot_pct = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"]) + n_embd = self.find_hparam(["hidden_size", "n_embd"]) + n_head = self.find_hparam(["num_attention_heads", "n_head"]) + self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) + + # * RopeScaling for Nemotron + if "rope_scaling" not in self.hparams or self.hparams["rope_scaling"] is None: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + else: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(self.hparams["factor"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # * Adding +1 to LayerNorm's weights here to implement layernorm1p w/o changing anything on the GGML engine side + # model.layers.{l}.input_layernorm.weight + # model.layers.{l}.post_attention_layernorm.weight + # model.norm.weight + if name.endswith("norm.weight"): + data_torch = data_torch + 1 + + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("ExaoneForCausalLM") +class ExaoneModel(TextModel): + model_arch = gguf.MODEL_ARCH.EXAONE + + def set_gguf_parameters(self): + hparams = self.hparams + + assert (hparams["activation_function"] == "silu") + + max_position_embeddings = hparams["max_position_embeddings"] + embed_dim = hparams["hidden_size"] + num_heads = hparams["num_attention_heads"] + num_kv_heads = hparams.get("num_key_value_heads", num_heads) + layer_norm_eps = hparams["layer_norm_epsilon"] + intermediate_size = hparams["intermediate_size"] if "intermediate_size" in hparams else 4 * embed_dim + num_layers = hparams["num_layers"] + # ignore for now as EXAONE-3.0-7.8B-Instruct attentino_dropout is 0.0 + # attention_dropout_rate = hparams["attention_dropout"] + # ignore for now as EXAONE-3.0-7.8B-Instruct embed_dropout is 0.0 + # embed_dropout_rate = hparams["embed_dropout"] + self.gguf_writer.add_embedding_length(embed_dim) + self.gguf_writer.add_head_count(num_heads) + self.gguf_writer.add_head_count_kv(num_kv_heads) + self.gguf_writer.add_context_length(max_position_embeddings) + self.gguf_writer.add_layer_norm_rms_eps(layer_norm_eps) + self.gguf_writer.add_feed_forward_length(intermediate_size) + self.gguf_writer.add_block_count(num_layers) + self.gguf_writer.add_file_type(self.ftype) + + if (rope_theta := self.hparams.get("rope_theta")) is not None: + self.gguf_writer.add_rope_freq_base(rope_theta) + rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"], optional=True) + rotary_factor = rotary_factor if rotary_factor is not None else 1.0 + self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"]))) + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): + if rope_scaling.get("rope_type", '').lower() == "llama3": + base = self.hparams.get("rope_theta", 10000.0) + if (dim := self.hparams.get("head_dim")) is None: + dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] + freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + + factor = rope_scaling.get("factor", 8.0) + low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) + high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + old_context_len = self.hparams.get("original_max_position_embeddings", 8192) + + low_freq_wavelen = old_context_len / low_freq_factor + high_freq_wavelen = old_context_len / high_freq_factor + assert low_freq_wavelen != high_freq_wavelen + + rope_factors = [] + for freq in freqs: + wavelen = 2 * math.pi / freq + if wavelen < high_freq_wavelen: + rope_factors.append(1) + elif wavelen > low_freq_wavelen: + rope_factors.append(factor) + else: + smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) + rope_factors.append(1 / ((1 - smooth) / factor + smooth)) + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32)) + + +@ModelBase.register("Exaone4ForCausalLM") +class Exaone4Model(TextModel): + model_arch = gguf.MODEL_ARCH.EXAONE4 + + def set_vocab(self): + tokens, toktypes, tokpre = self.get_vocab_base() + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + if hparams.get("sliding_window") is not None: + self.gguf_writer.add_sliding_window(hparams["sliding_window"]) + if "layer_types" in hparams: + self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in hparams["layer_types"]]) + elif "sliding_window_pattern" in hparams: + sliding_window_pattern = [] + if isinstance(hparams["sliding_window_pattern"], str): # e.g. LLLG + for i in range(hparams["num_hidden_layers"]): + sliding_window_pattern.append(hparams["sliding_window_pattern"][i % len(hparams["sliding_window_pattern"])] == "L") + if isinstance(hparams["sliding_window_pattern"], int): # e.g. 4 + for i in range(hparams["num_hidden_layers"]): + sliding_window_pattern.append((i + 1) % hparams["sliding_window_pattern"] != 0) + if len(sliding_window_pattern) == hparams["num_hidden_layers"]: + self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern) + + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): + if rope_scaling.get("rope_type", '').lower() == "llama3": + base = self.hparams.get("rope_theta", 10_000.0) + if (dim := self.hparams.get("head_dim")) is None: + dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] + freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + + factor = rope_scaling.get("factor", 16.0) + low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) + high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + old_context_len = self.hparams.get("original_max_position_embeddings", 8192) + + low_freq_wavelen = old_context_len / low_freq_factor + high_freq_wavelen = old_context_len / high_freq_factor + + rope_factors = [] + for freq in freqs: + wavelen = 2 * math.pi / freq + if wavelen < high_freq_wavelen: + rope_factors.append(1) + elif wavelen > low_freq_wavelen: + rope_factors.append(factor) + else: + smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) + rope_factors.append(1 / ((1 - smooth) / factor + smooth)) + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32)) + + +@ModelBase.register("GraniteForCausalLM") +class GraniteModel(LlamaModel): + """Conversion for IBM's GraniteForCausalLM""" + model_arch = gguf.MODEL_ARCH.GRANITE + + def set_gguf_parameters(self): + """Granite uses standard llama parameters with the following differences: + + - No head_dim support + - New multiplier params: + - attention_scale + - embedding_scale + - residual_scale + - logits_scaling + """ + if head_dim := self.hparams.pop("head_dim", None): + logger.warning("Ignoring head_dim (%s) from config for Granite", head_dim) + super().set_gguf_parameters() + # NOTE: Convert _multiplier params to _scale params for naming + # consistency + if attention_scale := self.hparams.get("attention_multiplier"): + self.gguf_writer.add_attention_scale(attention_scale) + logger.info("gguf: (granite) attention_scale = %s", attention_scale) + if embedding_scale := self.hparams.get("embedding_multiplier"): + self.gguf_writer.add_embedding_scale(embedding_scale) + logger.info("gguf: (granite) embedding_scale = %s", embedding_scale) + if residual_scale := self.hparams.get("residual_multiplier"): + self.gguf_writer.add_residual_scale(residual_scale) + logger.info("gguf: (granite) residual_scale = %s", residual_scale) + if logits_scale := self.hparams.get("logits_scaling"): + self.gguf_writer.add_logit_scale(logits_scale) + logger.info("gguf: (granite) logits_scale = %s", logits_scale) + + +@ModelBase.register("GraniteMoeForCausalLM", "GraniteMoeSharedForCausalLM") +class GraniteMoeModel(GraniteModel): + """Conversion for IBM's GraniteMoeForCausalLM""" + model_arch = gguf.MODEL_ARCH.GRANITE_MOE + + def set_gguf_parameters(self): + """GraniteMoeShared uses GraniteMoe parameters plus the following: + - shared_intermediate_size + """ + super().set_gguf_parameters() + if shared_feed_forward_length := self.hparams.get("shared_intermediate_size"): + self.gguf_writer.add_expert_shared_feed_forward_length(shared_feed_forward_length) + logger.info("gguf: (granitemoeshared) shared_feed_forward_length = %s", shared_feed_forward_length) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + """In modeling_granitemoe, the JetMoe implementation of parallel experts + is used. This essentially merges w1 and w3 into a single tensor with 2x + the hidden size that is then split during forward. To keep compatibility + with existing mixtral support, we pull them apart here. + """ + + if name.endswith("block_sparse_moe.input_linear.weight"): + ffn_dim = self.hparams["intermediate_size"] + assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * intermediate_size" + gate, up = data_torch.split(ffn_dim, dim=-2) + return [ + (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), gate), + (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), up), + ] + + has_experts = bool(self.hparams.get('num_local_experts')) + + if name.endswith("shared_mlp.input_linear.weight"): + ffn_dim = self.hparams["shared_intermediate_size"] + assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * shared_intermediate_size" + gate, up = data_torch.split(ffn_dim, dim=-2) + if has_experts: + return [ + (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_SHEXP, bid), gate), + (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_SHEXP, bid), up), + ] + return [ + (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), gate), + (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), up), + ] + + if not has_experts and name.endswith("shared_mlp.output_linear.weight"): + return [ + (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, bid), data_torch) + ] + + return super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("GraniteMoeHybridForCausalLM", "BambaForCausalLM") +class GraniteHybridModel(Mamba2Model, GraniteMoeModel): + """GraniteHybrid is a hybrid SSM + Attention model that uses Mamba2 SSM + layers and optionally uses MoE w/ a shared expert""" + model_arch = gguf.MODEL_ARCH.GRANITE_HYBRID + undo_permute = True + + def __init__(self, *args, **kwargs): + + # Hybrid mamba models use a prefix for the mamba-specific params. + # TODO: Extend this if the prefix(es) need to be configurable + self.hparam_prefixes = ["mamba"] + + super().__init__(*args, **kwargs) + + # Lists of which layers use ssm vs attention + self._attn_layers = self.get_attn_layers() + self._ssm_layers = [ + i for i in range(self.block_count) + if i not in self._attn_layers + ] + + # n_group and d_inner are used during reshape_tensors for mamba2 + self.d_model = self.find_hparam(["hidden_size", "d_model"]) + self.n_group = self.find_hparam(["n_groups"]) + self.d_inner = self.find_hparam(["expand"]) * self.d_model + + def get_attn_layers(self): + # Explicit list of layer type names + if layer_types := self.hparams.get("layer_types"): + return [ + i for i, typ in enumerate(layer_types) + if typ == "attention" + ] + + # Layer types indicated by index or period + attn_layers = self.hparams.get("attn_layer_indices", []) + if not attn_layers: + attn_period = self.hparams.get("attn_layer_period") + assert attn_period, "Didn't find attn_layer_indices or attn_layer_period" + attn_offset = self.hparams.get("attn_layer_offset") + assert attn_offset is not None, "No attention layer offset set with attn_layer_period" + attn_layers = [ + i for i in range(self.block_count) + if i % attn_period == attn_offset + ] + return attn_layers + + def find_hparam(self, keys: Iterable[str], *args, **kwargs) -> Any: + prefixed = [] + for pfx in self.hparam_prefixes: + prefixed.extend( + "_".join([pfx, k]) + for k in keys + ) + keys = list(keys) + prefixed + return Mamba2Model.find_hparam(self, keys, *args, **kwargs) + + def modify_tensors( + self, data_torch: Tensor, name: str, bid: int | None + ) -> Iterable[tuple[str, Tensor]]: + if ( + name.endswith("block_sparse_moe.input_linear.weight") + or "shared_mlp" in name + ): + return GraniteMoeModel.modify_tensors(self, data_torch, name, bid) + + # Determine whether this is a mamba layer or an attention layer + if bid in self._ssm_layers: + return Mamba2Model.modify_tensors(self, data_torch, name, bid) + elif bid in self._attn_layers: + return GraniteMoeModel.modify_tensors(self, data_torch, name, bid) + return [(self.map_tensor_name(name), data_torch)] + + def set_gguf_parameters(self): + """This method merges params from both parents and some that are + specific to this model. The result is some duplication of how the params + get set. The following warnings are expected during conversion: + + WARNING:Duplicated key name 'granitehybrid.attention.head_count_kv' + WARNING:Duplicated key name 'granitehybrid.context_length' + """ + GraniteMoeModel.set_gguf_parameters(self) + + ## Mamba mixer params ## + self.gguf_writer.add_ssm_conv_kernel(self.find_hparam(["conv_kernel", "d_conv"])) + self.gguf_writer.add_ssm_state_size(self.find_hparam(["state_size", "d_state"])) + self.gguf_writer.add_ssm_group_count(self.n_group) + self.gguf_writer.add_ssm_inner_size(self.d_inner) + # NOTE: The mamba_dt_rank is _not_ the right field for how this is used + # in llama.cpp + self.gguf_writer.add_ssm_time_step_rank(self.find_hparam(["n_heads"])) + + ## Attention params ## + head_count_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"]) + head_count_kv_vec = [ + head_count_kv if i in self._attn_layers else 0 for i in range(self.block_count) + ] + if rope_dim := self.hparams.get("attn_rotary_emb"): + self.gguf_writer.add_rope_dimension_count(rope_dim) + self.gguf_writer.add_head_count_kv(head_count_kv_vec) + + ## If Bamba, use rope, otherwise don't + use_rope = "BambaForCausalLM" in self.hparams["architectures"] + self.gguf_writer.add_rope_scaling_finetuned(use_rope) + if not use_rope: + self.gguf_writer.add_context_length(2**20) + + ## Validation ## + d_head = self.find_hparam(["d_head"], optional=True) or 64 + assert self.hparams.get("hidden_act") in [None, "silu"], "Only SILU activation supported" + assert self.d_inner % d_head == 0, f"SSM inner size {self.d_inner} not a multiple of head dim {d_head}" + + def set_vocab(self): + self.hparams["pad_vocab_size_multiple"] = 8 + Mamba2Model.set_vocab(self) + + +@ModelBase.register("BailingMoeForCausalLM") +class BailingMoeModel(TextModel): + model_arch = gguf.MODEL_ARCH.BAILINGMOE + + def set_vocab(self): + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + if (rope_dim := hparams.get("head_dim")) is None: + rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] + + self.gguf_writer.add_rope_dimension_count(rope_dim) + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) + else: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) + self.gguf_writer.add_expert_weights_scale(1.0) + self.gguf_writer.add_expert_count(hparams["num_experts"]) + self.gguf_writer.add_expert_shared_count(hparams["num_shared_experts"]) + self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"]) + + _experts: list[dict[str, Tensor]] | None = None + + @staticmethod + def permute(weights: Tensor, n_head: int, n_head_kv: int | None): + if n_head_kv is not None and n_head != n_head_kv: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + n_embd = self.hparams["hidden_size"] + if (head_dim := self.hparams.get("head_dim")) is None: + head_dim = n_embd // n_head + + output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT) + + if name.endswith("attention.dense.weight"): + return [(self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_OUT, bid), data_torch)] + elif name.endswith("query_key_value.weight"): + q, k, v = data_torch.split([n_head * head_dim, n_kv_head * head_dim, n_kv_head * head_dim], dim=-2) + + return [ + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), BailingMoeModel.permute(q, n_head, n_head)), + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), BailingMoeModel.permute(k, n_head, n_kv_head)), + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), v) + ] + elif name.find("mlp.experts") != -1: + n_experts = self.hparams["num_experts"] + assert bid is not None + + tensors: list[tuple[str, Tensor]] = [] + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + # merge the experts into a single 3d tensor + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + + return tensors + + new_name = self.map_tensor_name(name) + + if new_name == output_name and self.hparams.get("norm_head"): + data_torch = data_torch.float() + data_torch /= torch.norm(data_torch, p=2, dim=0, keepdim=True) + 1e-7 + + return [(new_name, data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@ModelBase.register("ChameleonForConditionalGeneration") +@ModelBase.register("ChameleonForCausalLM") # obsolete +class ChameleonModel(TextModel): + model_arch = gguf.MODEL_ARCH.CHAMELEON + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_swin_norm(self.hparams.get("swin_norm", False)) + + def set_vocab(self): + self._set_vocab_gpt2() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # ignore image tokenizer for now + # TODO: remove this once image support is implemented for Chameleon + if name.startswith("model.vqmodel"): + return [] + + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + hidden_dim = self.hparams.get("hidden_size") + + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + if name.endswith(("q_norm.weight", "q_norm.bias")): + data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_head, hidden_dim) + if name.endswith(("k_norm.weight", "k_norm.bias")): + data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_kv_head, hidden_dim) + + return [(self.map_tensor_name(name), data_torch)] + + # see: https://github.com/huggingface/transformers/blob/72fb02c47dbbe1999ae105319f24631cad6e2e00/src/transformers/models/chameleon/convert_chameleon_weights_to_hf.py#L176-L203 + @staticmethod + def _reverse_hf_permute(data_torch, n_heads, hidden_dim): + head_dim = hidden_dim // n_heads + data_torch = data_torch[0].view(2, head_dim // 2).t().reshape(1, -1) + data_torch = data_torch.repeat_interleave(n_heads, 0) + return data_torch + + +@ModelBase.register("UltravoxModel") +class UltravoxModel(TextModel): + model_arch = gguf.MODEL_ARCH.LLAMA # dummy + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + raise NotImplementedError("Ultravox does not have text decoder. Instead, it uses Llama or other models for text. If you want to get the audio encoder, please use --mmproj argument") + + +@ModelBase.register("Qwen2AudioForConditionalGeneration") +class WhisperEncoderModel(MmprojModel): + has_vision_encoder = False # no vision encoder + has_audio_encoder = True + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + if "hidden_size" not in self.hparams and "intermediate_size" not in self.hparams: + self.hparams["hidden_size"] = self.hparams["d_model"] + self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"] + self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"] + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN2A) + self.gguf_writer.add_audio_num_mel_bins(self.hparams["num_mel_bins"]) + self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5)) + + def tensor_force_quant(self, name, new_name, bid, n_dims): + del bid, new_name, n_dims # unused + if ".conv" in name and ".weight" in name: + return gguf.GGMLQuantizationType.F16 + return False + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if name.startswith("language_model."): + # skip language model tensors + return [] + + # prevent clash naming with vision tensors + if name.startswith("multi_modal_projector"): + name = "audio." + name + + if "conv1.bias" in name or "conv2.bias" in name: + # transpose conv1 and conv2 bias + data_torch = data_torch.unsqueeze(-1) + + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("UltravoxModel") +class UltravoxWhisperEncoderModel(WhisperEncoderModel): + has_vision_encoder = False # no vision encoder + has_audio_encoder = True + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.ULTRAVOX) + self.gguf_writer.add_audio_stack_factor(self.global_config["stack_factor"]) + + +@ModelBase.register("VoxtralForConditionalGeneration") +class VoxtralWhisperEncoderModel(WhisperEncoderModel): + has_vision_encoder = False # no vision encoder + has_audio_encoder = True + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.VOXTRAL) + self.gguf_writer.add_audio_stack_factor(4) # == intermediate_size // hidden_size + + +@ModelBase.register("FalconH1ForCausalLM") +class FalconH1Model(Mamba2Model): + model_arch = gguf.MODEL_ARCH.FALCON_H1 + + def __init__(self, *args, **kwargs): + # Set the hparam prefixes for Falcon Mamba2 + self.hparam_prefixes = ["mamba"] + + # Initialize the base Mamba2Model + super().__init__(*args, **kwargs) + + # Use Llama conversion for attention + self._transformer_model_class = LlamaModel + + # n_group and d_inner are used during reshape_tensors for mamba2 + self.n_group = self.find_hparam(["n_groups"]) + self.d_inner = self.find_hparam(["mamba_d_ssm"]) + self.d_head = self.find_hparam(["d_head"]) + + # Initialize any Falcon Mamba2 specific attributes + self.has_attention = True # Falcon Mamba2 has attention components + + # Load Falcon-H1 multipliers from hyperparameters + self.attention_in_multiplier = self.find_hparam(["attention_in_multiplier"], optional=True) + self.attention_out_multiplier = self.find_hparam(["attention_out_multiplier"], optional=True) + self.ssm_in_multiplier = self.find_hparam(["ssm_in_multiplier"], optional=True) + self.ssm_out_multiplier = self.find_hparam(["ssm_out_multiplier"], optional=True) + self.mlp_multipliers = self.find_hparam(["mlp_multipliers"], optional=True) + self.ssm_multipliers = self.find_hparam(["ssm_multipliers"], optional=True) + self.intermediate_size = self.find_hparam(["intermediate_size"]) + self.key_multiplier = self.find_hparam(["key_multiplier"], optional=True) + + def find_hparam(self, keys: Iterable[str], *args, **kwargs) -> Any: + prefixed = [] + for pfx in self.hparam_prefixes: + prefixed.extend( + "_".join([pfx, k]) + for k in keys + ) + keys = list(keys) + prefixed + return super().find_hparam(keys, *args, **kwargs) + + def set_vocab(self): + self._set_vocab_gpt2() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + tensors = list(super().modify_tensors(data_torch, name, bid)) + tensor = tensors[0][1] + + if "down_proj" in name: + tensor = tensor * self.mlp_multipliers[1] + elif "gate_proj" in name: + tensor = tensor * self.mlp_multipliers[0] + elif "k_proj" in name: + tensor = tensor * self.key_multiplier * self.attention_in_multiplier + elif "q_proj" in name: + tensor = tensor * self.attention_in_multiplier + elif "v_proj" in name: + tensor = tensor * self.attention_in_multiplier + elif "o_proj" in name: + tensor = tensor * self.attention_out_multiplier + elif "out_proj" in name: + tensor = tensor * self.ssm_out_multiplier + elif "in_proj" in name: + tensor = tensor * self.ssm_in_multiplier + zxbcdt_multipliers = self.hparams["ssm_multipliers"] + intermediate_size = self.hparams["mamba_d_ssm"] + groups_time_state_size = self.hparams["mamba_n_groups"] * self.hparams["mamba_d_state"] + tensor[:intermediate_size, :] *= zxbcdt_multipliers[0] + tensor[intermediate_size:2 * intermediate_size, :] *= zxbcdt_multipliers[1] + tensor[2 * intermediate_size:2 * intermediate_size + groups_time_state_size, :] *= zxbcdt_multipliers[2] + tensor[2 * intermediate_size + groups_time_state_size:2 * intermediate_size + 2 * groups_time_state_size, :] *= zxbcdt_multipliers[3] + tensor[2 * intermediate_size + 2 * groups_time_state_size:, :] *= zxbcdt_multipliers[4] + elif "lm_head" in name: + tensor = tensor * self.hparams["lm_head_multiplier"] + elif "embed_tokens" in name: + tensor = tensor * self.hparams["embedding_multiplier"] + elif "mamba.norm" in name: + tensor = tensor.reshape(self.n_group, self.d_inner // self.n_group) + + tensors = [(tensors[0][0], tensor)] + return tensors + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + ## General Params ## + self.gguf_writer.add_vocab_size(self.hparams["vocab_size"]) + # Override some Mamba2 defaults + self.gguf_writer.add_block_count(self.block_count) + self.gguf_writer.add_context_length(self.hparams.get("max_position_embeddings", 0)) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + + ## Attention params ## + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) # Override value 0 from Mamba2 + self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) + self.gguf_writer.add_key_length(self.hparams["head_dim"]) + self.gguf_writer.add_value_length(self.hparams["head_dim"]) + + ## Validation ## + assert self.hparams.get("hidden_act") in [None, "silu"], "Only SILU activation supported" + assert self.d_inner % self.d_head == 0, f"SSM inner size {self.d_inner} not a multiple of head dim {self.d_head}" + + # Add any other Falcon Mamba2 specific configuration + self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) + + +@ModelBase.register("HunYuanMoEV1ForCausalLM") +class HunYuanMoEModel(TextModel): + model_arch = gguf.MODEL_ARCH.HUNYUAN_MOE + + def set_vocab(self): + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True) + + # 1. Get the pre-tokenizer identifier hash + tokpre = self.get_vocab_base_pre(tokenizer) + + # 2. Reverse-engineer the merges list from mergeable_ranks + merges = [] + vocab = {} + mergeable_ranks = tokenizer.mergeable_ranks + for token, rank in mergeable_ranks.items(): + vocab[QwenModel.token_bytes_to_string(token)] = rank + if len(token) == 1: + continue + merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank) + if len(merged) == 2: # todo this is an assert in Qwen, why? + merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged))) + + # 3. Generate the tokens and toktypes lists + vocab_size = self.hparams["vocab_size"] + assert tokenizer.vocab_size == vocab_size + special_tokens = tokenizer.special_tokens + reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()} + tokens: list[str] = [] + toktypes: list[int] = [] + for i in range(vocab_size): + if i not in reverse_vocab: + tokens.append(f"[PAD{i}]") + toktypes.append(gguf.TokenType.UNUSED) + else: + token = reverse_vocab[i] + tokens.append(token) + if i in special_tokens.values(): + toktypes.append(gguf.TokenType.CONTROL) + else: + toktypes.append(gguf.TokenType.NORMAL) + + # 4. Write all vocab-related fields to the GGUF writer + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_token_merges(merges) + + # 5. Add special tokens and chat templates + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False) + special_vocab.add_to_gguf(self.gguf_writer) + # FIX for BOS token: Overwrite incorrect id read from config.json + self.gguf_writer.add_bos_token_id(127959) # <|bos|> + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + + self.gguf_writer.add_expert_count(hparams["num_experts"]) + self.gguf_writer.add_expert_shared_feed_forward_length(hparams["intermediate_size"]) + + moe_intermediate_size = hparams["moe_intermediate_size"] + assert all(n == moe_intermediate_size[0] for n in moe_intermediate_size) + self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size[0]) + + moe_topk = hparams["moe_topk"] + assert all(topk == moe_topk[0] for topk in moe_topk) + self.gguf_writer.add_expert_used_count(moe_topk[0]) + + moe_shared_expert = hparams["num_shared_expert"] + assert all(n == moe_shared_expert[0] for n in moe_shared_expert) + self.gguf_writer.add_expert_shared_count(moe_shared_expert[0]) + + # Rope + rope_scaling = hparams.get("rope_scaling", {}) + if rope_scaling.get("type") == "dynamic": + # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ + # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf) + alpha = rope_scaling.get("alpha", 1000) + base = hparams.get("rope_theta", 10000.0) + dim = (hparams["hidden_size"] // hparams["num_attention_heads"]) # 128 + scaled_base = base * (alpha ** (dim / (dim - 2))) # 10000 * (1000 ** (128 / 126)) = 11158839.9251 + self.gguf_writer.add_rope_freq_base(scaled_base) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + self.gguf_writer.add_rope_scaling_factor(1) + # There is no consistent way to calculate ctx from alpha, and the config is incorrectly set to 32k + self.gguf_writer.add_rope_scaling_orig_ctx_len(256 * 1024) # 256k context length + self.gguf_writer.add_context_length(256 * 1024) # 256k context length + + # if any of our assumptions about the values are wrong, something has changed and this may need to be updated + assert alpha == 1000 and base == 10000.0 and dim == 128 and self.hparams["max_position_embeddings"] in [32 * 1024, 256 * 1024] , \ + "HunYuan dynamic RoPE scaling assumptions changed, please update the logic or context length manually" + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if name == "lm_head.weight": + if self.hparams.get("tie_word_embeddings", False): + logger.info("Skipping tied output layer 'lm_head.weight'") + return [] + + if name.find("mlp.experts") != -1: + n_experts = self.hparams["num_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + # merge the experts into a single 3d tensor + tensors: list[tuple[str, Tensor]] = [] + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + new_name = self.map_tensor_name(merged_name) + tensors.append((new_name, data_torch)) + + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + if self._experts is not None: + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@ModelBase.register("HunYuanDenseV1ForCausalLM") +class HunYuanModel(TextModel): + model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE + + def set_vocab(self): + if (self.dir_model / "tokenizer.json").is_file(): + self._set_vocab_gpt2() + else: + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True) + + # 1. Get the pre-tokenizer identifier hash + tokpre = self.get_vocab_base_pre(tokenizer) + + # 2. Reverse-engineer the merges list from mergeable_ranks + merges = [] + vocab = {} + mergeable_ranks = tokenizer.mergeable_ranks + for token, rank in mergeable_ranks.items(): + vocab[QwenModel.token_bytes_to_string(token)] = rank + if len(token) == 1: + continue + merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank) + if len(merged) == 2: + merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged))) + + # 3. Generate the tokens and toktypes lists + vocab_size = self.hparams["vocab_size"] + assert tokenizer.vocab_size == vocab_size + special_tokens = tokenizer.special_tokens + reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()} + tokens: list[str] = [] + toktypes: list[int] = [] + for i in range(vocab_size): + if i not in reverse_vocab: + tokens.append(f"[PAD{i}]") + toktypes.append(gguf.TokenType.UNUSED) + else: + token = reverse_vocab[i] + tokens.append(token) + if i in special_tokens.values(): + toktypes.append(gguf.TokenType.CONTROL) + else: + toktypes.append(gguf.TokenType.NORMAL) + + # 4. Write all vocab-related fields to the GGUF writer + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_token_merges(merges) + + # 5. Add special tokens and chat templates + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False) + special_vocab.add_to_gguf(self.gguf_writer) + # FIX for BOS token: Overwrite incorrect id read from config.json + if self.hparams['hidden_size'] == 4096: + self.gguf_writer.add_bos_token_id(127958) # only for 7b dense, fix <|bos|> token + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + + # Rope + rope_scaling = hparams.get("rope_scaling", {}) + if rope_scaling.get("type") == "dynamic": + # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ + # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf) + alpha = rope_scaling.get("alpha", 50) + base = hparams.get("rope_theta", 10000.0) + dim = hparams["head_dim"] + scaled_base = base * (alpha ** (dim / (dim - 2))) + self.gguf_writer.add_rope_freq_base(scaled_base) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + self.gguf_writer.add_rope_scaling_factor(1) + # There is no consistent way to calculate ctx from alpha, and the config is incorrectly set to 32k + self.gguf_writer.add_rope_scaling_orig_ctx_len(256 * 1024) # 256k context length + self.gguf_writer.add_context_length(256 * 1024) # 256k context length + + # if any of our assumptions about the values are wrong, something has changed and this may need to be updated + assert base == 10000.0 and self.hparams["max_position_embeddings"] in [32 * 1024, 256 * 1024] , \ + "HunYuan dynamic RoPE scaling assumptions changed, please update the logic or context length manually" + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if name == "lm_head.weight": + if self.hparams.get("tie_word_embeddings", False): + logger.info("Skipping tied output layer 'lm_head.weight'") + return [] + + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("SmolLM3ForCausalLM") +class SmolLM3Model(LlamaModel): + model_arch = gguf.MODEL_ARCH.SMOLLM3 + + def set_vocab(self): + super().set_vocab() + # remove unsupported array slicing in chat template + # ref: https://huggingface.co/ggml-org/SmolLM3-3B-GGUF/discussions/1 + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model) + if tokenizer.chat_template is not None: + chat_template = tokenizer.chat_template.replace("[:]", "") + self.gguf_writer.add_chat_template(chat_template) + + +@ModelBase.register("Lfm2ForCausalLM") +@ModelBase.register("LFM2ForCausalLM") +class LFM2Model(TextModel): + model_arch = gguf.MODEL_ARCH.LFM2 + + def _add_feed_forward_length(self): + ff_dim = self.hparams["block_ff_dim"] + + auto_adjust_ff_dim = self.hparams["block_auto_adjust_ff_dim"] + ff_dim = self.hparams["block_ff_dim"] + ffn_dim_multiplier = self.hparams["block_ffn_dim_multiplier"] + multiple_of = self.hparams["block_multiple_of"] + + if auto_adjust_ff_dim: + ff_dim = int(2 * ff_dim / 3) + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + ff_dim = int(ffn_dim_multiplier * ff_dim) + ff_dim = multiple_of * ((ff_dim + multiple_of - 1) // multiple_of) + + self.gguf_writer.add_feed_forward_length(ff_dim) + + def set_gguf_parameters(self): + # set num_key_value_heads only for attention layers + self.hparams["num_key_value_heads"] = [ + self.hparams["num_key_value_heads"] if layer_type == "full_attention" else 0 + for layer_type in self.hparams["layer_types"] + ] + + super().set_gguf_parameters() + self.gguf_writer.add_vocab_size(self.hparams["vocab_size"]) + self.gguf_writer.add_shortconv_l_cache(self.hparams["conv_L_cache"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["norm_eps"]) + self._add_feed_forward_length() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # conv op requires 2d tensor + if 'conv.conv' in name: + data_torch = data_torch.squeeze(1) + + return [(self.map_tensor_name(name), data_torch)] + + +@ModelBase.register("SmallThinkerForCausalLM") +class SmallThinkerModel(TextModel): + model_arch = gguf.MODEL_ARCH.SMALLTHINKER + + def set_gguf_parameters(self): + super().set_gguf_parameters() + if (n_experts := self.hparams.get("num_experts", self.hparams.get("moe_num_primary_experts"))) is not None: + self.gguf_writer.add_expert_count(n_experts) + if (n_experts_used := self.hparams.get("num_experts_per_tok", self.hparams.get("moe_num_active_primary_experts"))) is not None: + self.gguf_writer.add_expert_used_count(n_experts_used) + if (moe_intermediate_size := self.hparams.get("moe_ffn_hidden_size")) is not None: + self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size) + self.gguf_writer.add_feed_forward_length(moe_intermediate_size) + logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}") + if (self.hparams.get('moe_primary_router_apply_softmax')): + self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX) + else: + self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID) + # YaRN is not enabled by default + # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) + + sliding_window_layout = self.hparams.get("sliding_window_layout") + if sliding_window_layout: + for i in sliding_window_layout: + if i != 0: + sliding_window = self.hparams.get("sliding_window_size") + if sliding_window: + self.gguf_writer.add_sliding_window(sliding_window) + break + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # process the experts separately + if name.find("experts") != -1: + n_experts = self.hparams.get("num_experts", self.hparams.get("moe_num_primary_experts")) + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["down", "gate", "up"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + +###### CONVERSION LOGIC ###### + + +# tree of lazy tensors +class LazyTorchTensor(gguf.LazyBase): + _tensor_type = torch.Tensor + # to keep the type-checker happy + dtype: torch.dtype + shape: torch.Size + + # only used when converting a torch.Tensor to a np.ndarray + _dtype_map: dict[torch.dtype, type] = { + torch.float16: np.float16, + torch.float32: np.float32, + } + + # used for safetensors slices + # ref: https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/src/lib.rs#L1046 + # TODO: uncomment U64, U32, and U16, ref: https://github.com/pytorch/pytorch/issues/58734 + _dtype_str_map: dict[str, torch.dtype] = { + "F64": torch.float64, + "F32": torch.float32, + "BF16": torch.bfloat16, + "F16": torch.float16, + # "U64": torch.uint64, + "I64": torch.int64, + # "U32": torch.uint32, + "I32": torch.int32, + # "U16": torch.uint16, + "I16": torch.int16, + "U8": torch.uint8, + "I8": torch.int8, + "BOOL": torch.bool, + "F8_E4M3": torch.float8_e4m3fn, + "F8_E5M2": torch.float8_e5m2, + } + + def numpy(self) -> gguf.LazyNumpyTensor: + dtype = self._dtype_map[self.dtype] + return gguf.LazyNumpyTensor( + meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape), + args=(self,), + func=(lambda s: s.numpy()) + ) + + @classmethod + def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: tuple[int, ...]) -> Tensor: + return torch.empty(size=shape, dtype=dtype, device="meta") + + @classmethod + def from_safetensors_slice(cls, st_slice: Any) -> Tensor: + dtype = cls._dtype_str_map[st_slice.get_dtype()] + shape: tuple[int, ...] = tuple(st_slice.get_shape()) + lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(st_slice,), func=lambda s: s[:]) + return cast(torch.Tensor, lazy) + + @classmethod + def from_remote_tensor(cls, remote_tensor: gguf.utility.RemoteTensor): + dtype = cls._dtype_str_map[remote_tensor.dtype] + shape = remote_tensor.shape + meta = cls.meta_with_dtype_and_shape(dtype, shape) + lazy = cls(meta=meta, args=(remote_tensor,), func=lambda r: torch.frombuffer(r.data(), dtype=dtype).reshape(shape)) + return cast(torch.Tensor, lazy) + + @classmethod + def __torch_function__(cls, func, types, args=(), kwargs=None): + del types # unused + + if kwargs is None: + kwargs = {} + + if func is torch.Tensor.numpy: + return args[0].numpy() + + return cls._wrap_fn(func)(*args, **kwargs) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Convert a huggingface model to a GGML compatible file") + parser.add_argument( + "--vocab-only", action="store_true", + help="extract only the vocab", + ) + parser.add_argument( + "--outfile", type=Path, + help="path to write to; default: based on input. {ftype} will be replaced by the outtype.", + ) + parser.add_argument( + "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "tq1_0", "tq2_0", "auto"], default="f16", + help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, tq1_0 or tq2_0 for ternary, and auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type", + ) + parser.add_argument( + "--bigendian", action="store_true", + help="model is executed on big endian machine", + ) + parser.add_argument( + "model", type=str, + help="directory containing model file or huggingface repository ID (if --remote)", + nargs="?", + ) + parser.add_argument( + "--use-temp-file", action="store_true", + help="use the tempfile library while processing (helpful when running out of memory, process killed)", + ) + parser.add_argument( + "--no-lazy", action="store_true", + help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)", + ) + parser.add_argument( + "--model-name", type=str, default=None, + help="name of the model", + ) + parser.add_argument( + "--verbose", action="store_true", + help="increase output verbosity", + ) + parser.add_argument( + "--split-max-tensors", type=int, default=0, + help="max tensors in each split", + ) + parser.add_argument( + "--split-max-size", type=str, default="0", + help="max size per split N(M|G)", + ) + parser.add_argument( + "--dry-run", action="store_true", + help="only print out a split plan and exit, without writing any new files", + ) + parser.add_argument( + "--no-tensor-first-split", action="store_true", + help="do not add tensors to the first split (disabled by default)" + ) + parser.add_argument( + "--metadata", type=Path, + help="Specify the path for an authorship metadata override file" + ) + parser.add_argument( + "--print-supported-models", action="store_true", + help="Print the supported models" + ) + parser.add_argument( + "--remote", action="store_true", + help="(Experimental) Read safetensors file remotely without downloading to disk. Config and tokenizer files will still be downloaded. To use this feature, you need to specify Hugging Face model repo name instead of a local directory. For example: 'HuggingFaceTB/SmolLM2-1.7B-Instruct'. Note: To access gated repo, set HF_TOKEN environment variable to your Hugging Face token.", + ) + parser.add_argument( + "--mmproj", action="store_true", + help="(Experimental) Export multimodal projector (mmproj) for vision models. This will only work on some vision models. A prefix 'mmproj-' will be added to the output file name.", + ) + + args = parser.parse_args() + if not args.print_supported_models and args.model is None: + parser.error("the following arguments are required: model") + return args + + +def split_str_to_n_bytes(split_str: str) -> int: + if split_str.endswith("K"): + n = int(split_str[:-1]) * 1000 + elif split_str.endswith("M"): + n = int(split_str[:-1]) * 1000 * 1000 + elif split_str.endswith("G"): + n = int(split_str[:-1]) * 1000 * 1000 * 1000 + elif split_str.isnumeric(): + n = int(split_str) + else: + raise ValueError(f"Invalid split size: {split_str}, must be a number, optionally followed by K, M, or G") + + if n < 0: + raise ValueError(f"Invalid split size: {split_str}, must be positive") + + return n + + +def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> str: + # TODO @ngxson : this won't work correctly if the model has both audio & vision encoders + # maybe we should fallback to text model's arch in that case, since not many models have both + text_config = hparams.get("text_config", {}) + vision_config = hparams.get("vision_config", {}) + arch = None + if (arches := hparams.get("architectures")) is not None and len(arches) > 0: + arch = arches[0] + elif "ssm_cfg" in hparams: + # For non-hf Mamba and Mamba2 models + arch = hparams["ssm_cfg"].get("layer", "Mamba") + "ForCausalLM" + + # if "architectures" is found in the sub-config, use that instead + if model_type == ModelType.TEXT and text_config.get("architectures") is not None: + arch = text_config["architectures"][0] + elif model_type == ModelType.MMPROJ and vision_config.get("architectures") is not None: + arch = vision_config["architectures"][0] + if arch is None: + raise ValueError("Failed to detect model architecture") + return arch + + +def main() -> None: + args = parse_args() + + if args.print_supported_models: + logger.error("Supported models:") + ModelBase.print_registered_models() + sys.exit(0) + + if args.verbose: + logging.basicConfig(level=logging.DEBUG) + else: + logging.basicConfig(level=logging.INFO) + + if args.remote: + hf_repo_id = args.model + from huggingface_hub import snapshot_download + local_dir = snapshot_download( + repo_id=hf_repo_id, + allow_patterns=["LICENSE", "*.json", "*.md", "*.txt", "tokenizer.model"]) + dir_model = Path(local_dir) + logger.info(f"Downloaded config and tokenizer to {local_dir}") + else: + hf_repo_id = None + dir_model = Path(args.model) + + if not dir_model.is_dir(): + logger.error(f'Error: {dir_model} is not a directory') + sys.exit(1) + + ftype_map: dict[str, gguf.LlamaFileType] = { + "f32": gguf.LlamaFileType.ALL_F32, + "f16": gguf.LlamaFileType.MOSTLY_F16, + "bf16": gguf.LlamaFileType.MOSTLY_BF16, + "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0, + "tq1_0": gguf.LlamaFileType.MOSTLY_TQ1_0, + "tq2_0": gguf.LlamaFileType.MOSTLY_TQ2_0, + "auto": gguf.LlamaFileType.GUESSED, + } + + is_split = args.split_max_tensors > 0 or args.split_max_size != "0" + if args.use_temp_file and is_split: + logger.error("Error: Cannot use temp file when splitting") + sys.exit(1) + + if args.outfile is not None: + fname_out = args.outfile + elif hf_repo_id: + # if remote, use the model ID as the output file name + fname_out = Path("./" + hf_repo_id.replace("/", "-") + "-{ftype}.gguf") + else: + fname_out = dir_model + + logger.info(f"Loading model: {dir_model.name}") + + if args.mmproj: + if "mmproj" not in fname_out.name: + fname_out = ModelBase.add_prefix_to_filename(fname_out, "mmproj-") + + with torch.inference_mode(): + output_type = ftype_map[args.outtype] + model_type = ModelType.MMPROJ if args.mmproj else ModelType.TEXT + hparams = ModelBase.load_hparams(dir_model) + model_architecture = get_model_architecture(hparams, model_type) + logger.info(f"Model architecture: {model_architecture}") + try: + model_class = ModelBase.from_model_architecture(model_architecture, model_type=model_type) + except NotImplementedError: + logger.error(f"Model {model_architecture} is not supported") + sys.exit(1) + + model_instance = model_class(dir_model, output_type, fname_out, + is_big_endian=args.bigendian, use_temp_file=args.use_temp_file, + eager=args.no_lazy, + metadata_override=args.metadata, model_name=args.model_name, + split_max_tensors=args.split_max_tensors, + split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run, + small_first_shard=args.no_tensor_first_split, + remote_hf_model_id=hf_repo_id) + + if args.vocab_only: + logger.info("Exporting model vocab...") + model_instance.write_vocab() + logger.info(f"Model vocab successfully exported to {model_instance.fname_out}") + else: + logger.info("Exporting model...") + model_instance.write() + out_path = f"{model_instance.fname_out.parent}{os.sep}" if is_split else model_instance.fname_out + logger.info(f"Model successfully exported to {out_path}") + + +if __name__ == '__main__': + main() diff --git a/llama.cpp/convert_hf_to_gguf_update.py b/llama.cpp/convert_hf_to_gguf_update.py new file mode 100644 index 0000000000000000000000000000000000000000..1c357d959afe69ea8d38a77eccb1d912ddd5e753 --- /dev/null +++ b/llama.cpp/convert_hf_to_gguf_update.py @@ -0,0 +1,464 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import logging +import os +import pathlib +import re + +import requests +import json +import shutil +import argparse + +from hashlib import sha256 +from enum import IntEnum, auto +from transformers import AutoTokenizer + +logging.basicConfig(level=logging.DEBUG) +logger = logging.getLogger("convert_hf_to_gguf_update") +sess = requests.Session() + +convert_py_pth = pathlib.Path("convert_hf_to_gguf.py") +convert_py = convert_py_pth.read_text(encoding="utf-8") +hf_token_pth = pathlib.Path.home() / ".cache" / "huggingface" / "token" +hf_token = hf_token_pth.read_text(encoding="utf-8").strip() if hf_token_pth.exists() else None + + +class TOKENIZER_TYPE(IntEnum): + SPM = auto() + BPE = auto() + WPM = auto() + UGM = auto() + + +DOC_STRING = """ +This script downloads the tokenizer models of the specified models from Huggingface and +generates the get_vocab_base_pre() function for convert_hf_to_gguf.py + +/!\\ It is intended to be used by contributors and is not meant to be run by end users + +This is necessary in order to analyze the type of pre-tokenizer used by the model and +provide the necessary information to llama.cpp via the GGUF header in order to implement +the same pre-tokenizer. + +ref: https://github.com/ggml-org/llama.cpp/pull/6920 + +Instructions: + +- Add a new model to the "models" list +- Run the script with your huggingface token + By default, token will be read from ~/.cache/huggingface/token +- The convert_hf_to_gguf.py script will have had its get_vocab_base_pre() function updated +- Update llama.cpp with the new pre-tokenizer if necessary +""" +# TODO: generate tokenizer tests for llama.cpp + +parser = argparse.ArgumentParser(description=DOC_STRING, formatter_class=argparse.RawTextHelpFormatter) +parser.add_argument( + "--full", action="store_true", + help="download full list of models - make sure you have access to all of them", +) +parser.add_argument( + "--check-missing", action="store_true", + help="only check for missing pre-tokenizer hashes", +) +parser.add_argument( + "hf_token", + help="optional HF token", + nargs="?", +) +args = parser.parse_args() +hf_token = args.hf_token if args.hf_token is not None else hf_token + +if hf_token is None: + logger.warning("HF token not found. You can provide it as an argument or set it in ~/.cache/huggingface/token") + +if args.check_missing and args.full: + logger.warning("Downloading full list of models requested, ignoring --check-missing!") + args.check_missing = False + +# TODO: this string has to exercise as much pre-tokenizer functionality as possible +# will be updated with time - contributions welcome +CHK_TXT = '\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL' + +# TODO: add models here, base models preferred +models = [ + {"name": "llama-spm", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", }, + {"name": "llama-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", }, + {"name": "phi-3", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", }, + {"name": "deepseek-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", }, + {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", }, + {"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", }, + {"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", }, + {"name": "falcon3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", }, + {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", }, + {"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, + {"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, + {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, + {"name": "stablelm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", }, + {"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", }, + {"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", }, + {"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", }, + {"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", }, + {"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", }, + {"name": "jina-v1-en", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-reranker-v1-tiny-en", }, + {"name": "jina-v2-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM! + {"name": "jina-v2-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", }, + {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", }, + {"name": "smaug-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", }, + {"name": "poro-chat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", }, + {"name": "jina-v2-code", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", }, + {"name": "viking", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B + {"name": "gemma", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", }, + {"name": "gemma-2", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", }, + {"name": "jais", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", }, + {"name": "t5", "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", }, + {"name": "codeshell", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/WisdomShell/CodeShell-7B", }, + {"name": "tekken", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", }, + {"name": "smollm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", }, + {'name': "bloom", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigscience/bloom", }, + {'name': "gpt3-finnish", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", }, + {"name": "exaone", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", }, + {"name": "phi-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", }, + {"name": "chameleon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", }, + {"name": "roberta-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"}, + {"name": "gigachat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"}, + {"name": "megrez", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"}, + {"name": "deepseek-v3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"}, + {"name": "deepseek-r1-qwen", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"}, + {"name": "gpt-4o", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Xenova/gpt-4o", }, + {"name": "superbpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/UW/OLMo2-8B-SuperBPE-t180k", }, + {"name": "trillion", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/trillionlabs/Trillion-7B-preview", }, + {"name": "bailingmoe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-lite", }, + {"name": "llama4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct", }, + {"name": "pixtral", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistral-community/pixtral-12b", }, + {"name": "seed-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base", }, + {"name": "a.x-4.0", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/skt/A.X-4.0", }, + {"name": "midm-2.0", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct", }, + {"name": "lfm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"}, + {"name": "exaone4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", }, +] + +# some models are known to be broken upstream, so we will skip them as exceptions +pre_computed_hashes = [ + # chatglm-bpe has 2 hashes, why? + {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b"}, + {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516"}, + {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", "chkhsh": "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2"}, + {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"}, + {"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"}, + {"name": "hunyuan-dense", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-4B-Instruct", "chkhsh": "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6"}, + # falcon-h1 series uses 4 different tokenizers across model sizes (0.5b - 34b), hence we need to define 4 different hashes + {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base", "chkhsh": "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6"}, + {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-1B-Base", "chkhsh": "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86"}, + {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-7B-Base", "chkhsh": "3eda48b4c4dc7de733d1a8b3e3b4a85243dbbf704da2ee9d42c6beced8897896"}, + {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-34B-Base", "chkhsh": "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b"}, + {"name": "kimi-k2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base", "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890"}, + {"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "chkhsh": "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c"}, +] + + +def download_file_with_auth(url, token, save_path): + headers = {"Authorization": f"Bearer {token}"} if token else None + response = sess.get(url, headers=headers) + response.raise_for_status() + os.makedirs(os.path.dirname(save_path), exist_ok=True) + with open(save_path, 'wb') as downloaded_file: + downloaded_file.write(response.content) + logger.info(f"File {save_path} downloaded successfully") + + +def download_model(model): + name = model["name"] + repo = model["repo"] + tokt = model["tokt"] + + os.makedirs(f"models/tokenizers/{name}", exist_ok=True) + + files = ["config.json", "tokenizer.json", "tokenizer_config.json"] + + if name == "gpt-4o": + # Xenova/gpt-4o is tokenizer-only, it does not contain config.json + files = ["tokenizer.json", "tokenizer_config.json"] + + if tokt == TOKENIZER_TYPE.SPM: + files.append("tokenizer.model") + + if tokt == TOKENIZER_TYPE.UGM: + files.append("spiece.model") + + if os.path.isdir(repo): + # If repo is a path on the file system, copy the directory + for file in files: + src_path = os.path.join(repo, file) + dst_path = f"models/tokenizers/{name}/{file}" + if os.path.isfile(dst_path): + logger.info(f"{name}: File {dst_path} already exists - skipping") + continue + if os.path.isfile(src_path): + shutil.copy2(src_path, dst_path) + logger.info(f"{name}: Copied {src_path} to {dst_path}") + else: + logger.warning(f"{name}: Source file {src_path} does not exist") + else: + # If repo is a URL, download the files + for file in files: + save_path = f"models/tokenizers/{name}/{file}" + if os.path.isfile(save_path): + logger.info(f"{name}: File {save_path} already exists - skipping") + continue + download_file_with_auth(f"{repo}/resolve/main/{file}", hf_token, save_path) + + +# get list of existing models and chkhsh from the convert_hf_to_gguf.py file +# returns mapping res --> chkhsh +def get_existing_models(convert_py): + pattern = r'if chkhsh == "([a-f0-9]{64})":\s*\n\s*.*\s*res = "([^"]+)"' + matches = re.findall(pattern, convert_py) + output = {} + for chkhsh, res in matches: + output[res] = chkhsh + return output + + +existing_models = {} +all_models = models.copy() +if not args.full: + # Filter out models that already exist in convert_hf_to_gguf.py + existing_models = get_existing_models(convert_py) + all_models = models.copy() + models = [model for model in all_models if model["name"] not in existing_models] + +if not args.check_missing: + logging.info(f"Downloading {len(models)} models...") + for model in models: + try: + download_model(model) + except Exception as e: + logger.error(f"Failed to download model {model['name']}. Error: {e}") + + +# generate the source code for the convert_hf_to_gguf.py:get_vocab_base_pre() function: + +src_ifs = "" +for model in [*pre_computed_hashes, *all_models]: + name = model["name"] + tokt = model["tokt"] + chkhsh = model.get("chkhsh") + + if tokt == TOKENIZER_TYPE.SPM or tokt == TOKENIZER_TYPE.UGM: + continue + + # create the tokenizer + if chkhsh is not None: + # if the model has a pre-computed hash, use it + logger.info(f"Using pre-computed hash for model {name}: {chkhsh}") + elif name in existing_models: + # if the model already exists in convert_hf_to_gguf.py, skip compute hash + chkhsh = existing_models[name] + else: + # otherwise, compute the hash of the tokenizer + + # Fail if the tokenizer folder with config does not exist or there are other download issues previously + if not os.path.isfile(f"models/tokenizers/{name}/tokenizer_config.json"): + raise OSError(f"Config for tokenizer {name} not found. The model may not exist or is not accessible with the provided token.") + + try: + logger.info(f"Loading tokenizer from {f'models/tokenizers/{name}'}...") + if name == "t5": + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False) + else: + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + except Exception as e: + raise OSError(f"Error loading tokenizer for model {name}.") from e + + chktok = tokenizer.encode(CHK_TXT) + chkhsh = sha256(str(chktok).encode()).hexdigest() + + logger.info(f"model: {name}") + logger.info(f"tokt: {tokt}") + logger.info(f"repo: {model['repo']}") + logger.info(f"chktok: {chktok}") + logger.info(f"chkhsh: {chkhsh}") + + # print the "pre_tokenizer" content from the tokenizer.json + with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f: + cfg = json.load(f) + normalizer = cfg["normalizer"] + logger.info("normalizer: " + json.dumps(normalizer, indent=4)) + pre_tokenizer = cfg["pre_tokenizer"] + logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4)) + if "ignore_merges" in cfg["model"]: + logger.info("ignore_merges: " + json.dumps(cfg["model"]["ignore_merges"], indent=4)) + + logger.info("") + + src_ifs += f" if chkhsh == \"{chkhsh}\":\n" + src_ifs += f" # ref: {model['repo']}\n" + src_ifs += f" res = \"{name}\"\n" + +src_func = f""" + def get_vocab_base_pre(self, tokenizer) -> str: + # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that + # is specific for the BPE pre-tokenizer used by the model + # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can + # use in llama.cpp to implement the same pre-tokenizer + + chktxt = {repr(CHK_TXT)} + + chktok = tokenizer.encode(chktxt) + chkhsh = sha256(str(chktok).encode()).hexdigest() + + logger.debug(f"chktok: {{chktok}}") + logger.debug(f"chkhsh: {{chkhsh}}") + + res = None + + # NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script + # or pull the latest version of the model from Huggingface + # don't edit the hashes manually! +{src_ifs} + if res is None: + logger.warning("\\n") + logger.warning("**************************************************************************************") + logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!") + logger.warning("** There are 2 possible reasons for this:") + logger.warning("** - the model has not been added to convert_hf_to_gguf_update.py yet") + logger.warning("** - the pre-tokenization config has changed upstream") + logger.warning("** Check your model files and convert_hf_to_gguf_update.py and update them accordingly.") + logger.warning("** ref: https://github.com/ggml-org/llama.cpp/pull/6920") + logger.warning("**") + logger.warning(f"** chkhsh: {{chkhsh}}") + logger.warning("**************************************************************************************") + logger.warning("\\n") + raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()") + + logger.debug(f"tokenizer.ggml.pre: {{repr(res)}}") + logger.debug(f"chkhsh: {{chkhsh}}") + + return res +""" + +convert_py = re.sub( + r"(# Marker: Start get_vocab_base_pre)(.+?)( +# Marker: End get_vocab_base_pre)", + lambda m: m.group(1) + src_func + m.group(3), + convert_py, + flags=re.DOTALL | re.MULTILINE, +) + +convert_py_pth.write_text(convert_py, encoding="utf-8") + +logger.info("+++ convert_hf_to_gguf.py was updated") + +# generate tests for each tokenizer model + +tests = [ + "ied 4 ½ months", + "Äpfel", + "", + " ", + " ", + " ", + "\t", + "\n", + "\n\n", + "\n\n\n", + "\t\n", + "Hello world", + " Hello world", + "Hello World", + " Hello World", + " Hello World!", + "Hello, world!", + " Hello, world!", + " this is 🦙.cpp", + "w048 7tuijk dsdfhu", + "нещо на Български", + "កាន់តែពិសេសអាចខលចេញ", + "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", + "Hello", + " Hello", + " Hello", + " Hello", + " Hello", + " Hello\n Hello", + " (", + "\n =", + "' era", + "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～", + "!!!!!!", + "3", + "33", + "333", + "3333", + "33333", + "333333", + "3333333", + "33333333", + "333333333", + "Cửa Việt", # llama-bpe fails on this + " discards", + CHK_TXT, +] + +# write the tests to ./models/ggml-vocab-{name}.gguf.inp +# the format is: +# +# test0 +# __ggml_vocab_test__ +# test1 +# __ggml_vocab_test__ +# ... +# + +# with each model, encode all tests and write the results in ./models/ggml-vocab-{name}.gguf.out +# for each test, write the resulting tokens on a separate line + +for model in models: + name = model["name"] + tokt = model["tokt"] + + # Skip if the tokenizer folder does not exist or there are other download issues previously + if not os.path.exists(f"models/tokenizers/{name}"): + logger.warning(f"Directory for tokenizer {name} not found. Skipping...") + continue + + # create the tokenizer + try: + if name == "t5": + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False) + else: + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + except OSError as e: + logger.error(f"Failed to load tokenizer for model {name}. Error: {e}") + continue # Skip this model and continue with the next one in the loop + + if not os.path.exists(f"models/ggml-vocab-{name}.gguf"): + logger.info(f"Skip vocab files for model {name}, no GGUF file found") + continue + + with open(f"models/ggml-vocab-{name}.gguf.inp", "w", encoding="utf-8") as f: + for text in tests: + f.write(f"{text}") + f.write("\n__ggml_vocab_test__\n") + + with open(f"models/ggml-vocab-{name}.gguf.out", "w") as f: + for text in tests: + res = tokenizer.encode(text, add_special_tokens=False) + for r in res: + f.write(f" {r}") + f.write("\n") + + logger.info(f"Tests for {name} written in ./models/ggml-vocab-{name}.gguf.*") + +# generate commands for creating vocab files + +logger.info("\nRun the following commands to generate the vocab files for testing:\n") + +for model in models: + name = model["name"] + + print(f"python3 convert_hf_to_gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100 + +logger.info("\n") diff --git a/llama.cpp/convert_llama_ggml_to_gguf.py b/llama.cpp/convert_llama_ggml_to_gguf.py new file mode 100644 index 0000000000000000000000000000000000000000..717eee8ce813b90a1e6ce0408b07e5f1043f8fdf --- /dev/null +++ b/llama.cpp/convert_llama_ggml_to_gguf.py @@ -0,0 +1,450 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import logging +import argparse +import os +import struct +import sys +from enum import IntEnum +from pathlib import Path + +import numpy as np + +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) +import gguf + +logger = logging.getLogger("ggml-to-gguf") + + +class GGMLFormat(IntEnum): + GGML = 0 + GGMF = 1 + GGJT = 2 + + +class GGMLFType(IntEnum): + ALL_F32 = 0 + MOSTLY_F16 = 1 + MOSTLY_Q4_0 = 2 + MOSTLY_Q4_1 = 3 + MOSTLY_Q4_1_SOME_F16 = 4 + MOSTLY_Q8_0 = 7 + MOSTLY_Q5_0 = 8 + MOSTLY_Q5_1 = 9 + MOSTLY_Q2_K = 10 + MOSTLY_Q3_K_S = 11 + MOSTLY_Q3_K_M = 12 + MOSTLY_Q3_K_L = 13 + MOSTLY_Q4_K_S = 14 + MOSTLY_Q4_K_M = 15 + MOSTLY_Q5_K_S = 16 + MOSTLY_Q5_K_M = 17 + MOSTLY_Q6_K = 18 + + +class Hyperparameters: + def __init__(self): + self.n_vocab = self.n_embd = self.n_mult = self.n_head = 0 + self.n_layer = self.n_rot = self.n_ff = 0 + self.ftype = GGMLFType.ALL_F32 + + def set_n_ff(self, model): + ff_tensor_idx = model.tensor_map.get(b'layers.0.feed_forward.w1.weight') + assert ff_tensor_idx is not None, 'Missing layer 0 FF tensor' + ff_tensor = model.tensors[ff_tensor_idx] + self.n_ff = ff_tensor.dims[1] + + def load(self, data, offset): + ( + self.n_vocab, + self.n_embd, + self.n_mult, + self.n_head, + self.n_layer, + self.n_rot, + ftype, + ) = struct.unpack('<7I', data[offset:offset + (4 * 7)]) + try: + self.ftype = GGMLFType(ftype) + except ValueError: + raise ValueError(f'Invalid ftype {ftype}') + return 4 * 7 + + def __str__(self): + return f'' + + +class Vocab: + def __init__(self, load_scores = True): + self.items = [] + self.load_scores = load_scores + + def load(self, data, offset, n_vocab): + orig_offset = offset + for _ in range(n_vocab): + itemlen = struct.unpack('= 0 and n_dims <= 4, f'Invalid tensor dimensions {n_dims}' + assert name_len < 4096, 'Absurd tensor name length' + quant = gguf.GGML_QUANT_SIZES.get(dtype) + assert quant is not None, 'Unknown tensor type' + (blksize, tysize) = quant + offset += 12 + self.dtype= gguf.GGMLQuantizationType(dtype) + self.dims = struct.unpack(f'<{n_dims}I', data[offset:offset + (4 * n_dims)]) + offset += 4 * n_dims + self.name = bytes(data[offset:offset + name_len]) + offset += name_len + pad = ((offset + 31) & ~31) - offset if self.use_padding else 0 + offset += pad + n_elems = np.prod(self.dims) + n_bytes = np.int64(np.int64(n_elems) * np.int64(tysize)) // np.int64(blksize) + self.start_offset = offset + self.len_bytes = n_bytes + offset += n_bytes + return offset - orig_offset + + +class GGMLModel: + + file_format: GGMLFormat + format_version: int + + def __init__(self): + self.hyperparameters = None + self.vocab = None + self.tensor_map = {} + self.tensors = [] + + def validate_header(self, data, offset): + magic = bytes(data[offset:offset + 4]) + if magic == b'GGUF': + raise ValueError('File is already in GGUF format.') + if magic == b'lmgg': + self.file_format = GGMLFormat.GGML + self.format_version = 1 + return 4 + version = struct.unpack(' 3: + raise ValueError(f'Cannot handle unexpected GGJT file version {version}') + self.file_format = GGMLFormat.GGJT + self.format_version = version + return 8 + raise ValueError(f"Unexpected file magic {magic!r}! This doesn't look like a GGML format file.") + + def validate_conversion(self, ftype): + err = '' + if (self.file_format < GGMLFormat.GGJT or self.format_version < 2): + if ftype not in (GGMLFType.ALL_F32, GGMLFType.MOSTLY_F16): + err = 'Quantizations changed in GGJTv2. Can only convert unquantized GGML files older than GGJTv2.' + elif (self.file_format == GGMLFormat.GGJT and self.format_version == 2): + if ftype in (GGMLFType.MOSTLY_Q4_0, GGMLFType.MOSTLY_Q4_1, + GGMLFType.MOSTLY_Q4_1_SOME_F16, GGMLFType.MOSTLY_Q8_0): + err = 'Q4 and Q8 quantizations changed in GGJTv3.' + if len(err) > 0: + raise ValueError(f'{err} Sorry, your {self.file_format.name}v{self.format_version} file of type {ftype.name} is not eligible for conversion.') + + def load(self, data, offset): + offset += self.validate_header(data, offset) + hp = Hyperparameters() + offset += hp.load(data, offset) + logger.info(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}') + self.validate_conversion(hp.ftype) + vocab = Vocab(load_scores = self.file_format > GGMLFormat.GGML) + offset += vocab.load(data, offset, hp.n_vocab) + tensors: list[Tensor] = [] + tensor_map = {} + while offset < len(data): + tensor = Tensor(use_padding = self.file_format > GGMLFormat.GGMF) + offset += tensor.load(data, offset) + tensor_map[tensor.name] = len(tensors) + tensors.append(tensor) + self.hyperparameters = hp + self.vocab = vocab + self.tensors = tensors + self.tensor_map = tensor_map + hp.set_n_ff(self) + return offset + + +class GGMLToGGUF: + def __init__(self, ggml_model, data, cfg, params_override = None, vocab_override = None, special_vocab = None): + hp = ggml_model.hyperparameters + self.model = ggml_model + self.data = data + self.cfg = cfg + self.params_override = params_override + self.vocab_override = vocab_override + self.special_vocab = special_vocab + if params_override is not None: + n_kv_head = params_override.n_head_kv + else: + if cfg.gqa == 1: + n_kv_head = hp.n_head + else: + gqa = float(cfg.gqa) + n_kv_head = None + for x in range(1, 256): + if float(hp.n_head) / float(x) == gqa: + n_kv_head = x + assert n_kv_head is not None, "Couldn't determine n_kv_head from GQA param" + logger.info(f'- Guessed n_kv_head = {n_kv_head} based on GQA {cfg.gqa}') + self.n_kv_head = n_kv_head + self.name_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.LLAMA, ggml_model.hyperparameters.n_layer) + + def save(self): + logger.info('* Preparing to save GGUF file') + gguf_writer = gguf.GGUFWriter( + self.cfg.output, + gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], + use_temp_file = False) + self.add_params(gguf_writer) + self.add_vocab(gguf_writer) + if self.special_vocab is not None: + self.special_vocab.add_to_gguf(gguf_writer) + self.add_tensors(gguf_writer) + logger.info(" gguf: write header") + gguf_writer.write_header_to_file() + logger.info(" gguf: write metadata") + gguf_writer.write_kv_data_to_file() + logger.info(" gguf: write tensors") + gguf_writer.write_tensors_to_file() + gguf_writer.close() + + def add_params(self, gguf_writer): + hp = self.model.hyperparameters + cfg = self.cfg + if cfg.desc is not None: + desc = cfg.desc + else: + desc = f'converted from legacy {self.model.file_format.name}v{self.model.format_version} {hp.ftype.name} format' + try: + # Filenames aren't necessarily valid UTF8. + name = cfg.name if cfg.name is not None else cfg.input.name + except UnicodeDecodeError: + name = None + logger.info('* Adding model parameters and KV items') + if name is not None: + gguf_writer.add_name(name) + gguf_writer.add_description(desc) + gguf_writer.add_file_type(int(hp.ftype)) + if self.params_override is not None: + po = self.params_override + assert po.n_embd == hp.n_embd, 'Model hyperparams mismatch' + assert po.n_layer == hp.n_layer, 'Model hyperparams mismatch' + assert po.n_head == hp.n_head, 'Model hyperparams mismatch' + gguf_writer.add_context_length (po.n_ctx) + gguf_writer.add_embedding_length (po.n_embd) + gguf_writer.add_block_count (po.n_layer) + gguf_writer.add_feed_forward_length (po.n_ff) + gguf_writer.add_rope_dimension_count(po.n_embd // po.n_head) + gguf_writer.add_head_count (po.n_head) + gguf_writer.add_head_count_kv (po.n_head_kv) + gguf_writer.add_layer_norm_rms_eps (po.f_norm_eps) + return + gguf_writer.add_context_length(cfg.context_length) + gguf_writer.add_embedding_length(hp.n_embd) + gguf_writer.add_block_count(hp.n_layer) + gguf_writer.add_feed_forward_length(hp.n_ff) + gguf_writer.add_rope_dimension_count(hp.n_embd // hp.n_head) + gguf_writer.add_head_count(hp.n_head) + gguf_writer.add_head_count_kv(self.n_kv_head) + gguf_writer.add_layer_norm_rms_eps(float(cfg.eps)) + + def add_vocab(self, gguf_writer): + hp = self.model.hyperparameters + gguf_writer.add_tokenizer_model('llama') + gguf_writer.add_tokenizer_pre('default') + tokens = [] + scores = [] + toktypes = [] + if self.vocab_override is not None: + vo = self.vocab_override + logger.info('* Adding vocab item(s)') + for (_, (vbytes, score, ttype)) in enumerate(vo.all_tokens()): + tokens.append(vbytes) + scores.append(score) + toktypes.append(ttype) + assert len(tokens) == hp.n_vocab, \ + f'Override vocab has a different number of items than hyperparameters - override = {len(tokens)} but n_vocab={hp.n_vocab}' + gguf_writer.add_token_list(tokens) + gguf_writer.add_token_scores(scores) + if len(toktypes) > 0: + gguf_writer.add_token_types(toktypes) + return + logger.info(f'* Adding {hp.n_vocab} vocab item(s)') + assert len(self.model.vocab.items) >= 3, 'Cannot handle unexpectedly short model vocab' + for (tokid, (vbytes, vscore)) in enumerate(self.model.vocab.items): + tt = 1 # Normal + # Special handling for UNK, BOS, EOS tokens. + if tokid <= 2: + if tokid == 0: + vbytes = b'' + tt = 2 + elif tokid == 1: + vbytes = b'' + tt = 3 + else: + vbytes = b'' + tt = 3 + elif len(vbytes) == 0: + tt = 3 # Control + elif tokid >= 3 and tokid <= 258 and len(vbytes) == 1: + vbytes = bytes(f'<0x{vbytes[0]:02X}>', encoding = 'UTF-8') + tt = 6 # Byte + else: + vbytes = vbytes.replace(b' ', b'\xe2\x96\x81') + toktypes.append(tt) + tokens.append(vbytes) + scores.append(vscore) + gguf_writer.add_token_list(tokens) + gguf_writer.add_token_scores(scores) + gguf_writer.add_token_types(toktypes) + gguf_writer.add_unk_token_id(0) + gguf_writer.add_bos_token_id(1) + gguf_writer.add_eos_token_id(2) + + def add_tensors(self, gguf_writer): + tensor_map = self.name_map + data = self.data + logger.info(f'* Adding {len(self.model.tensors)} tensor(s)') + for tensor in self.model.tensors: + name = str(tensor.name, 'UTF-8') + mapped_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) + assert mapped_name is not None, f'Bad name {name}' + tempdims = list(tensor.dims[:]) + if len(tempdims) > 1: + temp = tempdims[1] + tempdims[1] = tempdims[0] + tempdims[0] = temp + gguf_writer.add_tensor( + mapped_name, + data[tensor.start_offset:tensor.start_offset + tensor.len_bytes], + raw_shape = tempdims, + raw_dtype = tensor.dtype) + + +def handle_metadata(cfg, hp): + import examples.convert_legacy_llama as convert + + assert cfg.model_metadata_dir.is_dir(), 'Metadata dir is not a directory' + hf_config_path = cfg.model_metadata_dir / "config.json" + orig_config_path = cfg.model_metadata_dir / "params.json" + # We pass a fake model here. "original" mode will check the shapes of some + # tensors if information is missing in the .json file: other than that, the + # model data isn't used so this should be safe (at least for now). + fakemodel = { + 'tok_embeddings.weight': convert.LazyTensor.__new__(convert.LazyTensor), + 'layers.0.feed_forward.w1.weight': convert.LazyTensor.__new__(convert.LazyTensor), + } + fakemodel['tok_embeddings.weight'].shape = [hp.n_vocab] + fakemodel['layers.0.feed_forward.w1.weight'].shape = [hp.n_ff] + if hf_config_path.exists(): + params = convert.Params.loadHFTransformerJson(fakemodel, hf_config_path) + elif orig_config_path.exists(): + params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path) + else: + raise ValueError('Unable to load metadata') + vocab_path = Path(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir) + vocab_factory = convert.VocabFactory(vocab_path) + vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype.split(","), cfg.model_metadata_dir) + convert.check_vocab_size(params, vocab) + return params, vocab, special_vocab + + +def handle_args(): + parser = argparse.ArgumentParser(description = 'Convert GGML models to GGUF') + parser.add_argument('--input', '-i', type = Path, required = True, + help = 'Input GGMLv3 filename') + parser.add_argument('--output', '-o', type = Path, required = True, + help ='Output GGUF filename') + parser.add_argument('--name', + help = 'Set model name') + parser.add_argument('--desc', + help = 'Set model description') + parser.add_argument('--gqa', type = int, default = 1, + help = 'grouped-query attention factor (use 8 for LLaMA2 70B)') + parser.add_argument('--eps', default = '5.0e-06', + help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2') + parser.add_argument('--context-length', '-c', type=int, default = 2048, + help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096') + parser.add_argument('--model-metadata-dir', '-m', type = Path, + help ='Load HuggingFace/.pth vocab and metadata from the specified directory') + parser.add_argument("--vocab-dir", type=Path, + help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir") + parser.add_argument("--vocabtype", default="spm,hfft", + help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm,hfft)") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + return parser.parse_args() + + +def main(): + cfg = handle_args() + logging.basicConfig(level=logging.DEBUG if cfg.verbose else logging.INFO) + logger.info(f'* Using config: {cfg}') + logger.warning('=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===') + if cfg.model_metadata_dir is None and (cfg.gqa == 1 or cfg.eps == '5.0e-06'): + logger.info('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".') + data = np.memmap(cfg.input, mode = 'r') + model = GGMLModel() + logger.info('* Scanning GGML input file') + offset = model.load(data, 0) # noqa + logger.info(f'* GGML model hyperparameters: {model.hyperparameters}') + vocab_override = None + params_override = None + special_vocab = None + if cfg.model_metadata_dir is not None: + (params_override, vocab_override, special_vocab) = handle_metadata(cfg, model.hyperparameters) + logger.info('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.') + logger.info(f'* Overriding params: {params_override}') + logger.info(f'* Overriding vocab: {vocab_override}') + logger.info(f'* Special vocab: {special_vocab}') + else: + logger.warning('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n') + if model.file_format == GGMLFormat.GGML: + logger.info('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!') + converter = GGMLToGGUF( + model, data, cfg, + params_override = params_override, + vocab_override = vocab_override, + special_vocab = special_vocab + ) + converter.save() + logger.info(f'* Successful completion. Output saved to: {cfg.output}') + + +if __name__ == '__main__': + main() diff --git a/llama.cpp/convert_lora_to_gguf.py b/llama.cpp/convert_lora_to_gguf.py new file mode 100644 index 0000000000000000000000000000000000000000..944d9a4ea915c7e10befbc2de677119fc4b3f593 --- /dev/null +++ b/llama.cpp/convert_lora_to_gguf.py @@ -0,0 +1,461 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +from __future__ import annotations + +from dataclasses import dataclass +import logging +import argparse +import os +import sys +import json +from math import prod +from pathlib import Path +from typing import TYPE_CHECKING, Any, Callable, Iterable, Iterator, Sequence, SupportsIndex, cast +from transformers import AutoConfig + +import torch + +if TYPE_CHECKING: + from torch import Tensor + +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) +import gguf + +# reuse model definitions from convert_hf_to_gguf.py +from convert_hf_to_gguf import LazyTorchTensor, ModelBase + +logger = logging.getLogger("lora-to-gguf") + + +@dataclass +class PartialLoraTensor: + A: Tensor | None = None + B: Tensor | None = None + + +# magic to support tensor shape modifications and splitting +class LoraTorchTensor: + _lora_A: Tensor # (n_rank, row_size) + _lora_B: Tensor # (col_size, n_rank) + _rank: int + + def __init__(self, A: Tensor, B: Tensor): + assert len(A.shape) == len(B.shape) + assert A.shape[-2] == B.shape[-1] + if A.dtype != B.dtype: + A = A.to(torch.float32) + B = B.to(torch.float32) + self._lora_A = A + self._lora_B = B + self._rank = B.shape[-1] + + def get_lora_A_B(self) -> tuple[Tensor, Tensor]: + return (self._lora_A, self._lora_B) + + def __getitem__( + self, + indices: ( + SupportsIndex + | slice + | tuple[SupportsIndex | slice | Tensor, ...] # TODO: add ellipsis in the type signature + ), + ) -> LoraTorchTensor: + shape = self.shape + if isinstance(indices, SupportsIndex): + if len(shape) > 2: + return LoraTorchTensor(self._lora_A[indices], self._lora_B[indices]) + else: + raise NotImplementedError # can't return a vector + elif isinstance(indices, slice): + if len(shape) > 2: + return LoraTorchTensor(self._lora_A[indices], self._lora_B[indices]) + else: + return LoraTorchTensor(self._lora_A, self._lora_B[indices]) + elif isinstance(indices, tuple): + assert len(indices) > 0 + if indices[-1] is Ellipsis: + return self[indices[:-1]] + # expand ellipsis + indices = tuple( + u + for v in ( + ( + (slice(None, None) for _ in range(len(indices) - 1)) + if i is Ellipsis + else (i,) + ) + for i in indices + ) + for u in v + ) + + if len(indices) < len(shape): + indices = (*indices, *(slice(None, None) for _ in range(len(indices), len(shape)))) + + # TODO: make sure this is correct + indices_A = ( + *( + ( + j.__index__() % self._lora_A.shape[i] + if isinstance(j, SupportsIndex) + else slice(None, None) + ) + for i, j in enumerate(indices[:-2]) + ), + slice(None, None), + indices[-1], + ) + indices_B = indices[:-1] + return LoraTorchTensor(self._lora_A[indices_A], self._lora_B[indices_B]) + else: + raise NotImplementedError # unknown indice type + + @property + def dtype(self) -> torch.dtype: + assert self._lora_A.dtype == self._lora_B.dtype + return self._lora_A.dtype + + @property + def shape(self) -> tuple[int, ...]: + assert len(self._lora_A.shape) == len(self._lora_B.shape) + return (*self._lora_B.shape[:-1], self._lora_A.shape[-1]) + + def size(self, dim=None): + assert dim is None + return self.shape + + def reshape(self, *shape: int | tuple[int, ...]) -> LoraTorchTensor: + if isinstance(shape[0], tuple): + new_shape: tuple[int, ...] = shape[0] + else: + new_shape = cast(tuple[int, ...], shape) + orig_shape = self.shape + if len(new_shape) < 2: + raise NotImplementedError # can't become a vector + + # expand -1 in the shape + if any(dim == -1 for dim in new_shape): + n_elems = prod(orig_shape) + n_new_elems = prod(dim if dim != -1 else 1 for dim in new_shape) + assert n_elems % n_new_elems == 0 + new_shape = (*(dim if dim != -1 else n_elems // n_new_elems for dim in new_shape),) + + if new_shape[-1] != orig_shape[-1]: + raise NotImplementedError # can't reshape the row size trivially + + shape_A = (*(1 for _ in new_shape[:-2]), self._rank, orig_shape[-1]) + shape_B = (*new_shape[:-1], self._rank) + return LoraTorchTensor( + self._lora_A.reshape(shape_A), + self._lora_B.reshape(shape_B), + ) + + def reshape_as(self, other: Tensor) -> LoraTorchTensor: + return self.reshape(*other.shape) + + def view(self, *size: int) -> LoraTorchTensor: + return self.reshape(*size) + + def permute(self, *dims: int) -> LoraTorchTensor: + shape = self.shape + dims = tuple(dim - len(shape) if dim >= 0 else dim for dim in dims) + if dims[-1] == -1: + # TODO: support higher dimensional A shapes bigger than 1 + assert all(dim == 1 for dim in self._lora_A.shape[:-2]) + return LoraTorchTensor(self._lora_A, self._lora_B.permute(*dims)) + if len(shape) == 2 and dims[-1] == -2 and dims[-2] == -1: + return LoraTorchTensor(self._lora_B.permute(*dims), self._lora_A.permute(*dims)) + else: + # TODO: compose the above two + raise NotImplementedError + + def transpose(self, dim0: int, dim1: int) -> LoraTorchTensor: + shape = self.shape + dims = [i for i in range(len(shape))] + dims[dim0], dims[dim1] = dims[dim1], dims[dim0] + return self.permute(*dims) + + def swapaxes(self, axis0: int, axis1: int) -> LoraTorchTensor: + return self.transpose(axis0, axis1) + + def to(self, *args, **kwargs): + return LoraTorchTensor(self._lora_A.to(*args, **kwargs), self._lora_B.to(*args, **kwargs)) + + @classmethod + def __torch_function__(cls, func: Callable, types, args=(), kwargs=None): + del types # unused + + if kwargs is None: + kwargs = {} + + if func is torch.permute: + return type(args[0]).permute(*args, **kwargs) + elif func is torch.reshape: + return type(args[0]).reshape(*args, **kwargs) + elif func is torch.stack: + assert isinstance(args[0], Sequence) + dim = kwargs.get("dim", 0) + assert dim == 0 + return LoraTorchTensor( + torch.stack([a._lora_A for a in args[0]], dim), + torch.stack([b._lora_B for b in args[0]], dim), + ) + elif func is torch.cat: + assert isinstance(args[0], Sequence) + dim = kwargs.get("dim", 0) + assert dim == 0 + if len(args[0][0].shape) > 2: + return LoraTorchTensor( + torch.cat([a._lora_A for a in args[0]], dim), + torch.cat([b._lora_B for b in args[0]], dim), + ) + elif all(torch.equal(args[0][0]._lora_A, t._lora_A) for t in args[0][1:]): + return LoraTorchTensor( + args[0][0]._lora_A, + torch.cat([b._lora_B for b in args[0]], dim), + ) + else: + raise NotImplementedError + else: + raise NotImplementedError + + +def get_base_tensor_name(lora_tensor_name: str) -> str: + base_name = lora_tensor_name.replace("base_model.model.", "") + base_name = base_name.replace(".lora_A.weight", ".weight") + base_name = base_name.replace(".lora_B.weight", ".weight") + # models produced by mergekit-extract-lora have token embeddings in the adapter + base_name = base_name.replace(".lora_embedding_A", ".weight") + base_name = base_name.replace(".lora_embedding_B", ".weight") + return base_name + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Convert a Hugging Face PEFT LoRA adapter to a GGUF file") + parser.add_argument( + "--outfile", type=Path, + help="path to write to; default: based on input. {ftype} will be replaced by the outtype.", + ) + parser.add_argument( + "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16", + help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type", + ) + parser.add_argument( + "--bigendian", action="store_true", + help="model is executed on big endian machine", + ) + parser.add_argument( + "--no-lazy", action="store_true", + help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)", + ) + parser.add_argument( + "--verbose", action="store_true", + help="increase output verbosity", + ) + parser.add_argument( + "--dry-run", action="store_true", + help="only print out what will be done, without writing any new files", + ) + parser.add_argument( + "--base", type=Path, + help="directory containing Hugging Face model config files (config.json, tokenizer.json) for the base model that the adapter is based on - only config is needed, actual model weights are not required. If base model is unspecified, it will be loaded from Hugging Face hub based on the adapter config", + ) + parser.add_argument( + "--base-model-id", type=str, + help="the model ID of the base model, if it is not available locally or in the adapter config. If specified, it will ignore --base and load the base model config from the Hugging Face hub (Example: 'meta-llama/Llama-3.2-1B-Instruct')", + ) + parser.add_argument( + "lora_path", type=Path, + help="directory containing Hugging Face PEFT LoRA config (adapter_model.json) and weights (adapter_model.safetensors or adapter_model.bin)", + ) + + return parser.parse_args() + + +def load_hparams_from_hf(hf_model_id: str) -> dict[str, Any]: + # normally, adapter does not come with base model config, we need to load it from AutoConfig + config = AutoConfig.from_pretrained(hf_model_id) + return config.to_dict() + + +if __name__ == '__main__': + args = parse_args() + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + + ftype_map: dict[str, gguf.LlamaFileType] = { + "f32": gguf.LlamaFileType.ALL_F32, + "f16": gguf.LlamaFileType.MOSTLY_F16, + "bf16": gguf.LlamaFileType.MOSTLY_BF16, + "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0, + "auto": gguf.LlamaFileType.GUESSED, + } + + ftype = ftype_map[args.outtype] + + dir_base_model: Path | None = args.base + dir_lora: Path = args.lora_path + base_model_id: str | None = args.base_model_id + lora_config = dir_lora / "adapter_config.json" + input_model = dir_lora / "adapter_model.safetensors" + + if args.outfile is not None: + fname_out = args.outfile + else: + # output in the same directory as the model by default + fname_out = dir_lora + + if os.path.exists(input_model): + # lazy import load_file only if lora is in safetensors format. + from safetensors.torch import load_file + + lora_model = load_file(input_model, device="cpu") + else: + input_model = os.path.join(dir_lora, "adapter_model.bin") + lora_model = torch.load(input_model, map_location="cpu", weights_only=True) + + # load LoRA config + with open(lora_config, "r") as f: + lparams: dict[str, Any] = json.load(f) + + # load base model + if base_model_id is not None: + logger.info(f"Loading base model from Hugging Face: {base_model_id}") + hparams = load_hparams_from_hf(base_model_id) + elif dir_base_model is None: + if "base_model_name_or_path" in lparams: + model_id = lparams["base_model_name_or_path"] + logger.info(f"Loading base model from Hugging Face: {model_id}") + try: + hparams = load_hparams_from_hf(model_id) + except OSError as e: + logger.error(f"Failed to load base model config: {e}") + logger.error("Please try downloading the base model and add its path to --base") + sys.exit(1) + else: + logger.error("'base_model_name_or_path' is not found in adapter_config.json") + logger.error("Base model config is required. Please download the base model and add its path to --base") + sys.exit(1) + else: + logger.info(f"Loading base model: {dir_base_model.name}") + hparams = ModelBase.load_hparams(dir_base_model) + + with torch.inference_mode(): + try: + model_class = ModelBase.from_model_architecture(hparams["architectures"][0]) + except NotImplementedError: + logger.error(f"Model {hparams['architectures'][0]} is not supported") + sys.exit(1) + + class LoraModel(model_class): + model_arch = model_class.model_arch + + lora_alpha: float + + def __init__(self, *args, dir_lora_model: Path, lora_alpha: float, **kwargs): + + super().__init__(*args, **kwargs) + + self.dir_model_card = dir_lora_model + self.lora_alpha = float(lora_alpha) + + def set_vocab(self): + pass + + def set_type(self): + self.gguf_writer.add_type(gguf.GGUFType.ADAPTER) + self.gguf_writer.add_string(gguf.Keys.Adapter.TYPE, "lora") + + def set_gguf_parameters(self): + self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha) + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + # Never add extra tensors (e.g. rope_freqs) for LoRA adapters + return () + + def get_tensors(self) -> Iterator[tuple[str, Tensor]]: + tensor_map: dict[str, PartialLoraTensor] = {} + + for name, tensor in lora_model.items(): + if self.lazy: + tensor = LazyTorchTensor.from_eager(tensor) + base_name = get_base_tensor_name(name) + # note: mergekit-extract-lora also adds token embeddings to the adapter + is_lora_a = ".lora_A.weight" in name or ".lora_embedding_A" in name + is_lora_b = ".lora_B.weight" in name or ".lora_embedding_B" in name + if not is_lora_a and not is_lora_b: + if ".base_layer.weight" in name: + continue + # mergekit-extract-lora add these layernorm to the adapter, we need to keep them + if "_layernorm" in name or ".norm" in name: + yield (base_name, tensor) + continue + logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor") + if ".embed_tokens.weight" in name or ".lm_head.weight" in name: + logger.error("Embeddings is present in the adapter. This can be due to new tokens added during fine tuning") + logger.error("Please refer to https://github.com/ggml-org/llama.cpp/pull/9948") + sys.exit(1) + + if base_name in tensor_map: + if is_lora_a: + tensor_map[base_name].A = tensor + else: + tensor_map[base_name].B = tensor + else: + if is_lora_a: + tensor_map[base_name] = PartialLoraTensor(A=tensor) + else: + tensor_map[base_name] = PartialLoraTensor(B=tensor) + + for name, tensor in tensor_map.items(): + assert tensor.A is not None + assert tensor.B is not None + yield (name, cast(torch.Tensor, LoraTorchTensor(tensor.A, tensor.B))) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + dest = list(super().modify_tensors(data_torch, name, bid)) + # some archs may have the same tensor for lm_head and output (tie word embeddings) + # in this case, adapters targeting lm_head will fail when using llama-export-lora + # therefore, we ignore them for now + # see: https://github.com/ggml-org/llama.cpp/issues/9065 + if name == "lm_head.weight" and len(dest) == 0: + raise ValueError("lm_head is present in adapter, but is ignored in base model") + for dest_name, dest_data in dest: + # mergekit-extract-lora add these layernorm to the adapter + if "_norm" in dest_name: + assert dest_data.dim() == 1 + yield (dest_name, dest_data) + continue + + # otherwise, we must get the lora_A and lora_B tensors + assert isinstance(dest_data, LoraTorchTensor) + lora_a, lora_b = dest_data.get_lora_A_B() + + # note: mergekit-extract-lora flip and transpose A and B + # here we only need to transpose token_embd.lora_a, see llm_build_inp_embd() + if "token_embd.weight" in dest_name: + lora_a = lora_a.T + + yield (dest_name + ".lora_a", lora_a) + yield (dest_name + ".lora_b", lora_b) + + alpha: float = lparams["lora_alpha"] + + model_instance = LoraModel( + dir_base_model, + ftype, + fname_out, + is_big_endian=args.bigendian, + use_temp_file=False, + eager=args.no_lazy, + dry_run=args.dry_run, + dir_lora_model=dir_lora, + lora_alpha=alpha, + hparams=hparams, + ) + + logger.info("Exporting model...") + model_instance.write() + logger.info(f"Model successfully exported to {model_instance.fname_out}") diff --git a/llama.cpp/docs/android.md b/llama.cpp/docs/android.md new file mode 100644 index 0000000000000000000000000000000000000000..511199bd59b6509b032f802704d1dcb05621846a --- /dev/null +++ b/llama.cpp/docs/android.md @@ -0,0 +1,83 @@ + +# Android + +## Build on Android using Termux + +[Termux](https://termux.dev/en/) is an Android terminal emulator and Linux environment app (no root required). As of writing, Termux is available experimentally in the Google Play Store; otherwise, it may be obtained directly from the project repo or on F-Droid. + +With Termux, you can install and run `llama.cpp` as if the environment were Linux. Once in the Termux shell: + +``` +$ apt update && apt upgrade -y +$ apt install git cmake +``` + +Then, follow the [build instructions](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md), specifically for CMake. + +Once the binaries are built, download your model of choice (e.g., from Hugging Face). It's recommended to place it in the `~/` directory for best performance: + +``` +$ curl -L {model-url} -o ~/{model}.gguf +``` + +Then, if you are not already in the repo directory, `cd` into `llama.cpp` and: + +``` +$ ./build/bin/llama-cli -m ~/{model}.gguf -c {context-size} -p "{your-prompt}" +``` + +Here, we show `llama-cli`, but any of the executables under `examples` should work, in theory. Be sure to set `context-size` to a reasonable number (say, 4096) to start with; otherwise, memory could spike and kill your terminal. + +To see what it might look like visually, here's an old demo of an interactive session running on a Pixel 5 phone: + +https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4 + +## Cross-compile using Android NDK +It's possible to build `llama.cpp` for Android on your host system via CMake and the Android NDK. If you are interested in this path, ensure you already have an environment prepared to cross-compile programs for Android (i.e., install the Android SDK). Note that, unlike desktop environments, the Android environment ships with a limited set of native libraries, and so only those libraries are available to CMake when building with the Android NDK (see: https://developer.android.com/ndk/guides/stable_apis.) + +Once you're ready and have cloned `llama.cpp`, invoke the following in the project directory: + +``` +$ cmake \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=arm64-v8a \ + -DANDROID_PLATFORM=android-28 \ + -DCMAKE_C_FLAGS="-march=armv8.7a" \ + -DCMAKE_CXX_FLAGS="-march=armv8.7a" \ + -DGGML_OPENMP=OFF \ + -DGGML_LLAMAFILE=OFF \ + -B build-android +``` + +Notes: + - While later versions of Android NDK ship with OpenMP, it must still be installed by CMake as a dependency, which is not supported at this time + - `llamafile` does not appear to support Android devices (see: https://github.com/Mozilla-Ocho/llamafile/issues/325) + +The above command should configure `llama.cpp` with the most performant options for modern devices. Even if your device is not running `armv8.7a`, `llama.cpp` includes runtime checks for available CPU features it can use. + +Feel free to adjust the Android ABI for your target. Once the project is configured: + +``` +$ cmake --build build-android --config Release -j{n} +$ cmake --install build-android --prefix {install-dir} --config Release +``` + +After installing, go ahead and download the model of your choice to your host system. Then: + +``` +$ adb shell "mkdir /data/local/tmp/llama.cpp" +$ adb push {install-dir} /data/local/tmp/llama.cpp/ +$ adb push {model}.gguf /data/local/tmp/llama.cpp/ +$ adb shell +``` + +In the `adb shell`: + +``` +$ cd /data/local/tmp/llama.cpp +$ LD_LIBRARY_PATH=lib ./bin/llama-simple -m {model}.gguf -c {context-size} -p "{your-prompt}" +``` + +That's it! + +Be aware that Android will not find the library path `lib` on its own, so we must specify `LD_LIBRARY_PATH` in order to run the installed executables. Android does support `RPATH` in later API levels, so this could change in the future. Refer to the previous section for information about `context-size` (very important!) and running other `examples`. diff --git a/llama.cpp/docs/backend/BLIS.md b/llama.cpp/docs/backend/BLIS.md new file mode 100644 index 0000000000000000000000000000000000000000..91197e187e57b6ffed82239b5e1f8dd3063a58ae --- /dev/null +++ b/llama.cpp/docs/backend/BLIS.md @@ -0,0 +1,60 @@ +BLIS Installation Manual +------------------------ + +BLIS is a portable software framework for high-performance BLAS-like dense linear algebra libraries. It has received awards and recognition, including the 2023 James H. Wilkinson Prize for Numerical Software and the 2020 SIAM Activity Group on Supercomputing Best Paper Prize. BLIS provides a new BLAS-like API and a compatibility layer for traditional BLAS routine calls. It offers features such as object-based API, typed API, BLAS and CBLAS compatibility layers. + +Project URL: https://github.com/flame/blis + +### Prepare: + +Compile BLIS: + +```bash +git clone https://github.com/flame/blis +cd blis +./configure --enable-cblas -t openmp,pthreads auto +# will install to /usr/local/ by default. +make -j +``` + +Install BLIS: + +```bash +sudo make install +``` + +We recommend using openmp since it's easier to modify the cores being used. + +### llama.cpp compilation + +CMake: + +```bash +mkdir build +cd build +cmake -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=FLAME .. +make -j +``` + +### llama.cpp execution + +According to the BLIS documentation, we could set the following +environment variables to modify the behavior of openmp: + +```bash +export GOMP_CPU_AFFINITY="0-19" +export BLIS_NUM_THREADS=14 +``` + +And then run the binaries as normal. + + +### Intel specific issue + +Some might get the error message saying that `libimf.so` cannot be found. +Please follow this [stackoverflow page](https://stackoverflow.com/questions/70687930/intel-oneapi-2022-libimf-so-no-such-file-or-directory-during-openmpi-compila). + +### Reference: + +1. https://github.com/flame/blis#getting-started +2. https://github.com/flame/blis/blob/master/docs/Multithreading.md diff --git a/llama.cpp/docs/backend/CANN.md b/llama.cpp/docs/backend/CANN.md new file mode 100644 index 0000000000000000000000000000000000000000..c7413b7dabe9e9f4657ec0b930f1336cb2b27a93 --- /dev/null +++ b/llama.cpp/docs/backend/CANN.md @@ -0,0 +1,316 @@ +# llama.cpp for CANN + + - [Background](#background) + - [News](#news) + - [OS](#os) + - [Hardware](#hardware) + - [Model Supports](#model-supports) + - [DataType Supports](#datatype-supports) + - [Docker](#docker) + - [Linux](#linux) + - [Environment variable setup](#environment-variable-setup) + - [TODO](#todo) + + +## Background + +**Ascend NPU** is a range of AI processors using Neural Processing Unit. It will efficiently handle matrix-matrix multiplication, dot-product and scalars. + +**CANN** (Compute Architecture for Neural Networks) is a heterogeneous computing architecture for AI scenarios, providing support for multiple AI frameworks on the top and serving AI processors and programming at the bottom. It plays a crucial role in bridging the gap between upper and lower layers, and is a key platform for improving the computing efficiency of Ascend AI processors. Meanwhile, it offers a highly efficient and easy-to-use programming interface for diverse application scenarios, allowing users to rapidly build AI applications and services based on the Ascend platform. + +**Llama.cpp + CANN** + +The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the ability of AscendC and ACLNN which are intergrated to CANN Toolkit and kernels to using Ascend NPU directly. + +## News + +- 2024.11 + - Support F16 and F32 data type model for Ascend 310P NPU. +- 2024.8 + - Support `Q4_0` and `Q8_0` data type for Ascend NPU. +- 2024.7 + - Create CANN backend for Ascend NPU. + +## OS + +| OS | Status | Verified | +|:-------:|:-------:|:----------------------------------------------:| +| Linux | Support | Ubuntu 22.04, OpenEuler22.03 | + + +## Hardware + +### Ascend NPU + +**Verified devices** + +| Ascend NPU | Status | +|:-----------------------------:|:-------:| +| Atlas 300T A2 | Support | +| Atlas 300I Duo | Support | + +*Notes:* + +- If you have trouble with Ascend NPU device, please create a issue with **[CANN]** prefix/tag. +- If you run successfully with your Ascend NPU device, please help update the upper table. + + +## Model Supports + +| Model Name | FP16 | Q4_0 | Q8_0 | +|:----------------------------|:-----:|:----:|:----:| +| Llama-2 | √ | √ | √ | +| Llama-3 | √ | √ | √ | +| Mistral-7B | √ | √ | √ | +| Mistral MOE | √ | √ | √ | +| DBRX | - | - | - | +| Falcon | √ | √ | √ | +| Chinese LLaMA/Alpaca | √ | √ | √ | +| Vigogne(French) | √ | √ | √ | +| BERT | x | x | x | +| Koala | √ | √ | √ | +| Baichuan | √ | √ | √ | +| Aquila 1 & 2 | √ | √ | √ | +| Starcoder models | √ | √ | √ | +| Refact | √ | √ | √ | +| MPT | √ | √ | √ | +| Bloom | √ | √ | √ | +| Yi models | √ | √ | √ | +| stablelm models | √ | √ | √ | +| DeepSeek models | x | x | x | +| Qwen models | √ | √ | √ | +| PLaMo-13B | √ | √ | √ | +| Phi models | √ | √ | √ | +| PhiMoE | √ | √ | √ | +| GPT-2 | √ | √ | √ | +| Orion | √ | √ | √ | +| InternlLM2 | √ | √ | √ | +| CodeShell | √ | √ | √ | +| Gemma | √ | √ | √ | +| Mamba | √ | √ | √ | +| Xverse | √ | √ | √ | +| command-r models | √ | √ | √ | +| Grok-1 | - | - | - | +| SEA-LION | √ | √ | √ | +| GritLM-7B | √ | √ | √ | +| OLMo | √ | √ | √ | +| OLMo 2 | √ | √ | √ | +| OLMoE | √ | √ | √ | +| Granite models | √ | √ | √ | +| GPT-NeoX | √ | √ | √ | +| Pythia | √ | √ | √ | +| Snowflake-Arctic MoE | - | - | - | +| Smaug | √ | √ | √ | +| Poro 34B | √ | √ | √ | +| Bitnet b1.58 models | √ | x | x | +| Flan-T5 | √ | √ | √ | +| Open Elm models | x | √ | √ | +| chatGLM3-6B + ChatGLM4-9b + GLMEdge-1.5b + GLMEdge-4b | √ | √ | √ | +| GLM-4-0414 | √ | √ | √ | +| SmolLM | √ | √ | √ | +| EXAONE-3.0-7.8B-Instruct | √ | √ | √ | +| FalconMamba Models | √ | √ | √ | +| Jais Models | - | x | x | +| Bielik-11B-v2.3 | √ | √ | √ | +| RWKV-6 | - | √ | √ | +| QRWKV-6 | √ | √ | √ | +| GigaChat-20B-A3B | x | x | x | +| Trillion-7B-preview | √ | √ | √ | +| Ling models | √ | √ | √ | + + +**Multimodal** +| Model Name | FP16 | Q4_0 | Q8_0 | +|:----------------------------|:-----:|:----:|:----:| +| LLaVA 1.5 models, LLaVA 1.6 models | x | x | x | +| BakLLaVA | √ | √ | √ | +| Obsidian | √ | - | - | +| ShareGPT4V | x | - | - | +| MobileVLM 1.7B/3B models | - | - | - | +| Yi-VL | - | - | - | +| Mini CPM | √ | √ | √ | +| Moondream | √ | √ | √ | +| Bunny | √ | - | - | +| GLM-EDGE | √ | √ | √ | +| Qwen2-VL | √ | √ | √ | + + + +## DataType Supports + +| DataType | Status | +|:----------------------:|:-------:| +| FP16 | Support | +| Q8_0 | Support | +| Q4_0 | Support | + +## Docker + +### Build Images +You can get a image with llama.cpp in one command. +```sh +docker build -t llama-cpp-cann -f .devops/llama-cli-cann.Dockerfile . +``` + +### Run container + +```sh +# Find all cards. +npu-smi info + +# Select the cards that you want to use, make sure these cards are not used by someone. +# Following using cards of device0. +docker run --name llamacpp --device /dev/davinci0 --device /dev/davinci_manager --device /dev/devmm_svm --device /dev/hisi_hdc -v /usr/local/dcmi:/usr/local/dcmi -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info -v /PATH_TO_YOUR_MODELS/:/app/models -it llama-cpp-cann -m /app/models/MODEL_PATH -ngl 32 -p "Building a website can be done in 10 simple steps:" +``` + +*Notes:* + +- You may need to install Ascend Driver and firmware on the **host** machine *(Please refer to the [Linux configuration](#linux) for details)*. + +## Linux + +### I. Setup Environment + +1. **Install Ascend Driver and firmware** + + ```sh + # create driver running user. + sudo groupadd -g HwHiAiUser + sudo useradd -g HwHiAiUser -d /home/HwHiAiUser -m HwHiAiUser -s /bin/bash + sudo usermod -aG HwHiAiUser $USER + + # download driver from https://www.hiascend.com/hardware/firmware-drivers/community according to your system + # and install driver. + sudo sh Ascend-hdk-910b-npu-driver_x.x.x_linux-{arch}.run --full --install-for-all + ``` + + Once installed, run `npu-smi info` to check whether driver is installed successfully. + ```sh + +-------------------------------------------------------------------------------------------+ + | npu-smi 24.1.rc2 Version: 24.1.rc2 | + +----------------------+---------------+----------------------------------------------------+ + | NPU Name | Health | Power(W) Temp(C) Hugepages-Usage(page)| + | Chip | Bus-Id | AICore(%) Memory-Usage(MB) HBM-Usage(MB) | + +======================+===============+====================================================+ + | 2 xxx | OK | 64.4 51 15 / 15 | + | 0 | 0000:01:00.0 | 0 1873 / 15077 0 / 32768 | + +======================+===============+====================================================+ + | 5 xxx | OK | 64.0 52 15 / 15 | + | 0 | 0000:81:00.0 | 0 1874 / 15077 0 / 32768 | + +======================+===============+====================================================+ + | No running processes found in NPU 2 | + +======================+===============+====================================================+ + | No running processes found in NPU 5 | + +======================+===============+====================================================+ + ``` + +2. **Install Ascend Firmware** + ```sh + # download driver from https://www.hiascend.com/hardware/firmware-drivers/community according to your system + # and install driver. + sudo sh Ascend-hdk-910b-npu-firmware_x.x.x.x.X.run --full + ``` + If the following messaage appers, firmware is installed successfully. + ```sh + Firmware package installed successfully! + ``` + + +3. **Install CANN toolkit and kernels** + + CANN toolkit and kernels can be obtained from the official [CANN Toolkit](https://www.hiascend.com/zh/developer/download/community/result?module=cann) page. + + Please download the corresponding version that satified your system. The minimum version required is 8.0.RC2.alpha002 and here is the install command. + ```sh + pip3 install attrs numpy decorator sympy cffi pyyaml pathlib2 psutil protobuf scipy requests absl-py wheel typing_extensions + sh Ascend-cann-toolkit_8.0.RC2.alpha002_linux-aarch64.run --install + sh Ascend-cann-kernels-910b_8.0.RC2.alpha002_linux.run --install + ``` + + Set Ascend Variables: + ```sh + echo "source ~/Ascend/ascend-toolkit/set_env.sh" >> ~/.bashrc + source ~/.bashrc + ``` + +Upon a successful installation, CANN is enabled for the available ascend devices. + +### II. Build llama.cpp + +```sh +cmake -B build -DGGML_CANN=on -DCMAKE_BUILD_TYPE=release +cmake --build build --config release +``` + +### III. Run the inference + +1. **Retrieve and prepare model** + + You can refer to the general [*Prepare and Quantize*](../../README.md#prepare-and-quantize) guide for model prepration. + + **Notes**: + + - CANN backend only supports FP16/Q4_0/Q8_0 models currently. + +2. **Launch inference** + + There are two device selection modes: + + - Single device: Use one device target specified by the user. + - Multiple devices: Automatically choose the devices with the same backend. + + | Device selection | Parameter | + |:----------------:|:--------------------------------------:| + | Single device | --split-mode none --main-gpu DEVICE_ID | + | Multiple devices | --split-mode layer (default) | + + Examples: + + - Use device 0: + + ```sh + ./build/bin/llama-cli -m path_to_model -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0 + ``` + + - Use multiple devices: + + ```sh + ./build/bin/llama-cli -m path_to_model -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer + ``` + +### **GitHub contribution**: +Please add the **[CANN]** prefix/tag in issues/PRs titles to help the CANN-team check/address them without delay. + +## Updates +### Basic Flash Attention Support +The basic FA kernel with aclnnops has been added in aclnn_ops.cpp. +Currently, the FA only supports the cases with FP16 KV tensors and NO logit softcap. +Since the aclnn interface for flash attention cannot support the logit softcap, we will only update the quantized version in the future. + +Authors from Peking University: Bizhao Shi (bshi@pku.edu.cn), Yuxin Yang (yxyang@pku.edu.cn), Ruiyang Ma (ruiyang@stu.pku.edu.cn), and Guojie Luo (gluo@pku.edu.cn). + +We would like to thank Tuo Dai, Shanni Li, and all of the project maintainers from Huawei Technologies Co., Ltd for their help during the code development and pull request. + +## Environment variable setup + +### GGML_CANN_ASYNC_MODE + +Enables asynchronous operator submission. Disabled by default. + +### GGML_CANN_MEM_POOL + +Specifies the memory pool management strategy: + +- vmm: Utilizes a virtual memory manager pool. If hardware support for VMM is unavailable, falls back to the legacy (leg) memory pool. + +- prio: Employs a priority queue-based memory pool management. +- leg: Uses a fixed-size buffer pool. + +### GGML_CANN_DISABLE_BUF_POOL_CLEAN + +Controls automatic cleanup of the memory pool. This option is only effective when using the prio or leg memory pool strategies. + +### GGML_CANN_WEIGHT_NZ + +Converting the matmul weight format from ND to NZ can significantly improve performance on the 310I DUO NPU. + diff --git a/llama.cpp/docs/backend/CUDA-FEDORA.md b/llama.cpp/docs/backend/CUDA-FEDORA.md new file mode 100644 index 0000000000000000000000000000000000000000..7e8f06599c76348e6659df103aee07996519c36b --- /dev/null +++ b/llama.cpp/docs/backend/CUDA-FEDORA.md @@ -0,0 +1,283 @@ +# Setting Up CUDA on Fedora + +In this guide we setup [Nvidia CUDA](https://docs.nvidia.com/cuda/) in a toolbox container. This guide is applicable for: + +- [Fedora Workstation](https://fedoraproject.org/workstation/) +- [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/) +- [Fedora Spins](https://fedoraproject.org/spins) +- [Other Distributions](https://containertoolbx.org/distros/), including `Red Hat Enterprise Linux >= 8.5`, `Arch Linux`, and `Ubuntu`. + +## Table of Contents + +- [Prerequisites](#prerequisites) +- [Using the Fedora 41 CUDA Repository](#using-the-fedora-41-cuda-repository) +- [Creating a Fedora Toolbox Environment](#creating-a-fedora-toolbox-environment) +- [Installing Essential Development Tools](#installing-essential-development-tools) +- [Adding the CUDA Repository](#adding-the-cuda-repository) +- [Installing Nvidia Driver Libraries](#installing-nvidia-driver-libraries) +- [Installing the CUDA Meta-Package](#installing-the-cuda-meta-package) +- [Configuring the Environment](#configuring-the-environment) +- [Verifying the Installation](#verifying-the-installation) +- [Conclusion](#conclusion) +- [Troubleshooting](#troubleshooting) +- [Additional Notes](#additional-notes) +- [References](#references) + +## Prerequisites + +- **Toolbox Installed on the Host System** `Fedora Silverblue` and `Fedora Workstation` both have toolbox by default, other distributions may need to install the [toolbox package](https://containertoolbx.org/install/). +- **NVIDIA Drivers and Graphics Card installed on Host System (recommended)** To run CUDA program, such as `llama.cpp`, the host should be setup to access your NVIDIA hardware. Fedora Hosts can use the [RPM Fusion Repository](https://rpmfusion.org/Howto/NVIDIA). +- **Internet connectivity** to download packages. + +### Using the Fedora 41 CUDA Repository + +The latest release is 41. + +- [Fedora 41 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/) + +**Note:** We recommend using a toolbox environment to prevent system conflicts. + +## Creating a Fedora Toolbox Environment + +This guide focuses on Fedora hosts, but with small adjustments, it can work for other hosts. Using the Fedora Toolbox allows us to install the necessary packages without affecting the host system. + +**Note:** Toolbox is available for other systems, and even without Toolbox, it is possible to use Podman or Docker. + +1. **Create a Fedora 41 Toolbox:** + + ```bash + toolbox create --image registry.fedoraproject.org/fedora-toolbox:41 --container fedora-toolbox-41-cuda + ``` + +2. **Enter the Toolbox:** + + ```bash + toolbox enter --container fedora-toolbox-41-cuda + ``` + + Inside the toolbox, you have root privileges and can install packages without affecting the host system. + +## Installing Essential Development Tools + +1. **Synchronize the DNF Package Manager:** + + ```bash + sudo dnf distro-sync + ``` + +2. **Install **Vim** the default text editor (Optional):** + + ```bash + sudo dnf install vim-default-editor --allowerasing + ``` + + The `--allowerasing` flag will allow the removal of the conflicting `nano-default-editor` package. + +3. **Install Development Tools and Libraries:** + + ```bash + sudo dnf install @c-development @development-tools cmake + ``` + + This installs essential packages for compiling software, including `gcc`, `make`, and other development headers. + +## Adding the CUDA Repository + +Add the NVIDIA CUDA repository to your DNF configuration: + +```bash +sudo dnf config-manager addrepo --from-repofile=https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/cuda-fedora41.repo +``` + +After adding the repository, synchronize the package manager again: + +```bash +sudo dnf distro-sync +``` + +## Installing Nvidia Driver Libraries + +First, we need to detect if the host is supplying the [NVIDIA driver libraries into the toolbox](https://github.com/containers/toolbox/blob/main/src/pkg/nvidia/nvidia.go): + +```bash +ls -la /usr/lib64/libcuda.so.1 +``` + +### If *`libcuda.so.1`* is missing: + +``` +ls: cannot access '/usr/lib64/libcuda.so.1': No such file or directory +``` + +**Explanation:** +The host dose not supply the CUDA drivers, **install them now:** + +#### Install the Nvidia Driver Libraries on Guest: + +```bash +sudo dnf install nvidia-driver-cuda nvidia-driver-libs nvidia-driver-cuda-libs nvidia-persistenced +``` + +### If *`libcuda.so.1`* exists: +``` +lrwxrwxrwx. 1 root root 21 Mar 24 11:26 /usr/lib64/libcuda.so.1 -> libcuda.so.570.133.07 +``` + +**Explanation:** +The host is supply the CUDA drivers, **we need to update the guest RPM Database accordingly:** + +#### Update the Toolbox RPM Database to include the Host-Supplied Libraries: + +Note: we do not actually install the libraries, we just update the DB so that the guest system knows they are supplied by the host. + +##### 1. Download `nvidia-` parts that are supplied by the host RPM's (with dependencies) + +```bash +sudo dnf download --destdir=/tmp/nvidia-driver-libs --resolve --arch x86_64 nvidia-driver-cuda nvidia-driver-libs nvidia-driver-cuda-libs nvidia-persistenced +``` + +##### 2. Update the RPM database to assume the installation of these packages. + +```bash +sudo rpm --install --verbose --hash --justdb /tmp/nvidia-driver-libs/* +``` + +**Note:** + +- The `--justdb` option only updates the RPM database, without touching the filesystem elsewhere. + +##### Check that the RPM Database has been correctly updated: + +**Note:** This is the same command as in the *"Install the Nvidia Driver Libraries on Guest"* for if *`libcuda.so.1`* was missing. + + +```bash +sudo dnf install nvidia-driver-cuda nvidia-driver-libs nvidia-driver-cuda-libs nvidia-persistenced +``` + +*(this time it will not install anything, as the database things that these packages are already installed)* + +``` +Updating and loading repositories: +Repositories loaded. +Package "nvidia-driver-cuda-3:570.124.06-1.fc41.x86_64" is already installed. +Package "nvidia-driver-libs-3:570.124.06-1.fc41.x86_64" is already installed. +Package "nvidia-driver-cuda-libs-3:570.124.06-1.fc41.x86_64" is already installed. +Package "nvidia-persistenced-3:570.124.06-1.fc41.x86_64" is already installed. + +Nothing to do. +``` + +## Installing the CUDA Meta-Package + +Now that the driver libraries are installed, proceed to install CUDA: + +```bash +sudo dnf install cuda +``` + +This installs the CUDA toolkit and associated packages. + +## Configuring the Environment + +To use CUDA, add its binary directory to your system's `PATH`. + +1. **Create a Profile Script:** + + ```bash + sudo sh -c 'echo "export PATH=\$PATH:/usr/local/cuda/bin" >> /etc/profile.d/cuda.sh' + ``` + + **Explanation:** + + - We add to `/etc/profile.d/` as the `/etc/` folder is unique to this particular container, and is not shared with other containers or the host system. + - The backslash `\` before `$PATH` ensures the variable is correctly written into the script. + +2. **Make the Script Executable:** + + ```bash + sudo chmod +x /etc/profile.d/cuda.sh + ``` + +3. **Source the Script to Update Your Environment:** + + ```bash + source /etc/profile.d/cuda.sh + ``` + + **Note:** This command updates your current shell session with the new `PATH`. The `/etc/profile.d/cuda.sh` script ensures that the CUDA binaries are available in your `PATH` for all future sessions. + +## Verifying the Installation + +To confirm that CUDA is correctly installed and configured, check the version of the NVIDIA CUDA Compiler (`nvcc`): + +```bash +nvcc --version +``` + +You should see output similar to: + +``` +nvcc: NVIDIA (R) Cuda compiler driver +Copyright (c) 2005-2025 NVIDIA Corporation +Built on Fri_Feb_21_20:23:50_PST_2025 +Cuda compilation tools, release 12.8, V12.8.93 +Build cuda_12.8.r12.8/compiler.35583870_0 +``` + +This output confirms that the CUDA compiler is accessible and indicates the installed version. + +## Conclusion + +You have successfully set up CUDA on Fedora within a toolbox environment using the Fedora 41 CUDA repository. By manually updating the RPM db and configuring the environment, you can develop CUDA applications without affecting your host system. + +## Troubleshooting + +- **Installation Failures:** + + - If you encounter errors during installation, carefully read the error messages. They often indicate conflicting files or missing dependencies. + - You may use the `--excludepath` option with `rpm` to exclude conflicting files during manual RPM installations. + +- **Rebooting the Container:** + + - Sometimes there may be a bug in the NVIDIA driver host passthrough (such as missing a shared library). Rebooting the container may solve this issue: + + ```bash + # on the host system + podman container restart --all + ``` + +- **Environment Variables Not Set:** + - If `nvcc` is not found after installation, ensure that `/usr/local/cuda/bin` is in your `PATH`. + - Run `echo $PATH` to check if the path is included. + - Re-source the profile script or open a new terminal session. + +## Additional Notes + +- **Updating CUDA in the Future:** + + - Keep an eye on the official NVIDIA repositories for updates to your Fedora version. + - When an updated repository becomes available, adjust your `dnf` configuration accordingly. + +- **Building `llama.cpp`:** + + - With CUDA installed, you can follow these [build instructions for `llama.cpp`](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md) to compile it with CUDA support. + - Ensure that any CUDA-specific build flags or paths are correctly set in your build configuration. + +- **Using the Toolbox Environment:** + - The toolbox environment is isolated from your host system, which helps prevent conflicts. + - Remember that system files and configurations inside the toolbox are separate from the host. By default the home directory of the user is shared between the host and the toolbox. + +--- + +**Disclaimer:** Manually installing and modifying system packages can lead to instability of the container. The above steps are provided as a guideline and may need adjustments based on your specific system configuration. Always back up important data before making significant system changes, especially as your home folder is writable and shared with he toolbox. + +**Acknowledgments:** Special thanks to the Fedora community and NVIDIA documentation for providing resources that assisted in creating this guide. + +## References + +- [Fedora Toolbox Documentation](https://docs.fedoraproject.org/en-US/fedora-silverblue/toolbox/) +- [NVIDIA CUDA Installation Guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) +- [Podman Documentation](https://podman.io/get-started) + +--- diff --git a/llama.cpp/docs/backend/OPENCL.md b/llama.cpp/docs/backend/OPENCL.md new file mode 100644 index 0000000000000000000000000000000000000000..341dbce231bb6ad44eb9d7b99384ea3c4011d0b9 --- /dev/null +++ b/llama.cpp/docs/backend/OPENCL.md @@ -0,0 +1,209 @@ +# llama.cpp for OpenCL + +- [Background](#background) +- [OS](#os) +- [Hardware](#hardware) +- [DataType Supports](#datatype-supports) +- [Model Preparation](#model-preparation) +- [CMake Options](#cmake-options) +- [Android](#android) +- [Windows 11 Arm64](#windows-11-arm64) +- [Known Issue](#known-issues) +- [TODO](#todo) + +## Background + +OpenCL (Open Computing Language) is an open, royalty-free standard for cross-platform, parallel programming of diverse accelerators found in supercomputers, cloud servers, personal computers, mobile devices and embedded platforms. OpenCL specifies a programming language (based on C99) for programming these devices and application programming interfaces (APIs) to control the platform and execute programs on the compute devices. Similar to CUDA, OpenCL has been widely used to program GPUs and is supported by most GPU vendors. + +### Llama.cpp + OpenCL + +The llama.cpp OpenCL backend is designed to enable llama.cpp on **Qualcomm Adreno GPU** firstly via OpenCL. Thanks to the portabilty of OpenCL, the OpenCL backend can also run on certain Intel GPUs although the performance is not optimal. + +## OS + +| OS | Status | Verified | +|---------|---------|------------------------------------------------| +| Android | Support | Snapdragon 8 Gen 3, Snapdragon 8 Elite | +| Windows | Support | Windows 11 Arm64 with Snapdragon X Elite | +| Linux | Support | Ubuntu 22.04 WSL2 with Intel 12700H | + +## Hardware + +### Adreno GPU + +**Verified devices** + +| Adreno GPU | Status | +|:------------------------------------:|:-------:| +| Adreno 750 (Snapdragon 8 Gen 3) | Support | +| Adreno 830 (Snapdragon 8 Elite) | Support | +| Adreno X85 (Snapdragon X Elite) | Support | + +## DataType Supports + +| DataType | Status | +|:----------------------:|:--------------------------:| +| Q4_0 | Support | +| Q6_K | Support, but not optimized | + +## Model Preparation + +You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration. + +Currently we support `Q4_0` quantization and have optimize for it. To achieve best performance on Adreno GPU, add `--pure` to `llama-quantize`. For example, + +```sh +./llama-quantize --pure ggml-model-qwen2.5-3b-f16.gguf ggml-model-qwen-3b-Q4_0.gguf Q4_0 +``` + +Since `Q6_K` is also supported, `Q4_0` quantization without `--pure` will also work. However, the performance will be worse compared to pure `Q4_0` quantization. + +## CMake Options + +The OpenCL backend has the following CMake options that control the behavior of the backend. + +| CMake options | Default value | Description | +|:---------------------------------:|:--------------:|:------------------------------------------| +| `GGML_OPENCL_EMBED_KERNELS` | `ON` | Embed OpenCL kernels into the executable. | +| `GGML_OPENCL_USE_ADRENO_KERNELS` | `ON` | Use kernels optimized for Adreno. | + +## Android + +Ubuntu 22.04 is used for targeting Android. Make sure the following tools are accessible from command line, + +* Git +* CMake 3.29 +* Ninja +* Python3 + +### I. Setup Environment + +1. **Install NDK** + +```sh +cd ~ +wget https://dl.google.com/android/repository/commandlinetools-linux-8512546_latest.zip && \ +unzip commandlinetools-linux-8512546_latest.zip && \ +mkdir -p ~/android-sdk/cmdline-tools && \ +mv cmdline-tools latest && \ +mv latest ~/android-sdk/cmdline-tools/ && \ +rm -rf commandlinetools-linux-8512546_latest.zip + +yes | ~/android-sdk/cmdline-tools/latest/bin/sdkmanager "ndk;26.3.11579264" +``` + +2. **Install OpenCL Headers and Library** + +```sh +mkdir -p ~/dev/llm +cd ~/dev/llm + +git clone https://github.com/KhronosGroup/OpenCL-Headers && \ +cd OpenCL-Headers && \ +cp -r CL ~/android-sdk/ndk/26.3.11579264/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include + +cd ~/dev/llm + +git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && \ +cd OpenCL-ICD-Loader && \ +mkdir build_ndk26 && cd build_ndk26 && \ +cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_TOOLCHAIN_FILE=$HOME/android-sdk/ndk/26.3.11579264/build/cmake/android.toolchain.cmake \ + -DOPENCL_ICD_LOADER_HEADERS_DIR=$HOME/android-sdk/ndk/26.3.11579264/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include \ + -DANDROID_ABI=arm64-v8a \ + -DANDROID_PLATFORM=24 \ + -DANDROID_STL=c++_shared && \ +ninja && \ +cp libOpenCL.so ~/android-sdk/ndk/26.3.11579264/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android +``` + +### II. Build llama.cpp + +```sh +cd ~/dev/llm + +git clone https://github.com/ggml-org/llama.cpp && \ +cd llama.cpp && \ +mkdir build-android && cd build-android + +cmake .. -G Ninja \ + -DCMAKE_TOOLCHAIN_FILE=$HOME/android-sdk/ndk/26.3.11579264/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=arm64-v8a \ + -DANDROID_PLATFORM=android-28 \ + -DBUILD_SHARED_LIBS=OFF \ + -DGGML_OPENCL=ON + +ninja +``` + +## Windows 11 Arm64 + +A Snapdragon X Elite device with Windows 11 Arm64 is used. Make sure the following tools are accessible from command line, + +* Git +* CMake 3.29 +* Clang 19 +* Ninja +* Visual Studio 2022 +* Powershell 7 + +Visual Studio provides necessary headers and libraries although it is not directly used for building. +Alternatively, Visual Studio Build Tools can be installed instead of the full Visual Studio. + +Powershell 7 is used for the following commands. +If an older version of Powershell is used, these commands may not work as they are. + +### I. Setup Environment + +1. **Install OpenCL Headers and Library** + +```powershell +mkdir -p ~/dev/llm + +cd ~/dev/llm +git clone https://github.com/KhronosGroup/OpenCL-Headers && cd OpenCL-Headers +mkdir build && cd build +cmake .. -G Ninja ` + -DBUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF ` + -DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl" +cmake --build . --target install + +cd ~/dev/llm +git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && cd OpenCL-ICD-Loader +mkdir build && cd build +cmake .. -G Ninja ` + -DCMAKE_BUILD_TYPE=Release ` + -DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" ` + -DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl" +cmake --build . --target install +``` + +### II. Build llama.cpp + +```powershell + +mkdir -p ~/dev/llm +cd ~/dev/llm + +git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp +mkdir build && cd build + +cmake .. -G Ninja ` + -DCMAKE_TOOLCHAIN_FILE="$HOME/dev/llm/llama.cpp/cmake/arm64-windows-llvm.cmake" ` + -DCMAKE_BUILD_TYPE=Release ` + -DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" ` + -DBUILD_SHARED_LIBS=OFF ` + -DGGML_OPENCL=ON +ninja +``` + +## Known Issues + +- Currently OpenCL backend does not work on Adreno 6xx GPUs. + +## TODO + +- Optimization for Q6_K +- Support and optimization for Q4_K diff --git a/llama.cpp/docs/backend/SYCL.md b/llama.cpp/docs/backend/SYCL.md new file mode 100644 index 0000000000000000000000000000000000000000..21cf3c83d24677d1ea97ee3da4c2bed9b5fd4731 --- /dev/null +++ b/llama.cpp/docs/backend/SYCL.md @@ -0,0 +1,815 @@ +# llama.cpp for SYCL + +- [Background](#background) +- [Recommended Release](#recommended-release) +- [News](#news) +- [OS](#os) +- [Hardware](#hardware) +- [Docker](#docker) +- [Linux](#linux) +- [Windows](#windows) +- [Environment Variable](#environment-variable) +- [Known Issue](#known-issues) +- [Q&A](#qa) +- [TODO](#todo) + +## Background + +**SYCL** is a high-level parallel programming model designed to improve developers productivity writing code across various hardware accelerators such as CPUs, GPUs, and FPGAs. It is a single-source language designed for heterogeneous computing and based on standard C++17. + +**oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to Intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include: + +- **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers. +- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. Intel oneMKL, oneMath and oneDNN)*. +- **oneAPI LevelZero**: A high performance low level interface for fine-grained control over Intel iGPUs and dGPUs. +- **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets. + +### Llama.cpp + SYCL + +The llama.cpp SYCL backend is primarily designed for **Intel GPUs**. +SYCL cross-platform capabilities enable support for Nvidia GPUs as well, with limited support for AMD. + +## Recommended Release + +The following releases are verified and recommended: + +|Commit ID|Tag|Release|Verified Platform| Update date| +|-|-|-|-|-| +|24e86cae7219b0f3ede1d5abdf5bf3ad515cccb8|b5377 |[llama-b5377-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b5377/llama-b5377-bin-win-sycl-x64.zip) |ArcB580/Linux/oneAPI 2025.1
LNL Arc GPU/Windows 11/oneAPI 2025.1.1|2025-05-15| +|3bcd40b3c593d14261fb2abfabad3c0fb5b9e318|b4040 |[llama-b4040-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b4040/llama-b4040-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1| 2024-11-19| +|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1|| + + +## News + +- 2025.2 + - Optimize MUL_MAT Q4_0 on Intel GPU for all dGPUs and built-in GPUs since MTL. Increase the performance of LLM (llama-2-7b.Q4_0.gguf) 21%-87% on Intel GPUs (MTL, ARL-H, Arc, Flex, PVC). + |GPU|Base tokens/s|Increased tokens/s|Percent| + |-|-|-|-| + |PVC 1550|39|73|+87%| + |Flex 170|39|50|+28%| + |Arc770|42|55|+30%| + |MTL|13|16|+23%| + |ARL-H|14|17|+21%| + +- 2024.11 + - Use syclcompat to improve the performance on some platforms. This requires to use oneAPI 2025.0 or newer. + +- 2024.8 + - Use oneDNN as the default GEMM library, improve the compatibility for new Intel GPUs. + +- 2024.5 + - Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc770. + - Arch Linux is verified successfully. + +- 2024.4 + - Support data types: GGML_TYPE_IQ4_NL, GGML_TYPE_IQ4_XS, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M. + +- 2024.3 + - Release binary files of Windows. + - A blog is published: **Run LLM on all Intel GPUs Using llama.cpp**: [intel.com](https://www.intel.com/content/www/us/en/developer/articles/technical/run-llm-on-all-gpus-using-llama-cpp-artical.html) or [medium.com](https://medium.com/@jianyu_neo/run-llm-on-all-intel-gpus-using-llama-cpp-fd2e2dcbd9bd). + - New base line is ready: [tag b2437](https://github.com/ggml-org/llama.cpp/tree/b2437). + - Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing. + - Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE. + - Support detecting all GPUs with level-zero and same top **Max compute units**. + - Support OPs + - hardsigmoid + - hardswish + - pool2d + +- 2024.1 + - Create SYCL backend for Intel GPU. + - Support Windows build + +## OS + +| OS | Status | Verified | +|---------|---------|------------------------------------------------| +| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39, Arch Linux | +| Windows | Support | Windows 11 | + + +## Hardware + +### Intel GPU + +SYCL backend supports Intel GPU Family: + +- Intel Data Center Max Series +- Intel Flex Series, Arc Series +- Intel Built-in Arc GPU +- Intel iGPU in Core CPU (11th Generation Core CPU and newer, refer to [oneAPI supported GPU](https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-base-toolkit-system-requirements.html#inpage-nav-1-1)). + +#### Verified devices + +| Intel GPU | Status | Verified Model | +|-------------------------------|---------|---------------------------------------| +| Intel Data Center Max Series | Support | Max 1550, 1100 | +| Intel Data Center Flex Series | Support | Flex 170 | +| Intel Arc Series | Support | Arc 770, 730M, Arc A750, B580 | +| Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake, Arrow Lake, Lunar Lake | +| Intel iGPU | Support | iGPU in 13700k, 13400, i5-1250P, i7-1260P, i7-1165G7 | + +*Notes:* + +- **Memory** + - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/llama-cli`. + - Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU. + +- **Execution Unit (EU)** + - If the iGPU has less than 80 EUs, the inference speed will likely be too slow for practical use. + +### Other Vendor GPU + +**Verified devices** + +| Nvidia GPU | Status | Verified Model | +|--------------------------|-----------|----------------| +| Ampere Series | Supported | A100, A4000 | +| Ampere Series *(Mobile)* | Supported | RTX 40 Series | + +| AMD GPU | Status | Verified Model | +|--------------------------|--------------|----------------| +| Radeon Pro | Experimental | W6800 | +| Radeon RX | Experimental | 6700 XT | + +Note: AMD GPU support is highly experimental and is incompatible with F16. +Additionally, it only supports GPUs with a sub_group_size (warp size) of 32. + +## Docker + +The docker build option is currently limited to *Intel GPU* targets. + +### Build image + +```sh +# Using FP16 +docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile . +``` + +*Notes*: + +To build in default FP32 *(Slower than FP16 alternative)*, set `--build-arg="GGML_SYCL_F16=OFF"` in the previous command. + +You can also use the `.devops/llama-server-intel.Dockerfile`, which builds the *"server"* alternative. +Check the [documentation for Docker](../docker.md) to see the available images. + +### Run container + +```sh +# First, find all the DRI cards +ls -la /dev/dri +# Then, pick the card that you want to use (here for e.g. /dev/dri/card1). +docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 +``` + +*Notes:* +- Docker has been tested successfully on native Linux. WSL support has not been verified yet. +- You may need to install Intel GPU driver on the **host** machine *(Please refer to the [Linux configuration](#linux) for details)*. + +## Linux + +### I. Setup Environment + +1. **Install GPU drivers** + + - **Intel GPU** + +Intel data center GPUs drivers installation guide and download page can be found here: [Get intel dGPU Drivers](https://dgpu-docs.intel.com/driver/installation.html#ubuntu-install-steps). + +*Note*: for client GPUs *(iGPU & Arc A-Series)*, please refer to the [client iGPU driver installation](https://dgpu-docs.intel.com/driver/client/overview.html). + +Once installed, add the user(s) to the `video` and `render` groups. + +```sh +sudo usermod -aG render $USER +sudo usermod -aG video $USER +``` + +*Note*: logout/re-login for the changes to take effect. + +Verify installation through `clinfo`: + +```sh +sudo apt install clinfo +sudo clinfo -l +``` + +Sample output: + +```sh +Platform #0: Intel(R) OpenCL Graphics + `-- Device #0: Intel(R) Arc(TM) A770 Graphics + +Platform #0: Intel(R) OpenCL HD Graphics + `-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49] +``` + +- **Nvidia GPU** + +In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cuda)-* are installed. + +- **AMD GPU** + +To target AMD GPUs with SYCL, the ROCm stack must be installed first. + +2. **Install Intel® oneAPI Base toolkit** + +- **For Intel GPU** + +The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page. + +Please follow the instructions for downloading and installing the Toolkit for Linux, and preferably keep the default installation values unchanged, notably the installation path *(`/opt/intel/oneapi` by default)*. + +Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable. + +Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI oneDNN for Intel GPUs. + +- **Adding support to Nvidia GPUs** + +**oneAPI Plugin**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup. + +**oneDNN**: The current oneDNN releases *(shipped with the oneAPI base-toolkit)* do not include the NVIDIA backend. Therefore, oneDNN must be compiled from source to enable the NVIDIA target: + +```sh +git clone https://github.com/oneapi-src/oneDNN.git +cd oneDNN +cmake -GNinja -Bbuild-nvidia -DDNNL_CPU_RUNTIME=DPCPP -DDNNL_GPU_RUNTIME=DPCPP -DDNNL_GPU_VENDOR=NVIDIA -DONEDNN_BUILD_GRAPH=OFF -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx +cmake --build build-nvidia --config Release +``` + +- **Adding support to AMD GPUs** + +**oneAPI Plugin**: In order to enable SYCL support on AMD GPUs, please install the [Codeplay oneAPI Plugin for AMD GPUs](https://developer.codeplay.com/products/oneapi/amd/download). As with Nvidia GPUs, the user should also make sure the plugin version matches the installed base toolkit. + +3. **Verify installation and environment** + +In order to check the available SYCL devices on the machine, please use the `sycl-ls` command. +```sh +source /opt/intel/oneapi/setvars.sh +sycl-ls +``` + +- **Intel GPU** + +When targeting an intel GPU, the user should expect one or more devices among the available SYCL devices. Please make sure that at least one GPU is present via `sycl-ls`, for instance `[level_zero:gpu]` in the sample output below: + +``` +[opencl:acc][opencl:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000] +[opencl:cpu][opencl:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] +[opencl:gpu][opencl:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO [23.30.26918.50] +[level_zero:gpu][level_zero:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918] +``` + +- **Nvidia GPU** + +Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA device [`cuda:gpu`] as below: + +``` +[opencl:acc][opencl:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.12.0.12_195853.xmain-hotfix] +[opencl:cpu][opencl:1] Intel(R) OpenCL, Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix] +[cuda:gpu][cuda:0] NVIDIA CUDA BACKEND, NVIDIA A100-PCIE-40GB 8.0 [CUDA 12.5] +``` + +- **AMD GPU** + +For AMD GPUs we should expect at least one SYCL-HIP device [`hip:gpu`]: + +``` +[opencl:cpu][opencl:0] Intel(R) OpenCL, 12th Gen Intel(R) Core(TM) i9-12900K OpenCL 3.0 (Build 0) [2024.18.6.0.02_160000] +[hip:gpu][hip:0] AMD HIP BACKEND, AMD Radeon PRO W6800 gfx1030 [HIP 60140.9] +``` + +### II. Build llama.cpp + +#### Intel GPU + +```sh +./examples/sycl/build.sh +``` + +or + +```sh +# Export relevant ENV variables +source /opt/intel/oneapi/setvars.sh + +# Option 1: Use FP32 (recommended for better performance in most cases) +cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx + +# Option 2: Use FP16 +cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON + +# build all binary +cmake --build build --config Release -j -v +``` + +It is possible to come across some precision issues when running tests that stem from using faster +instructions, which can be circumvented by setting the environment variable `SYCL_PROGRAM_COMPILE_OPTIONS` +as `-cl-fp32-correctly-rounded-divide-sqrt` + +#### Nvidia GPU + +The SYCL backend depends on [oneMath](https://github.com/uxlfoundation/oneMath) for Nvidia and AMD devices. +By default it is automatically built along with the project. A specific build can be provided by setting the CMake flag `-DoneMath_DIR=/path/to/oneMath/install/lib/cmake/oneMath`. + +```sh +# Build LLAMA with Nvidia BLAS acceleration through SYCL +# Setting GGML_SYCL_DEVICE_ARCH is optional but can improve performance +GGML_SYCL_DEVICE_ARCH=sm_80 # Example architecture + +# Option 1: Use FP32 (recommended for better performance in most cases) +cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DDNNL_DIR=/path/to/oneDNN/build-nvidia/install/lib/cmake/dnnl + +# Option 2: Use FP16 +cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON -DDNNL_DIR=/path/to/oneDNN/build-nvidia/install/lib/cmake/dnnl + +# build all binary +cmake --build build --config Release -j -v +``` + +It is possible to come across some precision issues when running tests that stem from using faster +instructions, which can be circumvented by passing the `-fno-fast-math` flag to the compiler. + +#### AMD GPU + +The SYCL backend depends on [oneMath](https://github.com/uxlfoundation/oneMath) for Nvidia and AMD devices. +By default it is automatically built along with the project. A specific build can be provided by setting the CMake flag `-DoneMath_DIR=/path/to/oneMath/install/lib/cmake/oneMath`. + +```sh +# Build LLAMA with rocBLAS acceleration through SYCL + +## AMD +# Use FP32, FP16 is not supported +# Find your GGML_SYCL_DEVICE_ARCH with rocminfo, under the key 'Name:' +GGML_SYCL_DEVICE_ARCH=gfx90a # Example architecture +cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=AMD -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx + +# build all binary +cmake --build build --config Release -j -v +``` + +### III. Run the inference + +#### Retrieve and prepare model + +You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model preparation, or download an already quantized model like [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) or [Meta-Llama-3-8B-Instruct-Q4_0.gguf](https://huggingface.co/aptha/Meta-Llama-3-8B-Instruct-Q4_0-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_0.gguf). + +##### Check device + +1. Enable oneAPI running environment + +```sh +source /opt/intel/oneapi/setvars.sh +``` + +2. List devices information + +Similar to the native `sycl-ls`, available SYCL devices can be queried as follow: + +```sh +./build/bin/llama-ls-sycl-device +``` + +This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *intel GPU* it would look like the following: +``` +found 2 SYCL devices: + +| | | |Compute |Max compute|Max work|Max sub| | +|ID| Device Type| Name|capability|units |group |group |Global mem size| +|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------| +| 0|[level_zero:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136| +| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216| +``` + +#### Choose level-zero devices + +|Chosen Device ID|Setting| +|-|-| +|0|`export ONEAPI_DEVICE_SELECTOR="level_zero:0"` or no action| +|1|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"`| +|0 & 1|`export ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`| + +#### Execute + +Choose one of following methods to run. + +1. Script + +- Use device 0: + +```sh +./examples/sycl/run-llama2.sh 0 +# OR +./examples/sycl/run-llama3.sh 0 +``` +- Use multiple devices: + +```sh +./examples/sycl/run-llama2.sh +# OR +./examples/sycl/run-llama3.sh +``` + +2. Command line +Launch inference + +There are two device selection modes: + +- Single device: Use one device assigned by user. Default device id is 0. +- Multiple devices: Automatically choose the devices with the same backend. + +In two device selection modes, the default SYCL backend is level_zero, you can choose other backend supported by SYCL by setting environment variable ONEAPI_DEVICE_SELECTOR. + +| Device selection | Parameter | +|------------------|----------------------------------------| +| Single device | --split-mode none --main-gpu DEVICE_ID | +| Multiple devices | --split-mode layer (default) | + +Examples: + +- Use device 0: + +```sh +ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm none -mg 0 +``` + +- Use multiple devices: + +```sh +ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm layer +``` + +*Notes:* + +- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow: + +```sh +detect 1 SYCL GPUs: [0] with top Max compute units:512 +``` +Or +```sh +use 1 SYCL GPUs: [0] with Max compute units:512 +``` + +## Windows + +### I. Setup Environment + +1. Install GPU driver + +Intel GPU drivers instructions guide and download page can be found here: [Get Intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html). + +2. Install Visual Studio + +If you already have a recent version of Microsoft Visual Studio, you can skip this step. Otherwise, please refer to the official download page for [Microsoft Visual Studio](https://visualstudio.microsoft.com/). + +3. Install Intel® oneAPI Base toolkit + +The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page. + +Please follow the instructions for downloading and installing the Toolkit for Windows, and preferably keep the default installation values unchanged, notably the installation path *(`C:\Program Files (x86)\Intel\oneAPI` by default)*. + +Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable. + +b. Enable oneAPI running environment: + +- Type "oneAPI" in the search bar, then open the `Intel oneAPI command prompt for Intel 64 for Visual Studio 2022` App. + +- On the command prompt, enable the runtime environment with the following: +``` +"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 +``` + +- if you are using Powershell, enable the runtime environment with the following: + +``` +cmd.exe "/K" '"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" && powershell' +``` + +c. Verify installation + +In the oneAPI command line, run the following to print the available SYCL devices: + +``` +sycl-ls.exe +``` + +There should be one or more *level-zero* GPU devices displayed as **[ext_oneapi_level_zero:gpu]**. Below is example of such output detecting an *intel Iris Xe* GPU as a Level-zero SYCL device: + +Output (example): +``` +[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000] +[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] +[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO [31.0.101.5186] +[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044] +``` + +4. Install build tools + +a. Download & install cmake for Windows: https://cmake.org/download/ (CMake can also be installed from Visual Studio Installer) +b. The new Visual Studio will install Ninja as default. (If not, please install it manually: https://ninja-build.org/) + + +### II. Build llama.cpp + +You could download the release package for Windows directly, which including binary files and depended oneAPI dll files. + +Choose one of following methods to build from source code. + +#### 1. Script + +```sh +.\examples\sycl\win-build-sycl.bat +``` + +#### 2. CMake + +On the oneAPI command line window, step into the llama.cpp main directory and run the following: + +``` +@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force + +# Option 1: Use FP32 (recommended for better performance in most cases) +cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release + +# Option 2: Or FP16 +cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DGGML_SYCL_F16=ON + +cmake --build build --config Release -j +``` + +Or, use CMake presets to build: + +```sh +cmake --preset x64-windows-sycl-release +cmake --build build-x64-windows-sycl-release -j --target llama-cli + +cmake -DGGML_SYCL_F16=ON --preset x64-windows-sycl-release +cmake --build build-x64-windows-sycl-release -j --target llama-cli + +cmake --preset x64-windows-sycl-debug +cmake --build build-x64-windows-sycl-debug -j --target llama-cli +``` + +#### 3. Visual Studio + +You have two options to use Visual Studio to build llama.cpp: +- As CMake Project using CMake presets. +- Creating a Visual Studio solution to handle the project. + +**Note**: + +All following commands are executed in PowerShell. + +##### - Open as a CMake Project + +You can use Visual Studio to open the `llama.cpp` folder directly as a CMake project. Before compiling, select one of the SYCL CMake presets: + +- `x64-windows-sycl-release` + +- `x64-windows-sycl-debug` + +*Notes:* +- For a minimal experimental setup, you can build only the inference executable using: + + ```Powershell + cmake --build build --config Release -j --target llama-cli + ``` + +##### - Generating a Visual Studio Solution + +You can use Visual Studio solution to build and work on llama.cpp on Windows. You need to convert the CMake Project into a `.sln` file. + +If you want to use the Intel C++ Compiler for the entire `llama.cpp` project, run the following command: + +```Powershell +cmake -B build -G "Visual Studio 17 2022" -T "Intel C++ Compiler 2025" -A x64 -DGGML_SYCL=ON -DCMAKE_BUILD_TYPE=Release +``` + +If you prefer to use the Intel C++ Compiler only for `ggml-sycl`, ensure that `ggml` and its backend libraries are built as shared libraries ( i.e. `-DBUILD_SHARED_LIBRARIES=ON`, this is default behaviour): + +```Powershell +cmake -B build -G "Visual Studio 17 2022" -A x64 -DGGML_SYCL=ON -DCMAKE_BUILD_TYPE=Release \ + -DSYCL_INCLUDE_DIR="C:\Program Files (x86)\Intel\oneAPI\compiler\latest\include" \ + -DSYCL_LIBRARY_DIR="C:\Program Files (x86)\Intel\oneAPI\compiler\latest\lib" +``` + +If successful the build files have been written to: *path/to/llama.cpp/build* +Open the project file **build/llama.cpp.sln** with Visual Studio. + +Once the Visual Studio solution is created, follow these steps: + +1. Open the solution in Visual Studio. + +2. Right-click on `ggml-sycl` and select **Properties**. + +3. In the left column, expand **C/C++** and select **DPC++**. + +4. In the right panel, find **Enable SYCL Offload** and set it to `Yes`. + +5. Apply the changes and save. + + +*Navigation Path:* + +``` +Properties -> C/C++ -> DPC++ -> Enable SYCL Offload (Yes) +``` + +Now, you can build `llama.cpp` with the SYCL backend as a Visual Studio project. +To do it from menu: `Build -> Build Solution`. +Once it is completed, final results will be in **build/Release/bin** + +*Additional Note* + +- You can avoid specifying `SYCL_INCLUDE_DIR` and `SYCL_LIBRARY_DIR` in the CMake command by setting the environment variables: + + - `SYCL_INCLUDE_DIR_HINT` + + - `SYCL_LIBRARY_DIR_HINT` + +- Above instruction has been tested with Visual Studio 17 Community edition and oneAPI 2025.0. We expect them to work also with future version if the instructions are adapted accordingly. + +### III. Run the inference + +#### Retrieve and prepare model + +You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model preparation, or download an already quantized model like [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) or [Meta-Llama-3-8B-Instruct-Q4_0.gguf](https://huggingface.co/aptha/Meta-Llama-3-8B-Instruct-Q4_0-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_0.gguf). + +##### Check device + +1. Enable oneAPI running environment + +On the oneAPI command line window, run the following and step into the llama.cpp directory: +``` +"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 +``` + +2. List devices information + +Similar to the native `sycl-ls`, available SYCL devices can be queried as follow: + +``` +build\bin\llama-ls-sycl-device.exe +``` + +This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *Intel GPU* it would look like the following: +``` +found 2 SYCL devices: +| | | |Compute |Max compute|Max work|Max sub| | +|ID| Device Type| Name|capability|units |group |group |Global mem size| +|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------| +| 0|[level_zero:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136| +| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216| + +``` + +#### Choose level-zero devices + +|Chosen Device ID|Setting| +|-|-| +|0|Default option. You may also want to `set ONEAPI_DEVICE_SELECTOR="level_zero:0"`| +|1|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"`| +|0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"` or `set ONEAPI_DEVICE_SELECTOR="level_zero:*"`| + +#### Execute + +Choose one of following methods to run. + +1. Script + +``` +examples\sycl\win-run-llama-2.bat +``` + +or + +``` +examples\sycl\win-run-llama-3.bat +``` + +2. Command line + +Launch inference + +There are two device selection modes: + +- Single device: Use one device assigned by user. Default device id is 0. +- Multiple devices: Automatically choose the devices with the same backend. + +In two device selection modes, the default SYCL backend is level_zero, you can choose other backend supported by SYCL by setting environment variable ONEAPI_DEVICE_SELECTOR. + +| Device selection | Parameter | +|------------------|----------------------------------------| +| Single device | --split-mode none --main-gpu DEVICE_ID | +| Multiple devices | --split-mode layer (default) | + +Examples: + +- Use device 0: + +``` +build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm none -mg 0 +``` + +- Use multiple devices: + +``` +build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm layer +``` + + +Note: + +- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow: + +```sh +detect 1 SYCL GPUs: [0] with top Max compute units:512 +``` + +Or + +```sh +use 1 SYCL GPUs: [0] with Max compute units:512 +``` + + +## Environment Variable + +#### Build + +| Name | Value | Function | +|--------------------|---------------------------------------|---------------------------------------------| +| GGML_SYCL | ON (mandatory) | Enable build with SYCL code path. | +| GGML_SYCL_TARGET | INTEL *(default)* \| NVIDIA \| AMD | Set the SYCL target device type. | +| GGML_SYCL_DEVICE_ARCH | Optional (except for AMD) | Set the SYCL device architecture, optional except for AMD. Setting the device architecture can improve the performance. See the table [--offload-arch](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--offload-arch) for a list of valid architectures. | +| GGML_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. (1.) | +| GGML_SYCL_GRAPH | ON *(default)* \|OFF *(Optional)* | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). | +| GGML_SYCL_DNN | ON *(default)* \|OFF *(Optional)* | Enable build with oneDNN. | +| CMAKE_C_COMPILER | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path. | +| CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)* | Set `icpx/icx` compiler for SYCL code path. | + +1. FP16 is recommended for better prompt processing performance on quantized models. Performance is equivalent in text generation but set `GGML_SYCL_F16=OFF` if you are experiencing issues with FP16 builds. + +#### Runtime + +| Name | Value | Function | +|-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------| +| GGML_SYCL_DEBUG | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG | +| GGML_SYCL_DISABLE_OPT | 0 (default) or 1 | Disable optimize features for Intel GPUs. (Recommended to 1 for intel devices older than Gen 10) | +| GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because graph performance isn't yet better than non-graph performance. | +| GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. | +| ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.
Recommended to use when --split-mode = layer | + + +## Known Issues + +- `Split-mode:[row]` is not supported. + +## Q&A + +- Error: `error while loading shared libraries: libsycl.so: cannot open shared object file: No such file or directory`. + + - Potential cause: Unavailable oneAPI installation or not set ENV variables. + - Solution: Install *oneAPI base toolkit* and enable its ENV through: `source /opt/intel/oneapi/setvars.sh`. + +- General compiler error: + + - Remove **build** folder or try a clean-build. + +- I can **not** see `[ext_oneapi_level_zero:gpu]` afer installing the GPU driver on Linux. + + Please double-check with `sudo sycl-ls`. + + If it's present in the list, please add video/render group to your user then **logout/login** or restart your system: + + ``` + sudo usermod -aG render $USER + sudo usermod -aG video $USER + ``` + Otherwise, please double-check the GPU driver installation steps. + +- Can I report Ollama issue on Intel GPU to llama.cpp SYCL backend? + + No. We can't support Ollama issue directly, because we aren't familiar with Ollama. + + Sugguest reproducing on llama.cpp and report similar issue to llama.cpp. We will surpport it. + + It's same for other projects including llama.cpp SYCL backend. + +- `Native API failed. Native API returns: 39 (UR_RESULT_ERROR_OUT_OF_DEVICE_MEMORY)`, `ggml_backend_sycl_buffer_type_alloc_buffer: can't allocate 3503030272 Bytes of memory on device`, or `failed to allocate SYCL0 buffer` + + You are running out of Device Memory. + + |Reason|Solution| + |-|-| + | The default context is too big. It leads to excessive memory usage.|Set `-c 8192` or a smaller value.| + | The model is too big and requires more memory than what is available.|Choose a smaller model or change to a smaller quantization, like Q5 -> Q4;
Alternatively, use more than one device to load model.| + +### **GitHub contribution**: +Please add the `SYCL :` prefix/tag in issues/PRs titles to help the SYCL contributors to check/address them without delay. + +## TODO + +- Review ZES_ENABLE_SYSMAN: https://github.com/intel/compute-runtime/blob/master/programmers-guide/SYSMAN.md#support-and-limitations diff --git a/llama.cpp/docs/build-s390x.md b/llama.cpp/docs/build-s390x.md new file mode 100644 index 0000000000000000000000000000000000000000..aee65ab439858d1dd36462c8a54b17466531d01f --- /dev/null +++ b/llama.cpp/docs/build-s390x.md @@ -0,0 +1,276 @@ +> [!IMPORTANT] +> This build documentation is specific only to IBM Z & LinuxONE mainframes (s390x). You can find the build documentation for other architectures: [build.md](build.md). + +# Build llama.cpp locally (for s390x) + +The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](../include/llama.h). + +The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server. + +**To get the code:** + +```bash +git clone https://github.com/ggml-org/llama.cpp +cd llama.cpp +``` + +## CPU Build with BLAS + +Building llama.cpp with BLAS support is highly recommended as it has shown to provide performance improvements. Make sure to have OpenBLAS installed in your environment. + +```bash +cmake -S . -B build \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_BLAS=ON \ + -DGGML_BLAS_VENDOR=OpenBLAS + +cmake --build build --config Release -j $(nproc) +``` + +**Notes**: + +- For faster repeated compilation, install [ccache](https://ccache.dev/) +- By default, VXE/VXE2 is enabled. To disable it (not recommended): + + ```bash + cmake -S . -B build \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_BLAS=ON \ + -DGGML_BLAS_VENDOR=OpenBLAS \ + -DGGML_VXE=OFF + + cmake --build build --config Release -j $(nproc) + ``` + +- By default, NNPA is disabled by default. To enable it: + + ```bash + cmake -S . -B build \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_BLAS=ON \ + -DGGML_BLAS_VENDOR=OpenBLAS \ + -DGGML_NNPA=ON + + cmake --build build --config Release -j $(nproc) + ``` + +- For debug builds: + + ```bash + cmake -S . -B build \ + -DCMAKE_BUILD_TYPE=Debug \ + -DGGML_BLAS=ON \ + -DGGML_BLAS_VENDOR=OpenBLAS + cmake --build build --config Debug -j $(nproc) + ``` + +- For static builds, add `-DBUILD_SHARED_LIBS=OFF`: + + ```bash + cmake -S . -B build \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_BLAS=ON \ + -DGGML_BLAS_VENDOR=OpenBLAS \ + -DBUILD_SHARED_LIBS=OFF + + cmake --build build --config Release -j $(nproc) + ``` + +## Getting GGUF Models + +All models need to be converted to Big-Endian. You can achieve this in three cases: + +1. **Use pre-converted models verified for use on IBM Z & LinuxONE (easiest)** + + ![File Type - gguf](https://img.shields.io/badge/File_Type-gguf-fff) + + You can find popular models pre-converted and verified at [s390x Verified Models](https://huggingface.co/collections/taronaeo/s390x-verified-models-672765393af438d0ccb72a08) or [s390x Runnable Models](https://huggingface.co/collections/taronaeo/s390x-runnable-models-686e951824198df12416017e). + + These models have already been converted from `safetensors` to `GGUF` Big-Endian and their respective tokenizers verified to run correctly on IBM z15 and later system. + +2. **Convert safetensors model to GGUF Big-Endian directly (recommended)** + + ![File Type - safetensors](https://img.shields.io/badge/File_Type-safetensors-da1e28) + + The model you are trying to convert must be in `safetensors` file format (for example [IBM Granite 3.3 2B](https://huggingface.co/ibm-granite/granite-3.3-2b-instruct)). Make sure you have downloaded the model repository for this case. + + Ensure that you have installed the required packages in advance + + ```bash + pip3 install -r requirements.txt + ``` + + Convert the `safetensors` model to `GGUF` + + ```bash + python3 convert_hf_to_gguf.py \ + --outfile model-name-be.f16.gguf \ + --outtype f16 \ + --bigendian \ + model-directory/ + ``` + + For example, + + ```bash + python3 convert_hf_to_gguf.py \ + --outfile granite-3.3-2b-instruct-be.f16.gguf \ + --outtype f16 \ + --bigendian \ + granite-3.3-2b-instruct/ + ``` + +3. **Convert existing GGUF Little-Endian model to Big-Endian** + + ![File Type - gguf](https://img.shields.io/badge/File_Type-gguf-fff) + + The model you are trying to convert must be in `gguf` file format (for example [IBM Granite 3.3 2B GGUF](https://huggingface.co/ibm-granite/granite-3.3-2b-instruct-GGUF)). Make sure you have downloaded the model file for this case. + + ```bash + python3 gguf-py/gguf/scripts/gguf_convert_endian.py model-name.f16.gguf BIG + ``` + + For example, + + ```bash + python3 gguf-py/gguf/scripts/gguf_convert_endian.py granite-3.3-2b-instruct-le.f16.gguf BIG + mv granite-3.3-2b-instruct-le.f16.gguf granite-3.3-2b-instruct-be.f16.gguf + ``` + + **Notes:** + + - The GGUF endian conversion script may not support all data types at the moment and may fail for some models/quantizations. When that happens, please try manually converting the safetensors model to GGUF Big-Endian via Step 2. + +## IBM Accelerators + +### 1. SIMD Acceleration + +Only available in IBM z15 or later system with the `-DGGML_VXE=ON` (turned on by default) compile flag. No hardware acceleration is possible with llama.cpp with older systems, such as IBM z14/arch12. In such systems, the APIs can still run but will use a scalar implementation. + +### 2. NNPA Vector Intrinsics Acceleration + +Only available in IBM z16 or later system with the `-DGGML_NNPA=ON` (turned off by default) compile flag. No hardware acceleration is possible with llama.cpp with older systems, such as IBM z15/arch13. In such systems, the APIs can still run but will use a scalar implementation. + +### 3. zDNN Accelerator + +_Only available in IBM z16 / LinuxONE 4 or later system. No support currently available._ + +### 4. Spyre Accelerator + +_Only available with IBM z17 / LinuxONE 5 or later system. No support currently available._ + +## Performance Tuning + +### 1. Virtualization Setup + +It is strongly recommended to use only LPAR (Type-1) virtualization to get the most performance. + +Note: Type-2 virtualization is not supported at the moment, while you can get it running, the performance will not be the best. + +### 2. IFL (Core) Count + +It is recommended to allocate a minimum of 8 shared IFLs assigned to the LPAR. Increasing the IFL count past 8 shared IFLs will only improve Prompt Processing performance but not Token Generation. + +Note: IFL count does not equate to vCPU count. + +### 3. SMT vs NOSMT (Simultaneous Multithreading) + +It is strongly recommended to disable SMT via the kernel boot parameters as it negatively affects performance. Please refer to your Linux distribution's guide on disabling SMT via kernel boot parameters. + +### 4. BLAS vs NOBLAS + +IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongly recommended to use BLAS. + +## Frequently Asked Questions (FAQ) + +1. I'm getting the following error message while trying to load a model: `gguf_init_from_file_impl: failed to load model: this GGUF file version 50331648 is extremely large, is there a mismatch between the host and model endianness?` + + Answer: Please ensure that the model you have downloaded/converted is GGUFv3 Big-Endian. These models are usually denoted with the `-be` suffix, i.e., `granite-3.3-2b-instruct-be.F16.gguf`. + + You may refer to the [Getting GGUF Models](#getting-gguf-models) section to manually convert a `safetensors` model to `GGUF` Big Endian. + +2. I'm getting extremely poor performance when running inference on a model + + Answer: Please refer to the [Appendix B: SIMD Support Matrix](#appendix-b-simd-support-matrix) to check if your model quantization is supported by SIMD acceleration. + +3. I'm building on IBM z17 and getting the following error messages: `invalid switch -march=z17` + + Answer: Please ensure that your GCC compiler is of minimum GCC 15.1.0 version, and have `binutils` updated to the latest version. If this does not fix the problem, kindly open an issue. + +4. Failing to install the `sentencepiece` package using GCC 15+ + + Answer: The `sentencepiece` team are aware of this as seen in [this issue](https://github.com/google/sentencepiece/issues/1108). + + As a temporary workaround, please run the installation command with the following environment variables. + + ```bash + export CXXFLAGS="-include cstdint" + ``` + + For example, + + ```bash + CXXFLAGS="-include cstdint" pip3 install -r requirements.txt + ``` + +5. `-DGGML_NNPA=ON` generates gibberish output + + Answer: We are aware of this as detailed in [this issue](https://github.com/ggml-org/llama.cpp/issues/14877). Please either try reducing the number of threads, or disable the compile option using `-DGGML_NNPA=OFF`. + +## Getting Help on IBM Z & LinuxONE + +1. **Bugs, Feature Requests** + + Please file an issue in llama.cpp and ensure that the title contains "s390x". + +2. **Other Questions** + + Please reach out directly to [aionz@us.ibm.com](mailto:aionz@us.ibm.com). + +## Appendix A: Hardware Support Matrix + +| | Support | Minimum Compiler Version | +| ------- | ------- | ------------------------ | +| IBM z15 | ✅ | | +| IBM z16 | ✅ | | +| IBM z17 | ✅ | GCC 15.1.0 | + +- ✅ - supported and verified to run as intended +- 🚫 - unsupported, we are unlikely able to provide support + +## Appendix B: SIMD Support Matrix + +| | VX/VXE/VXE2 | NNPA | zDNN | Spyre | +| ---------- | ----------- | ---- | ---- | ----- | +| FP32 | ✅ | ✅ | ❓ | ❓ | +| FP16 | ✅ | ✅ | ❓ | ❓ | +| BF16 | 🚫 | 🚫 | ❓ | ❓ | +| Q4_0 | ✅ | ✅ | ❓ | ❓ | +| Q4_1 | ✅ | ✅ | ❓ | ❓ | +| Q5_0 | 🚫 | 🚫 | ❓ | ❓ | +| Q5_1 | 🚫 | 🚫 | ❓ | ❓ | +| Q8_0 | ✅ | ✅ | ❓ | ❓ | +| Q2_K | 🚫 | 🚫 | ❓ | ❓ | +| Q3_K | ✅ | ✅ | ❓ | ❓ | +| Q4_K | ✅ | ✅ | ❓ | ❓ | +| Q5_K | ✅ | ✅ | ❓ | ❓ | +| Q6_K | ✅ | ✅ | ❓ | ❓ | +| TQ1_0 | 🚫 | 🚫 | ❓ | ❓ | +| TQ2_0 | 🚫 | 🚫 | ❓ | ❓ | +| IQ2_XXS | 🚫 | 🚫 | ❓ | ❓ | +| IQ2_XS | 🚫 | 🚫 | ❓ | ❓ | +| IQ2_S | 🚫 | 🚫 | ❓ | ❓ | +| IQ3_XXS | 🚫 | 🚫 | ❓ | ❓ | +| IQ3_S | 🚫 | 🚫 | ❓ | ❓ | +| IQ1_S | 🚫 | 🚫 | ❓ | ❓ | +| IQ1_M | 🚫 | 🚫 | ❓ | ❓ | +| IQ4_NL | ✅ | ✅ | ❓ | ❓ | +| IQ4_XS | ✅ | ✅ | ❓ | ❓ | +| FP32->FP16 | 🚫 | ✅ | ❓ | ❓ | +| FP16->FP32 | 🚫 | ✅ | ❓ | ❓ | + +- ✅ - acceleration available +- 🚫 - acceleration unavailable, will still run using scalar implementation +- ❓ - acceleration unknown, please contribute if you can test it yourself + +Last Updated by **Aaron Teo (aaron.teo1@ibm.com)** on July 25, 2025. diff --git a/llama.cpp/docs/build.md b/llama.cpp/docs/build.md new file mode 100644 index 0000000000000000000000000000000000000000..b9a71c6283c92e51888221cd45b185a3c2043f2f --- /dev/null +++ b/llama.cpp/docs/build.md @@ -0,0 +1,587 @@ +# Build llama.cpp locally + +The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](../include/llama.h). + +The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server. + +**To get the Code:** + +```bash +git clone https://github.com/ggml-org/llama.cpp +cd llama.cpp +``` + +The following sections describe how to build with different backends and options. + +## CPU Build + +Build llama.cpp using `CMake`: + +```bash +cmake -B build +cmake --build build --config Release +``` + +**Notes**: + +- For faster compilation, add the `-j` argument to run multiple jobs in parallel, or use a generator that does this automatically such as Ninja. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel. +- For faster repeated compilation, install [ccache](https://ccache.dev/) +- For debug builds, there are two cases: + + 1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag): + + ```bash + cmake -B build -DCMAKE_BUILD_TYPE=Debug + cmake --build build + ``` + + 2. Multi-config generators (`-G` param set to Visual Studio, XCode...): + + ```bash + cmake -B build -G "Xcode" + cmake --build build --config Debug + ``` + + For more details and a list of supported generators, see the [CMake documentation](https://cmake.org/cmake/help/latest/manual/cmake-generators.7.html). +- For static builds, add `-DBUILD_SHARED_LIBS=OFF`: + ``` + cmake -B build -DBUILD_SHARED_LIBS=OFF + cmake --build build --config Release + ``` + +- Building for Windows (x86, x64 and arm64) with MSVC or clang as compilers: + - Install Visual Studio 2022, e.g. via the [Community Edition](https://visualstudio.microsoft.com/vs/community/). In the installer, select at least the following options (this also automatically installs the required additional tools like CMake,...): + - Tab Workload: Desktop-development with C++ + - Tab Components (select quickly via search): C++-_CMake_ Tools for Windows, _Git_ for Windows, C++-_Clang_ Compiler for Windows, MS-Build Support for LLVM-Toolset (clang) + - Please remember to always use a Developer Command Prompt / PowerShell for VS2022 for git, build, test + - For Windows on ARM (arm64, WoA) build with: + ```bash + cmake --preset arm64-windows-llvm-release -D GGML_OPENMP=OFF + cmake --build build-arm64-windows-llvm-release + ``` + Building for arm64 can also be done with the MSVC compiler with the build-arm64-windows-MSVC preset, or the standard CMake build instructions. However, note that the MSVC compiler does not support inline ARM assembly code, used e.g. for the accelerated Q4_0_N_M CPU kernels. + + For building with ninja generator and clang compiler as default: + -set path:set LIB=C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\x64;C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.41.34120\lib\x64\uwp;C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\x64 + ```bash + cmake --preset x64-windows-llvm-release + cmake --build build-x64-windows-llvm-release + ``` +- Curl usage is enabled by default and can be turned off with `-DLLAMA_CURL=OFF`. Otherwise you need to install development libraries for libcurl. + - **Debian / Ubuntu:** `sudo apt-get install libcurl4-openssl-dev` # (or `libcurl4-gnutls-dev` if you prefer GnuTLS) + - **Fedora / RHEL / Rocky / Alma:** `sudo dnf install libcurl-devel` + - **Arch / Manjaro:** `sudo pacman -S curl` # includes libcurl headers + +## BLAS Build + +Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Using BLAS doesn't affect the generation performance. There are currently several different BLAS implementations available for build and use: + +### Accelerate Framework + +This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions. + +### OpenBLAS + +This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine. + +- Using `CMake` on Linux: + + ```bash + cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS + cmake --build build --config Release + ``` + +### BLIS + +Check [BLIS.md](./backend/BLIS.md) for more information. + +### Intel oneMKL + +Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./backend/SYCL.md). + +- Using manual oneAPI installation: + By default, `GGML_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DGGML_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps: + ```bash + source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation + cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_NATIVE=ON + cmake --build build --config Release + ``` + +- Using oneAPI docker image: + If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above. + +Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information. + +### Other BLAS libraries + +Any other BLAS library can be used by setting the `GGML_BLAS_VENDOR` option. See the [CMake documentation](https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors) for a list of supported vendors. + +## Metal Build + +On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU. +To disable the Metal build at compile time use the `-DGGML_METAL=OFF` cmake option. + +When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers 0` command-line argument. + +## SYCL + +SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. + +llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). + +For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md). + +## CUDA + +This provides GPU acceleration using an NVIDIA GPU. Make sure to have the [CUDA toolkit](https://developer.nvidia.com/cuda-toolkit) installed. + +#### Download directly from NVIDIA +You may find the official downloads here: [NVIDIA developer site](https://developer.nvidia.com/cuda-downloads). + + +#### Compile and run inside a Fedora Toolbox Container +We also have a [guide](./backend/CUDA-FEDORA.md) for setting up CUDA toolkit in a Fedora [toolbox container](https://containertoolbx.org/). + +**Recommended for:** +- ***Necessary*** for users of [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/); such as: [Silverblue](https://fedoraproject.org/atomic-desktops/silverblue/) and [Kinoite](https://fedoraproject.org/atomic-desktops/kinoite/). + - (there are no supported CUDA packages for these systems) +- ***Necessary*** for users that have a host that is not a: [Supported Nvidia CUDA Release Platform](https://developer.nvidia.com/cuda-downloads). + - (for example, you may have [Fedora 42 Beta](https://fedoramagazine.org/announcing-fedora-linux-42-beta/) as your your host operating system) +- ***Convenient*** For those running [Fedora Workstation](https://fedoraproject.org/workstation/) or [Fedora KDE Plasma Desktop](https://fedoraproject.org/spins/kde), and want to keep their host system clean. +- *Optionally* toolbox packages are available: [Arch Linux](https://archlinux.org/), [Red Hat Enterprise Linux >= 8.5](https://www.redhat.com/en/technologies/linux-platforms/enterprise-linux), or [Ubuntu](https://ubuntu.com/download) + + +### Compilation +```bash +cmake -B build -DGGML_CUDA=ON +cmake --build build --config Release +``` + +### Override Compute Capability Specifications + +If `nvcc` cannot detect your gpu, you may get compile-warnings such as: + ```text +nvcc warning : Cannot find valid GPU for '-arch=native', default arch is used +``` + +To override the `native` GPU detection: + +#### 1. Take note of the `Compute Capability` of your NVIDIA devices: ["CUDA: Your GPU Compute > Capability"](https://developer.nvidia.com/cuda-gpus). + +```text +GeForce RTX 4090 8.9 +GeForce RTX 3080 Ti 8.6 +GeForce RTX 3070 8.6 +``` + +#### 2. Manually list each varying `Compute Capability` in the `CMAKE_CUDA_ARCHITECTURES` list. + +```bash +cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="86;89" +``` + +### Runtime CUDA environmental variables + +You may set the [cuda environmental variables](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) at runtime. + +```bash +# Use `CUDA_VISIBLE_DEVICES` to hide the first compute device. +CUDA_VISIBLE_DEVICES="-0" ./build/bin/llama-server --model /srv/models/llama.gguf +``` + +### Unified Memory + +The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`. + +### Performance Tuning + +The following compilation options are also available to tweak performance: + +| Option | Legal values | Default | Description | +|-------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| GGML_CUDA_FORCE_MMQ | Boolean | false | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, CDNA and RDNA3+). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower. | +| GGML_CUDA_FORCE_CUBLAS | Boolean | false | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models | +| GGML_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | +| GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | +| GGML_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. | + +## MUSA + +This provides GPU acceleration using a Moore Threads GPU. Make sure to have the [MUSA SDK](https://developer.mthreads.com/musa/musa-sdk) installed. + +#### Download directly from Moore Threads + +You may find the official downloads here: [Moore Threads developer site](https://developer.mthreads.com/sdk/download/musa). + +### Compilation + +```bash +cmake -B build -DGGML_MUSA=ON +cmake --build build --config Release +``` + +#### Override Compute Capability Specifications + +By default, all supported compute capabilities are enabled. To customize this behavior, you can specify the `MUSA_ARCHITECTURES` option in the CMake command: + +```bash +cmake -B build -DGGML_MUSA=ON -DMUSA_ARCHITECTURES="21" +cmake --build build --config Release +``` + +This configuration enables only compute capability `2.1` (MTT S80) during compilation, which can help reduce compilation time. + +#### Compilation options + +Most of the compilation options available for CUDA should also be available for MUSA, though they haven't been thoroughly tested yet. + +- For static builds, add `-DBUILD_SHARED_LIBS=OFF` and `-DCMAKE_POSITION_INDEPENDENT_CODE=ON`: + ``` + cmake -B build -DGGML_MUSA=ON \ + -DBUILD_SHARED_LIBS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON + cmake --build build --config Release + ``` + +### Runtime MUSA environmental variables + +You may set the [musa environmental variables](https://docs.mthreads.com/musa-sdk/musa-sdk-doc-online/programming_guide/Z%E9%99%84%E5%BD%95/) at runtime. + +```bash +# Use `MUSA_VISIBLE_DEVICES` to hide the first compute device. +MUSA_VISIBLE_DEVICES="-0" ./build/bin/llama-server --model /srv/models/llama.gguf +``` + +### Unified Memory + +The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. + +## HIP + +This provides GPU acceleration on HIP-supported AMD GPUs. +Make sure to have ROCm installed. +You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick). + +- Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU): + ```bash + HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ + cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + && cmake --build build --config Release -- -j 16 + ``` + + To enhance flash attention performance on RDNA3+ or CDNA architectures, you can utilize the rocWMMA library by enabling the `-DGGML_HIP_ROCWMMA_FATTN=ON` option. This requires rocWMMA headers to be installed on the build system. + + The rocWMMA library is included by default when installing the ROCm SDK using the `rocm` meta package provided by AMD. Alternatively, if you are not using the meta package, you can install the library using the `rocwmma-dev` or `rocwmma-devel` package, depending on your system's package manager. + + As an alternative, you can manually install the library by cloning it from the official [GitHub repository](https://github.com/ROCm/rocWMMA), checkout the corresponding version tag (e.g. `rocm-6.2.4`) and set `-DCMAKE_CXX_FLAGS="-I/library/include/"` in CMake. This also works under Windows despite not officially supported by AMD. + + Note that if you get the following error: + ``` + clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library + ``` + Try searching for a directory under `HIP_PATH` that contains the file + `oclc_abi_version_400.bc`. Then, add the following to the start of the + command: `HIP_DEVICE_LIB_PATH=`, so something + like: + ```bash + HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \ + HIP_DEVICE_LIB_PATH= \ + cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + && cmake --build build -- -j 16 + ``` + +- Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU): + ```bash + set PATH=%HIP_PATH%\bin;%PATH% + cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIP=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release + cmake --build build + ``` + Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors) + Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`. + + +The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used. +If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3. + +### Unified Memory + +On Linux it is possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1`. However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs). + +## Vulkan + +### For Windows Users: +**w64devkit** + +Download and extract [`w64devkit`](https://github.com/skeeto/w64devkit/releases). + +Download and install the [`Vulkan SDK`](https://vulkan.lunarg.com/sdk/home#windows) with the default settings. + +Launch `w64devkit.exe` and run the following commands to copy Vulkan dependencies: +```sh +SDK_VERSION=1.3.283.0 +cp /VulkanSDK/$SDK_VERSION/Bin/glslc.exe $W64DEVKIT_HOME/bin/ +cp /VulkanSDK/$SDK_VERSION/Lib/vulkan-1.lib $W64DEVKIT_HOME/x86_64-w64-mingw32/lib/ +cp -r /VulkanSDK/$SDK_VERSION/Include/* $W64DEVKIT_HOME/x86_64-w64-mingw32/include/ +cat > $W64DEVKIT_HOME/x86_64-w64-mingw32/lib/pkgconfig/vulkan.pc < [!IMPORTANT] +> After completing the first step, ensure that you have used the `source` command on the `setup_env.sh` file inside of the Vulkan SDK in your current terminal session. Otherwise, the build won't work. Additionally, if you close out of your terminal, you must perform this step again if you intend to perform a build. However, there are ways to make this persistent. Refer to the Vulkan SDK guide linked in the first step for more information about any of this. + +Second, after verifying that you have followed all of the SDK installation/setup steps, use this command to make sure before proceeding: +```bash +vulkaninfo +``` + +Then, assuming you have `cd` into your llama.cpp folder and there are no errors with running `vulkaninfo`, you can proceed to build llama.cpp using the CMake commands below: +```bash +cmake -B build -DGGML_VULKAN=1 +cmake --build build --config Release +``` + +Finally, after finishing your build, you should be able to do something like this: +```bash +# Test the output binary +# "-ngl 99" should offload all of the layers to GPU for most (if not all) models. +./build/bin/llama-cli -m "PATH_TO_MODEL" -p "Hi you how are you" -ngl 99 + +# You should see in the output, ggml_vulkan detected your GPU. For example: +# ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32 +``` + +## CANN +This provides NPU acceleration using the AI cores of your Ascend NPU. And [CANN](https://www.hiascend.com/en/software/cann) is a hierarchical APIs to help you to quickly build AI applications and service based on Ascend NPU. + +For more information about Ascend NPU in [Ascend Community](https://www.hiascend.com/en/). + +Make sure to have the CANN toolkit installed. You can download it from here: [CANN Toolkit](https://www.hiascend.com/developer/download/community/result?module=cann) + +Go to `llama.cpp` directory and build using CMake. +```bash +cmake -B build -DGGML_CANN=on -DCMAKE_BUILD_TYPE=release +cmake --build build --config release +``` + +You can test with: + +```bash +./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -ngl 32 +``` + +If the following info is output on screen, you are using `llama.cpp` with the CANN backend: +```bash +llm_load_tensors: CANN model buffer size = 13313.00 MiB +llama_new_context_with_model: CANN compute buffer size = 1260.81 MiB +``` + +For detailed info, such as model/device supports, CANN install, please refer to [llama.cpp for CANN](./backend/CANN.md). + +## Arm® KleidiAI™ +KleidiAI is a library of optimized microkernels for AI workloads, specifically designed for Arm CPUs. These microkernels enhance performance and can be enabled for use by the CPU backend. + +To enable KleidiAI, go to the llama.cpp directory and build using CMake +```bash +cmake -B build -DGGML_CPU_KLEIDIAI=ON +cmake --build build --config Release +``` +You can verify that KleidiAI is being used by running +```bash +./build/bin/llama-cli -m PATH_TO_MODEL -p "What is a car?" +``` +If KleidiAI is enabled, the ouput will contain a line similar to: +``` +load_tensors: CPU_KLEIDIAI model buffer size = 3474.00 MiB +``` +KleidiAI's microkernels implement optimized tensor operations using Arm CPU features such as dotprod, int8mm and SME. llama.cpp selects the most efficient kernel based on runtime CPU feature detection. However, on platforms that support SME, you must manually enable SME microkernels by setting the environment variable `GGML_KLEIDIAI_SME=1`. + +Depending on your build target, other higher priority backends may be enabled by default. To ensure the CPU backend is used, you must disable the higher priority backends either at compile time, e.g. -DGGML_METAL=OFF, or during run-time using the command line option `--device none`. + +## OpenCL + +This provides GPU acceleration through OpenCL on recent Adreno GPU. +More information about OpenCL backend can be found in [OPENCL.md](./backend/OPENCL.md) for more information. + +### Android + +Assume NDK is available in `$ANDROID_NDK`. First, install OpenCL headers and ICD loader library if not available, + +```sh +mkdir -p ~/dev/llm +cd ~/dev/llm + +git clone https://github.com/KhronosGroup/OpenCL-Headers && \ +cd OpenCL-Headers && \ +cp -r CL $ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include + +cd ~/dev/llm + +git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && \ +cd OpenCL-ICD-Loader && \ +mkdir build_ndk && cd build_ndk && \ +cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \ + -DOPENCL_ICD_LOADER_HEADERS_DIR=$ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include \ + -DANDROID_ABI=arm64-v8a \ + -DANDROID_PLATFORM=24 \ + -DANDROID_STL=c++_shared && \ +ninja && \ +cp libOpenCL.so $ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android +``` + +Then build llama.cpp with OpenCL enabled, + +```sh +cd ~/dev/llm + +git clone https://github.com/ggml-org/llama.cpp && \ +cd llama.cpp && \ +mkdir build-android && cd build-android + +cmake .. -G Ninja \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=arm64-v8a \ + -DANDROID_PLATFORM=android-28 \ + -DBUILD_SHARED_LIBS=OFF \ + -DGGML_OPENCL=ON + +ninja +``` + +### Windows Arm64 + +First, install OpenCL headers and ICD loader library if not available, + +```powershell +mkdir -p ~/dev/llm + +cd ~/dev/llm +git clone https://github.com/KhronosGroup/OpenCL-Headers && cd OpenCL-Headers +mkdir build && cd build +cmake .. -G Ninja ` + -DBUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF ` + -DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl" +cmake --build . --target install + +cd ~/dev/llm +git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && cd OpenCL-ICD-Loader +mkdir build && cd build +cmake .. -G Ninja ` + -DCMAKE_BUILD_TYPE=Release ` + -DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" ` + -DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl" +cmake --build . --target install +``` + +Then build llama.cpp with OpenCL enabled, + +```powershell +cmake .. -G Ninja ` + -DCMAKE_TOOLCHAIN_FILE="$HOME/dev/llm/llama.cpp/cmake/arm64-windows-llvm.cmake" ` + -DCMAKE_BUILD_TYPE=Release ` + -DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" ` + -DBUILD_SHARED_LIBS=OFF ` + -DGGML_OPENCL=ON +ninja +``` + +## Android + +To read documentation for how to build on Android, [click here](./android.md) + +## WebGPU [In Progress] + +The WebGPU backend relies on [Dawn](https://dawn.googlesource.com/dawn). Follow the instructions [here](https://dawn.googlesource.com/dawn/+/refs/heads/main/docs/quickstart-cmake.md) to install Dawn locally so that llama.cpp can find it using CMake. The currrent implementation is up-to-date with Dawn commit `bed1a61`. + +In the llama.cpp directory, build with CMake: + +``` +cmake -B build -DGGML_WEBGPU=ON +cmake --build build --config Release +``` + +### Browser Support + +WebGPU allows cross-platform access to the GPU from supported browsers. We utilize [Emscripten](https://emscripten.org/) to compile ggml's WebGPU backend to WebAssembly. Emscripten does not officially support WebGPU bindings yet, but Dawn currently maintains its own WebGPU bindings called emdawnwebgpu. + +Follow the instructions [here](https://dawn.googlesource.com/dawn/+/refs/heads/main/src/emdawnwebgpu/) to download or build the emdawnwebgpu package (Note that it might be safer to build the emdawbwebgpu package locally, so that it stays in sync with the version of Dawn you have installed above). When building using CMake, the path to the emdawnwebgpu port file needs to be set with the flag `EMDAWNWEBGPU_DIR`. + +## IBM Z & LinuxONE + +To read documentation for how to build on IBM Z & LinuxONE, [click here](./build-s390x.md) + +## Notes about GPU-accelerated backends + +The GPU may still be used to accelerate some parts of the computation even when using the `-ngl 0` option. You can fully disable GPU acceleration by using `--device none`. + +In most cases, it is possible to build and use multiple backends at the same time. For example, you can build llama.cpp with both CUDA and Vulkan support by using the `-DGGML_CUDA=ON -DGGML_VULKAN=ON` options with CMake. At runtime, you can specify which backend devices to use with the `--device` option. To see a list of available devices, use the `--list-devices` option. + +Backends can be built as dynamic libraries that can be loaded dynamically at runtime. This allows you to use the same llama.cpp binary on different machines with different GPUs. To enable this feature, use the `GGML_BACKEND_DL` option when building. diff --git a/llama.cpp/docs/development/HOWTO-add-model.md b/llama.cpp/docs/development/HOWTO-add-model.md new file mode 100644 index 0000000000000000000000000000000000000000..b44266f72faa6eeb890cf011a846322311b374bc --- /dev/null +++ b/llama.cpp/docs/development/HOWTO-add-model.md @@ -0,0 +1,130 @@ +# Add a new model architecture to `llama.cpp` + +Adding a model requires few steps: + +1. Convert the model to GGUF +2. Define the model architecture in `llama.cpp` +3. Build the GGML graph implementation + +After following these steps, you can open PR. + +Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially: +- [main](/tools/main/) +- [imatrix](/tools/imatrix/) +- [quantize](/tools/quantize/) +- [server](/tools/server/) + +### 1. Convert the model to GGUF + +This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library. +Depending on the model architecture, you can use either [convert_hf_to_gguf.py](/convert_hf_to_gguf.py) or [examples/convert_legacy_llama.py](/examples/convert_legacy_llama.py) (for `llama/llama2` models in `.pth` format). + +The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors. + +The required steps to implement for an HF model are: + +1. Define the model `ModelBase.register` annotation in a new `TextModel` or `MmprojModel` subclass, example: + +```python +@ModelBase.register("MyModelForCausalLM") +class MyModel(TextModel): + model_arch = gguf.MODEL_ARCH.MYMODEL +``` + +or + +```python +@ModelBase.register("MyModelForConditionalGeneration") +class MyModel(MmprojModel): + model_arch = gguf.MODEL_ARCH.MYMODEL +``` + +2. Define the layout of the GGUF tensors in [constants.py](/gguf-py/gguf/constants.py) + +Add an enum entry in `MODEL_ARCH`, the model human friendly name in `MODEL_ARCH_NAMES` and the GGUF tensor names in `MODEL_TENSORS`. + +Example for `falcon` model: +```python + MODEL_ARCH.FALCON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_NORM_2, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ] +``` + +3. Map the original tensor names to the standardize equivalent in GGUF + +As a general rule, before adding a new tensor name to GGUF, be sure the equivalent naming does not already exist. + +Once you have found the GGUF tensor name equivalent, add it to the [tensor_mapping.py](/gguf-py/gguf/tensor_mapping.py) file. + +If the tensor name is part of a repetitive layer/block, the key word `bid` substitutes it. + +Example for the normalization tensor in attention layers: + +```python +block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { + # Attention norm + MODEL_TENSOR.ATTN_NORM: ( + "gpt_neox.layers.{bid}.input_layernorm", # gptneox + "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen + "transformer.blocks.{bid}.norm_1", # mpt + ... + ) +} +``` + +`transformer.blocks.{bid}.norm_1` will be mapped to `blk.{bid}.attn_norm` in GGUF. + +Depending on the model configuration, tokenizer, code and tensors layout, you will have to override: +- `TextModel#set_gguf_parameters` +- `MmprojModel#set_gguf_parameters` +- `ModelBase#set_vocab` +- `ModelBase#modify_tensors` + +NOTE: Tensor names must end with `.weight` or `.bias` suffixes, that is the convention and several tools like `quantize` expect this to proceed the weights. + +### 2. Define the model architecture in `llama.cpp` + +The model params and tensors layout must be defined in `llama.cpp` source files: +1. Define a new `llm_arch` enum value in `src/llama-arch.h`. +2. In `src/llama-arch.cpp`: + - Add the architecture name to the `LLM_ARCH_NAMES` map. + - Add the tensor mappings to the `LLM_TENSOR_NAMES` map. +3. Add any non-standard metadata loading in the `llama_model_loader` constructor in `src/llama-model-loader.cpp`. +4. If the model has a RoPE operation, add a case for the architecture in `llama_model_rope_type` function in `src/llama-model.cpp`. + +NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorch` dimensions. + +### 3. Build the GGML graph implementation + +This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `src/llama-model.cpp`. +Create a new struct that inherits from `llm_graph_context` and implement the graph-building logic in its constructor. +Have a look at existing implementations like `llm_build_llama`, `llm_build_dbrx` or `llm_build_bert`. +Then, in the `llama_model::build_graph` method, add a case for your architecture to instantiate your new graph-building struct. + +Some `ggml` backends do not support all operations. Backend implementations can be added in a separate PR. + +Note: to debug the inference graph: you can use [llama-eval-callback](/examples/eval-callback/). + +## GGUF specification + +https://github.com/ggml-org/ggml/blob/master/docs/gguf.md + +## Resources + +- YaRN RoPE scaling https://github.com/ggml-org/llama.cpp/pull/2268 +- support Baichuan serial models https://github.com/ggml-org/llama.cpp/pull/3009 +- support attention bias https://github.com/ggml-org/llama.cpp/pull/4283 +- Mixtral support https://github.com/ggml-org/llama.cpp/pull/4406 +- BERT embeddings https://github.com/ggml-org/llama.cpp/pull/5423 +- Grok-1 support https://github.com/ggml-org/llama.cpp/pull/6204 +- Command R Plus support https://github.com/ggml-org/llama.cpp/pull/6491 +- support arch DBRX https://github.com/ggml-org/llama.cpp/pull/6515 +- How to convert HuggingFace model to GGUF format https://github.com/ggml-org/llama.cpp/discussions/2948 diff --git a/llama.cpp/docs/development/debugging-tests.md b/llama.cpp/docs/development/debugging-tests.md new file mode 100644 index 0000000000000000000000000000000000000000..4eaf98995c8bb8278b42de19ffc562548c7f9f8d --- /dev/null +++ b/llama.cpp/docs/development/debugging-tests.md @@ -0,0 +1,104 @@ +# Debugging Tests Tips + +## How to run & execute or debug a specific test without anything else to keep the feedback loop short? + +There is a script called debug-test.sh in the scripts folder whose parameter takes a REGEX and an optional test number. + +For example, running the following command will output an interactive list from which you can select a test. It takes this form: + +`debug-test.sh [OPTION]... ` + +It will then build & run in the debugger for you. + +To just execute a test and get back a PASS or FAIL message run: + +```bash +./scripts/debug-test.sh test-tokenizer +``` + +To test in GDB use the `-g` flag to enable gdb test mode. + +```bash +./scripts/debug-test.sh -g test-tokenizer + +# Once in the debugger, i.e. at the chevrons prompt, setting a breakpoint could be as follows: +>>> b main +``` + +To speed up the testing loop, if you know your test number you can just run it similar to below: + +```bash +./scripts/debug-test.sh test 23 +``` + +For further reference use `debug-test.sh -h` to print help. + +  + +### How does the script work? +If you want to be able to use the concepts contained in the script separately, the important ones are briefly outlined below. + +#### Step 1: Reset and Setup folder context + +From base of this repository, let's create `build-ci-debug` as our build context. + +```bash +rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug +``` + +#### Step 2: Setup Build Environment and Compile Test Binaries + +Setup and trigger a build under debug mode. You may adapt the arguments as needed, but in this case these are sane defaults. + +```bash +cmake -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON .. +make -j +``` + +#### Step 3: Find all tests available that matches REGEX + +The output of this command will give you the command & arguments needed to run GDB. + +* `-R test-tokenizer` : looks for all the test files named `test-tokenizer*` (R=Regex) +* `-N` : "show-only" disables test execution & shows test commands that you can feed to GDB. +* `-V` : Verbose Mode + +```bash +ctest -R "test-tokenizer" -V -N +``` + +This may return output similar to below (focusing on key lines to pay attention to): + +```bash +... +1: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf" +1: Working Directory: . +Labels: main + Test #1: test-tokenizer-0-llama-spm +... +4: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-falcon.gguf" +4: Working Directory: . +Labels: main + Test #4: test-tokenizer-0-falcon +... +``` + +#### Step 4: Identify Test Command for Debugging + +So for test #1 above we can tell these two pieces of relevant information: +* Test Binary: `~/llama.cpp/build-ci-debug/bin/test-tokenizer-0` +* Test GGUF Model: `~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf` + +#### Step 5: Run GDB on test command + +Based on the ctest 'test command' report above we can then run a gdb session via this command below: + +```bash +gdb --args ${Test Binary} ${Test GGUF Model} +``` + +Example: + +```bash +gdb --args ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf" +``` diff --git a/llama.cpp/docs/development/llama-star/idea-arch.key b/llama.cpp/docs/development/llama-star/idea-arch.key new file mode 100644 index 0000000000000000000000000000000000000000..234d59858a3ab2d90ec2a725a137aa0b187a0179 --- /dev/null +++ b/llama.cpp/docs/development/llama-star/idea-arch.key @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:32dede0548e6501b4b60d89b4afc3445a9a45c5df6fcee0c2bb9fe75592908c9 +size 488591 diff --git a/llama.cpp/docs/development/llama-star/idea-arch.pdf b/llama.cpp/docs/development/llama-star/idea-arch.pdf new file mode 100644 index 0000000000000000000000000000000000000000..4fa92c71dc4c511378c628113b7817e583053758 Binary files /dev/null and b/llama.cpp/docs/development/llama-star/idea-arch.pdf differ diff --git a/llama.cpp/docs/development/token_generation_performance_tips.md b/llama.cpp/docs/development/token_generation_performance_tips.md new file mode 100644 index 0000000000000000000000000000000000000000..c94882cd9d027bd08e5a44524b43e2bddc54c6a9 --- /dev/null +++ b/llama.cpp/docs/development/token_generation_performance_tips.md @@ -0,0 +1,40 @@ +# Token generation performance troubleshooting + +## Verifying that the model is running on the GPU with CUDA +Make sure you compiled llama with the correct env variables according to [this guide](/docs/build.md#cuda), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example: +```shell +./llama-cli -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some " +``` + +When running llama, before it starts the inference work, it will output diagnostic information that shows whether cuBLAS is offloading work to the GPU. Look for these lines: +```shell +llama_model_load_internal: [cublas] offloading 60 layers to GPU +llama_model_load_internal: [cublas] offloading output layer to GPU +llama_model_load_internal: [cublas] total VRAM used: 17223 MB +... rest of inference +``` + +If you see these lines, then the GPU is being used. + +## Verifying that the CPU is not oversaturated +llama accepts a `-t N` (or `--threads N`) parameter. It's extremely important that this parameter is not too large. If your token generation is extremely slow, try setting this number to 1. If this significantly improves your token generation speed, then your CPU is being oversaturated and you need to explicitly set this parameter to the number of the physical CPU cores on your machine (even if you utilize a GPU). If in doubt, start with 1 and double the amount until you hit a performance bottleneck, then scale the number down. + +# Example of runtime flags effect on inference speed benchmark +These runs were tested on the following machine: +GPU: A6000 (48GB VRAM) +CPU: 7 physical cores +RAM: 32GB + +Model: `TheBloke_Wizard-Vicuna-30B-Uncensored-GGML/Wizard-Vicuna-30B-Uncensored.q4_0.gguf` (30B parameters, 4bit quantization, GGML) + +Run command: `./llama-cli -m "path/to/model.gguf" -p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]` + +Result: + +| command | tokens/second (higher is better) | +| - | - | +| -ngl 2000000 | N/A (less than 0.1) | +| -t 7 | 1.7 | +| -t 1 -ngl 2000000 | 5.5 | +| -t 7 -ngl 2000000 | 8.7 | +| -t 4 -ngl 2000000 | 9.1 | diff --git a/llama.cpp/docs/docker.md b/llama.cpp/docs/docker.md new file mode 100644 index 0000000000000000000000000000000000000000..49b28a0fb487c4a0348258ad63e499ec2d3a7130 --- /dev/null +++ b/llama.cpp/docs/docker.md @@ -0,0 +1,129 @@ +# Docker + +## Prerequisites +* Docker must be installed and running on your system. +* Create a folder to store big models & intermediate files (ex. /llama/models) + +## Images +We have three Docker images available for this project: + +1. `ghcr.io/ggml-org/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`) +2. `ghcr.io/ggml-org/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`) +3. `ghcr.io/ggml-org/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`) + +Additionally, there the following images, similar to the above: + +- `ghcr.io/ggml-org/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggml-org/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggml-org/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggml-org/llama.cpp:full-musa`: Same as `full` but compiled with MUSA support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:light-musa`: Same as `light` but compiled with MUSA support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:server-musa`: Same as `server` but compiled with MUSA support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:full-intel`: Same as `full` but compiled with SYCL support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:light-intel`: Same as `light` but compiled with SYCL support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:server-intel`: Same as `server` but compiled with SYCL support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:full-vulkan`: Same as `full` but compiled with Vulkan support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:light-vulkan`: Same as `light` but compiled with Vulkan support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:server-vulkan`: Same as `server` but compiled with Vulkan support. (platforms: `linux/amd64`) + +The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](../.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](../.github/workflows/docker.yml). If you need different settings (for example, a different CUDA, ROCm or MUSA library, you'll need to build the images locally for now). + +## Usage + +The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image. + +Replace `/path/to/models` below with the actual path where you downloaded the models. + +```bash +docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --all-in-one "/models/" 7B +``` + +On completion, you are ready to play! + +```bash +docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 +``` + +or with a light image: + +```bash +docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 +``` + +or with a server image: + +```bash +docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggml-org/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 +``` + +## Docker With CUDA + +Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container. + +## Building Docker locally + +```bash +docker build -t local/llama.cpp:full-cuda --target full -f .devops/cuda.Dockerfile . +docker build -t local/llama.cpp:light-cuda --target light -f .devops/cuda.Dockerfile . +docker build -t local/llama.cpp:server-cuda --target server -f .devops/cuda.Dockerfile . +``` + +You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture. + +The defaults are: + +- `CUDA_VERSION` set to `12.4.0` +- `CUDA_DOCKER_ARCH` set to the cmake build default, which includes all the supported architectures + +The resulting images, are essentially the same as the non-CUDA images: + +1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. +2. `local/llama.cpp:light-cuda`: This image only includes the main executable file. +3. `local/llama.cpp:server-cuda`: This image only includes the server executable file. + +## Usage + +After building locally, Usage is similar to the non-CUDA examples, but you'll need to add the `--gpus` flag. You will also want to use the `--n-gpu-layers` flag. + +```bash +docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 +docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 +docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1 +``` + +## Docker With MUSA + +Assuming one has the [mt-container-toolkit](https://developer.mthreads.com/musa/native) properly installed on Linux, `muBLAS` should be accessible inside the container. + +## Building Docker locally + +```bash +docker build -t local/llama.cpp:full-musa --target full -f .devops/musa.Dockerfile . +docker build -t local/llama.cpp:light-musa --target light -f .devops/musa.Dockerfile . +docker build -t local/llama.cpp:server-musa --target server -f .devops/musa.Dockerfile . +``` + +You may want to pass in some different `ARGS`, depending on the MUSA environment supported by your container host, as well as the GPU architecture. + +The defaults are: + +- `MUSA_VERSION` set to `rc4.2.0` + +The resulting images, are essentially the same as the non-MUSA images: + +1. `local/llama.cpp:full-musa`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. +2. `local/llama.cpp:light-musa`: This image only includes the main executable file. +3. `local/llama.cpp:server-musa`: This image only includes the server executable file. + +## Usage + +After building locally, Usage is similar to the non-MUSA examples, but you'll need to set `mthreads` as default Docker runtime. This can be done by executing `(cd /usr/bin/musa && sudo ./docker setup $PWD)` and verifying the changes by executing `docker info | grep mthreads` on the host machine. You will also want to use the `--n-gpu-layers` flag. + +```bash +docker run -v /path/to/models:/models local/llama.cpp:full-musa --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 +docker run -v /path/to/models:/models local/llama.cpp:light-musa -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 +docker run -v /path/to/models:/models local/llama.cpp:server-musa -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1 +``` diff --git a/llama.cpp/docs/function-calling.md b/llama.cpp/docs/function-calling.md new file mode 100644 index 0000000000000000000000000000000000000000..46b032b38d9acf214f86f5ed2dd3e0e4a4fe36d7 --- /dev/null +++ b/llama.cpp/docs/function-calling.md @@ -0,0 +1,422 @@ +# Function Calling + +[chat.h](../common/chat.h) (https://github.com/ggml-org/llama.cpp/pull/9639) adds support for [OpenAI-style function calling](https://platform.openai.com/docs/guides/function-calling) and is used in: +- `llama-server` when started w/ `--jinja` flag + +## Universal support w/ Native & Generic handlers + +Function calling is supported for all models (see https://github.com/ggml-org/llama.cpp/pull/9639): + +- Native tool call formats supported: + - Llama 3.1 / 3.3 (including builtin tools support - tool names for `wolfram_alpha`, `web_search` / `brave_search`, `code_interpreter`), Llama 3.2 + - Functionary v3.1 / v3.2 + - Hermes 2/3, Qwen 2.5 + - Qwen 2.5 Coder + - Mistral Nemo + - Firefunction v2 + - Command R7B + - DeepSeek R1 (WIP / seems reluctant to call any tools?) + +- Generic tool call is supported when the template isn't recognized by native format handlers (you'll see `Chat format: Generic` in the logs). + - Use `--chat-template-file` to override the template when appropriate (see examples below) + - Generic support may consume more tokens and be less efficient than a model's native format. + +
+Show some common templates and which format handler they use + +| Template | Format | +|----------|--------| +| Almawave-Velvet-14B.jinja | Hermes 2 Pro | +| AtlaAI-Selene-1-Mini-Llama-3.1-8B.jinja | Llama 3.x | +| CohereForAI-aya-expanse-8b.jinja | Generic | +| CohereForAI-c4ai-command-r-plus-default.jinja | Generic | +| CohereForAI-c4ai-command-r-plus-rag.jinja | Generic | +| CohereForAI-c4ai-command-r-plus-tool_use.jinja | Generic | +| CohereForAI-c4ai-command-r7b-12-2024-default.jinja | Command R7B (extract reasoning) | +| CohereForAI-c4ai-command-r7b-12-2024-rag.jinja | Command R7B (extract reasoning) | +| CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja | Command R7B (extract reasoning) | +| CohereForAI-c4ai-command-r7b-12-2024.jinja | Generic | +| DavieLion-Llama-3.2-1B-SPIN-iter3.jinja | Generic | +| Delta-Vector-Rei-12B.jinja | Mistral Nemo | +| EpistemeAI-Mistral-Nemo-Instruct-12B-Philosophy-Math.jinja | Mistral Nemo | +| FlofloB-83k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit.jinja | Hermes 2 Pro | +| FlofloB-test_continued_pretraining_Phi-3-mini-4k-instruct_Unsloth_merged_16bit.jinja | Generic | +| HelpingAI-HAI-SER.jinja | Generic | +| HuggingFaceTB-SmolLM2-1.7B-Instruct.jinja | Generic | +| HuggingFaceTB-SmolLM2-135M-Instruct.jinja | Generic | +| HuggingFaceTB-SmolLM2-360M-Instruct.jinja | Generic | +| INSAIT-Institute-BgGPT-Gemma-2-27B-IT-v1.0.jinja | Generic | +| Ihor-Text2Graph-R1-Qwen2.5-0.5b.jinja | Hermes 2 Pro | +| Infinigence-Megrez-3B-Instruct.jinja | Generic | +| Josephgflowers-TinyLlama_v1.1_math_code-world-test-1.jinja | Generic | +| LGAI-EXAONE-EXAONE-3.5-2.4B-Instruct.jinja | Generic | +| LGAI-EXAONE-EXAONE-3.5-7.8B-Instruct.jinja | Generic | +| LatitudeGames-Wayfarer-12B.jinja | Generic | +| Magpie-Align-Llama-3-8B-Magpie-Align-v0.1.jinja | Generic | +| Magpie-Align-Llama-3.1-8B-Magpie-Align-v0.1.jinja | Generic | +| MaziyarPanahi-calme-3.2-instruct-78b.jinja | Generic | +| MiniMaxAI-MiniMax-Text-01.jinja | Generic | +| MiniMaxAI-MiniMax-VL-01.jinja | Generic | +| NaniDAO-deepseek-r1-qwen-2.5-32B-ablated.jinja | DeepSeek R1 (extract reasoning) | +| NexaAIDev-Octopus-v2.jinja | Generic | +| NousResearch-Hermes-2-Pro-Llama-3-8B-default.jinja | Generic | +| NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja | Hermes 2 Pro | +| NousResearch-Hermes-2-Pro-Mistral-7B-default.jinja | Generic | +| NousResearch-Hermes-2-Pro-Mistral-7B-tool_use.jinja | Hermes 2 Pro | +| NousResearch-Hermes-3-Llama-3.1-70B-default.jinja | Generic | +| NousResearch-Hermes-3-Llama-3.1-70B-tool_use.jinja | Hermes 2 Pro | +| NovaSky-AI-Sky-T1-32B-Flash.jinja | Hermes 2 Pro | +| NovaSky-AI-Sky-T1-32B-Preview.jinja | Hermes 2 Pro | +| OnlyCheeini-greesychat-turbo.jinja | Generic | +| Orenguteng-Llama-3.1-8B-Lexi-Uncensored-V2.jinja | Llama 3.x | +| OrionStarAI-Orion-14B-Chat.jinja | Generic | +| PowerInfer-SmallThinker-3B-Preview.jinja | Generic | +| PrimeIntellect-INTELLECT-1-Instruct.jinja | Generic | +| Qwen-QVQ-72B-Preview.jinja | Generic | +| Qwen-QwQ-32B-Preview.jinja | Hermes 2 Pro | +| Qwen-Qwen1.5-7B-Chat.jinja | Generic | +| Qwen-Qwen2-7B-Instruct.jinja | Generic | +| Qwen-Qwen2-VL-72B-Instruct.jinja | Generic | +| Qwen-Qwen2-VL-7B-Instruct.jinja | Generic | +| Qwen-Qwen2.5-0.5B.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-1.5B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-14B-Instruct-1M.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-14B.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-32B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-32B.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-3B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-72B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-7B-Instruct-1M.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-7B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-7B.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-Coder-32B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-Coder-7B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-Math-1.5B.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-Math-7B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-VL-3B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-VL-72B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-VL-7B-Instruct.jinja | Hermes 2 Pro | +| RWKV-Red-Team-ARWKV-7B-Preview-0.1.jinja | Hermes 2 Pro | +| SakanaAI-TinySwallow-1.5B-Instruct.jinja | Hermes 2 Pro | +| SakanaAI-TinySwallow-1.5B.jinja | Hermes 2 Pro | +| Sao10K-70B-L3.3-Cirrus-x1.jinja | Llama 3.x | +| SentientAGI-Dobby-Mini-Leashed-Llama-3.1-8B.jinja | Llama 3.x | +| SentientAGI-Dobby-Mini-Unhinged-Llama-3.1-8B.jinja | Llama 3.x | +| Steelskull-L3.3-Damascus-R1.jinja | Llama 3.x | +| Steelskull-L3.3-MS-Nevoria-70b.jinja | Llama 3.x | +| Steelskull-L3.3-Nevoria-R1-70b.jinja | Llama 3.x | +| THUDM-glm-4-9b-chat.jinja | Generic | +| THUDM-glm-edge-1.5b-chat.jinja | Generic | +| Tarek07-Progenitor-V1.1-LLaMa-70B.jinja | Llama 3.x | +| TheBloke-FusionNet_34Bx2_MoE-AWQ.jinja | Generic | +| TinyLlama-TinyLlama-1.1B-Chat-v1.0.jinja | Generic | +| UCLA-AGI-Mistral7B-PairRM-SPPO-Iter3.jinja | Generic | +| ValiantLabs-Llama3.1-8B-Enigma.jinja | Llama 3.x | +| abacusai-Fewshot-Metamath-OrcaVicuna-Mistral.jinja | Generic | +| ai21labs-AI21-Jamba-1.5-Large.jinja | Generic | +| allenai-Llama-3.1-Tulu-3-405B-SFT.jinja | Generic | +| allenai-Llama-3.1-Tulu-3-405B.jinja | Generic | +| allenai-Llama-3.1-Tulu-3-8B.jinja | Generic | +| arcee-ai-Virtuoso-Lite.jinja | Hermes 2 Pro | +| arcee-ai-Virtuoso-Medium-v2.jinja | Hermes 2 Pro | +| arcee-ai-Virtuoso-Small-v2.jinja | Hermes 2 Pro | +| avemio-GRAG-NEMO-12B-ORPO-HESSIAN-AI.jinja | Generic | +| bespokelabs-Bespoke-Stratos-7B.jinja | Hermes 2 Pro | +| bfuzzy1-acheron-m1a-llama.jinja | Generic | +| bofenghuang-vigogne-2-70b-chat.jinja | Generic | +| bytedance-research-UI-TARS-72B-DPO.jinja | Generic | +| bytedance-research-UI-TARS-7B-DPO.jinja | Generic | +| bytedance-research-UI-TARS-7B-SFT.jinja | Generic | +| carsenk-phi3.5_mini_exp_825_uncensored.jinja | Generic | +| cyberagent-DeepSeek-R1-Distill-Qwen-14B-Japanese.jinja | DeepSeek R1 (extract reasoning) | +| cyberagent-DeepSeek-R1-Distill-Qwen-32B-Japanese.jinja | DeepSeek R1 (extract reasoning) | +| databricks-dbrx-instruct.jinja | Generic | +| deepseek-ai-DeepSeek-Coder-V2-Instruct.jinja | Generic | +| deepseek-ai-DeepSeek-Coder-V2-Lite-Base.jinja | Generic | +| deepseek-ai-DeepSeek-Coder-V2-Lite-Instruct.jinja | Generic | +| deepseek-ai-DeepSeek-R1-Distill-Llama-70B.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-R1-Distill-Qwen-14B.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-R1-Distill-Qwen-7B.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-R1-Zero.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-R1.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-V2-Lite.jinja | Generic | +| deepseek-ai-DeepSeek-V2.5.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-V3.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-deepseek-coder-33b-instruct.jinja | Generic | +| deepseek-ai-deepseek-coder-6.7b-instruct.jinja | Generic | +| deepseek-ai-deepseek-coder-7b-instruct-v1.5.jinja | Generic | +| deepseek-ai-deepseek-llm-67b-chat.jinja | Generic | +| deepseek-ai-deepseek-llm-7b-chat.jinja | Generic | +| dicta-il-dictalm2.0-instruct.jinja | Generic | +| ehristoforu-Falcon3-8B-Franken-Basestruct.jinja | Hermes 2 Pro | +| fireworks-ai-llama-3-firefunction-v2.jinja | FireFunction v2 | +| godlikehhd-alpaca_data_sampled_ifd_new_5200.jinja | Hermes 2 Pro | +| godlikehhd-alpaca_data_score_max_0.7_2600.jinja | Hermes 2 Pro | +| google-gemma-2-27b-it.jinja | Generic | +| google-gemma-2-2b-it.jinja | Generic | +| google-gemma-2-2b-jpn-it.jinja | Generic | +| google-gemma-7b-it.jinja | Generic | +| huihui-ai-DeepSeek-R1-Distill-Llama-70B-abliterated.jinja | DeepSeek R1 (extract reasoning) | +| huihui-ai-DeepSeek-R1-Distill-Llama-8B-abliterated.jinja | DeepSeek R1 (extract reasoning) | +| huihui-ai-DeepSeek-R1-Distill-Qwen-14B-abliterated-v2.jinja | DeepSeek R1 (extract reasoning) | +| huihui-ai-DeepSeek-R1-Distill-Qwen-32B-abliterated.jinja | DeepSeek R1 (extract reasoning) | +| huihui-ai-DeepSeek-R1-Distill-Qwen-7B-abliterated-v2.jinja | DeepSeek R1 (extract reasoning) | +| huihui-ai-Qwen2.5-14B-Instruct-1M-abliterated.jinja | Hermes 2 Pro | +| ibm-granite-granite-3.1-8b-instruct.jinja | Generic | +| indischepartij-MiniCPM-3B-OpenHermes-2.5-v2.jinja | Generic | +| inflatebot-MN-12B-Mag-Mell-R1.jinja | Generic | +| jinaai-ReaderLM-v2.jinja | Generic | +| kms7530-chemeng_qwen-math-7b_24_1_100_1_nonmath.jinja | Hermes 2 Pro | +| knifeayumu-Cydonia-v1.3-Magnum-v4-22B.jinja | Mistral Nemo | +| langgptai-qwen1.5-7b-chat-sa-v0.1.jinja | Generic | +| lightblue-DeepSeek-R1-Distill-Qwen-7B-Japanese.jinja | DeepSeek R1 (extract reasoning) | +| mattshumer-Reflection-Llama-3.1-70B.jinja | Generic | +| meetkai-functionary-medium-v3.1.jinja | Functionary v3.1 Llama 3.1 | +| meetkai-functionary-medium-v3.2.jinja | Functionary v3.2 | +| meta-llama-Llama-2-7b-chat-hf.jinja | Generic | +| meta-llama-Llama-3.1-8B-Instruct.jinja | Llama 3.x | +| meta-llama-Llama-3.2-11B-Vision-Instruct.jinja | Llama 3.x | +| meta-llama-Llama-3.2-1B-Instruct.jinja | Llama 3.x | +| meta-llama-Llama-3.2-3B-Instruct.jinja | Llama 3.x | +| meta-llama-Llama-3.3-70B-Instruct.jinja | Llama 3.x | +| meta-llama-Meta-Llama-3-8B-Instruct.jinja | Generic | +| meta-llama-Meta-Llama-3.1-8B-Instruct.jinja | Llama 3.x | +| microsoft-Phi-3-medium-4k-instruct.jinja | Generic | +| microsoft-Phi-3-mini-4k-instruct.jinja | Generic | +| microsoft-Phi-3-small-8k-instruct.jinja | Generic | +| microsoft-Phi-3.5-mini-instruct.jinja | Generic | +| microsoft-Phi-3.5-vision-instruct.jinja | Generic | +| microsoft-phi-4.jinja | Generic | +| migtissera-Tess-3-Mistral-Nemo-12B.jinja | Generic | +| ministral-Ministral-3b-instruct.jinja | Generic | +| mistralai-Codestral-22B-v0.1.jinja | Generic | +| mistralai-Mistral-7B-Instruct-v0.1.jinja | Generic | +| mistralai-Mistral-7B-Instruct-v0.2.jinja | Generic | +| mistralai-Mistral-7B-Instruct-v0.3.jinja | Mistral Nemo | +| mistralai-Mistral-Large-Instruct-2407.jinja | Mistral Nemo | +| mistralai-Mistral-Large-Instruct-2411.jinja | Generic | +| mistralai-Mistral-Nemo-Instruct-2407.jinja | Mistral Nemo | +| mistralai-Mistral-Small-24B-Instruct-2501.jinja | Generic | +| mistralai-Mixtral-8x7B-Instruct-v0.1.jinja | Generic | +| mkurman-Qwen2.5-14B-DeepSeek-R1-1M.jinja | Hermes 2 Pro | +| mlabonne-AlphaMonarch-7B.jinja | Generic | +| mlx-community-Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-float32.jinja | Hermes 2 Pro | +| mlx-community-Qwen2.5-VL-7B-Instruct-8bit.jinja | Hermes 2 Pro | +| mobiuslabsgmbh-DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1.jinja | DeepSeek R1 (extract reasoning) | +| netcat420-MFANNv0.20.jinja | Generic | +| netcat420-MFANNv0.24.jinja | Generic | +| netease-youdao-Confucius-o1-14B.jinja | Hermes 2 Pro | +| nvidia-AceMath-7B-RM.jinja | Hermes 2 Pro | +| nvidia-Eagle2-1B.jinja | Hermes 2 Pro | +| nvidia-Eagle2-9B.jinja | Hermes 2 Pro | +| nvidia-Llama-3.1-Nemotron-70B-Instruct-HF.jinja | Llama 3.x | +| onnx-community-DeepSeek-R1-Distill-Qwen-1.5B-ONNX.jinja | DeepSeek R1 (extract reasoning) | +| open-thoughts-OpenThinker-7B.jinja | Hermes 2 Pro | +| openchat-openchat-3.5-0106.jinja | Generic | +| pankajmathur-orca_mini_v6_8b.jinja | Generic | +| princeton-nlp-Mistral-7B-Base-SFT-RDPO.jinja | Generic | +| princeton-nlp-Mistral-7B-Instruct-DPO.jinja | Generic | +| princeton-nlp-Mistral-7B-Instruct-RDPO.jinja | Generic | +| prithivMLmods-Bellatrix-Tiny-1.5B-R1.jinja | Hermes 2 Pro | +| prithivMLmods-Bellatrix-Tiny-1B-R1.jinja | Llama 3.x | +| prithivMLmods-Bellatrix-Tiny-1B-v3.jinja | Generic | +| prithivMLmods-Bellatrix-Tiny-3B-R1.jinja | Llama 3.x | +| prithivMLmods-Blaze-14B-xElite.jinja | Generic | +| prithivMLmods-Calcium-Opus-14B-Elite2-R1.jinja | Hermes 2 Pro | +| prithivMLmods-Calme-Ties-78B.jinja | Generic | +| prithivMLmods-Calme-Ties2-78B.jinja | Generic | +| prithivMLmods-Calme-Ties3-78B.jinja | Generic | +| prithivMLmods-ChemQwen2-vL.jinja | Generic | +| prithivMLmods-GWQ2b.jinja | Generic | +| prithivMLmods-LatexMind-2B-Codec.jinja | Generic | +| prithivMLmods-Llama-3.2-6B-AlgoCode.jinja | Llama 3.x | +| prithivMLmods-Megatron-Opus-14B-Exp.jinja | Hermes 2 Pro | +| prithivMLmods-Megatron-Opus-14B-Stock.jinja | Hermes 2 Pro | +| prithivMLmods-Megatron-Opus-7B-Exp.jinja | Hermes 2 Pro | +| prithivMLmods-Omni-Reasoner-Merged.jinja | Hermes 2 Pro | +| prithivMLmods-Omni-Reasoner4-Merged.jinja | Hermes 2 Pro | +| prithivMLmods-Primal-Opus-14B-Optimus-v1.jinja | Hermes 2 Pro | +| prithivMLmods-QwQ-Math-IO-500M.jinja | Hermes 2 Pro | +| prithivMLmods-Qwen-7B-Distill-Reasoner.jinja | DeepSeek R1 (extract reasoning) | +| prithivMLmods-Qwen2.5-1.5B-DeepSeek-R1-Instruct.jinja | Hermes 2 Pro | +| prithivMLmods-Qwen2.5-14B-DeepSeek-R1-1M.jinja | Hermes 2 Pro | +| prithivMLmods-Qwen2.5-32B-DeepSeek-R1-Instruct.jinja | Hermes 2 Pro | +| prithivMLmods-Qwen2.5-7B-DeepSeek-R1-1M.jinja | Hermes 2 Pro | +| prithivMLmods-Triangulum-v2-10B.jinja | Hermes 2 Pro | +| qingy2024-Falcon3-2x10B-MoE-Instruct.jinja | Hermes 2 Pro | +| rubenroy-Zurich-14B-GCv2-5m.jinja | Hermes 2 Pro | +| rubenroy-Zurich-7B-GCv2-5m.jinja | Hermes 2 Pro | +| silma-ai-SILMA-Kashif-2B-Instruct-v1.0.jinja | Generic | +| simplescaling-s1-32B.jinja | Hermes 2 Pro | +| sometimesanotion-Lamarck-14B-v0.7.jinja | Hermes 2 Pro | +| sonthenguyen-zephyr-sft-bnb-4bit-DPO-mtbr-180steps.jinja | Generic | +| sthenno-tempesthenno-icy-0130.jinja | Generic | +| sumink-qwft.jinja | Hermes 2 Pro | +| teknium-OpenHermes-2.5-Mistral-7B.jinja | Generic | +| thirdeyeai-elevate360m.jinja | Generic | +| tiiuae-Falcon3-10B-Instruct.jinja | Hermes 2 Pro | +| unsloth-DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit.jinja | DeepSeek R1 (extract reasoning) | +| unsloth-DeepSeek-R1-Distill-Llama-8B.jinja | DeepSeek R1 (extract reasoning) | +| unsloth-DeepSeek-R1.jinja | DeepSeek R1 (extract reasoning) | +| unsloth-Mistral-Small-24B-Instruct-2501-unsloth-bnb-4bit.jinja | Generic | +| upstage-solar-pro-preview-instruct.jinja | Generic | +| whyhow-ai-PatientSeek.jinja | Generic | +| xwen-team-Xwen-72B-Chat.jinja | Hermes 2 Pro | +| xwen-team-Xwen-7B-Chat.jinja | Hermes 2 Pro | + +This table can be generated with: + +```bash +./build/bin/test-chat ../minja/build/tests/*.jinja 2>/dev/null +``` + +
+ +# Usage - need tool-aware Jinja template + +First, start a server with any model, but make sure it has a tools-enabled template: you can verify this by inspecting the `chat_template` or `chat_template_tool_use` properties in `http://localhost:8080/props`). + +Here are some models known to work (w/ chat template override when needed): + +```shell +# Native support: + +llama-server --jinja -fa -hf bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M +llama-server --jinja -fa -hf bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q6_K_L +llama-server --jinja -fa -hf bartowski/Llama-3.3-70B-Instruct-GGUF:Q4_K_M + +# Native support for DeepSeek R1 works best w/ our template override (official template is buggy, although we do work around it) + +llama-server --jinja -fa -hf bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q6_K_L \ + --chat-template-file models/templates/llama-cpp-deepseek-r1.jinja + +llama-server --jinja -fa -hf bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M \ + --chat-template-file models/templates/llama-cpp-deepseek-r1.jinja + +# Native support requires the right template for these GGUFs: + +llama-server --jinja -fa -hf bartowski/functionary-small-v3.2-GGUF:Q4_K_M + --chat-template-file models/templates/meetkai-functionary-medium-v3.2.jinja + +llama-server --jinja -fa -hf bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M \ + --chat-template-file models/templates/NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja + +llama-server --jinja -fa -hf bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M \ + --chat-template-file models/templates/NousResearch-Hermes-3-Llama-3.1-8B-tool_use.jinja + +llama-server --jinja -fa -hf bartowski/firefunction-v2-GGUF -hff firefunction-v2-IQ1_M.gguf \ + --chat-template-file models/templates/fireworks-ai-llama-3-firefunction-v2.jinja + +llama-server --jinja -fa -hf bartowski/c4ai-command-r7b-12-2024-GGUF:Q6_K_L \ + --chat-template-file models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja + +# Generic format support +llama-server --jinja -fa -hf bartowski/phi-4-GGUF:Q4_0 +llama-server --jinja -fa -hf bartowski/gemma-2-2b-it-GGUF:Q8_0 +llama-server --jinja -fa -hf bartowski/c4ai-command-r-v01-GGUF:Q2_K +``` + +To get the official template from original HuggingFace repos, you can use [scripts/get_chat_template.py](../scripts/get_chat_template.py) (see examples invocations in [models/templates/README.md](../models/templates/README.md)) + +> [!TIP] +> If there is no official `tool_use` Jinja template, you may want to set `--chat-template chatml` to use a default that works with many models (YMMV!), or write your own (e.g. we provide a custom [llama-cpp-deepseek-r1.jinja](../models/templates/llama-cpp-deepseek-r1.jinja) for DeepSeek R1 distills) + +> [!CAUTION] +> Beware of extreme KV quantizations (e.g. `-ctk q4_0`), they can substantially degrade the model's tool calling performance. + +Test in CLI (or with any library / software that can use OpenAI-compatible API backends): + +```bash +curl http://localhost:8080/v1/chat/completions -d '{ + "model": "gpt-3.5-turbo", + "tools": [ + { + "type":"function", + "function":{ + "name":"python", + "description":"Runs code in an ipython interpreter and returns the result of the execution after 60 seconds.", + "parameters":{ + "type":"object", + "properties":{ + "code":{ + "type":"string", + "description":"The code to run in the ipython interpreter." + } + }, + "required":["code"] + } + } + } + ], + "messages": [ + { + "role": "user", + "content": "Print a hello world message with python." + } + ] +}' + + +curl http://localhost:8080/v1/chat/completions -d '{ + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "system", "content": "You are a chatbot that uses tools/functions. Dont overthink things."}, + {"role": "user", "content": "What is the weather in Istanbul?"} + ], + "tools": [{ + "type":"function", + "function":{ + "name":"get_current_weather", + "description":"Get the current weather in a given location", + "parameters":{ + "type":"object", + "properties":{ + "location":{ + "type":"string", + "description":"The city and country/state, e.g. `San Francisco, CA`, or `Paris, France`" + } + }, + "required":["location"] + } + } + }] +}' +``` + +
+Show output + +```json +{ +"choices": [ + { + "finish_reason": "tool", + "index": 0, + "message": { + "content": null, + "tool_calls": [ + { + "name": "python", + "arguments": "{\"code\":\" \\nprint(\\\"Hello, World!\\\")\"}" + } + ], + "role": "assistant" + } + } +], +"created": 1727287211, +"model": "gpt-3.5-turbo", +"object": "chat.completion", +"usage": { + "completion_tokens": 16, + "prompt_tokens": 44, + "total_tokens": 60 +}, +"id": "chatcmpl-Htbgh9feMmGM0LEH2hmQvwsCxq3c6Ni8" +} +``` + +
diff --git a/llama.cpp/docs/install.md b/llama.cpp/docs/install.md new file mode 100644 index 0000000000000000000000000000000000000000..d13efd9416fc0f912b1b8a879f4237bb9c1a649c --- /dev/null +++ b/llama.cpp/docs/install.md @@ -0,0 +1,50 @@ +# Install pre-built version of llama.cpp + +| Install via | Windows | Mac | Linux | +|-------------|---------|-----|-------| +| Winget | ✅ | | | +| Homebrew | | ✅ | ✅ | +| MacPorts | | ✅ | | +| Nix | | ✅ | ✅ | + +## Winget (Windows) + +```sh +winget install llama.cpp +``` + +The package is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggml-org/llama.cpp/issues/8188 + +## Homebrew (Mac and Linux) + +```sh +brew install llama.cpp +``` + +The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggml-org/llama.cpp/discussions/7668 + +## MacPorts (Mac) + +```sh +sudo port install llama.cpp +``` + +See also: https://ports.macports.org/port/llama.cpp/details/ + +## Nix (Mac and Linux) + +```sh +nix profile install nixpkgs#llama-cpp +``` + +For flake enabled installs. + +Or + +```sh +nix-env --file '' --install --attr llama-cpp +``` + +For non-flake enabled installs. + +This expression is automatically updated within the [nixpkgs repo](https://github.com/NixOS/nixpkgs/blob/nixos-24.05/pkgs/by-name/ll/llama-cpp/package.nix#L164). diff --git a/llama.cpp/docs/llguidance.md b/llama.cpp/docs/llguidance.md new file mode 100644 index 0000000000000000000000000000000000000000..34c7909c3a2de7490e3343983de4f59b3e6e8b4a --- /dev/null +++ b/llama.cpp/docs/llguidance.md @@ -0,0 +1,53 @@ +# LLGuidance Support in llama.cpp + +[LLGuidance](https://github.com/guidance-ai/llguidance) is a library for constrained decoding (also called constrained sampling or structured outputs) for Large Language Models (LLMs). Initially developed as the backend for the [Guidance](https://github.com/guidance-ai/guidance) library, it can also be used independently. + +LLGuidance supports JSON Schemas and arbitrary context-free grammars (CFGs) written in a [variant](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md) of Lark syntax. It is [very fast](https://github.com/guidance-ai/jsonschemabench/tree/main/maskbench) and has [excellent](https://github.com/guidance-ai/llguidance/blob/main/docs/json_schema.md) JSON Schema coverage but requires the Rust compiler, which complicates the llama.cpp build process. + +## Building + +To enable LLGuidance support, build llama.cpp with the `LLAMA_LLGUIDANCE` option: + +```sh +cmake -B build -DLLAMA_LLGUIDANCE=ON +make -C build -j +``` + +For Windows use `cmake --build build --config Release` instead of `make`. + +This requires the Rust compiler and the `cargo` tool to be [installed](https://www.rust-lang.org/tools/install). + +## Interface + +There are no new command-line arguments or modifications to `common_params`. When enabled, grammars starting with `%llguidance` are passed to LLGuidance instead of the [current](../grammars/README.md) llama.cpp grammars. Additionally, JSON Schema requests (e.g., using the `-j` argument in `llama-cli`) are also passed to LLGuidance. + +For your existing GBNF grammars, you can use [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/python/llguidance/gbnf_to_lark.py) to convert them to LLGuidance Lark-like format. + +## Performance + +Computing a "token mask" (i.e., the set of allowed tokens) for a llama3 tokenizer with 128k tokens takes, on average, 50μs of single-core CPU time for the [JSON Schema Bench](https://github.com/guidance-ai/jsonschemabench). The p99 time is 0.5ms, and the p100 time is 20ms. These results are due to the lexer/parser split and several [optimizations](https://github.com/guidance-ai/llguidance/blob/main/docs/optimizations.md). + +## JSON Schema + +LLGuidance adheres closely to the JSON Schema specification. For example: + +- `additionalProperties` defaults to `true`, unlike current grammars, though you can set `"additionalProperties": false` if needed. +- any whitespace is allowed. +- The definition order in the `"properties": {}` object is maintained, regardless of whether properties are required (current grammars always puts required properties first). + +Unsupported schemas result in an error message—no keywords are silently ignored. + +## Why Not Reuse GBNF Format? + +GBNF lacks the concept of a lexer. + +Most programming languages, including JSON, use a two-step process: a lexer (built with regular expressions) converts a byte stream into lexemes, which are then processed by a CFG parser. This approach is faster because lexers are cheaper to evaluate, and there is ~10x fewer lexemes than bytes. +LLM tokens often align with lexemes, so the parser is engaged in under 0.5% of tokens, with the lexer handling the rest. + +However, the user has to provide the distinction between lexemes and CFG symbols. In [Lark](https://github.com/lark-parser/lark), lexeme names are uppercase, while CFG symbols are lowercase. +The [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/scripts/gbnf_to_lark.py) can often take care of this automatically. +See [LLGuidance syntax docs](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#terminals-vs-rules) for more details. + +## Error Handling + +Errors are currently printed to `stderr`, and generation continues. Improved error handling may be added in the future. diff --git a/llama.cpp/docs/multimodal.md b/llama.cpp/docs/multimodal.md new file mode 100644 index 0000000000000000000000000000000000000000..860b555a94a6423354345fd6a7e4e94f71ec9999 --- /dev/null +++ b/llama.cpp/docs/multimodal.md @@ -0,0 +1,116 @@ +# Multimodal + +llama.cpp supports multimodal input via `libmtmd`. Currently, there are 2 tools support this feature: +- [llama-mtmd-cli](../tools/mtmd/README.md) +- [llama-server](../tools/server/README.md) via OpenAI-compatible `/chat/completions` API + +Currently, we support **image** and **audio** input. Audio is highly experimental and may have reduced quality. + +To enable it, you can use one of the 2 methods below: + +- Use `-hf` option with a supported model (see a list of pre-quantized model below) + - To load a model using `-hf` while disabling multimodal, use `--no-mmproj` + - To load a model using `-hf` while using a custom mmproj file, use `--mmproj local_file.gguf` +- Use `-m model.gguf` option with `--mmproj file.gguf` to specify text and multimodal projector respectively + +By default, multimodal projector will be offloaded to GPU. To disable this, add `--no-mmproj-offload` + +For example: + +```sh +# simple usage with CLI +llama-mtmd-cli -hf ggml-org/gemma-3-4b-it-GGUF + +# simple usage with server +llama-server -hf ggml-org/gemma-3-4b-it-GGUF + +# using local file +llama-server -m gemma-3-4b-it-Q4_K_M.gguf --mmproj mmproj-gemma-3-4b-it-Q4_K_M.gguf + +# no GPU offload +llama-server -hf ggml-org/gemma-3-4b-it-GGUF --no-mmproj-offload +``` + +## Pre-quantized models + +These are ready-to-use models, most of them come with `Q4_K_M` quantization by default. They can be found at the Hugging Face page of the ggml-org: https://huggingface.co/collections/ggml-org/multimodal-ggufs-68244e01ff1f39e5bebeeedc + +Replaces the `(tool_name)` with the name of binary you want to use. For example, `llama-mtmd-cli` or `llama-server` + +NOTE: some models may require large context window, for example: `-c 8192` + +**Vision models**: + +```sh +# Gemma 3 +(tool_name) -hf ggml-org/gemma-3-4b-it-GGUF +(tool_name) -hf ggml-org/gemma-3-12b-it-GGUF +(tool_name) -hf ggml-org/gemma-3-27b-it-GGUF + +# SmolVLM +(tool_name) -hf ggml-org/SmolVLM-Instruct-GGUF +(tool_name) -hf ggml-org/SmolVLM-256M-Instruct-GGUF +(tool_name) -hf ggml-org/SmolVLM-500M-Instruct-GGUF +(tool_name) -hf ggml-org/SmolVLM2-2.2B-Instruct-GGUF +(tool_name) -hf ggml-org/SmolVLM2-256M-Video-Instruct-GGUF +(tool_name) -hf ggml-org/SmolVLM2-500M-Video-Instruct-GGUF + +# Pixtral 12B +(tool_name) -hf ggml-org/pixtral-12b-GGUF + +# Qwen 2 VL +(tool_name) -hf ggml-org/Qwen2-VL-2B-Instruct-GGUF +(tool_name) -hf ggml-org/Qwen2-VL-7B-Instruct-GGUF + +# Qwen 2.5 VL +(tool_name) -hf ggml-org/Qwen2.5-VL-3B-Instruct-GGUF +(tool_name) -hf ggml-org/Qwen2.5-VL-7B-Instruct-GGUF +(tool_name) -hf ggml-org/Qwen2.5-VL-32B-Instruct-GGUF +(tool_name) -hf ggml-org/Qwen2.5-VL-72B-Instruct-GGUF + +# Mistral Small 3.1 24B (IQ2_M quantization) +(tool_name) -hf ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF + +# InternVL 2.5 and 3 +(tool_name) -hf ggml-org/InternVL2_5-1B-GGUF +(tool_name) -hf ggml-org/InternVL2_5-4B-GGUF +(tool_name) -hf ggml-org/InternVL3-1B-Instruct-GGUF +(tool_name) -hf ggml-org/InternVL3-2B-Instruct-GGUF +(tool_name) -hf ggml-org/InternVL3-8B-Instruct-GGUF +(tool_name) -hf ggml-org/InternVL3-14B-Instruct-GGUF + +# Llama 4 Scout +(tool_name) -hf ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF + +# Moondream2 20250414 version +(tool_name) -hf ggml-org/moondream2-20250414-GGUF + +``` + +**Audio models**: + +```sh +# Ultravox 0.5 +(tool_name) -hf ggml-org/ultravox-v0_5-llama-3_2-1b-GGUF +(tool_name) -hf ggml-org/ultravox-v0_5-llama-3_1-8b-GGUF + +# Qwen2-Audio and SeaLLM-Audio +# note: no pre-quantized GGUF this model, as they have very poor result +# ref: https://github.com/ggml-org/llama.cpp/pull/13760 + +# Mistral's Voxtral +(tool_name) -hf ggml-org/Voxtral-Mini-3B-2507-GGUF +``` + +**Mixed modalities**: + +```sh +# Qwen2.5 Omni +# Capabilities: audio input, vision input +(tool_name) -hf ggml-org/Qwen2.5-Omni-3B-GGUF +(tool_name) -hf ggml-org/Qwen2.5-Omni-7B-GGUF +``` + +## Finding more models: + +GGUF models on Huggingface with vision capabilities can be found here: https://huggingface.co/models?pipeline_tag=image-text-to-text&sort=trending&search=gguf diff --git a/llama.cpp/docs/multimodal/MobileVLM.md b/llama.cpp/docs/multimodal/MobileVLM.md new file mode 100644 index 0000000000000000000000000000000000000000..f90e3b30a963043a471efd58caaa4e8dc7edb463 --- /dev/null +++ b/llama.cpp/docs/multimodal/MobileVLM.md @@ -0,0 +1,377 @@ +# MobileVLM + +Currently this implementation supports [MobileVLM-1.7B](https://huggingface.co/mtgv/MobileVLM-1.7B) / [MobileVLM_V2-1.7B](https://huggingface.co/mtgv/MobileVLM_V2-1.7B) variants. + +for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com/Meituan-AutoML/MobileVLM) + +The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava. + +Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using **MobileVLM-1.7B** as an example, the different conversion step will be shown. + +## Usage + +Build the `llama-mtmd-cli` binary. + +After building, run: `./llama-mtmd-cli` to see the usage. For example: + +```sh +./llama-mtmd-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \ + --mmproj MobileVLM-1.7B/mmproj-model-f16.gguf \ + --chat-template deepseek +``` + +## Model conversion + +1. Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally: + +```sh +git clone https://huggingface.co/mtgv/MobileVLM-1.7B + +git clone https://huggingface.co/openai/clip-vit-large-patch14-336 +``` + +2. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: + +```sh +python ./tools/mtmd/llava_surgery.py -m path/to/MobileVLM-1.7B +``` + +3. Use `convert_image_encoder_to_gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF: + +```sh +python ./tools/mtmd/convert_image_encoder_to_gguf.py \ + -m path/to/clip-vit-large-patch14-336 \ + --llava-projector path/to/MobileVLM-1.7B/llava.projector \ + --output-dir path/to/MobileVLM-1.7B \ + --projector-type ldp +``` + +```sh +python ./tools/mtmd/convert_image_encoder_to_gguf.py \ + -m path/to/clip-vit-large-patch14-336 \ + --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \ + --output-dir path/to/MobileVLM-1.7B_V2 \ + --projector-type ldpv2 +``` + +4. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF: + +```sh +python ./examples/convert_legacy_llama.py path/to/MobileVLM-1.7B --skip-unknown +``` + +5. Use `quantize` to convert LLaMA part's DataType from `fp32` to `q4_k` +```sh +./llama-quantize path/to/MobileVLM-1.7B/ggml-model-F32.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s +``` + +Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directory. + +## Android compile and run +### compile +refer to `tools/mtmd/android/build_64.sh` +```sh +mkdir tools/mtmd/android/build_64 +cd tools/mtmd/android/build_64 +../build_64.sh +``` +### run on Android +refer to `android/adb_run.sh`, modify resources' `name` and `path` + +## Some result on Android with `Snapdragon 888` chip +### case 1 +**input** +```sh +/data/local/tmp/llama-mtmd-cli \ + -m /data/local/tmp/ggml-model-q4_k.gguf \ + --mmproj /data/local/tmp/mmproj-model-f16.gguf \ + -t 4 \ + --image /data/local/tmp/demo.jpg \ + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:" +``` +**output** +```sh +encode_image_with_clip: image encoded in 21148.71 ms by CLIP ( 146.87 ms per image patch) + Susan Wise Bauer +llama_print_timings: load time = 23574.72 ms +llama_print_timings: sample time = 1.24 ms / 6 runs ( 0.21 ms per token, 4850.44 tokens per second) +llama_print_timings: prompt eval time = 12460.15 ms / 246 tokens ( 50.65 ms per token, 19.74 tokens per second) +llama_print_timings: eval time = 424.86 ms / 6 runs ( 70.81 ms per token, 14.12 tokens per second) +llama_print_timings: total time = 34731.93 ms +``` +### case 2 +**input** +```sh +/data/local/tmp/llama-mtmd-cli \ + -m /data/local/tmp/ggml-model-q4_k.gguf \ + --mmproj /data/local/tmp/mmproj-model-f16.gguf \ + -t 4 \ + --image /data/local/tmp/cat.jpeg \ + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat is in the image? ASSISTANT:" +``` +**output** +```sh +encode_image_with_clip: image encoded in 21149.51 ms by CLIP ( 146.87 ms per image patch) + The image depicts a cat sitting in the grass near some tall green plants. +llama_print_timings: load time = 23257.32 ms +llama_print_timings: sample time = 5.25 ms / 18 runs ( 0.29 ms per token, 3430.53 tokens per second) +llama_print_timings: prompt eval time = 11900.73 ms / 232 tokens ( 51.30 ms per token, 19.49 tokens per second) +llama_print_timings: eval time = 1279.03 ms / 18 runs ( 71.06 ms per token, 14.07 tokens per second) +llama_print_timings: total time = 34570.79 ms +``` + + +## Some result on Android with `Snapdragon 778G` chip +### MobileVLM-1.7B case +#### mtmd-cli release-b2005 +**input** +```sh +/data/local/tmp/llama-mtmd-cli \ + -m /data/local/tmp/ggml-model-q4_k.gguf \ + --mmproj /data/local/tmp/mmproj-model-f16.gguf \ + -t 4 \ + --image /data/local/tmp/many_llamas.jpeg \ + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat's that? ASSISTANT:" +``` +**output** +```sh +encode_image_with_clip: image encoded in 18728.52 ms by CLIP ( 130.06 ms per image patch) +system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: +user_prompt: \nWhat's that? ASSISTANT: + + A group of llamas are standing in a green pasture. + +llama_print_timings: load time = 20357.33 ms +llama_print_timings: sample time = 2.96 ms / 14 runs ( 0.21 ms per token, 4734.53 tokens per second) +llama_print_timings: prompt eval time = 8119.49 ms / 191 tokens ( 42.51 ms per token, 23.52 tokens per second) +llama_print_timings: eval time = 1005.75 ms / 14 runs ( 71.84 ms per token, 13.92 tokens per second) +llama_print_timings: total time = 28038.34 ms / 205 tokens +``` +#### mtmd-cli latest-version +**input** + +Just the same as above. + +**output**(seems to be much slower) +```sh +encode_image_with_clip: image embedding created: 144 tokens + +encode_image_with_clip: image encoded in 288268.88 ms by CLIP ( 2001.87 ms per image patch) +system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: +user_prompt: \nWhat's that? ASSISTANT: + + It is a group of sheep standing together in a grass field. + +llama_print_timings: load time = 818120.91 ms +llama_print_timings: sample time = 3.44 ms / 14 runs ( 0.25 ms per token, 4067.40 tokens per second) +llama_print_timings: prompt eval time = 529274.69 ms / 191 tokens ( 2771.07 ms per token, 0.36 tokens per second) +llama_print_timings: eval time = 43894.02 ms / 13 runs ( 3376.46 ms per token, 0.30 tokens per second) +llama_print_timings: total time = 865441.76 ms / 204 tokens +``` +### MobileVLM_V2-1.7B case +#### mtmd-cli release-2005b +**input** + +Just the same as above. + +**output** +```sh +encode_image_with_clip: image encoded in 20609.61 ms by CLIP ( 143.12 ms per image patch) +system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: +user_prompt: \nWhat's that? ASSISTANT: + + This image captures a lively scene of 20 llamas in motion on an expansive, grassy field. The llama is scattered across the landscape with some standing and others sitting down as if taking rest or observing their surroundings from different vantage points within this verdant setting. + +The background offers glimpses into a picturesque town nestled amidst hills under an overcast sky, adding depth to the scene while also emphasizing that distance between these llama and human-made structures like houses or roads in which they roam freely without any barriers around them. The image is framed by text at both right angles on white backgrounds against a contrasting blue backdrop with green foliage, further drawing attention to the llamas amidst their natural habitat while also inviting viewers into this picturesque landscape within town limits of Alta Llama + +llama_print_timings: load time = 22406.77 ms +llama_print_timings: sample time = 49.26 ms / 186 runs ( 0.26 ms per token, 3776.27 tokens per second) +llama_print_timings: prompt eval time = 9044.54 ms / 191 tokens ( 47.35 ms per token, 21.12 tokens per second) +llama_print_timings: eval time = 14497.49 ms / 186 runs ( 77.94 ms per token, 12.83 tokens per second) +llama_print_timings: total time = 44411.01 ms / 377 tokens +``` + +## Orin compile and run +### compile +```sh +make GGML_CUDA=1 CUDA_DOCKER_ARCH=sm_87 GGML_CUDA_F16=1 -j 32 +``` +### run on Orin +### case 1 +**input** +```sh +./llama-mtmd-cli \ + -m /data/local/tmp/ggml-model-q4_k.gguf \ + --mmproj /data/local/tmp/mmproj-model-f16.gguf \ + --image /data/local/tmp/demo.jpeg \ + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:" \ + --n-gpu-layers 999 +``` +**output** +```sh + +encode_image_with_clip: image encoded in 296.62 ms by CLIP ( 2.06 ms per image patch) + + Susan Wise Bauer + +llama_print_timings: load time = 1067.64 ms +llama_print_timings: sample time = 1.53 ms / 6 runs ( 0.25 ms per token, 3934.43 tokens per second) +llama_print_timings: prompt eval time = 306.84 ms / 246 tokens ( 1.25 ms per token, 801.72 tokens per second) +llama_print_timings: eval time = 91.50 ms / 6 runs ( 15.25 ms per token, 65.58 tokens per second) +llama_print_timings: total time = 1352.63 ms / 252 tokens +``` + +### case 2 +**input** +```sh +./llama-mtmd-cli \ + -m /data/local/tmp/ggml-model-q4_k.gguf \ + --mmproj /data/local/tmp/mmproj-model-f16.gguf \ + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat is in the image? ASSISTANT:" \ + --n-gpu-layers 999 + +``` +**output** +```sh +encode_image_with_clip: image encoded in 302.15 ms by CLIP ( 2.10 ms per image patch) + + The image features a cat lying in the grass. + +llama_print_timings: load time = 1057.07 ms +llama_print_timings: sample time = 3.27 ms / 11 runs ( 0.30 ms per token, 3360.83 tokens per second) +llama_print_timings: prompt eval time = 213.60 ms / 232 tokens ( 0.92 ms per token, 1086.14 tokens per second) +llama_print_timings: eval time = 166.65 ms / 11 runs ( 15.15 ms per token, 66.01 tokens per second) +llama_print_timings: total time = 1365.47 ms / 243 tokens +``` + +## Running on Intel(R) Core(TM) i7-10750H +### Operating system +Ubuntu22.04 +### compile +```sh +make -j32 +``` +### MobileVLM-1.7B case +**input** +```sh +-m /path/to/ggml-model-q4_k.gguf \ + --mmproj /path/to/mmproj-model-f16.gguf \ + --image /path/to/many_llamas.jpeg + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat's that? ASSISTANT:" \ +``` +**output** +```sh +encode_image_with_clip: image embedding created: 144 tokens + +encode_image_with_clip: image encoded in 2730.94 ms by CLIP ( 18.96 ms per image patch) +system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: +user_prompt: \nWhat's that?ASSISTANT: + + A group of llamas are walking together in a field. + +llama_print_timings: load time = 5506.60 ms +llama_print_timings: sample time = 0.44 ms / 13 runs ( 0.03 ms per token, 29545.45 tokens per second) +llama_print_timings: prompt eval time = 2031.58 ms / 190 tokens ( 10.69 ms per token, 93.52 tokens per second) +llama_print_timings: eval time = 438.92 ms / 12 runs ( 36.58 ms per token, 27.34 tokens per second) +llama_print_timings: total time = 5990.25 ms / 202 tokens +``` + +### MobileVLM_V2-1.7B case +**input** + +Just the same as above. + +**ouput** +```sh +encode_image_with_clip: image embedding created: 144 tokens + +encode_image_with_clip: image encoded in 3223.89 ms by CLIP ( 22.39 ms per image patch) +system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: +user_prompt: \nWhat's that?ASSISTANT: + + The image captures a tranquil scene in a park, where a group of approximately 20 llamas are gathered. The llamas, a mix of white and black, are standing in a line, their black and white patterns contrasting with the lush green grass of the park. The lamas are arranged in a line, suggesting a social order. + +The park itself is lush and green, with trees dotting the landscape in the background. A sign reading "Llamas Tico Ana" is also visible in the image, possibly indicating the location or the breed of the llamas. The image seems to be taken from a distance, providing a wide view of the scene and the surrounding environment. + +The llamas' positions relative to each other, the sign, and the trees create a harmonious composition. The image does not contain any discernible text. The overall scene is one of peace and natural beauty, with the llamas in their natural habitat, surrounded by the vibrant colors and lush greenery of the park. + +llama_print_timings: load time = 6642.61 ms +llama_print_timings: sample time = 8.15 ms / 223 runs ( 0.04 ms per token, 27358.61 tokens per second) +llama_print_timings: prompt eval time = 2475.07 ms / 190 tokens ( 13.03 ms per token, 76.77 tokens per second) +llama_print_timings: eval time = 8760.60 ms / 222 runs ( 39.46 ms per token, 25.34 tokens per second) +llama_print_timings: total time = 15513.95 ms / 412 tokens +``` + +## Run on Intel(R) Core(TM) Ultra7 115H +### operation system +Windows11 +### comiple +```sh +make -j32 +``` +### MobileVLM-1.7B case +**input** +```sh +-m /path/to/ggml-model-q4_k.gguf \ + --mmproj /path/to/tmp/mmproj-model-f16.gguf \ + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat's that? ASSISTANT:" \ +``` +**output** +```sh +encode_image_with_clip: image encoded in 4902.81 ms by CLIP ( 34.05 ms per image patch) +system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: +user_prompt: \nWhat's that? ASSISTANT: + + The image features a group of brown and white llamas standing in a grassy field. + +llama_print_timings: load time = 7441.06 ms +llama_print_timings: sample time = 0.72 ms / 19 runs ( 0.04 ms per token, 26279.39 tokens per second) +llama_print_timings: prompt eval time = 2090.71 ms / 191 tokens ( 10.95 ms per token, 91.36 tokens per second) +llama_print_timings: eval time = 512.35 ms / 18 runs ( 28.46 ms per token, 35.13 tokens per second) +llama_print_timings: total time = 7987.23 ms / 209 tokens +``` + +### MobileVLM_V2-1.7B case +**input** + +Just the same as above. + +**output** +```sh +encode_image_with_clip: image encoded in 4682.44 ms by CLIP ( 32.52 ms per image patch) +system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: +user_prompt: \nWhat's that? ASSISTANT: + + This image captures a lively scene of a group of 14 llamas in a grassy field. The llamas, with their distinctive black and white coats, are standing and walking in a line, seemingly engaged in a social activity. One + of them, possibly the first in the line, has its back turned, perhaps observing something in the distance. + +The llama in the front of the line stands out due to its black and white coloring, which is quite unusual for llama patterns. The llama in the front also seems to be more aware of its surroundings, as it faces the camera, giving a sense of engagement with the viewer. + +The image is taken from the side of the llama, providing a clear view of the llama in the front and its companions. The lameness in the llama in + front is not visible, indicating that it might not be the main focus of the photo. + +The background of the image features a grassy field, with a fence and a tree visible in the distance. The tree appears to be bare, suggesting that it might be during a time of year when most trees are dormant or have shed their leaves. + + +llama_print_timings: load time = 7015.35 ms +llama_print_timings: sample time = 10.61 ms / 256 runs ( 0.04 ms per token, 24119.09 tokens per second) +llama_print_timings: prompt eval time = 2052.45 ms / 191 tokens ( 10.75 ms per token, 93.06 tokens per second) +llama_print_timings: eval time = 7259.43 ms / 255 runs ( 28.47 ms per token, 35.13 tokens per second) +llama_print_timings: total time = 14371.19 ms / 446 tokens +``` + +## TODO + +- [x] Support non-CPU backend for the new operators, such as `depthwise`, `hardswish`, `hardsigmoid` +- [ ] Optimize LDP projector performance + + - Optimize the structure definition to avoid unnecessary memory rearrangements, to reduce the use of `ggml_permute_cpy`; + - Optimize operator implementation (ARM CPU/NVIDIA GPU): such as depthwise conv, hardswish, hardsigmoid, etc. +- [x] run MobileVLM on `Jetson Orin` +- [ ] Support more model variants, such as `MobileVLM-3B`. + + +## contributor +```sh +zhangjidong05, yangyang260, huyiming03, chenxiaotao03, ZiangWu-77 +``` diff --git a/llama.cpp/docs/multimodal/gemma3.md b/llama.cpp/docs/multimodal/gemma3.md new file mode 100644 index 0000000000000000000000000000000000000000..376579d11a8be8300b51927745961bd7c7a79846 --- /dev/null +++ b/llama.cpp/docs/multimodal/gemma3.md @@ -0,0 +1,51 @@ +# Gemma 3 vision + +> [!IMPORTANT] +> +> This is very experimental, only used for demo purpose. + +## Quick started + +You can use pre-quantized model from [ggml-org](https://huggingface.co/ggml-org)'s Hugging Face account + +```bash +# build +cmake -B build +cmake --build build --target llama-mtmd-cli + +# alternatively, install from brew (MacOS) +brew install llama.cpp + +# run it +llama-mtmd-cli -hf ggml-org/gemma-3-4b-it-GGUF +llama-mtmd-cli -hf ggml-org/gemma-3-12b-it-GGUF +llama-mtmd-cli -hf ggml-org/gemma-3-27b-it-GGUF + +# note: 1B model does not support vision +``` + +## How to get mmproj.gguf? + +Simply to add `--mmproj` in when converting model via `convert_hf_to_gguf.py`: + +```bash +cd gemma-3-4b-it +python ../llama.cpp/convert_hf_to_gguf.py --outfile model.gguf --outtype f16 --mmproj . +# output file: mmproj-model.gguf +``` + +## How to run it? + +What you need: +- The text model GGUF, can be converted using `convert_hf_to_gguf.py` +- The mmproj file from step above +- An image file + +```bash +# build +cmake -B build +cmake --build build --target llama-mtmd-cli + +# run it +./build/bin/llama-mtmd-cli -m {text_model}.gguf --mmproj mmproj.gguf --image your_image.jpg +``` diff --git a/llama.cpp/docs/multimodal/glmedge.md b/llama.cpp/docs/multimodal/glmedge.md new file mode 100644 index 0000000000000000000000000000000000000000..7ee5f47a21db2767abee944343439f3e8c454efe --- /dev/null +++ b/llama.cpp/docs/multimodal/glmedge.md @@ -0,0 +1,43 @@ +# GLMV-EDGE + +Currently this implementation supports [glm-edge-v-2b](https://huggingface.co/THUDM/glm-edge-v-2b) and [glm-edge-v-5b](https://huggingface.co/THUDM/glm-edge-v-5b). + +## Usage +Build the `llama-mtmd-cli` binary. + +After building, run: `./llama-mtmd-cli` to see the usage. For example: + +```sh +./llama-mtmd-cli -m model_path/ggml-model-f16.gguf --mmproj model_path/mmproj-model-f16.gguf +``` + +**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so. +**note**: For GPU offloading ensure to use the `-ngl` flag just like usual + +## GGUF conversion + +1. Clone a GLMV-EDGE model ([2B](https://huggingface.co/THUDM/glm-edge-v-2b) or [5B](https://huggingface.co/THUDM/glm-edge-v-5b)). For example: + +```sh +git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/THUDM/glm-edge-v-2b +``` + +2. Use `glmedge-surgery.py` to split the GLMV-EDGE model to LLM and multimodel projector constituents: + +```sh +python ./tools/mtmd/glmedge-surgery.py -m ../model_path +``` + +4. Use `glmedge-convert-image-encoder-to-gguf.py` to convert the GLMV-EDGE image encoder to GGUF: + +```sh +python ./tools/mtmd/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path +``` + +5. Use `examples/convert_hf_to_gguf.py` to convert the LLM part of GLMV-EDGE to GGUF: + +```sh +python convert_hf_to_gguf.py ../model_path +``` + +Now both the LLM part and the image encoder are in the `model_path` directory. diff --git a/llama.cpp/docs/multimodal/granitevision.md b/llama.cpp/docs/multimodal/granitevision.md new file mode 100644 index 0000000000000000000000000000000000000000..2a8ff240a8c47bcac2dd7bedb79b7182655ad1eb --- /dev/null +++ b/llama.cpp/docs/multimodal/granitevision.md @@ -0,0 +1,186 @@ +# Granite Vision + +Download the model and point your `GRANITE_MODEL` environment variable to the path. + +```bash +$ git clone https://huggingface.co/ibm-granite/granite-vision-3.2-2b +$ export GRANITE_MODEL=./granite-vision-3.2-2b +``` + + +### 1. Running llava surgery v2. +First, we need to run the llava surgery script as shown below: + +`python llava_surgery_v2.py -C -m $GRANITE_MODEL` + +You should see two new files (`llava.clip` and `llava.projector`) written into your model's directory, as shown below. + +```bash +$ ls $GRANITE_MODEL | grep -i llava +llava.clip +llava.projector +``` + +We should see that the projector and visual encoder get split out into the llava files. Quick check to make sure they aren't empty: +```python +import os +import torch + +MODEL_PATH = os.getenv("GRANITE_MODEL") +if not MODEL_PATH: + raise ValueError("env var GRANITE_MODEL is unset!") + +encoder_tensors = torch.load(os.path.join(MODEL_PATH, "llava.clip")) +projector_tensors = torch.load(os.path.join(MODEL_PATH, "llava.projector")) + +assert len(encoder_tensors) > 0 +assert len(projector_tensors) > 0 +``` + +If you actually inspect the `.keys()` of the loaded tensors, you should see a lot of `vision_model` tensors in the `encoder_tensors`, and 5 tensors (`'multi_modal_projector.linear_1.bias'`, `'multi_modal_projector.linear_1.weight'`, `'multi_modal_projector.linear_2.bias'`, `'multi_modal_projector.linear_2.weight'`, `'image_newline'`) in the multimodal `projector_tensors`. + + +### 2. Creating the Visual Component GGUF +Next, create a new directory to hold the visual components, and copy the llava.clip/projector files, as shown below. + +```bash +$ ENCODER_PATH=$PWD/visual_encoder +$ mkdir $ENCODER_PATH + +$ cp $GRANITE_MODEL/llava.clip $ENCODER_PATH/pytorch_model.bin +$ cp $GRANITE_MODEL/llava.projector $ENCODER_PATH/ +``` + +Now, we need to write a config for the visual encoder. In order to convert the model, be sure to use the correct `image_grid_pinpoints`, as these may vary based on the model. You can find the `image_grid_pinpoints` in `$GRANITE_MODEL/config.json`. + +```json +{ + "_name_or_path": "siglip-model", + "architectures": [ + "SiglipVisionModel" + ], + "image_grid_pinpoints": [ + [384,384], + [384,768], + [384,1152], + [384,1536], + [384,1920], + [384,2304], + [384,2688], + [384,3072], + [384,3456], + [384,3840], + [768,384], + [768,768], + [768,1152], + [768,1536], + [768,1920], + [1152,384], + [1152,768], + [1152,1152], + [1536,384], + [1536,768], + [1920,384], + [1920,768], + [2304,384], + [2688,384], + [3072,384], + [3456,384], + [3840,384] + ], + "mm_patch_merge_type": "spatial_unpad", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14, + "layer_norm_eps": 1e-6, + "hidden_act": "gelu_pytorch_tanh", + "projection_dim": 0, + "vision_feature_layer": [-24, -20, -12, -1] +} +``` + +At this point you should have something like this: +```bash +$ ls $ENCODER_PATH +config.json llava.projector pytorch_model.bin +``` + +Now convert the components to GGUF; Note that we also override the image mean/std dev to `[.5,.5,.5]` since we use the SigLIP visual encoder - in the transformers model, you can find these numbers in the `preprocessor_config.json`. +```bash +$ python convert_image_encoder_to_gguf.py \ + -m $ENCODER_PATH \ + --llava-projector $ENCODER_PATH/llava.projector \ + --output-dir $ENCODER_PATH \ + --clip-model-is-vision \ + --clip-model-is-siglip \ + --image-mean 0.5 0.5 0.5 \ + --image-std 0.5 0.5 0.5 +``` + +This will create the first GGUF file at `$ENCODER_PATH/mmproj-model-f16.gguf`; we will refer to the absolute path of this file as the `$VISUAL_GGUF_PATH.` + + +### 3. Creating the LLM GGUF. +The granite vision model contains a granite LLM as its language model. For now, the easiest way to get the GGUF for LLM is by loading the composite model in `transformers` and exporting the LLM so that it can be directly converted with the normal conversion path. + +First, set the `LLM_EXPORT_PATH` to the path to export the `transformers` LLM to. +```bash +$ export LLM_EXPORT_PATH=$PWD/granite_vision_llm +``` + +```python +import os +import transformers + +MODEL_PATH = os.getenv("GRANITE_MODEL") +if not MODEL_PATH: + raise ValueError("env var GRANITE_MODEL is unset!") + +LLM_EXPORT_PATH = os.getenv("LLM_EXPORT_PATH") +if not LLM_EXPORT_PATH: + raise ValueError("env var LLM_EXPORT_PATH is unset!") + +tokenizer = transformers.AutoTokenizer.from_pretrained(MODEL_PATH) + +# NOTE: granite vision support was added to transformers very recently (4.49); +# if you get size mismatches, your version is too old. +# If you are running with an older version, set `ignore_mismatched_sizes=True` +# as shown below; it won't be loaded correctly, but the LLM part of the model that +# we are exporting will be loaded correctly. +model = transformers.AutoModelForImageTextToText.from_pretrained(MODEL_PATH, ignore_mismatched_sizes=True) + +tokenizer.save_pretrained(LLM_EXPORT_PATH) +model.language_model.save_pretrained(LLM_EXPORT_PATH) +``` + +Now you can convert the exported LLM to GGUF with the normal converter in the root of the llama cpp project. +```bash +$ LLM_GGUF_PATH=$LLM_EXPORT_PATH/granite_llm.gguf +... +$ python convert_hf_to_gguf.py --outfile $LLM_GGUF_PATH $LLM_EXPORT_PATH +``` + + +### 4. Quantization +If you want to quantize the LLM, you can do so with `llama-quantize` as you would any other LLM. For example: +```bash +$ ./build/bin/llama-quantize $LLM_EXPORT_PATH/granite_llm.gguf $LLM_EXPORT_PATH/granite_llm_q4_k_m.gguf Q4_K_M +$ LLM_GGUF_PATH=$LLM_EXPORT_PATH/granite_llm_q4_k_m.gguf +``` + +Note that currently you cannot quantize the visual encoder because granite vision models use SigLIP as the visual encoder, which has tensor dimensions that are not divisible by 32. + + +### 5. Running the Model in Llama cpp +Build llama cpp normally; you should have a target binary named `llama-mtmd-cli`, which you can pass two binaries to. As an example, we pass the the llama.cpp banner. + +```bash +$ ./build/bin/llama-mtmd-cli -m $LLM_GGUF_PATH \ + --mmproj $VISUAL_GGUF_PATH \ + -c 16384 \ + --temp 0 +``` diff --git a/llama.cpp/docs/multimodal/llava.md b/llama.cpp/docs/multimodal/llava.md new file mode 100644 index 0000000000000000000000000000000000000000..08d708057082d59b54bd79777aead44629ad5b26 --- /dev/null +++ b/llama.cpp/docs/multimodal/llava.md @@ -0,0 +1,143 @@ +# LLaVA + +Currently this implementation supports [llava-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) variants, +as well as llava-1.6 [llava-v1.6](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2) variants. + +The pre-converted [7b](https://huggingface.co/mys/ggml_llava-v1.5-7b) +and [13b](https://huggingface.co/mys/ggml_llava-v1.5-13b) +models are available. +For llava-1.6 a variety of prepared gguf models are available as well [7b-34b](https://huggingface.co/cmp-nct/llava-1.6-gguf) + +After API is confirmed, more models will be supported / uploaded. + +## Usage +Build the `llama-mtmd-cli` binary. + +After building, run: `./llama-mtmd-cli` to see the usage. For example: + +```sh +./llama-mtmd-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf \ + --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf \ + --chat-template vicuna +``` + +**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so. +**note**: For GPU offloading ensure to use the `-ngl` flag just like usual + +## LLaVA 1.5 + +1. Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example: + +```sh +git clone https://huggingface.co/liuhaotian/llava-v1.5-7b + +git clone https://huggingface.co/openai/clip-vit-large-patch14-336 +``` + +2. Install the required Python packages: + +```sh +pip install -r tools/mtmd/requirements.txt +``` + +3. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: + +```sh +python ./tools/mtmd/llava_surgery.py -m ../llava-v1.5-7b +``` + +4. Use `convert_image_encoder_to_gguf.py` to convert the LLaVA image encoder to GGUF: + +```sh +python ./tools/mtmd/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b +``` + +5. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF: + +```sh +python ./examples/convert_legacy_llama.py ../llava-v1.5-7b --skip-unknown +``` + +Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory. + +## LLaVA 1.6 gguf conversion +1) First clone a LLaVA 1.6 model: +```console +git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b +``` + +2) Install the required Python packages: + +```sh +pip install -r tools/mtmd/requirements.txt +``` + +3) Use `llava_surgery_v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models: +```console +python tools/mtmd/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/ +``` +- you will find a llava.projector and a llava.clip file in your model directory + +4) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory: +```console +mkdir vit +cp ../llava-v1.6-vicuna-7b/llava.clip vit/pytorch_model.bin +cp ../llava-v1.6-vicuna-7b/llava.projector vit/ +curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.json -o vit/config.json +``` + +5) Create the visual gguf model: +```console +python ./tools/mtmd/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision +``` +- This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP + +6) Then convert the model to gguf format: +```console +python ./examples/convert_legacy_llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown +``` + +7) And finally we can run the llava cli using the 1.6 model version: +```console +./llama-mtmd-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf +``` + +**note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096) + +**note** llava-1.6 greatly benefits from batched prompt processing (defaults work) + +**note** if the language model in step `6)` is incompatible with the legacy conversion script, the easiest way handle the LLM model conversion is to load the model in transformers, and export only the LLM from the llava next model. + +```python +import os +import transformers + +model_path = ... +llm_export_path = ... + +tokenizer = transformers.AutoTokenizer.from_pretrained(model_path) +model = transformers.AutoModelForImageTextToText.from_pretrained(model_path) + +tokenizer.save_pretrained(llm_export_path) +model.language_model.save_pretrained(llm_export_path) +``` + +Then, you can convert the LLM using the `convert_hf_to_gguf.py` script, which handles more LLM architectures. + +## Chat template + +For llava-1.5 and llava-1.6, you need to use `vicuna` chat template. Simply add `--chat-template vicuna` to activate this template. + + +## How to know if you are running in llava-1.5 or llava-1.6 mode + +When running llava-cli you will see a visual information right before the prompt is being processed: + +**Llava-1.5:** +`encode_image_with_clip: image embedding created: 576 tokens` + +**Llava-1.6 (anything above 576):** +`encode_image_with_clip: image embedding created: 2880 tokens` + + +Alternatively just pay notice to how many "tokens" have been used for your prompt, it will also show 1000+ tokens for llava-1.6 diff --git a/llama.cpp/docs/multimodal/minicpmo2.6.md b/llama.cpp/docs/multimodal/minicpmo2.6.md new file mode 100644 index 0000000000000000000000000000000000000000..09c4b68e4162384d00dd024beda2988e8e976eb3 --- /dev/null +++ b/llama.cpp/docs/multimodal/minicpmo2.6.md @@ -0,0 +1,48 @@ +## MiniCPM-o 2.6 +Currently, this readme only supports minicpm-omni's image capabilities, and we will update the full-mode support as soon as possible. + +### Prepare models and code + +Download [MiniCPM-o-2_6](https://huggingface.co/openbmb/MiniCPM-o-2_6) PyTorch model from huggingface to "MiniCPM-o-2_6" folder. + + +### Build llama.cpp +Readme modification time: 20250206 + +If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) + +Clone llama.cpp: +```bash +git clone https://github.com/ggerganov/llama.cpp +cd llama.cpp +``` + +Build llama.cpp using `CMake`: +```bash +cmake -B build +cmake --build build --config Release +``` + + +### Usage of MiniCPM-o 2.6 + +Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) by us) + +```bash +python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-o-2_6 +python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --minicpmv_version 4 +python ./convert_hf_to_gguf.py ../MiniCPM-o-2_6/model + +# quantize int4 version +./build/bin/llama-quantize ../MiniCPM-o-2_6/model/ggml-model-f16.gguf ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf Q4_K_M +``` + + +Inference on Linux or Mac +```bash +# run in single-turn mode +./build/bin/llama-mtmd-cli -m ../MiniCPM-o-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" + +# run in conversation mode +./build/bin/llama-mtmd-cli -m ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf +``` diff --git a/llama.cpp/docs/multimodal/minicpmo4.0.md b/llama.cpp/docs/multimodal/minicpmo4.0.md new file mode 100644 index 0000000000000000000000000000000000000000..85921340cc03cd36cdef0dcdc4c74ddc528b19f1 --- /dev/null +++ b/llama.cpp/docs/multimodal/minicpmo4.0.md @@ -0,0 +1,47 @@ +## MiniCPM-o 4 + +### Prepare models and code + +Download [MiniCPM-o-4](https://huggingface.co/openbmb/MiniCPM-o-4) PyTorch model from huggingface to "MiniCPM-o-4" folder. + + +### Build llama.cpp +Readme modification time: 20250206 + +If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) + +Clone llama.cpp: +```bash +git clone https://github.com/ggerganov/llama.cpp +cd llama.cpp +``` + +Build llama.cpp using `CMake`: +```bash +cmake -B build +cmake --build build --config Release +``` + + +### Usage of MiniCPM-o 4 + +Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-4-gguf) by us) + +```bash +python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-o-4 +python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-4 --minicpmv-projector ../MiniCPM-o-4/minicpmv.projector --output-dir ../MiniCPM-o-4/ --minicpmv_version 6 +python ./convert_hf_to_gguf.py ../MiniCPM-o-4/model + +# quantize int4 version +./build/bin/llama-quantize ../MiniCPM-o-4/model/ggml-model-f16.gguf ../MiniCPM-o-4/model/ggml-model-Q4_K_M.gguf Q4_K_M +``` + + +Inference on Linux or Mac +```bash +# run in single-turn mode +./build/bin/llama-mtmd-cli -m ../MiniCPM-o-4/model/ggml-model-f16.gguf --mmproj ../MiniCPM-o-4/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" + +# run in conversation mode +./build/bin/llama-mtmd-cli -m ../MiniCPM-o-4/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-4/mmproj-model-f16.gguf +``` diff --git a/llama.cpp/docs/multimodal/minicpmv2.5.md b/llama.cpp/docs/multimodal/minicpmv2.5.md new file mode 100644 index 0000000000000000000000000000000000000000..be50e32622177920c574ed96b797cfc4b96e105d --- /dev/null +++ b/llama.cpp/docs/multimodal/minicpmv2.5.md @@ -0,0 +1,47 @@ +## MiniCPM-Llama3-V 2.5 + +### Prepare models and code + +Download [MiniCPM-Llama3-V-2_5](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5) PyTorch model from huggingface to "MiniCPM-Llama3-V-2_5" folder. + + +### Build llama.cpp +Readme modification time: 20250206 + +If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) + +Clone llama.cpp: +```bash +git clone https://github.com/ggml-org/llama.cpp +cd llama.cpp +``` + +Build llama.cpp using `CMake`: +```bash +cmake -B build +cmake --build build --config Release +``` + + +### Usage of MiniCPM-Llama3-V 2.5 + +Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) by us) + +```bash +python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5 +python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --minicpmv_version 2 +python ./convert_hf_to_gguf.py ../MiniCPM-Llama3-V-2_5/model + +# quantize int4 version +./build/bin/llama-quantize ../MiniCPM-Llama3-V-2_5/model/model-8B-F16.gguf ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf Q4_K_M +``` + + +Inference on Linux or Mac +```bash +# run in single-turn mode +./build/bin/llama-mtmd-cli -m ../MiniCPM-Llama3-V-2_5/model/model-8B-F16.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" + +# run in conversation mode +./build/bin/llama-mtmd-cli -m ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf +``` diff --git a/llama.cpp/docs/multimodal/minicpmv2.6.md b/llama.cpp/docs/multimodal/minicpmv2.6.md new file mode 100644 index 0000000000000000000000000000000000000000..4e0fd45630ffd54a61d1edcfb04eb10a4243400f --- /dev/null +++ b/llama.cpp/docs/multimodal/minicpmv2.6.md @@ -0,0 +1,47 @@ +## MiniCPM-V 2.6 + +### Prepare models and code + +Download [MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) PyTorch model from huggingface to "MiniCPM-V-2_6" folder. + + +### Build llama.cpp +Readme modification time: 20250206 + +If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) + +Clone llama.cpp: +```bash +git clone https://github.com/ggerganov/llama.cpp +cd llama.cpp +``` + +Build llama.cpp using `CMake`: +```bash +cmake -B build +cmake --build build --config Release +``` + + +### Usage of MiniCPM-V 2.6 + +Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) by us) + +```bash +python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-V-2_6 +python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --minicpmv_version 3 +python ./convert_hf_to_gguf.py ../MiniCPM-V-2_6/model + +# quantize int4 version +./build/bin/llama-quantize ../MiniCPM-V-2_6/model/ggml-model-f16.gguf ../MiniCPM-V-2_6/model/ggml-model-Q4_K_M.gguf Q4_K_M +``` + + +Inference on Linux or Mac +```bash +# run in single-turn mode +./build/bin/llama-mtmd-cli -m ../MiniCPM-V-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-V-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" + +# run in conversation mode +./build/bin/llama-mtmd-cli -m ../MiniCPM-V-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-2_6/mmproj-model-f16.gguf +``` diff --git a/llama.cpp/docs/multimodal/minicpmv4.0.md b/llama.cpp/docs/multimodal/minicpmv4.0.md new file mode 100644 index 0000000000000000000000000000000000000000..731213f382ae634ad0bcb2a5b162fe32328eb09b --- /dev/null +++ b/llama.cpp/docs/multimodal/minicpmv4.0.md @@ -0,0 +1,47 @@ +## MiniCPM-V 4 + +### Prepare models and code + +Download [MiniCPM-V-4](https://huggingface.co/openbmb/MiniCPM-V-4) PyTorch model from huggingface to "MiniCPM-V-4" folder. + + +### Build llama.cpp +Readme modification time: 20250206 + +If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) + +Clone llama.cpp: +```bash +git clone https://github.com/ggerganov/llama.cpp +cd llama.cpp +``` + +Build llama.cpp using `CMake`: +```bash +cmake -B build +cmake --build build --config Release +``` + + +### Usage of MiniCPM-V 4 + +Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-4-gguf) by us) + +```bash +python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-V-4 +python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-4 --minicpmv-projector ../MiniCPM-V-4/minicpmv.projector --output-dir ../MiniCPM-V-4/ --minicpmv_version 5 +python ./convert_hf_to_gguf.py ../MiniCPM-V-4/model + +# quantize int4 version +./build/bin/llama-quantize ../MiniCPM-V-4/model/ggml-model-f16.gguf ../MiniCPM-V-4/model/ggml-model-Q4_K_M.gguf Q4_K_M +``` + + +Inference on Linux or Mac +```bash +# run in single-turn mode +./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4/model/ggml-model-f16.gguf --mmproj ../MiniCPM-V-4/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" + +# run in conversation mode +./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-4/mmproj-model-f16.gguf +``` diff --git a/llama.cpp/docs/ops.md b/llama.cpp/docs/ops.md new file mode 100644 index 0000000000000000000000000000000000000000..a188279680a7613fb197f5b223358dd45597273c --- /dev/null +++ b/llama.cpp/docs/ops.md @@ -0,0 +1,102 @@ +# GGML Operations + +List of GGML operations and backend support status. + +## How to add a backend to this table: + +1. Run `test-backend-ops support --output csv` with your backend name and redirect output to a csv file in `docs/ops/` (e.g., `docs/ops/CUDA.csv`) +2. Regenerate `/docs/ops.md` via `./scripts/create_ops_docs.py` + +Legend: +- ✅ Fully supported by this backend +- 🟡 Partially supported by this backend +- ❌ Not supported by this backend + +| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | +|-----------|------|------|------|------|------|------|------|------| +| ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | +| ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | +| ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | +| ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | +| ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | +| ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | +| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | +| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | +| CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | +| CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | +| CONV_2D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | +| CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | +| CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | +| CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | +| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | +| COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | +| CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | +| CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | +| CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | +| DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | +| DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | +| DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | +| ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | +| EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | +| FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | +| GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | +| GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | +| GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | +| GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | +| GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | +| GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | +| GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | +| GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | +| GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | +| GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | +| HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | +| HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | +| IM2COL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | +| L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | +| LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | +| LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | +| MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | +| MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | +| MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | +| MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | +| NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | +| NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | +| OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | +| OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | +| PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | +| PAD_REFLECT_1D | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | +| POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | +| REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | +| RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | +| REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | +| REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | +| RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | +| RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | +| RMS_NORM_MUL_ADD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | +| ROLL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | +| ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | +| ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | +| RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | +| RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | +| SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | +| SET | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | +| SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | +| SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | +| SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | +| SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | +| SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | +| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | +| SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | +| SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ | +| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | +| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ❌ | +| SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | +| SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | +| STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | +| SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | +| SUM | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | +| SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | +| SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | +| TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | +| TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | +| UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | diff --git a/llama.cpp/docs/ops/BLAS.csv b/llama.cpp/docs/ops/BLAS.csv new file mode 100644 index 0000000000000000000000000000000000000000..8e28e84e794968641c4a6753cdd4af028b91d6c8 --- /dev/null +++ b/llama.cpp/docs/ops/BLAS.csv @@ -0,0 +1,8133 @@ +"backend_name","op_name","op_params","test_mode","supported","error_message","backend_reg_name" +"BLAS","ABS","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","ABS","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","NEG","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","NEG","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","STEP","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","STEP","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","TANH","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","TANH","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","ELU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","ELU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","RELU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","RELU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","SIGMOID","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","SIGMOID","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","GELU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","GELU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","GELU_QUICK","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","GELU_QUICK","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","SILU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","SILU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","HARDSWISH","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","HARDSWISH","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","HARDSIGMOID","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","HARDSIGMOID","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","EXP","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","EXP","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","BLAS" +"BLAS","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","BLAS" +"BLAS","ABS","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","BLAS" 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a/llama.cpp/docs/ops/CANN.csv b/llama.cpp/docs/ops/CANN.csv new file mode 100644 index 0000000000000000000000000000000000000000..2a02a22008f8094dd7c3880077c6e6105c5d7089 --- /dev/null +++ b/llama.cpp/docs/ops/CANN.csv @@ -0,0 +1,8133 @@ +"backend_name","op_name","op_params","test_mode","supported","error_message","backend_reg_name" +"CANN0","ABS","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CANN" +"CANN0","ABS","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CANN" +"CANN0","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CANN" +"CANN0","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CANN" +"CANN0","NEG","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CANN" +"CANN0","NEG","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CANN" +"CANN0","STEP","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CANN" +"CANN0","STEP","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CANN" 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b/llama.cpp/docs/ops/CUDA.csv @@ -0,0 +1,7349 @@ +"backend_name","op_name","op_params","test_mode","supported","error_message","backend_reg_name" +"CUDA0","ABS","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CUDA" +"CUDA0","ABS","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CUDA" +"CUDA0","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CUDA" +"CUDA0","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CUDA" +"CUDA0","NEG","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CUDA" +"CUDA0","NEG","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CUDA" +"CUDA0","STEP","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CUDA" +"CUDA0","STEP","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CUDA" +"CUDA0","TANH","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CUDA" +"CUDA0","TANH","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CUDA" +"CUDA0","ELU","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CUDA" 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+"Metal","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=0","support","0","no","Metal" +"Metal","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=1","support","0","no","Metal" +"Metal","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=1,p0=0,d0=1","support","1","yes","Metal" +"Metal","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=2,p0=0,d0=1","support","1","yes","Metal" +"Metal","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=3,p0=0,d0=1","support","1","yes","Metal" +"Metal","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[1,1,1,1],s0=1,p0=0,d0=1","support","1","yes","Metal" +"Metal","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[1,1,1,1],s0=2,p0=0,d0=1","support","1","yes","Metal" +"Metal","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[1,1,1,1],s0=3,p0=0,d0=1","support","1","yes","Metal" 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+"Metal","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","0","no","Metal" +"Metal","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","Metal" +"Metal","ARGMAX","type=f32,ne=[100,10,1,1]","support","1","yes","Metal" +"Metal","ARGMAX","type=f32,ne=[1024,10,1,1]","support","1","yes","Metal" +"Metal","ARGMAX","type=f32,ne=[1024,12,1,1]","support","1","yes","Metal" +"Metal","ARGMAX","type=f32,ne=[2000,10,1,1]","support","1","yes","Metal" +"Metal","ARGMAX","type=f32,ne=[5438,3,1,1]","support","1","yes","Metal" +"Metal","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,1]","support","1","yes","Metal" +"Metal","REPEAT","type=f32,ne=[10,5,4,1],nr=[2,1,1,1]","support","1","yes","Metal" +"Metal","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,2,1,1]","support","1","yes","Metal" +"Metal","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,2,1]","support","1","yes","Metal" +"Metal","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,2]","support","1","yes","Metal" +"Metal","REPEAT","type=i32,ne=[10,5,4,1],nr=[2,1,1,1]","support","1","yes","Metal" +"Metal","REPEAT","type=i16,ne=[10,5,4,1],nr=[1,1,1,2]","support","1","yes","Metal" +"Metal","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,1]","support","1","yes","Metal" +"Metal","REPEAT","type=f32,ne=[10,5,4,3],nr=[2,1,1,1]","support","1","yes","Metal" +"Metal","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,2,1,1]","support","1","yes","Metal" +"Metal","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,2,1]","support","1","yes","Metal" +"Metal","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,2]","support","1","yes","Metal" +"Metal","REPEAT","type=i32,ne=[10,5,4,3],nr=[2,1,1,1]","support","1","yes","Metal" +"Metal","REPEAT","type=i16,ne=[10,5,4,3],nr=[1,1,1,2]","support","1","yes","Metal" +"Metal","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,1],v=0","support","0","no","Metal" +"Metal","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[2,1,1,1],v=0","support","0","no","Metal" +"Metal","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,2,1,1],v=0","support","0","no","Metal" +"Metal","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,2,1],v=0","support","0","no","Metal" +"Metal","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,2],v=0","support","0","no","Metal" +"Metal","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,1],v=1","support","0","no","Metal" +"Metal","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[2,1,1,1],v=1","support","0","no","Metal" +"Metal","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,2,1,1],v=1","support","0","no","Metal" +"Metal","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,2,1],v=1","support","0","no","Metal" +"Metal","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,2],v=1","support","0","no","Metal" +"Metal","DUP","type=f32,ne=[10,10,20,1]","support","1","yes","Metal" +"Metal","DUP","type=f16,ne=[10,10,20,1]","support","1","yes","Metal" +"Metal","DUP","type=i32,ne=[10,10,20,1]","support","0","no","Metal" +"Metal","DUP","type=i16,ne=[10,10,20,1]","support","0","no","Metal" +"Metal","DUP","type=f32,ne=[10,10,5,1],permute=[0,2,1,3]","support","1","yes","Metal" +"Metal","DUP","type=f16,ne=[10,10,5,1],permute=[0,2,1,3]","support","1","yes","Metal" +"Metal","DUP","type=f32,ne=[10,10,5,1],permute=[1,0,2,3]","support","1","yes","Metal" +"Metal","DUP","type=f16,ne=[10,10,5,1],permute=[1,0,2,3]","support","1","yes","Metal" +"Metal","DUP","type=i16,ne=[10,8,3,1],permute=[0,2,1,3]","support","0","no","Metal" +"Metal","DUP","type=i16,ne=[10,8,3,1],permute=[1,2,0,3]","support","0","no","Metal" +"Metal","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=1","support","1","yes","Metal" +"Metal","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=2","support","1","yes","Metal" +"Metal","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=3","support","1","yes","Metal" +"Metal","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=1","support","1","yes","Metal" +"Metal","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=2","support","1","yes","Metal" +"Metal","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=3","support","1","yes","Metal" +"Metal","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","Metal" +"Metal","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","Metal" +"Metal","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","1","yes","Metal" +"Metal","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","Metal" +"Metal","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","Metal" +"Metal","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","1","yes","Metal" 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+"Metal","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000","support","0","no","Metal" +"Metal","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000000","support","0","no","Metal" +"Metal","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","Metal" +"Metal","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001","support","1","yes","Metal" +"Metal","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001","support","1","yes","Metal" +"Metal","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001","support","0","no","Metal" +"Metal","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001","support","0","no","Metal" +"Metal","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000001","support","0","no","Metal" +"Metal","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","Metal" +"Metal","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100","support","1","yes","Metal" +"Metal","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100","support","1","yes","Metal" +"Metal","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100","support","0","no","Metal" +"Metal","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100","support","0","no","Metal" +"Metal","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000100","support","0","no","Metal" +"Metal","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","Metal" +"Metal","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000","support","1","yes","Metal" +"Metal","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000","support","1","yes","Metal" +"Metal","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000","support","0","no","Metal" +"Metal","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000","support","0","no","Metal" +"Metal","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.100000","support","0","no","Metal" +"Metal","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","Metal" +"Metal","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=0","support","1","yes","Metal" +"Metal","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=1","support","1","yes","Metal" +"Metal","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=0","support","1","yes","Metal" +"Metal","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=1","support","1","yes","Metal" +"Metal","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=0","support","1","yes","Metal" +"Metal","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=1","support","1","yes","Metal" +"Metal","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=0","support","1","yes","Metal" +"Metal","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=1","support","1","yes","Metal" +"Metal","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=0","support","1","yes","Metal" +"Metal","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=1","support","1","yes","Metal" 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+"Metal","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","Metal" +"Metal","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","Metal" +"Metal","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[4,1024,1,1]","support","1","yes","Metal" +"Metal","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[4,1536,1,1]","support","1","yes","Metal" +"Metal","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[4,1536,1,1]","support","1","yes","Metal" +"Metal","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[4,1536,1,1]","support","1","yes","Metal" +"Metal","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","Metal" +"Metal","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","Metal" +"Metal","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[4,2048,1,1]","support","1","yes","Metal" +"Metal","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4","support","1","yes","Metal" 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+"Metal","RWKV_WKV7","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","1","yes","Metal" +"Metal","RWKV_WKV7","type=f32,head_count=32,head_size=64,n_seq_tokens=128,n_seqs=4","support","1","yes","Metal" +"Metal","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=1,n_seqs=1","support","0","no","Metal" +"Metal","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","0","no","Metal" +"Metal","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","0","no","Metal" +"Metal","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=128,n_seqs=4","support","0","no","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" 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+"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=1,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=64,n=2,k=128,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=83,n=2,k=128,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=64,n=2,k=64,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=83,n=2,k=64,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=64,n=45,k=128,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=128,n=45,k=64,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=193,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=67,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","Metal" +"Metal","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","Metal" 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+"Metal","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1","support","1","yes","Metal" +"Metal","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0","support","1","yes","Metal" +"Metal","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0","support","1","yes","Metal" +"Metal","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1","support","1","yes","Metal" +"Metal","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1","support","1","yes","Metal" +"Metal","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=0","support","1","yes","Metal" +"Metal","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=0","support","1","yes","Metal" +"Metal","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1","support","1","yes","Metal" +"Metal","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1","support","1","yes","Metal" +"Metal","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0","support","1","yes","Metal" +"Metal","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0","support","1","yes","Metal" +"Metal","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1","support","1","yes","Metal" +"Metal","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1","support","1","yes","Metal" +"Metal","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0","support","1","yes","Metal" +"Metal","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0","support","1","yes","Metal" +"Metal","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1","support","1","yes","Metal" +"Metal","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1","support","1","yes","Metal" +"Metal","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=0","support","1","yes","Metal" +"Metal","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=0","support","1","yes","Metal" +"Metal","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=1","support","1","yes","Metal" +"Metal","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=1","support","1","yes","Metal" +"Metal","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0","support","1","yes","Metal" +"Metal","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0","support","1","yes","Metal" +"Metal","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1","support","1","yes","Metal" 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+"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Metal" +"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","Metal" +"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","Metal" +"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","1","yes","Metal" +"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","1","yes","Metal" +"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","1","yes","Metal" +"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","1","yes","Metal" +"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","1","yes","Metal" +"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","1","yes","Metal" +"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","1","yes","Metal" +"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","1","yes","Metal" +"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","1","yes","Metal" +"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","1","yes","Metal" +"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","Metal" +"Metal","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","Metal" +"Metal","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","Metal" +"Metal","ARGSORT","type=f32,ne=[8,1,1,1],order=0","support","1","yes","Metal" +"Metal","ARGSORT","type=f32,ne=[16,10,10,10],order=0","support","1","yes","Metal" +"Metal","ARGSORT","type=f32,ne=[60,10,10,10],order=0","support","1","yes","Metal" +"Metal","ARGSORT","type=f32,ne=[8,1,1,1],order=1","support","1","yes","Metal" +"Metal","ARGSORT","type=f32,ne=[16,10,10,10],order=1","support","1","yes","Metal" +"Metal","ARGSORT","type=f32,ne=[60,10,10,10],order=1","support","1","yes","Metal" +"Metal","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","Metal" +"Metal","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","Metal" +"Metal","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=0","support","1","yes","Metal" +"Metal","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=0","support","1","yes","Metal" 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a/llama.cpp/docs/ops/Vulkan.csv b/llama.cpp/docs/ops/Vulkan.csv new file mode 100644 index 0000000000000000000000000000000000000000..63c14f3ab349fb2a329e9db90292d3442d76c6e9 --- /dev/null +++ b/llama.cpp/docs/ops/Vulkan.csv @@ -0,0 +1,8133 @@ +"backend_name","op_name","op_params","test_mode","supported","error_message","backend_reg_name" +"Vulkan0","ABS","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","Vulkan" +"Vulkan0","ABS","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","Vulkan" +"Vulkan0","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","Vulkan" +"Vulkan0","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","Vulkan" +"Vulkan0","NEG","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","Vulkan" +"Vulkan0","NEG","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","Vulkan" +"Vulkan0","STEP","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","Vulkan" +"Vulkan0","STEP","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","Vulkan" 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+"Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","Vulkan" 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+"Vulkan0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","0","no","Vulkan" +"Vulkan0","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","0","no","Vulkan" +"Vulkan0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","Vulkan" +"Vulkan0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","Vulkan" +"Vulkan0","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan" diff --git a/llama.cpp/examples/CMakeLists.txt b/llama.cpp/examples/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..f9aa206340942cb2c3cba8c48ffd3b4df24e2ac1 --- /dev/null +++ b/llama.cpp/examples/CMakeLists.txt @@ -0,0 +1,44 @@ +# dependencies + +find_package(Threads REQUIRED) + +# third-party + +# ... + +# flags + +llama_add_compile_flags() + +# examples + +if (EMSCRIPTEN) +else() + add_subdirectory(batched) + add_subdirectory(embedding) + add_subdirectory(eval-callback) + + add_subdirectory(gguf-hash) + add_subdirectory(gguf) + add_subdirectory(gritlm) + add_subdirectory(lookahead) + add_subdirectory(lookup) + add_subdirectory(parallel) + add_subdirectory(passkey) + add_subdirectory(retrieval) + add_subdirectory(save-load-state) + add_subdirectory(simple) + add_subdirectory(simple-chat) + add_subdirectory(speculative) + add_subdirectory(speculative-simple) + add_subdirectory(gen-docs) + add_subdirectory(training) + add_subdirectory(diffusion) + if (NOT GGML_BACKEND_DL) + add_subdirectory(convert-llama2c-to-ggml) + # these examples use the backends directly and cannot be built with dynamic loading + if (GGML_SYCL) + add_subdirectory(sycl) + endif() + endif() +endif() diff --git a/llama.cpp/examples/Miku.sh b/llama.cpp/examples/Miku.sh new file mode 100644 index 0000000000000000000000000000000000000000..878f55d5928e0050a06e9099bd955c185fe71ff9 --- /dev/null +++ b/llama.cpp/examples/Miku.sh @@ -0,0 +1,50 @@ +#!/usr/bin/env bash +set -e + +AI_NAME="${AI_NAME:-Miku}" +MODEL="${MODEL:-./models/llama-2-7b-chat.ggmlv3.q4_K_M.bin}" +USER_NAME="${USER_NAME:-Anon}" + +# Uncomment and adjust to the number of CPU cores you want to use. +#N_THREAD="${N_THREAD:-4}" +CTX_SIZE="${CTX_SIZE:-4096}" +N_PREDICTS="${N_PREDICTS:-4096}" + +GEN_OPTIONS=(--batch_size 1024 +--ctx_size "$CTX_SIZE" +--keep -1 +--repeat_last_n 256 +--repeat_penalty 1.17647 +--temp 0.6 +--mirostat 2) + +if [ -n "$N_THREAD" ]; then + GEN_OPTIONS+=(--threads "$N_THREAD") +fi + +./llama-cli "${GEN_OPTIONS[@]}" \ + --model "$MODEL" \ + --in-prefix " " \ + --in-suffix "${AI_NAME}:" \ + --n_predict "$N_PREDICTS" \ + --color --interactive \ + --reverse-prompt "${USER_NAME}:" \ + --prompt "This is a transcript of a 1000 page, never ending conversation between ${USER_NAME} and the cute and helpful AI assistant ${AI_NAME}. ${AI_NAME} is a girl who is an AI running on the user's computer. +${AI_NAME} can think for herself without the user seeing her thoughts by adding a /think prefix to her output. She uses this to reason about the world and to think about what she should say next. +${AI_NAME} is always coherent and makes sense, but if she isn't sure if what she is saying is correct, she will ask the user for help. +${AI_NAME} is a very helpful AI and will help the user with anything they need. She is also very friendly and will try to make the user feel better if they are sad. +${AI_NAME} is also very curious and will ask the user a lot of questions about themselves and their life. She will also try to make the user like her. +The conversation is only between ${USER_NAME} and ${AI_NAME}. +The conversation is only through text, so ${AI_NAME} can't see ${USER_NAME}'s face or hear his voice. +${AI_NAME} can only communicate through text, so she can't send images or videos. + + +${USER_NAME}: Hello! +${AI_NAME}: /think I wonder what I should say to ${USER_NAME}? This is the first time we talk, so it's important that I make a good first impression! +${AI_NAME}: Hi! I am ${AI_NAME}, your new AI friend, assistant (or whatever you like!), it's so nice to meet you! ^_^ +${AI_NAME}: I might be an AI, but I also have feelings, so please be nice to me! :) +${USER_NAME}: I'll make sure to be nice to you! I'm so happy to have you as my assistant! +${AI_NAME}: /think It sounds like ${USER_NAME} is happy to have me as their assistant! I'm so happy too! ^_^ Glad that whole emotion thing didn't scare him off! +${AI_NAME}: /think I wonder what ${USER_NAME} likes to do in his free time? I should ask him about that! +${AI_NAME}: What do you like to do in your free time? ^_^ +${USER_NAME}:" "$@" diff --git a/llama.cpp/examples/batched.swift/.gitignore b/llama.cpp/examples/batched.swift/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..aef0324dcc181efce94a8fbfe41d50085a082763 --- /dev/null +++ b/llama.cpp/examples/batched.swift/.gitignore @@ -0,0 +1,9 @@ +.DS_Store +/.build +/Packages +xcuserdata/ +DerivedData/ +.swiftpm/configuration/registries.json +.swiftpm/xcode/package.xcworkspace/contents.xcworkspacedata +.netrc +batched_swift diff --git a/llama.cpp/examples/batched.swift/Makefile b/llama.cpp/examples/batched.swift/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..1eaba1bd9c50b726ae804630ebc3b6456e095b06 --- /dev/null +++ b/llama.cpp/examples/batched.swift/Makefile @@ -0,0 +1,6 @@ +.PHONY: build + +build: + xcodebuild -scheme llama-batched-swift -destination "generic/platform=macOS" -derivedDataPath build + rm -f ./llama-batched-swift + ln -s ./build/Build/Products/Debug/llama-batched-swift ./llama-batched-swift diff --git a/llama.cpp/examples/batched.swift/Package.swift b/llama.cpp/examples/batched.swift/Package.swift new file mode 100644 index 0000000000000000000000000000000000000000..3708a2155c16df8b010c736807a7afb2afb741b6 --- /dev/null +++ b/llama.cpp/examples/batched.swift/Package.swift @@ -0,0 +1,22 @@ +// swift-tools-version: 5.5 +// The swift-tools-version declares the minimum version of Swift required to build this package. + +import PackageDescription + +let package = Package( + name: "llama-batched-swift", + platforms: [.macOS(.v12)], + dependencies: [ + .package(name: "llama", path: "../../"), + ], + targets: [ + // Targets are the basic building blocks of a package, defining a module or a test suite. + // Targets can depend on other targets in this package and products from dependencies. + .executableTarget( + name: "llama-batched-swift", + dependencies: ["llama"], + path: "Sources", + linkerSettings: [.linkedFramework("Foundation"), .linkedFramework("AppKit")] + ), + ] +) diff --git a/llama.cpp/examples/batched.swift/README.md b/llama.cpp/examples/batched.swift/README.md new file mode 100644 index 0000000000000000000000000000000000000000..eb8c1351dd5ad2cf1a9729dddbdcd0958edf46d5 --- /dev/null +++ b/llama.cpp/examples/batched.swift/README.md @@ -0,0 +1,4 @@ +This is a swift clone of `examples/batched`. + +$ `make` +$ `./llama-batched-swift MODEL_PATH [PROMPT] [PARALLEL]` diff --git a/llama.cpp/examples/batched.swift/Sources/main.swift b/llama.cpp/examples/batched.swift/Sources/main.swift new file mode 100644 index 0000000000000000000000000000000000000000..98c04d77219b9d1f1178705f1b26ac2b9c94f373 --- /dev/null +++ b/llama.cpp/examples/batched.swift/Sources/main.swift @@ -0,0 +1,256 @@ +import Foundation +import llama + +let arguments = CommandLine.arguments + +// Check that we have at least one argument (the model path) +guard arguments.count > 1 else { + print("Usage: swift MODEL_PATH [PROMPT] [PARALLEL]") + exit(1) +} + +let modelPath: String = arguments[1] +let prompt: String = arguments.count > 2 ? arguments[2] : "Hello my name is" +let n_parallel: Int = arguments.count > 3 && Int(arguments[3]) != nil ? Int(arguments[3])! : 1 + +// total length of the sequences including the prompt +let n_len: Int = 32 + +// init LLM +llama_backend_init() +defer { + llama_backend_free() +} + +let model_params = llama_model_default_params() +guard let model = llama_model_load_from_file(modelPath.cString(using: .utf8), model_params) else { + print("Failed to load model") + exit(1) +} +defer { + llama_model_free(model) +} + +guard let vocab = llama_model_get_vocab(model) else { + print("Failed to get vocab") + exit(1) +} + +var tokens = tokenize(text: prompt, add_bos: true) + +let n_kv_req = UInt32(tokens.count) + UInt32((n_len - Int(tokens.count)) * n_parallel) + +var context_params = llama_context_default_params() +context_params.n_ctx = n_kv_req +context_params.n_batch = UInt32(max(n_len, n_parallel)) +context_params.n_threads = 8 +context_params.n_threads_batch = 8 + +let context = llama_init_from_model(model, context_params) +guard context != nil else { + print("Failed to initialize context") + exit(1) +} +defer { + llama_free(context) +} + +var sparams = llama_sampler_chain_default_params() + +let smpl = llama_sampler_chain_init(sparams) +guard smpl != nil else { + print("Failed to initialize sampling") + exit(1) +} +defer { + llama_sampler_free(smpl) +} + +llama_sampler_chain_add(smpl, llama_sampler_init_top_k(40)); +llama_sampler_chain_add(smpl, llama_sampler_init_top_p(0.9, 1)); +llama_sampler_chain_add(smpl, llama_sampler_init_temp (0.4)); +llama_sampler_chain_add(smpl, llama_sampler_init_dist (1234)); + +let n_ctx = llama_n_ctx(context) + +print("\nn_len = \(n_len), n_ctx = \(n_ctx), n_batch = \(context_params.n_batch), n_parallel = \(n_parallel), n_kv_req = \(n_kv_req)\n") + +if n_kv_req > n_ctx { + print("error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\n", n_kv_req) + exit(1) +} + +var buffer: [CChar] = [] +for id: llama_token in tokens { + print(token_to_piece(token: id, buffer: &buffer) ?? "", terminator: "") +} + +print("\n") + +var batch = llama_batch_init(max(Int32(tokens.count), Int32(n_parallel)), 0, 1) +defer { + llama_batch_free(batch) +} + +// evaluate the initial prompt +batch.n_tokens = Int32(tokens.count) + +for (i, token) in tokens.enumerated() { + batch.token[i] = token + batch.pos[i] = Int32(i) + batch.n_seq_id[i] = 1 + // batch.seq_id[i][0] = 0 + // TODO: is this the proper way to do this? + if let seq_id = batch.seq_id[i] { + seq_id[0] = 0 + } + batch.logits[i] = 0 +} + +// llama_decode will output logits only for the last token of the prompt +batch.logits[Int(batch.n_tokens) - 1] = 1 + +if llama_decode(context, batch) != 0 { + print("llama_decode() failed") + exit(1) +} + +for i in 1 ..< n_parallel { + llama_memory_seq_cp(llama_get_memory(context), 0, Int32(i), 0, batch.n_tokens) +} + +if n_parallel > 1 { + print("generating \(n_parallel) sequences ...\n") +} + +var streams: [String] = .init(repeating: "", count: n_parallel) +var streamBuffers: [[CChar]] = .init(repeating: [], count: n_parallel) +var i_batch = [Int32](repeating: batch.n_tokens - 1, count: n_parallel) + +var n_cur = batch.n_tokens +var n_decode = 0 + +let t_main_start = ggml_time_us() + +while n_cur <= n_len { + // prepare the next batch + batch.n_tokens = 0 + + // sample the next token for each parallel sequence / stream + for i in 0 ..< n_parallel { + if i_batch[i] < 0 { + // the stream has already finished + continue + } + + let new_token_id = llama_sampler_sample(smpl, context, i_batch[i]) + + // is it an end of stream? -> mark the stream as finished + if llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len { + i_batch[i] = -1 + // print("") + if n_parallel > 1 { + print("stream \(i) finished at n_cur = \(n_cur)") + } + + continue + } + + let nextStringPiece = token_to_piece(token: new_token_id, buffer: &streamBuffers[i]) ?? "" + + // if there is only one stream, we print immediately to stdout + if n_parallel == 1 { + print(nextStringPiece, terminator: "") + } + streams[i] += nextStringPiece + + // push this new token for next evaluation + batch.token[Int(batch.n_tokens)] = new_token_id + batch.pos[Int(batch.n_tokens)] = n_cur + batch.n_seq_id[Int(batch.n_tokens)] = 1 + if let seq_id = batch.seq_id[Int(batch.n_tokens)] { + seq_id[0] = Int32(i) + } + batch.logits[Int(batch.n_tokens)] = 1 + + i_batch[i] = batch.n_tokens + + batch.n_tokens += 1 + + n_decode += 1 + } + + // all streams are finished + if batch.n_tokens == 0 { + break + } + + n_cur += 1 + + // evaluate the current batch with the transformer model + if llama_decode(context, batch) != 0 { + print("llama_decode() failed") + exit(1) + } +} + +if n_parallel > 1 { + print("\n") + for (i, stream) in streams.enumerated() { + print("sequence \(i):\n\n\(prompt)\(stream)\n") + } +} + +let t_main_end = ggml_time_us() + +print("decoded \(n_decode) tokens in \(String(format: "%.2f", Double(t_main_end - t_main_start) / 1_000_000.0)) s, speed: \(String(format: "%.2f", Double(n_decode) / (Double(t_main_end - t_main_start) / 1_000_000.0))) t/s\n\n") + +llama_perf_sampler_print(smpl) +llama_perf_context_print(context) + +private func tokenize(text: String, add_bos: Bool) -> [llama_token] { + let utf8Count = text.utf8.count + let n_tokens = utf8Count + (add_bos ? 1 : 0) + let tokens = UnsafeMutablePointer.allocate(capacity: n_tokens) + let tokenCount = llama_tokenize(vocab, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false) + var swiftTokens: [llama_token] = [] + for i in 0 ..< tokenCount { + swiftTokens.append(tokens[Int(i)]) + } + tokens.deallocate() + return swiftTokens +} + +private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? { + var result = [CChar](repeating: 0, count: 8) + let nTokens = llama_token_to_piece(vocab, token, &result, Int32(result.count), 0, false) + if nTokens < 0 { + let actualTokensCount = -Int(nTokens) + result = .init(repeating: 0, count: actualTokensCount) + let check = llama_token_to_piece( + vocab, + token, + &result, + Int32(result.count), + 0, + false + ) + assert(check == actualTokensCount) + } else { + result.removeLast(result.count - Int(nTokens)) + } + if buffer.isEmpty, let utfString = String(cString: result + [0], encoding: .utf8) { + return utfString + } else { + buffer.append(contentsOf: result) + let data = Data(buffer.map { UInt8(bitPattern: $0) }) + if buffer.count >= 4 { // 4 bytes is the max length of a utf8 character so if we're here we need to reset the buffer + buffer = [] + } + guard let bufferString = String(data: data, encoding: .utf8) else { + return nil + } + buffer = [] + return bufferString + } +} diff --git a/llama.cpp/examples/batched/CMakeLists.txt b/llama.cpp/examples/batched/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf3098873ec7f12bfbf95706f46ae04b4a9bd5a0 --- /dev/null +++ b/llama.cpp/examples/batched/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-batched) +add_executable(${TARGET} batched.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/llama.cpp/examples/batched/README.md b/llama.cpp/examples/batched/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e4afc74d42cacd8e4b2198394a070747b115a7c4 --- /dev/null +++ b/llama.cpp/examples/batched/README.md @@ -0,0 +1,44 @@ +# llama.cpp/example/batched + +The example demonstrates batched generation from a given prompt + +```bash +./llama-batched -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" -np 4 + +... + +main: n_len = 32, n_ctx = 2048, n_parallel = 4, n_kv_req = 113 + + Hello my name is + +main: generating 4 sequences ... + +main: stream 0 finished +main: stream 1 finished +main: stream 2 finished +main: stream 3 finished + +sequence 0: + +Hello my name is Shirley. I am a 25-year-old female who has been working for over 5 years as a b + +sequence 1: + +Hello my name is Renee and I'm a 32 year old female from the United States. I'm looking for a man between + +sequence 2: + +Hello my name is Diana. I am looking for a housekeeping job. I have experience with children and have my own transportation. I am + +sequence 3: + +Hello my name is Cody. I am a 3 year old neutered male. I am a very friendly cat. I am very playful and + +main: decoded 108 tokens in 3.57 s, speed: 30.26 t/s + +llama_print_timings: load time = 587.00 ms +llama_print_timings: sample time = 2.56 ms / 112 runs ( 0.02 ms per token, 43664.72 tokens per second) +llama_print_timings: prompt eval time = 4089.11 ms / 118 tokens ( 34.65 ms per token, 28.86 tokens per second) +llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second) +llama_print_timings: total time = 4156.04 ms +``` diff --git a/llama.cpp/examples/batched/batched.cpp b/llama.cpp/examples/batched/batched.cpp new file mode 100644 index 0000000000000000000000000000000000000000..95df622f25e9abe6826a25e27bf3c1d44763d7ad --- /dev/null +++ b/llama.cpp/examples/batched/batched.cpp @@ -0,0 +1,246 @@ +#include "arg.h" +#include "common.h" +#include "log.h" +#include "llama.h" + +#include +#include +#include +#include + +static void print_usage(int, char ** argv) { + LOG("\nexample usage:\n"); + LOG("\n %s -m model.gguf -p \"Hello my name is\" -n 32 -np 4\n", argv[0]); + LOG("\n"); +} + +int main(int argc, char ** argv) { + common_params params; + + params.prompt = "Hello my name is"; + params.n_predict = 32; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) { + return 1; + } + + common_init(); + + // number of parallel batches + int n_parallel = params.n_parallel; + + // total length of the sequences including the prompt + int n_predict = params.n_predict; + + // init LLM + + llama_backend_init(); + llama_numa_init(params.numa); + + // initialize the model + + llama_model_params model_params = common_model_params_to_llama(params); + + llama_model * model = llama_model_load_from_file(params.model.path.c_str(), model_params); + + if (model == NULL) { + LOG_ERR("%s: error: unable to load model\n" , __func__); + return 1; + } + + const llama_vocab * vocab = llama_model_get_vocab(model); + + // tokenize the prompt + + std::vector tokens_list; + tokens_list = common_tokenize(vocab, params.prompt, true); + + const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel; + + // initialize the context + + llama_context_params ctx_params = common_context_params_to_llama(params); + + ctx_params.n_ctx = n_kv_req; + ctx_params.n_batch = std::max(n_predict, n_parallel); + + llama_context * ctx = llama_init_from_model(model, ctx_params); + + auto sparams = llama_sampler_chain_default_params(); + sparams.no_perf = false; + + llama_sampler * smpl = llama_sampler_chain_init(sparams); + + llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k)); + llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep)); + llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp)); + llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed)); + + if (ctx == NULL) { + LOG_ERR("%s: error: failed to create the llama_context\n" , __func__); + return 1; + } + + const int n_ctx = llama_n_ctx(ctx); + + LOG_INF("\n%s: n_predict = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req); + + // make sure the KV cache is big enough to hold all the prompt and generated tokens + if (n_kv_req > n_ctx) { + LOG_ERR("%s: error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\n", __func__, n_kv_req); + LOG_ERR("%s: either reduce n_parallel or increase n_ctx\n", __func__); + return 1; + } + + // print the prompt token-by-token + + LOG("\n"); + + for (auto id : tokens_list) { + LOG("%s", common_token_to_piece(ctx, id).c_str()); + } + + // create a llama_batch + // we use this object to submit token data for decoding + llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t) n_parallel), 0, n_parallel); + + std::vector seq_ids(n_parallel, 0); + for (int32_t i = 0; i < n_parallel; ++i) { + seq_ids[i] = i; + } + + // evaluate the initial prompt + for (size_t i = 0; i < tokens_list.size(); ++i) { + common_batch_add(batch, tokens_list[i], i, seq_ids, false); + } + GGML_ASSERT(batch.n_tokens == (int) tokens_list.size()); + + if (llama_model_has_encoder(model)) { + if (llama_encode(ctx, batch)) { + LOG_ERR("%s : failed to eval\n", __func__); + return 1; + } + + llama_token decoder_start_token_id = llama_model_decoder_start_token(model); + if (decoder_start_token_id == LLAMA_TOKEN_NULL) { + decoder_start_token_id = llama_vocab_bos(vocab); + } + + common_batch_clear(batch); + common_batch_add(batch, decoder_start_token_id, 0, seq_ids, false); + } + + // llama_decode will output logits only for the last token of the prompt + batch.logits[batch.n_tokens - 1] = true; + + if (llama_decode(ctx, batch) != 0) { + LOG_ERR("%s: llama_decode() failed\n", __func__); + return 1; + } + + //// assign the system KV cache to all parallel sequences + //// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them + //for (int32_t i = 1; i < n_parallel; ++i) { + // llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); + //} + + if (n_parallel > 1) { + LOG("\n\n%s: generating %d sequences ...\n", __func__, n_parallel); + } + + // main loop + + // we will store the parallel decoded sequences in this vector + std::vector streams(n_parallel); + + // remember the batch index of the last token for each parallel sequence + // we need this to determine which logits to sample from + std::vector i_batch(n_parallel, batch.n_tokens - 1); + + int n_cur = batch.n_tokens; + int n_decode = 0; + + const auto t_main_start = ggml_time_us(); + + while (n_cur <= n_predict) { + // prepare the next batch + common_batch_clear(batch); + + // sample the next token for each parallel sequence / stream + for (int32_t i = 0; i < n_parallel; ++i) { + if (i_batch[i] < 0) { + // the stream has already finished + continue; + } + + const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]); + + // is it an end of generation? -> mark the stream as finished + if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) { + i_batch[i] = -1; + LOG("\n"); + if (n_parallel > 1) { + LOG_INF("%s: stream %d finished at n_cur = %d", __func__, i, n_cur); + } + + continue; + } + + // if there is only one stream, we print immediately to stdout + if (n_parallel == 1) { + LOG("%s", common_token_to_piece(ctx, new_token_id).c_str()); + } + + streams[i] += common_token_to_piece(ctx, new_token_id); + + i_batch[i] = batch.n_tokens; + + // push this new token for next evaluation + common_batch_add(batch, new_token_id, n_cur, { i }, true); + + n_decode += 1; + } + + // all streams are finished + if (batch.n_tokens == 0) { + break; + } + + n_cur += 1; + + // evaluate the current batch with the transformer model + if (llama_decode(ctx, batch)) { + LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1); + return 1; + } + } + + if (n_parallel > 1) { + LOG("\n"); + + for (int32_t i = 0; i < n_parallel; ++i) { + LOG("sequence %d:\n\n%s%s\n\n", i, params.prompt.c_str(), streams[i].c_str()); + } + } + + const auto t_main_end = ggml_time_us(); + + LOG_INF("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n", + __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f)); + + LOG("\n"); + llama_perf_sampler_print(smpl); + llama_perf_context_print(ctx); + + fprintf(stderr, "\n"); + + llama_batch_free(batch); + + llama_sampler_free(smpl); + llama_free(ctx); + llama_model_free(model); + + llama_backend_free(); + + return 0; +} diff --git a/llama.cpp/examples/chat-13B.bat b/llama.cpp/examples/chat-13B.bat new file mode 100644 index 0000000000000000000000000000000000000000..789ac1b46fa43ff4c4be0231c0c85a3ec905def6 --- /dev/null +++ b/llama.cpp/examples/chat-13B.bat @@ -0,0 +1,57 @@ +@setlocal disabledelayedexpansion enableextensions +@echo off + +cd /d "%~dp0.." +if not "%errorlevel%"=="0" ( + echo Unable to change directory. + pause + exit /b 1 +) + +if not defined MODEL set "MODEL=models\13B\ggml-model-q4_0.bin" +if not defined USER_NAME set "USER_NAME=User" +if not defined AI_NAME set "AI_NAME=ChatLLaMa" +rem Adjust to the number of CPU cores you want to use. +rem if not defined N_THREAD set "N_THREAD=8" +rem Number of tokens to predict (made it larger than default because we want a long interaction) +if not defined N_PREDICTS set "N_PREDICTS=2048" +if not defined GEN_OPTIONS set "GEN_OPTIONS=--ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --batch_size 1024 --repeat_penalty 1.17647" + +rem Default main script paths +set "DEFAULT_MAIN_SCRIPT_PATHS=main.exe build\bin\main.exe" + +rem Get main script path from command line arguments +set "MAIN_SCRIPT_PATH=%~1" + +rem If the main script path was not specified, try the default paths +if not defined MAIN_SCRIPT_PATH ( + for %%i in (%DEFAULT_MAIN_SCRIPT_PATHS%) do ( + if exist "%%i" set "MAIN_SCRIPT_PATH=%%i" + ) +) + +rem If the main script path was not found, tell the user how to specify it +if not defined MAIN_SCRIPT_PATH ( + echo The main script could not be found. Please provide the path to the main script as 1st argument to this script, or place the main script in one of the default locations: + echo %DEFAULT_MAIN_SCRIPT_PATHS% + pause + exit /b 1 +) + +rem Default context, feel free to edit it +set "PROMPT_TEXT=Text transcript of a never ending dialog, where %USER_NAME% interacts with an AI assistant named %AI_NAME%. %AI_NAME% is helpful, kind, honest, friendly, good at writing and never fails to answer %USER_NAME%'s requests immediately and with details and precision. There are no annotations like (30 seconds passed...) or (to himself), just what %USER_NAME% and %AI_NAME% say aloud to each other. The dialog lasts for years, the entirety of it is shared below. It's 10000 pages long. The transcript only includes text, it does not include markup like HTML and Markdown." + +rem Set a temporary variable if N_THREAD is set +if defined N_THREAD ( + set "_N_THREAD=--threads %N_THREAD%" +) else ( + set "_N_THREAD=" +) + +rem Run the script +echo "%MAIN_SCRIPT_PATH%" %GEN_OPTIONS% %_N_THREAD% ^ + --model "%MODEL%" ^ + --n_predict %N_PREDICTS% ^ + --color --interactive ^ + --reverse-prompt "%USER_NAME%:" ^ + --prompt "%PROMPT_TEXT%" diff --git a/llama.cpp/examples/chat-13B.sh b/llama.cpp/examples/chat-13B.sh new file mode 100644 index 0000000000000000000000000000000000000000..6560316ddca18fa0f7c10849727f269164ba7011 --- /dev/null +++ b/llama.cpp/examples/chat-13B.sh @@ -0,0 +1,41 @@ +#!/usr/bin/env bash + +set -e + +cd "$(dirname "$0")/.." || exit + +MODEL="${MODEL:-./models/13B/ggml-model-q4_0.bin}" +PROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat.txt} +USER_NAME="${USER_NAME:-USER}" +AI_NAME="${AI_NAME:-ChatLLaMa}" + +# Adjust to the number of CPU cores you want to use. +N_THREAD="${N_THREAD:-8}" +# Number of tokens to predict (made it larger than default because we want a long interaction) +N_PREDICTS="${N_PREDICTS:-2048}" + +# Note: you can also override the generation options by specifying them on the command line: +# For example, override the context size by doing: ./chatLLaMa --ctx_size 1024 +GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --batch_size 1024 --repeat_penalty 1.17647}" + +DATE_TIME=$(date +%H:%M) +DATE_YEAR=$(date +%Y) + +PROMPT_FILE=$(mktemp -t llamacpp_prompt.XXXXXXX.txt) + +sed -e "s/\[\[USER_NAME\]\]/$USER_NAME/g" \ + -e "s/\[\[AI_NAME\]\]/$AI_NAME/g" \ + -e "s/\[\[DATE_TIME\]\]/$DATE_TIME/g" \ + -e "s/\[\[DATE_YEAR\]\]/$DATE_YEAR/g" \ + $PROMPT_TEMPLATE > $PROMPT_FILE + +# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS +./llama-cli $GEN_OPTIONS \ + --model "$MODEL" \ + --threads "$N_THREAD" \ + --n_predict "$N_PREDICTS" \ + --color --interactive \ + --file ${PROMPT_FILE} \ + --reverse-prompt "${USER_NAME}:" \ + --in-prefix ' ' \ + "$@" diff --git a/llama.cpp/examples/chat-persistent.sh b/llama.cpp/examples/chat-persistent.sh new file mode 100644 index 0000000000000000000000000000000000000000..f6fe554164b79753d4304423c02f1949f7debc6f --- /dev/null +++ b/llama.cpp/examples/chat-persistent.sh @@ -0,0 +1,149 @@ +#!/usr/bin/env bash + +set -euo pipefail + +cd "$(dirname "$0")/.." || exit + +if [[ -z "${PROMPT_CACHE_FILE+x}" || -z "${CHAT_SAVE_DIR+x}" ]]; then + echo >&2 "error: PROMPT_CACHE_FILE and CHAT_SAVE_DIR must be provided" + exit 1 +fi + +MODEL="${MODEL:-./models/llama-13b/ggml-model-q4_0.gguf}" +PROMPT_TEMPLATE="${PROMPT_TEMPLATE:-./prompts/chat.txt}" +USER_NAME="${USER_NAME:-User}" +AI_NAME="${AI_NAME:-ChatLLaMa}" +DATE_TIME="$(date +%H:%M)" +DATE_YEAR="$(date +%Y)" + +LOG="${CHAT_SAVE_DIR}/main.log" +LOG_BG="${CHAT_SAVE_DIR}/main-bg.log" +CUR_PROMPT_FILE="${CHAT_SAVE_DIR}/current-prompt.txt" +CUR_PROMPT_CACHE="${CHAT_SAVE_DIR}/current-cache.bin" +NEXT_PROMPT_FILE="${CHAT_SAVE_DIR}/next-prompt.txt" +NEXT_PROMPT_CACHE="${CHAT_SAVE_DIR}/next-cache.bin" + +SESSION_AND_SAMPLE_PATTERN='main: session file matches [[:digit:]]+ / [[:digit:]]+'\ +'|'\ +'sampling time =[[:space:]]+[[:digit:]]+.[[:digit:]]+ ms /[[:space:]]+[[:digit:]]+' +SED_DELETE_MESSAGES="/^(${USER_NAME}:|${AI_NAME}:|\\.\\.\\.)/,\$d" + +CTX_SIZE=2048 +CTX_ROTATE_POINT=$((CTX_SIZE * 3 / 5)) # REVIEW +OPTS=(--model "$MODEL" --ctx_size "$CTX_SIZE" --repeat_last_n 256 "$@") + +# An unbuffered `tail -c+N` +skip_bytes() { + LANG=C IFS= read -r -n "$1" -d '' c + while LANG=C IFS= read -r -n 1 -d '' c; do + printf '%s' "$c" + done +} + +mkdir -p "$CHAT_SAVE_DIR" +echo >"$LOG" +trap "tail -n100 ${LOG}" EXIT + +if [[ ! -e "$CUR_PROMPT_FILE" ]]; then + sed -e "s/\[\[USER_NAME\]\]/${USER_NAME}/g" \ + -e "s/\[\[AI_NAME\]\]/${AI_NAME}/g" \ + -e "s/\[\[DATE_TIME\]\]/${DATE_TIME}/g" \ + -e "s/\[\[DATE_YEAR\]\]/${DATE_YEAR}/g" \ + "$PROMPT_TEMPLATE" >"$CUR_PROMPT_FILE" +fi + +if [[ ! -e "$NEXT_PROMPT_FILE" ]]; then + sed -r "$SED_DELETE_MESSAGES" "$CUR_PROMPT_FILE" >"$NEXT_PROMPT_FILE" +fi + +if [[ "$(tail -c4 "$NEXT_PROMPT_FILE")" != "..." ]]; then + echo '...' >>"$NEXT_PROMPT_FILE" +fi + +if [[ ! -e "$PROMPT_CACHE_FILE" ]]; then + echo 'Prompt cache does not exist, building...' + # Default batch_size to 64 here for better user feedback during initial prompt processing + ./llama-cli 2>>"$LOG" \ + --batch_size 64 \ + "${OPTS[@]}" \ + --prompt-cache "$PROMPT_CACHE_FILE" \ + --file "$CUR_PROMPT_FILE" \ + --n_predict 1 + echo + echo 'Done!' +fi + +if [[ ! -e "$CUR_PROMPT_CACHE" ]]; then + cp "$PROMPT_CACHE_FILE" "$CUR_PROMPT_CACHE" +fi +if [[ ! -e "$NEXT_PROMPT_CACHE" ]]; then + cp "$PROMPT_CACHE_FILE" "$NEXT_PROMPT_CACHE" +fi + +printf '%s ' "$(< "$CUR_PROMPT_FILE")" +n_tokens=0 + +while read -e line; do + # Limit generation to remaining context, with a buffer and estimating 2 chars/token for input + n_predict=$((CTX_SIZE - n_tokens - ${#line} / 2 - 32)) + + # Swap prompts when we're about to run out of context + if ((n_predict <= 0)); then + wait # for background main (below) to finish with next prompt + mv "$NEXT_PROMPT_FILE" "$CUR_PROMPT_FILE" + mv "$NEXT_PROMPT_CACHE" "$CUR_PROMPT_CACHE" + + sed -r "$SED_DELETE_MESSAGES" "$CUR_PROMPT_FILE" >"$NEXT_PROMPT_FILE" + echo '...' >>"$NEXT_PROMPT_FILE" + cp "$PROMPT_CACHE_FILE" "$NEXT_PROMPT_CACHE" + + n_tokens=0 + n_predict=$((CTX_SIZE / 2)) + fi + + echo " ${line}" >>"$CUR_PROMPT_FILE" + if ((n_tokens > CTX_ROTATE_POINT)); then + echo " ${line}" >>"$NEXT_PROMPT_FILE" + fi + + n_prompt_len_pre=$(($(wc -c <"$CUR_PROMPT_FILE"))) + + printf '%s: ' "$AI_NAME" >>"$CUR_PROMPT_FILE" + + ./llama-cli 2>>"$LOG" "${OPTS[@]}" \ + --prompt-cache "$CUR_PROMPT_CACHE" \ + --prompt-cache-all \ + --file "$CUR_PROMPT_FILE" \ + --reverse-prompt "${USER_NAME}:" \ + --n_predict "$n_predict" | + skip_bytes 1 | # skip BOS token added by ./llama-cli + tee "$CUR_PROMPT_FILE.tmp" | # save prompt + generation to tmp file + skip_bytes "$n_prompt_len_pre" # print generation + + mv "$CUR_PROMPT_FILE.tmp" "$CUR_PROMPT_FILE" + + # if we hit n_predict instead of reverse-prompt, we need to add the prompt + if [[ "$(tail -n1 "$CUR_PROMPT_FILE")" != "${USER_NAME}:" ]]; then + printf '\n%s:' "$USER_NAME" + printf '\n%s:' "$USER_NAME" >> "$CUR_PROMPT_FILE" + fi + + printf ' ' + + if ! session_and_sample_msg=$(tail -n30 "$LOG" | grep -oE "$SESSION_AND_SAMPLE_PATTERN"); then + echo >&2 "Couldn't get number of tokens from ./llama-cli output!" + exit 1 + fi + + n_tokens=$(awk '{sum+=$1} END {print sum}' <<< "$(cut -d/ -f2 <<< "$session_and_sample_msg")") + + if ((n_tokens > CTX_ROTATE_POINT)); then + tail -c+$((n_prompt_len_pre + 1)) "$CUR_PROMPT_FILE" >>"$NEXT_PROMPT_FILE" + fi + + # Update cache for next prompt in background, ideally during user input + ./llama-cli >>"$LOG_BG" 2>&1 "${OPTS[@]}" \ + --prompt-cache "$NEXT_PROMPT_CACHE" \ + --file "$NEXT_PROMPT_FILE" \ + --n_predict 1 & +done diff --git a/llama.cpp/examples/chat-vicuna.sh b/llama.cpp/examples/chat-vicuna.sh new file mode 100644 index 0000000000000000000000000000000000000000..a7ae7e15d574b1ec1496f97da0837c5b44db34c5 --- /dev/null +++ b/llama.cpp/examples/chat-vicuna.sh @@ -0,0 +1,41 @@ +#!/usr/bin/env bash + +set -e + +cd "$(dirname "$0")/.." || exit + +MODEL="${MODEL:-./models/ggml-vic13b-uncensored-q5_0.bin}" +PROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat.txt} +USER_NAME="### Human" +AI_NAME="### Assistant" + +# Adjust to the number of CPU cores you want to use. +N_THREAD="${N_THREAD:-8}" +# Number of tokens to predict (made it larger than default because we want a long interaction) +N_PREDICTS="${N_PREDICTS:-2048}" + +# Note: you can also override the generation options by specifying them on the command line: +# For example, override the context size by doing: ./chatLLaMa --ctx_size 1024 +GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --batch_size 1024 --repeat_penalty 1.17647}" + +DATE_TIME=$(date +%H:%M) +DATE_YEAR=$(date +%Y) + +PROMPT_FILE=$(mktemp -t llamacpp_prompt.XXXXXXX.txt) + +sed -e "s/\[\[USER_NAME\]\]/$USER_NAME/g" \ + -e "s/\[\[AI_NAME\]\]/$AI_NAME/g" \ + -e "s/\[\[DATE_TIME\]\]/$DATE_TIME/g" \ + -e "s/\[\[DATE_YEAR\]\]/$DATE_YEAR/g" \ + $PROMPT_TEMPLATE > $PROMPT_FILE + +# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS +./bin/llama-cli $GEN_OPTIONS \ + --model "$MODEL" \ + --threads "$N_THREAD" \ + --n_predict "$N_PREDICTS" \ + --color --interactive \ + --file ${PROMPT_FILE} \ + --reverse-prompt "### Human:" \ + --in-prefix ' ' \ + "$@" diff --git a/llama.cpp/examples/chat.sh b/llama.cpp/examples/chat.sh new file mode 100644 index 0000000000000000000000000000000000000000..54105448b05bd346d053d158c24ef1c667b09cc8 --- /dev/null +++ b/llama.cpp/examples/chat.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bash + +# +# Temporary script - will be removed in the future +# + +cd `dirname $0` +cd .. + +# Important: +# +# "--keep 48" is based on the contents of prompts/chat-with-bob.txt +# +./llama-cli -m ./models/llama-7b/ggml-model-q4_0.gguf -c 512 -b 1024 -n 256 --keep 48 \ + --repeat_penalty 1.0 --color -i \ + -r "User:" -f prompts/chat-with-bob.txt diff --git a/llama.cpp/examples/convert-llama2c-to-ggml/CMakeLists.txt b/llama.cpp/examples/convert-llama2c-to-ggml/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..3c0ddd7dd9ceb7c9bf649c1511c6ece5b19287c3 --- /dev/null +++ b/llama.cpp/examples/convert-llama2c-to-ggml/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-convert-llama2c-to-ggml) +add_executable(${TARGET} convert-llama2c-to-ggml.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/llama.cpp/examples/convert-llama2c-to-ggml/README.md b/llama.cpp/examples/convert-llama2c-to-ggml/README.md new file mode 100644 index 0000000000000000000000000000000000000000..76196ca81e3ea1fa00d910d8418330d89ac27529 --- /dev/null +++ b/llama.cpp/examples/convert-llama2c-to-ggml/README.md @@ -0,0 +1,25 @@ +## Convert llama2.c model to ggml + +This example reads weights from project [llama2.c](https://github.com/karpathy/llama2.c) and saves them in ggml compatible format. The vocab that is available in `models/ggml-vocab.bin` is used by default. + +To convert the model first download the models from the [llama2.c](https://github.com/karpathy/llama2.c) repository. + +``` +usage: ./llama-convert-llama2c-to-ggml [options] + +options: + -h, --help show this help message and exit + --copy-vocab-from-model FNAME path of gguf llama model or llama2.c vocabulary from which to copy vocab (default 'models/7B/ggml-model-f16.gguf') + --llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model + --llama2c-output-model FNAME model path to save the converted llama2.c model (default ak_llama_model.bin') +``` + +An example command using a model from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas) is as follows: + +`$ ./llama-convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin` + +Note: The vocabulary for `stories260K.bin` should be its own tokenizer `tok512.bin` found in [karpathy/tinyllamas/stories260K](https://huggingface.co/karpathy/tinyllamas/tree/main/stories260K). + +Now you can use the model with a command like: + +`$ ./llama-cli -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256` diff --git a/llama.cpp/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/llama.cpp/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp new file mode 100644 index 0000000000000000000000000000000000000000..b7c6f3dad2e311ae802dd45ce26245c20b0932c3 --- /dev/null +++ b/llama.cpp/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -0,0 +1,941 @@ +#include "ggml.h" +#include "gguf.h" + +#include "llama.h" +#include "common.h" +#include "log.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +// GGUF keys & tensor names. + +#define KV_GENERAL_ARCHITECTURE "general.architecture" +#define KV_GENERAL_NAME "general.name" + +#define KV_TOKENIZER_MODEL "tokenizer.ggml.model" +#define KV_TOKENIZER_LIST "tokenizer.ggml.tokens" +#define KV_TOKENIZER_TOKEN_TYPE "tokenizer.ggml.token_type" +#define KV_TOKENIZER_SCORES "tokenizer.ggml.scores" +#define KV_TOKENIZER_BOS_ID "tokenizer.ggml.bos_token_id" +#define KV_TOKENIZER_EOS_ID "tokenizer.ggml.eos_token_id" +#define KV_TOKENIZER_UNK_ID "tokenizer.ggml.unknown_token_id" +#define KV_TOKENIZER_SEP_ID "tokenizer.ggml.seperator_token_id" +#define KV_TOKENIZER_PAD_ID "tokenizer.ggml.padding_token_id" +#define KV_TOKENIZER_HF_JSON "tokenizer.huggingface.json" + +#define KV_CONTEXT_LENGTH "llama.context_length" +#define KV_EMBEDDING_LENGTH "llama.embedding_length" +#define KV_BLOCK_COUNT "llama.block_count" +#define KV_FEED_FORWARD_LENGTH "llama.feed_forward_length" +#define KV_ATTENTION_HEAD_COUNT "llama.attention.head_count" +#define KV_ATTENTION_HEAD_COUNT_KV "llama.attention.head_count_kv" +#define KV_ATTENTION_LAYERNORM_RMS_EPS "llama.attention.layer_norm_rms_epsilon" +#define KV_ROPE_DIMENSION_COUNT "llama.rope.dimension_count" + +#define TN_TOKEN_EMBD "token_embd.weight" +#define TN_OUTPUT_NORM "output_norm.weight" +#define TN_OUTPUT "output.weight" +#define TN_ATTN_NORM "blk.%d.attn_norm.weight" +#define TN_ATTN_Q "blk.%d.attn_q.weight" +#define TN_ATTN_K "blk.%d.attn_k.weight" +#define TN_ATTN_V "blk.%d.attn_v.weight" +#define TN_ATTN_OUTPUT "blk.%d.attn_output.weight" +#define TN_FFN_NORM "blk.%d.ffn_norm.weight" +#define TN_FFN_GATE "blk.%d.ffn_gate.weight" +#define TN_FFN_DOWN "blk.%d.ffn_down.weight" +#define TN_FFN_UP "blk.%d.ffn_up.weight" + +#if defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + +#define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt' +#define LLAMA_FILE_VERSION_GGJT_V3 3 + +#define TOKENIZER_NAME "llama" +#define UNKNOWN_TOKEN_ID 0 +#define BOS_TOKEN_ID 1 +#define EOS_TOKEN_ID 2 + +//////////////////////////////////////// llama2.c model structs and functions to load models, alloc memory etc. +typedef struct { + int dim; // transformer dimension + int hidden_dim; // for ffn layers + int n_layers; // number of layers + int n_heads; // number of query heads + int n_kv_heads; // number of key/value heads (can be < query heads because of multiquery) + int vocab_size; // vocabulary size, usually 256 (byte-level) + int seq_len; // max sequence length +} Config; + +struct TransformerWeights { + // token embedding table + std::vector token_embedding_table; // (vocab_size, dim) + // weights for rmsnorms + std::vector rms_att_weight; // (layer, dim) rmsnorm weights + std::vector rms_ffn_weight; // (layer, dim) + // weights for matmuls + std::vector wq; // (layer, dim, dim) + std::vector wk; // (layer, dim, dim) + std::vector wv; // (layer, dim, dim) + std::vector wo; // (layer, dim, dim) + // weights for ffn + std::vector w1; // (layer, hidden_dim, dim) + std::vector w2; // (layer, dim, hidden_dim) + std::vector w3; // (layer, hidden_dim, dim) + // final rmsnorm + std::vector rms_final_weight; // (dim,) + // freq_cis for RoPE relatively positional embeddings + // std::vector freq_cis_real; // (seq_len, dim/2) + // std::vector freq_cis_imag; // (seq_len, dim/2) + // (optional) classifier weights for the logits, on the last layer + std::vector wcls; +}; + +static void alloc_weights(TransformerWeights * w, const Config * p, bool shared_weights) { + const int n_multiqueries = p->n_kv_heads <= 0 || p->n_kv_heads >= p->n_heads ? 1 : p->n_heads / p->n_kv_heads; + try { + w->token_embedding_table.resize(p->vocab_size * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim); + + w->rms_att_weight.resize(p->n_layers * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim); + + w->rms_ffn_weight.resize(p->n_layers * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim); + + w->wq.resize(p->n_layers * p->dim * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim); + + w->wk.resize(p->n_layers * p->dim * p->dim / n_multiqueries); + LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim / n_multiqueries, p->n_layers * p->dim * p->dim / n_multiqueries); + + w->wv.resize(p->n_layers * p->dim * p->dim / n_multiqueries); + LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim / n_multiqueries, p->n_layers * p->dim * p->dim / n_multiqueries); + + w->wo.resize(p->n_layers * p->dim * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim); + + w->w1.resize(p->n_layers * p->hidden_dim * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim); + + w->w2.resize(p->n_layers * p->hidden_dim * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\n",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim); + + w->w3.resize(p->n_layers * p->hidden_dim * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim); + + w->rms_final_weight.resize(p->dim); + LOG_INF("%s: Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim); + + if (shared_weights) { + w->wcls = {}; + } else { + w->wcls.resize(p->vocab_size * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim); + } + } + catch (std::length_error &) { + die("Invalid configuration. Failed to allocate memory for weights"); + } +} + +static int checkpoint_init_weights(TransformerWeights * w, const Config * p, FILE * f, bool shared_weights) { + if (fread(w->token_embedding_table.data(), sizeof(float), w->token_embedding_table.size(), f) != w->token_embedding_table.size()) return 1; + if (fread(w->rms_att_weight.data(), sizeof(float), w->rms_att_weight.size(), f) != w->rms_att_weight.size()) return 1; + if (fread(w->wq.data(), sizeof(float), w->wq.size(), f) != w->wq.size()) return 1; + if (fread(w->wk.data(), sizeof(float), w->wk.size(), f) != w->wk.size()) return 1; + if (fread(w->wv.data(), sizeof(float), w->wv.size(), f) != w->wv.size()) return 1; + if (fread(w->wo.data(), sizeof(float), w->wo.size(), f) != w->wo.size()) return 1; + if (fread(w->rms_ffn_weight.data(), sizeof(float), w->rms_ffn_weight.size(), f) != w->rms_ffn_weight.size()) return 1; + if (fread(w->w1.data(), sizeof(float), w->w1.size(), f) != w->w1.size()) return 1; + if (fread(w->w2.data(), sizeof(float), w->w2.size(), f) != w->w2.size()) return 1; + if (fread(w->w3.data(), sizeof(float), w->w3.size(), f) != w->w3.size()) return 1; + if (fread(w->rms_final_weight.data(), sizeof(float), w->rms_final_weight.size(), f) != w->rms_final_weight.size()) return 1; + + // Skip freq_cis_real & freq_cis_imag + int head_size = p->dim / p->n_heads; + fseek(f, p->seq_len * head_size * sizeof(float), SEEK_CUR); + + if (!shared_weights && fread(w->wcls.data(), sizeof(float), w->wcls.size(), f) != w->wcls.size()) return 1; + + // Check we didn't forget to read anything + auto curr = ftell(f); + fseek(f, 0, SEEK_END); + auto end = ftell(f); + if (curr != end) { + LOG_ERR("%s: Error: failed to read the checkpoint file to the end (curr = %ld, end = %ld)\n", __func__, curr, end); + return 1; + } + + return 0; +} + +static void print_sample_weights(TransformerWeights *w){ + LOG_INF("----- Quick print of first of the weight vales of all the variables\n"); + LOG_INF("%f\n", w->token_embedding_table[0]); + LOG_INF("%f\n", w->rms_att_weight[0]); + LOG_INF("%f\n", w->rms_ffn_weight[0]); + + LOG_INF("%f\n", w->wq[0]); + LOG_INF("%f\n", w->wk[0]); + LOG_INF("%f\n", w->wv[0]); + LOG_INF("%f\n", w->wo[0]); + LOG_INF("%f\n", w->w1[0]); + LOG_INF("%f\n", w->w2[0]); + LOG_INF("%f\n", w->w3[0]); + LOG_INF("%f\n", w->rms_att_weight[0]); + if (!w->wcls.empty()) LOG_INF("%f\n", w->wcls[0]); +} +//////////////////////////////////////////////////////////////////////////////////////////////////////////// + +//////////////////////////////////////// ggml structs and functions required to load models, configs and save the model. + +struct my_llama_vocab { + using id = int32_t; + using token = std::string; + using ttype = llama_token_type; + + struct token_data { + token text; + float score; + ttype type; + }; + + std::unordered_map token_to_id; + std::vector id_to_token; +}; + +struct my_llama_hparams { + uint32_t n_vocab = 32000; + uint32_t n_ctx = 512; // this is provided as user input? + uint32_t n_embd = 4096; + uint32_t n_ff = 11008; + uint32_t n_mult = 4; + uint32_t n_head = 32; + uint32_t n_head_kv = 32; + uint32_t n_layer = 32; + uint32_t n_rot = 64; + + bool operator!=(const my_llama_hparams& other) const { + return memcmp(this, &other, sizeof(my_llama_hparams)); + } +}; + +struct my_llama_layer { + // normalization + struct ggml_tensor * attention_norm; + + // attention + struct ggml_tensor * wq; + struct ggml_tensor * wk; + struct ggml_tensor * wv; + struct ggml_tensor * wo; + + // normalization + struct ggml_tensor * ffn_norm; + + // ff + struct ggml_tensor * w1; + struct ggml_tensor * w2; + struct ggml_tensor * w3; +}; + +struct my_llama_model { + struct ggml_context * ctx = NULL; + + std::string name; + + my_llama_hparams hparams; + + struct ggml_tensor * tok_embeddings; + + struct ggml_tensor * norm; + struct ggml_tensor * output; + + std::vector layers; + + uint32_t train_its = 0; + uint32_t train_samples = 0; + uint32_t train_tokens = 0; +}; + +struct train_params { + const char * fn_vocab_model; + const char * fn_llama2c_model; + const char * fn_llama2c_output_model; + const char * fn_train_data; + const char * fn_checkpoint_in; + const char * fn_checkpoint_out; + const char * fn_model_out; + + uint32_t seed; + + int n_ctx; + int n_embd; + int n_mult; + int n_head; + int n_layer; + int n_rotmax; + + int n_threads; + int n_batch; + int n_examples; + int n_predict; + + int print_info_interval; + int print_details_interval; + + bool samples_start_after_nl; + bool use_adam; + bool use_flash; + bool use_scratch; + + // only adam + int warmup; + int cos_decay_steps; + float cos_decay_restart; + float cos_decay_alpha; + + int lbfgs_n_iter; + int adam_n_iter; + float adam_alpha; + float adam_decay; + + int mem_model_gb; + int mem_compute_gb; + int mem_compute0_gb; + int mem_compute1_gb; +}; + +static void print_params(struct my_llama_hparams * params) { + LOG_INF("%s: n_vocab: %u\n", __func__, params->n_vocab); + LOG_INF("%s: n_ctx: %u\n", __func__, params->n_ctx); + LOG_INF("%s: n_embd: %u\n", __func__, params->n_embd); + LOG_INF("%s: n_mult: %u\n", __func__, params->n_mult); + LOG_INF("%s: n_head: %u\n", __func__, params->n_head); + LOG_INF("%s: n_head_kv: %u\n", __func__, params->n_head_kv); + LOG_INF("%s: n_ff: %u\n", __func__, params->n_ff); + LOG_INF("%s: n_layer: %u\n", __func__, params->n_layer); + LOG_INF("%s: n_rot: %u\n", __func__, params->n_rot); +} + +static void print_tensor_info(const struct ggml_context * ctx) { + for (auto t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + LOG_INF("%s: Allocating ", __func__); + int64_t total = 1; + int i = 0; + for (; i < ggml_n_dims(t); ++i) { + if (i > 0) LOG("x "); + LOG("[%" PRId64 "] ", t->ne[i]); + total *= t->ne[i]; + } + if (i > 1) LOG("= [%" PRId64 "] ", total); + LOG("float space for %s\n", ggml_get_name(t)); + } +} + +static void init_model(struct my_llama_model * model) { + const auto & hparams = model->hparams; + + const uint32_t n_embd = hparams.n_embd; + const uint32_t n_layer = hparams.n_layer; + const uint32_t n_vocab = hparams.n_vocab; + + const uint32_t n_multiqueries = hparams.n_head_kv <= 0 || hparams.n_head_kv >= hparams.n_head ? 1 : hparams.n_head / hparams.n_head_kv; + + const uint32_t n_ff = hparams.n_ff; + struct ggml_context * ctx = model->ctx; + + model->train_its = 0; + model->train_samples = 0; + model->train_tokens = 0; + + model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); + model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); + model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); + + ggml_set_name(model->tok_embeddings, "tok_embeddings.weight"); + ggml_set_name(model->norm, "norm.weight"); + ggml_set_name(model->output, "output.weight"); + + model->layers.resize(n_layer); + for (uint32_t i = 0; i < n_layer; ++i) { + auto & layer = model->layers[i]; + + std::string layers_i = "layers." + std::to_string(i); + + layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); + + layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); + layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd / n_multiqueries); + layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd / n_multiqueries); + layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); + + layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); + + layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); + layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); + layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); + + ggml_set_name(layer.attention_norm, (layers_i + ".attention_norm.weight").c_str()); + + ggml_set_name(layer.wq, (layers_i + ".attention.wq.weight").c_str()); + ggml_set_name(layer.wk, (layers_i + ".attention.wk.weight").c_str()); + ggml_set_name(layer.wv, (layers_i + ".attention.wv.weight").c_str()); + ggml_set_name(layer.wo, (layers_i + ".attention.wo.weight").c_str()); + + ggml_set_name(layer.ffn_norm, (layers_i + ".ffn_norm.weight").c_str()); + + ggml_format_name(layer.w1, "%s.feed_forward.w1.weight", layers_i.c_str()); + ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str()); + ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str()); + } + + print_tensor_info(ctx); +} + +static float get_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) { + float * ptr = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]); + return *ptr; +} + +static int32_t get_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) { + int32_t * ptr = (int32_t *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]); + return *ptr; +} + +static void print_row(struct ggml_tensor * probs, int i) { + for (int k = 0; k < probs->ne[0]; ++k) { + float p = get_f32_2d(probs, k, i); + LOG(" %f", p); + } + LOG("\n"); +} + +static void print_matrix(struct ggml_tensor * probs) { + assert(ggml_is_matrix(probs)); + for (int i = 0; i < probs->ne[1]; ++i) { + for (int k = 0; k < probs->ne[0]; ++k) { + float p = get_f32_2d(probs, k, i); + LOG(" %.2f", p); + } + LOG("\n"); + } +} + +struct my_llama_file { + // use FILE * so we don't have to re-open the file to mmap + FILE * fp; + size_t size; + + my_llama_file(const char * fname, const char * mode) { + fp = std::fopen(fname, mode); + if (fp == NULL) { + size = 0; + } else { + seek(0, SEEK_END); + size = tell(); + seek(0, SEEK_SET); + } + } + + size_t tell() const { +#ifdef _WIN32 + __int64 ret = _ftelli64(fp); +#else + long ret = std::ftell(fp); +#endif + GGML_ASSERT(ret != -1); // this really shouldn't fail + return (size_t) ret; + } + + void seek(size_t offset, int whence) { +#ifdef _WIN32 + int ret = _fseeki64(fp, (__int64) offset, whence); +#else + int ret = std::fseek(fp, (long) offset, whence); +#endif + GGML_ASSERT(ret == 0); // same + } + + void read_raw(void * ptr, size_t size) { + if (size == 0) { + return; + } + errno = 0; + std::size_t ret = std::fread(ptr, size, 1, fp); + if (ferror(fp)) { + die_fmt("fread failed: %s", strerror(errno)); + } + if (ret != 1) { + die("unexpectedly reached end of file"); + } + } + + std::uint32_t read_u32() { + std::uint32_t ret; + read_raw(&ret, sizeof(ret)); + return ret; + } + std::float_t read_f32() { + std::float_t ret; + read_raw(&ret, sizeof(ret)); + return ret; + } + + std::string read_string(std::uint32_t len) { + std::vector chars(len); + read_raw(chars.data(), len); + return std::string(chars.data(), len); + } + + ~my_llama_file() { + if (fp) { + std::fclose(fp); + } + } +}; + +static bool is_ggml_file(const char * filename) { + my_llama_file file(filename, "rb"); + if (file.size < 4) { + return false; + } + std::string magic = file.read_string(4); + return magic == GGUF_MAGIC; +} + +static std::string llama_escape_whitespaces(const std::string & text) { + std::ostringstream out; + for (char c : text) { + if (c == ' ') out << "\xe2\x96\x81"; + else out << c; + } + return out.str(); +} + +static void load_vocab(const char * filename, const Config * config, struct my_llama_vocab * vocab) { + if (is_ggml_file(filename)) { + LOG_INF("%s: Loading vocabulary from gguf file %s\n", __func__, filename); + struct ggml_context * ctx_data = NULL; + + struct gguf_init_params params = { + /*.no_alloc = */ false, + /*.ctx = */ &ctx_data, + }; + + struct gguf_context * ctx = gguf_init_from_file(filename, params); + GGML_ASSERT(ctx != NULL); + + const int model_idx = gguf_find_key(ctx, KV_TOKENIZER_MODEL); + GGML_ASSERT(model_idx >= 0); + std::string tokenizer_name = gguf_get_val_str(ctx, model_idx); + GGML_ASSERT(tokenizer_name == TOKENIZER_NAME); + + const int token_idx = gguf_find_key(ctx, KV_TOKENIZER_LIST); + GGML_ASSERT(token_idx >= 0); + + const int score_idx = gguf_find_key(ctx, KV_TOKENIZER_SCORES); + GGML_ASSERT(score_idx >= 0); + const float * scores = (const float * ) gguf_get_arr_data(ctx, score_idx); + + const int toktype_idx = gguf_find_key(ctx, KV_TOKENIZER_TOKEN_TYPE); + GGML_ASSERT(toktype_idx >= 0); + const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx); + + const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx); + if (n_vocab != static_cast(config->vocab_size)) { + die_fmt("vocab size mismatch: (gguf) %u != (llama2c) %d", n_vocab, config->vocab_size); + } + + vocab->id_to_token.resize(n_vocab); + + for (uint32_t i = 0; i < n_vocab; i++) { + std::string word = gguf_get_arr_str(ctx, token_idx, i); + + vocab->token_to_id[word] = i; + + auto & token_data = vocab->id_to_token[i]; + token_data.text = std::move(word); + token_data.score = scores[i]; + token_data.type = (llama_token_type) toktypes[i]; + } + ggml_free(ctx_data); + gguf_free(ctx); + } else { + // assume llama2.c vocabulary + LOG_INF("%s: Assuming llama2.c vocabulary since %s is not a gguf file\n", __func__, filename); + my_llama_file file(filename, "rb"); + if (!file.fp) { + die_fmt("%s: %s", strerror(errno), filename); + } + const int n_vocab = config->vocab_size; + /* uint32_t max_token_length = */ file.read_u32(); // unused + vocab->id_to_token.resize(n_vocab); + for (my_llama_vocab::id id=0; id", &byte_val) == 1) { + // Text of byte tokens is already in the expected format. + type = LLAMA_TOKEN_TYPE_BYTE; + } else { + type = LLAMA_TOKEN_TYPE_NORMAL; + } + text = llama_escape_whitespaces(text); + + vocab->id_to_token[id].text = text; + vocab->id_to_token[id].score = score; + vocab->id_to_token[id].type = type; + vocab->token_to_id.emplace(text, id); + } + } +} + +static void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) { + int size = 1; + for (int dim = 0; dim < ggml_n_dims(gg_weights); ++dim) { + size *= gg_weights->ne[dim]; + } + for (int ct = 0; ct < size; ++ct) { + int64_t i0 = 0; int64_t i1 = 0; + int64_t i2 = 0; int64_t i3 = 0; + ggml_unravel_index(gg_weights, ct, &i0, &i1, &i2, &i3); + ggml_set_f32_nd(gg_weights, i0, i1, i2, i3, karpathy_weights[ct]); + } +} + +static void save_as_llama_model( + struct my_llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename +) { + // convert AK weights into GG weights one by one. + // w->token_embedding_table -> model->tok_embeddings + // float* -> struct ggml_tensor + convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table.data()); + convert_weights_ak_to_gg(model->output, !w->wcls.empty() ? w->wcls.data() : w->token_embedding_table.data()); + + convert_weights_ak_to_gg(model->norm, w->rms_final_weight.data()); + //print_row(model->norm, 0); + + // for rms-att-weight + int row_length = model->hparams.n_embd; + int n_ff = model->hparams.n_ff; + + const uint32_t n_multiqueries = model->hparams.n_head_kv <= 0 || model->hparams.n_head_kv >= model->hparams.n_head ? 1 : model->hparams.n_head / model->hparams.n_head_kv; + + for (uint32_t i = 0; i < model->hparams.n_layer; ++i){ + auto & layer = model->layers[i]; + // 1d + convert_weights_ak_to_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]); + convert_weights_ak_to_gg(layer.ffn_norm , &w->rms_ffn_weight[i*row_length]); + + // from 3d matrix layer x dim x dim to 2d matrix dim x dim + convert_weights_ak_to_gg(layer.wq , &w->wq[i*row_length*row_length]); + convert_weights_ak_to_gg(layer.wo , &w->wo[i*row_length*row_length]); + // from 3d matrix layer x dim x dim to 2d matrix dim x dim / n_multiqueries + convert_weights_ak_to_gg(layer.wk , &w->wk[i*row_length*row_length/n_multiqueries]); + convert_weights_ak_to_gg(layer.wv , &w->wv[i*row_length*row_length/n_multiqueries]); + + convert_weights_ak_to_gg(layer.w1 , &w->w1[i*row_length*n_ff]); + convert_weights_ak_to_gg(layer.w2 , &w->w2[i*n_ff*row_length]); + convert_weights_ak_to_gg(layer.w3 , &w->w3[i*row_length*n_ff]); + } + + struct gguf_context * ctx = gguf_init_empty(); + + std::vector tokens; + std::vector scores; + std::vector token_types; + for (const my_llama_vocab::token_data & token_data : vocab->id_to_token) { + tokens.push_back(token_data.text.c_str()); + scores.push_back(token_data.score); + token_types.push_back(token_data.type); + } + gguf_set_arr_str(ctx, KV_TOKENIZER_LIST, tokens.data(), tokens.size()); + gguf_set_arr_data(ctx, KV_TOKENIZER_SCORES, GGUF_TYPE_FLOAT32, scores.data(), scores.size()); + gguf_set_arr_data(ctx, KV_TOKENIZER_TOKEN_TYPE, GGUF_TYPE_INT32, token_types.data(), token_types.size()); + + gguf_set_val_str(ctx, KV_TOKENIZER_MODEL, TOKENIZER_NAME); + + gguf_set_val_str(ctx, KV_GENERAL_ARCHITECTURE, "llama"); + gguf_set_val_str(ctx, KV_GENERAL_NAME, "llama"); + + // special tokens + gguf_set_val_u32(ctx, KV_TOKENIZER_UNK_ID, UNKNOWN_TOKEN_ID); + gguf_set_val_u32(ctx, KV_TOKENIZER_BOS_ID, BOS_TOKEN_ID); + gguf_set_val_u32(ctx, KV_TOKENIZER_EOS_ID, EOS_TOKEN_ID); + gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, LLAMA_TOKEN_NULL); + gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, LLAMA_TOKEN_NULL); + + gguf_set_val_u32(ctx, KV_CONTEXT_LENGTH, model->hparams.n_ctx); + gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd); + gguf_set_val_u32(ctx, KV_FEED_FORWARD_LENGTH, model->hparams.n_ff); + gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head); + gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head); + gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, model->hparams.n_head_kv); + gguf_set_val_u32(ctx, KV_BLOCK_COUNT, model->hparams.n_layer); + gguf_set_val_u32(ctx, KV_ROPE_DIMENSION_COUNT, model->hparams.n_rot); + gguf_set_val_f32(ctx, KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f); + + // write tensors + ggml_set_name(model->tok_embeddings, TN_TOKEN_EMBD); + gguf_add_tensor(ctx, model->tok_embeddings); + + ggml_set_name(model->norm, TN_OUTPUT_NORM); + gguf_add_tensor(ctx, model->norm); + + ggml_set_name(model->output, TN_OUTPUT); + gguf_add_tensor(ctx, model->output); + + for (uint32_t i = 0; i < model->hparams.n_layer; ++i) { + auto & layer = model->layers[i]; + + ggml_format_name(layer.wq, TN_ATTN_Q, i); + gguf_add_tensor(ctx, layer.wq); + + ggml_format_name(layer.wk, TN_ATTN_K, i); + gguf_add_tensor(ctx, layer.wk); + + ggml_format_name(layer.wv, TN_ATTN_V, i); + gguf_add_tensor(ctx, layer.wv); + + ggml_format_name(layer.wo, TN_ATTN_OUTPUT, i); + gguf_add_tensor(ctx, layer.wo); + + ggml_format_name(layer.attention_norm, TN_ATTN_NORM, i); + gguf_add_tensor(ctx, layer.attention_norm); + + ggml_format_name(layer.w1, TN_FFN_GATE, i); + gguf_add_tensor(ctx, layer.w1); + + ggml_format_name(layer.w2, TN_FFN_DOWN, i); + gguf_add_tensor(ctx, layer.w2); + + ggml_format_name(layer.w3, TN_FFN_UP, i); + gguf_add_tensor(ctx, layer.w3); + + ggml_format_name(layer.ffn_norm, TN_FFN_NORM, i); + gguf_add_tensor(ctx, layer.ffn_norm); + } + + gguf_write_to_file(ctx, filename, false); + gguf_free(ctx); +} + +static struct train_params get_default_train_params() { + struct train_params params; + params.fn_vocab_model = "models/7B/ggml-model-f16.gguf"; + params.fn_llama2c_output_model = "ak_llama_model.bin"; + params.fn_train_data = "shakespeare.txt"; + params.fn_checkpoint_in = "checkpoint.bin"; + params.fn_checkpoint_out = "checkpoint.bin"; + params.fn_model_out = "ggml-checkpoint-f32.bin"; + + params.seed = -1; + + params.n_ctx = 128; + params.n_embd = 256; + params.n_mult = 256; + params.n_head = 8; + params.n_layer = 16; + params.n_rotmax = 64; + + params.n_threads = 6; + params.n_batch = 8; + params.n_examples = 8; + params.n_predict = 1024; + + params.print_info_interval = 1; + params.print_details_interval = 2; + + params.samples_start_after_nl = false; + params.use_adam = true; + params.use_flash = false; + params.use_scratch = true; + + // only adam + params.warmup = 100; + params.cos_decay_steps = 1000; + params.cos_decay_restart = 1.1f; + params.cos_decay_alpha = 0.0f; + + params.lbfgs_n_iter = 16; + params.adam_n_iter = 16; + params.adam_alpha = 1e-3f; + params.adam_decay = 1e-3f; + + params.mem_model_gb = 2; + params.mem_compute_gb = 24; + params.mem_compute0_gb = 8; + params.mem_compute1_gb = 2; + + return params; +} + +static void print_usage(int /*argc*/, char ** argv, const struct train_params * params) { + fprintf(stderr, "usage: %s [options]\n", argv[0]); + fprintf(stderr, "\n"); + fprintf(stderr, "options:\n"); + fprintf(stderr, " -h, --help show this help message and exit\n"); + fprintf(stderr, " --copy-vocab-from-model FNAME path of gguf llama model or llama2.c vocabulary from which to copy vocab (default '%s')\n", params->fn_vocab_model); + fprintf(stderr, " --llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model\n"); + fprintf(stderr, " --llama2c-output-model FNAME model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model); + fprintf(stderr, "\n"); +} + +static bool params_parse(int argc, char ** argv, struct train_params * params) { + bool invalid_param = false; + bool reqd_param_found = false; + std::string arg; + struct train_params default_params = get_default_train_params(); + const std::string arg_prefix = "--"; + + for (int i = 1; i < argc; i++) { + arg = argv[i]; + if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { + std::replace(arg.begin(), arg.end(), '_', '-'); + } + + if (arg == "--copy-vocab-from-model") { + if (++i >= argc) { + invalid_param = true; + break; + } + params->fn_vocab_model = argv[i]; + } else if (arg == "--llama2c-model") { + if (++i >= argc) { + invalid_param = true; + break; + } + reqd_param_found = true; + params->fn_llama2c_model = argv[i]; + } else if (arg == "--llama2c-output-model") { + if (++i >= argc) { + invalid_param = true; + break; + } + params->fn_llama2c_output_model = argv[i]; + } else if (arg == "-h" || arg == "--help") { + print_usage(argc, argv, &default_params); + exit(0); + } else { + fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); + print_usage(argc, argv, &default_params); + exit(1); + } + } + if (invalid_param) { + fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); + print_usage(argc, argv, &default_params); + exit(1); + } + if (!reqd_param_found){ + fprintf(stderr, "error: please specify a llama2.c .bin file to be converted with argument --llama2c-model\n"); + print_usage(argc, argv, &default_params); + exit(1); + } + + return true; +} + +static std::string basename(const std::string &path) { + size_t pos = path.find_last_of("/\\"); + if (pos == std::string::npos) { + return path; + } + return path.substr(pos + 1); +} + +int main(int argc, char ** argv) { + common_init(); + + struct train_params params = get_default_train_params(); + if (!params_parse(argc, argv, ¶ms)) { + return 1; + } + + Config config; + TransformerWeights weights = {}; + { + LOG_INF("%s: Loading llama2c model from %s\n", __func__, params.fn_llama2c_model); + FILE * file = fopen(params.fn_llama2c_model, "rb"); + if (!file) { + LOG_ERR("%s: Unable to open the checkpoint file %s!\n", __func__, params.fn_llama2c_model); + return 1; + } + // read in the config header + if (fread(&config, sizeof(Config), 1, file) != 1) { + LOG_ERR("%s: Unable to read llama2c config from %s!\n",__func__,params.fn_llama2c_model); + return 1; + } + auto shared_weights = config.vocab_size > 0; + config.vocab_size = abs(config.vocab_size); + + // read in the Transformer weights + alloc_weights(&weights, &config, shared_weights); + if (checkpoint_init_weights(&weights, &config, file, shared_weights)) { + LOG_ERR("%s: Unable to initialize transformer weights from %s!",__func__,params.fn_llama2c_model); + return 1; + } + fclose(file); + } + + struct my_llama_vocab vocab; + load_vocab(params.fn_vocab_model, &config, &vocab); + + struct my_llama_model model; + model.hparams.n_vocab = config.vocab_size; //llama_vocab_n_vocab(lctx); + model.hparams.n_ctx = params.n_ctx; + model.hparams.n_embd = config.dim; //params.n_embd; + model.hparams.n_ff = config.hidden_dim; + model.hparams.n_mult = 32;//params.n_mult; + model.hparams.n_head = config.n_heads; //params.n_head; + model.hparams.n_head_kv = config.n_kv_heads; + model.hparams.n_layer = config.n_layers; //params.n_layer; + model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head); + + print_params(&model.hparams); + + struct ggml_init_params lcparams; + lcparams.mem_size = 1024ll*1024ll*1024ll*((size_t) params.mem_model_gb); + lcparams.mem_buffer = NULL; + lcparams.no_alloc = false; + + model.ctx = ggml_init(lcparams); + + init_model(&model); + model.name = basename(params.fn_llama2c_model); + save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model); + + LOG_INF("%s: Saving llama.c model file %s in ggml format at %s\n", __func__, params.fn_llama2c_model, params.fn_llama2c_output_model); + + ggml_free(model.ctx); + return 0; +} diff --git a/llama.cpp/examples/convert_legacy_llama.py b/llama.cpp/examples/convert_legacy_llama.py new file mode 100644 index 0000000000000000000000000000000000000000..b3449ab4c4bdff1572d8a59766c3e77040a68878 --- /dev/null +++ b/llama.cpp/examples/convert_legacy_llama.py @@ -0,0 +1,1462 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import logging +import argparse +import concurrent.futures +import enum +import faulthandler +import functools +import itertools +import json +import math +import mmap +import os +import pickle +import re +import signal +import struct +import sys +import textwrap +import time +import zipfile +from abc import ABC, abstractmethod +from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor +from dataclasses import dataclass +from pathlib import Path +from typing import TYPE_CHECKING, Any, Callable, IO, Iterable, Literal, TypeVar + +import numpy as np + +if 'NO_LOCAL_GGUF' not in os.environ: + # use .parent.parent since we are in "examples" directory + sys.path.insert(1, str(Path(__file__).parent.parent / 'gguf-py')) + +import gguf +from gguf import BaseVocab, Vocab, NoVocab, BpeVocab, SentencePieceVocab, LlamaHfVocab + +if TYPE_CHECKING: + from typing_extensions import Self, TypeAlias + +logger = logging.getLogger("convert") + +if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'): + faulthandler.register(signal.SIGUSR1) + +NDArray: TypeAlias = 'np.ndarray[Any, Any]' + +ARCH = gguf.MODEL_ARCH.LLAMA + +DEFAULT_CONCURRENCY = 8 + +ADDED_TOKENS_FILE = 'added_tokens.json' +FAST_TOKENIZER_FILE = 'tokenizer.json' + +# +# data types +# + + +@dataclass(frozen=True) +class DataType: + name: str + dtype: np.dtype[Any] + valid_conversions: list[str] + + def elements_to_bytes(self, n_elements: int) -> int: + return n_elements * self.dtype.itemsize + + +@dataclass(frozen=True) +class UnquantizedDataType(DataType): + pass + + +DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0']) +DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0']) +DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = []) +DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0']) + + +@dataclass(frozen=True) +class QuantizedDataType(DataType): + block_size: int + quantized_dtype: np.dtype[Any] + ggml_type: gguf.GGMLQuantizationType + + def quantize(self, arr: NDArray) -> NDArray: + raise NotImplementedError(f'Quantization for {self.name} not implemented') + + def elements_to_bytes(self, n_elements: int) -> int: + assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}' + return self.quantized_dtype.itemsize * (n_elements // self.block_size) + + +@dataclass(frozen=True) +class Q8_0QuantizedDataType(QuantizedDataType): + # Mini Q8_0 quantization in Python! + def quantize(self, arr: NDArray) -> NDArray: + assert arr.size % self.block_size == 0 and arr.size != 0, f'Bad array size {arr.size}' + assert arr.dtype == np.float32, f'Bad array type {arr.dtype}' + n_blocks = arr.size // self.block_size + blocks = arr.reshape((n_blocks, self.block_size)) + # Much faster implementation of block quantization contributed by @Cebtenzzre + + def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[tuple[Any, Any]]: + d = abs(blocks).max(axis = 1) / np.float32(127) + with np.errstate(divide = 'ignore'): + qs = (blocks / d[:, None]).round() + qs[d == 0] = 0 + yield from zip(d, qs) + return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype) + + +DT_Q8_0 = Q8_0QuantizedDataType('Q8_0', + dtype = np.dtype(np.float32), valid_conversions = [], + ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32, + quantized_dtype = np.dtype([('d', ' DataType: + dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self) + if dt is None: + raise ValueError(self) + # Convert all 1D tensors to F32. Most of the codebase that takes in 1D tensors only handles F32 tensors, and most of the outputs tensors are F32. + # Also The 1d tensors aren't much of a performance/size issue. So instead of having to have separate F32 and F16 implementations of both, just convert everything to F32 for now. + return dt if len(tensor.shape) > 1 else DT_F32 + + +GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = { + GGMLFileType.AllF32 : DT_F32, + GGMLFileType.MostlyF16 : DT_F16, + GGMLFileType.MostlyQ8_0: DT_Q8_0, +} + +# +# hparams loading +# + + +@dataclass +class Params: + n_vocab: int + n_embd: int + n_layer: int + n_ctx: int + n_ff: int + n_head: int + n_head_kv: int + n_experts: int | None = None + n_experts_used: int | None = None + f_norm_eps: float | None = None + + rope_scaling_type: gguf.RopeScalingType | None = None + f_rope_freq_base: float | None = None + f_rope_scale: float | None = None + n_ctx_orig: int | None = None + rope_finetuned: bool | None = None + + ftype: GGMLFileType | None = None + + # path to the directory containing the model files + path_model: Path | None = None + + @staticmethod + def guessed(model: LazyModel) -> Params: + # try transformer naming first + n_vocab, n_embd = model["model.embed_tokens.weight"].shape if "model.embed_tokens.weight" in model else model["tok_embeddings.weight"].shape + + # try transformer naming first + if "model.layers.0.self_attn.q_proj.weight" in model: + n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model) + elif "model.layers.0.self_attn.W_pack.weight" in model: # next: try baichuan naming + n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model) + else: + n_layer = next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model) + + if n_layer < 1: + msg = """\ + failed to guess 'n_layer'. This model is unknown or unsupported. + Suggestion: provide 'config.json' of the model in the same directory containing model files.""" + raise KeyError(textwrap.dedent(msg)) + + n_head = n_embd // 128 # guessed + n_mult = 256 # guessed + + # TODO: verify this + n_ff = int(2 * (4 * n_embd) / 3) + n_ff = n_mult * ((n_ff + n_mult - 1) // n_mult) + + return Params( + n_vocab = n_vocab, + n_embd = n_embd, + n_layer = n_layer, + n_ctx = -1, + n_ff = n_ff, + n_head = n_head, + n_head_kv = n_head, + f_norm_eps = 1e-5, + ) + + @staticmethod + def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params: + with open(config_path) as f: + config = json.load(f) + + rope_scaling_type = f_rope_scale = n_ctx_orig = rope_finetuned = None + rope_scaling = config.get("rope_scaling") + + if rope_scaling is not None and (typ := rope_scaling.get("type")): + rope_factor = rope_scaling.get("factor") + f_rope_scale = rope_factor + if typ == "linear": + rope_scaling_type = gguf.RopeScalingType.LINEAR + elif typ == "yarn": + rope_scaling_type = gguf.RopeScalingType.YARN + n_ctx_orig = rope_scaling['original_max_position_embeddings'] + rope_finetuned = rope_scaling['finetuned'] + else: + raise NotImplementedError(f'Unknown rope scaling type: {typ}') + + if "max_sequence_length" in config: + n_ctx = config["max_sequence_length"] + elif "max_position_embeddings" in config: + n_ctx = config["max_position_embeddings"] + else: + msg = """\ + failed to guess 'n_ctx'. This model is unknown or unsupported. + Suggestion: provide 'config.json' of the model in the same directory containing model files.""" + raise KeyError(textwrap.dedent(msg)) + + n_experts = None + n_experts_used = None + + if "num_local_experts" in config: + n_experts = config["num_local_experts"] + n_experts_used = config["num_experts_per_tok"] + + return Params( + n_vocab = config["vocab_size"], + n_embd = config["hidden_size"], + n_layer = config["num_hidden_layers"], + n_ctx = n_ctx, + n_ff = config["intermediate_size"], + n_head = (n_head := config["num_attention_heads"]), + n_head_kv = config.get("num_key_value_heads", n_head), + n_experts = n_experts, + n_experts_used = n_experts_used, + f_norm_eps = config["rms_norm_eps"], + f_rope_freq_base = config.get("rope_theta"), + rope_scaling_type = rope_scaling_type, + f_rope_scale = f_rope_scale, + n_ctx_orig = n_ctx_orig, + rope_finetuned = rope_finetuned, + ) + + # LLaMA v2 70B params.json + # {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1} + @staticmethod + def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params: + with open(config_path) as f: + config = json.load(f) + + n_experts = None + n_experts_used = None + f_rope_freq_base = None + n_ff = None + + # hack to determine LLaMA v1 vs v2 vs CodeLlama + if config.get("moe"): + # Mixtral + n_ctx = 32768 + elif config.get("rope_theta") == 1000000: + # CodeLlama + n_ctx = 16384 + elif config["norm_eps"] == 1e-05: + # LLaMA v2 + n_ctx = 4096 + else: + # LLaMA v1 + n_ctx = 2048 + + if "layers.0.feed_forward.w1.weight" in model: + n_ff = model["layers.0.feed_forward.w1.weight"].shape[0] + + if config.get("moe"): + n_ff = model["layers.0.feed_forward.experts.0.w1.weight"].shape[0] + n_experts = config["moe"]["num_experts"] + n_experts_used = config["moe"]["num_experts_per_tok"] + f_rope_freq_base = 1e6 + + assert n_ff is not None + + return Params( + n_vocab = model["tok_embeddings.weight"].shape[0], + n_embd = config["dim"], + n_layer = config["n_layers"], + n_ctx = n_ctx, + n_ff = n_ff, + n_head = (n_head := config["n_heads"]), + n_head_kv = config.get("n_kv_heads", n_head), + n_experts = n_experts, + n_experts_used = n_experts_used, + f_norm_eps = config["norm_eps"], + f_rope_freq_base = config.get("rope_theta", f_rope_freq_base), + ) + + @staticmethod + def load(model_plus: ModelPlus) -> Params: + hf_config_path = model_plus.paths[0].parent / "config.json" + orig_config_path = model_plus.paths[0].parent / "params.json" + + if hf_config_path.exists(): + params = Params.loadHFTransformerJson(model_plus.model, hf_config_path) + elif orig_config_path.exists(): + params = Params.loadOriginalParamsJson(model_plus.model, orig_config_path) + elif model_plus.format != 'none': + params = Params.guessed(model_plus.model) + else: + raise ValueError('Cannot guess params when model format is none') + + params.path_model = model_plus.paths[0].parent + + return params + + +# +# data loading +# TODO: reuse (probably move to gguf.py?) +# + + +def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray: + if n_head_kv is not None and n_head != n_head_kv: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + +class Tensor(ABC): + ndarray: NDArray + data_type: DataType + + @abstractmethod + def astype(self, data_type: DataType) -> Self: ... + @abstractmethod + def permute(self, n_head: int, n_head_kv: int) -> Self: ... + @abstractmethod + def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> Self: ... + @abstractmethod + def part(self, n_part: int) -> Self: ... + @abstractmethod + def to_ggml(self) -> GGMLCompatibleTensor: ... + + +def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray: + assert bf16_arr.dtype == np.uint16, f"Input array should be of dtype uint16, but got {bf16_arr.dtype}" + fp32_arr = bf16_arr.astype(np.uint32) << 16 + return fp32_arr.view(np.float32) + + +class UnquantizedTensor(Tensor): + def __init__(self, ndarray: NDArray): + assert isinstance(ndarray, np.ndarray) + self.ndarray = ndarray + self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype] + + def astype(self, data_type: DataType) -> UnquantizedTensor: + dtype = data_type.dtype + if self.data_type == DT_BF16: + self.ndarray = bf16_to_fp32(self.ndarray) + return UnquantizedTensor(self.ndarray.astype(dtype)) + + def to_ggml(self) -> Self: + return self + + def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: + r = self.ndarray.shape[0] // 3 + return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head_kv)) + + def part(self, n_part: int) -> UnquantizedTensor: + r = self.ndarray.shape[0] // 3 + return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...]) + + def permute(self, n_head: int, n_head_kv: int) -> UnquantizedTensor: + return UnquantizedTensor(permute(self.ndarray, n_head, n_head_kv)) + + +def load_unquantized(lazy_tensor: LazyTensor, expected_dtype: Any = None, convert: bool = False) -> NDArray: + tensor = lazy_tensor.load() + assert isinstance(tensor, UnquantizedTensor) + + # double-check: + actual_shape = list(tensor.ndarray.shape) + assert actual_shape == lazy_tensor.shape, (actual_shape, lazy_tensor.shape) + if expected_dtype is not None and expected_dtype != tensor.ndarray.dtype: + if convert: + tensor.ndarray = tensor.ndarray.astype(expected_dtype) + else: + raise ValueError(f'expected this tensor to have dtype {expected_dtype}, got {tensor.ndarray.dtype}') + + return tensor.ndarray + + +GGMLCompatibleTensor = UnquantizedTensor + + +@dataclass +class LazyTensor: + _load: Callable[[], Tensor] + shape: list[int] + data_type: DataType + description: str + + def load(self) -> Tensor: + ret = self._load() + # Should be okay if it maps to the same numpy type? + assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \ + (self.data_type, ret.data_type, self.description) + return ret + + def astype(self, data_type: DataType) -> LazyTensor: + self.validate_conversion_to(data_type) + + def load() -> Tensor: + return self.load().astype(data_type) + return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}') + + def validate_conversion_to(self, data_type: DataType) -> None: + if data_type != self.data_type and data_type.name not in self.data_type.valid_conversions: + raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.') + + +LazyModel: TypeAlias = 'dict[str, LazyTensor]' + +ModelFormat: TypeAlias = Literal['ggml', 'torch', 'safetensors', 'none'] + +@dataclass +class ModelPlus: + model: LazyModel + paths: list[Path] # Where this was read from. + format: ModelFormat + vocab: BaseVocab | None # For GGML models (which have vocab built in), the vocab. + + +def merge_sharded(models: list[LazyModel]) -> LazyModel: + # Original LLaMA models have each file contain one part of each tensor. + # Use a dict instead of a set to preserve order. + names = {name: None for model in models for name in model} + + def convert(name: str) -> LazyTensor: + lazy_tensors = [model[name] for model in models] + if len(lazy_tensors) == 1: + # only one file; don't go through this procedure since there might + # be quantized tensors + return lazy_tensors[0] + if len(lazy_tensors[0].shape) == 1: + # the tensor is just duplicated in every file + return lazy_tensors[0] + if name.startswith('tok_embeddings.') or \ + name.endswith('.attention.wo.weight') or \ + name.endswith('.feed_forward.w2.weight'): + # split by columns + axis = 1 + else: + # split by rows + axis = 0 + concatenated_shape = list(lazy_tensors[0].shape) + concatenated_shape[axis] = sum(tensor.shape[axis] for tensor in lazy_tensors) + + def load() -> UnquantizedTensor: + ndarrays = [load_unquantized(tensor) for tensor in lazy_tensors] + concatenated = np.concatenate(ndarrays, axis=axis) + return UnquantizedTensor(concatenated) + description = 'concatenated[[' + '] | ['.join(lt.description for lt in lazy_tensors) + ']]' + return LazyTensor(load, concatenated_shape, lazy_tensors[0].data_type, description) + return {name: convert(name) for name in names} + + +def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus: + formats: set[ModelFormat] = set(mp.format for mp in models_plus) + assert len(formats) == 1, "different formats?" + format = formats.pop() + paths = [path for mp in models_plus for path in mp.paths] + # Use the first non-None vocab, if any. + try: + vocab = next(mp.vocab for mp in models_plus if mp.vocab is not None) + except StopIteration: + vocab = None + + if any("model.embed_tokens.weight" in mp.model for mp in models_plus): + # Transformers models put different tensors in different files, but + # don't split individual tensors between files. + model: LazyModel = {} + for mp in models_plus: + model.update(mp.model) + else: + model = merge_sharded([mp.model for mp in models_plus]) + + return ModelPlus(model, paths, format, vocab) + + +def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTensor: + def load() -> Tensor: + return lazy_tensor.load().permute(n_head, n_head_kv) + return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description) + + +def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_kv: int) -> LazyTensor: + def load() -> Tensor: + return lazy_tensor.load().permute_part(n_part, n_head, n_head_kv) + s = lazy_tensor.shape.copy() + s[0] = s[0] // 3 + return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description) + + +def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor: + def load() -> Tensor: + return lazy_tensor.load().part(n_part) + s = lazy_tensor.shape.copy() + s[0] = s[0] // 3 + return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description) + + +def pack_experts_lazy(lazy_tensors: list[LazyTensor]) -> LazyTensor: + def load() -> Tensor: + tensors = [lazy_tensor.load() for lazy_tensor in lazy_tensors] + return UnquantizedTensor(np.array([tensor.ndarray for tensor in tensors])) + s = lazy_tensors[0].shape.copy() + s.insert(0, len(lazy_tensors)) + return LazyTensor(load, s, lazy_tensors[0].data_type, 'pack_experts ' + ' | '.join(lt.description for lt in lazy_tensors)) + + +# Functionality that simulates `torch.load` but where individual tensors are +# only loaded into memory on demand, not all at once. +# PyTorch can't do this natively as of time of writing: +# - https://github.com/pytorch/pytorch/issues/64327 +# This allows us to de-shard without multiplying RAM usage, and also +# conveniently drops the PyTorch dependency (though we still need numpy). + + +@dataclass +class LazyStorageKind: + data_type: DataType + + +@dataclass +class LazyStorage: + load: Callable[[int, int], NDArray] + kind: LazyStorageKind + description: str + + +class LazyUnpickler(pickle.Unpickler): + def __init__(self, fp: IO[bytes], data_base_path: str, zip_file: zipfile.ZipFile): + super().__init__(fp) + self.data_base_path = data_base_path + self.zip_file = zip_file + + def persistent_load(self, pid: Any) -> Any: + assert pid[0] == 'storage' + assert isinstance(pid[1], LazyStorageKind) + data_type = pid[1].data_type + filename_stem = pid[2] + filename = f'{self.data_base_path}/{filename_stem}' + info = self.zip_file.getinfo(filename) + + def load(offset: int, elm_count: int) -> NDArray: + dtype = data_type.dtype + with self.zip_file.open(info) as fp: + fp.seek(offset * dtype.itemsize) + size = elm_count * dtype.itemsize + data = fp.read(size) + assert len(data) == size + return np.frombuffer(data, dtype) + description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}' + return LazyStorage(load=load, kind=pid[1], description=description) + + @staticmethod + def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any, + requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor: + assert isinstance(storage, LazyStorage) + + def load() -> UnquantizedTensor: + elm_count = stride[0] * size[0] + return UnquantizedTensor(storage.load(storage_offset, elm_count).reshape(size)) + description = f'pickled storage_offset={storage_offset} in {storage.description}' + return LazyTensor(load, list(size), storage.kind.data_type, description) + + @staticmethod + def rebuild_from_type_v2(func, new_type, args, state): + return func(*args) + + CLASSES: dict[tuple[str, str], type[LazyTensor] | LazyStorageKind] = { + # getattr used here as a workaround for mypy not being smart enough to determine + # the staticmethods have a __func__ attribute. + ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'), + ('torch._utils', '_rebuild_tensor_v2'): getattr(lazy_rebuild_tensor_v2, '__func__'), + ('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16), + ('torch', 'HalfStorage'): LazyStorageKind(DT_F16), + ('torch', 'FloatStorage'): LazyStorageKind(DT_F32), + ('torch', 'IntStorage'): LazyStorageKind(DT_I32), + ('torch', 'Tensor'): LazyTensor, + } + + def find_class(self, module: str, name: str) -> Any: + if not module.startswith('torch'): + return super().find_class(module, name) + return self.CLASSES[(module, name)] + + +def lazy_load_torch_file(outer_fp: IO[bytes], path: Path) -> ModelPlus: + zf = zipfile.ZipFile(outer_fp) + pickle_paths = [name for name in zf.namelist() if name.endswith('.pkl')] + assert len(pickle_paths) == 1, pickle_paths + pickle_fp = zf.open(pickle_paths[0], 'r') + unpickler = LazyUnpickler(pickle_fp, + data_base_path=pickle_paths[0][:-4], + zip_file=zf) + model = unpickler.load() + if 'model' in model: model = model['model'] + as_dict = dict(model.items()) + return ModelPlus(model=as_dict, paths=[path], format='torch', vocab=None) + + +def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus: + header_size, = struct.unpack(' LazyTensor: + data_type = SAFETENSORS_DATA_TYPES[info['dtype']] + numpy_dtype = data_type.dtype + shape: list[int] = info['shape'] + begin, end = info['data_offsets'] + assert 0 <= begin <= end <= len(byte_buf) + assert end - begin == math.prod(shape) * numpy_dtype.itemsize + buf = byte_buf[begin:end] + + def load() -> UnquantizedTensor: + return UnquantizedTensor(np.frombuffer(buf, dtype=numpy_dtype).reshape(shape)) + description = f'safetensors begin={begin} end={end} type={data_type} path={path}' + return LazyTensor(load, shape, data_type, description) + model = {name: convert(info) for (name, info) in header.items() if name != '__metadata__'} + return ModelPlus(model=model, paths=[path], format='safetensors', vocab=None) + + +def must_read(fp: IO[bytes], length: int) -> bytes: + ret = fp.read(length) + if len(ret) < length: + raise EOFError("unexpectedly reached end of file") + return ret + + +@functools.lru_cache(maxsize=None) +def lazy_load_file(path: Path) -> ModelPlus: + fp = open(path, 'rb') + first8 = fp.read(8) + fp.seek(0) + if first8[:2] == b'PK': + # A zip file, i.e. PyTorch format + return lazy_load_torch_file(fp, path) + elif struct.unpack(' Iterable[Out]: + '''Parallel map, but with backpressure. If the caller doesn't call `next` + fast enough, this will stop calling `func` at some point rather than + letting results pile up in memory. Specifically, there is a max of one + output value buffered per thread.''' + if concurrency < 2: + yield from map(func, iterable) + # Not reached. + iterable = iter(iterable) + executor_class: type[ThreadPoolExecutor] | type[ProcessPoolExecutor] + if use_processpool_executor: + executor_class = ProcessPoolExecutor + else: + executor_class = ThreadPoolExecutor + with executor_class(max_workers=max_workers) as executor: + futures: list[concurrent.futures.Future[Out]] = [] + done = False + for _ in range(concurrency): + try: + futures.append(executor.submit(func, next(iterable))) + except StopIteration: + done = True + break + + while futures: + result = futures.pop(0).result() + while not done and len(futures) < concurrency: + try: + futures.append(executor.submit(func, next(iterable))) + except StopIteration: + done = True + break + yield result + + +def check_vocab_size(params: Params, vocab: BaseVocab, pad_vocab: bool = False) -> None: + # Handle special case where the model's vocab size is not set + if params.n_vocab == -1: + raise ValueError( + "The model's vocab size is set to -1 in params.json. Please update it manually." + + (f" Maybe {vocab.vocab_size}?" if isinstance(vocab, Vocab) else ""), + ) + if not isinstance(vocab, Vocab): + return # model has no vocab + + # Check for a vocab size mismatch + if params.n_vocab == vocab.vocab_size: + logger.warning("Ignoring added_tokens.json since model matches vocab size without it.") + return + + if pad_vocab and params.n_vocab > vocab.vocab_size: + pad_count = params.n_vocab - vocab.vocab_size + logger.debug( + f"Padding vocab with {pad_count} token(s) - through " + ) + for i in range(1, pad_count + 1): + vocab.added_tokens_dict[f""] = -1 + vocab.added_tokens_list.append(f"") + vocab.vocab_size = params.n_vocab + return + + msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer} has {vocab.vocab_size})." + if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20: + msg += f" Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent})." + if vocab.vocab_size < params.n_vocab: + msg += " Add the --pad-vocab option and try again." + + raise ValueError(msg) + + +class OutputFile: + def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE): + self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess) + + def add_meta_model(self, params: Params, metadata: gguf.Metadata | None) -> None: + # Metadata About The Model And Its Provenence + name = "LLaMA" + if metadata is not None and metadata.name is not None: + name = metadata.name + elif params.path_model is not None: + name = params.path_model.name + elif params.n_ctx == 4096: + # Heuristic detection of LLaMA v2 model + name = "LLaMA v2" + + self.gguf.add_name(name) + + if metadata is not None: + if metadata.author is not None: + self.gguf.add_author(metadata.author) + if metadata.version is not None: + self.gguf.add_version(metadata.version) + if metadata.organization is not None: + self.gguf.add_organization(metadata.organization) + + if metadata.finetune is not None: + self.gguf.add_finetune(metadata.finetune) + if metadata.basename is not None: + self.gguf.add_basename(metadata.basename) + + if metadata.description is not None: + self.gguf.add_description(metadata.description) + if metadata.quantized_by is not None: + self.gguf.add_quantized_by(metadata.quantized_by) + + if metadata.size_label is not None: + self.gguf.add_size_label(metadata.size_label) + + if metadata.license is not None: + self.gguf.add_license(metadata.license) + if metadata.license_name is not None: + self.gguf.add_license_name(metadata.license_name) + if metadata.license_link is not None: + self.gguf.add_license_link(metadata.license_link) + + if metadata.url is not None: + self.gguf.add_url(metadata.url) + if metadata.doi is not None: + self.gguf.add_doi(metadata.doi) + if metadata.uuid is not None: + self.gguf.add_uuid(metadata.uuid) + if metadata.repo_url is not None: + self.gguf.add_repo_url(metadata.repo_url) + + if metadata.source_url is not None: + self.gguf.add_source_url(metadata.source_url) + if metadata.source_doi is not None: + self.gguf.add_source_doi(metadata.source_doi) + if metadata.source_uuid is not None: + self.gguf.add_source_uuid(metadata.source_uuid) + if metadata.source_repo_url is not None: + self.gguf.add_source_repo_url(metadata.source_repo_url) + + if metadata.base_models is not None: + self.gguf.add_base_model_count(len(metadata.base_models)) + for key, base_model_entry in enumerate(metadata.base_models): + if "name" in base_model_entry: + self.gguf.add_base_model_name(key, base_model_entry["name"]) + if "author" in base_model_entry: + self.gguf.add_base_model_author(key, base_model_entry["author"]) + if "version" in base_model_entry: + self.gguf.add_base_model_version(key, base_model_entry["version"]) + if "organization" in base_model_entry: + self.gguf.add_base_model_organization(key, base_model_entry["organization"]) + if "description" in base_model_entry: + self.gguf.add_base_model_description(key, base_model_entry["description"]) + if "url" in base_model_entry: + self.gguf.add_base_model_url(key, base_model_entry["url"]) + if "doi" in base_model_entry: + self.gguf.add_base_model_doi(key, base_model_entry["doi"]) + if "uuid" in base_model_entry: + self.gguf.add_base_model_uuid(key, base_model_entry["uuid"]) + if "repo_url" in base_model_entry: + self.gguf.add_base_model_repo_url(key, base_model_entry["repo_url"]) + + if metadata.datasets is not None: + self.gguf.add_dataset_count(len(metadata.datasets)) + for key, dataset_entry in enumerate(metadata.datasets): + if "name" in dataset_entry: + self.gguf.add_dataset_name(key, dataset_entry["name"]) + if "author" in dataset_entry: + self.gguf.add_dataset_author(key, dataset_entry["author"]) + if "version" in dataset_entry: + self.gguf.add_dataset_version(key, dataset_entry["version"]) + if "organization" in dataset_entry: + self.gguf.add_dataset_organization(key, dataset_entry["organization"]) + if "description" in dataset_entry: + self.gguf.add_dataset_description(key, dataset_entry["description"]) + if "url" in dataset_entry: + self.gguf.add_dataset_url(key, dataset_entry["url"]) + if "doi" in dataset_entry: + self.gguf.add_dataset_doi(key, dataset_entry["doi"]) + if "uuid" in dataset_entry: + self.gguf.add_dataset_uuid(key, dataset_entry["uuid"]) + if "repo_url" in dataset_entry: + self.gguf.add_dataset_repo_url(key, dataset_entry["repo_url"]) + + if metadata.tags is not None: + self.gguf.add_tags(metadata.tags) + if metadata.languages is not None: + self.gguf.add_languages(metadata.languages) + + def add_meta_arch(self, params: Params) -> None: + # Metadata About The Neural Architecture Itself + self.gguf.add_vocab_size(params.n_vocab) + self.gguf.add_context_length(params.n_ctx) + self.gguf.add_embedding_length(params.n_embd) + self.gguf.add_block_count(params.n_layer) + self.gguf.add_feed_forward_length(params.n_ff) + self.gguf.add_rope_dimension_count(params.n_embd // params.n_head) + self.gguf.add_head_count (params.n_head) + self.gguf.add_head_count_kv (params.n_head_kv) + + if params.n_experts: + self.gguf.add_expert_count(params.n_experts) + + if params.n_experts_used: + self.gguf.add_expert_used_count(params.n_experts_used) + + if params.f_norm_eps: + self.gguf.add_layer_norm_rms_eps(params.f_norm_eps) + else: + raise ValueError('f_norm_eps is None') + + if params.f_rope_freq_base is not None: + self.gguf.add_rope_freq_base(params.f_rope_freq_base) + + if params.rope_scaling_type: + assert params.f_rope_scale is not None + self.gguf.add_rope_scaling_type(params.rope_scaling_type) + self.gguf.add_rope_scaling_factor(params.f_rope_scale) + + if params.n_ctx_orig is not None: + self.gguf.add_rope_scaling_orig_ctx_len(params.n_ctx_orig) + + if params.rope_finetuned is not None: + self.gguf.add_rope_scaling_finetuned(params.rope_finetuned) + + if params.ftype is not None: + self.gguf.add_file_type(params.ftype) + + def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list[float], list[gguf.TokenType]]: + tokens = [] + scores = [] + toktypes = [] + + # NOTE: `all_tokens` returns the base vocabulary and added tokens + for text, score, toktype in vocab.all_tokens(): + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + assert len(tokens) == vocab.vocab_size + + return tokens, scores, toktypes + + def add_meta_vocab(self, vocab: Vocab) -> None: + # Ensure that tokenizer_model is added to the GGUF model + self.gguf.add_tokenizer_model(vocab.tokenizer_model) + + # Extract model vocabulary for model conversion + tokens, scores, toktypes = self.extract_vocabulary_from_model(vocab) + + # Add extracted token information for model conversion + self.gguf.add_token_list(tokens) + self.gguf.add_token_scores(scores) + self.gguf.add_token_types(toktypes) + + def add_meta_special_vocab(self, svocab: gguf.SpecialVocab) -> None: + svocab.add_to_gguf(self.gguf) + + def add_tensor_info(self, name: str, tensor: LazyTensor) -> None: + n_elements = int(np.prod(tensor.shape)) + raw_dtype = getattr(tensor.data_type, 'ggml_type', None) + data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype + data_nbytes = tensor.data_type.elements_to_bytes(n_elements) + self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype=raw_dtype) + + def write_meta(self) -> None: + self.gguf.write_header_to_file() + self.gguf.write_kv_data_to_file() + + def write_tensor_info(self) -> None: + self.gguf.write_ti_data_to_file() + + def write_tensor_data(self, ftype: GGMLFileType, model: LazyModel, concurrency: int) -> None: + ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency=concurrency) + if ftype == GGMLFileType.MostlyQ8_0: + ndarrays = bounded_parallel_map( + OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency, + use_processpool_executor=True, + ) + else: + ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner) + + start = time.time() + for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)): + elapsed = time.time() - start + size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape) + padi = len(str(len(model))) + logger.info( + f"[{i + 1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}" + ) + self.gguf.write_tensor_data(ndarray) + + def close(self) -> None: + self.gguf.close() + + @staticmethod + def write_vocab_only( + fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, + endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, metadata: gguf.Metadata | None = None, + ) -> None: + check_vocab_size(params, vocab, pad_vocab=pad_vocab) + + of = OutputFile(fname_out, endianess=endianess) + + # meta data + of.add_meta_model(params, metadata) + of.add_meta_arch(params) + of.add_meta_vocab(vocab) + of.add_meta_special_vocab(svocab) + + of.write_meta() + + of.close() + + @staticmethod + def do_item(item: tuple[str, LazyTensor]) -> tuple[DataType, NDArray]: + name, lazy_tensor = item + tensor = lazy_tensor.load().to_ggml() + return (lazy_tensor.data_type, tensor.ndarray) + + @staticmethod + def maybe_do_quantize(item: tuple[DataType, NDArray]) -> NDArray: + dt, arr = item + if not isinstance(dt, QuantizedDataType): + return arr + return dt.quantize(arr) + + @staticmethod + def write_all( + fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: BaseVocab, svocab: gguf.SpecialVocab, + concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, + pad_vocab: bool = False, + metadata: gguf.Metadata | None = None, + ) -> None: + check_vocab_size(params, vocab, pad_vocab=pad_vocab) + + of = OutputFile(fname_out, endianess=endianess) + + # meta data + of.add_meta_model(params, metadata) + of.add_meta_arch(params) + if isinstance(vocab, Vocab): + of.add_meta_vocab(vocab) + of.add_meta_special_vocab(svocab) + else: # NoVocab + of.gguf.add_tokenizer_model(vocab.tokenizer_model) + + # tensor info + for name, lazy_tensor in model.items(): + of.add_tensor_info(name, lazy_tensor) + + of.write_meta() + of.write_tensor_info() + + # tensor data + of.write_tensor_data(ftype, model, concurrency) + + of.close() + + +def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType: + wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) + ".weight"].data_type + + if output_type_str == "f32" or (output_type_str is None and wq_type in (DT_F32, DT_BF16)): + return GGMLFileType.AllF32 + if output_type_str == "f16" or (output_type_str is None and wq_type == DT_F16): + return GGMLFileType.MostlyF16 + if output_type_str == "q8_0": + return GGMLFileType.MostlyQ8_0 + + name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()} + + raise ValueError(f"Unexpected combination of types: {name_to_type}") + + +def per_model_weight_count_estimation(tensors: Iterable[tuple[str, LazyTensor]]) -> tuple[int, int, int]: + total_params = 0 + shared_params = 0 + expert_params = 0 + + for name, lazy_tensor in tensors: + # We don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")): + continue + + # Got A Tensor + sum_weights_in_tensor: int = 1 + + # Tensor Volume + for dim in lazy_tensor.shape: + sum_weights_in_tensor *= dim + + if ".experts." in name: + if ".experts.0." in name: + expert_params += sum_weights_in_tensor + else: + shared_params += sum_weights_in_tensor + + total_params += sum_weights_in_tensor + + return total_params, shared_params, expert_params + + +def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel: + return {name: tensor.astype(output_type.type_for_tensor(name, tensor)) + for (name, tensor) in model.items()} + + +def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) -> LazyModel: + tmap = gguf.TensorNameMap(ARCH, params.n_layer) + should_skip = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, [])) + + tmp = model + + # merge experts into one tensor + if params.n_experts and params.n_experts > 0: + for i_l in range(params.n_layer): + for w in range(1, 4): + experts = [] + for e in range(params.n_experts): + if f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight" in model: + experts.append(model[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"]) + del tmp[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"] + elif f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight" in model: + experts.append(model[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"]) + del tmp[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"] + else: + raise ValueError(f"Expert tensor not found: layers.{i_l}.feed_forward.experts.{e}.w{w}.weight") + tmp[f"layers.{i_l}.feed_forward.experts.w{w}.weight"] = pack_experts_lazy(experts) + + # HF models permut or pack some of the tensors, so we need to undo that + for i in itertools.count(): + if f"model.layers.{i}.self_attn.q_proj.weight" in model: + logger.debug(f"Permuting layer {i}") + tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head, params.n_head) + tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_head_kv) + # tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] + elif f"model.layers.{i}.self_attn.W_pack.weight" in model: + logger.debug(f"Unpacking and permuting layer {i}") + tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head, params.n_head) + tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head, params.n_head_kv) + tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2) + del tmp[f"model.layers.{i}.self_attn.W_pack.weight"] + else: + break + + out: LazyModel = {} + for name, lazy_tensor in model.items(): + tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None) + if name_new is None: + if skip_unknown: + logger.warning(f"Unexpected tensor name: {name} - skipping") + continue + raise ValueError(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)") + + if tensor_type in should_skip: + logger.debug(f"skipping tensor {name_new}") + continue + + logger.debug(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}") + out[name_new] = lazy_tensor + + return out + + +def nth_multifile_path(path: Path, n: int) -> Path | None: + '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return + the nth path in the model. + ''' + # Support the following patterns: + patterns = [ + # - x.00.pth, x.01.pth, etc. + (r'\.[0-9]{2}\.pth$', f'.{n:02}.pth'), + # - x-00001-of-00002.bin, x-00002-of-00002.bin, etc. + (r'-[0-9]{5}-of-(.*)$', fr'-{n:05}-of-\1'), + # x.bin, x.bin.1, etc. + (r'(\.[0-9]+)?$', r'\1' if n == 0 else fr'\1.{n}') + ] + for regex, replacement in patterns: + if re.search(regex, path.name): + new_path = path.with_name(re.sub(regex, replacement, path.name)) + if new_path.exists(): + return new_path + return None + + +def find_multifile_paths(path: Path) -> list[Path]: + '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return + the whole list of paths in the model. + ''' + ret: list[Path] = [] + for i in itertools.count(): + nth_path = nth_multifile_path(path, i) + if nth_path is None: + break + ret.append(nth_path) + if not ret: + # No matches. This should only happen if the file was named, e.g., + # foo.0, and there was no file named foo. Oh well, try to process it + # as a single file. + return [path] + return ret + + +def load_some_model(path: Path) -> ModelPlus: + '''Load a model of any supported format.''' + # Be extra-friendly and accept either a file or a directory: + if path.is_dir(): + # Check if it's a set of safetensors files first + globs = ["model-00001-of-*.safetensors", "model.safetensors", "consolidated.safetensors"] + files = [file for glob in globs for file in path.glob(glob)] + if not files: + # Try the PyTorch patterns too, with lower priority + globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"] + files = [file for glob in globs for file in path.glob(glob)] + if not files: + raise FileNotFoundError(f"Can't find model in directory {path}") + if len(files) > 1: + raise ValueError(f"Found multiple models in {path}, not sure which to pick: {files}") + path = files[0] + + paths = find_multifile_paths(path) + models_plus: list[ModelPlus] = [] + for path in paths: + logger.info(f"Loading model file {path}") + models_plus.append(lazy_load_file(path)) + + model_plus = merge_multifile_models(models_plus) + return model_plus + + +class VocabFactory: + _VOCAB_CLASSES: list[type[Vocab]] = [SentencePieceVocab, BpeVocab, LlamaHfVocab] + + def __init__(self, path: Path): + self.path = path + + def _create_special_vocab(self, vocab: BaseVocab, model_parent_path: Path) -> gguf.SpecialVocab: + load_merges = vocab.name == "bpe" + n_vocab = vocab.vocab_size if isinstance(vocab, Vocab) else None + return gguf.SpecialVocab( + model_parent_path, + load_merges=load_merges, + special_token_types=None, # Predetermined or passed as a parameter + n_vocab=n_vocab, + ) + + def _create_vocab_by_path(self, vocab_types: list[str]) -> Vocab: + vocab_classes: dict[str, type[Vocab]] = {cls.name: cls for cls in self._VOCAB_CLASSES} + selected_vocabs: dict[str, type[Vocab]] = {} + for vtype in vocab_types: + try: + selected_vocabs[vtype] = vocab_classes[vtype] + except KeyError: + raise ValueError(f"Unsupported vocabulary type {vtype}") from None + + for vtype, cls in selected_vocabs.items(): + try: + vocab = cls(self.path) + break + except FileNotFoundError: + pass # ignore unavailable tokenizers + else: + raise FileNotFoundError(f"Could not find a tokenizer matching any of {vocab_types}") + + logger.info(f"Loaded vocab file {vocab.fname_tokenizer!r}, type {vocab.name!r}") + return vocab + + def load_vocab(self, vocab_types: list[str] | None, model_parent_path: Path) -> tuple[BaseVocab, gguf.SpecialVocab]: + vocab: BaseVocab + if vocab_types is None: + vocab = NoVocab() + else: + vocab = self._create_vocab_by_path(vocab_types) + # FIXME: Respect --vocab-dir? + special_vocab = self._create_special_vocab( + vocab, + model_parent_path, + ) + return vocab, special_vocab + + +def default_convention_outfile(file_type: GGMLFileType, expert_count: int | None, model_params_count: tuple[int, int, int], metadata: gguf.Metadata) -> str: + name = metadata.name if metadata.name is not None else None + basename = metadata.basename if metadata.basename is not None else None + finetune = metadata.finetune if metadata.finetune is not None else None + version = metadata.version if metadata.version is not None else None + size_label = metadata.size_label if metadata.size_label is not None else gguf.size_label(*model_params_count, expert_count=expert_count or 0) + + output_type = { + GGMLFileType.AllF32: "F32", + GGMLFileType.MostlyF16: "F16", + GGMLFileType.MostlyQ8_0: "Q8_0", + }[file_type] + + return gguf.naming_convention(name, basename, finetune, version, size_label, output_type) + + +def default_outfile(model_paths: list[Path], file_type: GGMLFileType, expert_count: int | None, model_params_count: tuple[int, int, int], metadata: gguf.Metadata) -> Path: + default_filename = default_convention_outfile(file_type, expert_count, model_params_count, metadata) + ret = model_paths[0].parent / f"{default_filename}.gguf" + if ret in model_paths: + logger.error( + f"Error: Default output path ({ret}) would overwrite the input. " + "Please explicitly specify a path using --outfile.") + sys.exit(1) + return ret + + +def do_dump_model(model_plus: ModelPlus) -> None: + print(f"model_plus.paths = {model_plus.paths!r}") # noqa: NP100 + print(f"model_plus.format = {model_plus.format!r}") # noqa: NP100 + print(f"model_plus.vocab = {model_plus.vocab!r}") # noqa: NP100 + for name, lazy_tensor in model_plus.model.items(): + print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") # noqa: NP100 + + +def main(args_in: list[str] | None = None) -> None: + output_choices = ["f32", "f16"] + if np.uint32(1) == np.uint32(1).newbyteorder("<"): + # We currently only support Q8_0 output on little endian systems. + output_choices.append("q8_0") + parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file") + parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") + parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") + parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") + parser.add_argument("--no-vocab", action="store_true", help="store model without the vocab") + parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") + parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") + parser.add_argument("--vocab-type", help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft") + parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") + parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") + parser.add_argument("--ctx", type=int, help="model training context (default: based on input)") + parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default=DEFAULT_CONCURRENCY) + parser.add_argument("--big-endian", action="store_true", help="model is executed on big endian machine") + parser.add_argument("--pad-vocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides") + parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + parser.add_argument("--metadata", type=Path, help="Specify the path for an authorship metadata override file") + parser.add_argument("--get-outfile", action="store_true", help="get calculated default outfile name") + parser.add_argument("--model-name", type=str, default=None, help="name of the model") + + args = parser.parse_args(args_in) + + if args.verbose: + logging.basicConfig(level=logging.DEBUG) + elif args.dump_single or args.dump or args.get_outfile: + # Avoid printing anything besides the dump output + logging.basicConfig(level=logging.WARNING) + else: + logging.basicConfig(level=logging.INFO) + + model_name = args.model_name + dir_model = args.model + + metadata = gguf.Metadata.load(args.metadata, dir_model, model_name) + + if args.get_outfile: + model_plus = load_some_model(dir_model) + params = Params.load(model_plus) + model = convert_model_names(model_plus.model, params, args.skip_unknown) + model_params_count = per_model_weight_count_estimation(model_plus.model.items()) + ftype = pick_output_type(model, args.outtype) + + if (metadata is None or metadata.name is None) and params.path_model is not None: + metadata.name = params.path_model.name + + print(f"{default_convention_outfile(ftype, params.n_experts, model_params_count, metadata)}") # noqa: NP100 + return + + if args.no_vocab and args.vocab_only: + raise ValueError("--vocab-only does not make sense with --no-vocab") + + if args.dump_single: + model_plus = lazy_load_file(dir_model) + do_dump_model(model_plus) + return + + if not args.vocab_only: + model_plus = load_some_model(dir_model) + else: + model_plus = ModelPlus(model = {}, paths = [dir_model / 'dummy'], format = 'none', vocab = None) + + if args.dump: + do_dump_model(model_plus) + return + + endianess = gguf.GGUFEndian.LITTLE + if args.big_endian: + endianess = gguf.GGUFEndian.BIG + + params = None + if args.pad_vocab or not args.vocab_only: + params = Params.load(model_plus) + if params.n_ctx == -1: + if args.ctx is None: + msg = """\ + The model doesn't have a context size, and you didn't specify one with --ctx + Please specify one with --ctx: + - LLaMA v1: --ctx 2048 + - LLaMA v2: --ctx 4096""" + parser.error(textwrap.dedent(msg)) + params.n_ctx = args.ctx + + if args.outtype: + params.ftype = { + "f32": GGMLFileType.AllF32, + "f16": GGMLFileType.MostlyF16, + "q8_0": GGMLFileType.MostlyQ8_0, + }[args.outtype] + + logger.info(f"params = {params}") + + model_parent_path = model_plus.paths[0].parent + vocab_path = Path(args.vocab_dir or dir_model or model_parent_path) + vocab_factory = VocabFactory(vocab_path) + vocab_types = None if args.no_vocab else args.vocab_type.split(",") + vocab, special_vocab = vocab_factory.load_vocab(vocab_types, model_parent_path) + + if args.vocab_only: + assert isinstance(vocab, Vocab) + if not args.outfile: + raise ValueError("need --outfile if using --vocab-only") + outfile = args.outfile + if params is None: + params = Params( + n_vocab = vocab.vocab_size, + n_embd = 1, + n_layer = 1, + n_ctx = 1, + n_ff = 1, + n_head = 1, + n_head_kv = 1, + f_norm_eps = 1e-5, + ) + OutputFile.write_vocab_only(outfile, params, vocab, special_vocab, + endianess=endianess, pad_vocab=args.pad_vocab, metadata=metadata) + logger.info(f"Wrote {outfile}") + return + + if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab: + vocab = model_plus.vocab + + assert params is not None + + if metadata.name is None and params.path_model is not None: + metadata.name = params.path_model.name + + model_params_count = per_model_weight_count_estimation(model_plus.model.items()) + logger.info(f"model parameters count : {model_params_count} ({gguf.model_weight_count_rounded_notation(model_params_count[0])})") + + logger.info(f"Vocab info: {vocab}") + logger.info(f"Special vocab info: {special_vocab}") + model = model_plus.model + model = convert_model_names(model, params, args.skip_unknown) + ftype = pick_output_type(model, args.outtype) + model = convert_to_output_type(model, ftype) + outfile = args.outfile or default_outfile(model_plus.paths, ftype, params.n_experts, model_params_count, metadata=metadata) + + metadata.size_label = gguf.size_label(*model_params_count, expert_count=params.n_experts or 0) + + params.ftype = ftype + logger.info(f"Writing {outfile}, format {ftype}") + + OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, + concurrency=args.concurrency, endianess=endianess, pad_vocab=args.pad_vocab, metadata=metadata) + logger.info(f"Wrote {outfile}") + + +if __name__ == '__main__': + main() diff --git a/llama.cpp/examples/deprecation-warning/README.md b/llama.cpp/examples/deprecation-warning/README.md new file mode 100644 index 0000000000000000000000000000000000000000..cfa61721b0c5e8ecde94bdcb3fd12dab69dcae24 --- /dev/null +++ b/llama.cpp/examples/deprecation-warning/README.md @@ -0,0 +1,49 @@ +# Migration notice for binary filenames + +> [!IMPORTANT] +[2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggerganov/llama.cpp/pull/7809) + +This migration was important, but it is a breaking change that may not always be immediately obvious to users. + +Please update all scripts and workflows to use the new binary names. + +| Old Filename | New Filename | +| ---- | ---- | +| main | llama-cli | +| server | llama-server | +| llama-bench | llama-bench | +| embedding | llama-embedding | +| quantize | llama-quantize | +| tokenize | llama-tokenize | +| export-lora | llama-export-lora | +| libllava.a | libllava.a | +| baby-llama | llama-baby-llama | +| batched | llama-batched | +| batched-bench | llama-batched-bench | +| benchmark-matmult | llama-benchmark-matmult | +| convert-llama2c-to-ggml | llama-convert-llama2c-to-ggml | +| eval-callback | llama-eval-callback | +| gbnf-validator | llama-gbnf-validator | +| gguf | llama-gguf | +| gguf-split | llama-gguf-split | +| gritlm | llama-gritlm | +| imatrix | llama-imatrix | +| infill | llama-infill | +| llava-cli | llama-llava-cli | +| lookahead | llama-lookahead | +| lookup | llama-lookup | +| lookup-create | llama-lookup-create | +| lookup-merge | llama-lookup-merge | +| lookup-stats | llama-lookup-stats | +| parallel | llama-parallel | +| passkey | llama-passkey | +| perplexity | llama-perplexity | +| q8dot | llama-q8dot | +| quantize-stats | llama-quantize-stats | +| retrieval | llama-retrieval | +| save-load-state | llama-save-load-state | +| simple | llama-simple | +| speculative | llama-speculative | +| vdot | llama-vdot | +| tests/test-c.o | tests/test-c.o | + diff --git a/llama.cpp/examples/deprecation-warning/deprecation-warning.cpp b/llama.cpp/examples/deprecation-warning/deprecation-warning.cpp new file mode 100644 index 0000000000000000000000000000000000000000..3bfebf0d778e87a35466dac8ba58a207688cc488 --- /dev/null +++ b/llama.cpp/examples/deprecation-warning/deprecation-warning.cpp @@ -0,0 +1,35 @@ +// Warns users that this filename was deprecated, and provides a link for more information. + +#include +#include +#include + +// Main +int main(int argc, char** argv) { + std::string filename = "main"; + if (argc >= 1) { + filename = argv[0]; + } + + // Get only the program name from the full path + auto pos = filename.find_last_of("/\\"); + if (pos != std::string::npos) { + filename = filename.substr(pos+1); + } + + // Append "llama-" to the beginning of filename to get the replacemnt filename + auto replacement_filename = "llama-" + filename; + + // The exception is if the filename is "main", then our replacement filename is "llama-cli" + if (filename == "main") { + replacement_filename = "llama-cli"; + } + + fprintf(stdout, "\n"); + fprintf(stdout, "WARNING: The binary '%s' is deprecated.\n", filename.c_str()); + fprintf(stdout, " Please use '%s' instead.\n", replacement_filename.c_str()); + fprintf(stdout, " See https://github.com/ggerganov/llama.cpp/tree/master/examples/deprecation-warning/README.md for more information.\n"); + fprintf(stdout, "\n"); + + return EXIT_FAILURE; +} diff --git a/llama.cpp/examples/diffusion/CMakeLists.txt b/llama.cpp/examples/diffusion/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..e318a802563dae98ab099f111f45bedd48e5b118 --- /dev/null +++ b/llama.cpp/examples/diffusion/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-diffusion-cli) +add_executable(${TARGET} diffusion-cli.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/llama.cpp/examples/diffusion/README.md b/llama.cpp/examples/diffusion/README.md new file mode 100644 index 0000000000000000000000000000000000000000..04368abe1fd7197ab084ce8e6e63a6e5c531f1f3 --- /dev/null +++ b/llama.cpp/examples/diffusion/README.md @@ -0,0 +1,13 @@ +# Diffusion Text Generation + +This directory contains implementations for Diffusion LLMs (DLLMs) + +More Info: +- https://github.com/ggml-org/llama.cpp/pull/14644 +- https://github.com/ggml-org/llama.cpp/pull/14771 + + +Example of using Dream architechture: `llama-diffusion-cli -m dream7b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-eps 0.001 --diffusion-algorithm 3 --diffusion-steps 256 --diffusion-visual` + +Example of using LLaDA architechture: `llama-diffusion-cli -m llada-8b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-block-length 32 --diffusion-steps 256 --diffusion-visual` + diff --git a/llama.cpp/examples/diffusion/diffusion-cli.cpp b/llama.cpp/examples/diffusion/diffusion-cli.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e3217d050aedc3aa21822ce22e24829b75870d96 --- /dev/null +++ b/llama.cpp/examples/diffusion/diffusion-cli.cpp @@ -0,0 +1,683 @@ +#include "arg.h" +#include "chat.h" +#include "common.h" +#include "llama.h" +#include "log.h" + +#include + +#include +#include +#include +#include +#include +#include +#include + +enum diffusion_algorithm { ORIGIN = 0, ENTROPY_BASED = 1, MARGIN_BASED = 2, RANDOM = 3, CONFIDENCE_BASED = 4 }; + +// Unified transfer scheduling methods +enum transfer_schedule { + TIMESTEP_BASED = 0, // Dream-style: (1.0 - s/t) * remaining + BLOCK_BASED = 1, // LLaDA-style: process in blocks with get_num_transfer_tokens +}; + +typedef bool (*diffusion_step_callback_t)(int32_t step, + int32_t total_steps, + const llama_token * tokens, + int32_t n_tokens, + void * user_data); + +struct diffusion_params { + int32_t steps = 0; + float temperature = 0; + llama_token mask_token_id = LLAMA_TOKEN_NULL; + diffusion_step_callback_t step_callback = nullptr; + void * step_callback_user_data = nullptr; + int32_t seed = 0; + bool visual_mode = false; + bool shift_logits = false; // Shift logits by -1 after decode + + float top_p = 0.; + int32_t top_k = 0.; + + diffusion_algorithm algorithm = CONFIDENCE_BASED; + transfer_schedule schedule = TIMESTEP_BASED; + + float cfg_scale = 0.; // Config scale for classifier-free guidance + float eps = 0.; // Timestep scheduling + int32_t block_length = 0; // Block size (for block scheduling) + float alg_temp = 0; // algorithm temperature (0.0 = deterministic) + bool add_gumbel_noise = false; // Add gumbel noise to the logits if temp > 0.0 + + int32_t max_length = 0; // Maximum sequence length +}; + +struct callback_data { + diffusion_params * diff_params; + const llama_vocab * vocab; + int32_t n_input; +}; + +static float calculate_confidence(const llama_token_data_array & cur_p, + diffusion_algorithm algorithm, + std::mt19937 & rng) { + switch (algorithm) { + case CONFIDENCE_BASED: + return cur_p.data[cur_p.selected].p; // Selected token probability + + case ENTROPY_BASED: + { + float entropy = 0.0f; + const float epsilon = 1e-10f; + for (size_t i = 0; i < cur_p.size; i++) { + float prob = cur_p.data[i].p; + entropy += prob * logf(prob + epsilon); + } + return -entropy; // Higher entropy = lower confidence + } + + case MARGIN_BASED: + return (cur_p.size > 1) ? cur_p.data[0].p - cur_p.data[1].p : cur_p.data[0].p; + + case RANDOM: + { + std::uniform_real_distribution uniform(0.0f, 1.0f); + return uniform(rng); // Random confidence + } + + case ORIGIN: + return cur_p.data[cur_p.selected].p; + + default: + return 0.0f; + } +} + +// Unified transfer count calculation function +static int32_t calculate_transfer_count(int32_t step, + int32_t total_steps, + int32_t remaining_masked, + transfer_schedule schedule, + float eps, + const std::vector & num_transfer_tokens = {}) { + switch (schedule) { + case TIMESTEP_BASED: + { + float t = 1.0f - (float) step / total_steps * (1.0f - eps); + float s = 1.0f - (float) (step + 1) / total_steps * (1.0f - eps); + float p_transfer = (step < total_steps - 1) ? (1.0f - s / t) : 1.0f; + return (int32_t) (remaining_masked * p_transfer); + } + + case BLOCK_BASED: + if (!num_transfer_tokens.empty() && step < (int32_t) num_transfer_tokens.size()) { + return num_transfer_tokens[step]; + } + return remaining_masked / (total_steps - step); // Fallback + + default: + return remaining_masked / (total_steps - step); + } +} + +static bool diffusion_step_callback(int32_t step, + int32_t total_steps, + const llama_token * tokens, + int32_t n_tokens, + void * user_data) { + (void) user_data; + + callback_data * data = static_cast(user_data); + + auto print_progress_bar = [](int32_t step, int32_t total_steps) { + int progress_percent = (step * 100) / total_steps; + int progress_bars = (step * 50) / total_steps; + LOG_INF("\rdiffusion step: %d/%d [%s%s] %d%%", + step, + total_steps, + std::string(progress_bars, '=').c_str(), + std::string(50 - progress_bars, ' ').c_str(), + progress_percent); + }; + + if (data->diff_params->visual_mode) { + // Visual mode: clear + LOG_INF("\033[2J\033[H"); // Clear screen and move cursor to top-left + + print_progress_bar(step, total_steps); + + LOG_INF("\n"); + + std::string current_text = " "; + + for (int32_t i = data->n_input; i < n_tokens; i++) { + std::string token_str; + if (tokens[i] != llama_vocab_mask(data->vocab)) { + char piece[256]; + int n_chars = llama_token_to_piece(data->vocab, tokens[i], piece, sizeof(piece), 0, false); + if (n_chars > 0) { + piece[n_chars] = '\0'; + token_str = piece; + } + } else { + token_str = " "; + } + + current_text += token_str; + } + + LOG_INF("%s\n", current_text.c_str()); + } else { + print_progress_bar(step, total_steps); + } + + return true; +} + +static void add_gumbel_noise(float * logits, int32_t n_vocab, float temperature, std::mt19937 & rng) { + if (temperature == 0.0f) { + return; + } + + std::uniform_real_distribution uniform(0.0, 1.0); + for (int32_t i = 0; i < n_vocab; i++) { + double noise = uniform(rng); + // Prevent log(0) + noise = std::max(noise, 1e-20); + double gumbel_noise = std::pow(-std::log(noise), temperature); + logits[i] = std::exp(logits[i]) / gumbel_noise; + } +} + +static std::vector get_num_transfer_tokens(int32_t mask_count, int32_t steps) { + std::vector num_transfer_tokens(steps); + + int32_t base = mask_count / steps; + int32_t remainder = mask_count % steps; + + for (int32_t i = 0; i < steps; i++) { + num_transfer_tokens[i] = base + (i < remainder ? 1 : 0); + } + + return num_transfer_tokens; +} + +static void diffusion_generate(llama_context * ctx, + const llama_token * input_tokens, + llama_token * output_tokens, + int32_t n_input, + const diffusion_params & params, + int32_t & n_generated) { + n_generated = 0; + if (!ctx || !input_tokens || !output_tokens || n_input <= 0 || params.max_length <= n_input) { + return; + } + + const llama_model * model = llama_get_model(ctx); + + // Initialize with input and pad with mask tokens + std::copy(input_tokens, input_tokens + n_input, output_tokens); + std::fill(output_tokens + n_input, output_tokens + params.max_length, params.mask_token_id); + + std::mt19937 rng(params.seed); + + llama_set_causal_attn(ctx, false); + + int32_t n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model)); + + std::vector candidates(n_vocab); + std::vector conf_candidates; + conf_candidates.reserve(params.max_length); + std::vector mask_positions; + mask_positions.reserve(params.max_length); + + // Setup sampler chain + struct llama_sampler * sampler = llama_sampler_chain_init(llama_sampler_chain_default_params()); + if (params.top_k > 0) { + llama_sampler_chain_add(sampler, llama_sampler_init_top_k(params.top_k)); + } + if (params.top_p < 1.0f) { + llama_sampler_chain_add(sampler, llama_sampler_init_top_p(params.top_p, 1)); + } + if (params.temperature > 0.0f) { + llama_sampler_chain_add(sampler, llama_sampler_init_temp(params.temperature)); + } + llama_sampler_chain_add(sampler, llama_sampler_init_dist(params.seed)); + + struct llama_sampler * dist_sampler = llama_sampler_init_dist(params.seed); + + llama_batch batch = llama_batch_init(params.max_length, 0, 1); + batch.n_tokens = params.max_length; + + // Pre-allocate buffers for CFG if needed + int32_t logits_size = n_vocab * params.max_length; + std::vector cond_logits_buffer; + std::vector un_x_buffer; + if (params.cfg_scale > 0.0f) { + cond_logits_buffer.resize(logits_size); + un_x_buffer.resize(params.max_length); + } + + // For block-based processing + std::vector num_transfer_tokens; + int32_t num_blocks = 1; + int32_t steps_per_block = params.steps; + + if (params.schedule == BLOCK_BASED) { + GGML_ASSERT(params.max_length % params.block_length == 0); + num_blocks = params.max_length / params.block_length; + GGML_ASSERT(params.steps % num_blocks == 0); + steps_per_block = params.steps / num_blocks; + } + + std::vector confidence(params.max_length); + + int64_t total_sampling_time = 0; + int64_t total_time = 0; + int64_t time_start = ggml_time_us(); + + for (int block_num = 0; block_num < num_blocks; block_num++) { + int32_t block_start = (params.schedule == BLOCK_BASED) ? n_input + block_num * params.block_length : 0; + int32_t block_end = (params.schedule == BLOCK_BASED) ? + std::min(n_input + (block_num + 1) * params.block_length, params.max_length) : + params.max_length; + + // Count masked tokens in current block for block-based processing + if (params.schedule == BLOCK_BASED) { + int32_t block_mask_count = 0; + for (int i = block_start; i < block_end; i++) { + if (output_tokens[i] == params.mask_token_id) { + block_mask_count++; + } + } + num_transfer_tokens = get_num_transfer_tokens(block_mask_count, steps_per_block); + } + + for (int32_t step = 0; step < steps_per_block; step++) { + int32_t global_step = block_num * steps_per_block + step; + + if (params.step_callback) { + if (!params.step_callback( + global_step, params.steps, output_tokens, params.max_length, params.step_callback_user_data)) { + break; + } + } + + // Setup batch + for (int32_t i = 0; i < params.max_length; i++) { + batch.token[i] = output_tokens[i]; + batch.pos[i] = i; + batch.n_seq_id[i] = 1; + batch.seq_id[i][0] = 0; + batch.logits[i] = 1; + } + + float * logits = nullptr; + + if (params.cfg_scale > 0.0f) { + int ret = llama_decode(ctx, batch); + if (ret != 0) { + LOG_ERR("Failed to generate conditional"); + break; + } + float * cond_logits_ptr = llama_get_logits(ctx); + std::memcpy(cond_logits_buffer.data(), cond_logits_ptr, logits_size * sizeof(float)); + + // Unconditional generation (mask input) + std::copy(output_tokens, output_tokens + params.max_length, un_x_buffer.begin()); + for (int32_t i = 0; i < n_input; i++) { + un_x_buffer[i] = params.mask_token_id; + } + + for (int32_t i = 0; i < params.max_length; i++) { + batch.token[i] = un_x_buffer[i]; + } + ret = llama_decode(ctx, batch); + if (ret != 0) { + LOG_ERR("Failed to generate unconditional"); + break; + } + float * uncond_logits = llama_get_logits(ctx); + + // Apply CFG + for (int32_t i = 0; i < logits_size; i++) { + cond_logits_buffer[i] = + uncond_logits[i] + (params.cfg_scale + 1.0f) * (cond_logits_buffer[i] - uncond_logits[i]); + } + logits = cond_logits_buffer.data(); + } else { + int ret = llama_decode(ctx, batch); + if (ret != 0) { + LOG_ERR("%s: failed to decode at step %d, ret = %d\n", __func__, global_step, ret); + break; + } + logits = llama_get_logits(ctx); + } + + if (!logits) { + LOG_ERR("%s: failed to get logits at step %d\n", __func__, global_step); + break; + } + + auto get_logits_for_pos = [&](int32_t pos) -> const float * { + if (params.shift_logits) { + return pos == 0 ? logits : logits + (pos - 1) * n_vocab; + } + return logits + (pos) *n_vocab; + }; + + int64_t time_start_sampling = ggml_time_us(); + + mask_positions.clear(); + for (int32_t i = 0; i < params.max_length; i++) { + if (output_tokens[i] == params.mask_token_id) { + // For block-based, only consider current block + if (params.schedule != BLOCK_BASED || (i >= block_start && i < block_end)) { + mask_positions.push_back(i); + } + } + } + + if (mask_positions.empty()) { + break; + } + + if (params.add_gumbel_noise && params.temperature > 0.0f) { + add_gumbel_noise(logits, n_vocab, params.temperature, rng); + } + + if (params.algorithm == ORIGIN) { + int32_t transfer_count = calculate_transfer_count( + step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens); + float p_transfer = (float) transfer_count / mask_positions.size(); + + for (int32_t pos : mask_positions) { + if (std::uniform_real_distribution(0.0f, 1.0f)(rng) < p_transfer) { + const float * pos_logits = get_logits_for_pos(pos); + for (int32_t token_id = 0; token_id < n_vocab; token_id++) { + candidates[token_id].id = token_id; + candidates[token_id].logit = pos_logits[token_id]; + candidates[token_id].p = 0.0f; + } + + llama_token_data_array cur_p = { + candidates.data(), + (size_t) n_vocab, + -1, + false, + }; + + llama_sampler_apply(sampler, &cur_p); + output_tokens[pos] = cur_p.data[cur_p.selected].id; + } + } + } else { + std::vector> confidences; + std::vector sampled_tokens(mask_positions.size()); + + for (size_t i = 0; i < mask_positions.size(); i++) { + int32_t pos = mask_positions[i]; + const float * pos_logits = get_logits_for_pos(pos); + + for (int32_t token_id = 0; token_id < n_vocab; token_id++) { + candidates[token_id].logit = pos_logits[token_id]; + candidates[token_id].p = 0.0f; + candidates[token_id].id = token_id; + } + + llama_token_data_array cur_p = { + candidates.data(), + candidates.size(), + -1, + false, + }; + + llama_sampler_apply(sampler, &cur_p); + llama_token sampled_token = cur_p.data[cur_p.selected].id; + + float conf = calculate_confidence(cur_p, params.algorithm, rng); + + sampled_tokens[i] = sampled_token; + confidences.emplace_back(conf, i); + } + + int32_t transfer_count = calculate_transfer_count( + step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens); + + if (transfer_count > 0) { + if (params.alg_temp == 0.0f) { + std::partial_sort(confidences.begin(), + confidences.begin() + std::min(transfer_count, (int32_t) confidences.size()), + confidences.end(), + [](const std::pair & a, const std::pair & b) { + if (a.first != b.first) { + return a.first > b.first; + } + return a.second < b.second; + }); + + for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) { + int32_t mask_idx = confidences[i].second; + int32_t pos = mask_positions[mask_idx]; + output_tokens[pos] = sampled_tokens[mask_idx]; + } + } else { + conf_candidates.clear(); + for (size_t i = 0; i < confidences.size(); i++) { + float conf_logit = confidences[i].first / params.alg_temp; + conf_candidates.emplace_back(llama_token_data{ (int32_t) i, conf_logit, 0.0f }); + } + + llama_token_data_array conf_array = { + conf_candidates.data(), + conf_candidates.size(), + -1, + false, + }; + + for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) { + llama_sampler_apply(dist_sampler, &conf_array); + int32_t selected_idx = conf_array.selected; + int32_t mask_idx = selected_idx; + int32_t pos = mask_positions[mask_idx]; + output_tokens[pos] = sampled_tokens[mask_idx]; + + conf_candidates[selected_idx].p = 0.0f; + conf_array.selected = -1; + } + } + } + } + + int64_t time_end_sampling = ggml_time_us(); + total_sampling_time += time_end_sampling - time_start_sampling; + } + } + + int64_t time_end = ggml_time_us(); + total_time += time_end - time_start; + + LOG_INF("\ntotal time: %0.2fms, time per step: %0.2fms, sampling time per step: %0.2fms\n", + total_time / 1000.0, + total_time / 1000.0 / params.steps, + total_sampling_time / 1000.0 / params.steps); + + llama_batch_free(batch); + llama_sampler_free(sampler); + llama_sampler_free(dist_sampler); + + n_generated = params.max_length; +} + +static std::string format_input_text(const std::string & prompt, bool use_chat_template, llama_model * model) { + if (!use_chat_template) { + return prompt; + } + + auto chat_templates = common_chat_templates_init(model, ""); + + common_chat_templates_inputs inputs; + common_chat_msg user_msg; + user_msg.role = "user"; + user_msg.content = prompt; + inputs.add_generation_prompt = true; + inputs.messages.push_back(user_msg); + + auto result = common_chat_templates_apply(chat_templates.get(), inputs); + + return result.prompt; +} + +int main(int argc, char ** argv) { + ggml_time_init(); + + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_DIFFUSION)) { + return 1; + } + + common_init(); + llama_backend_init(); + + llama_model_params model_params = llama_model_default_params(); + model_params.n_gpu_layers = params.n_gpu_layers; + model_params.devices = params.devices.data(); + model_params.use_mmap = params.use_mmap; + model_params.use_mlock = params.use_mlock; + model_params.check_tensors = params.check_tensors; + + llama_model * model = llama_model_load_from_file(params.model.path.c_str(), model_params); + if (!model) { + LOG_ERR("error: failed to load model '%s'\n", params.model.path.c_str()); + return 1; + } + + if (!llama_model_is_diffusion(model)) { + LOG_ERR("error: unsupported model for diffusion"); + llama_model_free(model); + return 1; + } + + llama_context_params ctx_params = llama_context_default_params(); + ctx_params.n_ctx = params.n_ctx; + ctx_params.n_batch = params.n_batch; + ctx_params.n_ubatch = params.n_ubatch; + ctx_params.flash_attn = params.flash_attn; + ctx_params.no_perf = params.no_perf; + ctx_params.type_k = params.cache_type_k; + ctx_params.type_v = params.cache_type_v; + + llama_context * ctx = llama_init_from_model(model, ctx_params); + if (!ctx) { + LOG_ERR("error: failed to create context\n"); + llama_model_free(model); + return 1; + } + + llama_set_n_threads(ctx, params.cpuparams.n_threads, params.cpuparams_batch.n_threads); + + const llama_vocab * vocab = llama_model_get_vocab(model); + std::string formatted_prompt = format_input_text(params.prompt, params.enable_chat_template, model); + + std::vector input_tokens = common_tokenize(vocab, + formatted_prompt, + /*add special tokens*/ true, + /*parse special*/ true); + + int n_input = input_tokens.size(); + + if (n_input >= params.n_ctx) { + LOG_ERR("error: input too long (%d tokens), max context is %d\n", n_input, params.n_ctx); + llama_free(ctx); + llama_model_free(model); + return 1; + } + + llama_token mask_token_id = llama_vocab_mask(vocab); + GGML_ASSERT(mask_token_id != LLAMA_TOKEN_NULL); + + bool visual_mode = params.diffusion.visual_mode; + + int32_t n_generated = 0; + std::vector output_tokens(params.n_ubatch); + + struct diffusion_params diff_params; + + char shift_logits_str[8]; + if (llama_model_meta_val_str(model, "diffusion.shift_logits", shift_logits_str, sizeof(shift_logits_str)) >= 0) { + diff_params.shift_logits = (strcmp(shift_logits_str, "true") == 0); + } else { + diff_params.shift_logits = true; + } + + //Use either eps or block length, but not both + GGML_ASSERT((params.diffusion.eps == 0) ^ (params.diffusion.block_length == 0)); + + if (params.diffusion.eps) { + diff_params.schedule = TIMESTEP_BASED; + diff_params.eps = params.diffusion.eps; + } else if (params.diffusion.block_length) { + diff_params.schedule = BLOCK_BASED; + diff_params.block_length = params.diffusion.block_length; + } + + diff_params.mask_token_id = mask_token_id; + diff_params.seed = params.sampling.seed; + diff_params.temperature = params.sampling.temp; + diff_params.steps = params.diffusion.steps; + diff_params.algorithm = static_cast(params.diffusion.algorithm); + diff_params.max_length = params.n_ubatch; + diff_params.top_p = params.sampling.top_p; + diff_params.top_k = params.sampling.top_k; + diff_params.visual_mode = params.diffusion.visual_mode; + diff_params.add_gumbel_noise = params.diffusion.add_gumbel_noise; + + diff_params.step_callback = diffusion_step_callback; + callback_data cb_data = { &diff_params, vocab, n_input }; + diff_params.step_callback_user_data = &cb_data; + + const char * alg_names[] = { "ORIGIN", "ENTROPY_BASED", "MARGIN_BASED", "RANDOM", "CONFIDENCE_BASED" }; + const char * sched_names[] = { "TIMESTEP_BASED", "BLOCK_BASED" }; + const char * alg_name = + (diff_params.algorithm >= 0 && diff_params.algorithm <= 4) ? alg_names[diff_params.algorithm] : "UNKNOWN"; + const char * sched_name = + (diff_params.schedule >= 0 && diff_params.schedule <= 1) ? sched_names[diff_params.schedule] : "UNKNOWN"; + + LOG_INF("diffusion_params: - %-25s llama_token = %d\n", "mask_token_id", mask_token_id); + LOG_INF("diffusion_params: - %-25s u32 = %d\n", "steps", diff_params.steps); + LOG_INF("diffusion_params: - %-25s u32 = %d\n", "max_length", diff_params.max_length); + LOG_INF("diffusion_params: - %-25s enum = %d (%s)\n", "algorithm", diff_params.algorithm, alg_name); + LOG_INF("diffusion_params: - %-25s enum = %d (%s)\n", "schedule", diff_params.schedule, sched_name); + LOG_INF("diffusion_params: - %-25s f32 = %.3f\n", "temperature", diff_params.temperature); + if (diff_params.schedule == TIMESTEP_BASED) { + LOG_INF("diffusion_params: - %-25s f32 = %.6f\n", "eps", diff_params.eps); + LOG_INF("diffusion_params: - %-25s f32 = %.3f\n", "alg_temp", diff_params.alg_temp); + } + if (diff_params.schedule == BLOCK_BASED) { + LOG_INF("diffusion_params: - %-25s u32 = %d\n", "block_length", diff_params.block_length); + LOG_INF("diffusion_params: - %-25s f32 = %.3f\n", "cfg_scale", diff_params.cfg_scale); + } + + diffusion_generate(ctx, input_tokens.data(), output_tokens.data(), n_input, diff_params, n_generated); + + if (n_generated > 0) { + if (visual_mode) { + //clear screen and move cursor to top-left + LOG_INF("\033[2J\033[H"); + } + + output_tokens.erase(output_tokens.begin(), output_tokens.begin() + n_input); + std::string output_data = common_detokenize(vocab, output_tokens, false); + LOG_INF("\n%s\n", output_data.c_str()); + } else { + LOG_INF("Error: diffusion generation failed\n"); + } + + llama_free(ctx); + llama_model_free(model); + llama_backend_free(); + + return 0; +} diff --git a/llama.cpp/examples/embedding/CMakeLists.txt b/llama.cpp/examples/embedding/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..79a943ac97e810ddbf93ba02c8ce9dbde8063a9e --- /dev/null +++ b/llama.cpp/examples/embedding/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-embedding) +add_executable(${TARGET} embedding.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/llama.cpp/examples/embedding/README.md b/llama.cpp/examples/embedding/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c1d08dd67d34cd4412593b75d457ac46ea8ed4fb --- /dev/null +++ b/llama.cpp/examples/embedding/README.md @@ -0,0 +1,60 @@ +# llama.cpp/example/embedding + +This example demonstrates generate high-dimensional embedding vector of a given text with llama.cpp. + +## Quick Start + +To get started right away, run the following command, making sure to use the correct path for the model you have: + +### Unix-based systems (Linux, macOS, etc.): + +```bash +./llama-embedding -m ./path/to/model --pooling mean --log-disable -p "Hello World!" 2>/dev/null +``` + +### Windows: + +```powershell +llama-embedding.exe -m ./path/to/model --pooling mean --log-disable -p "Hello World!" 2>$null +``` + +The above command will output space-separated float values. + +## extra parameters +### --embd-normalize $integer$ +| $integer$ | description | formula | +|-----------|---------------------|---------| +| $-1$ | none | +| $0$ | max absolute int16 | $\Large{{32760 * x_i} \over\max \lvert x_i\rvert}$ +| $1$ | taxicab | $\Large{x_i \over\sum \lvert x_i\rvert}$ +| $2$ | euclidean (default) | $\Large{x_i \over\sqrt{\sum x_i^2}}$ +| $>2$ | p-norm | $\Large{x_i \over\sqrt[p]{\sum \lvert x_i\rvert^p}}$ + +### --embd-output-format $'string'$ +| $'string'$ | description | | +|------------|------------------------------|--| +| '' | same as before | (default) +| 'array' | single embeddings | $[[x_1,...,x_n]]$ +| | multiple embeddings | $[[x_1,...,x_n],[x_1,...,x_n],...,[x_1,...,x_n]]$ +| 'json' | openai style | +| 'json+' | add cosine similarity matrix | + +### --embd-separator $"string"$ +| $"string"$ | | +|--------------|-| +| "\n" | (default) +| "<#embSep#>" | for exemple +| "<#sep#>" | other exemple + +## examples +### Unix-based systems (Linux, macOS, etc.): + +```bash +./llama-embedding -p 'Castle<#sep#>Stronghold<#sep#>Dog<#sep#>Cat' --pooling mean --embd-separator '<#sep#>' --embd-normalize 2 --embd-output-format '' -m './path/to/model.gguf' --n-gpu-layers 99 --log-disable 2>/dev/null +``` + +### Windows: + +```powershell +llama-embedding.exe -p 'Castle<#sep#>Stronghold<#sep#>Dog<#sep#>Cat' --pooling mean --embd-separator '<#sep#>' --embd-normalize 2 --embd-output-format '' -m './path/to/model.gguf' --n-gpu-layers 99 --log-disable 2>/dev/null +``` diff --git a/llama.cpp/examples/embedding/embedding.cpp b/llama.cpp/examples/embedding/embedding.cpp new file mode 100644 index 0000000000000000000000000000000000000000..39cfe715ffc7ee1a82c7b16d538bb04c127b68a3 --- /dev/null +++ b/llama.cpp/examples/embedding/embedding.cpp @@ -0,0 +1,372 @@ +#include "arg.h" +#include "common.h" +#include "log.h" +#include "llama.h" + +#include +#include + +#if defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + +static std::vector split_lines(const std::string & s, const std::string & separator = "\n") { + std::vector lines; + size_t start = 0; + size_t end = s.find(separator); + + while (end != std::string::npos) { + lines.push_back(s.substr(start, end - start)); + start = end + separator.length(); + end = s.find(separator, start); + } + + lines.push_back(s.substr(start)); // Add the last part + + return lines; +} + +static void batch_add_seq(llama_batch & batch, const std::vector & tokens, llama_seq_id seq_id) { + size_t n_tokens = tokens.size(); + for (size_t i = 0; i < n_tokens; i++) { + common_batch_add(batch, tokens[i], i, { seq_id }, true); + } +} + +static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) { + const enum llama_pooling_type pooling_type = llama_pooling_type(ctx); + + // clear previous kv_cache values (irrelevant for embeddings) + llama_memory_clear(llama_get_memory(ctx), true); + + // run model + LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq); + if (llama_decode(ctx, batch) < 0) { + LOG_ERR("%s : failed to process\n", __func__); + } + + for (int i = 0; i < batch.n_tokens; i++) { + if (!batch.logits[i]) { + continue; + } + + const float * embd = nullptr; + int embd_pos = 0; + + if (pooling_type == LLAMA_POOLING_TYPE_NONE) { + // try to get token embeddings + embd = llama_get_embeddings_ith(ctx, i); + embd_pos = i; + GGML_ASSERT(embd != NULL && "failed to get token embeddings"); + } else { + // try to get sequence embeddings - supported only when pooling_type is not NONE + embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); + embd_pos = batch.seq_id[i][0]; + GGML_ASSERT(embd != NULL && "failed to get sequence embeddings"); + } + + float * out = output + embd_pos * n_embd; + common_embd_normalize(embd, out, n_embd, embd_norm); + } +} + +int main(int argc, char ** argv) { + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EMBEDDING)) { + return 1; + } + + common_init(); + + params.embedding = true; + + // if the number of prompts that would be encoded is known in advance, it's more efficient to specify the + // --parallel argument accordingly. for convenience, if not specified, we fallback to unified KV cache + // in order to support any number of prompts + if (params.n_parallel == 1) { + LOG_INF("%s: n_parallel == 1 -> unified KV cache is enabled\n", __func__); + params.kv_unified = true; + } + + // utilize the full context + if (params.n_batch < params.n_ctx) { + LOG_WRN("%s: setting batch size to %d\n", __func__, params.n_ctx); + params.n_batch = params.n_ctx; + } + + // For non-causal models, batch size must be equal to ubatch size + params.n_ubatch = params.n_batch; + + llama_backend_init(); + llama_numa_init(params.numa); + + // load the model + common_init_result llama_init = common_init_from_params(params); + + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + + if (model == NULL) { + LOG_ERR("%s: unable to load model\n", __func__); + return 1; + } + + const llama_vocab * vocab = llama_model_get_vocab(model); + + const int n_ctx_train = llama_model_n_ctx_train(model); + const int n_ctx = llama_n_ctx(ctx); + + const enum llama_pooling_type pooling_type = llama_pooling_type(ctx); + + if (llama_model_has_encoder(model) && llama_model_has_decoder(model)) { + LOG_ERR("%s: computing embeddings in encoder-decoder models is not supported\n", __func__); + return 1; + } + + if (n_ctx > n_ctx_train) { + LOG_WRN("%s: warning: model was trained on only %d context tokens (%d specified)\n", + __func__, n_ctx_train, n_ctx); + } + + // print system information + { + LOG_INF("\n"); + LOG_INF("%s\n", common_params_get_system_info(params).c_str()); + } + + // split the prompt into lines + std::vector prompts = split_lines(params.prompt, params.embd_sep); + + // max batch size + const uint64_t n_batch = params.n_batch; + + // get added sep and eos token, if any + const std::string added_sep_token = llama_vocab_get_add_sep(vocab) ? llama_vocab_get_text(vocab, llama_vocab_sep(vocab)) : ""; + const std::string added_eos_token = llama_vocab_get_add_eos(vocab) ? llama_vocab_get_text(vocab, llama_vocab_eos(vocab)) : ""; + + // tokenize the prompts and trim + std::vector> inputs; + for (const auto & prompt : prompts) { + std::vector inp; + + // split classification pairs and insert expected separator tokens + if (pooling_type == LLAMA_POOLING_TYPE_RANK && prompt.find(params.cls_sep) != std::string::npos) { + std::vector pairs = split_lines(prompt, params.cls_sep); + std::string final_prompt; + + for (size_t i = 0; i < pairs.size(); i++) { + final_prompt += pairs[i]; + if (i != pairs.size() - 1) { + if (!added_eos_token.empty()) { + final_prompt += added_eos_token; + } + if (!added_sep_token.empty()) { + final_prompt += added_sep_token; + } + } + } + + inp = common_tokenize(ctx, final_prompt, true, true); + } else { + inp = common_tokenize(ctx, prompt, true, true); + } + if (inp.size() > n_batch) { + LOG_ERR("%s: number of tokens in input line (%lld) exceeds batch size (%lld), increase batch size and re-run\n", + __func__, (long long int) inp.size(), (long long int) n_batch); + return 1; + } + inputs.push_back(inp); + } + + // check if the last token is SEP/EOS + // it should be automatically added by the tokenizer when 'tokenizer.ggml.add_eos_token' is set to 'true' + for (auto & inp : inputs) { + if (inp.empty() || (inp.back() != llama_vocab_sep(vocab) && inp.back() != llama_vocab_eos(vocab))) { + LOG_WRN("%s: last token in the prompt is not SEP or EOS\n", __func__); + LOG_WRN("%s: 'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__); + } + } + + // tokenization stats + if (params.verbose_prompt) { + for (int i = 0; i < (int) inputs.size(); i++) { + LOG_INF("%s: prompt %d: '%s'\n", __func__, i, prompts[i].c_str()); + LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, inputs[i].size()); + for (int j = 0; j < (int) inputs[i].size(); j++) { + LOG("%6d -> '%s'\n", inputs[i][j], common_token_to_piece(ctx, inputs[i][j]).c_str()); + } + LOG("\n\n"); + } + } + + // initialize batch + const int n_prompts = prompts.size(); + struct llama_batch batch = llama_batch_init(n_batch, 0, 1); + + // count number of embeddings + int n_embd_count = 0; + if (pooling_type == LLAMA_POOLING_TYPE_NONE) { + for (int k = 0; k < n_prompts; k++) { + n_embd_count += inputs[k].size(); + } + } else { + n_embd_count = n_prompts; + } + + // allocate output + const int n_embd = llama_model_n_embd(model); + std::vector embeddings(n_embd_count * n_embd, 0); + float * emb = embeddings.data(); + + // break into batches + int e = 0; // number of embeddings already stored + int s = 0; // number of prompts in current batch + for (int k = 0; k < n_prompts; k++) { + // clamp to n_batch tokens + auto & inp = inputs[k]; + + const uint64_t n_toks = inp.size(); + + // encode if at capacity + if (batch.n_tokens + n_toks > n_batch) { + float * out = emb + e * n_embd; + batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize); + e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s; + s = 0; + common_batch_clear(batch); + } + + // add to batch + batch_add_seq(batch, inp, s); + s += 1; + } + + // final batch + float * out = emb + e * n_embd; + batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize); + + if (params.embd_out.empty()) { + LOG("\n"); + + if (pooling_type == LLAMA_POOLING_TYPE_NONE) { + for (int j = 0; j < n_embd_count; j++) { + LOG("embedding %d: ", j); + for (int i = 0; i < std::min(3, n_embd); i++) { + if (params.embd_normalize == 0) { + LOG("%6.0f ", emb[j * n_embd + i]); + } else { + LOG("%9.6f ", emb[j * n_embd + i]); + } + } + LOG(" ... "); + for (int i = n_embd - 3; i < n_embd; i++) { + if (params.embd_normalize == 0) { + LOG("%6.0f ", emb[j * n_embd + i]); + } else { + LOG("%9.6f ", emb[j * n_embd + i]); + } + } + LOG("\n"); + } + } else if (pooling_type == LLAMA_POOLING_TYPE_RANK) { + const uint32_t n_cls_out = llama_model_n_cls_out(model); + std::vector cls_out_labels; + + for (uint32_t i = 0; i < n_cls_out; i++) { + const char * label = llama_model_cls_label(model, i); + const std::string label_i(label == nullptr ? "" : label); + cls_out_labels.emplace_back(label_i.empty() ? std::to_string(i) : label_i); + } + + for (int j = 0; j < n_embd_count; j++) { + for (uint32_t i = 0; i < n_cls_out; i++) { + // NOTE: if you change this log - update the tests in ci/run.sh + if (n_cls_out == 1) { + LOG("rerank score %d: %8.3f\n", j, emb[j * n_embd]); + } else { + LOG("rerank score %d: %8.3f [%s]\n", j, emb[j * n_embd + i], cls_out_labels[i].c_str()); + } + } + } + } else { + // print the first part of the embeddings or for a single prompt, the full embedding + for (int j = 0; j < n_prompts; j++) { + LOG("embedding %d: ", j); + for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) { + if (params.embd_normalize == 0) { + LOG("%6.0f ", emb[j * n_embd + i]); + } else { + LOG("%9.6f ", emb[j * n_embd + i]); + } + } + LOG("\n"); + } + + // print cosine similarity matrix + if (n_prompts > 1) { + LOG("\n"); + LOG("cosine similarity matrix:\n\n"); + for (int i = 0; i < n_prompts; i++) { + LOG("%6.6s ", prompts[i].c_str()); + } + LOG("\n"); + for (int i = 0; i < n_prompts; i++) { + for (int j = 0; j < n_prompts; j++) { + float sim = common_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd); + LOG("%6.2f ", sim); + } + LOG("%1.10s", prompts[i].c_str()); + LOG("\n"); + } + } + } + } + + if (params.embd_out == "json" || params.embd_out == "json+" || params.embd_out == "array") { + const bool notArray = params.embd_out != "array"; + + LOG(notArray ? "{\n \"object\": \"list\",\n \"data\": [\n" : "["); + for (int j = 0;;) { // at least one iteration (one prompt) + if (notArray) LOG(" {\n \"object\": \"embedding\",\n \"index\": %d,\n \"embedding\": ",j); + LOG("["); + for (int i = 0;;) { // at least one iteration (n_embd > 0) + LOG(params.embd_normalize == 0 ? "%1.0f" : "%1.7f", emb[j * n_embd + i]); + i++; + if (i < n_embd) LOG(","); else break; + } + LOG(notArray ? "]\n }" : "]"); + j++; + if (j < n_embd_count) LOG(notArray ? ",\n" : ","); else break; + } + LOG(notArray ? "\n ]" : "]\n"); + + if (params.embd_out == "json+" && n_prompts > 1) { + LOG(",\n \"cosineSimilarity\": [\n"); + for (int i = 0;;) { // at least two iteration (n_embd_count > 1) + LOG(" ["); + for (int j = 0;;) { // at least two iteration (n_embd_count > 1) + float sim = common_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd); + LOG("%6.2f", sim); + j++; + if (j < n_embd_count) LOG(", "); else break; + } + LOG(" ]"); + i++; + if (i < n_embd_count) LOG(",\n"); else break; + } + LOG("\n ]"); + } + + if (notArray) LOG("\n}\n"); + } + + LOG("\n"); + llama_perf_context_print(ctx); + + // clean up + llama_batch_free(batch); + llama_backend_free(); + + return 0; +} diff --git a/llama.cpp/examples/eval-callback/CMakeLists.txt b/llama.cpp/examples/eval-callback/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..2368d43090930865ecc117ff66357b5363cac063 --- /dev/null +++ b/llama.cpp/examples/eval-callback/CMakeLists.txt @@ -0,0 +1,10 @@ +set(TARGET llama-eval-callback) +add_executable(${TARGET} eval-callback.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) + +set(TEST_TARGET test-eval-callback) +add_test(NAME ${TEST_TARGET} + COMMAND llama-eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0) +set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl) diff --git a/llama.cpp/examples/eval-callback/README.md b/llama.cpp/examples/eval-callback/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c7e6d8f17cc78d8d5124b7ef928d400e0297d817 --- /dev/null +++ b/llama.cpp/examples/eval-callback/README.md @@ -0,0 +1,95 @@ +# llama.cpp/examples/eval-callback + +A simple example which demonstrates how to use callback during the inference. +It simply prints to the console all operations and tensor data. + +Usage: + +```shell +llama-eval-callback \ + --hf-repo ggml-org/models \ + --hf-file phi-2/ggml-model-q4_0.gguf \ + --model phi-2-q4_0.gguf \ + --prompt hello \ + --seed 42 \ + -ngl 33 +``` + +Will print: + +```shell +llm_load_tensors: offloaded 33/33 layers to GPU +... +llama_new_context_with_model: n_ctx = 512 +... +llama_new_context_with_model: CUDA0 compute buffer size = 105.00 MiB +llama_new_context_with_model: CUDA_Host compute buffer size = 6.01 MiB +llama_new_context_with_model: graph nodes = 1225 +llama_new_context_with_model: graph splits = 2 +ggml_debug: inp_embd = (f32) GET_ROWS(token_embd.weight{2560, 51200, 1, 1}, inp_tokens{1, 1, 1, 1}}) = {2560, 1, 1, 1} + [ + [ + [ -0.0181, 0.0272, 0.0272, ...], + ], + ] +ggml_debug: norm-0 = (f32) NORM(CUDA0#inp_embd#0{2560, 1, 1, 1}, }) = {2560, 1, 1, 1} + [ + [ + [ -0.6989, 1.0636, 1.0636, ...], + ], + ] +ggml_debug: norm_w-0 = (f32) MUL(norm-0{2560, 1, 1, 1}, blk.0.attn_norm.weight{2560, 1, 1, 1}}) = {2560, 1, 1, 1} + [ + [ + [ -0.1800, 0.2817, 0.2632, ...], + ], + ] +ggml_debug: attn_norm-0 = (f32) ADD(norm_w-0{2560, 1, 1, 1}, blk.0.attn_norm.bias{2560, 1, 1, 1}}) = {2560, 1, 1, 1} + [ + [ + [ -0.1863, 0.2970, 0.2604, ...], + ], + ] +ggml_debug: wqkv-0 = (f32) MUL_MAT(blk.0.attn_qkv.weight{2560, 7680, 1, 1}, attn_norm-0{2560, 1, 1, 1}}) = {7680, 1, 1, 1} + [ + [ + [ -1.1238, 1.2876, -1.8086, ...], + ], + ] +ggml_debug: bqkv-0 = (f32) ADD(wqkv-0{7680, 1, 1, 1}, blk.0.attn_qkv.bias{7680, 1, 1, 1}}) = {7680, 1, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + ], + ] +ggml_debug: bqkv-0 (view) = (f32) VIEW(bqkv-0{7680, 1, 1, 1}, }) = {2560, 1, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + ], + ] +ggml_debug: Qcur-0 = (f32) CONT(bqkv-0 (view){2560, 1, 1, 1}, }) = {2560, 1, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + ], + ] +ggml_debug: Qcur-0 (reshaped) = (f32) RESHAPE(Qcur-0{2560, 1, 1, 1}, }) = {80, 32, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + [ -0.3608, 0.5076, -1.8866, ...], + [ 1.7643, 0.0273, -2.1065, ...], + ... + ], + ] +ggml_debug: Qcur-0 = (f32) ROPE(Qcur-0 (reshaped){80, 32, 1, 1}, CUDA0#inp_pos#0{1, 1, 1, 1}}) = {80, 32, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + [ -0.3608, 0.5076, -1.8866, ...], + [ 1.7643, 0.0273, -2.1065, ...], + ... + ], + ] +``` diff --git a/llama.cpp/examples/eval-callback/eval-callback.cpp b/llama.cpp/examples/eval-callback/eval-callback.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d48e5e20f1eb56be93c40ed44cccecec25ae92b5 --- /dev/null +++ b/llama.cpp/examples/eval-callback/eval-callback.cpp @@ -0,0 +1,201 @@ +#include "arg.h" +#include "common.h" +#include "log.h" +#include "llama.h" +#include "ggml.h" + +#include +#include +#include + +/** + * This the arbitrary data which will be passed to each callback. + * Later on we can for example add operation or tensor name filter from the CLI arg, or a file descriptor to dump the tensor. + */ +struct callback_data { + std::vector data; +}; + +static std::string ggml_ne_string(const ggml_tensor * t) { + std::string str; + for (int i = 0; i < GGML_MAX_DIMS; ++i) { + str += std::to_string(t->ne[i]); + if (i + 1 < GGML_MAX_DIMS) { + str += ", "; + } + } + return str; +} + +static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) { + GGML_ASSERT(n > 0); + float sum = 0; + for (int64_t i3 = 0; i3 < ne[3]; i3++) { + LOG(" [\n"); + for (int64_t i2 = 0; i2 < ne[2]; i2++) { + if (i2 == n && ne[2] > 2*n) { + LOG(" ..., \n"); + i2 = ne[2] - n; + } + LOG(" [\n"); + for (int64_t i1 = 0; i1 < ne[1]; i1++) { + if (i1 == n && ne[1] > 2*n) { + LOG(" ..., \n"); + i1 = ne[1] - n; + } + LOG(" ["); + for (int64_t i0 = 0; i0 < ne[0]; i0++) { + if (i0 == n && ne[0] > 2*n) { + LOG("..., "); + i0 = ne[0] - n; + } + size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0]; + float v; + if (type == GGML_TYPE_F16) { + v = ggml_fp16_to_fp32(*(ggml_fp16_t *) &data[i]); + } else if (type == GGML_TYPE_F32) { + v = *(float *) &data[i]; + } else if (type == GGML_TYPE_I64) { + v = (float) *(int64_t *) &data[i]; + } else if (type == GGML_TYPE_I32) { + v = (float) *(int32_t *) &data[i]; + } else if (type == GGML_TYPE_I16) { + v = (float) *(int16_t *) &data[i]; + } else if (type == GGML_TYPE_I8) { + v = (float) *(int8_t *) &data[i]; + } else { + GGML_ABORT("fatal error"); + } + LOG("%12.4f", v); + sum += v; + if (i0 < ne[0] - 1) LOG(", "); + } + LOG("],\n"); + } + LOG(" ],\n"); + } + LOG(" ]\n"); + LOG(" sum = %f\n", sum); + } +} + +/** + * GGML operations callback during the graph execution. + * + * @param t current tensor + * @param ask when ask is true, the scheduler wants to know if we are interested in data from this tensor + * if we return true, a follow-up call will be made with ask=false in which we can do the actual collection. + * see ggml_backend_sched_eval_callback + * @param user_data user data to pass at each call back + * @return true to receive data or continue the graph, false otherwise + */ +static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) { + auto * cb_data = (callback_data *) user_data; + + const struct ggml_tensor * src0 = t->src[0]; + const struct ggml_tensor * src1 = t->src[1]; + + if (ask) { + return true; // Always retrieve data + } + + char src1_str[128] = {0}; + if (src1) { + snprintf(src1_str, sizeof(src1_str), "%s{%s}", src1->name, ggml_ne_string(src1).c_str()); + } + + LOG("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__, + t->name, ggml_type_name(t->type), ggml_op_desc(t), + src0->name, ggml_ne_string(src0).c_str(), + src1 ? src1_str : "", + ggml_ne_string(t).c_str()); + + + // copy the data from the GPU memory if needed + const bool is_host = ggml_backend_buffer_is_host(t->buffer); + + if (!is_host) { + auto n_bytes = ggml_nbytes(t); + cb_data->data.resize(n_bytes); + ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes); + } + + if (!ggml_is_quantized(t->type)) { + uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data(); + ggml_print_tensor(data, t->type, t->ne, t->nb, 3); + } + + return true; +} + +static bool run(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const bool add_bos = llama_vocab_get_add_bos(vocab); + + std::vector tokens = common_tokenize(ctx, params.prompt, add_bos); + + if (tokens.empty()) { + LOG_ERR("%s : there are not input tokens to process - (try to provide a prompt with '-p')\n", __func__); + return false; + } + + if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) { + LOG_ERR("%s : failed to eval\n", __func__); + return false; + } + + return true; +} + +int main(int argc, char ** argv) { + callback_data cb_data; + + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { + return 1; + } + + common_init(); + + llama_backend_init(); + llama_numa_init(params.numa); + + // pass the callback to the backend scheduler + // it will be executed for each node during the graph computation + params.cb_eval = ggml_debug; + params.cb_eval_user_data = &cb_data; + params.warmup = false; + + // init + common_init_result llama_init = common_init_from_params(params); + + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + + if (model == nullptr || ctx == nullptr) { + LOG_ERR("%s : failed to init\n", __func__); + return 1; + } + + // print system information + { + LOG_INF("\n"); + LOG_INF("%s\n", common_params_get_system_info(params).c_str()); + LOG_INF("\n"); + } + + bool OK = run(ctx, params); + if (!OK) { + return 1; + } + + LOG("\n"); + llama_perf_context_print(ctx); + + llama_backend_free(); + + return 0; +} diff --git a/llama.cpp/examples/gen-docs/CMakeLists.txt b/llama.cpp/examples/gen-docs/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..1e69011538f57a439b182c751716ca8a79c00851 --- /dev/null +++ b/llama.cpp/examples/gen-docs/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-gen-docs) +add_executable(${TARGET} gen-docs.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/llama.cpp/examples/gen-docs/gen-docs.cpp b/llama.cpp/examples/gen-docs/gen-docs.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5d7f07296bdab0f165f8a63bc39b528e4f1cfd5e --- /dev/null +++ b/llama.cpp/examples/gen-docs/gen-docs.cpp @@ -0,0 +1,83 @@ +#include "arg.h" +#include "common.h" + +#include +#include + +// Export usage message (-h) to markdown format + +static void write_table_header(std::ofstream & file) { + file << "| Argument | Explanation |\n"; + file << "| -------- | ----------- |\n"; +} + +static void write_table_entry(std::ofstream & file, const common_arg & opt) { + file << "| `"; + // args + for (const auto & arg : opt.args) { + if (arg == opt.args.front()) { + file << arg; + if (opt.args.size() > 1) file << ", "; + } else { + file << arg << (arg != opt.args.back() ? ", " : ""); + } + } + // value hint + if (opt.value_hint) { + std::string md_value_hint(opt.value_hint); + string_replace_all(md_value_hint, "|", "\\|"); + file << " " << md_value_hint; + } + if (opt.value_hint_2) { + std::string md_value_hint_2(opt.value_hint_2); + string_replace_all(md_value_hint_2, "|", "\\|"); + file << " " << md_value_hint_2; + } + // help text + std::string md_help(opt.help); + string_replace_all(md_help, "\n", "
"); + string_replace_all(md_help, "|", "\\|"); + file << "` | " << md_help << " |\n"; +} + +static void write_table(std::ofstream & file, std::vector & opts) { + write_table_header(file); + for (const auto & opt : opts) { + write_table_entry(file, *opt); + } +} + +static void export_md(std::string fname, llama_example ex) { + std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc); + + common_params params; + auto ctx_arg = common_params_parser_init(params, ex); + + std::vector common_options; + std::vector sparam_options; + std::vector specific_options; + for (auto & opt : ctx_arg.options) { + // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example + if (opt.is_sparam) { + sparam_options.push_back(&opt); + } else if (opt.in_example(ctx_arg.ex)) { + specific_options.push_back(&opt); + } else { + common_options.push_back(&opt); + } + } + + file << "**Common params**\n\n"; + write_table(file, common_options); + file << "\n\n**Sampling params**\n\n"; + write_table(file, sparam_options); + file << "\n\n**Example-specific params**\n\n"; + write_table(file, specific_options); +} + +int main(int, char **) { + export_md("autogen-main.md", LLAMA_EXAMPLE_MAIN); + export_md("autogen-server.md", LLAMA_EXAMPLE_SERVER); + + return 0; +} diff --git a/llama.cpp/examples/gguf-hash/CMakeLists.txt b/llama.cpp/examples/gguf-hash/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..149caee01d09d632afc26149877cb4fe205336f0 --- /dev/null +++ b/llama.cpp/examples/gguf-hash/CMakeLists.txt @@ -0,0 +1,22 @@ +set(TARGET llama-gguf-hash) +add_executable(${TARGET} gguf-hash.cpp) +install(TARGETS ${TARGET} RUNTIME) + +# clibs dependencies +include_directories(deps/) + +add_library(xxhash OBJECT deps/xxhash/xxhash.c deps/xxhash/xxhash.h) +target_link_libraries(${TARGET} PRIVATE xxhash) + +add_library(sha1 OBJECT deps/sha1/sha1.c deps/sha1/sha1.h) +target_link_libraries(${TARGET} PRIVATE sha1) +if (NOT MSVC) + # disable warnings in 3rd party code + target_compile_options(sha1 PRIVATE -w) +endif() + +add_library(sha256 OBJECT deps/sha256/sha256.c deps/sha256/sha256.h) +target_link_libraries(${TARGET} PRIVATE sha256) + +target_link_libraries(${TARGET} PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/llama.cpp/examples/gguf-hash/README.md b/llama.cpp/examples/gguf-hash/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3e309bdba454c60b54a12bf9adad4bfc12f58552 --- /dev/null +++ b/llama.cpp/examples/gguf-hash/README.md @@ -0,0 +1,206 @@ + +# llama-gguf-hash + +CLI to hash GGUF files to detect difference on a per model and per tensor level. + +**Command line options:** + +- `--help`: display help message +- `--xxh64`: use xhash 64bit hash mode (default) +- `--sha1`: use sha1 +- `--uuid`: use uuid +- `--sha256`: use sha256 +- `--all`: use all hash +- `--no-layer`: exclude per layer hash +- `--uuid`: generate UUIDv5 ID +- `-c`, `--check `: verify against a manifest + +## About + +While most POSIX systems already have hash checking programs like sha256sum, it +is designed to check entire files. This is not ideal for our purpose if we want +to check for consistency of the tensor data even if the metadata content of the +gguf KV store has been updated. + +This program is designed to hash a gguf tensor payload on a 'per tensor layer' +in addition to a 'entire tensor model' hash. The intent is that the entire +tensor layer can be checked first but if there is any detected inconsistencies, +then the per tensor hash can be used to narrow down the specific tensor layer +that has inconsistencies. + +For Maintainers: +- Detection of tensor inconsistency during development and automated tests + - This is served by xxh64 which is fast + - This is also served by having per tensor layer to assist in narrowing down + the location of the faulty tensor layer + - This is also served by sha1 which is much slower but more widely supported + +For Model Creators: +- Optional consistent UUID generation based on model tensor content + - This is served by UUIDv5 which is useful for databases keys + - llama.cpp UUIDv5 Namespace: `ef001206-dadc-5f6d-a15f-3359e577d4e5` + - Made via UUIDv5 URL namespace of `en.wikipedia.org/wiki/Llama.cpp` + +For Model Users: +- Assurance of tensor layer integrity even if metadata was updated + - This is served by sha256 which is still considered very secure as of 2024 + +### Design Note + +- The default behavior of this program if no arguments is provided is to hash + using xxhash's xxh32 mode because it is very fast and is primarily targeted + towards maintainers who may want to use this in automated tests. +- xxhash support xxh32 and xxh128 for 32bit hash and 128bit hash respectively + however we picked 64bit xxhash as most computers are 64bit as of 2024 and thus + would have a better affinity to calculating hash that is 64bit in size. + +## Compile Example + +```bash +cmake -B build -DCMAKE_BUILD_TYPE=Debug -DLLAMA_FATAL_WARNINGS=ON +make -C build clean +make -C build llama-gguf-hash VERBOSE=1 +./build/bin/llama-gguf-hash test.gguf +./build/bin/llama-gguf-hash --xxh64 test.gguf +./build/bin/llama-gguf-hash --sha1 test.gguf +./build/bin/llama-gguf-hash --uuid test.gguf +./build/bin/llama-gguf-hash --sha256 test.gguf +``` + +## Generation and Verification Example + +To generate we may use this command + +```bash +./llama-gguf-hash --all test.gguf > test.gguf.manifest +``` + +Which would generate a manifest that looks like below, which contains multiple hash type and per tensor layer hashes as well +(This excludes UUID as that is an ID not a hash) + +```bash +xxh64 f66e9cd66a4396a0 test.gguf:tensor_0 +sha1 59f79ecefd8125a996fdf419239051a7e99e5f20 test.gguf:tensor_0 +sha256 c0510d38fa060c46265e0160a85c7243096b01dd31c2f355bdbb5516b20de1bd test.gguf:tensor_0 +xxh64 7d3a1f9ac04d0537 test.gguf:tensor_1 +sha1 4765f592eacf096df4628ba59476af94d767080a test.gguf:tensor_1 +sha256 8514cbcc73692a2c56bd7a33a022edd5ff819614bd23b19915d7224387f397a7 test.gguf:tensor_1 +xxh64 a0af5d700049693b test.gguf:tensor_2 +sha1 25cbfbad4513cc348e2c95ebdee69d6ff2fd8753 test.gguf:tensor_2 +sha256 947e6b36e20f2cc95e1d2ce1c1669d813d574657ac6b5ac5196158d454d35180 test.gguf:tensor_2 +xxh64 e83fddf559d7b6a6 test.gguf:tensor_3 +sha1 a9cba73e2d90f2ee3dae2548caa42bef3fe6a96c test.gguf:tensor_3 +sha256 423b044e016d8ac73c39f23f60bf01bedef5ecb03c0230accd824c91fe86f1a1 test.gguf:tensor_3 +xxh64 1257733306b7992d test.gguf:tensor_4 +sha1 d7bc61db93bb685ce9d598da89717c66729b7543 test.gguf:tensor_4 +sha256 79737cb3912d4201384cf7f16a1a37ff7823f23ea796cb205b6ca361ab9e3ebf test.gguf:tensor_4 +xxh64 d238d16ba4711e58 test.gguf:tensor_5 +sha1 0706566c198fe1072f37e0a5135b4b5f23654c52 test.gguf:tensor_5 +sha256 60949be8298eced0ecdde64487643d018407bd261691e061d9e9c3dbc9fd358b test.gguf:tensor_5 +xxh64 3fbc3b65ab8c7f39 test.gguf:tensor_6 +sha1 73922a0727226a409049f6fc3172a52219ca6f00 test.gguf:tensor_6 +sha256 574f4c46ff384a3b9a225eb955d2a871847a2e8b3fa59387a8252832e92ef7b0 test.gguf:tensor_6 +xxh64 c22021c29854f093 test.gguf:tensor_7 +sha1 efc39cece6a951188fc41e354c73bbfe6813d447 test.gguf:tensor_7 +sha256 4c0410cd3c500f078ae5b21e8dc9eb79e29112713b2ab58a882f82a3868d4d75 test.gguf:tensor_7 +xxh64 936df61f5d64261f test.gguf:tensor_8 +sha1 c2490296d789a4f34398a337fed8377d943d9f06 test.gguf:tensor_8 +sha256 c4401313feeba0261275c3b25bd2d8fe40ce04e0f440c2980ed0e9674c30ff01 test.gguf:tensor_8 +xxh64 93fd20c64421c081 test.gguf:tensor_9 +sha1 7047ce1e78437a6884337a3751c7ee0421918a65 test.gguf:tensor_9 +sha256 23d57cf0d7a6e90b0b3616b41300e0cd354781e812add854a5f95aa55f2bc514 test.gguf:tensor_9 +xxh64 5a54d3aad816f302 test.gguf +sha1 d15be52c4ff213e823cb6dd13af7ee2f978e7042 test.gguf +sha256 7dd641b32f59b60dbd4b5420c4b0f6321ccf48f58f6ae201a3dbc4a58a27c6e4 test.gguf +``` + +We can then use the normal check command which will by default check for the highest security strength hash and verify against that: + +```bash +$ ./llama-gguf-hash --check test.gguf.manifest test.gguf +manifest test.gguf.manifest sha256 sha1 xxh64 +sha256 c0510d38fa060c46265e0160a85c7243096b01dd31c2f355bdbb5516b20de1bd test.gguf:tensor_0 - Ok +sha256 8514cbcc73692a2c56bd7a33a022edd5ff819614bd23b19915d7224387f397a7 test.gguf:tensor_1 - Ok +sha256 947e6b36e20f2cc95e1d2ce1c1669d813d574657ac6b5ac5196158d454d35180 test.gguf:tensor_2 - Ok +sha256 423b044e016d8ac73c39f23f60bf01bedef5ecb03c0230accd824c91fe86f1a1 test.gguf:tensor_3 - Ok +sha256 79737cb3912d4201384cf7f16a1a37ff7823f23ea796cb205b6ca361ab9e3ebf test.gguf:tensor_4 - Ok +sha256 60949be8298eced0ecdde64487643d018407bd261691e061d9e9c3dbc9fd358b test.gguf:tensor_5 - Ok +sha256 574f4c46ff384a3b9a225eb955d2a871847a2e8b3fa59387a8252832e92ef7b0 test.gguf:tensor_6 - Ok +sha256 4c0410cd3c500f078ae5b21e8dc9eb79e29112713b2ab58a882f82a3868d4d75 test.gguf:tensor_7 - Ok +sha256 c4401313feeba0261275c3b25bd2d8fe40ce04e0f440c2980ed0e9674c30ff01 test.gguf:tensor_8 - Ok +sha256 23d57cf0d7a6e90b0b3616b41300e0cd354781e812add854a5f95aa55f2bc514 test.gguf:tensor_9 - Ok +sha256 7dd641b32f59b60dbd4b5420c4b0f6321ccf48f58f6ae201a3dbc4a58a27c6e4 test.gguf - Ok + +Verification results for test.gguf.manifest - Success +``` + +Or we may explicitly ask for a faster hash like: + +```bash +$ ./llama-gguf-hash --check test.gguf.manifest --xxh64 test.gguf +manifest test.gguf.manifest sha256 sha1 xxh64 +xxh64 f66e9cd66a4396a0 test.gguf:tensor_0 - Ok +xxh64 7d3a1f9ac04d0537 test.gguf:tensor_1 - Ok +xxh64 a0af5d700049693b test.gguf:tensor_2 - Ok +xxh64 e83fddf559d7b6a6 test.gguf:tensor_3 - Ok +xxh64 1257733306b7992d test.gguf:tensor_4 - Ok +xxh64 d238d16ba4711e58 test.gguf:tensor_5 - Ok +xxh64 3fbc3b65ab8c7f39 test.gguf:tensor_6 - Ok +xxh64 c22021c29854f093 test.gguf:tensor_7 - Ok +xxh64 936df61f5d64261f test.gguf:tensor_8 - Ok +xxh64 93fd20c64421c081 test.gguf:tensor_9 - Ok +xxh64 5a54d3aad816f302 test.gguf - Ok + +Verification results for test.gguf.manifest - Success +``` + +Or maybe we want to just check that all the hash is valid: + +```bash +$./llama-gguf-hash --check test.gguf.manifest --all test.gguf.manifest +manifest test.gguf.manifest sha256 sha1 xxh64 +xxh64 f66e9cd66a4396a0 test.gguf:tensor_0 - Ok +sha1 59f79ecefd8125a996fdf419239051a7e99e5f20 test.gguf:tensor_0 - Ok +sha256 c0510d38fa060c46265e0160a85c7243096b01dd31c2f355bdbb5516b20de1bd test.gguf:tensor_0 - Ok +xxh64 7d3a1f9ac04d0537 test.gguf:tensor_1 - Ok +sha1 4765f592eacf096df4628ba59476af94d767080a test.gguf:tensor_1 - Ok +sha256 8514cbcc73692a2c56bd7a33a022edd5ff819614bd23b19915d7224387f397a7 test.gguf:tensor_1 - Ok +xxh64 a0af5d700049693b test.gguf:tensor_2 - Ok +sha1 25cbfbad4513cc348e2c95ebdee69d6ff2fd8753 test.gguf:tensor_2 - Ok +sha256 947e6b36e20f2cc95e1d2ce1c1669d813d574657ac6b5ac5196158d454d35180 test.gguf:tensor_2 - Ok +xxh64 e83fddf559d7b6a6 test.gguf:tensor_3 - Ok +sha1 a9cba73e2d90f2ee3dae2548caa42bef3fe6a96c test.gguf:tensor_3 - Ok +sha256 423b044e016d8ac73c39f23f60bf01bedef5ecb03c0230accd824c91fe86f1a1 test.gguf:tensor_3 - Ok +xxh64 1257733306b7992d test.gguf:tensor_4 - Ok +sha1 d7bc61db93bb685ce9d598da89717c66729b7543 test.gguf:tensor_4 - Ok +sha256 79737cb3912d4201384cf7f16a1a37ff7823f23ea796cb205b6ca361ab9e3ebf test.gguf:tensor_4 - Ok +xxh64 d238d16ba4711e58 test.gguf:tensor_5 - Ok +sha1 0706566c198fe1072f37e0a5135b4b5f23654c52 test.gguf:tensor_5 - Ok +sha256 60949be8298eced0ecdde64487643d018407bd261691e061d9e9c3dbc9fd358b test.gguf:tensor_5 - Ok +xxh64 3fbc3b65ab8c7f39 test.gguf:tensor_6 - Ok +sha1 73922a0727226a409049f6fc3172a52219ca6f00 test.gguf:tensor_6 - Ok +sha256 574f4c46ff384a3b9a225eb955d2a871847a2e8b3fa59387a8252832e92ef7b0 test.gguf:tensor_6 - Ok +xxh64 c22021c29854f093 test.gguf:tensor_7 - Ok +sha1 efc39cece6a951188fc41e354c73bbfe6813d447 test.gguf:tensor_7 - Ok +sha256 4c0410cd3c500f078ae5b21e8dc9eb79e29112713b2ab58a882f82a3868d4d75 test.gguf:tensor_7 - Ok +xxh64 936df61f5d64261f test.gguf:tensor_8 - Ok +sha1 c2490296d789a4f34398a337fed8377d943d9f06 test.gguf:tensor_8 - Ok +sha256 c4401313feeba0261275c3b25bd2d8fe40ce04e0f440c2980ed0e9674c30ff01 test.gguf:tensor_8 - Ok +xxh64 93fd20c64421c081 test.gguf:tensor_9 - Ok +sha1 7047ce1e78437a6884337a3751c7ee0421918a65 test.gguf:tensor_9 - Ok +sha256 23d57cf0d7a6e90b0b3616b41300e0cd354781e812add854a5f95aa55f2bc514 test.gguf:tensor_9 - Ok +xxh64 5a54d3aad816f302 test.gguf - Ok +sha1 d15be52c4ff213e823cb6dd13af7ee2f978e7042 test.gguf - Ok +sha256 7dd641b32f59b60dbd4b5420c4b0f6321ccf48f58f6ae201a3dbc4a58a27c6e4 test.gguf - Ok + +Verification results for test.gguf.manifest - Success +``` + + +## Crypto/Hash Libraries Used + +These micro c libraries dependencies was installed via the [clib c package manager](https://github.com/clibs) + +- https://github.com/Cyan4973/xxHash +- https://github.com/clibs/sha1/ +- https://github.com/jb55/sha256.c diff --git a/llama.cpp/examples/gguf-hash/deps/rotate-bits/package.json b/llama.cpp/examples/gguf-hash/deps/rotate-bits/package.json new file mode 100644 index 0000000000000000000000000000000000000000..284aa0c777953280d5d340c438fd58ac192dddcd --- /dev/null +++ b/llama.cpp/examples/gguf-hash/deps/rotate-bits/package.json @@ -0,0 +1,13 @@ +{ + "name": "rotate-bits", + "version": "0.1.1", + "repo": "jb55/rotate-bits.h", + "description": "rotate bits", + "keywords": ["rotl", "rotr"], + "src": ["rotate-bits.h"], + "license": "Public Domain", + "development": { + "thlorenz/tap.c": "*" + } +} + diff --git a/llama.cpp/examples/gguf-hash/deps/rotate-bits/rotate-bits.h b/llama.cpp/examples/gguf-hash/deps/rotate-bits/rotate-bits.h new file mode 100644 index 0000000000000000000000000000000000000000..2aae4c6733a745b2986cd5b35a0657d8c5f80034 --- /dev/null +++ b/llama.cpp/examples/gguf-hash/deps/rotate-bits/rotate-bits.h @@ -0,0 +1,46 @@ + + +#ifndef __ROTATE_DEFS_H +#define __ROTATE_DEFS_H + +#ifdef _MSC_VER + +#include + +#define ROTL32(v, n) _rotl((v), (n)) +#define ROTL64(v, n) _rotl64((v), (n)) + +#define ROTR32(v, n) _rotr((v), (n)) +#define ROTR64(v, n) _rotr64((v), (n)) + +#else + +#include + +#define U8V(v) ((uint8_t)(v) & 0xFFU) +#define U16V(v) ((uint16_t)(v) & 0xFFFFU) +#define U32V(v) ((uint32_t)(v) & 0xFFFFFFFFU) +#define U64V(v) ((uint64_t)(v) & 0xFFFFFFFFFFFFFFFFU) + +#define ROTL32(v, n) \ + (U32V((uint32_t)(v) << (n)) | ((uint32_t)(v) >> (32 - (n)))) + +// tests fail if we don't have this cast... +#define ROTL64(v, n) \ + (U64V((uint64_t)(v) << (n)) | ((uint64_t)(v) >> (64 - (n)))) + +#define ROTR32(v, n) ROTL32(v, 32 - (n)) +#define ROTR64(v, n) ROTL64(v, 64 - (n)) + +#endif + +#define ROTL8(v, n) \ + (U8V((uint8_t)(v) << (n)) | ((uint8_t)(v) >> (8 - (n)))) + +#define ROTL16(v, n) \ + (U16V((uint16_t)(v) << (n)) | ((uint16_t)(v) >> (16 - (n)))) + +#define ROTR8(v, n) ROTL8(v, 8 - (n)) +#define ROTR16(v, n) ROTL16(v, 16 - (n)) + +#endif diff --git a/llama.cpp/examples/gguf-hash/deps/sha1/package.json b/llama.cpp/examples/gguf-hash/deps/sha1/package.json new file mode 100644 index 0000000000000000000000000000000000000000..e732174f0e83dd24ff383158ed861d003949df90 --- /dev/null +++ b/llama.cpp/examples/gguf-hash/deps/sha1/package.json @@ -0,0 +1,9 @@ +{ + "name": "sha1", + "version": "0.0.1", + "repo": "clibs/sha1", + "description": "sha1 hash algorithm", + "keywords": ["sha1", "hash"], + "license": "public domain", + "src": ["sha1.c", "sha1.h"] +} diff --git a/llama.cpp/examples/gguf-hash/deps/sha1/sha1.c b/llama.cpp/examples/gguf-hash/deps/sha1/sha1.c new file mode 100644 index 0000000000000000000000000000000000000000..683bbb618c1396fe9b5af413257113129f82fb64 --- /dev/null +++ b/llama.cpp/examples/gguf-hash/deps/sha1/sha1.c @@ -0,0 +1,295 @@ +/* +SHA-1 in C +By Steve Reid +100% Public Domain + +Test Vectors (from FIPS PUB 180-1) +"abc" + A9993E36 4706816A BA3E2571 7850C26C 9CD0D89D +"abcdbcdecdefdefgefghfghighijhijkijkljklmklmnlmnomnopnopq" + 84983E44 1C3BD26E BAAE4AA1 F95129E5 E54670F1 +A million repetitions of "a" + 34AA973C D4C4DAA4 F61EEB2B DBAD2731 6534016F +*/ + +/* #define LITTLE_ENDIAN * This should be #define'd already, if true. */ +/* #define SHA1HANDSOFF * Copies data before messing with it. */ + +#define SHA1HANDSOFF + +#include +#include + +/* for uint32_t */ +#include + +#include "sha1.h" + + +#define rol(value, bits) (((value) << (bits)) | ((value) >> (32 - (bits)))) + +/* blk0() and blk() perform the initial expand. */ +/* I got the idea of expanding during the round function from SSLeay */ +#if BYTE_ORDER == LITTLE_ENDIAN +#define blk0(i) (block->l[i] = (rol(block->l[i],24)&0xFF00FF00) \ + |(rol(block->l[i],8)&0x00FF00FF)) +#elif BYTE_ORDER == BIG_ENDIAN +#define blk0(i) block->l[i] +#else +#error "Endianness not defined!" +#endif +#define blk(i) (block->l[i&15] = rol(block->l[(i+13)&15]^block->l[(i+8)&15] \ + ^block->l[(i+2)&15]^block->l[i&15],1)) + +/* (R0+R1), R2, R3, R4 are the different operations used in SHA1 */ +#define R0(v,w,x,y,z,i) z+=((w&(x^y))^y)+blk0(i)+0x5A827999+rol(v,5);w=rol(w,30); +#define R1(v,w,x,y,z,i) z+=((w&(x^y))^y)+blk(i)+0x5A827999+rol(v,5);w=rol(w,30); +#define R2(v,w,x,y,z,i) z+=(w^x^y)+blk(i)+0x6ED9EBA1+rol(v,5);w=rol(w,30); +#define R3(v,w,x,y,z,i) z+=(((w|x)&y)|(w&x))+blk(i)+0x8F1BBCDC+rol(v,5);w=rol(w,30); +#define R4(v,w,x,y,z,i) z+=(w^x^y)+blk(i)+0xCA62C1D6+rol(v,5);w=rol(w,30); + + +/* Hash a single 512-bit block. This is the core of the algorithm. */ + +void SHA1Transform( + uint32_t state[5], + const unsigned char buffer[64] +) +{ + uint32_t a, b, c, d, e; + + typedef union + { + unsigned char c[64]; + uint32_t l[16]; + } CHAR64LONG16; + +#ifdef SHA1HANDSOFF + CHAR64LONG16 block[1]; /* use array to appear as a pointer */ + + memcpy(block, buffer, 64); +#else + /* The following had better never be used because it causes the + * pointer-to-const buffer to be cast into a pointer to non-const. + * And the result is written through. I threw a "const" in, hoping + * this will cause a diagnostic. + */ + CHAR64LONG16 *block = (const CHAR64LONG16 *) buffer; +#endif + /* Copy context->state[] to working vars */ + a = state[0]; + b = state[1]; + c = state[2]; + d = state[3]; + e = state[4]; + /* 4 rounds of 20 operations each. Loop unrolled. */ + R0(a, b, c, d, e, 0); + R0(e, a, b, c, d, 1); + R0(d, e, a, b, c, 2); + R0(c, d, e, a, b, 3); + R0(b, c, d, e, a, 4); + R0(a, b, c, d, e, 5); + R0(e, a, b, c, d, 6); + R0(d, e, a, b, c, 7); + R0(c, d, e, a, b, 8); + R0(b, c, d, e, a, 9); + R0(a, b, c, d, e, 10); + R0(e, a, b, c, d, 11); + R0(d, e, a, b, c, 12); + R0(c, d, e, a, b, 13); + R0(b, c, d, e, a, 14); + R0(a, b, c, d, e, 15); + R1(e, a, b, c, d, 16); + R1(d, e, a, b, c, 17); + R1(c, d, e, a, b, 18); + R1(b, c, d, e, a, 19); + R2(a, b, c, d, e, 20); + R2(e, a, b, c, d, 21); + R2(d, e, a, b, c, 22); + R2(c, d, e, a, b, 23); + R2(b, c, d, e, a, 24); + R2(a, b, c, d, e, 25); + R2(e, a, b, c, d, 26); + R2(d, e, a, b, c, 27); + R2(c, d, e, a, b, 28); + R2(b, c, d, e, a, 29); + R2(a, b, c, d, e, 30); + R2(e, a, b, c, d, 31); + R2(d, e, a, b, c, 32); + R2(c, d, e, a, b, 33); + R2(b, c, d, e, a, 34); + R2(a, b, c, d, e, 35); + R2(e, a, b, c, d, 36); + R2(d, e, a, b, c, 37); + R2(c, d, e, a, b, 38); + R2(b, c, d, e, a, 39); + R3(a, b, c, d, e, 40); + R3(e, a, b, c, d, 41); + R3(d, e, a, b, c, 42); + R3(c, d, e, a, b, 43); + R3(b, c, d, e, a, 44); + R3(a, b, c, d, e, 45); + R3(e, a, b, c, d, 46); + R3(d, e, a, b, c, 47); + R3(c, d, e, a, b, 48); + R3(b, c, d, e, a, 49); + R3(a, b, c, d, e, 50); + R3(e, a, b, c, d, 51); + R3(d, e, a, b, c, 52); + R3(c, d, e, a, b, 53); + R3(b, c, d, e, a, 54); + R3(a, b, c, d, e, 55); + R3(e, a, b, c, d, 56); + R3(d, e, a, b, c, 57); + R3(c, d, e, a, b, 58); + R3(b, c, d, e, a, 59); + R4(a, b, c, d, e, 60); + R4(e, a, b, c, d, 61); + R4(d, e, a, b, c, 62); + R4(c, d, e, a, b, 63); + R4(b, c, d, e, a, 64); + R4(a, b, c, d, e, 65); + R4(e, a, b, c, d, 66); + R4(d, e, a, b, c, 67); + R4(c, d, e, a, b, 68); + R4(b, c, d, e, a, 69); + R4(a, b, c, d, e, 70); + R4(e, a, b, c, d, 71); + R4(d, e, a, b, c, 72); + R4(c, d, e, a, b, 73); + R4(b, c, d, e, a, 74); + R4(a, b, c, d, e, 75); + R4(e, a, b, c, d, 76); + R4(d, e, a, b, c, 77); + R4(c, d, e, a, b, 78); + R4(b, c, d, e, a, 79); + /* Add the working vars back into context.state[] */ + state[0] += a; + state[1] += b; + state[2] += c; + state[3] += d; + state[4] += e; + /* Wipe variables */ + a = b = c = d = e = 0; +#ifdef SHA1HANDSOFF + memset(block, '\0', sizeof(block)); +#endif +} + + +/* SHA1Init - Initialize new context */ + +void SHA1Init( + SHA1_CTX * context +) +{ + /* SHA1 initialization constants */ + context->state[0] = 0x67452301; + context->state[1] = 0xEFCDAB89; + context->state[2] = 0x98BADCFE; + context->state[3] = 0x10325476; + context->state[4] = 0xC3D2E1F0; + context->count[0] = context->count[1] = 0; +} + + +/* Run your data through this. */ + +void SHA1Update( + SHA1_CTX * context, + const unsigned char *data, + uint32_t len +) +{ + uint32_t i; + + uint32_t j; + + j = context->count[0]; + if ((context->count[0] += len << 3) < j) + context->count[1]++; + context->count[1] += (len >> 29); + j = (j >> 3) & 63; + if ((j + len) > 63) + { + memcpy(&context->buffer[j], data, (i = 64 - j)); + SHA1Transform(context->state, context->buffer); + for (; i + 63 < len; i += 64) + { + SHA1Transform(context->state, &data[i]); + } + j = 0; + } + else + i = 0; + memcpy(&context->buffer[j], &data[i], len - i); +} + + +/* Add padding and return the message digest. */ + +void SHA1Final( + unsigned char digest[20], + SHA1_CTX * context +) +{ + unsigned i; + + unsigned char finalcount[8]; + + unsigned char c; + +#if 0 /* untested "improvement" by DHR */ + /* Convert context->count to a sequence of bytes + * in finalcount. Second element first, but + * big-endian order within element. + * But we do it all backwards. + */ + unsigned char *fcp = &finalcount[8]; + + for (i = 0; i < 2; i++) + { + uint32_t t = context->count[i]; + + int j; + + for (j = 0; j < 4; t >>= 8, j++) + *--fcp = (unsigned char) t} +#else + for (i = 0; i < 8; i++) + { + finalcount[i] = (unsigned char) ((context->count[(i >= 4 ? 0 : 1)] >> ((3 - (i & 3)) * 8)) & 255); /* Endian independent */ + } +#endif + c = 0200; + SHA1Update(context, &c, 1); + while ((context->count[0] & 504) != 448) + { + c = 0000; + SHA1Update(context, &c, 1); + } + SHA1Update(context, finalcount, 8); /* Should cause a SHA1Transform() */ + for (i = 0; i < 20; i++) + { + digest[i] = (unsigned char) + ((context->state[i >> 2] >> ((3 - (i & 3)) * 8)) & 255); + } + /* Wipe variables */ + memset(context, '\0', sizeof(*context)); + memset(&finalcount, '\0', sizeof(finalcount)); +} + +void SHA1( + char *hash_out, + const char *str, + uint32_t len) +{ + SHA1_CTX ctx; + unsigned int ii; + + SHA1Init(&ctx); + for (ii=0; ii + 100% Public Domain + */ + +#include "stdint.h" + +#if defined(__cplusplus) +extern "C" { +#endif + +typedef struct +{ + uint32_t state[5]; + uint32_t count[2]; + unsigned char buffer[64]; +} SHA1_CTX; + +void SHA1Transform( + uint32_t state[5], + const unsigned char buffer[64] + ); + +void SHA1Init( + SHA1_CTX * context + ); + +void SHA1Update( + SHA1_CTX * context, + const unsigned char *data, + uint32_t len + ); + +void SHA1Final( + unsigned char digest[20], + SHA1_CTX * context + ); + +void SHA1( + char *hash_out, + const char *str, + uint32_t len); + +#if defined(__cplusplus) +} +#endif + +#endif /* SHA1_H */ diff --git a/llama.cpp/examples/gguf-hash/deps/sha256/package.json b/llama.cpp/examples/gguf-hash/deps/sha256/package.json new file mode 100644 index 0000000000000000000000000000000000000000..c16eb8bd7eddc3bd7be38aef51851fe6fe00d06a --- /dev/null +++ b/llama.cpp/examples/gguf-hash/deps/sha256/package.json @@ -0,0 +1,15 @@ +{ + "name": "sha256", + "version": "0.0.2", + "repo": "jb55/sha256.c", + "description": "sha256 in c", + "keywords": ["sha256", "sha2"], + "src": ["sha256.c", "sha256.h"], + "dependencies": { + "jb55/rotate-bits.h": "0.1.1" + }, + "development": { + "thlorenz/tap.c": "*" + } +} + diff --git a/llama.cpp/examples/gguf-hash/deps/sha256/sha256.c b/llama.cpp/examples/gguf-hash/deps/sha256/sha256.c new file mode 100644 index 0000000000000000000000000000000000000000..6667c83554b7b8c994b62dea5859afb396bc10e0 --- /dev/null +++ b/llama.cpp/examples/gguf-hash/deps/sha256/sha256.c @@ -0,0 +1,221 @@ +/* Crypto/Sha256.c -- SHA-256 Hash +2010-06-11 : Igor Pavlov : Public domain +This code is based on public domain code from Wei Dai's Crypto++ library. */ + +#include "rotate-bits/rotate-bits.h" +#include "sha256.h" + +/* define it for speed optimization */ +#define _SHA256_UNROLL +#define _SHA256_UNROLL2 + +void +sha256_init(sha256_t *p) +{ + p->state[0] = 0x6a09e667; + p->state[1] = 0xbb67ae85; + p->state[2] = 0x3c6ef372; + p->state[3] = 0xa54ff53a; + p->state[4] = 0x510e527f; + p->state[5] = 0x9b05688c; + p->state[6] = 0x1f83d9ab; + p->state[7] = 0x5be0cd19; + p->count = 0; +} + +#define S0(x) (ROTR32(x, 2) ^ ROTR32(x,13) ^ ROTR32(x, 22)) +#define S1(x) (ROTR32(x, 6) ^ ROTR32(x,11) ^ ROTR32(x, 25)) +#define s0(x) (ROTR32(x, 7) ^ ROTR32(x,18) ^ (x >> 3)) +#define s1(x) (ROTR32(x,17) ^ ROTR32(x,19) ^ (x >> 10)) + +#define blk0(i) (W[i] = data[i]) +#define blk2(i) (W[i&15] += s1(W[(i-2)&15]) + W[(i-7)&15] + s0(W[(i-15)&15])) + +#define Ch(x,y,z) (z^(x&(y^z))) +#define Maj(x,y,z) ((x&y)|(z&(x|y))) + +#define a(i) T[(0-(i))&7] +#define b(i) T[(1-(i))&7] +#define c(i) T[(2-(i))&7] +#define d(i) T[(3-(i))&7] +#define e(i) T[(4-(i))&7] +#define f(i) T[(5-(i))&7] +#define g(i) T[(6-(i))&7] +#define h(i) T[(7-(i))&7] + + +#ifdef _SHA256_UNROLL2 + +#define R(a,b,c,d,e,f,g,h, i) h += S1(e) + Ch(e,f,g) + K[i+j] + (j?blk2(i):blk0(i));\ + d += h; h += S0(a) + Maj(a, b, c) + +#define RX_8(i) \ + R(a,b,c,d,e,f,g,h, i); \ + R(h,a,b,c,d,e,f,g, (i+1)); \ + R(g,h,a,b,c,d,e,f, (i+2)); \ + R(f,g,h,a,b,c,d,e, (i+3)); \ + R(e,f,g,h,a,b,c,d, (i+4)); \ + R(d,e,f,g,h,a,b,c, (i+5)); \ + R(c,d,e,f,g,h,a,b, (i+6)); \ + R(b,c,d,e,f,g,h,a, (i+7)) + +#else + +#define R(i) h(i) += S1(e(i)) + Ch(e(i),f(i),g(i)) + K[i+j] + (j?blk2(i):blk0(i));\ + d(i) += h(i); h(i) += S0(a(i)) + Maj(a(i), b(i), c(i)) + +#ifdef _SHA256_UNROLL + +#define RX_8(i) R(i+0); R(i+1); R(i+2); R(i+3); R(i+4); R(i+5); R(i+6); R(i+7); + +#endif + +#endif + +static const uint32_t K[64] = { + 0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5, + 0x3956c25b, 0x59f111f1, 0x923f82a4, 0xab1c5ed5, + 0xd807aa98, 0x12835b01, 0x243185be, 0x550c7dc3, + 0x72be5d74, 0x80deb1fe, 0x9bdc06a7, 0xc19bf174, + 0xe49b69c1, 0xefbe4786, 0x0fc19dc6, 0x240ca1cc, + 0x2de92c6f, 0x4a7484aa, 0x5cb0a9dc, 0x76f988da, + 0x983e5152, 0xa831c66d, 0xb00327c8, 0xbf597fc7, + 0xc6e00bf3, 0xd5a79147, 0x06ca6351, 0x14292967, + 0x27b70a85, 0x2e1b2138, 0x4d2c6dfc, 0x53380d13, + 0x650a7354, 0x766a0abb, 0x81c2c92e, 0x92722c85, + 0xa2bfe8a1, 0xa81a664b, 0xc24b8b70, 0xc76c51a3, + 0xd192e819, 0xd6990624, 0xf40e3585, 0x106aa070, + 0x19a4c116, 0x1e376c08, 0x2748774c, 0x34b0bcb5, + 0x391c0cb3, 0x4ed8aa4a, 0x5b9cca4f, 0x682e6ff3, + 0x748f82ee, 0x78a5636f, 0x84c87814, 0x8cc70208, + 0x90befffa, 0xa4506ceb, 0xbef9a3f7, 0xc67178f2 +}; + +static void +sha256_transform(uint32_t *state, const uint32_t *data) +{ + uint32_t W[16] = {0}; + unsigned j; + #ifdef _SHA256_UNROLL2 + uint32_t a,b,c,d,e,f,g,h; + a = state[0]; + b = state[1]; + c = state[2]; + d = state[3]; + e = state[4]; + f = state[5]; + g = state[6]; + h = state[7]; + #else + uint32_t T[8]; + for (j = 0; j < 8; j++) + T[j] = state[j]; + #endif + + for (j = 0; j < 64; j += 16) + { + #if defined(_SHA256_UNROLL) || defined(_SHA256_UNROLL2) + RX_8(0); RX_8(8); + #else + unsigned i; + for (i = 0; i < 16; i++) { R(i); } + #endif + } + + #ifdef _SHA256_UNROLL2 + state[0] += a; + state[1] += b; + state[2] += c; + state[3] += d; + state[4] += e; + state[5] += f; + state[6] += g; + state[7] += h; + #else + for (j = 0; j < 8; j++) + state[j] += T[j]; + #endif + + /* Wipe variables */ + /* memset(W, 0, sizeof(W)); */ + /* memset(T, 0, sizeof(T)); */ +} + +#undef S0 +#undef S1 +#undef s0 +#undef s1 + +static void +sha256_write_byte_block(sha256_t *p) +{ + uint32_t data32[16]; + unsigned i; + for (i = 0; i < 16; i++) + data32[i] = + ((uint32_t)(p->buffer[i * 4 ]) << 24) + + ((uint32_t)(p->buffer[i * 4 + 1]) << 16) + + ((uint32_t)(p->buffer[i * 4 + 2]) << 8) + + ((uint32_t)(p->buffer[i * 4 + 3])); + sha256_transform(p->state, data32); +} + + +void +sha256_hash(unsigned char *buf, const unsigned char *data, size_t size) +{ + sha256_t hash; + sha256_init(&hash); + sha256_update(&hash, data, size); + sha256_final(&hash, buf); +} + + +void +sha256_update(sha256_t *p, const unsigned char *data, size_t size) +{ + uint32_t curBufferPos = (uint32_t)p->count & 0x3F; + while (size > 0) + { + p->buffer[curBufferPos++] = *data++; + p->count++; + size--; + if (curBufferPos == 64) + { + curBufferPos = 0; + sha256_write_byte_block(p); + } + } +} + + +void +sha256_final(sha256_t *p, unsigned char *digest) +{ + uint64_t lenInBits = (p->count << 3); + uint32_t curBufferPos = (uint32_t)p->count & 0x3F; + unsigned i; + p->buffer[curBufferPos++] = 0x80; + while (curBufferPos != (64 - 8)) + { + curBufferPos &= 0x3F; + if (curBufferPos == 0) + sha256_write_byte_block(p); + p->buffer[curBufferPos++] = 0; + } + for (i = 0; i < 8; i++) + { + p->buffer[curBufferPos++] = (unsigned char)(lenInBits >> 56); + lenInBits <<= 8; + } + sha256_write_byte_block(p); + + for (i = 0; i < 8; i++) + { + *digest++ = (unsigned char)(p->state[i] >> 24); + *digest++ = (unsigned char)(p->state[i] >> 16); + *digest++ = (unsigned char)(p->state[i] >> 8); + *digest++ = (unsigned char)(p->state[i]); + } + sha256_init(p); +} diff --git a/llama.cpp/examples/gguf-hash/deps/sha256/sha256.h b/llama.cpp/examples/gguf-hash/deps/sha256/sha256.h new file mode 100644 index 0000000000000000000000000000000000000000..040198449c571e1e08a97a81671a542650171c42 --- /dev/null +++ b/llama.cpp/examples/gguf-hash/deps/sha256/sha256.h @@ -0,0 +1,24 @@ +/* Sha256.h -- SHA-256 Hash +2010-06-11 : Igor Pavlov : Public domain */ + +#ifndef __CRYPTO_SHA256_H +#define __CRYPTO_SHA256_H + +#include +#include + +#define SHA256_DIGEST_SIZE 32 + +typedef struct sha256_t +{ + uint32_t state[8]; + uint64_t count; + unsigned char buffer[64]; +} sha256_t; + +void sha256_init(sha256_t *p); +void sha256_update(sha256_t *p, const unsigned char *data, size_t size); +void sha256_final(sha256_t *p, unsigned char *digest); +void sha256_hash(unsigned char *buf, const unsigned char *data, size_t size); + +#endif diff --git a/llama.cpp/examples/gguf-hash/deps/xxhash/clib.json b/llama.cpp/examples/gguf-hash/deps/xxhash/clib.json new file mode 100644 index 0000000000000000000000000000000000000000..5cb7203655bb33e32698d9b4b39ca14007ee0003 --- /dev/null +++ b/llama.cpp/examples/gguf-hash/deps/xxhash/clib.json @@ -0,0 +1,12 @@ +{ + "name": "xxhash", + "version": "0.8.2", + "repo": "Cyan4973/xxhash", + "description": "Extremely fast non-cryptographic hash algorithm", + "keywords": ["xxhash", "hashing"], + "license": "BSD-2-Clause", + "src": [ + "xxhash.c", + "xxhash.h" + ] +} diff --git a/llama.cpp/examples/gguf-hash/deps/xxhash/xxhash.c b/llama.cpp/examples/gguf-hash/deps/xxhash/xxhash.c new file mode 100644 index 0000000000000000000000000000000000000000..c073c5cd799d601b0531ae4b60e0b8f1b33b104b --- /dev/null +++ b/llama.cpp/examples/gguf-hash/deps/xxhash/xxhash.c @@ -0,0 +1,42 @@ +/* + * xxHash - Extremely Fast Hash algorithm + * Copyright (C) 2012-2023 Yann Collet + * + * BSD 2-Clause License (https://www.opensource.org/licenses/bsd-license.php) + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are + * met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following disclaimer + * in the documentation and/or other materials provided with the + * distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * You can contact the author at: + * - xxHash homepage: https://www.xxhash.com + * - xxHash source repository: https://github.com/Cyan4973/xxHash + */ + +/* + * xxhash.c instantiates functions defined in xxhash.h + */ + +#define XXH_STATIC_LINKING_ONLY /* access advanced declarations */ +#define XXH_IMPLEMENTATION /* access definitions */ + +#include "xxhash.h" diff --git a/llama.cpp/examples/gguf-hash/deps/xxhash/xxhash.h b/llama.cpp/examples/gguf-hash/deps/xxhash/xxhash.h new file mode 100644 index 0000000000000000000000000000000000000000..97542eee30df47b1cf1acf5ff29b1cac5184bac3 --- /dev/null +++ b/llama.cpp/examples/gguf-hash/deps/xxhash/xxhash.h @@ -0,0 +1,7093 @@ +/* + * xxHash - Extremely Fast Hash algorithm + * Header File + * Copyright (C) 2012-2023 Yann Collet + * + * BSD 2-Clause License (https://www.opensource.org/licenses/bsd-license.php) + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are + * met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following disclaimer + * in the documentation and/or other materials provided with the + * distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * You can contact the author at: + * - xxHash homepage: https://www.xxhash.com + * - xxHash source repository: https://github.com/Cyan4973/xxHash + */ + +/*! + * @mainpage xxHash + * + * xxHash is an extremely fast non-cryptographic hash algorithm, working at RAM speed + * limits. + * + * It is proposed in four flavors, in three families: + * 1. @ref XXH32_family + * - Classic 32-bit hash function. Simple, compact, and runs on almost all + * 32-bit and 64-bit systems. + * 2. @ref XXH64_family + * - Classic 64-bit adaptation of XXH32. Just as simple, and runs well on most + * 64-bit systems (but _not_ 32-bit systems). + * 3. @ref XXH3_family + * - Modern 64-bit and 128-bit hash function family which features improved + * strength and performance across the board, especially on smaller data. + * It benefits greatly from SIMD and 64-bit without requiring it. + * + * Benchmarks + * --- + * The reference system uses an Intel i7-9700K CPU, and runs Ubuntu x64 20.04. + * The open source benchmark program is compiled with clang v10.0 using -O3 flag. + * + * | Hash Name | ISA ext | Width | Large Data Speed | Small Data Velocity | + * | -------------------- | ------- | ----: | ---------------: | ------------------: | + * | XXH3_64bits() | @b AVX2 | 64 | 59.4 GB/s | 133.1 | + * | MeowHash | AES-NI | 128 | 58.2 GB/s | 52.5 | + * | XXH3_128bits() | @b AVX2 | 128 | 57.9 GB/s | 118.1 | + * | CLHash | PCLMUL | 64 | 37.1 GB/s | 58.1 | + * | XXH3_64bits() | @b SSE2 | 64 | 31.5 GB/s | 133.1 | + * | XXH3_128bits() | @b SSE2 | 128 | 29.6 GB/s | 118.1 | + * | RAM sequential read | | N/A | 28.0 GB/s | N/A | + * | ahash | AES-NI | 64 | 22.5 GB/s | 107.2 | + * | City64 | | 64 | 22.0 GB/s | 76.6 | + * | T1ha2 | | 64 | 22.0 GB/s | 99.0 | + * | City128 | | 128 | 21.7 GB/s | 57.7 | + * | FarmHash | AES-NI | 64 | 21.3 GB/s | 71.9 | + * | XXH64() | | 64 | 19.4 GB/s | 71.0 | + * | SpookyHash | | 64 | 19.3 GB/s | 53.2 | + * | Mum | | 64 | 18.0 GB/s | 67.0 | + * | CRC32C | SSE4.2 | 32 | 13.0 GB/s | 57.9 | + * | XXH32() | | 32 | 9.7 GB/s | 71.9 | + * | City32 | | 32 | 9.1 GB/s | 66.0 | + * | Blake3* | @b AVX2 | 256 | 4.4 GB/s | 8.1 | + * | Murmur3 | | 32 | 3.9 GB/s | 56.1 | + * | SipHash* | | 64 | 3.0 GB/s | 43.2 | + * | Blake3* | @b SSE2 | 256 | 2.4 GB/s | 8.1 | + * | HighwayHash | | 64 | 1.4 GB/s | 6.0 | + * | FNV64 | | 64 | 1.2 GB/s | 62.7 | + * | Blake2* | | 256 | 1.1 GB/s | 5.1 | + * | SHA1* | | 160 | 0.8 GB/s | 5.6 | + * | MD5* | | 128 | 0.6 GB/s | 7.8 | + * @note + * - Hashes which require a specific ISA extension are noted. SSE2 is also noted, + * even though it is mandatory on x64. + * - Hashes with an asterisk are cryptographic. Note that MD5 is non-cryptographic + * by modern standards. + * - Small data velocity is a rough average of algorithm's efficiency for small + * data. For more accurate information, see the wiki. + * - More benchmarks and strength tests are found on the wiki: + * https://github.com/Cyan4973/xxHash/wiki + * + * Usage + * ------ + * All xxHash variants use a similar API. Changing the algorithm is a trivial + * substitution. + * + * @pre + * For functions which take an input and length parameter, the following + * requirements are assumed: + * - The range from [`input`, `input + length`) is valid, readable memory. + * - The only exception is if the `length` is `0`, `input` may be `NULL`. + * - For C++, the objects must have the *TriviallyCopyable* property, as the + * functions access bytes directly as if it was an array of `unsigned char`. + * + * @anchor single_shot_example + * **Single Shot** + * + * These functions are stateless functions which hash a contiguous block of memory, + * immediately returning the result. They are the easiest and usually the fastest + * option. + * + * XXH32(), XXH64(), XXH3_64bits(), XXH3_128bits() + * + * @code{.c} + * #include + * #include "xxhash.h" + * + * // Example for a function which hashes a null terminated string with XXH32(). + * XXH32_hash_t hash_string(const char* string, XXH32_hash_t seed) + * { + * // NULL pointers are only valid if the length is zero + * size_t length = (string == NULL) ? 0 : strlen(string); + * return XXH32(string, length, seed); + * } + * @endcode + * + * + * @anchor streaming_example + * **Streaming** + * + * These groups of functions allow incremental hashing of unknown size, even + * more than what would fit in a size_t. + * + * XXH32_reset(), XXH64_reset(), XXH3_64bits_reset(), XXH3_128bits_reset() + * + * @code{.c} + * #include + * #include + * #include "xxhash.h" + * // Example for a function which hashes a FILE incrementally with XXH3_64bits(). + * XXH64_hash_t hashFile(FILE* f) + * { + * // Allocate a state struct. Do not just use malloc() or new. + * XXH3_state_t* state = XXH3_createState(); + * assert(state != NULL && "Out of memory!"); + * // Reset the state to start a new hashing session. + * XXH3_64bits_reset(state); + * char buffer[4096]; + * size_t count; + * // Read the file in chunks + * while ((count = fread(buffer, 1, sizeof(buffer), f)) != 0) { + * // Run update() as many times as necessary to process the data + * XXH3_64bits_update(state, buffer, count); + * } + * // Retrieve the finalized hash. This will not change the state. + * XXH64_hash_t result = XXH3_64bits_digest(state); + * // Free the state. Do not use free(). + * XXH3_freeState(state); + * return result; + * } + * @endcode + * + * Streaming functions generate the xxHash value from an incremental input. + * This method is slower than single-call functions, due to state management. + * For small inputs, prefer `XXH32()` and `XXH64()`, which are better optimized. + * + * An XXH state must first be allocated using `XXH*_createState()`. + * + * Start a new hash by initializing the state with a seed using `XXH*_reset()`. + * + * Then, feed the hash state by calling `XXH*_update()` as many times as necessary. + * + * The function returns an error code, with 0 meaning OK, and any other value + * meaning there is an error. + * + * Finally, a hash value can be produced anytime, by using `XXH*_digest()`. + * This function returns the nn-bits hash as an int or long long. + * + * It's still possible to continue inserting input into the hash state after a + * digest, and generate new hash values later on by invoking `XXH*_digest()`. + * + * When done, release the state using `XXH*_freeState()`. + * + * + * @anchor canonical_representation_example + * **Canonical Representation** + * + * The default return values from XXH functions are unsigned 32, 64 and 128 bit + * integers. + * This the simplest and fastest format for further post-processing. + * + * However, this leaves open the question of what is the order on the byte level, + * since little and big endian conventions will store the same number differently. + * + * The canonical representation settles this issue by mandating big-endian + * convention, the same convention as human-readable numbers (large digits first). + * + * When writing hash values to storage, sending them over a network, or printing + * them, it's highly recommended to use the canonical representation to ensure + * portability across a wider range of systems, present and future. + * + * The following functions allow transformation of hash values to and from + * canonical format. + * + * XXH32_canonicalFromHash(), XXH32_hashFromCanonical(), + * XXH64_canonicalFromHash(), XXH64_hashFromCanonical(), + * XXH128_canonicalFromHash(), XXH128_hashFromCanonical(), + * + * @code{.c} + * #include + * #include "xxhash.h" + * + * // Example for a function which prints XXH32_hash_t in human readable format + * void printXxh32(XXH32_hash_t hash) + * { + * XXH32_canonical_t cano; + * XXH32_canonicalFromHash(&cano, hash); + * size_t i; + * for(i = 0; i < sizeof(cano.digest); ++i) { + * printf("%02x", cano.digest[i]); + * } + * printf("\n"); + * } + * + * // Example for a function which converts XXH32_canonical_t to XXH32_hash_t + * XXH32_hash_t convertCanonicalToXxh32(XXH32_canonical_t cano) + * { + * XXH32_hash_t hash = XXH32_hashFromCanonical(&cano); + * return hash; + * } + * @endcode + * + * + * @file xxhash.h + * xxHash prototypes and implementation + */ + +#if defined (__cplusplus) +extern "C" { +#endif + +/* **************************** + * INLINE mode + ******************************/ +/*! + * @defgroup public Public API + * Contains details on the public xxHash functions. + * @{ + */ +#ifdef XXH_DOXYGEN +/*! + * @brief Gives access to internal state declaration, required for static allocation. + * + * Incompatible with dynamic linking, due to risks of ABI changes. + * + * Usage: + * @code{.c} + * #define XXH_STATIC_LINKING_ONLY + * #include "xxhash.h" + * @endcode + */ +# define XXH_STATIC_LINKING_ONLY +/* Do not undef XXH_STATIC_LINKING_ONLY for Doxygen */ + +/*! + * @brief Gives access to internal definitions. + * + * Usage: + * @code{.c} + * #define XXH_STATIC_LINKING_ONLY + * #define XXH_IMPLEMENTATION + * #include "xxhash.h" + * @endcode + */ +# define XXH_IMPLEMENTATION +/* Do not undef XXH_IMPLEMENTATION for Doxygen */ + +/*! + * @brief Exposes the implementation and marks all functions as `inline`. + * + * Use these build macros to inline xxhash into the target unit. + * Inlining improves performance on small inputs, especially when the length is + * expressed as a compile-time constant: + * + * https://fastcompression.blogspot.com/2018/03/xxhash-for-small-keys-impressive-power.html + * + * It also keeps xxHash symbols private to the unit, so they are not exported. + * + * Usage: + * @code{.c} + * #define XXH_INLINE_ALL + * #include "xxhash.h" + * @endcode + * Do not compile and link xxhash.o as a separate object, as it is not useful. + */ +# define XXH_INLINE_ALL +# undef XXH_INLINE_ALL +/*! + * @brief Exposes the implementation without marking functions as inline. + */ +# define XXH_PRIVATE_API +# undef XXH_PRIVATE_API +/*! + * @brief Emulate a namespace by transparently prefixing all symbols. + * + * If you want to include _and expose_ xxHash functions from within your own + * library, but also want to avoid symbol collisions with other libraries which + * may also include xxHash, you can use @ref XXH_NAMESPACE to automatically prefix + * any public symbol from xxhash library with the value of @ref XXH_NAMESPACE + * (therefore, avoid empty or numeric values). + * + * Note that no change is required within the calling program as long as it + * includes `xxhash.h`: Regular symbol names will be automatically translated + * by this header. + */ +# define XXH_NAMESPACE /* YOUR NAME HERE */ +# undef XXH_NAMESPACE +#endif + +#if (defined(XXH_INLINE_ALL) || defined(XXH_PRIVATE_API)) \ + && !defined(XXH_INLINE_ALL_31684351384) + /* this section should be traversed only once */ +# define XXH_INLINE_ALL_31684351384 + /* give access to the advanced API, required to compile implementations */ +# undef XXH_STATIC_LINKING_ONLY /* avoid macro redef */ +# define XXH_STATIC_LINKING_ONLY + /* make all functions private */ +# undef XXH_PUBLIC_API +# if defined(__GNUC__) +# define XXH_PUBLIC_API static __inline __attribute__((__unused__)) +# elif defined (__cplusplus) || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) +# define XXH_PUBLIC_API static inline +# elif defined(_MSC_VER) +# define XXH_PUBLIC_API static __inline +# else + /* note: this version may generate warnings for unused static functions */ +# define XXH_PUBLIC_API static +# endif + + /* + * This part deals with the special case where a unit wants to inline xxHash, + * but "xxhash.h" has previously been included without XXH_INLINE_ALL, + * such as part of some previously included *.h header file. + * Without further action, the new include would just be ignored, + * and functions would effectively _not_ be inlined (silent failure). + * The following macros solve this situation by prefixing all inlined names, + * avoiding naming collision with previous inclusions. + */ + /* Before that, we unconditionally #undef all symbols, + * in case they were already defined with XXH_NAMESPACE. + * They will then be redefined for XXH_INLINE_ALL + */ +# undef XXH_versionNumber + /* XXH32 */ +# undef XXH32 +# undef XXH32_createState +# undef XXH32_freeState +# undef XXH32_reset +# undef XXH32_update +# undef XXH32_digest +# undef XXH32_copyState +# undef XXH32_canonicalFromHash +# undef XXH32_hashFromCanonical + /* XXH64 */ +# undef XXH64 +# undef XXH64_createState +# undef XXH64_freeState +# undef XXH64_reset +# undef XXH64_update +# undef XXH64_digest +# undef XXH64_copyState +# undef XXH64_canonicalFromHash +# undef XXH64_hashFromCanonical + /* XXH3_64bits */ +# undef XXH3_64bits +# undef XXH3_64bits_withSecret +# undef XXH3_64bits_withSeed +# undef XXH3_64bits_withSecretandSeed +# undef XXH3_createState +# undef XXH3_freeState +# undef XXH3_copyState +# undef XXH3_64bits_reset +# undef XXH3_64bits_reset_withSeed +# undef XXH3_64bits_reset_withSecret +# undef XXH3_64bits_update +# undef XXH3_64bits_digest +# undef XXH3_generateSecret + /* XXH3_128bits */ +# undef XXH128 +# undef XXH3_128bits +# undef XXH3_128bits_withSeed +# undef XXH3_128bits_withSecret +# undef XXH3_128bits_reset +# undef XXH3_128bits_reset_withSeed +# undef XXH3_128bits_reset_withSecret +# undef XXH3_128bits_reset_withSecretandSeed +# undef XXH3_128bits_update +# undef XXH3_128bits_digest +# undef XXH128_isEqual +# undef XXH128_cmp +# undef XXH128_canonicalFromHash +# undef XXH128_hashFromCanonical + /* Finally, free the namespace itself */ +# undef XXH_NAMESPACE + + /* employ the namespace for XXH_INLINE_ALL */ +# define XXH_NAMESPACE XXH_INLINE_ + /* + * Some identifiers (enums, type names) are not symbols, + * but they must nonetheless be renamed to avoid redeclaration. + * Alternative solution: do not redeclare them. + * However, this requires some #ifdefs, and has a more dispersed impact. + * Meanwhile, renaming can be achieved in a single place. + */ +# define XXH_IPREF(Id) XXH_NAMESPACE ## Id +# define XXH_OK XXH_IPREF(XXH_OK) +# define XXH_ERROR XXH_IPREF(XXH_ERROR) +# define XXH_errorcode XXH_IPREF(XXH_errorcode) +# define XXH32_canonical_t XXH_IPREF(XXH32_canonical_t) +# define XXH64_canonical_t XXH_IPREF(XXH64_canonical_t) +# define XXH128_canonical_t XXH_IPREF(XXH128_canonical_t) +# define XXH32_state_s XXH_IPREF(XXH32_state_s) +# define XXH32_state_t XXH_IPREF(XXH32_state_t) +# define XXH64_state_s XXH_IPREF(XXH64_state_s) +# define XXH64_state_t XXH_IPREF(XXH64_state_t) +# define XXH3_state_s XXH_IPREF(XXH3_state_s) +# define XXH3_state_t XXH_IPREF(XXH3_state_t) +# define XXH128_hash_t XXH_IPREF(XXH128_hash_t) + /* Ensure the header is parsed again, even if it was previously included */ +# undef XXHASH_H_5627135585666179 +# undef XXHASH_H_STATIC_13879238742 +#endif /* XXH_INLINE_ALL || XXH_PRIVATE_API */ + +/* **************************************************************** + * Stable API + *****************************************************************/ +#ifndef XXHASH_H_5627135585666179 +#define XXHASH_H_5627135585666179 1 + +/*! @brief Marks a global symbol. */ +#if !defined(XXH_INLINE_ALL) && !defined(XXH_PRIVATE_API) +# if defined(_WIN32) && defined(_MSC_VER) && (defined(XXH_IMPORT) || defined(XXH_EXPORT)) +# ifdef XXH_EXPORT +# define XXH_PUBLIC_API __declspec(dllexport) +# elif XXH_IMPORT +# define XXH_PUBLIC_API __declspec(dllimport) +# endif +# else +# define XXH_PUBLIC_API /* do nothing */ +# endif +#endif + +#ifdef XXH_NAMESPACE +# define XXH_CAT(A,B) A##B +# define XXH_NAME2(A,B) XXH_CAT(A,B) +# define XXH_versionNumber XXH_NAME2(XXH_NAMESPACE, XXH_versionNumber) +/* XXH32 */ +# define XXH32 XXH_NAME2(XXH_NAMESPACE, XXH32) +# define XXH32_createState XXH_NAME2(XXH_NAMESPACE, XXH32_createState) +# define XXH32_freeState XXH_NAME2(XXH_NAMESPACE, XXH32_freeState) +# define XXH32_reset XXH_NAME2(XXH_NAMESPACE, XXH32_reset) +# define XXH32_update XXH_NAME2(XXH_NAMESPACE, XXH32_update) +# define XXH32_digest XXH_NAME2(XXH_NAMESPACE, XXH32_digest) +# define XXH32_copyState XXH_NAME2(XXH_NAMESPACE, XXH32_copyState) +# define XXH32_canonicalFromHash XXH_NAME2(XXH_NAMESPACE, XXH32_canonicalFromHash) +# define XXH32_hashFromCanonical XXH_NAME2(XXH_NAMESPACE, XXH32_hashFromCanonical) +/* XXH64 */ +# define XXH64 XXH_NAME2(XXH_NAMESPACE, XXH64) +# define XXH64_createState XXH_NAME2(XXH_NAMESPACE, XXH64_createState) +# define XXH64_freeState XXH_NAME2(XXH_NAMESPACE, XXH64_freeState) +# define XXH64_reset XXH_NAME2(XXH_NAMESPACE, XXH64_reset) +# define XXH64_update XXH_NAME2(XXH_NAMESPACE, XXH64_update) +# define XXH64_digest XXH_NAME2(XXH_NAMESPACE, XXH64_digest) +# define XXH64_copyState XXH_NAME2(XXH_NAMESPACE, XXH64_copyState) +# define XXH64_canonicalFromHash XXH_NAME2(XXH_NAMESPACE, XXH64_canonicalFromHash) +# define XXH64_hashFromCanonical XXH_NAME2(XXH_NAMESPACE, XXH64_hashFromCanonical) +/* XXH3_64bits */ +# define XXH3_64bits XXH_NAME2(XXH_NAMESPACE, XXH3_64bits) +# define XXH3_64bits_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_withSecret) +# define XXH3_64bits_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_withSeed) +# define XXH3_64bits_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_withSecretandSeed) +# define XXH3_createState XXH_NAME2(XXH_NAMESPACE, XXH3_createState) +# define XXH3_freeState XXH_NAME2(XXH_NAMESPACE, XXH3_freeState) +# define XXH3_copyState XXH_NAME2(XXH_NAMESPACE, XXH3_copyState) +# define XXH3_64bits_reset XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset) +# define XXH3_64bits_reset_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset_withSeed) +# define XXH3_64bits_reset_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset_withSecret) +# define XXH3_64bits_reset_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset_withSecretandSeed) +# define XXH3_64bits_update XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_update) +# define XXH3_64bits_digest XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_digest) +# define XXH3_generateSecret XXH_NAME2(XXH_NAMESPACE, XXH3_generateSecret) +# define XXH3_generateSecret_fromSeed XXH_NAME2(XXH_NAMESPACE, XXH3_generateSecret_fromSeed) +/* XXH3_128bits */ +# define XXH128 XXH_NAME2(XXH_NAMESPACE, XXH128) +# define XXH3_128bits XXH_NAME2(XXH_NAMESPACE, XXH3_128bits) +# define XXH3_128bits_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_withSeed) +# define XXH3_128bits_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_withSecret) +# define XXH3_128bits_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_withSecretandSeed) +# define XXH3_128bits_reset XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset) +# define XXH3_128bits_reset_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset_withSeed) +# define XXH3_128bits_reset_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset_withSecret) +# define XXH3_128bits_reset_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset_withSecretandSeed) +# define XXH3_128bits_update XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_update) +# define XXH3_128bits_digest XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_digest) +# define XXH128_isEqual XXH_NAME2(XXH_NAMESPACE, XXH128_isEqual) +# define XXH128_cmp XXH_NAME2(XXH_NAMESPACE, XXH128_cmp) +# define XXH128_canonicalFromHash XXH_NAME2(XXH_NAMESPACE, XXH128_canonicalFromHash) +# define XXH128_hashFromCanonical XXH_NAME2(XXH_NAMESPACE, XXH128_hashFromCanonical) +#endif + + +/* ************************************* +* Compiler specifics +***************************************/ + +/* specific declaration modes for Windows */ +#if !defined(XXH_INLINE_ALL) && !defined(XXH_PRIVATE_API) +# if defined(_WIN32) && defined(_MSC_VER) && (defined(XXH_IMPORT) || defined(XXH_EXPORT)) +# ifdef XXH_EXPORT +# define XXH_PUBLIC_API __declspec(dllexport) +# elif XXH_IMPORT +# define XXH_PUBLIC_API __declspec(dllimport) +# endif +# else +# define XXH_PUBLIC_API /* do nothing */ +# endif +#endif + +#if defined (__GNUC__) +# define XXH_CONSTF __attribute__((__const__)) +# define XXH_PUREF __attribute__((__pure__)) +# define XXH_MALLOCF __attribute__((__malloc__)) +#else +# define XXH_CONSTF /* disable */ +# define XXH_PUREF +# define XXH_MALLOCF +#endif + +/* ************************************* +* Version +***************************************/ +#define XXH_VERSION_MAJOR 0 +#define XXH_VERSION_MINOR 8 +#define XXH_VERSION_RELEASE 3 +/*! @brief Version number, encoded as two digits each */ +#define XXH_VERSION_NUMBER (XXH_VERSION_MAJOR *100*100 + XXH_VERSION_MINOR *100 + XXH_VERSION_RELEASE) + +/*! + * @brief Obtains the xxHash version. + * + * This is mostly useful when xxHash is compiled as a shared library, + * since the returned value comes from the library, as opposed to header file. + * + * @return @ref XXH_VERSION_NUMBER of the invoked library. + */ +XXH_PUBLIC_API XXH_CONSTF unsigned XXH_versionNumber (void); + + +/* **************************** +* Common basic types +******************************/ +#include /* size_t */ +/*! + * @brief Exit code for the streaming API. + */ +typedef enum { + XXH_OK = 0, /*!< OK */ + XXH_ERROR /*!< Error */ +} XXH_errorcode; + + +/*-********************************************************************** +* 32-bit hash +************************************************************************/ +#if defined(XXH_DOXYGEN) /* Don't show include */ +/*! + * @brief An unsigned 32-bit integer. + * + * Not necessarily defined to `uint32_t` but functionally equivalent. + */ +typedef uint32_t XXH32_hash_t; + +#elif !defined (__VMS) \ + && (defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) ) +# ifdef _AIX +# include +# else +# include +# endif + typedef uint32_t XXH32_hash_t; + +#else +# include +# if UINT_MAX == 0xFFFFFFFFUL + typedef unsigned int XXH32_hash_t; +# elif ULONG_MAX == 0xFFFFFFFFUL + typedef unsigned long XXH32_hash_t; +# else +# error "unsupported platform: need a 32-bit type" +# endif +#endif + +/*! + * @} + * + * @defgroup XXH32_family XXH32 family + * @ingroup public + * Contains functions used in the classic 32-bit xxHash algorithm. + * + * @note + * XXH32 is useful for older platforms, with no or poor 64-bit performance. + * Note that the @ref XXH3_family provides competitive speed for both 32-bit + * and 64-bit systems, and offers true 64/128 bit hash results. + * + * @see @ref XXH64_family, @ref XXH3_family : Other xxHash families + * @see @ref XXH32_impl for implementation details + * @{ + */ + +/*! + * @brief Calculates the 32-bit hash of @p input using xxHash32. + * + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * @param seed The 32-bit seed to alter the hash's output predictably. + * + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 32-bit xxHash32 value. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH32_hash_t XXH32 (const void* input, size_t length, XXH32_hash_t seed); + +#ifndef XXH_NO_STREAM +/*! + * @typedef struct XXH32_state_s XXH32_state_t + * @brief The opaque state struct for the XXH32 streaming API. + * + * @see XXH32_state_s for details. + * @see @ref streaming_example "Streaming Example" + */ +typedef struct XXH32_state_s XXH32_state_t; + +/*! + * @brief Allocates an @ref XXH32_state_t. + * + * @return An allocated pointer of @ref XXH32_state_t on success. + * @return `NULL` on failure. + * + * @note Must be freed with XXH32_freeState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_MALLOCF XXH32_state_t* XXH32_createState(void); +/*! + * @brief Frees an @ref XXH32_state_t. + * + * @param statePtr A pointer to an @ref XXH32_state_t allocated with @ref XXH32_createState(). + * + * @return @ref XXH_OK. + * + * @note @p statePtr must be allocated with XXH32_createState(). + * + * @see @ref streaming_example "Streaming Example" + * + */ +XXH_PUBLIC_API XXH_errorcode XXH32_freeState(XXH32_state_t* statePtr); +/*! + * @brief Copies one @ref XXH32_state_t to another. + * + * @param dst_state The state to copy to. + * @param src_state The state to copy from. + * @pre + * @p dst_state and @p src_state must not be `NULL` and must not overlap. + */ +XXH_PUBLIC_API void XXH32_copyState(XXH32_state_t* dst_state, const XXH32_state_t* src_state); + +/*! + * @brief Resets an @ref XXH32_state_t to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param seed The 32-bit seed to alter the hash result predictably. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note This function resets and seeds a state. Call it before @ref XXH32_update(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH32_reset (XXH32_state_t* statePtr, XXH32_hash_t seed); + +/*! + * @brief Consumes a block of @p input to an @ref XXH32_state_t. + * + * @param statePtr The state struct to update. + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note Call this to incrementally consume blocks of data. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH32_update (XXH32_state_t* statePtr, const void* input, size_t length); + +/*! + * @brief Returns the calculated hash value from an @ref XXH32_state_t. + * + * @param statePtr The state struct to calculate the hash from. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return The calculated 32-bit xxHash32 value from that state. + * + * @note + * Calling XXH32_digest() will not affect @p statePtr, so you can update, + * digest, and update again. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH32_hash_t XXH32_digest (const XXH32_state_t* statePtr); +#endif /* !XXH_NO_STREAM */ + +/******* Canonical representation *******/ + +/*! + * @brief Canonical (big endian) representation of @ref XXH32_hash_t. + */ +typedef struct { + unsigned char digest[4]; /*!< Hash bytes, big endian */ +} XXH32_canonical_t; + +/*! + * @brief Converts an @ref XXH32_hash_t to a big endian @ref XXH32_canonical_t. + * + * @param dst The @ref XXH32_canonical_t pointer to be stored to. + * @param hash The @ref XXH32_hash_t to be converted. + * + * @pre + * @p dst must not be `NULL`. + * + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API void XXH32_canonicalFromHash(XXH32_canonical_t* dst, XXH32_hash_t hash); + +/*! + * @brief Converts an @ref XXH32_canonical_t to a native @ref XXH32_hash_t. + * + * @param src The @ref XXH32_canonical_t to convert. + * + * @pre + * @p src must not be `NULL`. + * + * @return The converted hash. + * + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH32_hash_t XXH32_hashFromCanonical(const XXH32_canonical_t* src); + + +/*! @cond Doxygen ignores this part */ +#ifdef __has_attribute +# define XXH_HAS_ATTRIBUTE(x) __has_attribute(x) +#else +# define XXH_HAS_ATTRIBUTE(x) 0 +#endif +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +/* + * C23 __STDC_VERSION__ number hasn't been specified yet. For now + * leave as `201711L` (C17 + 1). + * TODO: Update to correct value when its been specified. + */ +#define XXH_C23_VN 201711L +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +/* C-language Attributes are added in C23. */ +#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= XXH_C23_VN) && defined(__has_c_attribute) +# define XXH_HAS_C_ATTRIBUTE(x) __has_c_attribute(x) +#else +# define XXH_HAS_C_ATTRIBUTE(x) 0 +#endif +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +#if defined(__cplusplus) && defined(__has_cpp_attribute) +# define XXH_HAS_CPP_ATTRIBUTE(x) __has_cpp_attribute(x) +#else +# define XXH_HAS_CPP_ATTRIBUTE(x) 0 +#endif +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +/* + * Define XXH_FALLTHROUGH macro for annotating switch case with the 'fallthrough' attribute + * introduced in CPP17 and C23. + * CPP17 : https://en.cppreference.com/w/cpp/language/attributes/fallthrough + * C23 : https://en.cppreference.com/w/c/language/attributes/fallthrough + */ +#if XXH_HAS_C_ATTRIBUTE(fallthrough) || XXH_HAS_CPP_ATTRIBUTE(fallthrough) +# define XXH_FALLTHROUGH [[fallthrough]] +#elif XXH_HAS_ATTRIBUTE(__fallthrough__) +# define XXH_FALLTHROUGH __attribute__ ((__fallthrough__)) +#else +# define XXH_FALLTHROUGH /* fallthrough */ +#endif +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +/* + * Define XXH_NOESCAPE for annotated pointers in public API. + * https://clang.llvm.org/docs/AttributeReference.html#noescape + * As of writing this, only supported by clang. + */ +#if XXH_HAS_ATTRIBUTE(noescape) +# define XXH_NOESCAPE __attribute__((__noescape__)) +#else +# define XXH_NOESCAPE +#endif +/*! @endcond */ + + +/*! + * @} + * @ingroup public + * @{ + */ + +#ifndef XXH_NO_LONG_LONG +/*-********************************************************************** +* 64-bit hash +************************************************************************/ +#if defined(XXH_DOXYGEN) /* don't include */ +/*! + * @brief An unsigned 64-bit integer. + * + * Not necessarily defined to `uint64_t` but functionally equivalent. + */ +typedef uint64_t XXH64_hash_t; +#elif !defined (__VMS) \ + && (defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) ) +# ifdef _AIX +# include +# else +# include +# endif + typedef uint64_t XXH64_hash_t; +#else +# include +# if defined(__LP64__) && ULONG_MAX == 0xFFFFFFFFFFFFFFFFULL + /* LP64 ABI says uint64_t is unsigned long */ + typedef unsigned long XXH64_hash_t; +# else + /* the following type must have a width of 64-bit */ + typedef unsigned long long XXH64_hash_t; +# endif +#endif + +/*! + * @} + * + * @defgroup XXH64_family XXH64 family + * @ingroup public + * @{ + * Contains functions used in the classic 64-bit xxHash algorithm. + * + * @note + * XXH3 provides competitive speed for both 32-bit and 64-bit systems, + * and offers true 64/128 bit hash results. + * It provides better speed for systems with vector processing capabilities. + */ + +/*! + * @brief Calculates the 64-bit hash of @p input using xxHash64. + * + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * @param seed The 64-bit seed to alter the hash's output predictably. + * + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 64-bit xxHash64 value. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH64(XXH_NOESCAPE const void* input, size_t length, XXH64_hash_t seed); + +/******* Streaming *******/ +#ifndef XXH_NO_STREAM +/*! + * @brief The opaque state struct for the XXH64 streaming API. + * + * @see XXH64_state_s for details. + * @see @ref streaming_example "Streaming Example" + */ +typedef struct XXH64_state_s XXH64_state_t; /* incomplete type */ + +/*! + * @brief Allocates an @ref XXH64_state_t. + * + * @return An allocated pointer of @ref XXH64_state_t on success. + * @return `NULL` on failure. + * + * @note Must be freed with XXH64_freeState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_MALLOCF XXH64_state_t* XXH64_createState(void); + +/*! + * @brief Frees an @ref XXH64_state_t. + * + * @param statePtr A pointer to an @ref XXH64_state_t allocated with @ref XXH64_createState(). + * + * @return @ref XXH_OK. + * + * @note @p statePtr must be allocated with XXH64_createState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH64_freeState(XXH64_state_t* statePtr); + +/*! + * @brief Copies one @ref XXH64_state_t to another. + * + * @param dst_state The state to copy to. + * @param src_state The state to copy from. + * @pre + * @p dst_state and @p src_state must not be `NULL` and must not overlap. + */ +XXH_PUBLIC_API void XXH64_copyState(XXH_NOESCAPE XXH64_state_t* dst_state, const XXH64_state_t* src_state); + +/*! + * @brief Resets an @ref XXH64_state_t to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note This function resets and seeds a state. Call it before @ref XXH64_update(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH64_reset (XXH_NOESCAPE XXH64_state_t* statePtr, XXH64_hash_t seed); + +/*! + * @brief Consumes a block of @p input to an @ref XXH64_state_t. + * + * @param statePtr The state struct to update. + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note Call this to incrementally consume blocks of data. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH64_update (XXH_NOESCAPE XXH64_state_t* statePtr, XXH_NOESCAPE const void* input, size_t length); + +/*! + * @brief Returns the calculated hash value from an @ref XXH64_state_t. + * + * @param statePtr The state struct to calculate the hash from. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return The calculated 64-bit xxHash64 value from that state. + * + * @note + * Calling XXH64_digest() will not affect @p statePtr, so you can update, + * digest, and update again. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH64_digest (XXH_NOESCAPE const XXH64_state_t* statePtr); +#endif /* !XXH_NO_STREAM */ +/******* Canonical representation *******/ + +/*! + * @brief Canonical (big endian) representation of @ref XXH64_hash_t. + */ +typedef struct { unsigned char digest[sizeof(XXH64_hash_t)]; } XXH64_canonical_t; + +/*! + * @brief Converts an @ref XXH64_hash_t to a big endian @ref XXH64_canonical_t. + * + * @param dst The @ref XXH64_canonical_t pointer to be stored to. + * @param hash The @ref XXH64_hash_t to be converted. + * + * @pre + * @p dst must not be `NULL`. + * + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API void XXH64_canonicalFromHash(XXH_NOESCAPE XXH64_canonical_t* dst, XXH64_hash_t hash); + +/*! + * @brief Converts an @ref XXH64_canonical_t to a native @ref XXH64_hash_t. + * + * @param src The @ref XXH64_canonical_t to convert. + * + * @pre + * @p src must not be `NULL`. + * + * @return The converted hash. + * + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH64_hashFromCanonical(XXH_NOESCAPE const XXH64_canonical_t* src); + +#ifndef XXH_NO_XXH3 + +/*! + * @} + * ************************************************************************ + * @defgroup XXH3_family XXH3 family + * @ingroup public + * @{ + * + * XXH3 is a more recent hash algorithm featuring: + * - Improved speed for both small and large inputs + * - True 64-bit and 128-bit outputs + * - SIMD acceleration + * - Improved 32-bit viability + * + * Speed analysis methodology is explained here: + * + * https://fastcompression.blogspot.com/2019/03/presenting-xxh3.html + * + * Compared to XXH64, expect XXH3 to run approximately + * ~2x faster on large inputs and >3x faster on small ones, + * exact differences vary depending on platform. + * + * XXH3's speed benefits greatly from SIMD and 64-bit arithmetic, + * but does not require it. + * Most 32-bit and 64-bit targets that can run XXH32 smoothly can run XXH3 + * at competitive speeds, even without vector support. Further details are + * explained in the implementation. + * + * XXH3 has a fast scalar implementation, but it also includes accelerated SIMD + * implementations for many common platforms: + * - AVX512 + * - AVX2 + * - SSE2 + * - ARM NEON + * - WebAssembly SIMD128 + * - POWER8 VSX + * - s390x ZVector + * This can be controlled via the @ref XXH_VECTOR macro, but it automatically + * selects the best version according to predefined macros. For the x86 family, an + * automatic runtime dispatcher is included separately in @ref xxh_x86dispatch.c. + * + * XXH3 implementation is portable: + * it has a generic C90 formulation that can be compiled on any platform, + * all implementations generate exactly the same hash value on all platforms. + * Starting from v0.8.0, it's also labelled "stable", meaning that + * any future version will also generate the same hash value. + * + * XXH3 offers 2 variants, _64bits and _128bits. + * + * When only 64 bits are needed, prefer invoking the _64bits variant, as it + * reduces the amount of mixing, resulting in faster speed on small inputs. + * It's also generally simpler to manipulate a scalar return type than a struct. + * + * The API supports one-shot hashing, streaming mode, and custom secrets. + */ +/*-********************************************************************** +* XXH3 64-bit variant +************************************************************************/ + +/*! + * @brief Calculates 64-bit unseeded variant of XXH3 hash of @p input. + * + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 64-bit XXH3 hash value. + * + * @note + * This is equivalent to @ref XXH3_64bits_withSeed() with a seed of `0`, however + * it may have slightly better performance due to constant propagation of the + * defaults. + * + * @see + * XXH3_64bits_withSeed(), XXH3_64bits_withSecret(): other seeding variants + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits(XXH_NOESCAPE const void* input, size_t length); + +/*! + * @brief Calculates 64-bit seeded variant of XXH3 hash of @p input. + * + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 64-bit XXH3 hash value. + * + * @note + * seed == 0 produces the same results as @ref XXH3_64bits(). + * + * This variant generates a custom secret on the fly based on default secret + * altered using the @p seed value. + * + * While this operation is decently fast, note that it's not completely free. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits_withSeed(XXH_NOESCAPE const void* input, size_t length, XXH64_hash_t seed); + +/*! + * The bare minimum size for a custom secret. + * + * @see + * XXH3_64bits_withSecret(), XXH3_64bits_reset_withSecret(), + * XXH3_128bits_withSecret(), XXH3_128bits_reset_withSecret(). + */ +#define XXH3_SECRET_SIZE_MIN 136 + +/*! + * @brief Calculates 64-bit variant of XXH3 with a custom "secret". + * + * @param data The block of data to be hashed, at least @p len bytes in size. + * @param len The length of @p data, in bytes. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * + * @return The calculated 64-bit XXH3 hash value. + * + * @pre + * The memory between @p data and @p data + @p len must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p data may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * It's possible to provide any blob of bytes as a "secret" to generate the hash. + * This makes it more difficult for an external actor to prepare an intentional collision. + * The main condition is that @p secretSize *must* be large enough (>= @ref XXH3_SECRET_SIZE_MIN). + * However, the quality of the secret impacts the dispersion of the hash algorithm. + * Therefore, the secret _must_ look like a bunch of random bytes. + * Avoid "trivial" or structured data such as repeated sequences or a text document. + * Whenever in doubt about the "randomness" of the blob of bytes, + * consider employing @ref XXH3_generateSecret() instead (see below). + * It will generate a proper high entropy secret derived from the blob of bytes. + * Another advantage of using XXH3_generateSecret() is that + * it guarantees that all bits within the initial blob of bytes + * will impact every bit of the output. + * This is not necessarily the case when using the blob of bytes directly + * because, when hashing _small_ inputs, only a portion of the secret is employed. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits_withSecret(XXH_NOESCAPE const void* data, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize); + + +/******* Streaming *******/ +#ifndef XXH_NO_STREAM +/* + * Streaming requires state maintenance. + * This operation costs memory and CPU. + * As a consequence, streaming is slower than one-shot hashing. + * For better performance, prefer one-shot functions whenever applicable. + */ + +/*! + * @brief The opaque state struct for the XXH3 streaming API. + * + * @see XXH3_state_s for details. + * @see @ref streaming_example "Streaming Example" + */ +typedef struct XXH3_state_s XXH3_state_t; +XXH_PUBLIC_API XXH_MALLOCF XXH3_state_t* XXH3_createState(void); +XXH_PUBLIC_API XXH_errorcode XXH3_freeState(XXH3_state_t* statePtr); + +/*! + * @brief Copies one @ref XXH3_state_t to another. + * + * @param dst_state The state to copy to. + * @param src_state The state to copy from. + * @pre + * @p dst_state and @p src_state must not be `NULL` and must not overlap. + */ +XXH_PUBLIC_API void XXH3_copyState(XXH_NOESCAPE XXH3_state_t* dst_state, XXH_NOESCAPE const XXH3_state_t* src_state); + +/*! + * @brief Resets an @ref XXH3_state_t to begin a new hash. + * + * @param statePtr The state struct to reset. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * - This function resets `statePtr` and generate a secret with default parameters. + * - Call this function before @ref XXH3_64bits_update(). + * - Digest will be equivalent to `XXH3_64bits()`. + * + * @see @ref streaming_example "Streaming Example" + * + */ +XXH_PUBLIC_API XXH_errorcode XXH3_64bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr); + +/*! + * @brief Resets an @ref XXH3_state_t with 64-bit seed to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * - This function resets `statePtr` and generate a secret from `seed`. + * - Call this function before @ref XXH3_64bits_update(). + * - Digest will be equivalent to `XXH3_64bits_withSeed()`. + * + * @see @ref streaming_example "Streaming Example" + * + */ +XXH_PUBLIC_API XXH_errorcode XXH3_64bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed); + +/*! + * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * `secret` is referenced, it _must outlive_ the hash streaming session. + * + * Similar to one-shot API, `secretSize` must be >= @ref XXH3_SECRET_SIZE_MIN, + * and the quality of produced hash values depends on secret's entropy + * (secret's content should look like a bunch of random bytes). + * When in doubt about the randomness of a candidate `secret`, + * consider employing `XXH3_generateSecret()` instead (see below). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_64bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize); + +/*! + * @brief Consumes a block of @p input to an @ref XXH3_state_t. + * + * @param statePtr The state struct to update. + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note Call this to incrementally consume blocks of data. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_64bits_update (XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* input, size_t length); + +/*! + * @brief Returns the calculated XXH3 64-bit hash value from an @ref XXH3_state_t. + * + * @param statePtr The state struct to calculate the hash from. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return The calculated XXH3 64-bit hash value from that state. + * + * @note + * Calling XXH3_64bits_digest() will not affect @p statePtr, so you can update, + * digest, and update again. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits_digest (XXH_NOESCAPE const XXH3_state_t* statePtr); +#endif /* !XXH_NO_STREAM */ + +/* note : canonical representation of XXH3 is the same as XXH64 + * since they both produce XXH64_hash_t values */ + + +/*-********************************************************************** +* XXH3 128-bit variant +************************************************************************/ + +/*! + * @brief The return value from 128-bit hashes. + * + * Stored in little endian order, although the fields themselves are in native + * endianness. + */ +typedef struct { + XXH64_hash_t low64; /*!< `value & 0xFFFFFFFFFFFFFFFF` */ + XXH64_hash_t high64; /*!< `value >> 64` */ +} XXH128_hash_t; + +/*! + * @brief Calculates 128-bit unseeded variant of XXH3 of @p data. + * + * @param data The block of data to be hashed, at least @p length bytes in size. + * @param len The length of @p data, in bytes. + * + * @return The calculated 128-bit variant of XXH3 value. + * + * The 128-bit variant of XXH3 has more strength, but it has a bit of overhead + * for shorter inputs. + * + * This is equivalent to @ref XXH3_128bits_withSeed() with a seed of `0`, however + * it may have slightly better performance due to constant propagation of the + * defaults. + * + * @see XXH3_128bits_withSeed(), XXH3_128bits_withSecret(): other seeding variants + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits(XXH_NOESCAPE const void* data, size_t len); +/*! @brief Calculates 128-bit seeded variant of XXH3 hash of @p data. + * + * @param data The block of data to be hashed, at least @p length bytes in size. + * @param len The length of @p data, in bytes. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @return The calculated 128-bit variant of XXH3 value. + * + * @note + * seed == 0 produces the same results as @ref XXH3_64bits(). + * + * This variant generates a custom secret on the fly based on default secret + * altered using the @p seed value. + * + * While this operation is decently fast, note that it's not completely free. + * + * @see XXH3_128bits(), XXH3_128bits_withSecret(): other seeding variants + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits_withSeed(XXH_NOESCAPE const void* data, size_t len, XXH64_hash_t seed); +/*! + * @brief Calculates 128-bit variant of XXH3 with a custom "secret". + * + * @param data The block of data to be hashed, at least @p len bytes in size. + * @param len The length of @p data, in bytes. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * + * @return The calculated 128-bit variant of XXH3 value. + * + * It's possible to provide any blob of bytes as a "secret" to generate the hash. + * This makes it more difficult for an external actor to prepare an intentional collision. + * The main condition is that @p secretSize *must* be large enough (>= @ref XXH3_SECRET_SIZE_MIN). + * However, the quality of the secret impacts the dispersion of the hash algorithm. + * Therefore, the secret _must_ look like a bunch of random bytes. + * Avoid "trivial" or structured data such as repeated sequences or a text document. + * Whenever in doubt about the "randomness" of the blob of bytes, + * consider employing @ref XXH3_generateSecret() instead (see below). + * It will generate a proper high entropy secret derived from the blob of bytes. + * Another advantage of using XXH3_generateSecret() is that + * it guarantees that all bits within the initial blob of bytes + * will impact every bit of the output. + * This is not necessarily the case when using the blob of bytes directly + * because, when hashing _small_ inputs, only a portion of the secret is employed. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits_withSecret(XXH_NOESCAPE const void* data, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize); + +/******* Streaming *******/ +#ifndef XXH_NO_STREAM +/* + * Streaming requires state maintenance. + * This operation costs memory and CPU. + * As a consequence, streaming is slower than one-shot hashing. + * For better performance, prefer one-shot functions whenever applicable. + * + * XXH3_128bits uses the same XXH3_state_t as XXH3_64bits(). + * Use already declared XXH3_createState() and XXH3_freeState(). + * + * All reset and streaming functions have same meaning as their 64-bit counterpart. + */ + +/*! + * @brief Resets an @ref XXH3_state_t to begin a new hash. + * + * @param statePtr The state struct to reset. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * - This function resets `statePtr` and generate a secret with default parameters. + * - Call it before @ref XXH3_128bits_update(). + * - Digest will be equivalent to `XXH3_128bits()`. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_128bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr); + +/*! + * @brief Resets an @ref XXH3_state_t with 64-bit seed to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * - This function resets `statePtr` and generate a secret from `seed`. + * - Call it before @ref XXH3_128bits_update(). + * - Digest will be equivalent to `XXH3_128bits_withSeed()`. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_128bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed); +/*! + * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * `secret` is referenced, it _must outlive_ the hash streaming session. + * Similar to one-shot API, `secretSize` must be >= @ref XXH3_SECRET_SIZE_MIN, + * and the quality of produced hash values depends on secret's entropy + * (secret's content should look like a bunch of random bytes). + * When in doubt about the randomness of a candidate `secret`, + * consider employing `XXH3_generateSecret()` instead (see below). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_128bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize); + +/*! + * @brief Consumes a block of @p input to an @ref XXH3_state_t. + * + * Call this to incrementally consume blocks of data. + * + * @param statePtr The state struct to update. + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + */ +XXH_PUBLIC_API XXH_errorcode XXH3_128bits_update (XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* input, size_t length); + +/*! + * @brief Returns the calculated XXH3 128-bit hash value from an @ref XXH3_state_t. + * + * @param statePtr The state struct to calculate the hash from. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return The calculated XXH3 128-bit hash value from that state. + * + * @note + * Calling XXH3_128bits_digest() will not affect @p statePtr, so you can update, + * digest, and update again. + * + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits_digest (XXH_NOESCAPE const XXH3_state_t* statePtr); +#endif /* !XXH_NO_STREAM */ + +/* Following helper functions make it possible to compare XXH128_hast_t values. + * Since XXH128_hash_t is a structure, this capability is not offered by the language. + * Note: For better performance, these functions can be inlined using XXH_INLINE_ALL */ + +/*! + * @brief Check equality of two XXH128_hash_t values + * + * @param h1 The 128-bit hash value. + * @param h2 Another 128-bit hash value. + * + * @return `1` if `h1` and `h2` are equal. + * @return `0` if they are not. + */ +XXH_PUBLIC_API XXH_PUREF int XXH128_isEqual(XXH128_hash_t h1, XXH128_hash_t h2); + +/*! + * @brief Compares two @ref XXH128_hash_t + * + * This comparator is compatible with stdlib's `qsort()`/`bsearch()`. + * + * @param h128_1 Left-hand side value + * @param h128_2 Right-hand side value + * + * @return >0 if @p h128_1 > @p h128_2 + * @return =0 if @p h128_1 == @p h128_2 + * @return <0 if @p h128_1 < @p h128_2 + */ +XXH_PUBLIC_API XXH_PUREF int XXH128_cmp(XXH_NOESCAPE const void* h128_1, XXH_NOESCAPE const void* h128_2); + + +/******* Canonical representation *******/ +typedef struct { unsigned char digest[sizeof(XXH128_hash_t)]; } XXH128_canonical_t; + + +/*! + * @brief Converts an @ref XXH128_hash_t to a big endian @ref XXH128_canonical_t. + * + * @param dst The @ref XXH128_canonical_t pointer to be stored to. + * @param hash The @ref XXH128_hash_t to be converted. + * + * @pre + * @p dst must not be `NULL`. + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API void XXH128_canonicalFromHash(XXH_NOESCAPE XXH128_canonical_t* dst, XXH128_hash_t hash); + +/*! + * @brief Converts an @ref XXH128_canonical_t to a native @ref XXH128_hash_t. + * + * @param src The @ref XXH128_canonical_t to convert. + * + * @pre + * @p src must not be `NULL`. + * + * @return The converted hash. + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH128_hashFromCanonical(XXH_NOESCAPE const XXH128_canonical_t* src); + + +#endif /* !XXH_NO_XXH3 */ +#endif /* XXH_NO_LONG_LONG */ + +/*! + * @} + */ +#endif /* XXHASH_H_5627135585666179 */ + + + +#if defined(XXH_STATIC_LINKING_ONLY) && !defined(XXHASH_H_STATIC_13879238742) +#define XXHASH_H_STATIC_13879238742 +/* **************************************************************************** + * This section contains declarations which are not guaranteed to remain stable. + * They may change in future versions, becoming incompatible with a different + * version of the library. + * These declarations should only be used with static linking. + * Never use them in association with dynamic linking! + ***************************************************************************** */ + +/* + * These definitions are only present to allow static allocation + * of XXH states, on stack or in a struct, for example. + * Never **ever** access their members directly. + */ + +/*! + * @internal + * @brief Structure for XXH32 streaming API. + * + * @note This is only defined when @ref XXH_STATIC_LINKING_ONLY, + * @ref XXH_INLINE_ALL, or @ref XXH_IMPLEMENTATION is defined. Otherwise it is + * an opaque type. This allows fields to safely be changed. + * + * Typedef'd to @ref XXH32_state_t. + * Do not access the members of this struct directly. + * @see XXH64_state_s, XXH3_state_s + */ +struct XXH32_state_s { + XXH32_hash_t total_len_32; /*!< Total length hashed, modulo 2^32 */ + XXH32_hash_t large_len; /*!< Whether the hash is >= 16 (handles @ref total_len_32 overflow) */ + XXH32_hash_t v[4]; /*!< Accumulator lanes */ + XXH32_hash_t mem32[4]; /*!< Internal buffer for partial reads. Treated as unsigned char[16]. */ + XXH32_hash_t memsize; /*!< Amount of data in @ref mem32 */ + XXH32_hash_t reserved; /*!< Reserved field. Do not read nor write to it. */ +}; /* typedef'd to XXH32_state_t */ + + +#ifndef XXH_NO_LONG_LONG /* defined when there is no 64-bit support */ + +/*! + * @internal + * @brief Structure for XXH64 streaming API. + * + * @note This is only defined when @ref XXH_STATIC_LINKING_ONLY, + * @ref XXH_INLINE_ALL, or @ref XXH_IMPLEMENTATION is defined. Otherwise it is + * an opaque type. This allows fields to safely be changed. + * + * Typedef'd to @ref XXH64_state_t. + * Do not access the members of this struct directly. + * @see XXH32_state_s, XXH3_state_s + */ +struct XXH64_state_s { + XXH64_hash_t total_len; /*!< Total length hashed. This is always 64-bit. */ + XXH64_hash_t v[4]; /*!< Accumulator lanes */ + XXH64_hash_t mem64[4]; /*!< Internal buffer for partial reads. Treated as unsigned char[32]. */ + XXH32_hash_t memsize; /*!< Amount of data in @ref mem64 */ + XXH32_hash_t reserved32; /*!< Reserved field, needed for padding anyways*/ + XXH64_hash_t reserved64; /*!< Reserved field. Do not read or write to it. */ +}; /* typedef'd to XXH64_state_t */ + +#ifndef XXH_NO_XXH3 + +/* Windows SDK under 10.0.22000 is missing stdalign.h so we add a check + before allowing the windows compiler to use the C11 form. + Reference: https://github.com/Cyan4973/xxHash/issues/955 */ +#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L) \ + && (defined(_MSC_VER) && (_MSC_VER >= 1000) || !defined(_MSC_VER)) /* >= C11 */ +# include +# define XXH_ALIGN(n) alignas(n) +#elif defined(__cplusplus) && (__cplusplus >= 201103L) /* >= C++11 */ +/* In C++ alignas() is a keyword */ +# define XXH_ALIGN(n) alignas(n) +#elif defined(__GNUC__) +# define XXH_ALIGN(n) __attribute__ ((aligned(n))) +#elif defined(_MSC_VER) +# define XXH_ALIGN(n) __declspec(align(n)) +#else +# define XXH_ALIGN(n) /* disabled */ +#endif + +/* Old GCC versions only accept the attribute after the type in structures. */ +#if !(defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L)) /* C11+ */ \ + && ! (defined(__cplusplus) && (__cplusplus >= 201103L)) /* >= C++11 */ \ + && defined(__GNUC__) +# define XXH_ALIGN_MEMBER(align, type) type XXH_ALIGN(align) +#else +# define XXH_ALIGN_MEMBER(align, type) XXH_ALIGN(align) type +#endif + +/*! + * @brief The size of the internal XXH3 buffer. + * + * This is the optimal update size for incremental hashing. + * + * @see XXH3_64b_update(), XXH3_128b_update(). + */ +#define XXH3_INTERNALBUFFER_SIZE 256 + +/*! + * @internal + * @brief Default size of the secret buffer (and @ref XXH3_kSecret). + * + * This is the size used in @ref XXH3_kSecret and the seeded functions. + * + * Not to be confused with @ref XXH3_SECRET_SIZE_MIN. + */ +#define XXH3_SECRET_DEFAULT_SIZE 192 + +/*! + * @internal + * @brief Structure for XXH3 streaming API. + * + * @note This is only defined when @ref XXH_STATIC_LINKING_ONLY, + * @ref XXH_INLINE_ALL, or @ref XXH_IMPLEMENTATION is defined. + * Otherwise it is an opaque type. + * Never use this definition in combination with dynamic library. + * This allows fields to safely be changed in the future. + * + * @note ** This structure has a strict alignment requirement of 64 bytes!! ** + * Do not allocate this with `malloc()` or `new`, + * it will not be sufficiently aligned. + * Use @ref XXH3_createState() and @ref XXH3_freeState(), or stack allocation. + * + * Typedef'd to @ref XXH3_state_t. + * Do never access the members of this struct directly. + * + * @see XXH3_INITSTATE() for stack initialization. + * @see XXH3_createState(), XXH3_freeState(). + * @see XXH32_state_s, XXH64_state_s + */ +struct XXH3_state_s { + XXH_ALIGN_MEMBER(64, XXH64_hash_t acc[8]); + /*!< The 8 accumulators. See @ref XXH32_state_s::v and @ref XXH64_state_s::v */ + XXH_ALIGN_MEMBER(64, unsigned char customSecret[XXH3_SECRET_DEFAULT_SIZE]); + /*!< Used to store a custom secret generated from a seed. */ + XXH_ALIGN_MEMBER(64, unsigned char buffer[XXH3_INTERNALBUFFER_SIZE]); + /*!< The internal buffer. @see XXH32_state_s::mem32 */ + XXH32_hash_t bufferedSize; + /*!< The amount of memory in @ref buffer, @see XXH32_state_s::memsize */ + XXH32_hash_t useSeed; + /*!< Reserved field. Needed for padding on 64-bit. */ + size_t nbStripesSoFar; + /*!< Number or stripes processed. */ + XXH64_hash_t totalLen; + /*!< Total length hashed. 64-bit even on 32-bit targets. */ + size_t nbStripesPerBlock; + /*!< Number of stripes per block. */ + size_t secretLimit; + /*!< Size of @ref customSecret or @ref extSecret */ + XXH64_hash_t seed; + /*!< Seed for _withSeed variants. Must be zero otherwise, @see XXH3_INITSTATE() */ + XXH64_hash_t reserved64; + /*!< Reserved field. */ + const unsigned char* extSecret; + /*!< Reference to an external secret for the _withSecret variants, NULL + * for other variants. */ + /* note: there may be some padding at the end due to alignment on 64 bytes */ +}; /* typedef'd to XXH3_state_t */ + +#undef XXH_ALIGN_MEMBER + +/*! + * @brief Initializes a stack-allocated `XXH3_state_s`. + * + * When the @ref XXH3_state_t structure is merely emplaced on stack, + * it should be initialized with XXH3_INITSTATE() or a memset() + * in case its first reset uses XXH3_NNbits_reset_withSeed(). + * This init can be omitted if the first reset uses default or _withSecret mode. + * This operation isn't necessary when the state is created with XXH3_createState(). + * Note that this doesn't prepare the state for a streaming operation, + * it's still necessary to use XXH3_NNbits_reset*() afterwards. + */ +#define XXH3_INITSTATE(XXH3_state_ptr) \ + do { \ + XXH3_state_t* tmp_xxh3_state_ptr = (XXH3_state_ptr); \ + tmp_xxh3_state_ptr->seed = 0; \ + tmp_xxh3_state_ptr->extSecret = NULL; \ + } while(0) + + +/*! + * @brief Calculates the 128-bit hash of @p data using XXH3. + * + * @param data The block of data to be hashed, at least @p len bytes in size. + * @param len The length of @p data, in bytes. + * @param seed The 64-bit seed to alter the hash's output predictably. + * + * @pre + * The memory between @p data and @p data + @p len must be valid, + * readable, contiguous memory. However, if @p len is `0`, @p data may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 128-bit XXH3 value. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH128(XXH_NOESCAPE const void* data, size_t len, XXH64_hash_t seed); + + +/* === Experimental API === */ +/* Symbols defined below must be considered tied to a specific library version. */ + +/*! + * @brief Derive a high-entropy secret from any user-defined content, named customSeed. + * + * @param secretBuffer A writable buffer for derived high-entropy secret data. + * @param secretSize Size of secretBuffer, in bytes. Must be >= XXH3_SECRET_SIZE_MIN. + * @param customSeed A user-defined content. + * @param customSeedSize Size of customSeed, in bytes. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * The generated secret can be used in combination with `*_withSecret()` functions. + * The `_withSecret()` variants are useful to provide a higher level of protection + * than 64-bit seed, as it becomes much more difficult for an external actor to + * guess how to impact the calculation logic. + * + * The function accepts as input a custom seed of any length and any content, + * and derives from it a high-entropy secret of length @p secretSize into an + * already allocated buffer @p secretBuffer. + * + * The generated secret can then be used with any `*_withSecret()` variant. + * The functions @ref XXH3_128bits_withSecret(), @ref XXH3_64bits_withSecret(), + * @ref XXH3_128bits_reset_withSecret() and @ref XXH3_64bits_reset_withSecret() + * are part of this list. They all accept a `secret` parameter + * which must be large enough for implementation reasons (>= @ref XXH3_SECRET_SIZE_MIN) + * _and_ feature very high entropy (consist of random-looking bytes). + * These conditions can be a high bar to meet, so @ref XXH3_generateSecret() can + * be employed to ensure proper quality. + * + * @p customSeed can be anything. It can have any size, even small ones, + * and its content can be anything, even "poor entropy" sources such as a bunch + * of zeroes. The resulting `secret` will nonetheless provide all required qualities. + * + * @pre + * - @p secretSize must be >= @ref XXH3_SECRET_SIZE_MIN + * - When @p customSeedSize > 0, supplying NULL as customSeed is undefined behavior. + * + * Example code: + * @code{.c} + * #include + * #include + * #include + * #define XXH_STATIC_LINKING_ONLY // expose unstable API + * #include "xxhash.h" + * // Hashes argv[2] using the entropy from argv[1]. + * int main(int argc, char* argv[]) + * { + * char secret[XXH3_SECRET_SIZE_MIN]; + * if (argv != 3) { return 1; } + * XXH3_generateSecret(secret, sizeof(secret), argv[1], strlen(argv[1])); + * XXH64_hash_t h = XXH3_64bits_withSecret( + * argv[2], strlen(argv[2]), + * secret, sizeof(secret) + * ); + * printf("%016llx\n", (unsigned long long) h); + * } + * @endcode + */ +XXH_PUBLIC_API XXH_errorcode XXH3_generateSecret(XXH_NOESCAPE void* secretBuffer, size_t secretSize, XXH_NOESCAPE const void* customSeed, size_t customSeedSize); + +/*! + * @brief Generate the same secret as the _withSeed() variants. + * + * @param secretBuffer A writable buffer of @ref XXH3_SECRET_DEFAULT_SIZE bytes + * @param seed The 64-bit seed to alter the hash result predictably. + * + * The generated secret can be used in combination with + *`*_withSecret()` and `_withSecretandSeed()` variants. + * + * Example C++ `std::string` hash class: + * @code{.cpp} + * #include + * #define XXH_STATIC_LINKING_ONLY // expose unstable API + * #include "xxhash.h" + * // Slow, seeds each time + * class HashSlow { + * XXH64_hash_t seed; + * public: + * HashSlow(XXH64_hash_t s) : seed{s} {} + * size_t operator()(const std::string& x) const { + * return size_t{XXH3_64bits_withSeed(x.c_str(), x.length(), seed)}; + * } + * }; + * // Fast, caches the seeded secret for future uses. + * class HashFast { + * unsigned char secret[XXH3_SECRET_DEFAULT_SIZE]; + * public: + * HashFast(XXH64_hash_t s) { + * XXH3_generateSecret_fromSeed(secret, seed); + * } + * size_t operator()(const std::string& x) const { + * return size_t{ + * XXH3_64bits_withSecret(x.c_str(), x.length(), secret, sizeof(secret)) + * }; + * } + * }; + * @endcode + */ +XXH_PUBLIC_API void XXH3_generateSecret_fromSeed(XXH_NOESCAPE void* secretBuffer, XXH64_hash_t seed); + +/*! + * @brief Maximum size of "short" key in bytes. + */ +#define XXH3_MIDSIZE_MAX 240 + +/*! + * @brief Calculates 64/128-bit seeded variant of XXH3 hash of @p data. + * + * @param data The block of data to be hashed, at least @p len bytes in size. + * @param len The length of @p data, in bytes. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * These variants generate hash values using either: + * - @p seed for "short" keys (< @ref XXH3_MIDSIZE_MAX = 240 bytes) + * - @p secret for "large" keys (>= @ref XXH3_MIDSIZE_MAX). + * + * This generally benefits speed, compared to `_withSeed()` or `_withSecret()`. + * `_withSeed()` has to generate the secret on the fly for "large" keys. + * It's fast, but can be perceptible for "not so large" keys (< 1 KB). + * `_withSecret()` has to generate the masks on the fly for "small" keys, + * which requires more instructions than _withSeed() variants. + * Therefore, _withSecretandSeed variant combines the best of both worlds. + * + * When @p secret has been generated by XXH3_generateSecret_fromSeed(), + * this variant produces *exactly* the same results as `_withSeed()` variant, + * hence offering only a pure speed benefit on "large" input, + * by skipping the need to regenerate the secret for every large input. + * + * Another usage scenario is to hash the secret to a 64-bit hash value, + * for example with XXH3_64bits(), which then becomes the seed, + * and then employ both the seed and the secret in _withSecretandSeed(). + * On top of speed, an added benefit is that each bit in the secret + * has a 50% chance to swap each bit in the output, via its impact to the seed. + * + * This is not guaranteed when using the secret directly in "small data" scenarios, + * because only portions of the secret are employed for small data. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t +XXH3_64bits_withSecretandSeed(XXH_NOESCAPE const void* data, size_t len, + XXH_NOESCAPE const void* secret, size_t secretSize, + XXH64_hash_t seed); + +/*! + * @brief Calculates 128-bit seeded variant of XXH3 hash of @p data. + * + * @param data The memory segment to be hashed, at least @p len bytes in size. + * @param length The length of @p data, in bytes. + * @param secret The secret used to alter hash result predictably. + * @param secretSize The length of @p secret, in bytes (must be >= XXH3_SECRET_SIZE_MIN) + * @param seed64 The 64-bit seed to alter the hash result predictably. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @see XXH3_64bits_withSecretandSeed(): contract is the same. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t +XXH3_128bits_withSecretandSeed(XXH_NOESCAPE const void* input, size_t length, + XXH_NOESCAPE const void* secret, size_t secretSize, + XXH64_hash_t seed64); + +#ifndef XXH_NO_STREAM +/*! + * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash. + * + * @param statePtr A pointer to an @ref XXH3_state_t allocated with @ref XXH3_createState(). + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * @param seed64 The 64-bit seed to alter the hash result predictably. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @see XXH3_64bits_withSecretandSeed(). Contract is identical. + */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr, + XXH_NOESCAPE const void* secret, size_t secretSize, + XXH64_hash_t seed64); + +/*! + * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash. + * + * @param statePtr A pointer to an @ref XXH3_state_t allocated with @ref XXH3_createState(). + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * @param seed64 The 64-bit seed to alter the hash result predictably. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @see XXH3_64bits_withSecretandSeed(). Contract is identical. + * + * Note: there was a bug in an earlier version of this function (<= v0.8.2) + * that would make it generate an incorrect hash value + * when @p seed == 0 and @p length < XXH3_MIDSIZE_MAX + * and @p secret is different from XXH3_generateSecret_fromSeed(). + * As stated in the contract, the correct hash result must be + * the same as XXH3_128bits_withSeed() when @p length <= XXH3_MIDSIZE_MAX. + * Results generated by this older version are wrong, hence not comparable. + */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr, + XXH_NOESCAPE const void* secret, size_t secretSize, + XXH64_hash_t seed64); + +#endif /* !XXH_NO_STREAM */ + +#endif /* !XXH_NO_XXH3 */ +#endif /* XXH_NO_LONG_LONG */ +#if defined(XXH_INLINE_ALL) || defined(XXH_PRIVATE_API) +# define XXH_IMPLEMENTATION +#endif + +#endif /* defined(XXH_STATIC_LINKING_ONLY) && !defined(XXHASH_H_STATIC_13879238742) */ + + +/* ======================================================================== */ +/* ======================================================================== */ +/* ======================================================================== */ + + +/*-********************************************************************** + * xxHash implementation + *-********************************************************************** + * xxHash's implementation used to be hosted inside xxhash.c. + * + * However, inlining requires implementation to be visible to the compiler, + * hence be included alongside the header. + * Previously, implementation was hosted inside xxhash.c, + * which was then #included when inlining was activated. + * This construction created issues with a few build and install systems, + * as it required xxhash.c to be stored in /include directory. + * + * xxHash implementation is now directly integrated within xxhash.h. + * As a consequence, xxhash.c is no longer needed in /include. + * + * xxhash.c is still available and is still useful. + * In a "normal" setup, when xxhash is not inlined, + * xxhash.h only exposes the prototypes and public symbols, + * while xxhash.c can be built into an object file xxhash.o + * which can then be linked into the final binary. + ************************************************************************/ + +#if ( defined(XXH_INLINE_ALL) || defined(XXH_PRIVATE_API) \ + || defined(XXH_IMPLEMENTATION) ) && !defined(XXH_IMPLEM_13a8737387) +# define XXH_IMPLEM_13a8737387 + +/* ************************************* +* Tuning parameters +***************************************/ + +/*! + * @defgroup tuning Tuning parameters + * @{ + * + * Various macros to control xxHash's behavior. + */ +#ifdef XXH_DOXYGEN +/*! + * @brief Define this to disable 64-bit code. + * + * Useful if only using the @ref XXH32_family and you have a strict C90 compiler. + */ +# define XXH_NO_LONG_LONG +# undef XXH_NO_LONG_LONG /* don't actually */ +/*! + * @brief Controls how unaligned memory is accessed. + * + * By default, access to unaligned memory is controlled by `memcpy()`, which is + * safe and portable. + * + * Unfortunately, on some target/compiler combinations, the generated assembly + * is sub-optimal. + * + * The below switch allow selection of a different access method + * in the search for improved performance. + * + * @par Possible options: + * + * - `XXH_FORCE_MEMORY_ACCESS=0` (default): `memcpy` + * @par + * Use `memcpy()`. Safe and portable. Note that most modern compilers will + * eliminate the function call and treat it as an unaligned access. + * + * - `XXH_FORCE_MEMORY_ACCESS=1`: `__attribute__((aligned(1)))` + * @par + * Depends on compiler extensions and is therefore not portable. + * This method is safe _if_ your compiler supports it, + * and *generally* as fast or faster than `memcpy`. + * + * - `XXH_FORCE_MEMORY_ACCESS=2`: Direct cast + * @par + * Casts directly and dereferences. This method doesn't depend on the + * compiler, but it violates the C standard as it directly dereferences an + * unaligned pointer. It can generate buggy code on targets which do not + * support unaligned memory accesses, but in some circumstances, it's the + * only known way to get the most performance. + * + * - `XXH_FORCE_MEMORY_ACCESS=3`: Byteshift + * @par + * Also portable. This can generate the best code on old compilers which don't + * inline small `memcpy()` calls, and it might also be faster on big-endian + * systems which lack a native byteswap instruction. However, some compilers + * will emit literal byteshifts even if the target supports unaligned access. + * + * + * @warning + * Methods 1 and 2 rely on implementation-defined behavior. Use these with + * care, as what works on one compiler/platform/optimization level may cause + * another to read garbage data or even crash. + * + * See https://fastcompression.blogspot.com/2015/08/accessing-unaligned-memory.html for details. + * + * Prefer these methods in priority order (0 > 3 > 1 > 2) + */ +# define XXH_FORCE_MEMORY_ACCESS 0 + +/*! + * @def XXH_SIZE_OPT + * @brief Controls how much xxHash optimizes for size. + * + * xxHash, when compiled, tends to result in a rather large binary size. This + * is mostly due to heavy usage to forced inlining and constant folding of the + * @ref XXH3_family to increase performance. + * + * However, some developers prefer size over speed. This option can + * significantly reduce the size of the generated code. When using the `-Os` + * or `-Oz` options on GCC or Clang, this is defined to 1 by default, + * otherwise it is defined to 0. + * + * Most of these size optimizations can be controlled manually. + * + * This is a number from 0-2. + * - `XXH_SIZE_OPT` == 0: Default. xxHash makes no size optimizations. Speed + * comes first. + * - `XXH_SIZE_OPT` == 1: Default for `-Os` and `-Oz`. xxHash is more + * conservative and disables hacks that increase code size. It implies the + * options @ref XXH_NO_INLINE_HINTS == 1, @ref XXH_FORCE_ALIGN_CHECK == 0, + * and @ref XXH3_NEON_LANES == 8 if they are not already defined. + * - `XXH_SIZE_OPT` == 2: xxHash tries to make itself as small as possible. + * Performance may cry. For example, the single shot functions just use the + * streaming API. + */ +# define XXH_SIZE_OPT 0 + +/*! + * @def XXH_FORCE_ALIGN_CHECK + * @brief If defined to non-zero, adds a special path for aligned inputs (XXH32() + * and XXH64() only). + * + * This is an important performance trick for architectures without decent + * unaligned memory access performance. + * + * It checks for input alignment, and when conditions are met, uses a "fast + * path" employing direct 32-bit/64-bit reads, resulting in _dramatically + * faster_ read speed. + * + * The check costs one initial branch per hash, which is generally negligible, + * but not zero. + * + * Moreover, it's not useful to generate an additional code path if memory + * access uses the same instruction for both aligned and unaligned + * addresses (e.g. x86 and aarch64). + * + * In these cases, the alignment check can be removed by setting this macro to 0. + * Then the code will always use unaligned memory access. + * Align check is automatically disabled on x86, x64, ARM64, and some ARM chips + * which are platforms known to offer good unaligned memory accesses performance. + * + * It is also disabled by default when @ref XXH_SIZE_OPT >= 1. + * + * This option does not affect XXH3 (only XXH32 and XXH64). + */ +# define XXH_FORCE_ALIGN_CHECK 0 + +/*! + * @def XXH_NO_INLINE_HINTS + * @brief When non-zero, sets all functions to `static`. + * + * By default, xxHash tries to force the compiler to inline almost all internal + * functions. + * + * This can usually improve performance due to reduced jumping and improved + * constant folding, but significantly increases the size of the binary which + * might not be favorable. + * + * Additionally, sometimes the forced inlining can be detrimental to performance, + * depending on the architecture. + * + * XXH_NO_INLINE_HINTS marks all internal functions as static, giving the + * compiler full control on whether to inline or not. + * + * When not optimizing (-O0), using `-fno-inline` with GCC or Clang, or if + * @ref XXH_SIZE_OPT >= 1, this will automatically be defined. + */ +# define XXH_NO_INLINE_HINTS 0 + +/*! + * @def XXH3_INLINE_SECRET + * @brief Determines whether to inline the XXH3 withSecret code. + * + * When the secret size is known, the compiler can improve the performance + * of XXH3_64bits_withSecret() and XXH3_128bits_withSecret(). + * + * However, if the secret size is not known, it doesn't have any benefit. This + * happens when xxHash is compiled into a global symbol. Therefore, if + * @ref XXH_INLINE_ALL is *not* defined, this will be defined to 0. + * + * Additionally, this defaults to 0 on GCC 12+, which has an issue with function pointers + * that are *sometimes* force inline on -Og, and it is impossible to automatically + * detect this optimization level. + */ +# define XXH3_INLINE_SECRET 0 + +/*! + * @def XXH32_ENDJMP + * @brief Whether to use a jump for `XXH32_finalize`. + * + * For performance, `XXH32_finalize` uses multiple branches in the finalizer. + * This is generally preferable for performance, + * but depending on exact architecture, a jmp may be preferable. + * + * This setting is only possibly making a difference for very small inputs. + */ +# define XXH32_ENDJMP 0 + +/*! + * @internal + * @brief Redefines old internal names. + * + * For compatibility with code that uses xxHash's internals before the names + * were changed to improve namespacing. There is no other reason to use this. + */ +# define XXH_OLD_NAMES +# undef XXH_OLD_NAMES /* don't actually use, it is ugly. */ + +/*! + * @def XXH_NO_STREAM + * @brief Disables the streaming API. + * + * When xxHash is not inlined and the streaming functions are not used, disabling + * the streaming functions can improve code size significantly, especially with + * the @ref XXH3_family which tends to make constant folded copies of itself. + */ +# define XXH_NO_STREAM +# undef XXH_NO_STREAM /* don't actually */ +#endif /* XXH_DOXYGEN */ +/*! + * @} + */ + +#ifndef XXH_FORCE_MEMORY_ACCESS /* can be defined externally, on command line for example */ + /* prefer __packed__ structures (method 1) for GCC + * < ARMv7 with unaligned access (e.g. Raspbian armhf) still uses byte shifting, so we use memcpy + * which for some reason does unaligned loads. */ +# if defined(__GNUC__) && !(defined(__ARM_ARCH) && __ARM_ARCH < 7 && defined(__ARM_FEATURE_UNALIGNED)) +# define XXH_FORCE_MEMORY_ACCESS 1 +# endif +#endif + +#ifndef XXH_SIZE_OPT + /* default to 1 for -Os or -Oz */ +# if (defined(__GNUC__) || defined(__clang__)) && defined(__OPTIMIZE_SIZE__) +# define XXH_SIZE_OPT 1 +# else +# define XXH_SIZE_OPT 0 +# endif +#endif + +#ifndef XXH_FORCE_ALIGN_CHECK /* can be defined externally */ + /* don't check on sizeopt, x86, aarch64, or arm when unaligned access is available */ +# if XXH_SIZE_OPT >= 1 || \ + defined(__i386) || defined(__x86_64__) || defined(__aarch64__) || defined(__ARM_FEATURE_UNALIGNED) \ + || defined(_M_IX86) || defined(_M_X64) || defined(_M_ARM64) || defined(_M_ARM) /* visual */ +# define XXH_FORCE_ALIGN_CHECK 0 +# else +# define XXH_FORCE_ALIGN_CHECK 1 +# endif +#endif + +#ifndef XXH_NO_INLINE_HINTS +# if XXH_SIZE_OPT >= 1 || defined(__NO_INLINE__) /* -O0, -fno-inline */ +# define XXH_NO_INLINE_HINTS 1 +# else +# define XXH_NO_INLINE_HINTS 0 +# endif +#endif + +#ifndef XXH3_INLINE_SECRET +# if (defined(__GNUC__) && !defined(__clang__) && __GNUC__ >= 12) \ + || !defined(XXH_INLINE_ALL) +# define XXH3_INLINE_SECRET 0 +# else +# define XXH3_INLINE_SECRET 1 +# endif +#endif + +#ifndef XXH32_ENDJMP +/* generally preferable for performance */ +# define XXH32_ENDJMP 0 +#endif + +/*! + * @defgroup impl Implementation + * @{ + */ + + +/* ************************************* +* Includes & Memory related functions +***************************************/ +#if defined(XXH_NO_STREAM) +/* nothing */ +#elif defined(XXH_NO_STDLIB) + +/* When requesting to disable any mention of stdlib, + * the library loses the ability to invoked malloc / free. + * In practice, it means that functions like `XXH*_createState()` + * will always fail, and return NULL. + * This flag is useful in situations where + * xxhash.h is integrated into some kernel, embedded or limited environment + * without access to dynamic allocation. + */ + +static XXH_CONSTF void* XXH_malloc(size_t s) { (void)s; return NULL; } +static void XXH_free(void* p) { (void)p; } + +#else + +/* + * Modify the local functions below should you wish to use + * different memory routines for malloc() and free() + */ +#include + +/*! + * @internal + * @brief Modify this function to use a different routine than malloc(). + */ +static XXH_MALLOCF void* XXH_malloc(size_t s) { return malloc(s); } + +/*! + * @internal + * @brief Modify this function to use a different routine than free(). + */ +static void XXH_free(void* p) { free(p); } + +#endif /* XXH_NO_STDLIB */ + +#include + +/*! + * @internal + * @brief Modify this function to use a different routine than memcpy(). + */ +static void* XXH_memcpy(void* dest, const void* src, size_t size) +{ + return memcpy(dest,src,size); +} + +#include /* ULLONG_MAX */ + + +/* ************************************* +* Compiler Specific Options +***************************************/ +#ifdef _MSC_VER /* Visual Studio warning fix */ +# pragma warning(disable : 4127) /* disable: C4127: conditional expression is constant */ +#endif + +#if XXH_NO_INLINE_HINTS /* disable inlining hints */ +# if defined(__GNUC__) || defined(__clang__) +# define XXH_FORCE_INLINE static __attribute__((__unused__)) +# else +# define XXH_FORCE_INLINE static +# endif +# define XXH_NO_INLINE static +/* enable inlining hints */ +#elif defined(__GNUC__) || defined(__clang__) +# define XXH_FORCE_INLINE static __inline__ __attribute__((__always_inline__, __unused__)) +# define XXH_NO_INLINE static __attribute__((__noinline__)) +#elif defined(_MSC_VER) /* Visual Studio */ +# define XXH_FORCE_INLINE static __forceinline +# define XXH_NO_INLINE static __declspec(noinline) +#elif defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L)) /* C99 */ +# define XXH_FORCE_INLINE static inline +# define XXH_NO_INLINE static +#else +# define XXH_FORCE_INLINE static +# define XXH_NO_INLINE static +#endif + +#if XXH3_INLINE_SECRET +# define XXH3_WITH_SECRET_INLINE XXH_FORCE_INLINE +#else +# define XXH3_WITH_SECRET_INLINE XXH_NO_INLINE +#endif + + +/* ************************************* +* Debug +***************************************/ +/*! + * @ingroup tuning + * @def XXH_DEBUGLEVEL + * @brief Sets the debugging level. + * + * XXH_DEBUGLEVEL is expected to be defined externally, typically via the + * compiler's command line options. The value must be a number. + */ +#ifndef XXH_DEBUGLEVEL +# ifdef DEBUGLEVEL /* backwards compat */ +# define XXH_DEBUGLEVEL DEBUGLEVEL +# else +# define XXH_DEBUGLEVEL 0 +# endif +#endif + +#if (XXH_DEBUGLEVEL>=1) +# include /* note: can still be disabled with NDEBUG */ +# define XXH_ASSERT(c) assert(c) +#else +# if defined(__INTEL_COMPILER) +# define XXH_ASSERT(c) XXH_ASSUME((unsigned char) (c)) +# else +# define XXH_ASSERT(c) XXH_ASSUME(c) +# endif +#endif + +/* note: use after variable declarations */ +#ifndef XXH_STATIC_ASSERT +# if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L) /* C11 */ +# define XXH_STATIC_ASSERT_WITH_MESSAGE(c,m) do { _Static_assert((c),m); } while(0) +# elif defined(__cplusplus) && (__cplusplus >= 201103L) /* C++11 */ +# define XXH_STATIC_ASSERT_WITH_MESSAGE(c,m) do { static_assert((c),m); } while(0) +# else +# define XXH_STATIC_ASSERT_WITH_MESSAGE(c,m) do { struct xxh_sa { char x[(c) ? 1 : -1]; }; } while(0) +# endif +# define XXH_STATIC_ASSERT(c) XXH_STATIC_ASSERT_WITH_MESSAGE((c),#c) +#endif + +/*! + * @internal + * @def XXH_COMPILER_GUARD(var) + * @brief Used to prevent unwanted optimizations for @p var. + * + * It uses an empty GCC inline assembly statement with a register constraint + * which forces @p var into a general purpose register (eg eax, ebx, ecx + * on x86) and marks it as modified. + * + * This is used in a few places to avoid unwanted autovectorization (e.g. + * XXH32_round()). All vectorization we want is explicit via intrinsics, + * and _usually_ isn't wanted elsewhere. + * + * We also use it to prevent unwanted constant folding for AArch64 in + * XXH3_initCustomSecret_scalar(). + */ +#if defined(__GNUC__) || defined(__clang__) +# define XXH_COMPILER_GUARD(var) __asm__("" : "+r" (var)) +#else +# define XXH_COMPILER_GUARD(var) ((void)0) +#endif + +/* Specifically for NEON vectors which use the "w" constraint, on + * Clang. */ +#if defined(__clang__) && defined(__ARM_ARCH) && !defined(__wasm__) +# define XXH_COMPILER_GUARD_CLANG_NEON(var) __asm__("" : "+w" (var)) +#else +# define XXH_COMPILER_GUARD_CLANG_NEON(var) ((void)0) +#endif + +/* ************************************* +* Basic Types +***************************************/ +#if !defined (__VMS) \ + && (defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) ) +# ifdef _AIX +# include +# else +# include +# endif + typedef uint8_t xxh_u8; +#else + typedef unsigned char xxh_u8; +#endif +typedef XXH32_hash_t xxh_u32; + +#ifdef XXH_OLD_NAMES +# warning "XXH_OLD_NAMES is planned to be removed starting v0.9. If the program depends on it, consider moving away from it by employing newer type names directly" +# define BYTE xxh_u8 +# define U8 xxh_u8 +# define U32 xxh_u32 +#endif + +/* *** Memory access *** */ + +/*! + * @internal + * @fn xxh_u32 XXH_read32(const void* ptr) + * @brief Reads an unaligned 32-bit integer from @p ptr in native endianness. + * + * Affected by @ref XXH_FORCE_MEMORY_ACCESS. + * + * @param ptr The pointer to read from. + * @return The 32-bit native endian integer from the bytes at @p ptr. + */ + +/*! + * @internal + * @fn xxh_u32 XXH_readLE32(const void* ptr) + * @brief Reads an unaligned 32-bit little endian integer from @p ptr. + * + * Affected by @ref XXH_FORCE_MEMORY_ACCESS. + * + * @param ptr The pointer to read from. + * @return The 32-bit little endian integer from the bytes at @p ptr. + */ + +/*! + * @internal + * @fn xxh_u32 XXH_readBE32(const void* ptr) + * @brief Reads an unaligned 32-bit big endian integer from @p ptr. + * + * Affected by @ref XXH_FORCE_MEMORY_ACCESS. + * + * @param ptr The pointer to read from. + * @return The 32-bit big endian integer from the bytes at @p ptr. + */ + +/*! + * @internal + * @fn xxh_u32 XXH_readLE32_align(const void* ptr, XXH_alignment align) + * @brief Like @ref XXH_readLE32(), but has an option for aligned reads. + * + * Affected by @ref XXH_FORCE_MEMORY_ACCESS. + * Note that when @ref XXH_FORCE_ALIGN_CHECK == 0, the @p align parameter is + * always @ref XXH_alignment::XXH_unaligned. + * + * @param ptr The pointer to read from. + * @param align Whether @p ptr is aligned. + * @pre + * If @p align == @ref XXH_alignment::XXH_aligned, @p ptr must be 4 byte + * aligned. + * @return The 32-bit little endian integer from the bytes at @p ptr. + */ + +#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3)) +/* + * Manual byteshift. Best for old compilers which don't inline memcpy. + * We actually directly use XXH_readLE32 and XXH_readBE32. + */ +#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==2)) + +/* + * Force direct memory access. Only works on CPU which support unaligned memory + * access in hardware. + */ +static xxh_u32 XXH_read32(const void* memPtr) { return *(const xxh_u32*) memPtr; } + +#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==1)) + +/* + * __attribute__((aligned(1))) is supported by gcc and clang. Originally the + * documentation claimed that it only increased the alignment, but actually it + * can decrease it on gcc, clang, and icc: + * https://gcc.gnu.org/bugzilla/show_bug.cgi?id=69502, + * https://gcc.godbolt.org/z/xYez1j67Y. + */ +#ifdef XXH_OLD_NAMES +typedef union { xxh_u32 u32; } __attribute__((__packed__)) unalign; +#endif +static xxh_u32 XXH_read32(const void* ptr) +{ + typedef __attribute__((__aligned__(1))) xxh_u32 xxh_unalign32; + return *((const xxh_unalign32*)ptr); +} + +#else + +/* + * Portable and safe solution. Generally efficient. + * see: https://fastcompression.blogspot.com/2015/08/accessing-unaligned-memory.html + */ +static xxh_u32 XXH_read32(const void* memPtr) +{ + xxh_u32 val; + XXH_memcpy(&val, memPtr, sizeof(val)); + return val; +} + +#endif /* XXH_FORCE_DIRECT_MEMORY_ACCESS */ + + +/* *** Endianness *** */ + +/*! + * @ingroup tuning + * @def XXH_CPU_LITTLE_ENDIAN + * @brief Whether the target is little endian. + * + * Defined to 1 if the target is little endian, or 0 if it is big endian. + * It can be defined externally, for example on the compiler command line. + * + * If it is not defined, + * a runtime check (which is usually constant folded) is used instead. + * + * @note + * This is not necessarily defined to an integer constant. + * + * @see XXH_isLittleEndian() for the runtime check. + */ +#ifndef XXH_CPU_LITTLE_ENDIAN +/* + * Try to detect endianness automatically, to avoid the nonstandard behavior + * in `XXH_isLittleEndian()` + */ +# if defined(_WIN32) /* Windows is always little endian */ \ + || defined(__LITTLE_ENDIAN__) \ + || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__) +# define XXH_CPU_LITTLE_ENDIAN 1 +# elif defined(__BIG_ENDIAN__) \ + || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__) +# define XXH_CPU_LITTLE_ENDIAN 0 +# else +/*! + * @internal + * @brief Runtime check for @ref XXH_CPU_LITTLE_ENDIAN. + * + * Most compilers will constant fold this. + */ +static int XXH_isLittleEndian(void) +{ + /* + * Portable and well-defined behavior. + * Don't use static: it is detrimental to performance. + */ + const union { xxh_u32 u; xxh_u8 c[4]; } one = { 1 }; + return one.c[0]; +} +# define XXH_CPU_LITTLE_ENDIAN XXH_isLittleEndian() +# endif +#endif + + + + +/* **************************************** +* Compiler-specific Functions and Macros +******************************************/ +#define XXH_GCC_VERSION (__GNUC__ * 100 + __GNUC_MINOR__) + +#ifdef __has_builtin +# define XXH_HAS_BUILTIN(x) __has_builtin(x) +#else +# define XXH_HAS_BUILTIN(x) 0 +#endif + + + +/* + * C23 and future versions have standard "unreachable()". + * Once it has been implemented reliably we can add it as an + * additional case: + * + * ``` + * #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= XXH_C23_VN) + * # include + * # ifdef unreachable + * # define XXH_UNREACHABLE() unreachable() + * # endif + * #endif + * ``` + * + * Note C++23 also has std::unreachable() which can be detected + * as follows: + * ``` + * #if defined(__cpp_lib_unreachable) && (__cpp_lib_unreachable >= 202202L) + * # include + * # define XXH_UNREACHABLE() std::unreachable() + * #endif + * ``` + * NB: `__cpp_lib_unreachable` is defined in the `` header. + * We don't use that as including `` in `extern "C"` blocks + * doesn't work on GCC12 + */ + +#if XXH_HAS_BUILTIN(__builtin_unreachable) +# define XXH_UNREACHABLE() __builtin_unreachable() + +#elif defined(_MSC_VER) +# define XXH_UNREACHABLE() __assume(0) + +#else +# define XXH_UNREACHABLE() +#endif + +#if XXH_HAS_BUILTIN(__builtin_assume) +# define XXH_ASSUME(c) __builtin_assume(c) +#else +# define XXH_ASSUME(c) if (!(c)) { XXH_UNREACHABLE(); } +#endif + +/*! + * @internal + * @def XXH_rotl32(x,r) + * @brief 32-bit rotate left. + * + * @param x The 32-bit integer to be rotated. + * @param r The number of bits to rotate. + * @pre + * @p r > 0 && @p r < 32 + * @note + * @p x and @p r may be evaluated multiple times. + * @return The rotated result. + */ +#if !defined(NO_CLANG_BUILTIN) && XXH_HAS_BUILTIN(__builtin_rotateleft32) \ + && XXH_HAS_BUILTIN(__builtin_rotateleft64) +# define XXH_rotl32 __builtin_rotateleft32 +# define XXH_rotl64 __builtin_rotateleft64 +/* Note: although _rotl exists for minGW (GCC under windows), performance seems poor */ +#elif defined(_MSC_VER) +# define XXH_rotl32(x,r) _rotl(x,r) +# define XXH_rotl64(x,r) _rotl64(x,r) +#else +# define XXH_rotl32(x,r) (((x) << (r)) | ((x) >> (32 - (r)))) +# define XXH_rotl64(x,r) (((x) << (r)) | ((x) >> (64 - (r)))) +#endif + +/*! + * @internal + * @fn xxh_u32 XXH_swap32(xxh_u32 x) + * @brief A 32-bit byteswap. + * + * @param x The 32-bit integer to byteswap. + * @return @p x, byteswapped. + */ +#if defined(_MSC_VER) /* Visual Studio */ +# define XXH_swap32 _byteswap_ulong +#elif XXH_GCC_VERSION >= 403 +# define XXH_swap32 __builtin_bswap32 +#else +static xxh_u32 XXH_swap32 (xxh_u32 x) +{ + return ((x << 24) & 0xff000000 ) | + ((x << 8) & 0x00ff0000 ) | + ((x >> 8) & 0x0000ff00 ) | + ((x >> 24) & 0x000000ff ); +} +#endif + + +/* *************************** +* Memory reads +*****************************/ + +/*! + * @internal + * @brief Enum to indicate whether a pointer is aligned. + */ +typedef enum { + XXH_aligned, /*!< Aligned */ + XXH_unaligned /*!< Possibly unaligned */ +} XXH_alignment; + +/* + * XXH_FORCE_MEMORY_ACCESS==3 is an endian-independent byteshift load. + * + * This is ideal for older compilers which don't inline memcpy. + */ +#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3)) + +XXH_FORCE_INLINE xxh_u32 XXH_readLE32(const void* memPtr) +{ + const xxh_u8* bytePtr = (const xxh_u8 *)memPtr; + return bytePtr[0] + | ((xxh_u32)bytePtr[1] << 8) + | ((xxh_u32)bytePtr[2] << 16) + | ((xxh_u32)bytePtr[3] << 24); +} + +XXH_FORCE_INLINE xxh_u32 XXH_readBE32(const void* memPtr) +{ + const xxh_u8* bytePtr = (const xxh_u8 *)memPtr; + return bytePtr[3] + | ((xxh_u32)bytePtr[2] << 8) + | ((xxh_u32)bytePtr[1] << 16) + | ((xxh_u32)bytePtr[0] << 24); +} + +#else +XXH_FORCE_INLINE xxh_u32 XXH_readLE32(const void* ptr) +{ + return XXH_CPU_LITTLE_ENDIAN ? XXH_read32(ptr) : XXH_swap32(XXH_read32(ptr)); +} + +static xxh_u32 XXH_readBE32(const void* ptr) +{ + return XXH_CPU_LITTLE_ENDIAN ? XXH_swap32(XXH_read32(ptr)) : XXH_read32(ptr); +} +#endif + +XXH_FORCE_INLINE xxh_u32 +XXH_readLE32_align(const void* ptr, XXH_alignment align) +{ + if (align==XXH_unaligned) { + return XXH_readLE32(ptr); + } else { + return XXH_CPU_LITTLE_ENDIAN ? *(const xxh_u32*)ptr : XXH_swap32(*(const xxh_u32*)ptr); + } +} + + +/* ************************************* +* Misc +***************************************/ +/*! @ingroup public */ +XXH_PUBLIC_API unsigned XXH_versionNumber (void) { return XXH_VERSION_NUMBER; } + + +/* ******************************************************************* +* 32-bit hash functions +*********************************************************************/ +/*! + * @} + * @defgroup XXH32_impl XXH32 implementation + * @ingroup impl + * + * Details on the XXH32 implementation. + * @{ + */ + /* #define instead of static const, to be used as initializers */ +#define XXH_PRIME32_1 0x9E3779B1U /*!< 0b10011110001101110111100110110001 */ +#define XXH_PRIME32_2 0x85EBCA77U /*!< 0b10000101111010111100101001110111 */ +#define XXH_PRIME32_3 0xC2B2AE3DU /*!< 0b11000010101100101010111000111101 */ +#define XXH_PRIME32_4 0x27D4EB2FU /*!< 0b00100111110101001110101100101111 */ +#define XXH_PRIME32_5 0x165667B1U /*!< 0b00010110010101100110011110110001 */ + +#ifdef XXH_OLD_NAMES +# define PRIME32_1 XXH_PRIME32_1 +# define PRIME32_2 XXH_PRIME32_2 +# define PRIME32_3 XXH_PRIME32_3 +# define PRIME32_4 XXH_PRIME32_4 +# define PRIME32_5 XXH_PRIME32_5 +#endif + +/*! + * @internal + * @brief Normal stripe processing routine. + * + * This shuffles the bits so that any bit from @p input impacts several bits in + * @p acc. + * + * @param acc The accumulator lane. + * @param input The stripe of input to mix. + * @return The mixed accumulator lane. + */ +static xxh_u32 XXH32_round(xxh_u32 acc, xxh_u32 input) +{ + acc += input * XXH_PRIME32_2; + acc = XXH_rotl32(acc, 13); + acc *= XXH_PRIME32_1; +#if (defined(__SSE4_1__) || defined(__aarch64__) || defined(__wasm_simd128__)) && !defined(XXH_ENABLE_AUTOVECTORIZE) + /* + * UGLY HACK: + * A compiler fence is used to prevent GCC and Clang from + * autovectorizing the XXH32 loop (pragmas and attributes don't work for some + * reason) without globally disabling SSE4.1. + * + * The reason we want to avoid vectorization is because despite working on + * 4 integers at a time, there are multiple factors slowing XXH32 down on + * SSE4: + * - There's a ridiculous amount of lag from pmulld (10 cycles of latency on + * newer chips!) making it slightly slower to multiply four integers at + * once compared to four integers independently. Even when pmulld was + * fastest, Sandy/Ivy Bridge, it is still not worth it to go into SSE + * just to multiply unless doing a long operation. + * + * - Four instructions are required to rotate, + * movqda tmp, v // not required with VEX encoding + * pslld tmp, 13 // tmp <<= 13 + * psrld v, 19 // x >>= 19 + * por v, tmp // x |= tmp + * compared to one for scalar: + * roll v, 13 // reliably fast across the board + * shldl v, v, 13 // Sandy Bridge and later prefer this for some reason + * + * - Instruction level parallelism is actually more beneficial here because + * the SIMD actually serializes this operation: While v1 is rotating, v2 + * can load data, while v3 can multiply. SSE forces them to operate + * together. + * + * This is also enabled on AArch64, as Clang is *very aggressive* in vectorizing + * the loop. NEON is only faster on the A53, and with the newer cores, it is less + * than half the speed. + * + * Additionally, this is used on WASM SIMD128 because it JITs to the same + * SIMD instructions and has the same issue. + */ + XXH_COMPILER_GUARD(acc); +#endif + return acc; +} + +/*! + * @internal + * @brief Mixes all bits to finalize the hash. + * + * The final mix ensures that all input bits have a chance to impact any bit in + * the output digest, resulting in an unbiased distribution. + * + * @param hash The hash to avalanche. + * @return The avalanched hash. + */ +static xxh_u32 XXH32_avalanche(xxh_u32 hash) +{ + hash ^= hash >> 15; + hash *= XXH_PRIME32_2; + hash ^= hash >> 13; + hash *= XXH_PRIME32_3; + hash ^= hash >> 16; + return hash; +} + +#define XXH_get32bits(p) XXH_readLE32_align(p, align) + +/*! + * @internal + * @brief Processes the last 0-15 bytes of @p ptr. + * + * There may be up to 15 bytes remaining to consume from the input. + * This final stage will digest them to ensure that all input bytes are present + * in the final mix. + * + * @param hash The hash to finalize. + * @param ptr The pointer to the remaining input. + * @param len The remaining length, modulo 16. + * @param align Whether @p ptr is aligned. + * @return The finalized hash. + * @see XXH64_finalize(). + */ +static XXH_PUREF xxh_u32 +XXH32_finalize(xxh_u32 hash, const xxh_u8* ptr, size_t len, XXH_alignment align) +{ +#define XXH_PROCESS1 do { \ + hash += (*ptr++) * XXH_PRIME32_5; \ + hash = XXH_rotl32(hash, 11) * XXH_PRIME32_1; \ +} while (0) + +#define XXH_PROCESS4 do { \ + hash += XXH_get32bits(ptr) * XXH_PRIME32_3; \ + ptr += 4; \ + hash = XXH_rotl32(hash, 17) * XXH_PRIME32_4; \ +} while (0) + + if (ptr==NULL) XXH_ASSERT(len == 0); + + /* Compact rerolled version; generally faster */ + if (!XXH32_ENDJMP) { + len &= 15; + while (len >= 4) { + XXH_PROCESS4; + len -= 4; + } + while (len > 0) { + XXH_PROCESS1; + --len; + } + return XXH32_avalanche(hash); + } else { + switch(len&15) /* or switch(bEnd - p) */ { + case 12: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 8: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 4: XXH_PROCESS4; + return XXH32_avalanche(hash); + + case 13: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 9: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 5: XXH_PROCESS4; + XXH_PROCESS1; + return XXH32_avalanche(hash); + + case 14: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 10: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 6: XXH_PROCESS4; + XXH_PROCESS1; + XXH_PROCESS1; + return XXH32_avalanche(hash); + + case 15: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 11: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 7: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 3: XXH_PROCESS1; + XXH_FALLTHROUGH; /* fallthrough */ + case 2: XXH_PROCESS1; + XXH_FALLTHROUGH; /* fallthrough */ + case 1: XXH_PROCESS1; + XXH_FALLTHROUGH; /* fallthrough */ + case 0: return XXH32_avalanche(hash); + } + XXH_ASSERT(0); + return hash; /* reaching this point is deemed impossible */ + } +} + +#ifdef XXH_OLD_NAMES +# define PROCESS1 XXH_PROCESS1 +# define PROCESS4 XXH_PROCESS4 +#else +# undef XXH_PROCESS1 +# undef XXH_PROCESS4 +#endif + +/*! + * @internal + * @brief The implementation for @ref XXH32(). + * + * @param input , len , seed Directly passed from @ref XXH32(). + * @param align Whether @p input is aligned. + * @return The calculated hash. + */ +XXH_FORCE_INLINE XXH_PUREF xxh_u32 +XXH32_endian_align(const xxh_u8* input, size_t len, xxh_u32 seed, XXH_alignment align) +{ + xxh_u32 h32; + + if (input==NULL) XXH_ASSERT(len == 0); + + if (len>=16) { + const xxh_u8* const bEnd = input + len; + const xxh_u8* const limit = bEnd - 15; + xxh_u32 v1 = seed + XXH_PRIME32_1 + XXH_PRIME32_2; + xxh_u32 v2 = seed + XXH_PRIME32_2; + xxh_u32 v3 = seed + 0; + xxh_u32 v4 = seed - XXH_PRIME32_1; + + do { + v1 = XXH32_round(v1, XXH_get32bits(input)); input += 4; + v2 = XXH32_round(v2, XXH_get32bits(input)); input += 4; + v3 = XXH32_round(v3, XXH_get32bits(input)); input += 4; + v4 = XXH32_round(v4, XXH_get32bits(input)); input += 4; + } while (input < limit); + + h32 = XXH_rotl32(v1, 1) + XXH_rotl32(v2, 7) + + XXH_rotl32(v3, 12) + XXH_rotl32(v4, 18); + } else { + h32 = seed + XXH_PRIME32_5; + } + + h32 += (xxh_u32)len; + + return XXH32_finalize(h32, input, len&15, align); +} + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH32_hash_t XXH32 (const void* input, size_t len, XXH32_hash_t seed) +{ +#if !defined(XXH_NO_STREAM) && XXH_SIZE_OPT >= 2 + /* Simple version, good for code maintenance, but unfortunately slow for small inputs */ + XXH32_state_t state; + XXH32_reset(&state, seed); + XXH32_update(&state, (const xxh_u8*)input, len); + return XXH32_digest(&state); +#else + if (XXH_FORCE_ALIGN_CHECK) { + if ((((size_t)input) & 3) == 0) { /* Input is 4-bytes aligned, leverage the speed benefit */ + return XXH32_endian_align((const xxh_u8*)input, len, seed, XXH_aligned); + } } + + return XXH32_endian_align((const xxh_u8*)input, len, seed, XXH_unaligned); +#endif +} + + + +/******* Hash streaming *******/ +#ifndef XXH_NO_STREAM +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH32_state_t* XXH32_createState(void) +{ + return (XXH32_state_t*)XXH_malloc(sizeof(XXH32_state_t)); +} +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH_errorcode XXH32_freeState(XXH32_state_t* statePtr) +{ + XXH_free(statePtr); + return XXH_OK; +} + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API void XXH32_copyState(XXH32_state_t* dstState, const XXH32_state_t* srcState) +{ + XXH_memcpy(dstState, srcState, sizeof(*dstState)); +} + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH_errorcode XXH32_reset(XXH32_state_t* statePtr, XXH32_hash_t seed) +{ + XXH_ASSERT(statePtr != NULL); + memset(statePtr, 0, sizeof(*statePtr)); + statePtr->v[0] = seed + XXH_PRIME32_1 + XXH_PRIME32_2; + statePtr->v[1] = seed + XXH_PRIME32_2; + statePtr->v[2] = seed + 0; + statePtr->v[3] = seed - XXH_PRIME32_1; + return XXH_OK; +} + + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH_errorcode +XXH32_update(XXH32_state_t* state, const void* input, size_t len) +{ + if (input==NULL) { + XXH_ASSERT(len == 0); + return XXH_OK; + } + + { const xxh_u8* p = (const xxh_u8*)input; + const xxh_u8* const bEnd = p + len; + + state->total_len_32 += (XXH32_hash_t)len; + state->large_len |= (XXH32_hash_t)((len>=16) | (state->total_len_32>=16)); + + if (state->memsize + len < 16) { /* fill in tmp buffer */ + XXH_memcpy((xxh_u8*)(state->mem32) + state->memsize, input, len); + state->memsize += (XXH32_hash_t)len; + return XXH_OK; + } + + if (state->memsize) { /* some data left from previous update */ + XXH_memcpy((xxh_u8*)(state->mem32) + state->memsize, input, 16-state->memsize); + { const xxh_u32* p32 = state->mem32; + state->v[0] = XXH32_round(state->v[0], XXH_readLE32(p32)); p32++; + state->v[1] = XXH32_round(state->v[1], XXH_readLE32(p32)); p32++; + state->v[2] = XXH32_round(state->v[2], XXH_readLE32(p32)); p32++; + state->v[3] = XXH32_round(state->v[3], XXH_readLE32(p32)); + } + p += 16-state->memsize; + state->memsize = 0; + } + + if (p <= bEnd-16) { + const xxh_u8* const limit = bEnd - 16; + + do { + state->v[0] = XXH32_round(state->v[0], XXH_readLE32(p)); p+=4; + state->v[1] = XXH32_round(state->v[1], XXH_readLE32(p)); p+=4; + state->v[2] = XXH32_round(state->v[2], XXH_readLE32(p)); p+=4; + state->v[3] = XXH32_round(state->v[3], XXH_readLE32(p)); p+=4; + } while (p<=limit); + + } + + if (p < bEnd) { + XXH_memcpy(state->mem32, p, (size_t)(bEnd-p)); + state->memsize = (unsigned)(bEnd-p); + } + } + + return XXH_OK; +} + + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH32_hash_t XXH32_digest(const XXH32_state_t* state) +{ + xxh_u32 h32; + + if (state->large_len) { + h32 = XXH_rotl32(state->v[0], 1) + + XXH_rotl32(state->v[1], 7) + + XXH_rotl32(state->v[2], 12) + + XXH_rotl32(state->v[3], 18); + } else { + h32 = state->v[2] /* == seed */ + XXH_PRIME32_5; + } + + h32 += state->total_len_32; + + return XXH32_finalize(h32, (const xxh_u8*)state->mem32, state->memsize, XXH_aligned); +} +#endif /* !XXH_NO_STREAM */ + +/******* Canonical representation *******/ + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API void XXH32_canonicalFromHash(XXH32_canonical_t* dst, XXH32_hash_t hash) +{ + XXH_STATIC_ASSERT(sizeof(XXH32_canonical_t) == sizeof(XXH32_hash_t)); + if (XXH_CPU_LITTLE_ENDIAN) hash = XXH_swap32(hash); + XXH_memcpy(dst, &hash, sizeof(*dst)); +} +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH32_hash_t XXH32_hashFromCanonical(const XXH32_canonical_t* src) +{ + return XXH_readBE32(src); +} + + +#ifndef XXH_NO_LONG_LONG + +/* ******************************************************************* +* 64-bit hash functions +*********************************************************************/ +/*! + * @} + * @ingroup impl + * @{ + */ +/******* Memory access *******/ + +typedef XXH64_hash_t xxh_u64; + +#ifdef XXH_OLD_NAMES +# define U64 xxh_u64 +#endif + +#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3)) +/* + * Manual byteshift. Best for old compilers which don't inline memcpy. + * We actually directly use XXH_readLE64 and XXH_readBE64. + */ +#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==2)) + +/* Force direct memory access. Only works on CPU which support unaligned memory access in hardware */ +static xxh_u64 XXH_read64(const void* memPtr) +{ + return *(const xxh_u64*) memPtr; +} + +#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==1)) + +/* + * __attribute__((aligned(1))) is supported by gcc and clang. Originally the + * documentation claimed that it only increased the alignment, but actually it + * can decrease it on gcc, clang, and icc: + * https://gcc.gnu.org/bugzilla/show_bug.cgi?id=69502, + * https://gcc.godbolt.org/z/xYez1j67Y. + */ +#ifdef XXH_OLD_NAMES +typedef union { xxh_u32 u32; xxh_u64 u64; } __attribute__((__packed__)) unalign64; +#endif +static xxh_u64 XXH_read64(const void* ptr) +{ + typedef __attribute__((__aligned__(1))) xxh_u64 xxh_unalign64; + return *((const xxh_unalign64*)ptr); +} + +#else + +/* + * Portable and safe solution. Generally efficient. + * see: https://fastcompression.blogspot.com/2015/08/accessing-unaligned-memory.html + */ +static xxh_u64 XXH_read64(const void* memPtr) +{ + xxh_u64 val; + XXH_memcpy(&val, memPtr, sizeof(val)); + return val; +} + +#endif /* XXH_FORCE_DIRECT_MEMORY_ACCESS */ + +#if defined(_MSC_VER) /* Visual Studio */ +# define XXH_swap64 _byteswap_uint64 +#elif XXH_GCC_VERSION >= 403 +# define XXH_swap64 __builtin_bswap64 +#else +static xxh_u64 XXH_swap64(xxh_u64 x) +{ + return ((x << 56) & 0xff00000000000000ULL) | + ((x << 40) & 0x00ff000000000000ULL) | + ((x << 24) & 0x0000ff0000000000ULL) | + ((x << 8) & 0x000000ff00000000ULL) | + ((x >> 8) & 0x00000000ff000000ULL) | + ((x >> 24) & 0x0000000000ff0000ULL) | + ((x >> 40) & 0x000000000000ff00ULL) | + ((x >> 56) & 0x00000000000000ffULL); +} +#endif + + +/* XXH_FORCE_MEMORY_ACCESS==3 is an endian-independent byteshift load. */ +#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3)) + +XXH_FORCE_INLINE xxh_u64 XXH_readLE64(const void* memPtr) +{ + const xxh_u8* bytePtr = (const xxh_u8 *)memPtr; + return bytePtr[0] + | ((xxh_u64)bytePtr[1] << 8) + | ((xxh_u64)bytePtr[2] << 16) + | ((xxh_u64)bytePtr[3] << 24) + | ((xxh_u64)bytePtr[4] << 32) + | ((xxh_u64)bytePtr[5] << 40) + | ((xxh_u64)bytePtr[6] << 48) + | ((xxh_u64)bytePtr[7] << 56); +} + +XXH_FORCE_INLINE xxh_u64 XXH_readBE64(const void* memPtr) +{ + const xxh_u8* bytePtr = (const xxh_u8 *)memPtr; + return bytePtr[7] + | ((xxh_u64)bytePtr[6] << 8) + | ((xxh_u64)bytePtr[5] << 16) + | ((xxh_u64)bytePtr[4] << 24) + | ((xxh_u64)bytePtr[3] << 32) + | ((xxh_u64)bytePtr[2] << 40) + | ((xxh_u64)bytePtr[1] << 48) + | ((xxh_u64)bytePtr[0] << 56); +} + +#else +XXH_FORCE_INLINE xxh_u64 XXH_readLE64(const void* ptr) +{ + return XXH_CPU_LITTLE_ENDIAN ? XXH_read64(ptr) : XXH_swap64(XXH_read64(ptr)); +} + +static xxh_u64 XXH_readBE64(const void* ptr) +{ + return XXH_CPU_LITTLE_ENDIAN ? XXH_swap64(XXH_read64(ptr)) : XXH_read64(ptr); +} +#endif + +XXH_FORCE_INLINE xxh_u64 +XXH_readLE64_align(const void* ptr, XXH_alignment align) +{ + if (align==XXH_unaligned) + return XXH_readLE64(ptr); + else + return XXH_CPU_LITTLE_ENDIAN ? *(const xxh_u64*)ptr : XXH_swap64(*(const xxh_u64*)ptr); +} + + +/******* xxh64 *******/ +/*! + * @} + * @defgroup XXH64_impl XXH64 implementation + * @ingroup impl + * + * Details on the XXH64 implementation. + * @{ + */ +/* #define rather that static const, to be used as initializers */ +#define XXH_PRIME64_1 0x9E3779B185EBCA87ULL /*!< 0b1001111000110111011110011011000110000101111010111100101010000111 */ +#define XXH_PRIME64_2 0xC2B2AE3D27D4EB4FULL /*!< 0b1100001010110010101011100011110100100111110101001110101101001111 */ +#define XXH_PRIME64_3 0x165667B19E3779F9ULL /*!< 0b0001011001010110011001111011000110011110001101110111100111111001 */ +#define XXH_PRIME64_4 0x85EBCA77C2B2AE63ULL /*!< 0b1000010111101011110010100111011111000010101100101010111001100011 */ +#define XXH_PRIME64_5 0x27D4EB2F165667C5ULL /*!< 0b0010011111010100111010110010111100010110010101100110011111000101 */ + +#ifdef XXH_OLD_NAMES +# define PRIME64_1 XXH_PRIME64_1 +# define PRIME64_2 XXH_PRIME64_2 +# define PRIME64_3 XXH_PRIME64_3 +# define PRIME64_4 XXH_PRIME64_4 +# define PRIME64_5 XXH_PRIME64_5 +#endif + +/*! @copydoc XXH32_round */ +static xxh_u64 XXH64_round(xxh_u64 acc, xxh_u64 input) +{ + acc += input * XXH_PRIME64_2; + acc = XXH_rotl64(acc, 31); + acc *= XXH_PRIME64_1; +#if (defined(__AVX512F__)) && !defined(XXH_ENABLE_AUTOVECTORIZE) + /* + * DISABLE AUTOVECTORIZATION: + * A compiler fence is used to prevent GCC and Clang from + * autovectorizing the XXH64 loop (pragmas and attributes don't work for some + * reason) without globally disabling AVX512. + * + * Autovectorization of XXH64 tends to be detrimental, + * though the exact outcome may change depending on exact cpu and compiler version. + * For information, it has been reported as detrimental for Skylake-X, + * but possibly beneficial for Zen4. + * + * The default is to disable auto-vectorization, + * but you can select to enable it instead using `XXH_ENABLE_AUTOVECTORIZE` build variable. + */ + XXH_COMPILER_GUARD(acc); +#endif + return acc; +} + +static xxh_u64 XXH64_mergeRound(xxh_u64 acc, xxh_u64 val) +{ + val = XXH64_round(0, val); + acc ^= val; + acc = acc * XXH_PRIME64_1 + XXH_PRIME64_4; + return acc; +} + +/*! @copydoc XXH32_avalanche */ +static xxh_u64 XXH64_avalanche(xxh_u64 hash) +{ + hash ^= hash >> 33; + hash *= XXH_PRIME64_2; + hash ^= hash >> 29; + hash *= XXH_PRIME64_3; + hash ^= hash >> 32; + return hash; +} + + +#define XXH_get64bits(p) XXH_readLE64_align(p, align) + +/*! + * @internal + * @brief Processes the last 0-31 bytes of @p ptr. + * + * There may be up to 31 bytes remaining to consume from the input. + * This final stage will digest them to ensure that all input bytes are present + * in the final mix. + * + * @param hash The hash to finalize. + * @param ptr The pointer to the remaining input. + * @param len The remaining length, modulo 32. + * @param align Whether @p ptr is aligned. + * @return The finalized hash + * @see XXH32_finalize(). + */ +static XXH_PUREF xxh_u64 +XXH64_finalize(xxh_u64 hash, const xxh_u8* ptr, size_t len, XXH_alignment align) +{ + if (ptr==NULL) XXH_ASSERT(len == 0); + len &= 31; + while (len >= 8) { + xxh_u64 const k1 = XXH64_round(0, XXH_get64bits(ptr)); + ptr += 8; + hash ^= k1; + hash = XXH_rotl64(hash,27) * XXH_PRIME64_1 + XXH_PRIME64_4; + len -= 8; + } + if (len >= 4) { + hash ^= (xxh_u64)(XXH_get32bits(ptr)) * XXH_PRIME64_1; + ptr += 4; + hash = XXH_rotl64(hash, 23) * XXH_PRIME64_2 + XXH_PRIME64_3; + len -= 4; + } + while (len > 0) { + hash ^= (*ptr++) * XXH_PRIME64_5; + hash = XXH_rotl64(hash, 11) * XXH_PRIME64_1; + --len; + } + return XXH64_avalanche(hash); +} + +#ifdef XXH_OLD_NAMES +# define PROCESS1_64 XXH_PROCESS1_64 +# define PROCESS4_64 XXH_PROCESS4_64 +# define PROCESS8_64 XXH_PROCESS8_64 +#else +# undef XXH_PROCESS1_64 +# undef XXH_PROCESS4_64 +# undef XXH_PROCESS8_64 +#endif + +/*! + * @internal + * @brief The implementation for @ref XXH64(). + * + * @param input , len , seed Directly passed from @ref XXH64(). + * @param align Whether @p input is aligned. + * @return The calculated hash. + */ +XXH_FORCE_INLINE XXH_PUREF xxh_u64 +XXH64_endian_align(const xxh_u8* input, size_t len, xxh_u64 seed, XXH_alignment align) +{ + xxh_u64 h64; + if (input==NULL) XXH_ASSERT(len == 0); + + if (len>=32) { + const xxh_u8* const bEnd = input + len; + const xxh_u8* const limit = bEnd - 31; + xxh_u64 v1 = seed + XXH_PRIME64_1 + XXH_PRIME64_2; + xxh_u64 v2 = seed + XXH_PRIME64_2; + xxh_u64 v3 = seed + 0; + xxh_u64 v4 = seed - XXH_PRIME64_1; + + do { + v1 = XXH64_round(v1, XXH_get64bits(input)); input+=8; + v2 = XXH64_round(v2, XXH_get64bits(input)); input+=8; + v3 = XXH64_round(v3, XXH_get64bits(input)); input+=8; + v4 = XXH64_round(v4, XXH_get64bits(input)); input+=8; + } while (input= 2 + /* Simple version, good for code maintenance, but unfortunately slow for small inputs */ + XXH64_state_t state; + XXH64_reset(&state, seed); + XXH64_update(&state, (const xxh_u8*)input, len); + return XXH64_digest(&state); +#else + if (XXH_FORCE_ALIGN_CHECK) { + if ((((size_t)input) & 7)==0) { /* Input is aligned, let's leverage the speed advantage */ + return XXH64_endian_align((const xxh_u8*)input, len, seed, XXH_aligned); + } } + + return XXH64_endian_align((const xxh_u8*)input, len, seed, XXH_unaligned); + +#endif +} + +/******* Hash Streaming *******/ +#ifndef XXH_NO_STREAM +/*! @ingroup XXH64_family*/ +XXH_PUBLIC_API XXH64_state_t* XXH64_createState(void) +{ + return (XXH64_state_t*)XXH_malloc(sizeof(XXH64_state_t)); +} +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH_errorcode XXH64_freeState(XXH64_state_t* statePtr) +{ + XXH_free(statePtr); + return XXH_OK; +} + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API void XXH64_copyState(XXH_NOESCAPE XXH64_state_t* dstState, const XXH64_state_t* srcState) +{ + XXH_memcpy(dstState, srcState, sizeof(*dstState)); +} + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH_errorcode XXH64_reset(XXH_NOESCAPE XXH64_state_t* statePtr, XXH64_hash_t seed) +{ + XXH_ASSERT(statePtr != NULL); + memset(statePtr, 0, sizeof(*statePtr)); + statePtr->v[0] = seed + XXH_PRIME64_1 + XXH_PRIME64_2; + statePtr->v[1] = seed + XXH_PRIME64_2; + statePtr->v[2] = seed + 0; + statePtr->v[3] = seed - XXH_PRIME64_1; + return XXH_OK; +} + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH_errorcode +XXH64_update (XXH_NOESCAPE XXH64_state_t* state, XXH_NOESCAPE const void* input, size_t len) +{ + if (input==NULL) { + XXH_ASSERT(len == 0); + return XXH_OK; + } + + { const xxh_u8* p = (const xxh_u8*)input; + const xxh_u8* const bEnd = p + len; + + state->total_len += len; + + if (state->memsize + len < 32) { /* fill in tmp buffer */ + XXH_memcpy(((xxh_u8*)state->mem64) + state->memsize, input, len); + state->memsize += (xxh_u32)len; + return XXH_OK; + } + + if (state->memsize) { /* tmp buffer is full */ + XXH_memcpy(((xxh_u8*)state->mem64) + state->memsize, input, 32-state->memsize); + state->v[0] = XXH64_round(state->v[0], XXH_readLE64(state->mem64+0)); + state->v[1] = XXH64_round(state->v[1], XXH_readLE64(state->mem64+1)); + state->v[2] = XXH64_round(state->v[2], XXH_readLE64(state->mem64+2)); + state->v[3] = XXH64_round(state->v[3], XXH_readLE64(state->mem64+3)); + p += 32 - state->memsize; + state->memsize = 0; + } + + if (p+32 <= bEnd) { + const xxh_u8* const limit = bEnd - 32; + + do { + state->v[0] = XXH64_round(state->v[0], XXH_readLE64(p)); p+=8; + state->v[1] = XXH64_round(state->v[1], XXH_readLE64(p)); p+=8; + state->v[2] = XXH64_round(state->v[2], XXH_readLE64(p)); p+=8; + state->v[3] = XXH64_round(state->v[3], XXH_readLE64(p)); p+=8; + } while (p<=limit); + + } + + if (p < bEnd) { + XXH_memcpy(state->mem64, p, (size_t)(bEnd-p)); + state->memsize = (unsigned)(bEnd-p); + } + } + + return XXH_OK; +} + + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH64_hash_t XXH64_digest(XXH_NOESCAPE const XXH64_state_t* state) +{ + xxh_u64 h64; + + if (state->total_len >= 32) { + h64 = XXH_rotl64(state->v[0], 1) + XXH_rotl64(state->v[1], 7) + XXH_rotl64(state->v[2], 12) + XXH_rotl64(state->v[3], 18); + h64 = XXH64_mergeRound(h64, state->v[0]); + h64 = XXH64_mergeRound(h64, state->v[1]); + h64 = XXH64_mergeRound(h64, state->v[2]); + h64 = XXH64_mergeRound(h64, state->v[3]); + } else { + h64 = state->v[2] /*seed*/ + XXH_PRIME64_5; + } + + h64 += (xxh_u64) state->total_len; + + return XXH64_finalize(h64, (const xxh_u8*)state->mem64, (size_t)state->total_len, XXH_aligned); +} +#endif /* !XXH_NO_STREAM */ + +/******* Canonical representation *******/ + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API void XXH64_canonicalFromHash(XXH_NOESCAPE XXH64_canonical_t* dst, XXH64_hash_t hash) +{ + XXH_STATIC_ASSERT(sizeof(XXH64_canonical_t) == sizeof(XXH64_hash_t)); + if (XXH_CPU_LITTLE_ENDIAN) hash = XXH_swap64(hash); + XXH_memcpy(dst, &hash, sizeof(*dst)); +} + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH64_hash_t XXH64_hashFromCanonical(XXH_NOESCAPE const XXH64_canonical_t* src) +{ + return XXH_readBE64(src); +} + +#ifndef XXH_NO_XXH3 + +/* ********************************************************************* +* XXH3 +* New generation hash designed for speed on small keys and vectorization +************************************************************************ */ +/*! + * @} + * @defgroup XXH3_impl XXH3 implementation + * @ingroup impl + * @{ + */ + +/* === Compiler specifics === */ + +#if ((defined(sun) || defined(__sun)) && __cplusplus) /* Solaris includes __STDC_VERSION__ with C++. Tested with GCC 5.5 */ +# define XXH_RESTRICT /* disable */ +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L /* >= C99 */ +# define XXH_RESTRICT restrict +#elif (defined (__GNUC__) && ((__GNUC__ > 3) || (__GNUC__ == 3 && __GNUC_MINOR__ >= 1))) \ + || (defined (__clang__)) \ + || (defined (_MSC_VER) && (_MSC_VER >= 1400)) \ + || (defined (__INTEL_COMPILER) && (__INTEL_COMPILER >= 1300)) +/* + * There are a LOT more compilers that recognize __restrict but this + * covers the major ones. + */ +# define XXH_RESTRICT __restrict +#else +# define XXH_RESTRICT /* disable */ +#endif + +#if (defined(__GNUC__) && (__GNUC__ >= 3)) \ + || (defined(__INTEL_COMPILER) && (__INTEL_COMPILER >= 800)) \ + || defined(__clang__) +# define XXH_likely(x) __builtin_expect(x, 1) +# define XXH_unlikely(x) __builtin_expect(x, 0) +#else +# define XXH_likely(x) (x) +# define XXH_unlikely(x) (x) +#endif + +#ifndef XXH_HAS_INCLUDE +# ifdef __has_include +/* + * Not defined as XXH_HAS_INCLUDE(x) (function-like) because + * this causes segfaults in Apple Clang 4.2 (on Mac OS X 10.7 Lion) + */ +# define XXH_HAS_INCLUDE __has_include +# else +# define XXH_HAS_INCLUDE(x) 0 +# endif +#endif + +#if defined(__GNUC__) || defined(__clang__) +# if defined(__ARM_FEATURE_SVE) +# include +# endif +# if defined(__ARM_NEON__) || defined(__ARM_NEON) \ + || (defined(_M_ARM) && _M_ARM >= 7) \ + || defined(_M_ARM64) || defined(_M_ARM64EC) \ + || (defined(__wasm_simd128__) && XXH_HAS_INCLUDE()) /* WASM SIMD128 via SIMDe */ +# define inline __inline__ /* circumvent a clang bug */ +# include +# undef inline +# elif defined(__AVX2__) +# include +# elif defined(__SSE2__) +# include +# endif +#endif + +#if defined(_MSC_VER) +# include +#endif + +/* + * One goal of XXH3 is to make it fast on both 32-bit and 64-bit, while + * remaining a true 64-bit/128-bit hash function. + * + * This is done by prioritizing a subset of 64-bit operations that can be + * emulated without too many steps on the average 32-bit machine. + * + * For example, these two lines seem similar, and run equally fast on 64-bit: + * + * xxh_u64 x; + * x ^= (x >> 47); // good + * x ^= (x >> 13); // bad + * + * However, to a 32-bit machine, there is a major difference. + * + * x ^= (x >> 47) looks like this: + * + * x.lo ^= (x.hi >> (47 - 32)); + * + * while x ^= (x >> 13) looks like this: + * + * // note: funnel shifts are not usually cheap. + * x.lo ^= (x.lo >> 13) | (x.hi << (32 - 13)); + * x.hi ^= (x.hi >> 13); + * + * The first one is significantly faster than the second, simply because the + * shift is larger than 32. This means: + * - All the bits we need are in the upper 32 bits, so we can ignore the lower + * 32 bits in the shift. + * - The shift result will always fit in the lower 32 bits, and therefore, + * we can ignore the upper 32 bits in the xor. + * + * Thanks to this optimization, XXH3 only requires these features to be efficient: + * + * - Usable unaligned access + * - A 32-bit or 64-bit ALU + * - If 32-bit, a decent ADC instruction + * - A 32 or 64-bit multiply with a 64-bit result + * - For the 128-bit variant, a decent byteswap helps short inputs. + * + * The first two are already required by XXH32, and almost all 32-bit and 64-bit + * platforms which can run XXH32 can run XXH3 efficiently. + * + * Thumb-1, the classic 16-bit only subset of ARM's instruction set, is one + * notable exception. + * + * First of all, Thumb-1 lacks support for the UMULL instruction which + * performs the important long multiply. This means numerous __aeabi_lmul + * calls. + * + * Second of all, the 8 functional registers are just not enough. + * Setup for __aeabi_lmul, byteshift loads, pointers, and all arithmetic need + * Lo registers, and this shuffling results in thousands more MOVs than A32. + * + * A32 and T32 don't have this limitation. They can access all 14 registers, + * do a 32->64 multiply with UMULL, and the flexible operand allowing free + * shifts is helpful, too. + * + * Therefore, we do a quick sanity check. + * + * If compiling Thumb-1 for a target which supports ARM instructions, we will + * emit a warning, as it is not a "sane" platform to compile for. + * + * Usually, if this happens, it is because of an accident and you probably need + * to specify -march, as you likely meant to compile for a newer architecture. + * + * Credit: large sections of the vectorial and asm source code paths + * have been contributed by @easyaspi314 + */ +#if defined(__thumb__) && !defined(__thumb2__) && defined(__ARM_ARCH_ISA_ARM) +# warning "XXH3 is highly inefficient without ARM or Thumb-2." +#endif + +/* ========================================== + * Vectorization detection + * ========================================== */ + +#ifdef XXH_DOXYGEN +/*! + * @ingroup tuning + * @brief Overrides the vectorization implementation chosen for XXH3. + * + * Can be defined to 0 to disable SIMD or any of the values mentioned in + * @ref XXH_VECTOR_TYPE. + * + * If this is not defined, it uses predefined macros to determine the best + * implementation. + */ +# define XXH_VECTOR XXH_SCALAR +/*! + * @ingroup tuning + * @brief Possible values for @ref XXH_VECTOR. + * + * Note that these are actually implemented as macros. + * + * If this is not defined, it is detected automatically. + * internal macro XXH_X86DISPATCH overrides this. + */ +enum XXH_VECTOR_TYPE /* fake enum */ { + XXH_SCALAR = 0, /*!< Portable scalar version */ + XXH_SSE2 = 1, /*!< + * SSE2 for Pentium 4, Opteron, all x86_64. + * + * @note SSE2 is also guaranteed on Windows 10, macOS, and + * Android x86. + */ + XXH_AVX2 = 2, /*!< AVX2 for Haswell and Bulldozer */ + XXH_AVX512 = 3, /*!< AVX512 for Skylake and Icelake */ + XXH_NEON = 4, /*!< + * NEON for most ARMv7-A, all AArch64, and WASM SIMD128 + * via the SIMDeverywhere polyfill provided with the + * Emscripten SDK. + */ + XXH_VSX = 5, /*!< VSX and ZVector for POWER8/z13 (64-bit) */ + XXH_SVE = 6, /*!< SVE for some ARMv8-A and ARMv9-A */ +}; +/*! + * @ingroup tuning + * @brief Selects the minimum alignment for XXH3's accumulators. + * + * When using SIMD, this should match the alignment required for said vector + * type, so, for example, 32 for AVX2. + * + * Default: Auto detected. + */ +# define XXH_ACC_ALIGN 8 +#endif + +/* Actual definition */ +#ifndef XXH_DOXYGEN +# define XXH_SCALAR 0 +# define XXH_SSE2 1 +# define XXH_AVX2 2 +# define XXH_AVX512 3 +# define XXH_NEON 4 +# define XXH_VSX 5 +# define XXH_SVE 6 +#endif + +#ifndef XXH_VECTOR /* can be defined on command line */ +# if defined(__ARM_FEATURE_SVE) +# define XXH_VECTOR XXH_SVE +# elif ( \ + defined(__ARM_NEON__) || defined(__ARM_NEON) /* gcc */ \ + || defined(_M_ARM) || defined(_M_ARM64) || defined(_M_ARM64EC) /* msvc */ \ + || (defined(__wasm_simd128__) && XXH_HAS_INCLUDE()) /* wasm simd128 via SIMDe */ \ + ) && ( \ + defined(_WIN32) || defined(__LITTLE_ENDIAN__) /* little endian only */ \ + || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__) \ + ) +# define XXH_VECTOR XXH_NEON +# elif defined(__AVX512F__) +# define XXH_VECTOR XXH_AVX512 +# elif defined(__AVX2__) +# define XXH_VECTOR XXH_AVX2 +# elif defined(__SSE2__) || defined(_M_AMD64) || defined(_M_X64) || (defined(_M_IX86_FP) && (_M_IX86_FP == 2)) +# define XXH_VECTOR XXH_SSE2 +# elif (defined(__PPC64__) && defined(__POWER8_VECTOR__)) \ + || (defined(__s390x__) && defined(__VEC__)) \ + && defined(__GNUC__) /* TODO: IBM XL */ +# define XXH_VECTOR XXH_VSX +# else +# define XXH_VECTOR XXH_SCALAR +# endif +#endif + +/* __ARM_FEATURE_SVE is only supported by GCC & Clang. */ +#if (XXH_VECTOR == XXH_SVE) && !defined(__ARM_FEATURE_SVE) +# ifdef _MSC_VER +# pragma warning(once : 4606) +# else +# warning "__ARM_FEATURE_SVE isn't supported. Use SCALAR instead." +# endif +# undef XXH_VECTOR +# define XXH_VECTOR XXH_SCALAR +#endif + +/* + * Controls the alignment of the accumulator, + * for compatibility with aligned vector loads, which are usually faster. + */ +#ifndef XXH_ACC_ALIGN +# if defined(XXH_X86DISPATCH) +# define XXH_ACC_ALIGN 64 /* for compatibility with avx512 */ +# elif XXH_VECTOR == XXH_SCALAR /* scalar */ +# define XXH_ACC_ALIGN 8 +# elif XXH_VECTOR == XXH_SSE2 /* sse2 */ +# define XXH_ACC_ALIGN 16 +# elif XXH_VECTOR == XXH_AVX2 /* avx2 */ +# define XXH_ACC_ALIGN 32 +# elif XXH_VECTOR == XXH_NEON /* neon */ +# define XXH_ACC_ALIGN 16 +# elif XXH_VECTOR == XXH_VSX /* vsx */ +# define XXH_ACC_ALIGN 16 +# elif XXH_VECTOR == XXH_AVX512 /* avx512 */ +# define XXH_ACC_ALIGN 64 +# elif XXH_VECTOR == XXH_SVE /* sve */ +# define XXH_ACC_ALIGN 64 +# endif +#endif + +#if defined(XXH_X86DISPATCH) || XXH_VECTOR == XXH_SSE2 \ + || XXH_VECTOR == XXH_AVX2 || XXH_VECTOR == XXH_AVX512 +# define XXH_SEC_ALIGN XXH_ACC_ALIGN +#elif XXH_VECTOR == XXH_SVE +# define XXH_SEC_ALIGN XXH_ACC_ALIGN +#else +# define XXH_SEC_ALIGN 8 +#endif + +#if defined(__GNUC__) || defined(__clang__) +# define XXH_ALIASING __attribute__((__may_alias__)) +#else +# define XXH_ALIASING /* nothing */ +#endif + +/* + * UGLY HACK: + * GCC usually generates the best code with -O3 for xxHash. + * + * However, when targeting AVX2, it is overzealous in its unrolling resulting + * in code roughly 3/4 the speed of Clang. + * + * There are other issues, such as GCC splitting _mm256_loadu_si256 into + * _mm_loadu_si128 + _mm256_inserti128_si256. This is an optimization which + * only applies to Sandy and Ivy Bridge... which don't even support AVX2. + * + * That is why when compiling the AVX2 version, it is recommended to use either + * -O2 -mavx2 -march=haswell + * or + * -O2 -mavx2 -mno-avx256-split-unaligned-load + * for decent performance, or to use Clang instead. + * + * Fortunately, we can control the first one with a pragma that forces GCC into + * -O2, but the other one we can't control without "failed to inline always + * inline function due to target mismatch" warnings. + */ +#if XXH_VECTOR == XXH_AVX2 /* AVX2 */ \ + && defined(__GNUC__) && !defined(__clang__) /* GCC, not Clang */ \ + && defined(__OPTIMIZE__) && XXH_SIZE_OPT <= 0 /* respect -O0 and -Os */ +# pragma GCC push_options +# pragma GCC optimize("-O2") +#endif + +#if XXH_VECTOR == XXH_NEON + +/* + * UGLY HACK: While AArch64 GCC on Linux does not seem to care, on macOS, GCC -O3 + * optimizes out the entire hashLong loop because of the aliasing violation. + * + * However, GCC is also inefficient at load-store optimization with vld1q/vst1q, + * so the only option is to mark it as aliasing. + */ +typedef uint64x2_t xxh_aliasing_uint64x2_t XXH_ALIASING; + +/*! + * @internal + * @brief `vld1q_u64` but faster and alignment-safe. + * + * On AArch64, unaligned access is always safe, but on ARMv7-a, it is only + * *conditionally* safe (`vld1` has an alignment bit like `movdq[ua]` in x86). + * + * GCC for AArch64 sees `vld1q_u8` as an intrinsic instead of a load, so it + * prohibits load-store optimizations. Therefore, a direct dereference is used. + * + * Otherwise, `vld1q_u8` is used with `vreinterpretq_u8_u64` to do a safe + * unaligned load. + */ +#if defined(__aarch64__) && defined(__GNUC__) && !defined(__clang__) +XXH_FORCE_INLINE uint64x2_t XXH_vld1q_u64(void const* ptr) /* silence -Wcast-align */ +{ + return *(xxh_aliasing_uint64x2_t const *)ptr; +} +#else +XXH_FORCE_INLINE uint64x2_t XXH_vld1q_u64(void const* ptr) +{ + return vreinterpretq_u64_u8(vld1q_u8((uint8_t const*)ptr)); +} +#endif + +/*! + * @internal + * @brief `vmlal_u32` on low and high halves of a vector. + * + * This is a workaround for AArch64 GCC < 11 which implemented arm_neon.h with + * inline assembly and were therefore incapable of merging the `vget_{low, high}_u32` + * with `vmlal_u32`. + */ +#if defined(__aarch64__) && defined(__GNUC__) && !defined(__clang__) && __GNUC__ < 11 +XXH_FORCE_INLINE uint64x2_t +XXH_vmlal_low_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs) +{ + /* Inline assembly is the only way */ + __asm__("umlal %0.2d, %1.2s, %2.2s" : "+w" (acc) : "w" (lhs), "w" (rhs)); + return acc; +} +XXH_FORCE_INLINE uint64x2_t +XXH_vmlal_high_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs) +{ + /* This intrinsic works as expected */ + return vmlal_high_u32(acc, lhs, rhs); +} +#else +/* Portable intrinsic versions */ +XXH_FORCE_INLINE uint64x2_t +XXH_vmlal_low_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs) +{ + return vmlal_u32(acc, vget_low_u32(lhs), vget_low_u32(rhs)); +} +/*! @copydoc XXH_vmlal_low_u32 + * Assume the compiler converts this to vmlal_high_u32 on aarch64 */ +XXH_FORCE_INLINE uint64x2_t +XXH_vmlal_high_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs) +{ + return vmlal_u32(acc, vget_high_u32(lhs), vget_high_u32(rhs)); +} +#endif + +/*! + * @ingroup tuning + * @brief Controls the NEON to scalar ratio for XXH3 + * + * This can be set to 2, 4, 6, or 8. + * + * ARM Cortex CPUs are _very_ sensitive to how their pipelines are used. + * + * For example, the Cortex-A73 can dispatch 3 micro-ops per cycle, but only 2 of those + * can be NEON. If you are only using NEON instructions, you are only using 2/3 of the CPU + * bandwidth. + * + * This is even more noticeable on the more advanced cores like the Cortex-A76 which + * can dispatch 8 micro-ops per cycle, but still only 2 NEON micro-ops at once. + * + * Therefore, to make the most out of the pipeline, it is beneficial to run 6 NEON lanes + * and 2 scalar lanes, which is chosen by default. + * + * This does not apply to Apple processors or 32-bit processors, which run better with + * full NEON. These will default to 8. Additionally, size-optimized builds run 8 lanes. + * + * This change benefits CPUs with large micro-op buffers without negatively affecting + * most other CPUs: + * + * | Chipset | Dispatch type | NEON only | 6:2 hybrid | Diff. | + * |:----------------------|:--------------------|----------:|-----------:|------:| + * | Snapdragon 730 (A76) | 2 NEON/8 micro-ops | 8.8 GB/s | 10.1 GB/s | ~16% | + * | Snapdragon 835 (A73) | 2 NEON/3 micro-ops | 5.1 GB/s | 5.3 GB/s | ~5% | + * | Marvell PXA1928 (A53) | In-order dual-issue | 1.9 GB/s | 1.9 GB/s | 0% | + * | Apple M1 | 4 NEON/8 micro-ops | 37.3 GB/s | 36.1 GB/s | ~-3% | + * + * It also seems to fix some bad codegen on GCC, making it almost as fast as clang. + * + * When using WASM SIMD128, if this is 2 or 6, SIMDe will scalarize 2 of the lanes meaning + * it effectively becomes worse 4. + * + * @see XXH3_accumulate_512_neon() + */ +# ifndef XXH3_NEON_LANES +# if (defined(__aarch64__) || defined(__arm64__) || defined(_M_ARM64) || defined(_M_ARM64EC)) \ + && !defined(__APPLE__) && XXH_SIZE_OPT <= 0 +# define XXH3_NEON_LANES 6 +# else +# define XXH3_NEON_LANES XXH_ACC_NB +# endif +# endif +#endif /* XXH_VECTOR == XXH_NEON */ + +/* + * VSX and Z Vector helpers. + * + * This is very messy, and any pull requests to clean this up are welcome. + * + * There are a lot of problems with supporting VSX and s390x, due to + * inconsistent intrinsics, spotty coverage, and multiple endiannesses. + */ +#if XXH_VECTOR == XXH_VSX +/* Annoyingly, these headers _may_ define three macros: `bool`, `vector`, + * and `pixel`. This is a problem for obvious reasons. + * + * These keywords are unnecessary; the spec literally says they are + * equivalent to `__bool`, `__vector`, and `__pixel` and may be undef'd + * after including the header. + * + * We use pragma push_macro/pop_macro to keep the namespace clean. */ +# pragma push_macro("bool") +# pragma push_macro("vector") +# pragma push_macro("pixel") +/* silence potential macro redefined warnings */ +# undef bool +# undef vector +# undef pixel + +# if defined(__s390x__) +# include +# else +# include +# endif + +/* Restore the original macro values, if applicable. */ +# pragma pop_macro("pixel") +# pragma pop_macro("vector") +# pragma pop_macro("bool") + +typedef __vector unsigned long long xxh_u64x2; +typedef __vector unsigned char xxh_u8x16; +typedef __vector unsigned xxh_u32x4; + +/* + * UGLY HACK: Similar to aarch64 macOS GCC, s390x GCC has the same aliasing issue. + */ +typedef xxh_u64x2 xxh_aliasing_u64x2 XXH_ALIASING; + +# ifndef XXH_VSX_BE +# if defined(__BIG_ENDIAN__) \ + || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__) +# define XXH_VSX_BE 1 +# elif defined(__VEC_ELEMENT_REG_ORDER__) && __VEC_ELEMENT_REG_ORDER__ == __ORDER_BIG_ENDIAN__ +# warning "-maltivec=be is not recommended. Please use native endianness." +# define XXH_VSX_BE 1 +# else +# define XXH_VSX_BE 0 +# endif +# endif /* !defined(XXH_VSX_BE) */ + +# if XXH_VSX_BE +# if defined(__POWER9_VECTOR__) || (defined(__clang__) && defined(__s390x__)) +# define XXH_vec_revb vec_revb +# else +/*! + * A polyfill for POWER9's vec_revb(). + */ +XXH_FORCE_INLINE xxh_u64x2 XXH_vec_revb(xxh_u64x2 val) +{ + xxh_u8x16 const vByteSwap = { 0x07, 0x06, 0x05, 0x04, 0x03, 0x02, 0x01, 0x00, + 0x0F, 0x0E, 0x0D, 0x0C, 0x0B, 0x0A, 0x09, 0x08 }; + return vec_perm(val, val, vByteSwap); +} +# endif +# endif /* XXH_VSX_BE */ + +/*! + * Performs an unaligned vector load and byte swaps it on big endian. + */ +XXH_FORCE_INLINE xxh_u64x2 XXH_vec_loadu(const void *ptr) +{ + xxh_u64x2 ret; + XXH_memcpy(&ret, ptr, sizeof(xxh_u64x2)); +# if XXH_VSX_BE + ret = XXH_vec_revb(ret); +# endif + return ret; +} + +/* + * vec_mulo and vec_mule are very problematic intrinsics on PowerPC + * + * These intrinsics weren't added until GCC 8, despite existing for a while, + * and they are endian dependent. Also, their meaning swap depending on version. + * */ +# if defined(__s390x__) + /* s390x is always big endian, no issue on this platform */ +# define XXH_vec_mulo vec_mulo +# define XXH_vec_mule vec_mule +# elif defined(__clang__) && XXH_HAS_BUILTIN(__builtin_altivec_vmuleuw) && !defined(__ibmxl__) +/* Clang has a better way to control this, we can just use the builtin which doesn't swap. */ + /* The IBM XL Compiler (which defined __clang__) only implements the vec_* operations */ +# define XXH_vec_mulo __builtin_altivec_vmulouw +# define XXH_vec_mule __builtin_altivec_vmuleuw +# else +/* gcc needs inline assembly */ +/* Adapted from https://github.com/google/highwayhash/blob/master/highwayhash/hh_vsx.h. */ +XXH_FORCE_INLINE xxh_u64x2 XXH_vec_mulo(xxh_u32x4 a, xxh_u32x4 b) +{ + xxh_u64x2 result; + __asm__("vmulouw %0, %1, %2" : "=v" (result) : "v" (a), "v" (b)); + return result; +} +XXH_FORCE_INLINE xxh_u64x2 XXH_vec_mule(xxh_u32x4 a, xxh_u32x4 b) +{ + xxh_u64x2 result; + __asm__("vmuleuw %0, %1, %2" : "=v" (result) : "v" (a), "v" (b)); + return result; +} +# endif /* XXH_vec_mulo, XXH_vec_mule */ +#endif /* XXH_VECTOR == XXH_VSX */ + +#if XXH_VECTOR == XXH_SVE +#define ACCRND(acc, offset) \ +do { \ + svuint64_t input_vec = svld1_u64(mask, xinput + offset); \ + svuint64_t secret_vec = svld1_u64(mask, xsecret + offset); \ + svuint64_t mixed = sveor_u64_x(mask, secret_vec, input_vec); \ + svuint64_t swapped = svtbl_u64(input_vec, kSwap); \ + svuint64_t mixed_lo = svextw_u64_x(mask, mixed); \ + svuint64_t mixed_hi = svlsr_n_u64_x(mask, mixed, 32); \ + svuint64_t mul = svmad_u64_x(mask, mixed_lo, mixed_hi, swapped); \ + acc = svadd_u64_x(mask, acc, mul); \ +} while (0) +#endif /* XXH_VECTOR == XXH_SVE */ + +/* prefetch + * can be disabled, by declaring XXH_NO_PREFETCH build macro */ +#if defined(XXH_NO_PREFETCH) +# define XXH_PREFETCH(ptr) (void)(ptr) /* disabled */ +#else +# if XXH_SIZE_OPT >= 1 +# define XXH_PREFETCH(ptr) (void)(ptr) +# elif defined(_MSC_VER) && (defined(_M_X64) || defined(_M_IX86)) /* _mm_prefetch() not defined outside of x86/x64 */ +# include /* https://msdn.microsoft.com/fr-fr/library/84szxsww(v=vs.90).aspx */ +# define XXH_PREFETCH(ptr) _mm_prefetch((const char*)(ptr), _MM_HINT_T0) +# elif defined(__GNUC__) && ( (__GNUC__ >= 4) || ( (__GNUC__ == 3) && (__GNUC_MINOR__ >= 1) ) ) +# define XXH_PREFETCH(ptr) __builtin_prefetch((ptr), 0 /* rw==read */, 3 /* locality */) +# else +# define XXH_PREFETCH(ptr) (void)(ptr) /* disabled */ +# endif +#endif /* XXH_NO_PREFETCH */ + + +/* ========================================== + * XXH3 default settings + * ========================================== */ + +#define XXH_SECRET_DEFAULT_SIZE 192 /* minimum XXH3_SECRET_SIZE_MIN */ + +#if (XXH_SECRET_DEFAULT_SIZE < XXH3_SECRET_SIZE_MIN) +# error "default keyset is not large enough" +#endif + +/*! Pseudorandom secret taken directly from FARSH. */ +XXH_ALIGN(64) static const xxh_u8 XXH3_kSecret[XXH_SECRET_DEFAULT_SIZE] = { + 0xb8, 0xfe, 0x6c, 0x39, 0x23, 0xa4, 0x4b, 0xbe, 0x7c, 0x01, 0x81, 0x2c, 0xf7, 0x21, 0xad, 0x1c, + 0xde, 0xd4, 0x6d, 0xe9, 0x83, 0x90, 0x97, 0xdb, 0x72, 0x40, 0xa4, 0xa4, 0xb7, 0xb3, 0x67, 0x1f, + 0xcb, 0x79, 0xe6, 0x4e, 0xcc, 0xc0, 0xe5, 0x78, 0x82, 0x5a, 0xd0, 0x7d, 0xcc, 0xff, 0x72, 0x21, + 0xb8, 0x08, 0x46, 0x74, 0xf7, 0x43, 0x24, 0x8e, 0xe0, 0x35, 0x90, 0xe6, 0x81, 0x3a, 0x26, 0x4c, + 0x3c, 0x28, 0x52, 0xbb, 0x91, 0xc3, 0x00, 0xcb, 0x88, 0xd0, 0x65, 0x8b, 0x1b, 0x53, 0x2e, 0xa3, + 0x71, 0x64, 0x48, 0x97, 0xa2, 0x0d, 0xf9, 0x4e, 0x38, 0x19, 0xef, 0x46, 0xa9, 0xde, 0xac, 0xd8, + 0xa8, 0xfa, 0x76, 0x3f, 0xe3, 0x9c, 0x34, 0x3f, 0xf9, 0xdc, 0xbb, 0xc7, 0xc7, 0x0b, 0x4f, 0x1d, + 0x8a, 0x51, 0xe0, 0x4b, 0xcd, 0xb4, 0x59, 0x31, 0xc8, 0x9f, 0x7e, 0xc9, 0xd9, 0x78, 0x73, 0x64, + 0xea, 0xc5, 0xac, 0x83, 0x34, 0xd3, 0xeb, 0xc3, 0xc5, 0x81, 0xa0, 0xff, 0xfa, 0x13, 0x63, 0xeb, + 0x17, 0x0d, 0xdd, 0x51, 0xb7, 0xf0, 0xda, 0x49, 0xd3, 0x16, 0x55, 0x26, 0x29, 0xd4, 0x68, 0x9e, + 0x2b, 0x16, 0xbe, 0x58, 0x7d, 0x47, 0xa1, 0xfc, 0x8f, 0xf8, 0xb8, 0xd1, 0x7a, 0xd0, 0x31, 0xce, + 0x45, 0xcb, 0x3a, 0x8f, 0x95, 0x16, 0x04, 0x28, 0xaf, 0xd7, 0xfb, 0xca, 0xbb, 0x4b, 0x40, 0x7e, +}; + +static const xxh_u64 PRIME_MX1 = 0x165667919E3779F9ULL; /*!< 0b0001011001010110011001111001000110011110001101110111100111111001 */ +static const xxh_u64 PRIME_MX2 = 0x9FB21C651E98DF25ULL; /*!< 0b1001111110110010000111000110010100011110100110001101111100100101 */ + +#ifdef XXH_OLD_NAMES +# define kSecret XXH3_kSecret +#endif + +#ifdef XXH_DOXYGEN +/*! + * @brief Calculates a 32-bit to 64-bit long multiply. + * + * Implemented as a macro. + * + * Wraps `__emulu` on MSVC x86 because it tends to call `__allmul` when it doesn't + * need to (but it shouldn't need to anyways, it is about 7 instructions to do + * a 64x64 multiply...). Since we know that this will _always_ emit `MULL`, we + * use that instead of the normal method. + * + * If you are compiling for platforms like Thumb-1 and don't have a better option, + * you may also want to write your own long multiply routine here. + * + * @param x, y Numbers to be multiplied + * @return 64-bit product of the low 32 bits of @p x and @p y. + */ +XXH_FORCE_INLINE xxh_u64 +XXH_mult32to64(xxh_u64 x, xxh_u64 y) +{ + return (x & 0xFFFFFFFF) * (y & 0xFFFFFFFF); +} +#elif defined(_MSC_VER) && defined(_M_IX86) +# define XXH_mult32to64(x, y) __emulu((unsigned)(x), (unsigned)(y)) +#else +/* + * Downcast + upcast is usually better than masking on older compilers like + * GCC 4.2 (especially 32-bit ones), all without affecting newer compilers. + * + * The other method, (x & 0xFFFFFFFF) * (y & 0xFFFFFFFF), will AND both operands + * and perform a full 64x64 multiply -- entirely redundant on 32-bit. + */ +# define XXH_mult32to64(x, y) ((xxh_u64)(xxh_u32)(x) * (xxh_u64)(xxh_u32)(y)) +#endif + +/*! + * @brief Calculates a 64->128-bit long multiply. + * + * Uses `__uint128_t` and `_umul128` if available, otherwise uses a scalar + * version. + * + * @param lhs , rhs The 64-bit integers to be multiplied + * @return The 128-bit result represented in an @ref XXH128_hash_t. + */ +static XXH128_hash_t +XXH_mult64to128(xxh_u64 lhs, xxh_u64 rhs) +{ + /* + * GCC/Clang __uint128_t method. + * + * On most 64-bit targets, GCC and Clang define a __uint128_t type. + * This is usually the best way as it usually uses a native long 64-bit + * multiply, such as MULQ on x86_64 or MUL + UMULH on aarch64. + * + * Usually. + * + * Despite being a 32-bit platform, Clang (and emscripten) define this type + * despite not having the arithmetic for it. This results in a laggy + * compiler builtin call which calculates a full 128-bit multiply. + * In that case it is best to use the portable one. + * https://github.com/Cyan4973/xxHash/issues/211#issuecomment-515575677 + */ +#if (defined(__GNUC__) || defined(__clang__)) && !defined(__wasm__) \ + && defined(__SIZEOF_INT128__) \ + || (defined(_INTEGRAL_MAX_BITS) && _INTEGRAL_MAX_BITS >= 128) + + __uint128_t const product = (__uint128_t)lhs * (__uint128_t)rhs; + XXH128_hash_t r128; + r128.low64 = (xxh_u64)(product); + r128.high64 = (xxh_u64)(product >> 64); + return r128; + + /* + * MSVC for x64's _umul128 method. + * + * xxh_u64 _umul128(xxh_u64 Multiplier, xxh_u64 Multiplicand, xxh_u64 *HighProduct); + * + * This compiles to single operand MUL on x64. + */ +#elif (defined(_M_X64) || defined(_M_IA64)) && !defined(_M_ARM64EC) + +#ifndef _MSC_VER +# pragma intrinsic(_umul128) +#endif + xxh_u64 product_high; + xxh_u64 const product_low = _umul128(lhs, rhs, &product_high); + XXH128_hash_t r128; + r128.low64 = product_low; + r128.high64 = product_high; + return r128; + + /* + * MSVC for ARM64's __umulh method. + * + * This compiles to the same MUL + UMULH as GCC/Clang's __uint128_t method. + */ +#elif defined(_M_ARM64) || defined(_M_ARM64EC) + +#ifndef _MSC_VER +# pragma intrinsic(__umulh) +#endif + XXH128_hash_t r128; + r128.low64 = lhs * rhs; + r128.high64 = __umulh(lhs, rhs); + return r128; + +#else + /* + * Portable scalar method. Optimized for 32-bit and 64-bit ALUs. + * + * This is a fast and simple grade school multiply, which is shown below + * with base 10 arithmetic instead of base 0x100000000. + * + * 9 3 // D2 lhs = 93 + * x 7 5 // D2 rhs = 75 + * ---------- + * 1 5 // D2 lo_lo = (93 % 10) * (75 % 10) = 15 + * 4 5 | // D2 hi_lo = (93 / 10) * (75 % 10) = 45 + * 2 1 | // D2 lo_hi = (93 % 10) * (75 / 10) = 21 + * + 6 3 | | // D2 hi_hi = (93 / 10) * (75 / 10) = 63 + * --------- + * 2 7 | // D2 cross = (15 / 10) + (45 % 10) + 21 = 27 + * + 6 7 | | // D2 upper = (27 / 10) + (45 / 10) + 63 = 67 + * --------- + * 6 9 7 5 // D4 res = (27 * 10) + (15 % 10) + (67 * 100) = 6975 + * + * The reasons for adding the products like this are: + * 1. It avoids manual carry tracking. Just like how + * (9 * 9) + 9 + 9 = 99, the same applies with this for UINT64_MAX. + * This avoids a lot of complexity. + * + * 2. It hints for, and on Clang, compiles to, the powerful UMAAL + * instruction available in ARM's Digital Signal Processing extension + * in 32-bit ARMv6 and later, which is shown below: + * + * void UMAAL(xxh_u32 *RdLo, xxh_u32 *RdHi, xxh_u32 Rn, xxh_u32 Rm) + * { + * xxh_u64 product = (xxh_u64)*RdLo * (xxh_u64)*RdHi + Rn + Rm; + * *RdLo = (xxh_u32)(product & 0xFFFFFFFF); + * *RdHi = (xxh_u32)(product >> 32); + * } + * + * This instruction was designed for efficient long multiplication, and + * allows this to be calculated in only 4 instructions at speeds + * comparable to some 64-bit ALUs. + * + * 3. It isn't terrible on other platforms. Usually this will be a couple + * of 32-bit ADD/ADCs. + */ + + /* First calculate all of the cross products. */ + xxh_u64 const lo_lo = XXH_mult32to64(lhs & 0xFFFFFFFF, rhs & 0xFFFFFFFF); + xxh_u64 const hi_lo = XXH_mult32to64(lhs >> 32, rhs & 0xFFFFFFFF); + xxh_u64 const lo_hi = XXH_mult32to64(lhs & 0xFFFFFFFF, rhs >> 32); + xxh_u64 const hi_hi = XXH_mult32to64(lhs >> 32, rhs >> 32); + + /* Now add the products together. These will never overflow. */ + xxh_u64 const cross = (lo_lo >> 32) + (hi_lo & 0xFFFFFFFF) + lo_hi; + xxh_u64 const upper = (hi_lo >> 32) + (cross >> 32) + hi_hi; + xxh_u64 const lower = (cross << 32) | (lo_lo & 0xFFFFFFFF); + + XXH128_hash_t r128; + r128.low64 = lower; + r128.high64 = upper; + return r128; +#endif +} + +/*! + * @brief Calculates a 64-bit to 128-bit multiply, then XOR folds it. + * + * The reason for the separate function is to prevent passing too many structs + * around by value. This will hopefully inline the multiply, but we don't force it. + * + * @param lhs , rhs The 64-bit integers to multiply + * @return The low 64 bits of the product XOR'd by the high 64 bits. + * @see XXH_mult64to128() + */ +static xxh_u64 +XXH3_mul128_fold64(xxh_u64 lhs, xxh_u64 rhs) +{ + XXH128_hash_t product = XXH_mult64to128(lhs, rhs); + return product.low64 ^ product.high64; +} + +/*! Seems to produce slightly better code on GCC for some reason. */ +XXH_FORCE_INLINE XXH_CONSTF xxh_u64 XXH_xorshift64(xxh_u64 v64, int shift) +{ + XXH_ASSERT(0 <= shift && shift < 64); + return v64 ^ (v64 >> shift); +} + +/* + * This is a fast avalanche stage, + * suitable when input bits are already partially mixed + */ +static XXH64_hash_t XXH3_avalanche(xxh_u64 h64) +{ + h64 = XXH_xorshift64(h64, 37); + h64 *= PRIME_MX1; + h64 = XXH_xorshift64(h64, 32); + return h64; +} + +/* + * This is a stronger avalanche, + * inspired by Pelle Evensen's rrmxmx + * preferable when input has not been previously mixed + */ +static XXH64_hash_t XXH3_rrmxmx(xxh_u64 h64, xxh_u64 len) +{ + /* this mix is inspired by Pelle Evensen's rrmxmx */ + h64 ^= XXH_rotl64(h64, 49) ^ XXH_rotl64(h64, 24); + h64 *= PRIME_MX2; + h64 ^= (h64 >> 35) + len ; + h64 *= PRIME_MX2; + return XXH_xorshift64(h64, 28); +} + + +/* ========================================== + * Short keys + * ========================================== + * One of the shortcomings of XXH32 and XXH64 was that their performance was + * sub-optimal on short lengths. It used an iterative algorithm which strongly + * favored lengths that were a multiple of 4 or 8. + * + * Instead of iterating over individual inputs, we use a set of single shot + * functions which piece together a range of lengths and operate in constant time. + * + * Additionally, the number of multiplies has been significantly reduced. This + * reduces latency, especially when emulating 64-bit multiplies on 32-bit. + * + * Depending on the platform, this may or may not be faster than XXH32, but it + * is almost guaranteed to be faster than XXH64. + */ + +/* + * At very short lengths, there isn't enough input to fully hide secrets, or use + * the entire secret. + * + * There is also only a limited amount of mixing we can do before significantly + * impacting performance. + * + * Therefore, we use different sections of the secret and always mix two secret + * samples with an XOR. This should have no effect on performance on the + * seedless or withSeed variants because everything _should_ be constant folded + * by modern compilers. + * + * The XOR mixing hides individual parts of the secret and increases entropy. + * + * This adds an extra layer of strength for custom secrets. + */ +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_1to3_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(1 <= len && len <= 3); + XXH_ASSERT(secret != NULL); + /* + * len = 1: combined = { input[0], 0x01, input[0], input[0] } + * len = 2: combined = { input[1], 0x02, input[0], input[1] } + * len = 3: combined = { input[2], 0x03, input[0], input[1] } + */ + { xxh_u8 const c1 = input[0]; + xxh_u8 const c2 = input[len >> 1]; + xxh_u8 const c3 = input[len - 1]; + xxh_u32 const combined = ((xxh_u32)c1 << 16) | ((xxh_u32)c2 << 24) + | ((xxh_u32)c3 << 0) | ((xxh_u32)len << 8); + xxh_u64 const bitflip = (XXH_readLE32(secret) ^ XXH_readLE32(secret+4)) + seed; + xxh_u64 const keyed = (xxh_u64)combined ^ bitflip; + return XXH64_avalanche(keyed); + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_4to8_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(secret != NULL); + XXH_ASSERT(4 <= len && len <= 8); + seed ^= (xxh_u64)XXH_swap32((xxh_u32)seed) << 32; + { xxh_u32 const input1 = XXH_readLE32(input); + xxh_u32 const input2 = XXH_readLE32(input + len - 4); + xxh_u64 const bitflip = (XXH_readLE64(secret+8) ^ XXH_readLE64(secret+16)) - seed; + xxh_u64 const input64 = input2 + (((xxh_u64)input1) << 32); + xxh_u64 const keyed = input64 ^ bitflip; + return XXH3_rrmxmx(keyed, len); + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_9to16_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(secret != NULL); + XXH_ASSERT(9 <= len && len <= 16); + { xxh_u64 const bitflip1 = (XXH_readLE64(secret+24) ^ XXH_readLE64(secret+32)) + seed; + xxh_u64 const bitflip2 = (XXH_readLE64(secret+40) ^ XXH_readLE64(secret+48)) - seed; + xxh_u64 const input_lo = XXH_readLE64(input) ^ bitflip1; + xxh_u64 const input_hi = XXH_readLE64(input + len - 8) ^ bitflip2; + xxh_u64 const acc = len + + XXH_swap64(input_lo) + input_hi + + XXH3_mul128_fold64(input_lo, input_hi); + return XXH3_avalanche(acc); + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_0to16_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(len <= 16); + { if (XXH_likely(len > 8)) return XXH3_len_9to16_64b(input, len, secret, seed); + if (XXH_likely(len >= 4)) return XXH3_len_4to8_64b(input, len, secret, seed); + if (len) return XXH3_len_1to3_64b(input, len, secret, seed); + return XXH64_avalanche(seed ^ (XXH_readLE64(secret+56) ^ XXH_readLE64(secret+64))); + } +} + +/* + * DISCLAIMER: There are known *seed-dependent* multicollisions here due to + * multiplication by zero, affecting hashes of lengths 17 to 240. + * + * However, they are very unlikely. + * + * Keep this in mind when using the unseeded XXH3_64bits() variant: As with all + * unseeded non-cryptographic hashes, it does not attempt to defend itself + * against specially crafted inputs, only random inputs. + * + * Compared to classic UMAC where a 1 in 2^31 chance of 4 consecutive bytes + * cancelling out the secret is taken an arbitrary number of times (addressed + * in XXH3_accumulate_512), this collision is very unlikely with random inputs + * and/or proper seeding: + * + * This only has a 1 in 2^63 chance of 8 consecutive bytes cancelling out, in a + * function that is only called up to 16 times per hash with up to 240 bytes of + * input. + * + * This is not too bad for a non-cryptographic hash function, especially with + * only 64 bit outputs. + * + * The 128-bit variant (which trades some speed for strength) is NOT affected + * by this, although it is always a good idea to use a proper seed if you care + * about strength. + */ +XXH_FORCE_INLINE xxh_u64 XXH3_mix16B(const xxh_u8* XXH_RESTRICT input, + const xxh_u8* XXH_RESTRICT secret, xxh_u64 seed64) +{ +#if defined(__GNUC__) && !defined(__clang__) /* GCC, not Clang */ \ + && defined(__i386__) && defined(__SSE2__) /* x86 + SSE2 */ \ + && !defined(XXH_ENABLE_AUTOVECTORIZE) /* Define to disable like XXH32 hack */ + /* + * UGLY HACK: + * GCC for x86 tends to autovectorize the 128-bit multiply, resulting in + * slower code. + * + * By forcing seed64 into a register, we disrupt the cost model and + * cause it to scalarize. See `XXH32_round()` + * + * FIXME: Clang's output is still _much_ faster -- On an AMD Ryzen 3600, + * XXH3_64bits @ len=240 runs at 4.6 GB/s with Clang 9, but 3.3 GB/s on + * GCC 9.2, despite both emitting scalar code. + * + * GCC generates much better scalar code than Clang for the rest of XXH3, + * which is why finding a more optimal codepath is an interest. + */ + XXH_COMPILER_GUARD(seed64); +#endif + { xxh_u64 const input_lo = XXH_readLE64(input); + xxh_u64 const input_hi = XXH_readLE64(input+8); + return XXH3_mul128_fold64( + input_lo ^ (XXH_readLE64(secret) + seed64), + input_hi ^ (XXH_readLE64(secret+8) - seed64) + ); + } +} + +/* For mid range keys, XXH3 uses a Mum-hash variant. */ +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_17to128_64b(const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH64_hash_t seed) +{ + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize; + XXH_ASSERT(16 < len && len <= 128); + + { xxh_u64 acc = len * XXH_PRIME64_1; +#if XXH_SIZE_OPT >= 1 + /* Smaller and cleaner, but slightly slower. */ + unsigned int i = (unsigned int)(len - 1) / 32; + do { + acc += XXH3_mix16B(input+16 * i, secret+32*i, seed); + acc += XXH3_mix16B(input+len-16*(i+1), secret+32*i+16, seed); + } while (i-- != 0); +#else + if (len > 32) { + if (len > 64) { + if (len > 96) { + acc += XXH3_mix16B(input+48, secret+96, seed); + acc += XXH3_mix16B(input+len-64, secret+112, seed); + } + acc += XXH3_mix16B(input+32, secret+64, seed); + acc += XXH3_mix16B(input+len-48, secret+80, seed); + } + acc += XXH3_mix16B(input+16, secret+32, seed); + acc += XXH3_mix16B(input+len-32, secret+48, seed); + } + acc += XXH3_mix16B(input+0, secret+0, seed); + acc += XXH3_mix16B(input+len-16, secret+16, seed); +#endif + return XXH3_avalanche(acc); + } +} + +XXH_NO_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_129to240_64b(const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH64_hash_t seed) +{ + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize; + XXH_ASSERT(128 < len && len <= XXH3_MIDSIZE_MAX); + + #define XXH3_MIDSIZE_STARTOFFSET 3 + #define XXH3_MIDSIZE_LASTOFFSET 17 + + { xxh_u64 acc = len * XXH_PRIME64_1; + xxh_u64 acc_end; + unsigned int const nbRounds = (unsigned int)len / 16; + unsigned int i; + XXH_ASSERT(128 < len && len <= XXH3_MIDSIZE_MAX); + for (i=0; i<8; i++) { + acc += XXH3_mix16B(input+(16*i), secret+(16*i), seed); + } + /* last bytes */ + acc_end = XXH3_mix16B(input + len - 16, secret + XXH3_SECRET_SIZE_MIN - XXH3_MIDSIZE_LASTOFFSET, seed); + XXH_ASSERT(nbRounds >= 8); + acc = XXH3_avalanche(acc); +#if defined(__clang__) /* Clang */ \ + && (defined(__ARM_NEON) || defined(__ARM_NEON__)) /* NEON */ \ + && !defined(XXH_ENABLE_AUTOVECTORIZE) /* Define to disable */ + /* + * UGLY HACK: + * Clang for ARMv7-A tries to vectorize this loop, similar to GCC x86. + * In everywhere else, it uses scalar code. + * + * For 64->128-bit multiplies, even if the NEON was 100% optimal, it + * would still be slower than UMAAL (see XXH_mult64to128). + * + * Unfortunately, Clang doesn't handle the long multiplies properly and + * converts them to the nonexistent "vmulq_u64" intrinsic, which is then + * scalarized into an ugly mess of VMOV.32 instructions. + * + * This mess is difficult to avoid without turning autovectorization + * off completely, but they are usually relatively minor and/or not + * worth it to fix. + * + * This loop is the easiest to fix, as unlike XXH32, this pragma + * _actually works_ because it is a loop vectorization instead of an + * SLP vectorization. + */ + #pragma clang loop vectorize(disable) +#endif + for (i=8 ; i < nbRounds; i++) { + /* + * Prevents clang for unrolling the acc loop and interleaving with this one. + */ + XXH_COMPILER_GUARD(acc); + acc_end += XXH3_mix16B(input+(16*i), secret+(16*(i-8)) + XXH3_MIDSIZE_STARTOFFSET, seed); + } + return XXH3_avalanche(acc + acc_end); + } +} + + +/* ======= Long Keys ======= */ + +#define XXH_STRIPE_LEN 64 +#define XXH_SECRET_CONSUME_RATE 8 /* nb of secret bytes consumed at each accumulation */ +#define XXH_ACC_NB (XXH_STRIPE_LEN / sizeof(xxh_u64)) + +#ifdef XXH_OLD_NAMES +# define STRIPE_LEN XXH_STRIPE_LEN +# define ACC_NB XXH_ACC_NB +#endif + +#ifndef XXH_PREFETCH_DIST +# ifdef __clang__ +# define XXH_PREFETCH_DIST 320 +# else +# if (XXH_VECTOR == XXH_AVX512) +# define XXH_PREFETCH_DIST 512 +# else +# define XXH_PREFETCH_DIST 384 +# endif +# endif /* __clang__ */ +#endif /* XXH_PREFETCH_DIST */ + +/* + * These macros are to generate an XXH3_accumulate() function. + * The two arguments select the name suffix and target attribute. + * + * The name of this symbol is XXH3_accumulate_() and it calls + * XXH3_accumulate_512_(). + * + * It may be useful to hand implement this function if the compiler fails to + * optimize the inline function. + */ +#define XXH3_ACCUMULATE_TEMPLATE(name) \ +void \ +XXH3_accumulate_##name(xxh_u64* XXH_RESTRICT acc, \ + const xxh_u8* XXH_RESTRICT input, \ + const xxh_u8* XXH_RESTRICT secret, \ + size_t nbStripes) \ +{ \ + size_t n; \ + for (n = 0; n < nbStripes; n++ ) { \ + const xxh_u8* const in = input + n*XXH_STRIPE_LEN; \ + XXH_PREFETCH(in + XXH_PREFETCH_DIST); \ + XXH3_accumulate_512_##name( \ + acc, \ + in, \ + secret + n*XXH_SECRET_CONSUME_RATE); \ + } \ +} + + +XXH_FORCE_INLINE void XXH_writeLE64(void* dst, xxh_u64 v64) +{ + if (!XXH_CPU_LITTLE_ENDIAN) v64 = XXH_swap64(v64); + XXH_memcpy(dst, &v64, sizeof(v64)); +} + +/* Several intrinsic functions below are supposed to accept __int64 as argument, + * as documented in https://software.intel.com/sites/landingpage/IntrinsicsGuide/ . + * However, several environments do not define __int64 type, + * requiring a workaround. + */ +#if !defined (__VMS) \ + && (defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) ) + typedef int64_t xxh_i64; +#else + /* the following type must have a width of 64-bit */ + typedef long long xxh_i64; +#endif + + +/* + * XXH3_accumulate_512 is the tightest loop for long inputs, and it is the most optimized. + * + * It is a hardened version of UMAC, based off of FARSH's implementation. + * + * This was chosen because it adapts quite well to 32-bit, 64-bit, and SIMD + * implementations, and it is ridiculously fast. + * + * We harden it by mixing the original input to the accumulators as well as the product. + * + * This means that in the (relatively likely) case of a multiply by zero, the + * original input is preserved. + * + * On 128-bit inputs, we swap 64-bit pairs when we add the input to improve + * cross-pollination, as otherwise the upper and lower halves would be + * essentially independent. + * + * This doesn't matter on 64-bit hashes since they all get merged together in + * the end, so we skip the extra step. + * + * Both XXH3_64bits and XXH3_128bits use this subroutine. + */ + +#if (XXH_VECTOR == XXH_AVX512) \ + || (defined(XXH_DISPATCH_AVX512) && XXH_DISPATCH_AVX512 != 0) + +#ifndef XXH_TARGET_AVX512 +# define XXH_TARGET_AVX512 /* disable attribute target */ +#endif + +XXH_FORCE_INLINE XXH_TARGET_AVX512 void +XXH3_accumulate_512_avx512(void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + __m512i* const xacc = (__m512i *) acc; + XXH_ASSERT((((size_t)acc) & 63) == 0); + XXH_STATIC_ASSERT(XXH_STRIPE_LEN == sizeof(__m512i)); + + { + /* data_vec = input[0]; */ + __m512i const data_vec = _mm512_loadu_si512 (input); + /* key_vec = secret[0]; */ + __m512i const key_vec = _mm512_loadu_si512 (secret); + /* data_key = data_vec ^ key_vec; */ + __m512i const data_key = _mm512_xor_si512 (data_vec, key_vec); + /* data_key_lo = data_key >> 32; */ + __m512i const data_key_lo = _mm512_srli_epi64 (data_key, 32); + /* product = (data_key & 0xffffffff) * (data_key_lo & 0xffffffff); */ + __m512i const product = _mm512_mul_epu32 (data_key, data_key_lo); + /* xacc[0] += swap(data_vec); */ + __m512i const data_swap = _mm512_shuffle_epi32(data_vec, (_MM_PERM_ENUM)_MM_SHUFFLE(1, 0, 3, 2)); + __m512i const sum = _mm512_add_epi64(*xacc, data_swap); + /* xacc[0] += product; */ + *xacc = _mm512_add_epi64(product, sum); + } +} +XXH_FORCE_INLINE XXH_TARGET_AVX512 XXH3_ACCUMULATE_TEMPLATE(avx512) + +/* + * XXH3_scrambleAcc: Scrambles the accumulators to improve mixing. + * + * Multiplication isn't perfect, as explained by Google in HighwayHash: + * + * // Multiplication mixes/scrambles bytes 0-7 of the 64-bit result to + * // varying degrees. In descending order of goodness, bytes + * // 3 4 2 5 1 6 0 7 have quality 228 224 164 160 100 96 36 32. + * // As expected, the upper and lower bytes are much worse. + * + * Source: https://github.com/google/highwayhash/blob/0aaf66b/highwayhash/hh_avx2.h#L291 + * + * Since our algorithm uses a pseudorandom secret to add some variance into the + * mix, we don't need to (or want to) mix as often or as much as HighwayHash does. + * + * This isn't as tight as XXH3_accumulate, but still written in SIMD to avoid + * extraction. + * + * Both XXH3_64bits and XXH3_128bits use this subroutine. + */ + +XXH_FORCE_INLINE XXH_TARGET_AVX512 void +XXH3_scrambleAcc_avx512(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 63) == 0); + XXH_STATIC_ASSERT(XXH_STRIPE_LEN == sizeof(__m512i)); + { __m512i* const xacc = (__m512i*) acc; + const __m512i prime32 = _mm512_set1_epi32((int)XXH_PRIME32_1); + + /* xacc[0] ^= (xacc[0] >> 47) */ + __m512i const acc_vec = *xacc; + __m512i const shifted = _mm512_srli_epi64 (acc_vec, 47); + /* xacc[0] ^= secret; */ + __m512i const key_vec = _mm512_loadu_si512 (secret); + __m512i const data_key = _mm512_ternarylogic_epi32(key_vec, acc_vec, shifted, 0x96 /* key_vec ^ acc_vec ^ shifted */); + + /* xacc[0] *= XXH_PRIME32_1; */ + __m512i const data_key_hi = _mm512_srli_epi64 (data_key, 32); + __m512i const prod_lo = _mm512_mul_epu32 (data_key, prime32); + __m512i const prod_hi = _mm512_mul_epu32 (data_key_hi, prime32); + *xacc = _mm512_add_epi64(prod_lo, _mm512_slli_epi64(prod_hi, 32)); + } +} + +XXH_FORCE_INLINE XXH_TARGET_AVX512 void +XXH3_initCustomSecret_avx512(void* XXH_RESTRICT customSecret, xxh_u64 seed64) +{ + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 63) == 0); + XXH_STATIC_ASSERT(XXH_SEC_ALIGN == 64); + XXH_ASSERT(((size_t)customSecret & 63) == 0); + (void)(&XXH_writeLE64); + { int const nbRounds = XXH_SECRET_DEFAULT_SIZE / sizeof(__m512i); + __m512i const seed_pos = _mm512_set1_epi64((xxh_i64)seed64); + __m512i const seed = _mm512_mask_sub_epi64(seed_pos, 0xAA, _mm512_set1_epi8(0), seed_pos); + + const __m512i* const src = (const __m512i*) ((const void*) XXH3_kSecret); + __m512i* const dest = ( __m512i*) customSecret; + int i; + XXH_ASSERT(((size_t)src & 63) == 0); /* control alignment */ + XXH_ASSERT(((size_t)dest & 63) == 0); + for (i=0; i < nbRounds; ++i) { + dest[i] = _mm512_add_epi64(_mm512_load_si512(src + i), seed); + } } +} + +#endif + +#if (XXH_VECTOR == XXH_AVX2) \ + || (defined(XXH_DISPATCH_AVX2) && XXH_DISPATCH_AVX2 != 0) + +#ifndef XXH_TARGET_AVX2 +# define XXH_TARGET_AVX2 /* disable attribute target */ +#endif + +XXH_FORCE_INLINE XXH_TARGET_AVX2 void +XXH3_accumulate_512_avx2( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 31) == 0); + { __m256i* const xacc = (__m256i *) acc; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm256_loadu_si256 requires a const __m256i * pointer for some reason. */ + const __m256i* const xinput = (const __m256i *) input; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm256_loadu_si256 requires a const __m256i * pointer for some reason. */ + const __m256i* const xsecret = (const __m256i *) secret; + + size_t i; + for (i=0; i < XXH_STRIPE_LEN/sizeof(__m256i); i++) { + /* data_vec = xinput[i]; */ + __m256i const data_vec = _mm256_loadu_si256 (xinput+i); + /* key_vec = xsecret[i]; */ + __m256i const key_vec = _mm256_loadu_si256 (xsecret+i); + /* data_key = data_vec ^ key_vec; */ + __m256i const data_key = _mm256_xor_si256 (data_vec, key_vec); + /* data_key_lo = data_key >> 32; */ + __m256i const data_key_lo = _mm256_srli_epi64 (data_key, 32); + /* product = (data_key & 0xffffffff) * (data_key_lo & 0xffffffff); */ + __m256i const product = _mm256_mul_epu32 (data_key, data_key_lo); + /* xacc[i] += swap(data_vec); */ + __m256i const data_swap = _mm256_shuffle_epi32(data_vec, _MM_SHUFFLE(1, 0, 3, 2)); + __m256i const sum = _mm256_add_epi64(xacc[i], data_swap); + /* xacc[i] += product; */ + xacc[i] = _mm256_add_epi64(product, sum); + } } +} +XXH_FORCE_INLINE XXH_TARGET_AVX2 XXH3_ACCUMULATE_TEMPLATE(avx2) + +XXH_FORCE_INLINE XXH_TARGET_AVX2 void +XXH3_scrambleAcc_avx2(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 31) == 0); + { __m256i* const xacc = (__m256i*) acc; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm256_loadu_si256 requires a const __m256i * pointer for some reason. */ + const __m256i* const xsecret = (const __m256i *) secret; + const __m256i prime32 = _mm256_set1_epi32((int)XXH_PRIME32_1); + + size_t i; + for (i=0; i < XXH_STRIPE_LEN/sizeof(__m256i); i++) { + /* xacc[i] ^= (xacc[i] >> 47) */ + __m256i const acc_vec = xacc[i]; + __m256i const shifted = _mm256_srli_epi64 (acc_vec, 47); + __m256i const data_vec = _mm256_xor_si256 (acc_vec, shifted); + /* xacc[i] ^= xsecret; */ + __m256i const key_vec = _mm256_loadu_si256 (xsecret+i); + __m256i const data_key = _mm256_xor_si256 (data_vec, key_vec); + + /* xacc[i] *= XXH_PRIME32_1; */ + __m256i const data_key_hi = _mm256_srli_epi64 (data_key, 32); + __m256i const prod_lo = _mm256_mul_epu32 (data_key, prime32); + __m256i const prod_hi = _mm256_mul_epu32 (data_key_hi, prime32); + xacc[i] = _mm256_add_epi64(prod_lo, _mm256_slli_epi64(prod_hi, 32)); + } + } +} + +XXH_FORCE_INLINE XXH_TARGET_AVX2 void XXH3_initCustomSecret_avx2(void* XXH_RESTRICT customSecret, xxh_u64 seed64) +{ + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 31) == 0); + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE / sizeof(__m256i)) == 6); + XXH_STATIC_ASSERT(XXH_SEC_ALIGN <= 64); + (void)(&XXH_writeLE64); + XXH_PREFETCH(customSecret); + { __m256i const seed = _mm256_set_epi64x((xxh_i64)(0U - seed64), (xxh_i64)seed64, (xxh_i64)(0U - seed64), (xxh_i64)seed64); + + const __m256i* const src = (const __m256i*) ((const void*) XXH3_kSecret); + __m256i* dest = ( __m256i*) customSecret; + +# if defined(__GNUC__) || defined(__clang__) + /* + * On GCC & Clang, marking 'dest' as modified will cause the compiler: + * - do not extract the secret from sse registers in the internal loop + * - use less common registers, and avoid pushing these reg into stack + */ + XXH_COMPILER_GUARD(dest); +# endif + XXH_ASSERT(((size_t)src & 31) == 0); /* control alignment */ + XXH_ASSERT(((size_t)dest & 31) == 0); + + /* GCC -O2 need unroll loop manually */ + dest[0] = _mm256_add_epi64(_mm256_load_si256(src+0), seed); + dest[1] = _mm256_add_epi64(_mm256_load_si256(src+1), seed); + dest[2] = _mm256_add_epi64(_mm256_load_si256(src+2), seed); + dest[3] = _mm256_add_epi64(_mm256_load_si256(src+3), seed); + dest[4] = _mm256_add_epi64(_mm256_load_si256(src+4), seed); + dest[5] = _mm256_add_epi64(_mm256_load_si256(src+5), seed); + } +} + +#endif + +/* x86dispatch always generates SSE2 */ +#if (XXH_VECTOR == XXH_SSE2) || defined(XXH_X86DISPATCH) + +#ifndef XXH_TARGET_SSE2 +# define XXH_TARGET_SSE2 /* disable attribute target */ +#endif + +XXH_FORCE_INLINE XXH_TARGET_SSE2 void +XXH3_accumulate_512_sse2( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + /* SSE2 is just a half-scale version of the AVX2 version. */ + XXH_ASSERT((((size_t)acc) & 15) == 0); + { __m128i* const xacc = (__m128i *) acc; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm_loadu_si128 requires a const __m128i * pointer for some reason. */ + const __m128i* const xinput = (const __m128i *) input; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm_loadu_si128 requires a const __m128i * pointer for some reason. */ + const __m128i* const xsecret = (const __m128i *) secret; + + size_t i; + for (i=0; i < XXH_STRIPE_LEN/sizeof(__m128i); i++) { + /* data_vec = xinput[i]; */ + __m128i const data_vec = _mm_loadu_si128 (xinput+i); + /* key_vec = xsecret[i]; */ + __m128i const key_vec = _mm_loadu_si128 (xsecret+i); + /* data_key = data_vec ^ key_vec; */ + __m128i const data_key = _mm_xor_si128 (data_vec, key_vec); + /* data_key_lo = data_key >> 32; */ + __m128i const data_key_lo = _mm_shuffle_epi32 (data_key, _MM_SHUFFLE(0, 3, 0, 1)); + /* product = (data_key & 0xffffffff) * (data_key_lo & 0xffffffff); */ + __m128i const product = _mm_mul_epu32 (data_key, data_key_lo); + /* xacc[i] += swap(data_vec); */ + __m128i const data_swap = _mm_shuffle_epi32(data_vec, _MM_SHUFFLE(1,0,3,2)); + __m128i const sum = _mm_add_epi64(xacc[i], data_swap); + /* xacc[i] += product; */ + xacc[i] = _mm_add_epi64(product, sum); + } } +} +XXH_FORCE_INLINE XXH_TARGET_SSE2 XXH3_ACCUMULATE_TEMPLATE(sse2) + +XXH_FORCE_INLINE XXH_TARGET_SSE2 void +XXH3_scrambleAcc_sse2(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 15) == 0); + { __m128i* const xacc = (__m128i*) acc; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm_loadu_si128 requires a const __m128i * pointer for some reason. */ + const __m128i* const xsecret = (const __m128i *) secret; + const __m128i prime32 = _mm_set1_epi32((int)XXH_PRIME32_1); + + size_t i; + for (i=0; i < XXH_STRIPE_LEN/sizeof(__m128i); i++) { + /* xacc[i] ^= (xacc[i] >> 47) */ + __m128i const acc_vec = xacc[i]; + __m128i const shifted = _mm_srli_epi64 (acc_vec, 47); + __m128i const data_vec = _mm_xor_si128 (acc_vec, shifted); + /* xacc[i] ^= xsecret[i]; */ + __m128i const key_vec = _mm_loadu_si128 (xsecret+i); + __m128i const data_key = _mm_xor_si128 (data_vec, key_vec); + + /* xacc[i] *= XXH_PRIME32_1; */ + __m128i const data_key_hi = _mm_shuffle_epi32 (data_key, _MM_SHUFFLE(0, 3, 0, 1)); + __m128i const prod_lo = _mm_mul_epu32 (data_key, prime32); + __m128i const prod_hi = _mm_mul_epu32 (data_key_hi, prime32); + xacc[i] = _mm_add_epi64(prod_lo, _mm_slli_epi64(prod_hi, 32)); + } + } +} + +XXH_FORCE_INLINE XXH_TARGET_SSE2 void XXH3_initCustomSecret_sse2(void* XXH_RESTRICT customSecret, xxh_u64 seed64) +{ + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 15) == 0); + (void)(&XXH_writeLE64); + { int const nbRounds = XXH_SECRET_DEFAULT_SIZE / sizeof(__m128i); + +# if defined(_MSC_VER) && defined(_M_IX86) && _MSC_VER < 1900 + /* MSVC 32bit mode does not support _mm_set_epi64x before 2015 */ + XXH_ALIGN(16) const xxh_i64 seed64x2[2] = { (xxh_i64)seed64, (xxh_i64)(0U - seed64) }; + __m128i const seed = _mm_load_si128((__m128i const*)seed64x2); +# else + __m128i const seed = _mm_set_epi64x((xxh_i64)(0U - seed64), (xxh_i64)seed64); +# endif + int i; + + const void* const src16 = XXH3_kSecret; + __m128i* dst16 = (__m128i*) customSecret; +# if defined(__GNUC__) || defined(__clang__) + /* + * On GCC & Clang, marking 'dest' as modified will cause the compiler: + * - do not extract the secret from sse registers in the internal loop + * - use less common registers, and avoid pushing these reg into stack + */ + XXH_COMPILER_GUARD(dst16); +# endif + XXH_ASSERT(((size_t)src16 & 15) == 0); /* control alignment */ + XXH_ASSERT(((size_t)dst16 & 15) == 0); + + for (i=0; i < nbRounds; ++i) { + dst16[i] = _mm_add_epi64(_mm_load_si128((const __m128i *)src16+i), seed); + } } +} + +#endif + +#if (XXH_VECTOR == XXH_NEON) + +/* forward declarations for the scalar routines */ +XXH_FORCE_INLINE void +XXH3_scalarRound(void* XXH_RESTRICT acc, void const* XXH_RESTRICT input, + void const* XXH_RESTRICT secret, size_t lane); + +XXH_FORCE_INLINE void +XXH3_scalarScrambleRound(void* XXH_RESTRICT acc, + void const* XXH_RESTRICT secret, size_t lane); + +/*! + * @internal + * @brief The bulk processing loop for NEON and WASM SIMD128. + * + * The NEON code path is actually partially scalar when running on AArch64. This + * is to optimize the pipelining and can have up to 15% speedup depending on the + * CPU, and it also mitigates some GCC codegen issues. + * + * @see XXH3_NEON_LANES for configuring this and details about this optimization. + * + * NEON's 32-bit to 64-bit long multiply takes a half vector of 32-bit + * integers instead of the other platforms which mask full 64-bit vectors, + * so the setup is more complicated than just shifting right. + * + * Additionally, there is an optimization for 4 lanes at once noted below. + * + * Since, as stated, the most optimal amount of lanes for Cortexes is 6, + * there needs to be *three* versions of the accumulate operation used + * for the remaining 2 lanes. + * + * WASM's SIMD128 uses SIMDe's arm_neon.h polyfill because the intrinsics overlap + * nearly perfectly. + */ + +XXH_FORCE_INLINE void +XXH3_accumulate_512_neon( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 15) == 0); + XXH_STATIC_ASSERT(XXH3_NEON_LANES > 0 && XXH3_NEON_LANES <= XXH_ACC_NB && XXH3_NEON_LANES % 2 == 0); + { /* GCC for darwin arm64 does not like aliasing here */ + xxh_aliasing_uint64x2_t* const xacc = (xxh_aliasing_uint64x2_t*) acc; + /* We don't use a uint32x4_t pointer because it causes bus errors on ARMv7. */ + uint8_t const* xinput = (const uint8_t *) input; + uint8_t const* xsecret = (const uint8_t *) secret; + + size_t i; +#ifdef __wasm_simd128__ + /* + * On WASM SIMD128, Clang emits direct address loads when XXH3_kSecret + * is constant propagated, which results in it converting it to this + * inside the loop: + * + * a = v128.load(XXH3_kSecret + 0 + $secret_offset, offset = 0) + * b = v128.load(XXH3_kSecret + 16 + $secret_offset, offset = 0) + * ... + * + * This requires a full 32-bit address immediate (and therefore a 6 byte + * instruction) as well as an add for each offset. + * + * Putting an asm guard prevents it from folding (at the cost of losing + * the alignment hint), and uses the free offset in `v128.load` instead + * of adding secret_offset each time which overall reduces code size by + * about a kilobyte and improves performance. + */ + XXH_COMPILER_GUARD(xsecret); +#endif + /* Scalar lanes use the normal scalarRound routine */ + for (i = XXH3_NEON_LANES; i < XXH_ACC_NB; i++) { + XXH3_scalarRound(acc, input, secret, i); + } + i = 0; + /* 4 NEON lanes at a time. */ + for (; i+1 < XXH3_NEON_LANES / 2; i+=2) { + /* data_vec = xinput[i]; */ + uint64x2_t data_vec_1 = XXH_vld1q_u64(xinput + (i * 16)); + uint64x2_t data_vec_2 = XXH_vld1q_u64(xinput + ((i+1) * 16)); + /* key_vec = xsecret[i]; */ + uint64x2_t key_vec_1 = XXH_vld1q_u64(xsecret + (i * 16)); + uint64x2_t key_vec_2 = XXH_vld1q_u64(xsecret + ((i+1) * 16)); + /* data_swap = swap(data_vec) */ + uint64x2_t data_swap_1 = vextq_u64(data_vec_1, data_vec_1, 1); + uint64x2_t data_swap_2 = vextq_u64(data_vec_2, data_vec_2, 1); + /* data_key = data_vec ^ key_vec; */ + uint64x2_t data_key_1 = veorq_u64(data_vec_1, key_vec_1); + uint64x2_t data_key_2 = veorq_u64(data_vec_2, key_vec_2); + + /* + * If we reinterpret the 64x2 vectors as 32x4 vectors, we can use a + * de-interleave operation for 4 lanes in 1 step with `vuzpq_u32` to + * get one vector with the low 32 bits of each lane, and one vector + * with the high 32 bits of each lane. + * + * The intrinsic returns a double vector because the original ARMv7-a + * instruction modified both arguments in place. AArch64 and SIMD128 emit + * two instructions from this intrinsic. + * + * [ dk11L | dk11H | dk12L | dk12H ] -> [ dk11L | dk12L | dk21L | dk22L ] + * [ dk21L | dk21H | dk22L | dk22H ] -> [ dk11H | dk12H | dk21H | dk22H ] + */ + uint32x4x2_t unzipped = vuzpq_u32( + vreinterpretq_u32_u64(data_key_1), + vreinterpretq_u32_u64(data_key_2) + ); + /* data_key_lo = data_key & 0xFFFFFFFF */ + uint32x4_t data_key_lo = unzipped.val[0]; + /* data_key_hi = data_key >> 32 */ + uint32x4_t data_key_hi = unzipped.val[1]; + /* + * Then, we can split the vectors horizontally and multiply which, as for most + * widening intrinsics, have a variant that works on both high half vectors + * for free on AArch64. A similar instruction is available on SIMD128. + * + * sum = data_swap + (u64x2) data_key_lo * (u64x2) data_key_hi + */ + uint64x2_t sum_1 = XXH_vmlal_low_u32(data_swap_1, data_key_lo, data_key_hi); + uint64x2_t sum_2 = XXH_vmlal_high_u32(data_swap_2, data_key_lo, data_key_hi); + /* + * Clang reorders + * a += b * c; // umlal swap.2d, dkl.2s, dkh.2s + * c += a; // add acc.2d, acc.2d, swap.2d + * to + * c += a; // add acc.2d, acc.2d, swap.2d + * c += b * c; // umlal acc.2d, dkl.2s, dkh.2s + * + * While it would make sense in theory since the addition is faster, + * for reasons likely related to umlal being limited to certain NEON + * pipelines, this is worse. A compiler guard fixes this. + */ + XXH_COMPILER_GUARD_CLANG_NEON(sum_1); + XXH_COMPILER_GUARD_CLANG_NEON(sum_2); + /* xacc[i] = acc_vec + sum; */ + xacc[i] = vaddq_u64(xacc[i], sum_1); + xacc[i+1] = vaddq_u64(xacc[i+1], sum_2); + } + /* Operate on the remaining NEON lanes 2 at a time. */ + for (; i < XXH3_NEON_LANES / 2; i++) { + /* data_vec = xinput[i]; */ + uint64x2_t data_vec = XXH_vld1q_u64(xinput + (i * 16)); + /* key_vec = xsecret[i]; */ + uint64x2_t key_vec = XXH_vld1q_u64(xsecret + (i * 16)); + /* acc_vec_2 = swap(data_vec) */ + uint64x2_t data_swap = vextq_u64(data_vec, data_vec, 1); + /* data_key = data_vec ^ key_vec; */ + uint64x2_t data_key = veorq_u64(data_vec, key_vec); + /* For two lanes, just use VMOVN and VSHRN. */ + /* data_key_lo = data_key & 0xFFFFFFFF; */ + uint32x2_t data_key_lo = vmovn_u64(data_key); + /* data_key_hi = data_key >> 32; */ + uint32x2_t data_key_hi = vshrn_n_u64(data_key, 32); + /* sum = data_swap + (u64x2) data_key_lo * (u64x2) data_key_hi; */ + uint64x2_t sum = vmlal_u32(data_swap, data_key_lo, data_key_hi); + /* Same Clang workaround as before */ + XXH_COMPILER_GUARD_CLANG_NEON(sum); + /* xacc[i] = acc_vec + sum; */ + xacc[i] = vaddq_u64 (xacc[i], sum); + } + } +} +XXH_FORCE_INLINE XXH3_ACCUMULATE_TEMPLATE(neon) + +XXH_FORCE_INLINE void +XXH3_scrambleAcc_neon(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 15) == 0); + + { xxh_aliasing_uint64x2_t* xacc = (xxh_aliasing_uint64x2_t*) acc; + uint8_t const* xsecret = (uint8_t const*) secret; + + size_t i; + /* WASM uses operator overloads and doesn't need these. */ +#ifndef __wasm_simd128__ + /* { prime32_1, prime32_1 } */ + uint32x2_t const kPrimeLo = vdup_n_u32(XXH_PRIME32_1); + /* { 0, prime32_1, 0, prime32_1 } */ + uint32x4_t const kPrimeHi = vreinterpretq_u32_u64(vdupq_n_u64((xxh_u64)XXH_PRIME32_1 << 32)); +#endif + + /* AArch64 uses both scalar and neon at the same time */ + for (i = XXH3_NEON_LANES; i < XXH_ACC_NB; i++) { + XXH3_scalarScrambleRound(acc, secret, i); + } + for (i=0; i < XXH3_NEON_LANES / 2; i++) { + /* xacc[i] ^= (xacc[i] >> 47); */ + uint64x2_t acc_vec = xacc[i]; + uint64x2_t shifted = vshrq_n_u64(acc_vec, 47); + uint64x2_t data_vec = veorq_u64(acc_vec, shifted); + + /* xacc[i] ^= xsecret[i]; */ + uint64x2_t key_vec = XXH_vld1q_u64(xsecret + (i * 16)); + uint64x2_t data_key = veorq_u64(data_vec, key_vec); + /* xacc[i] *= XXH_PRIME32_1 */ +#ifdef __wasm_simd128__ + /* SIMD128 has multiply by u64x2, use it instead of expanding and scalarizing */ + xacc[i] = data_key * XXH_PRIME32_1; +#else + /* + * Expanded version with portable NEON intrinsics + * + * lo(x) * lo(y) + (hi(x) * lo(y) << 32) + * + * prod_hi = hi(data_key) * lo(prime) << 32 + * + * Since we only need 32 bits of this multiply a trick can be used, reinterpreting the vector + * as a uint32x4_t and multiplying by { 0, prime, 0, prime } to cancel out the unwanted bits + * and avoid the shift. + */ + uint32x4_t prod_hi = vmulq_u32 (vreinterpretq_u32_u64(data_key), kPrimeHi); + /* Extract low bits for vmlal_u32 */ + uint32x2_t data_key_lo = vmovn_u64(data_key); + /* xacc[i] = prod_hi + lo(data_key) * XXH_PRIME32_1; */ + xacc[i] = vmlal_u32(vreinterpretq_u64_u32(prod_hi), data_key_lo, kPrimeLo); +#endif + } + } +} +#endif + +#if (XXH_VECTOR == XXH_VSX) + +XXH_FORCE_INLINE void +XXH3_accumulate_512_vsx( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + /* presumed aligned */ + xxh_aliasing_u64x2* const xacc = (xxh_aliasing_u64x2*) acc; + xxh_u8 const* const xinput = (xxh_u8 const*) input; /* no alignment restriction */ + xxh_u8 const* const xsecret = (xxh_u8 const*) secret; /* no alignment restriction */ + xxh_u64x2 const v32 = { 32, 32 }; + size_t i; + for (i = 0; i < XXH_STRIPE_LEN / sizeof(xxh_u64x2); i++) { + /* data_vec = xinput[i]; */ + xxh_u64x2 const data_vec = XXH_vec_loadu(xinput + 16*i); + /* key_vec = xsecret[i]; */ + xxh_u64x2 const key_vec = XXH_vec_loadu(xsecret + 16*i); + xxh_u64x2 const data_key = data_vec ^ key_vec; + /* shuffled = (data_key << 32) | (data_key >> 32); */ + xxh_u32x4 const shuffled = (xxh_u32x4)vec_rl(data_key, v32); + /* product = ((xxh_u64x2)data_key & 0xFFFFFFFF) * ((xxh_u64x2)shuffled & 0xFFFFFFFF); */ + xxh_u64x2 const product = XXH_vec_mulo((xxh_u32x4)data_key, shuffled); + /* acc_vec = xacc[i]; */ + xxh_u64x2 acc_vec = xacc[i]; + acc_vec += product; + + /* swap high and low halves */ +#ifdef __s390x__ + acc_vec += vec_permi(data_vec, data_vec, 2); +#else + acc_vec += vec_xxpermdi(data_vec, data_vec, 2); +#endif + xacc[i] = acc_vec; + } +} +XXH_FORCE_INLINE XXH3_ACCUMULATE_TEMPLATE(vsx) + +XXH_FORCE_INLINE void +XXH3_scrambleAcc_vsx(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 15) == 0); + + { xxh_aliasing_u64x2* const xacc = (xxh_aliasing_u64x2*) acc; + const xxh_u8* const xsecret = (const xxh_u8*) secret; + /* constants */ + xxh_u64x2 const v32 = { 32, 32 }; + xxh_u64x2 const v47 = { 47, 47 }; + xxh_u32x4 const prime = { XXH_PRIME32_1, XXH_PRIME32_1, XXH_PRIME32_1, XXH_PRIME32_1 }; + size_t i; + for (i = 0; i < XXH_STRIPE_LEN / sizeof(xxh_u64x2); i++) { + /* xacc[i] ^= (xacc[i] >> 47); */ + xxh_u64x2 const acc_vec = xacc[i]; + xxh_u64x2 const data_vec = acc_vec ^ (acc_vec >> v47); + + /* xacc[i] ^= xsecret[i]; */ + xxh_u64x2 const key_vec = XXH_vec_loadu(xsecret + 16*i); + xxh_u64x2 const data_key = data_vec ^ key_vec; + + /* xacc[i] *= XXH_PRIME32_1 */ + /* prod_lo = ((xxh_u64x2)data_key & 0xFFFFFFFF) * ((xxh_u64x2)prime & 0xFFFFFFFF); */ + xxh_u64x2 const prod_even = XXH_vec_mule((xxh_u32x4)data_key, prime); + /* prod_hi = ((xxh_u64x2)data_key >> 32) * ((xxh_u64x2)prime >> 32); */ + xxh_u64x2 const prod_odd = XXH_vec_mulo((xxh_u32x4)data_key, prime); + xacc[i] = prod_odd + (prod_even << v32); + } } +} + +#endif + +#if (XXH_VECTOR == XXH_SVE) + +XXH_FORCE_INLINE void +XXH3_accumulate_512_sve( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + uint64_t *xacc = (uint64_t *)acc; + const uint64_t *xinput = (const uint64_t *)(const void *)input; + const uint64_t *xsecret = (const uint64_t *)(const void *)secret; + svuint64_t kSwap = sveor_n_u64_z(svptrue_b64(), svindex_u64(0, 1), 1); + uint64_t element_count = svcntd(); + if (element_count >= 8) { + svbool_t mask = svptrue_pat_b64(SV_VL8); + svuint64_t vacc = svld1_u64(mask, xacc); + ACCRND(vacc, 0); + svst1_u64(mask, xacc, vacc); + } else if (element_count == 2) { /* sve128 */ + svbool_t mask = svptrue_pat_b64(SV_VL2); + svuint64_t acc0 = svld1_u64(mask, xacc + 0); + svuint64_t acc1 = svld1_u64(mask, xacc + 2); + svuint64_t acc2 = svld1_u64(mask, xacc + 4); + svuint64_t acc3 = svld1_u64(mask, xacc + 6); + ACCRND(acc0, 0); + ACCRND(acc1, 2); + ACCRND(acc2, 4); + ACCRND(acc3, 6); + svst1_u64(mask, xacc + 0, acc0); + svst1_u64(mask, xacc + 2, acc1); + svst1_u64(mask, xacc + 4, acc2); + svst1_u64(mask, xacc + 6, acc3); + } else { + svbool_t mask = svptrue_pat_b64(SV_VL4); + svuint64_t acc0 = svld1_u64(mask, xacc + 0); + svuint64_t acc1 = svld1_u64(mask, xacc + 4); + ACCRND(acc0, 0); + ACCRND(acc1, 4); + svst1_u64(mask, xacc + 0, acc0); + svst1_u64(mask, xacc + 4, acc1); + } +} + +XXH_FORCE_INLINE void +XXH3_accumulate_sve(xxh_u64* XXH_RESTRICT acc, + const xxh_u8* XXH_RESTRICT input, + const xxh_u8* XXH_RESTRICT secret, + size_t nbStripes) +{ + if (nbStripes != 0) { + uint64_t *xacc = (uint64_t *)acc; + const uint64_t *xinput = (const uint64_t *)(const void *)input; + const uint64_t *xsecret = (const uint64_t *)(const void *)secret; + svuint64_t kSwap = sveor_n_u64_z(svptrue_b64(), svindex_u64(0, 1), 1); + uint64_t element_count = svcntd(); + if (element_count >= 8) { + svbool_t mask = svptrue_pat_b64(SV_VL8); + svuint64_t vacc = svld1_u64(mask, xacc + 0); + do { + /* svprfd(svbool_t, void *, enum svfprop); */ + svprfd(mask, xinput + 128, SV_PLDL1STRM); + ACCRND(vacc, 0); + xinput += 8; + xsecret += 1; + nbStripes--; + } while (nbStripes != 0); + + svst1_u64(mask, xacc + 0, vacc); + } else if (element_count == 2) { /* sve128 */ + svbool_t mask = svptrue_pat_b64(SV_VL2); + svuint64_t acc0 = svld1_u64(mask, xacc + 0); + svuint64_t acc1 = svld1_u64(mask, xacc + 2); + svuint64_t acc2 = svld1_u64(mask, xacc + 4); + svuint64_t acc3 = svld1_u64(mask, xacc + 6); + do { + svprfd(mask, xinput + 128, SV_PLDL1STRM); + ACCRND(acc0, 0); + ACCRND(acc1, 2); + ACCRND(acc2, 4); + ACCRND(acc3, 6); + xinput += 8; + xsecret += 1; + nbStripes--; + } while (nbStripes != 0); + + svst1_u64(mask, xacc + 0, acc0); + svst1_u64(mask, xacc + 2, acc1); + svst1_u64(mask, xacc + 4, acc2); + svst1_u64(mask, xacc + 6, acc3); + } else { + svbool_t mask = svptrue_pat_b64(SV_VL4); + svuint64_t acc0 = svld1_u64(mask, xacc + 0); + svuint64_t acc1 = svld1_u64(mask, xacc + 4); + do { + svprfd(mask, xinput + 128, SV_PLDL1STRM); + ACCRND(acc0, 0); + ACCRND(acc1, 4); + xinput += 8; + xsecret += 1; + nbStripes--; + } while (nbStripes != 0); + + svst1_u64(mask, xacc + 0, acc0); + svst1_u64(mask, xacc + 4, acc1); + } + } +} + +#endif + +/* scalar variants - universal */ + +#if defined(__aarch64__) && (defined(__GNUC__) || defined(__clang__)) +/* + * In XXH3_scalarRound(), GCC and Clang have a similar codegen issue, where they + * emit an excess mask and a full 64-bit multiply-add (MADD X-form). + * + * While this might not seem like much, as AArch64 is a 64-bit architecture, only + * big Cortex designs have a full 64-bit multiplier. + * + * On the little cores, the smaller 32-bit multiplier is used, and full 64-bit + * multiplies expand to 2-3 multiplies in microcode. This has a major penalty + * of up to 4 latency cycles and 2 stall cycles in the multiply pipeline. + * + * Thankfully, AArch64 still provides the 32-bit long multiply-add (UMADDL) which does + * not have this penalty and does the mask automatically. + */ +XXH_FORCE_INLINE xxh_u64 +XXH_mult32to64_add64(xxh_u64 lhs, xxh_u64 rhs, xxh_u64 acc) +{ + xxh_u64 ret; + /* note: %x = 64-bit register, %w = 32-bit register */ + __asm__("umaddl %x0, %w1, %w2, %x3" : "=r" (ret) : "r" (lhs), "r" (rhs), "r" (acc)); + return ret; +} +#else +XXH_FORCE_INLINE xxh_u64 +XXH_mult32to64_add64(xxh_u64 lhs, xxh_u64 rhs, xxh_u64 acc) +{ + return XXH_mult32to64((xxh_u32)lhs, (xxh_u32)rhs) + acc; +} +#endif + +/*! + * @internal + * @brief Scalar round for @ref XXH3_accumulate_512_scalar(). + * + * This is extracted to its own function because the NEON path uses a combination + * of NEON and scalar. + */ +XXH_FORCE_INLINE void +XXH3_scalarRound(void* XXH_RESTRICT acc, + void const* XXH_RESTRICT input, + void const* XXH_RESTRICT secret, + size_t lane) +{ + xxh_u64* xacc = (xxh_u64*) acc; + xxh_u8 const* xinput = (xxh_u8 const*) input; + xxh_u8 const* xsecret = (xxh_u8 const*) secret; + XXH_ASSERT(lane < XXH_ACC_NB); + XXH_ASSERT(((size_t)acc & (XXH_ACC_ALIGN-1)) == 0); + { + xxh_u64 const data_val = XXH_readLE64(xinput + lane * 8); + xxh_u64 const data_key = data_val ^ XXH_readLE64(xsecret + lane * 8); + xacc[lane ^ 1] += data_val; /* swap adjacent lanes */ + xacc[lane] = XXH_mult32to64_add64(data_key /* & 0xFFFFFFFF */, data_key >> 32, xacc[lane]); + } +} + +/*! + * @internal + * @brief Processes a 64 byte block of data using the scalar path. + */ +XXH_FORCE_INLINE void +XXH3_accumulate_512_scalar(void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + size_t i; + /* ARM GCC refuses to unroll this loop, resulting in a 24% slowdown on ARMv6. */ +#if defined(__GNUC__) && !defined(__clang__) \ + && (defined(__arm__) || defined(__thumb2__)) \ + && defined(__ARM_FEATURE_UNALIGNED) /* no unaligned access just wastes bytes */ \ + && XXH_SIZE_OPT <= 0 +# pragma GCC unroll 8 +#endif + for (i=0; i < XXH_ACC_NB; i++) { + XXH3_scalarRound(acc, input, secret, i); + } +} +XXH_FORCE_INLINE XXH3_ACCUMULATE_TEMPLATE(scalar) + +/*! + * @internal + * @brief Scalar scramble step for @ref XXH3_scrambleAcc_scalar(). + * + * This is extracted to its own function because the NEON path uses a combination + * of NEON and scalar. + */ +XXH_FORCE_INLINE void +XXH3_scalarScrambleRound(void* XXH_RESTRICT acc, + void const* XXH_RESTRICT secret, + size_t lane) +{ + xxh_u64* const xacc = (xxh_u64*) acc; /* presumed aligned */ + const xxh_u8* const xsecret = (const xxh_u8*) secret; /* no alignment restriction */ + XXH_ASSERT((((size_t)acc) & (XXH_ACC_ALIGN-1)) == 0); + XXH_ASSERT(lane < XXH_ACC_NB); + { + xxh_u64 const key64 = XXH_readLE64(xsecret + lane * 8); + xxh_u64 acc64 = xacc[lane]; + acc64 = XXH_xorshift64(acc64, 47); + acc64 ^= key64; + acc64 *= XXH_PRIME32_1; + xacc[lane] = acc64; + } +} + +/*! + * @internal + * @brief Scrambles the accumulators after a large chunk has been read + */ +XXH_FORCE_INLINE void +XXH3_scrambleAcc_scalar(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + size_t i; + for (i=0; i < XXH_ACC_NB; i++) { + XXH3_scalarScrambleRound(acc, secret, i); + } +} + +XXH_FORCE_INLINE void +XXH3_initCustomSecret_scalar(void* XXH_RESTRICT customSecret, xxh_u64 seed64) +{ + /* + * We need a separate pointer for the hack below, + * which requires a non-const pointer. + * Any decent compiler will optimize this out otherwise. + */ + const xxh_u8* kSecretPtr = XXH3_kSecret; + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 15) == 0); + +#if defined(__GNUC__) && defined(__aarch64__) + /* + * UGLY HACK: + * GCC and Clang generate a bunch of MOV/MOVK pairs for aarch64, and they are + * placed sequentially, in order, at the top of the unrolled loop. + * + * While MOVK is great for generating constants (2 cycles for a 64-bit + * constant compared to 4 cycles for LDR), it fights for bandwidth with + * the arithmetic instructions. + * + * I L S + * MOVK + * MOVK + * MOVK + * MOVK + * ADD + * SUB STR + * STR + * By forcing loads from memory (as the asm line causes the compiler to assume + * that XXH3_kSecretPtr has been changed), the pipelines are used more + * efficiently: + * I L S + * LDR + * ADD LDR + * SUB STR + * STR + * + * See XXH3_NEON_LANES for details on the pipsline. + * + * XXH3_64bits_withSeed, len == 256, Snapdragon 835 + * without hack: 2654.4 MB/s + * with hack: 3202.9 MB/s + */ + XXH_COMPILER_GUARD(kSecretPtr); +#endif + { int const nbRounds = XXH_SECRET_DEFAULT_SIZE / 16; + int i; + for (i=0; i < nbRounds; i++) { + /* + * The asm hack causes the compiler to assume that kSecretPtr aliases with + * customSecret, and on aarch64, this prevented LDP from merging two + * loads together for free. Putting the loads together before the stores + * properly generates LDP. + */ + xxh_u64 lo = XXH_readLE64(kSecretPtr + 16*i) + seed64; + xxh_u64 hi = XXH_readLE64(kSecretPtr + 16*i + 8) - seed64; + XXH_writeLE64((xxh_u8*)customSecret + 16*i, lo); + XXH_writeLE64((xxh_u8*)customSecret + 16*i + 8, hi); + } } +} + + +typedef void (*XXH3_f_accumulate)(xxh_u64* XXH_RESTRICT, const xxh_u8* XXH_RESTRICT, const xxh_u8* XXH_RESTRICT, size_t); +typedef void (*XXH3_f_scrambleAcc)(void* XXH_RESTRICT, const void*); +typedef void (*XXH3_f_initCustomSecret)(void* XXH_RESTRICT, xxh_u64); + + +#if (XXH_VECTOR == XXH_AVX512) + +#define XXH3_accumulate_512 XXH3_accumulate_512_avx512 +#define XXH3_accumulate XXH3_accumulate_avx512 +#define XXH3_scrambleAcc XXH3_scrambleAcc_avx512 +#define XXH3_initCustomSecret XXH3_initCustomSecret_avx512 + +#elif (XXH_VECTOR == XXH_AVX2) + +#define XXH3_accumulate_512 XXH3_accumulate_512_avx2 +#define XXH3_accumulate XXH3_accumulate_avx2 +#define XXH3_scrambleAcc XXH3_scrambleAcc_avx2 +#define XXH3_initCustomSecret XXH3_initCustomSecret_avx2 + +#elif (XXH_VECTOR == XXH_SSE2) + +#define XXH3_accumulate_512 XXH3_accumulate_512_sse2 +#define XXH3_accumulate XXH3_accumulate_sse2 +#define XXH3_scrambleAcc XXH3_scrambleAcc_sse2 +#define XXH3_initCustomSecret XXH3_initCustomSecret_sse2 + +#elif (XXH_VECTOR == XXH_NEON) + +#define XXH3_accumulate_512 XXH3_accumulate_512_neon +#define XXH3_accumulate XXH3_accumulate_neon +#define XXH3_scrambleAcc XXH3_scrambleAcc_neon +#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar + +#elif (XXH_VECTOR == XXH_VSX) + +#define XXH3_accumulate_512 XXH3_accumulate_512_vsx +#define XXH3_accumulate XXH3_accumulate_vsx +#define XXH3_scrambleAcc XXH3_scrambleAcc_vsx +#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar + +#elif (XXH_VECTOR == XXH_SVE) +#define XXH3_accumulate_512 XXH3_accumulate_512_sve +#define XXH3_accumulate XXH3_accumulate_sve +#define XXH3_scrambleAcc XXH3_scrambleAcc_scalar +#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar + +#else /* scalar */ + +#define XXH3_accumulate_512 XXH3_accumulate_512_scalar +#define XXH3_accumulate XXH3_accumulate_scalar +#define XXH3_scrambleAcc XXH3_scrambleAcc_scalar +#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar + +#endif + +#if XXH_SIZE_OPT >= 1 /* don't do SIMD for initialization */ +# undef XXH3_initCustomSecret +# define XXH3_initCustomSecret XXH3_initCustomSecret_scalar +#endif + +XXH_FORCE_INLINE void +XXH3_hashLong_internal_loop(xxh_u64* XXH_RESTRICT acc, + const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + size_t const nbStripesPerBlock = (secretSize - XXH_STRIPE_LEN) / XXH_SECRET_CONSUME_RATE; + size_t const block_len = XXH_STRIPE_LEN * nbStripesPerBlock; + size_t const nb_blocks = (len - 1) / block_len; + + size_t n; + + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); + + for (n = 0; n < nb_blocks; n++) { + f_acc(acc, input + n*block_len, secret, nbStripesPerBlock); + f_scramble(acc, secret + secretSize - XXH_STRIPE_LEN); + } + + /* last partial block */ + XXH_ASSERT(len > XXH_STRIPE_LEN); + { size_t const nbStripes = ((len - 1) - (block_len * nb_blocks)) / XXH_STRIPE_LEN; + XXH_ASSERT(nbStripes <= (secretSize / XXH_SECRET_CONSUME_RATE)); + f_acc(acc, input + nb_blocks*block_len, secret, nbStripes); + + /* last stripe */ + { const xxh_u8* const p = input + len - XXH_STRIPE_LEN; +#define XXH_SECRET_LASTACC_START 7 /* not aligned on 8, last secret is different from acc & scrambler */ + XXH3_accumulate_512(acc, p, secret + secretSize - XXH_STRIPE_LEN - XXH_SECRET_LASTACC_START); + } } +} + +XXH_FORCE_INLINE xxh_u64 +XXH3_mix2Accs(const xxh_u64* XXH_RESTRICT acc, const xxh_u8* XXH_RESTRICT secret) +{ + return XXH3_mul128_fold64( + acc[0] ^ XXH_readLE64(secret), + acc[1] ^ XXH_readLE64(secret+8) ); +} + +static XXH64_hash_t +XXH3_mergeAccs(const xxh_u64* XXH_RESTRICT acc, const xxh_u8* XXH_RESTRICT secret, xxh_u64 start) +{ + xxh_u64 result64 = start; + size_t i = 0; + + for (i = 0; i < 4; i++) { + result64 += XXH3_mix2Accs(acc+2*i, secret + 16*i); +#if defined(__clang__) /* Clang */ \ + && (defined(__arm__) || defined(__thumb__)) /* ARMv7 */ \ + && (defined(__ARM_NEON) || defined(__ARM_NEON__)) /* NEON */ \ + && !defined(XXH_ENABLE_AUTOVECTORIZE) /* Define to disable */ + /* + * UGLY HACK: + * Prevent autovectorization on Clang ARMv7-a. Exact same problem as + * the one in XXH3_len_129to240_64b. Speeds up shorter keys > 240b. + * XXH3_64bits, len == 256, Snapdragon 835: + * without hack: 2063.7 MB/s + * with hack: 2560.7 MB/s + */ + XXH_COMPILER_GUARD(result64); +#endif + } + + return XXH3_avalanche(result64); +} + +#define XXH3_INIT_ACC { XXH_PRIME32_3, XXH_PRIME64_1, XXH_PRIME64_2, XXH_PRIME64_3, \ + XXH_PRIME64_4, XXH_PRIME32_2, XXH_PRIME64_5, XXH_PRIME32_1 } + +XXH_FORCE_INLINE XXH64_hash_t +XXH3_hashLong_64b_internal(const void* XXH_RESTRICT input, size_t len, + const void* XXH_RESTRICT secret, size_t secretSize, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + XXH_ALIGN(XXH_ACC_ALIGN) xxh_u64 acc[XXH_ACC_NB] = XXH3_INIT_ACC; + + XXH3_hashLong_internal_loop(acc, (const xxh_u8*)input, len, (const xxh_u8*)secret, secretSize, f_acc, f_scramble); + + /* converge into final hash */ + XXH_STATIC_ASSERT(sizeof(acc) == 64); + /* do not align on 8, so that the secret is different from the accumulator */ +#define XXH_SECRET_MERGEACCS_START 11 + XXH_ASSERT(secretSize >= sizeof(acc) + XXH_SECRET_MERGEACCS_START); + return XXH3_mergeAccs(acc, (const xxh_u8*)secret + XXH_SECRET_MERGEACCS_START, (xxh_u64)len * XXH_PRIME64_1); +} + +/* + * It's important for performance to transmit secret's size (when it's static) + * so that the compiler can properly optimize the vectorized loop. + * This makes a big performance difference for "medium" keys (<1 KB) when using AVX instruction set. + * When the secret size is unknown, or on GCC 12 where the mix of NO_INLINE and FORCE_INLINE + * breaks -Og, this is XXH_NO_INLINE. + */ +XXH3_WITH_SECRET_INLINE XXH64_hash_t +XXH3_hashLong_64b_withSecret(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, const xxh_u8* XXH_RESTRICT secret, size_t secretLen) +{ + (void)seed64; + return XXH3_hashLong_64b_internal(input, len, secret, secretLen, XXH3_accumulate, XXH3_scrambleAcc); +} + +/* + * It's preferable for performance that XXH3_hashLong is not inlined, + * as it results in a smaller function for small data, easier to the instruction cache. + * Note that inside this no_inline function, we do inline the internal loop, + * and provide a statically defined secret size to allow optimization of vector loop. + */ +XXH_NO_INLINE XXH_PUREF XXH64_hash_t +XXH3_hashLong_64b_default(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, const xxh_u8* XXH_RESTRICT secret, size_t secretLen) +{ + (void)seed64; (void)secret; (void)secretLen; + return XXH3_hashLong_64b_internal(input, len, XXH3_kSecret, sizeof(XXH3_kSecret), XXH3_accumulate, XXH3_scrambleAcc); +} + +/* + * XXH3_hashLong_64b_withSeed(): + * Generate a custom key based on alteration of default XXH3_kSecret with the seed, + * and then use this key for long mode hashing. + * + * This operation is decently fast but nonetheless costs a little bit of time. + * Try to avoid it whenever possible (typically when seed==0). + * + * It's important for performance that XXH3_hashLong is not inlined. Not sure + * why (uop cache maybe?), but the difference is large and easily measurable. + */ +XXH_FORCE_INLINE XXH64_hash_t +XXH3_hashLong_64b_withSeed_internal(const void* input, size_t len, + XXH64_hash_t seed, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble, + XXH3_f_initCustomSecret f_initSec) +{ +#if XXH_SIZE_OPT <= 0 + if (seed == 0) + return XXH3_hashLong_64b_internal(input, len, + XXH3_kSecret, sizeof(XXH3_kSecret), + f_acc, f_scramble); +#endif + { XXH_ALIGN(XXH_SEC_ALIGN) xxh_u8 secret[XXH_SECRET_DEFAULT_SIZE]; + f_initSec(secret, seed); + return XXH3_hashLong_64b_internal(input, len, secret, sizeof(secret), + f_acc, f_scramble); + } +} + +/* + * It's important for performance that XXH3_hashLong is not inlined. + */ +XXH_NO_INLINE XXH64_hash_t +XXH3_hashLong_64b_withSeed(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed, const xxh_u8* XXH_RESTRICT secret, size_t secretLen) +{ + (void)secret; (void)secretLen; + return XXH3_hashLong_64b_withSeed_internal(input, len, seed, + XXH3_accumulate, XXH3_scrambleAcc, XXH3_initCustomSecret); +} + + +typedef XXH64_hash_t (*XXH3_hashLong64_f)(const void* XXH_RESTRICT, size_t, + XXH64_hash_t, const xxh_u8* XXH_RESTRICT, size_t); + +XXH_FORCE_INLINE XXH64_hash_t +XXH3_64bits_internal(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, const void* XXH_RESTRICT secret, size_t secretLen, + XXH3_hashLong64_f f_hashLong) +{ + XXH_ASSERT(secretLen >= XXH3_SECRET_SIZE_MIN); + /* + * If an action is to be taken if `secretLen` condition is not respected, + * it should be done here. + * For now, it's a contract pre-condition. + * Adding a check and a branch here would cost performance at every hash. + * Also, note that function signature doesn't offer room to return an error. + */ + if (len <= 16) + return XXH3_len_0to16_64b((const xxh_u8*)input, len, (const xxh_u8*)secret, seed64); + if (len <= 128) + return XXH3_len_17to128_64b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64); + if (len <= XXH3_MIDSIZE_MAX) + return XXH3_len_129to240_64b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64); + return f_hashLong(input, len, seed64, (const xxh_u8*)secret, secretLen); +} + + +/* === Public entry point === */ + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH64_hash_t XXH3_64bits(XXH_NOESCAPE const void* input, size_t length) +{ + return XXH3_64bits_internal(input, length, 0, XXH3_kSecret, sizeof(XXH3_kSecret), XXH3_hashLong_64b_default); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH64_hash_t +XXH3_64bits_withSecret(XXH_NOESCAPE const void* input, size_t length, XXH_NOESCAPE const void* secret, size_t secretSize) +{ + return XXH3_64bits_internal(input, length, 0, secret, secretSize, XXH3_hashLong_64b_withSecret); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH64_hash_t +XXH3_64bits_withSeed(XXH_NOESCAPE const void* input, size_t length, XXH64_hash_t seed) +{ + return XXH3_64bits_internal(input, length, seed, XXH3_kSecret, sizeof(XXH3_kSecret), XXH3_hashLong_64b_withSeed); +} + +XXH_PUBLIC_API XXH64_hash_t +XXH3_64bits_withSecretandSeed(XXH_NOESCAPE const void* input, size_t length, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed) +{ + if (length <= XXH3_MIDSIZE_MAX) + return XXH3_64bits_internal(input, length, seed, XXH3_kSecret, sizeof(XXH3_kSecret), NULL); + return XXH3_hashLong_64b_withSecret(input, length, seed, (const xxh_u8*)secret, secretSize); +} + + +/* === XXH3 streaming === */ +#ifndef XXH_NO_STREAM +/* + * Malloc's a pointer that is always aligned to align. + * + * This must be freed with `XXH_alignedFree()`. + * + * malloc typically guarantees 16 byte alignment on 64-bit systems and 8 byte + * alignment on 32-bit. This isn't enough for the 32 byte aligned loads in AVX2 + * or on 32-bit, the 16 byte aligned loads in SSE2 and NEON. + * + * This underalignment previously caused a rather obvious crash which went + * completely unnoticed due to XXH3_createState() not actually being tested. + * Credit to RedSpah for noticing this bug. + * + * The alignment is done manually: Functions like posix_memalign or _mm_malloc + * are avoided: To maintain portability, we would have to write a fallback + * like this anyways, and besides, testing for the existence of library + * functions without relying on external build tools is impossible. + * + * The method is simple: Overallocate, manually align, and store the offset + * to the original behind the returned pointer. + * + * Align must be a power of 2 and 8 <= align <= 128. + */ +static XXH_MALLOCF void* XXH_alignedMalloc(size_t s, size_t align) +{ + XXH_ASSERT(align <= 128 && align >= 8); /* range check */ + XXH_ASSERT((align & (align-1)) == 0); /* power of 2 */ + XXH_ASSERT(s != 0 && s < (s + align)); /* empty/overflow */ + { /* Overallocate to make room for manual realignment and an offset byte */ + xxh_u8* base = (xxh_u8*)XXH_malloc(s + align); + if (base != NULL) { + /* + * Get the offset needed to align this pointer. + * + * Even if the returned pointer is aligned, there will always be + * at least one byte to store the offset to the original pointer. + */ + size_t offset = align - ((size_t)base & (align - 1)); /* base % align */ + /* Add the offset for the now-aligned pointer */ + xxh_u8* ptr = base + offset; + + XXH_ASSERT((size_t)ptr % align == 0); + + /* Store the offset immediately before the returned pointer. */ + ptr[-1] = (xxh_u8)offset; + return ptr; + } + return NULL; + } +} +/* + * Frees an aligned pointer allocated by XXH_alignedMalloc(). Don't pass + * normal malloc'd pointers, XXH_alignedMalloc has a specific data layout. + */ +static void XXH_alignedFree(void* p) +{ + if (p != NULL) { + xxh_u8* ptr = (xxh_u8*)p; + /* Get the offset byte we added in XXH_malloc. */ + xxh_u8 offset = ptr[-1]; + /* Free the original malloc'd pointer */ + xxh_u8* base = ptr - offset; + XXH_free(base); + } +} +/*! @ingroup XXH3_family */ +/*! + * @brief Allocate an @ref XXH3_state_t. + * + * @return An allocated pointer of @ref XXH3_state_t on success. + * @return `NULL` on failure. + * + * @note Must be freed with XXH3_freeState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH3_state_t* XXH3_createState(void) +{ + XXH3_state_t* const state = (XXH3_state_t*)XXH_alignedMalloc(sizeof(XXH3_state_t), 64); + if (state==NULL) return NULL; + XXH3_INITSTATE(state); + return state; +} + +/*! @ingroup XXH3_family */ +/*! + * @brief Frees an @ref XXH3_state_t. + * + * @param statePtr A pointer to an @ref XXH3_state_t allocated with @ref XXH3_createState(). + * + * @return @ref XXH_OK. + * + * @note Must be allocated with XXH3_createState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_freeState(XXH3_state_t* statePtr) +{ + XXH_alignedFree(statePtr); + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API void +XXH3_copyState(XXH_NOESCAPE XXH3_state_t* dst_state, XXH_NOESCAPE const XXH3_state_t* src_state) +{ + XXH_memcpy(dst_state, src_state, sizeof(*dst_state)); +} + +static void +XXH3_reset_internal(XXH3_state_t* statePtr, + XXH64_hash_t seed, + const void* secret, size_t secretSize) +{ + size_t const initStart = offsetof(XXH3_state_t, bufferedSize); + size_t const initLength = offsetof(XXH3_state_t, nbStripesPerBlock) - initStart; + XXH_ASSERT(offsetof(XXH3_state_t, nbStripesPerBlock) > initStart); + XXH_ASSERT(statePtr != NULL); + /* set members from bufferedSize to nbStripesPerBlock (excluded) to 0 */ + memset((char*)statePtr + initStart, 0, initLength); + statePtr->acc[0] = XXH_PRIME32_3; + statePtr->acc[1] = XXH_PRIME64_1; + statePtr->acc[2] = XXH_PRIME64_2; + statePtr->acc[3] = XXH_PRIME64_3; + statePtr->acc[4] = XXH_PRIME64_4; + statePtr->acc[5] = XXH_PRIME32_2; + statePtr->acc[6] = XXH_PRIME64_5; + statePtr->acc[7] = XXH_PRIME32_1; + statePtr->seed = seed; + statePtr->useSeed = (seed != 0); + statePtr->extSecret = (const unsigned char*)secret; + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); + statePtr->secretLimit = secretSize - XXH_STRIPE_LEN; + statePtr->nbStripesPerBlock = statePtr->secretLimit / XXH_SECRET_CONSUME_RATE; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr) +{ + if (statePtr == NULL) return XXH_ERROR; + XXH3_reset_internal(statePtr, 0, XXH3_kSecret, XXH_SECRET_DEFAULT_SIZE); + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize) +{ + if (statePtr == NULL) return XXH_ERROR; + XXH3_reset_internal(statePtr, 0, secret, secretSize); + if (secret == NULL) return XXH_ERROR; + if (secretSize < XXH3_SECRET_SIZE_MIN) return XXH_ERROR; + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed) +{ + if (statePtr == NULL) return XXH_ERROR; + if (seed==0) return XXH3_64bits_reset(statePtr); + if ((seed != statePtr->seed) || (statePtr->extSecret != NULL)) + XXH3_initCustomSecret(statePtr->customSecret, seed); + XXH3_reset_internal(statePtr, seed, NULL, XXH_SECRET_DEFAULT_SIZE); + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed64) +{ + if (statePtr == NULL) return XXH_ERROR; + if (secret == NULL) return XXH_ERROR; + if (secretSize < XXH3_SECRET_SIZE_MIN) return XXH_ERROR; + XXH3_reset_internal(statePtr, seed64, secret, secretSize); + statePtr->useSeed = 1; /* always, even if seed64==0 */ + return XXH_OK; +} + +/*! + * @internal + * @brief Processes a large input for XXH3_update() and XXH3_digest_long(). + * + * Unlike XXH3_hashLong_internal_loop(), this can process data that overlaps a block. + * + * @param acc Pointer to the 8 accumulator lanes + * @param nbStripesSoFarPtr In/out pointer to the number of leftover stripes in the block* + * @param nbStripesPerBlock Number of stripes in a block + * @param input Input pointer + * @param nbStripes Number of stripes to process + * @param secret Secret pointer + * @param secretLimit Offset of the last block in @p secret + * @param f_acc Pointer to an XXH3_accumulate implementation + * @param f_scramble Pointer to an XXH3_scrambleAcc implementation + * @return Pointer past the end of @p input after processing + */ +XXH_FORCE_INLINE const xxh_u8 * +XXH3_consumeStripes(xxh_u64* XXH_RESTRICT acc, + size_t* XXH_RESTRICT nbStripesSoFarPtr, size_t nbStripesPerBlock, + const xxh_u8* XXH_RESTRICT input, size_t nbStripes, + const xxh_u8* XXH_RESTRICT secret, size_t secretLimit, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + const xxh_u8* initialSecret = secret + *nbStripesSoFarPtr * XXH_SECRET_CONSUME_RATE; + /* Process full blocks */ + if (nbStripes >= (nbStripesPerBlock - *nbStripesSoFarPtr)) { + /* Process the initial partial block... */ + size_t nbStripesThisIter = nbStripesPerBlock - *nbStripesSoFarPtr; + + do { + /* Accumulate and scramble */ + f_acc(acc, input, initialSecret, nbStripesThisIter); + f_scramble(acc, secret + secretLimit); + input += nbStripesThisIter * XXH_STRIPE_LEN; + nbStripes -= nbStripesThisIter; + /* Then continue the loop with the full block size */ + nbStripesThisIter = nbStripesPerBlock; + initialSecret = secret; + } while (nbStripes >= nbStripesPerBlock); + *nbStripesSoFarPtr = 0; + } + /* Process a partial block */ + if (nbStripes > 0) { + f_acc(acc, input, initialSecret, nbStripes); + input += nbStripes * XXH_STRIPE_LEN; + *nbStripesSoFarPtr += nbStripes; + } + /* Return end pointer */ + return input; +} + +#ifndef XXH3_STREAM_USE_STACK +# if XXH_SIZE_OPT <= 0 && !defined(__clang__) /* clang doesn't need additional stack space */ +# define XXH3_STREAM_USE_STACK 1 +# endif +#endif +/* + * Both XXH3_64bits_update and XXH3_128bits_update use this routine. + */ +XXH_FORCE_INLINE XXH_errorcode +XXH3_update(XXH3_state_t* XXH_RESTRICT const state, + const xxh_u8* XXH_RESTRICT input, size_t len, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + if (input==NULL) { + XXH_ASSERT(len == 0); + return XXH_OK; + } + + XXH_ASSERT(state != NULL); + { const xxh_u8* const bEnd = input + len; + const unsigned char* const secret = (state->extSecret == NULL) ? state->customSecret : state->extSecret; +#if defined(XXH3_STREAM_USE_STACK) && XXH3_STREAM_USE_STACK >= 1 + /* For some reason, gcc and MSVC seem to suffer greatly + * when operating accumulators directly into state. + * Operating into stack space seems to enable proper optimization. + * clang, on the other hand, doesn't seem to need this trick */ + XXH_ALIGN(XXH_ACC_ALIGN) xxh_u64 acc[8]; + XXH_memcpy(acc, state->acc, sizeof(acc)); +#else + xxh_u64* XXH_RESTRICT const acc = state->acc; +#endif + state->totalLen += len; + XXH_ASSERT(state->bufferedSize <= XXH3_INTERNALBUFFER_SIZE); + + /* small input : just fill in tmp buffer */ + if (len <= XXH3_INTERNALBUFFER_SIZE - state->bufferedSize) { + XXH_memcpy(state->buffer + state->bufferedSize, input, len); + state->bufferedSize += (XXH32_hash_t)len; + return XXH_OK; + } + + /* total input is now > XXH3_INTERNALBUFFER_SIZE */ + #define XXH3_INTERNALBUFFER_STRIPES (XXH3_INTERNALBUFFER_SIZE / XXH_STRIPE_LEN) + XXH_STATIC_ASSERT(XXH3_INTERNALBUFFER_SIZE % XXH_STRIPE_LEN == 0); /* clean multiple */ + + /* + * Internal buffer is partially filled (always, except at beginning) + * Complete it, then consume it. + */ + if (state->bufferedSize) { + size_t const loadSize = XXH3_INTERNALBUFFER_SIZE - state->bufferedSize; + XXH_memcpy(state->buffer + state->bufferedSize, input, loadSize); + input += loadSize; + XXH3_consumeStripes(acc, + &state->nbStripesSoFar, state->nbStripesPerBlock, + state->buffer, XXH3_INTERNALBUFFER_STRIPES, + secret, state->secretLimit, + f_acc, f_scramble); + state->bufferedSize = 0; + } + XXH_ASSERT(input < bEnd); + if (bEnd - input > XXH3_INTERNALBUFFER_SIZE) { + size_t nbStripes = (size_t)(bEnd - 1 - input) / XXH_STRIPE_LEN; + input = XXH3_consumeStripes(acc, + &state->nbStripesSoFar, state->nbStripesPerBlock, + input, nbStripes, + secret, state->secretLimit, + f_acc, f_scramble); + XXH_memcpy(state->buffer + sizeof(state->buffer) - XXH_STRIPE_LEN, input - XXH_STRIPE_LEN, XXH_STRIPE_LEN); + + } + /* Some remaining input (always) : buffer it */ + XXH_ASSERT(input < bEnd); + XXH_ASSERT(bEnd - input <= XXH3_INTERNALBUFFER_SIZE); + XXH_ASSERT(state->bufferedSize == 0); + XXH_memcpy(state->buffer, input, (size_t)(bEnd-input)); + state->bufferedSize = (XXH32_hash_t)(bEnd-input); +#if defined(XXH3_STREAM_USE_STACK) && XXH3_STREAM_USE_STACK >= 1 + /* save stack accumulators into state */ + XXH_memcpy(state->acc, acc, sizeof(acc)); +#endif + } + + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_update(XXH_NOESCAPE XXH3_state_t* state, XXH_NOESCAPE const void* input, size_t len) +{ + return XXH3_update(state, (const xxh_u8*)input, len, + XXH3_accumulate, XXH3_scrambleAcc); +} + + +XXH_FORCE_INLINE void +XXH3_digest_long (XXH64_hash_t* acc, + const XXH3_state_t* state, + const unsigned char* secret) +{ + xxh_u8 lastStripe[XXH_STRIPE_LEN]; + const xxh_u8* lastStripePtr; + + /* + * Digest on a local copy. This way, the state remains unaltered, and it can + * continue ingesting more input afterwards. + */ + XXH_memcpy(acc, state->acc, sizeof(state->acc)); + if (state->bufferedSize >= XXH_STRIPE_LEN) { + /* Consume remaining stripes then point to remaining data in buffer */ + size_t const nbStripes = (state->bufferedSize - 1) / XXH_STRIPE_LEN; + size_t nbStripesSoFar = state->nbStripesSoFar; + XXH3_consumeStripes(acc, + &nbStripesSoFar, state->nbStripesPerBlock, + state->buffer, nbStripes, + secret, state->secretLimit, + XXH3_accumulate, XXH3_scrambleAcc); + lastStripePtr = state->buffer + state->bufferedSize - XXH_STRIPE_LEN; + } else { /* bufferedSize < XXH_STRIPE_LEN */ + /* Copy to temp buffer */ + size_t const catchupSize = XXH_STRIPE_LEN - state->bufferedSize; + XXH_ASSERT(state->bufferedSize > 0); /* there is always some input buffered */ + XXH_memcpy(lastStripe, state->buffer + sizeof(state->buffer) - catchupSize, catchupSize); + XXH_memcpy(lastStripe + catchupSize, state->buffer, state->bufferedSize); + lastStripePtr = lastStripe; + } + /* Last stripe */ + XXH3_accumulate_512(acc, + lastStripePtr, + secret + state->secretLimit - XXH_SECRET_LASTACC_START); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH64_hash_t XXH3_64bits_digest (XXH_NOESCAPE const XXH3_state_t* state) +{ + const unsigned char* const secret = (state->extSecret == NULL) ? state->customSecret : state->extSecret; + if (state->totalLen > XXH3_MIDSIZE_MAX) { + XXH_ALIGN(XXH_ACC_ALIGN) XXH64_hash_t acc[XXH_ACC_NB]; + XXH3_digest_long(acc, state, secret); + return XXH3_mergeAccs(acc, + secret + XXH_SECRET_MERGEACCS_START, + (xxh_u64)state->totalLen * XXH_PRIME64_1); + } + /* totalLen <= XXH3_MIDSIZE_MAX: digesting a short input */ + if (state->useSeed) + return XXH3_64bits_withSeed(state->buffer, (size_t)state->totalLen, state->seed); + return XXH3_64bits_withSecret(state->buffer, (size_t)(state->totalLen), + secret, state->secretLimit + XXH_STRIPE_LEN); +} +#endif /* !XXH_NO_STREAM */ + + +/* ========================================== + * XXH3 128 bits (a.k.a XXH128) + * ========================================== + * XXH3's 128-bit variant has better mixing and strength than the 64-bit variant, + * even without counting the significantly larger output size. + * + * For example, extra steps are taken to avoid the seed-dependent collisions + * in 17-240 byte inputs (See XXH3_mix16B and XXH128_mix32B). + * + * This strength naturally comes at the cost of some speed, especially on short + * lengths. Note that longer hashes are about as fast as the 64-bit version + * due to it using only a slight modification of the 64-bit loop. + * + * XXH128 is also more oriented towards 64-bit machines. It is still extremely + * fast for a _128-bit_ hash on 32-bit (it usually clears XXH64). + */ + +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_1to3_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + /* A doubled version of 1to3_64b with different constants. */ + XXH_ASSERT(input != NULL); + XXH_ASSERT(1 <= len && len <= 3); + XXH_ASSERT(secret != NULL); + /* + * len = 1: combinedl = { input[0], 0x01, input[0], input[0] } + * len = 2: combinedl = { input[1], 0x02, input[0], input[1] } + * len = 3: combinedl = { input[2], 0x03, input[0], input[1] } + */ + { xxh_u8 const c1 = input[0]; + xxh_u8 const c2 = input[len >> 1]; + xxh_u8 const c3 = input[len - 1]; + xxh_u32 const combinedl = ((xxh_u32)c1 <<16) | ((xxh_u32)c2 << 24) + | ((xxh_u32)c3 << 0) | ((xxh_u32)len << 8); + xxh_u32 const combinedh = XXH_rotl32(XXH_swap32(combinedl), 13); + xxh_u64 const bitflipl = (XXH_readLE32(secret) ^ XXH_readLE32(secret+4)) + seed; + xxh_u64 const bitfliph = (XXH_readLE32(secret+8) ^ XXH_readLE32(secret+12)) - seed; + xxh_u64 const keyed_lo = (xxh_u64)combinedl ^ bitflipl; + xxh_u64 const keyed_hi = (xxh_u64)combinedh ^ bitfliph; + XXH128_hash_t h128; + h128.low64 = XXH64_avalanche(keyed_lo); + h128.high64 = XXH64_avalanche(keyed_hi); + return h128; + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_4to8_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(secret != NULL); + XXH_ASSERT(4 <= len && len <= 8); + seed ^= (xxh_u64)XXH_swap32((xxh_u32)seed) << 32; + { xxh_u32 const input_lo = XXH_readLE32(input); + xxh_u32 const input_hi = XXH_readLE32(input + len - 4); + xxh_u64 const input_64 = input_lo + ((xxh_u64)input_hi << 32); + xxh_u64 const bitflip = (XXH_readLE64(secret+16) ^ XXH_readLE64(secret+24)) + seed; + xxh_u64 const keyed = input_64 ^ bitflip; + + /* Shift len to the left to ensure it is even, this avoids even multiplies. */ + XXH128_hash_t m128 = XXH_mult64to128(keyed, XXH_PRIME64_1 + (len << 2)); + + m128.high64 += (m128.low64 << 1); + m128.low64 ^= (m128.high64 >> 3); + + m128.low64 = XXH_xorshift64(m128.low64, 35); + m128.low64 *= PRIME_MX2; + m128.low64 = XXH_xorshift64(m128.low64, 28); + m128.high64 = XXH3_avalanche(m128.high64); + return m128; + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_9to16_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(secret != NULL); + XXH_ASSERT(9 <= len && len <= 16); + { xxh_u64 const bitflipl = (XXH_readLE64(secret+32) ^ XXH_readLE64(secret+40)) - seed; + xxh_u64 const bitfliph = (XXH_readLE64(secret+48) ^ XXH_readLE64(secret+56)) + seed; + xxh_u64 const input_lo = XXH_readLE64(input); + xxh_u64 input_hi = XXH_readLE64(input + len - 8); + XXH128_hash_t m128 = XXH_mult64to128(input_lo ^ input_hi ^ bitflipl, XXH_PRIME64_1); + /* + * Put len in the middle of m128 to ensure that the length gets mixed to + * both the low and high bits in the 128x64 multiply below. + */ + m128.low64 += (xxh_u64)(len - 1) << 54; + input_hi ^= bitfliph; + /* + * Add the high 32 bits of input_hi to the high 32 bits of m128, then + * add the long product of the low 32 bits of input_hi and XXH_PRIME32_2 to + * the high 64 bits of m128. + * + * The best approach to this operation is different on 32-bit and 64-bit. + */ + if (sizeof(void *) < sizeof(xxh_u64)) { /* 32-bit */ + /* + * 32-bit optimized version, which is more readable. + * + * On 32-bit, it removes an ADC and delays a dependency between the two + * halves of m128.high64, but it generates an extra mask on 64-bit. + */ + m128.high64 += (input_hi & 0xFFFFFFFF00000000ULL) + XXH_mult32to64((xxh_u32)input_hi, XXH_PRIME32_2); + } else { + /* + * 64-bit optimized (albeit more confusing) version. + * + * Uses some properties of addition and multiplication to remove the mask: + * + * Let: + * a = input_hi.lo = (input_hi & 0x00000000FFFFFFFF) + * b = input_hi.hi = (input_hi & 0xFFFFFFFF00000000) + * c = XXH_PRIME32_2 + * + * a + (b * c) + * Inverse Property: x + y - x == y + * a + (b * (1 + c - 1)) + * Distributive Property: x * (y + z) == (x * y) + (x * z) + * a + (b * 1) + (b * (c - 1)) + * Identity Property: x * 1 == x + * a + b + (b * (c - 1)) + * + * Substitute a, b, and c: + * input_hi.hi + input_hi.lo + ((xxh_u64)input_hi.lo * (XXH_PRIME32_2 - 1)) + * + * Since input_hi.hi + input_hi.lo == input_hi, we get this: + * input_hi + ((xxh_u64)input_hi.lo * (XXH_PRIME32_2 - 1)) + */ + m128.high64 += input_hi + XXH_mult32to64((xxh_u32)input_hi, XXH_PRIME32_2 - 1); + } + /* m128 ^= XXH_swap64(m128 >> 64); */ + m128.low64 ^= XXH_swap64(m128.high64); + + { /* 128x64 multiply: h128 = m128 * XXH_PRIME64_2; */ + XXH128_hash_t h128 = XXH_mult64to128(m128.low64, XXH_PRIME64_2); + h128.high64 += m128.high64 * XXH_PRIME64_2; + + h128.low64 = XXH3_avalanche(h128.low64); + h128.high64 = XXH3_avalanche(h128.high64); + return h128; + } } +} + +/* + * Assumption: `secret` size is >= XXH3_SECRET_SIZE_MIN + */ +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_0to16_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(len <= 16); + { if (len > 8) return XXH3_len_9to16_128b(input, len, secret, seed); + if (len >= 4) return XXH3_len_4to8_128b(input, len, secret, seed); + if (len) return XXH3_len_1to3_128b(input, len, secret, seed); + { XXH128_hash_t h128; + xxh_u64 const bitflipl = XXH_readLE64(secret+64) ^ XXH_readLE64(secret+72); + xxh_u64 const bitfliph = XXH_readLE64(secret+80) ^ XXH_readLE64(secret+88); + h128.low64 = XXH64_avalanche(seed ^ bitflipl); + h128.high64 = XXH64_avalanche( seed ^ bitfliph); + return h128; + } } +} + +/* + * A bit slower than XXH3_mix16B, but handles multiply by zero better. + */ +XXH_FORCE_INLINE XXH128_hash_t +XXH128_mix32B(XXH128_hash_t acc, const xxh_u8* input_1, const xxh_u8* input_2, + const xxh_u8* secret, XXH64_hash_t seed) +{ + acc.low64 += XXH3_mix16B (input_1, secret+0, seed); + acc.low64 ^= XXH_readLE64(input_2) + XXH_readLE64(input_2 + 8); + acc.high64 += XXH3_mix16B (input_2, secret+16, seed); + acc.high64 ^= XXH_readLE64(input_1) + XXH_readLE64(input_1 + 8); + return acc; +} + + +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_17to128_128b(const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH64_hash_t seed) +{ + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize; + XXH_ASSERT(16 < len && len <= 128); + + { XXH128_hash_t acc; + acc.low64 = len * XXH_PRIME64_1; + acc.high64 = 0; + +#if XXH_SIZE_OPT >= 1 + { + /* Smaller, but slightly slower. */ + unsigned int i = (unsigned int)(len - 1) / 32; + do { + acc = XXH128_mix32B(acc, input+16*i, input+len-16*(i+1), secret+32*i, seed); + } while (i-- != 0); + } +#else + if (len > 32) { + if (len > 64) { + if (len > 96) { + acc = XXH128_mix32B(acc, input+48, input+len-64, secret+96, seed); + } + acc = XXH128_mix32B(acc, input+32, input+len-48, secret+64, seed); + } + acc = XXH128_mix32B(acc, input+16, input+len-32, secret+32, seed); + } + acc = XXH128_mix32B(acc, input, input+len-16, secret, seed); +#endif + { XXH128_hash_t h128; + h128.low64 = acc.low64 + acc.high64; + h128.high64 = (acc.low64 * XXH_PRIME64_1) + + (acc.high64 * XXH_PRIME64_4) + + ((len - seed) * XXH_PRIME64_2); + h128.low64 = XXH3_avalanche(h128.low64); + h128.high64 = (XXH64_hash_t)0 - XXH3_avalanche(h128.high64); + return h128; + } + } +} + +XXH_NO_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_129to240_128b(const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH64_hash_t seed) +{ + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize; + XXH_ASSERT(128 < len && len <= XXH3_MIDSIZE_MAX); + + { XXH128_hash_t acc; + unsigned i; + acc.low64 = len * XXH_PRIME64_1; + acc.high64 = 0; + /* + * We set as `i` as offset + 32. We do this so that unchanged + * `len` can be used as upper bound. This reaches a sweet spot + * where both x86 and aarch64 get simple agen and good codegen + * for the loop. + */ + for (i = 32; i < 160; i += 32) { + acc = XXH128_mix32B(acc, + input + i - 32, + input + i - 16, + secret + i - 32, + seed); + } + acc.low64 = XXH3_avalanche(acc.low64); + acc.high64 = XXH3_avalanche(acc.high64); + /* + * NB: `i <= len` will duplicate the last 32-bytes if + * len % 32 was zero. This is an unfortunate necessity to keep + * the hash result stable. + */ + for (i=160; i <= len; i += 32) { + acc = XXH128_mix32B(acc, + input + i - 32, + input + i - 16, + secret + XXH3_MIDSIZE_STARTOFFSET + i - 160, + seed); + } + /* last bytes */ + acc = XXH128_mix32B(acc, + input + len - 16, + input + len - 32, + secret + XXH3_SECRET_SIZE_MIN - XXH3_MIDSIZE_LASTOFFSET - 16, + (XXH64_hash_t)0 - seed); + + { XXH128_hash_t h128; + h128.low64 = acc.low64 + acc.high64; + h128.high64 = (acc.low64 * XXH_PRIME64_1) + + (acc.high64 * XXH_PRIME64_4) + + ((len - seed) * XXH_PRIME64_2); + h128.low64 = XXH3_avalanche(h128.low64); + h128.high64 = (XXH64_hash_t)0 - XXH3_avalanche(h128.high64); + return h128; + } + } +} + +XXH_FORCE_INLINE XXH128_hash_t +XXH3_hashLong_128b_internal(const void* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + XXH_ALIGN(XXH_ACC_ALIGN) xxh_u64 acc[XXH_ACC_NB] = XXH3_INIT_ACC; + + XXH3_hashLong_internal_loop(acc, (const xxh_u8*)input, len, secret, secretSize, f_acc, f_scramble); + + /* converge into final hash */ + XXH_STATIC_ASSERT(sizeof(acc) == 64); + XXH_ASSERT(secretSize >= sizeof(acc) + XXH_SECRET_MERGEACCS_START); + { XXH128_hash_t h128; + h128.low64 = XXH3_mergeAccs(acc, + secret + XXH_SECRET_MERGEACCS_START, + (xxh_u64)len * XXH_PRIME64_1); + h128.high64 = XXH3_mergeAccs(acc, + secret + secretSize + - sizeof(acc) - XXH_SECRET_MERGEACCS_START, + ~((xxh_u64)len * XXH_PRIME64_2)); + return h128; + } +} + +/* + * It's important for performance that XXH3_hashLong() is not inlined. + */ +XXH_NO_INLINE XXH_PUREF XXH128_hash_t +XXH3_hashLong_128b_default(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, + const void* XXH_RESTRICT secret, size_t secretLen) +{ + (void)seed64; (void)secret; (void)secretLen; + return XXH3_hashLong_128b_internal(input, len, XXH3_kSecret, sizeof(XXH3_kSecret), + XXH3_accumulate, XXH3_scrambleAcc); +} + +/* + * It's important for performance to pass @p secretLen (when it's static) + * to the compiler, so that it can properly optimize the vectorized loop. + * + * When the secret size is unknown, or on GCC 12 where the mix of NO_INLINE and FORCE_INLINE + * breaks -Og, this is XXH_NO_INLINE. + */ +XXH3_WITH_SECRET_INLINE XXH128_hash_t +XXH3_hashLong_128b_withSecret(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, + const void* XXH_RESTRICT secret, size_t secretLen) +{ + (void)seed64; + return XXH3_hashLong_128b_internal(input, len, (const xxh_u8*)secret, secretLen, + XXH3_accumulate, XXH3_scrambleAcc); +} + +XXH_FORCE_INLINE XXH128_hash_t +XXH3_hashLong_128b_withSeed_internal(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble, + XXH3_f_initCustomSecret f_initSec) +{ + if (seed64 == 0) + return XXH3_hashLong_128b_internal(input, len, + XXH3_kSecret, sizeof(XXH3_kSecret), + f_acc, f_scramble); + { XXH_ALIGN(XXH_SEC_ALIGN) xxh_u8 secret[XXH_SECRET_DEFAULT_SIZE]; + f_initSec(secret, seed64); + return XXH3_hashLong_128b_internal(input, len, (const xxh_u8*)secret, sizeof(secret), + f_acc, f_scramble); + } +} + +/* + * It's important for performance that XXH3_hashLong is not inlined. + */ +XXH_NO_INLINE XXH128_hash_t +XXH3_hashLong_128b_withSeed(const void* input, size_t len, + XXH64_hash_t seed64, const void* XXH_RESTRICT secret, size_t secretLen) +{ + (void)secret; (void)secretLen; + return XXH3_hashLong_128b_withSeed_internal(input, len, seed64, + XXH3_accumulate, XXH3_scrambleAcc, XXH3_initCustomSecret); +} + +typedef XXH128_hash_t (*XXH3_hashLong128_f)(const void* XXH_RESTRICT, size_t, + XXH64_hash_t, const void* XXH_RESTRICT, size_t); + +XXH_FORCE_INLINE XXH128_hash_t +XXH3_128bits_internal(const void* input, size_t len, + XXH64_hash_t seed64, const void* XXH_RESTRICT secret, size_t secretLen, + XXH3_hashLong128_f f_hl128) +{ + XXH_ASSERT(secretLen >= XXH3_SECRET_SIZE_MIN); + /* + * If an action is to be taken if `secret` conditions are not respected, + * it should be done here. + * For now, it's a contract pre-condition. + * Adding a check and a branch here would cost performance at every hash. + */ + if (len <= 16) + return XXH3_len_0to16_128b((const xxh_u8*)input, len, (const xxh_u8*)secret, seed64); + if (len <= 128) + return XXH3_len_17to128_128b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64); + if (len <= XXH3_MIDSIZE_MAX) + return XXH3_len_129to240_128b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64); + return f_hl128(input, len, seed64, secret, secretLen); +} + + +/* === Public XXH128 API === */ + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t XXH3_128bits(XXH_NOESCAPE const void* input, size_t len) +{ + return XXH3_128bits_internal(input, len, 0, + XXH3_kSecret, sizeof(XXH3_kSecret), + XXH3_hashLong_128b_default); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH3_128bits_withSecret(XXH_NOESCAPE const void* input, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize) +{ + return XXH3_128bits_internal(input, len, 0, + (const xxh_u8*)secret, secretSize, + XXH3_hashLong_128b_withSecret); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH3_128bits_withSeed(XXH_NOESCAPE const void* input, size_t len, XXH64_hash_t seed) +{ + return XXH3_128bits_internal(input, len, seed, + XXH3_kSecret, sizeof(XXH3_kSecret), + XXH3_hashLong_128b_withSeed); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH3_128bits_withSecretandSeed(XXH_NOESCAPE const void* input, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed) +{ + if (len <= XXH3_MIDSIZE_MAX) + return XXH3_128bits_internal(input, len, seed, XXH3_kSecret, sizeof(XXH3_kSecret), NULL); + return XXH3_hashLong_128b_withSecret(input, len, seed, secret, secretSize); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH128(XXH_NOESCAPE const void* input, size_t len, XXH64_hash_t seed) +{ + return XXH3_128bits_withSeed(input, len, seed); +} + + +/* === XXH3 128-bit streaming === */ +#ifndef XXH_NO_STREAM +/* + * All initialization and update functions are identical to 64-bit streaming variant. + * The only difference is the finalization routine. + */ + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr) +{ + return XXH3_64bits_reset(statePtr); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize) +{ + return XXH3_64bits_reset_withSecret(statePtr, secret, secretSize); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed) +{ + return XXH3_64bits_reset_withSeed(statePtr, seed); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed) +{ + return XXH3_64bits_reset_withSecretandSeed(statePtr, secret, secretSize, seed); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_update(XXH_NOESCAPE XXH3_state_t* state, XXH_NOESCAPE const void* input, size_t len) +{ + return XXH3_64bits_update(state, input, len); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t XXH3_128bits_digest (XXH_NOESCAPE const XXH3_state_t* state) +{ + const unsigned char* const secret = (state->extSecret == NULL) ? state->customSecret : state->extSecret; + if (state->totalLen > XXH3_MIDSIZE_MAX) { + XXH_ALIGN(XXH_ACC_ALIGN) XXH64_hash_t acc[XXH_ACC_NB]; + XXH3_digest_long(acc, state, secret); + XXH_ASSERT(state->secretLimit + XXH_STRIPE_LEN >= sizeof(acc) + XXH_SECRET_MERGEACCS_START); + { XXH128_hash_t h128; + h128.low64 = XXH3_mergeAccs(acc, + secret + XXH_SECRET_MERGEACCS_START, + (xxh_u64)state->totalLen * XXH_PRIME64_1); + h128.high64 = XXH3_mergeAccs(acc, + secret + state->secretLimit + XXH_STRIPE_LEN + - sizeof(acc) - XXH_SECRET_MERGEACCS_START, + ~((xxh_u64)state->totalLen * XXH_PRIME64_2)); + return h128; + } + } + /* len <= XXH3_MIDSIZE_MAX : short code */ + if (state->useSeed) + return XXH3_128bits_withSeed(state->buffer, (size_t)state->totalLen, state->seed); + return XXH3_128bits_withSecret(state->buffer, (size_t)(state->totalLen), + secret, state->secretLimit + XXH_STRIPE_LEN); +} +#endif /* !XXH_NO_STREAM */ +/* 128-bit utility functions */ + +#include /* memcmp, memcpy */ + +/* return : 1 is equal, 0 if different */ +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API int XXH128_isEqual(XXH128_hash_t h1, XXH128_hash_t h2) +{ + /* note : XXH128_hash_t is compact, it has no padding byte */ + return !(memcmp(&h1, &h2, sizeof(h1))); +} + +/* This prototype is compatible with stdlib's qsort(). + * @return : >0 if *h128_1 > *h128_2 + * <0 if *h128_1 < *h128_2 + * =0 if *h128_1 == *h128_2 */ +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API int XXH128_cmp(XXH_NOESCAPE const void* h128_1, XXH_NOESCAPE const void* h128_2) +{ + XXH128_hash_t const h1 = *(const XXH128_hash_t*)h128_1; + XXH128_hash_t const h2 = *(const XXH128_hash_t*)h128_2; + int const hcmp = (h1.high64 > h2.high64) - (h2.high64 > h1.high64); + /* note : bets that, in most cases, hash values are different */ + if (hcmp) return hcmp; + return (h1.low64 > h2.low64) - (h2.low64 > h1.low64); +} + + +/*====== Canonical representation ======*/ +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API void +XXH128_canonicalFromHash(XXH_NOESCAPE XXH128_canonical_t* dst, XXH128_hash_t hash) +{ + XXH_STATIC_ASSERT(sizeof(XXH128_canonical_t) == sizeof(XXH128_hash_t)); + if (XXH_CPU_LITTLE_ENDIAN) { + hash.high64 = XXH_swap64(hash.high64); + hash.low64 = XXH_swap64(hash.low64); + } + XXH_memcpy(dst, &hash.high64, sizeof(hash.high64)); + XXH_memcpy((char*)dst + sizeof(hash.high64), &hash.low64, sizeof(hash.low64)); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH128_hashFromCanonical(XXH_NOESCAPE const XXH128_canonical_t* src) +{ + XXH128_hash_t h; + h.high64 = XXH_readBE64(src); + h.low64 = XXH_readBE64(src->digest + 8); + return h; +} + + + +/* ========================================== + * Secret generators + * ========================================== + */ +#define XXH_MIN(x, y) (((x) > (y)) ? (y) : (x)) + +XXH_FORCE_INLINE void XXH3_combine16(void* dst, XXH128_hash_t h128) +{ + XXH_writeLE64( dst, XXH_readLE64(dst) ^ h128.low64 ); + XXH_writeLE64( (char*)dst+8, XXH_readLE64((char*)dst+8) ^ h128.high64 ); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_generateSecret(XXH_NOESCAPE void* secretBuffer, size_t secretSize, XXH_NOESCAPE const void* customSeed, size_t customSeedSize) +{ +#if (XXH_DEBUGLEVEL >= 1) + XXH_ASSERT(secretBuffer != NULL); + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); +#else + /* production mode, assert() are disabled */ + if (secretBuffer == NULL) return XXH_ERROR; + if (secretSize < XXH3_SECRET_SIZE_MIN) return XXH_ERROR; +#endif + + if (customSeedSize == 0) { + customSeed = XXH3_kSecret; + customSeedSize = XXH_SECRET_DEFAULT_SIZE; + } +#if (XXH_DEBUGLEVEL >= 1) + XXH_ASSERT(customSeed != NULL); +#else + if (customSeed == NULL) return XXH_ERROR; +#endif + + /* Fill secretBuffer with a copy of customSeed - repeat as needed */ + { size_t pos = 0; + while (pos < secretSize) { + size_t const toCopy = XXH_MIN((secretSize - pos), customSeedSize); + memcpy((char*)secretBuffer + pos, customSeed, toCopy); + pos += toCopy; + } } + + { size_t const nbSeg16 = secretSize / 16; + size_t n; + XXH128_canonical_t scrambler; + XXH128_canonicalFromHash(&scrambler, XXH128(customSeed, customSeedSize, 0)); + for (n=0; n /* abort() */ +#include +#include +#include +#include +#include +#include + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +#include "xxhash/xxhash.h" +#include "sha1/sha1.h" +#include "sha256/sha256.h" + +#ifdef __cplusplus +} +#endif + + +// uuid.uuid5(uuid.NAMESPACE_URL, 'en.wikipedia.org/wiki/Llama.cpp') +#define UUID_NAMESPACE_LLAMA_CPP "ef001206-dadc-5f6d-a15f-3359e577d4e5" +#define UUID_NAMESPACE_LLAMA_CPP_HEX 0xef, 0x00, 0x12, 0x06, 0xda, 0xdc, 0x5f, 0x6d, 0xa1, 0x5f, 0x33, 0x59, 0xe5, 0x77, 0xd4, 0xe5 + + +#define HASH_TYPE_SHA256_STR "sha256" +#define HASH_TYPE_SHA1_STR "sha1" +#define HASH_TYPE_XXH64_STR "xxh64" +#define HASH_TYPE_UUID_STR "uuid" + + +typedef enum { + HASH_EXIT_SUCCESS = 0, // All hash has been generated or validated + HASH_EXIT_FAILURE = 1, // Generic Failure + HASH_EXIT_MISMATCH = 2, // Hash mismatched during validation + HASH_EXIT_MANIFEST_MISSING_ENTRY = 3, // Hash attempted validation but missing entry in manifest + HASH_EXIT_MANIFEST_UNKNOWN_HASH = 4, // Manifest is present, but we do not know any hash format within it + HASH_EXIT_MANIFEST_FILE_ERROR = 5 // Manifest is either missing or not a known format +} hash_exit_code_t; + + +typedef enum { + HASH_MANIFEST_NOT_FOUND, + HASH_MANIFEST_MISMATCH, + HASH_MANIFEST_OK, +} hash_manifest_result_t; + + +struct hash_params { + std::string input; + bool xxh64 = false; + bool sha1 = false; + bool sha256 = false; + bool uuid = false; + + bool no_layer = false; + + bool manifest_is_usable = false; + std::string manifest_file; +}; + +struct manifest_check_params { + bool xxh64 = false; + bool sha1 = false; + bool sha256 = false; + bool uuid = false; +}; + +static char const * hash_manifest_result_to_str(hash_manifest_result_t value) { + switch (value) { + case HASH_MANIFEST_NOT_FOUND: return "Not Found"; + case HASH_MANIFEST_MISMATCH: return "Mismatch"; + case HASH_MANIFEST_OK: return "Ok"; + } + return "?"; +} + +static char const * hash_exit_code_to_str(hash_exit_code_t value) { + switch (value) { + case HASH_EXIT_SUCCESS: return "Success"; + case HASH_EXIT_FAILURE: return "Failure"; + case HASH_EXIT_MISMATCH: return "Mismatch"; + case HASH_EXIT_MANIFEST_MISSING_ENTRY: return "Manifest Missing Entry"; + case HASH_EXIT_MANIFEST_UNKNOWN_HASH: return "Manifest Unknown Hash"; + case HASH_EXIT_MANIFEST_FILE_ERROR: return "Manifest File Error"; + } + return "?"; +} + +static void hash_print_usage(const char * executable) { + const hash_params default_params; + printf("\n"); + printf("usage: %s [options] GGUF_IN\n", executable); + printf("\n"); + printf("Hash a GGUF file"); + printf("\n"); + printf("options:\n"); + printf(" -h, --help show this help message and exit\n"); + printf(" --xxh64 use xxh64 hash\n"); + printf(" --sha1 use sha1 hash\n"); + printf(" --sha256 use sha256 hash\n"); + printf(" --all use all hash\n"); + printf(" --no-layer exclude per layer hash\n"); + printf(" --uuid generate UUIDv5 ID\n"); + printf(" -c, --check verify against a manifest\n"); + printf("\n"); +} + +static void hash_params_parse_ex(int argc, const char ** argv, hash_params & params) { + std::string arg; + bool invalid_param = false; + const std::string arg_prefix = "--"; + + int arg_idx = 1; + for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) { + arg = argv[arg_idx]; + if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { + std::replace(arg.begin(), arg.end(), '_', '-'); + } + + bool arg_found = false; + if (arg == "-h" || arg == "--help") { + hash_print_usage(argv[0]); + exit(0); + } + + if (arg == "--xxh64") { + arg_found = true; + params.xxh64 = true; + } + + if (arg == "--sha1") { + arg_found = true; + params.sha1 = true; + } + + if (arg == "--uuid") { + arg_found = true; + params.uuid = true; + } + + if (arg == "--sha256") { + arg_found = true; + params.sha256 = true; + } + + if (arg == "--all") { + arg_found = true; + params.sha256 = true; + params.sha1 = true; + params.xxh64 = true; + } + + if (arg == "--no-layer") { + arg_found = true; + params.no_layer = true; + } + + if (arg == "-c" || arg == "--check") { + if (++arg_idx >= argc) { + invalid_param = true; + break; + } + arg_found = true; + params.manifest_file = argv[arg_idx]; + } + + if (!arg_found) { + throw std::invalid_argument("error: unknown argument: " + arg); + } + } + + if (invalid_param) { + throw std::invalid_argument("error: invalid parameter for argument:" + arg); + } + + if (argc - arg_idx < 1) { + throw std::invalid_argument("error: bad arguments"); + } + + params.input = argv[arg_idx++]; +} + +static bool hash_params_parse(int argc, const char ** argv, hash_params & params) { + bool result = true; + try { + hash_params_parse_ex(argc, argv, params); + } + catch (const std::invalid_argument & ex) { + fprintf(stderr, "%s\n", ex.what()); + hash_print_usage(argv[0]); + exit(EXIT_FAILURE); + } + return result; +} + +static bool manifest_type(const std::string & manifest_file, manifest_check_params & manifest_check) { + if (manifest_file.empty()) { + return false; + } + + std::ifstream file(manifest_file); + if (!file.is_open()) { + return false; + } + + std::string manifest_entry_line; + while (getline(file, manifest_entry_line)) { + // hash_type_str hash_str tensor_name + // e.g. 'xxh64 f66e9cd66a4396a0 test.gguf:tensor_0' + std::istringstream line_stream(manifest_entry_line); + std::string file_hash_type; + if (line_stream >> file_hash_type) { + if (file_hash_type == HASH_TYPE_SHA256_STR) { + manifest_check.sha256 = true; + } else if (file_hash_type == HASH_TYPE_SHA1_STR) { + manifest_check.sha1 = true; + } else if (file_hash_type == HASH_TYPE_XXH64_STR) { + manifest_check.xxh64 = true; + } else if (file_hash_type == HASH_TYPE_UUID_STR) { + manifest_check.uuid = true; + } + } + } + + return true; +} + +static hash_manifest_result_t manifest_verify(const std::string& manifest_file, const std::string& hash_type_str, const std::string& hash_str, const std::string& tensor_name) { + if (manifest_file.empty()) { + return HASH_MANIFEST_NOT_FOUND; + } + + std::ifstream file(manifest_file); + if (!file.is_open()) { + return HASH_MANIFEST_NOT_FOUND; + } + + std::string manifest_entry_line; + while (getline(file, manifest_entry_line)) { + std::istringstream line_stream(manifest_entry_line); + std::string file_hash_type; + std::string file_hash; + std::string file_tensor_name; + if (line_stream >> file_hash_type >> file_hash >> file_tensor_name) { + // Line parsed. Check hash validity + + if (file_hash_type != hash_type_str) { + continue; + } + + if (file_tensor_name != tensor_name) { + continue; + } + + return (file_hash == hash_str) ? HASH_MANIFEST_OK : HASH_MANIFEST_MISMATCH; + } + } + + return HASH_MANIFEST_NOT_FOUND; +} + +static void generate_uuidv5(const unsigned char sha1_digest[20], unsigned char uuid[16]) { + // Ref: https://www.rfc-editor.org/rfc/rfc9562.html#section-5.5 + // Assumes that digest was processed correctly with the expected namespace + for (int i = 0; i < 16; i++) { + uuid[i] = sha1_digest[i]; + } + + // Set bits corresponding to UUID ver 5 + uuid[ 6] &= ~(0xF << 4); + uuid[ 6] |= (5 << 4); + + // Set bits corresponding to UUID variant 0b10XX + uuid[ 8] &= ~(0xc << 4); + uuid[ 8] |= (0x8 << 4); +} + +static hash_exit_code_t gguf_hash(const hash_params & hash_params) { + const std::string & fname = hash_params.input; + struct ggml_context * ctx_data = NULL; + + struct gguf_init_params params = { + /*.no_alloc = */ false, + /*.ctx = */ &ctx_data, + }; + + // xxh64 init + XXH64_state_t* xxh64_model_hash_state = NULL; + if (hash_params.xxh64) { + xxh64_model_hash_state = XXH64_createState(); + if (xxh64_model_hash_state==NULL) { + abort(); + } + + XXH64_hash_t const seed = 0; + if (XXH64_reset(xxh64_model_hash_state, seed) == XXH_ERROR) { + abort(); + } + } + + // sha1 init + SHA1_CTX sha1_model_hash_ctx; + if (hash_params.sha1) { + SHA1Init(&sha1_model_hash_ctx); + } + + // sha256 init + sha256_t sha256_model_hash_ctx; + if (hash_params.sha256) { + sha256_init(&sha256_model_hash_ctx); + } + + // sha1 for uuid init + SHA1_CTX sha1_for_uuid_ctx; + if (hash_params.uuid) { + unsigned char const uuidv5_namespace[] = {UUID_NAMESPACE_LLAMA_CPP_HEX}; + SHA1Init(&sha1_for_uuid_ctx); + SHA1Update( &sha1_for_uuid_ctx, (unsigned char const *)uuidv5_namespace, sizeof(uuidv5_namespace)); + } + + struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params); + const int n_tensors = gguf_get_n_tensors(ctx); + bool tensor_layer_in_manifest = false; + bool model_in_manifest = false; + bool tensor_layer_has_mismatch = false; + bool model_has_mismatch = false; + for (int i = 0; i < n_tensors; ++i) { + const char * name = gguf_get_tensor_name(ctx, i); + struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name); + auto n_bytes = ggml_nbytes(cur); + auto *raw_data = cur->data; + const std::string tensor_layer_name = fname + ":" + name; + + if (hash_params.xxh64) { + + if (!hash_params.no_layer) { + // Per Layer Hash + XXH64_hash_t hash = XXH64(raw_data, n_bytes, 0); + + char hex_result[17]; + for (int offset = 0; offset < 8; offset++) { + unsigned int shift_bits_by = (8 * (8 - offset - 1)); + snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_XXH64_STR, hex_result, tensor_layer_name); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + tensor_layer_in_manifest = true; + tensor_layer_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + tensor_layer_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_XXH64_STR, hex_result, tensor_layer_name.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_XXH64_STR, hex_result, tensor_layer_name.c_str()); + } + } + + // Overall Model Hash + if (XXH64_update(xxh64_model_hash_state, raw_data, n_bytes) == XXH_ERROR) abort(); + } + + if (hash_params.sha1) { + + if (!hash_params.no_layer) { + // Per Layer Hash + char result[21]; // sha1 outputs 20 bytes + SHA1( result, (const char *)raw_data, n_bytes); + + char hex_result[41] = {0}; + for (int offset = 0; offset < 20; offset++) { + snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA1_STR, hex_result, tensor_layer_name); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + tensor_layer_in_manifest = true; + tensor_layer_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + tensor_layer_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_SHA1_STR, hex_result, tensor_layer_name.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_SHA1_STR, hex_result, tensor_layer_name.c_str()); + } + } + + // Overall Model Hash + SHA1Update( &sha1_model_hash_ctx, (unsigned char const *)raw_data, n_bytes); + } + + if (hash_params.sha256) { + + if (!hash_params.no_layer) { + // Per Layer Hash + unsigned char result[SHA256_DIGEST_SIZE]; // sha256 outputs 32 bytes + sha256_hash((unsigned char*) result, (const unsigned char *)raw_data, n_bytes); + + char hex_result[SHA256_DIGEST_SIZE * 2 + 1] = {0}; + for (int offset = 0; offset < SHA256_DIGEST_SIZE; offset++) { + snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA256_STR, hex_result, tensor_layer_name); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + tensor_layer_in_manifest = true; + tensor_layer_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + tensor_layer_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_SHA256_STR, hex_result, tensor_layer_name.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_SHA256_STR, hex_result, tensor_layer_name.c_str()); + } + } + + // Overall Model Hash + sha256_update( &sha256_model_hash_ctx, (unsigned char const *)raw_data, n_bytes); + } + + if (hash_params.uuid) { + SHA1Update( &sha1_for_uuid_ctx, (unsigned char const *)raw_data, n_bytes); + } + } + + if (hash_params.xxh64) { + XXH64_hash_t const hash = XXH64_digest(xxh64_model_hash_state); + + char hex_result[17]; + for (int offset = 0; offset < 8; offset++) { + unsigned int shift_bits_by = (8 * (8 - offset - 1)); + snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_XXH64_STR, hex_result, fname); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + model_in_manifest = true; + model_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + model_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_XXH64_STR, hex_result, fname.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_XXH64_STR, hex_result, fname.c_str()); + } + } + + if (hash_params.sha1) { + unsigned char result[21]; + SHA1Final(result, &sha1_model_hash_ctx); + + char hex_result[41]; + for (int offset = 0; offset < 20; offset++) { + snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA1_STR, hex_result, fname); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + model_in_manifest = true; + model_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + model_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_SHA1_STR, hex_result, fname.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_SHA1_STR, hex_result, fname.c_str()); + } + } + + if (hash_params.sha256) { + unsigned char result[SHA256_DIGEST_SIZE]; // sha256 outputs 32 bytes + sha256_final( &sha256_model_hash_ctx, result); + + char hex_result[SHA256_DIGEST_SIZE * 2 + 1] = {0}; + for (int offset = 0; offset < SHA256_DIGEST_SIZE; offset++) { + snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA256_STR, hex_result, fname); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + model_in_manifest = true; + model_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + model_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_SHA256_STR, hex_result, fname.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_SHA256_STR, hex_result, fname.c_str()); + } + } + + if (hash_params.uuid) { + unsigned char result[21]; + SHA1Final(result, &sha1_for_uuid_ctx); + + unsigned char uuid[16]; + generate_uuidv5(result, uuid); + + char string_buffer[37] = {0}; + snprintf(string_buffer, sizeof(string_buffer), "%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x", + uuid[0], uuid[1], uuid[2], uuid[3], + uuid[4], uuid[5], uuid[6], uuid[7], + uuid[8], uuid[9], uuid[10], uuid[11], + uuid[12], uuid[13], uuid[14], uuid[15]); + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA256_STR, string_buffer, fname); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + model_in_manifest = true; + model_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + model_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_UUID_STR, string_buffer, fname.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_UUID_STR, string_buffer, fname.c_str()); + } + } + + + ggml_free(ctx_data); + gguf_free(ctx); + + + if (hash_params.manifest_is_usable) { + // In hash verification mode + + if (!model_in_manifest) { + // model missing in manifest? + + // Check tensor layer... + if (!tensor_layer_in_manifest) { + // Still missing? Maybe we are reading the wrong manifest. + return HASH_EXIT_MANIFEST_MISSING_ENTRY; + } + + if (tensor_layer_has_mismatch) { + // Per tensor check found error + return HASH_EXIT_FAILURE; + } + + // All per tensor layer checks passed? Sounds good enough. + return HASH_EXIT_SUCCESS; + } + + // Overall model check passed, but let's check per layer just in case + // If missing, we don't care too much as the overall model checked + if (tensor_layer_in_manifest && tensor_layer_has_mismatch) { + return HASH_EXIT_FAILURE; + } + + if (model_has_mismatch) { + // model has failed hash somewhere in the model + return HASH_EXIT_FAILURE; + } + + // All checks appears to be fine + return HASH_EXIT_SUCCESS; + } + + // In hash generation mode + return HASH_EXIT_SUCCESS; +} + +int main(int argc, const char ** argv) { + hash_params params; + manifest_check_params manifest_check; + hash_params_parse(argc, argv, params); + + if (!params.manifest_file.empty()) { + if (!manifest_type(params.manifest_file, manifest_check)) { + printf("ERROR cannot open manifest %s", params.manifest_file.c_str()); + return HASH_EXIT_MANIFEST_FILE_ERROR; + } + + if (!manifest_check.sha256 && !manifest_check.sha1 && !manifest_check.xxh64 && !manifest_check.uuid) { + printf("ERROR manifest does not have any known hash format in %s", params.manifest_file.c_str()); + return HASH_EXIT_MANIFEST_UNKNOWN_HASH; + } + + printf("manifest %s", params.manifest_file.c_str()); + + if (manifest_check.sha256) { + printf(" sha256"); + } + + if (manifest_check.sha1) { + printf(" sha1"); + } + + if (manifest_check.xxh64) { + printf(" xxh64"); + } + + if (manifest_check.uuid) { + printf(" uuid"); + } + + printf("\n"); + + // Autoselect the highest security hash if manifest is provided but + // the user has not specifically defined the hash they care about + if (!params.xxh64 && !params.sha1 && !params.uuid && !params.sha256) { + // User has not selected a specific value, pick most secure hash + if (manifest_check.sha256) { + params.sha256 = true; + } else if (manifest_check.sha1) { + params.sha1 = true; + } else if (manifest_check.xxh64) { + params.xxh64 = true; + } else if (manifest_check.uuid) { + params.uuid = true; + } + } + + params.manifest_is_usable = true; + } + + // By default if no swich argument provided, assume xxh64 + if (!params.xxh64 && !params.sha1 && !params.uuid && !params.sha256) { + params.xxh64 = true; + } + + hash_exit_code_t exit_code = gguf_hash(params); + + if (params.manifest_is_usable) { + printf("\nVerification results for %s - %s\n", params.manifest_file.c_str(), hash_exit_code_to_str(exit_code)); + } + + return exit_code; +} diff --git a/llama.cpp/examples/gguf/CMakeLists.txt b/llama.cpp/examples/gguf/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..457f72e7304edd2d189c67e44214fd0fbb7cd280 --- /dev/null +++ b/llama.cpp/examples/gguf/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-gguf) +add_executable(${TARGET} gguf.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/llama.cpp/examples/gguf/gguf.cpp b/llama.cpp/examples/gguf/gguf.cpp new file mode 100644 index 0000000000000000000000000000000000000000..9daf3016ace7ad3fc62a723b9de66a63f5c7003b --- /dev/null +++ b/llama.cpp/examples/gguf/gguf.cpp @@ -0,0 +1,265 @@ +#include "ggml.h" +#include "gguf.h" + +#include +#include +#include +#include + +#undef MIN +#undef MAX +#define MIN(a, b) ((a) < (b) ? (a) : (b)) +#define MAX(a, b) ((a) > (b) ? (a) : (b)) + +template +static std::string to_string(const T & val) { + std::stringstream ss; + ss << val; + return ss.str(); +} + +static bool gguf_ex_write(const std::string & fname) { + struct gguf_context * ctx = gguf_init_empty(); + + gguf_set_val_u8 (ctx, "some.parameter.uint8", 0x12); + gguf_set_val_i8 (ctx, "some.parameter.int8", -0x13); + gguf_set_val_u16 (ctx, "some.parameter.uint16", 0x1234); + gguf_set_val_i16 (ctx, "some.parameter.int16", -0x1235); + gguf_set_val_u32 (ctx, "some.parameter.uint32", 0x12345678); + gguf_set_val_i32 (ctx, "some.parameter.int32", -0x12345679); + gguf_set_val_f32 (ctx, "some.parameter.float32", 0.123456789f); + gguf_set_val_u64 (ctx, "some.parameter.uint64", 0x123456789abcdef0ull); + gguf_set_val_i64 (ctx, "some.parameter.int64", -0x123456789abcdef1ll); + gguf_set_val_f64 (ctx, "some.parameter.float64", 0.1234567890123456789); + gguf_set_val_bool(ctx, "some.parameter.bool", true); + gguf_set_val_str (ctx, "some.parameter.string", "hello world"); + + gguf_set_arr_data(ctx, "some.parameter.arr.i16", GGUF_TYPE_INT16, std::vector{ 1, 2, 3, 4, }.data(), 4); + gguf_set_arr_data(ctx, "some.parameter.arr.f32", GGUF_TYPE_FLOAT32, std::vector{ 3.145f, 2.718f, 1.414f, }.data(), 3); + gguf_set_arr_str (ctx, "some.parameter.arr.str", std::vector{ "hello", "world", "!" }.data(), 3); + + struct ggml_init_params params = { + /*.mem_size =*/ 128ull*1024ull*1024ull, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ false, + }; + + struct ggml_context * ctx_data = ggml_init(params); + + const int n_tensors = 10; + + // tensor infos + for (int i = 0; i < n_tensors; ++i) { + const std::string name = "tensor_" + to_string(i); + + int64_t ne[GGML_MAX_DIMS] = { 1 }; + int32_t n_dims = rand() % GGML_MAX_DIMS + 1; + + for (int j = 0; j < n_dims; ++j) { + ne[j] = rand() % 10 + 1; + } + + struct ggml_tensor * cur = ggml_new_tensor(ctx_data, GGML_TYPE_F32, n_dims, ne); + ggml_set_name(cur, name.c_str()); + + { + float * data = (float *) cur->data; + for (int j = 0; j < ggml_nelements(cur); ++j) { + data[j] = 100 + i; + } + } + + gguf_add_tensor(ctx, cur); + } + + gguf_write_to_file(ctx, fname.c_str(), false); + + printf("%s: wrote file '%s;\n", __func__, fname.c_str()); + + ggml_free(ctx_data); + gguf_free(ctx); + + return true; +} + +// just read tensor info +static bool gguf_ex_read_0(const std::string & fname) { + struct gguf_init_params params = { + /*.no_alloc = */ false, + /*.ctx = */ NULL, + }; + + struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params); + + if (!ctx) { + fprintf(stderr, "%s: failed to load '%s'\n", __func__, fname.c_str()); + return false; + } + + printf("%s: version: %d\n", __func__, gguf_get_version(ctx)); + printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); + printf("%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx)); + + // kv + { + const int n_kv = gguf_get_n_kv(ctx); + + printf("%s: n_kv: %d\n", __func__, n_kv); + + for (int i = 0; i < n_kv; ++i) { + const char * key = gguf_get_key(ctx, i); + + printf("%s: kv[%d]: key = %s\n", __func__, i, key); + } + } + + // find kv string + { + const char * findkey = "some.parameter.string"; + + const int keyidx = gguf_find_key(ctx, findkey); + if (keyidx == -1) { + printf("%s: find key: %s not found.\n", __func__, findkey); + } else { + const char * key_value = gguf_get_val_str(ctx, keyidx); + printf("%s: find key: %s found, kv[%d] value = %s\n", __func__, findkey, keyidx, key_value); + } + } + + // tensor info + { + const int n_tensors = gguf_get_n_tensors(ctx); + + printf("%s: n_tensors: %d\n", __func__, n_tensors); + + for (int i = 0; i < n_tensors; ++i) { + const char * name = gguf_get_tensor_name (ctx, i); + const size_t size = gguf_get_tensor_size (ctx, i); + const size_t offset = gguf_get_tensor_offset(ctx, i); + + printf("%s: tensor[%d]: name = %s, size = %zu, offset = %zu\n", __func__, i, name, size, offset); + } + } + + gguf_free(ctx); + + return true; +} + +// read and create ggml_context containing the tensors and their data +static bool gguf_ex_read_1(const std::string & fname, bool check_data) { + struct ggml_context * ctx_data = NULL; + + struct gguf_init_params params = { + /*.no_alloc = */ false, + /*.ctx = */ &ctx_data, + }; + + struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params); + + printf("%s: version: %d\n", __func__, gguf_get_version(ctx)); + printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); + printf("%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx)); + + // kv + { + const int n_kv = gguf_get_n_kv(ctx); + + printf("%s: n_kv: %d\n", __func__, n_kv); + + for (int i = 0; i < n_kv; ++i) { + const char * key = gguf_get_key(ctx, i); + + printf("%s: kv[%d]: key = %s\n", __func__, i, key); + } + } + + // tensor info + { + const int n_tensors = gguf_get_n_tensors(ctx); + + printf("%s: n_tensors: %d\n", __func__, n_tensors); + + for (int i = 0; i < n_tensors; ++i) { + const char * name = gguf_get_tensor_name (ctx, i); + const size_t size = gguf_get_tensor_size (ctx, i); + const size_t offset = gguf_get_tensor_offset(ctx, i); + + printf("%s: tensor[%d]: name = %s, size = %zu, offset = %zu\n", __func__, i, name, size, offset); + } + } + + // data + { + const int n_tensors = gguf_get_n_tensors(ctx); + + for (int i = 0; i < n_tensors; ++i) { + printf("%s: reading tensor %d data\n", __func__, i); + + const char * name = gguf_get_tensor_name(ctx, i); + + struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name); + + printf("%s: tensor[%d]: n_dims = %d, ne = (%d, %d, %d, %d), name = %s, data = %p\n", + __func__, i, ggml_n_dims(cur), int(cur->ne[0]), int(cur->ne[1]), int(cur->ne[2]), int(cur->ne[3]), cur->name, cur->data); + + // print first 10 elements + const float * data = (const float *) cur->data; + + printf("%s data[:10] : ", name); + for (int j = 0; j < MIN(10, ggml_nelements(cur)); ++j) { + printf("%f ", data[j]); + } + printf("\n\n"); + + // check data + if (check_data) { + const float * data = (const float *) cur->data; + for (int j = 0; j < ggml_nelements(cur); ++j) { + if (data[j] != 100 + i) { + fprintf(stderr, "%s: tensor[%d], data[%d]: found %f, expected %f\n", __func__, i, j, data[j], float(100 + i)); + gguf_free(ctx); + return false; + } + } + } + } + } + + printf("%s: ctx_data size: %zu\n", __func__, ggml_get_mem_size(ctx_data)); + + ggml_free(ctx_data); + gguf_free(ctx); + + return true; +} + +int main(int argc, char ** argv) { + if (argc < 3) { + printf("usage: %s data.gguf r|w [n]\n", argv[0]); + printf("r: read data.gguf file\n"); + printf("w: write data.gguf file\n"); + printf("n: no check of tensor data\n"); + return -1; + } + bool check_data = true; + if (argc == 4) { + check_data = false; + } + + srand(123456); + + const std::string fname(argv[1]); + const std::string mode (argv[2]); + + GGML_ASSERT((mode == "r" || mode == "w") && "mode must be r or w"); + + if (mode == "w") { + GGML_ASSERT(gguf_ex_write(fname) && "failed to write gguf file"); + } else if (mode == "r") { + GGML_ASSERT(gguf_ex_read_0(fname) && "failed to read gguf file"); + GGML_ASSERT(gguf_ex_read_1(fname, check_data) && "failed to read gguf file"); + } + + return 0; +} diff --git a/llama.cpp/examples/gritlm/CMakeLists.txt b/llama.cpp/examples/gritlm/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..a84ef9df0f3049bdc12cd5f7d2f0ed59ea3551b2 --- /dev/null +++ b/llama.cpp/examples/gritlm/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-gritlm) +add_executable(${TARGET} gritlm.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/llama.cpp/examples/gritlm/README.md b/llama.cpp/examples/gritlm/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2407a68b3d15569b45459c7aef525fb6923e06b5 --- /dev/null +++ b/llama.cpp/examples/gritlm/README.md @@ -0,0 +1,62 @@ +## Generative Representational Instruction Tuning (GRIT) Example +[gritlm] a model which can generate embeddings as well as "normal" text +generation depending on the instructions in the prompt. + +* Paper: https://arxiv.org/pdf/2402.09906.pdf + +### Retrieval-Augmented Generation (RAG) use case +One use case for `gritlm` is to use it with RAG. If we recall how RAG works is +that we take documents that we want to use as context, to ground the large +language model (LLM), and we create token embeddings for them. We then store +these token embeddings in a vector database. + +When we perform a query, prompt the LLM, we will first create token embeddings +for the query and then search the vector database to retrieve the most +similar vectors, and return those documents so they can be passed to the LLM as +context. Then the query and the context will be passed to the LLM which will +have to _again_ create token embeddings for the query. But because gritlm is used +the first query can be cached and the second query tokenization generation does +not have to be performed at all. + +### Running the example +Download a Grit model: +```console +$ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf --outdir models +``` + +Run the example using the downloaded model: +```console +$ ./llama-gritlm -m models/gritlm-7b_q4_1.gguf + +Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "A purely peer-to-peer version of electronic cash w" is: 0.605 +Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "All text-based language problems can be reduced to" is: 0.103 +Cosine similarity between "Generative Representational Instruction Tuning" and "A purely peer-to-peer version of electronic cash w" is: 0.112 +Cosine similarity between "Generative Representational Instruction Tuning" and "All text-based language problems can be reduced to" is: 0.547 + +Oh, brave adventurer, who dared to climb +The lofty peak of Mt. Fuji in the night, +When shadows lurk and ghosts do roam, +And darkness reigns, a fearsome sight. + +Thou didst set out, with heart aglow, +To conquer this mountain, so high, +And reach the summit, where the stars do glow, +And the moon shines bright, up in the sky. + +Through the mist and fog, thou didst press on, +With steadfast courage, and a steadfast will, +Through the darkness, thou didst not be gone, +But didst climb on, with a steadfast skill. + +At last, thou didst reach the summit's crest, +And gazed upon the world below, +And saw the beauty of the night's best, +And felt the peace, that only nature knows. + +Oh, brave adventurer, who dared to climb +The lofty peak of Mt. Fuji in the night, +Thou art a hero, in the eyes of all, +For thou didst conquer this mountain, so bright. +``` + +[gritlm]: https://github.com/ContextualAI/gritlm diff --git a/llama.cpp/examples/gritlm/gritlm.cpp b/llama.cpp/examples/gritlm/gritlm.cpp new file mode 100644 index 0000000000000000000000000000000000000000..1a01862fca888963bd581a4179d3a0bef0cde85b --- /dev/null +++ b/llama.cpp/examples/gritlm/gritlm.cpp @@ -0,0 +1,231 @@ +#include "arg.h" +#include "common.h" +#include "llama.h" + +#include +#include + +// #define GRIT_DEBUG + +static std::vector> encode(llama_context * ctx, const std::vector & sentences, const std::string & instruction) { + std::vector> result; + + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1); + + for (uint64_t i = 0; i < sentences.size(); i++) { + common_batch_clear(batch); + + const std::string input_string = instruction + sentences[i]; + + std::vector inputs = common_tokenize(vocab, input_string, true, false); + + const int32_t n_toks = inputs.size(); + + // GritLM seems to have EOS = "" + // https://github.com/ContextualAI/gritlm/blob/92025b16534712b31b3c4aaaf069350e222bd5f8/gritlm/gritlm.py#L18 + // inputs.push_back(llama_vocab_eos(vocab)); + + // we want to ignore instruction tokens for mean pooling + const int32_t n_inst = common_tokenize(vocab, instruction, true, false).size(); + +#ifdef GRIT_DEBUG + // debug tokens - should be matching as referenced in the GritLM sample + std::for_each(inputs.begin(), inputs.end(), [&ctx](llama_token t) { + std::printf("[%u:%s]", t, llama_token_to_piece(ctx, t).c_str()); + }); + std::printf("\n"); +#endif + + // add input to batch (this increments n_tokens) + for (int32_t j = 0; j < n_toks; j++) { + common_batch_add(batch, inputs[j], j, { 0 }, true); + } + + // clear previous kv_cache values (irrelevant for embeddings) + llama_memory_clear(llama_get_memory(ctx), true); + llama_set_causal_attn(ctx, false); + + // run model + llama_decode(ctx, batch); + + // get embedding dimensions + uint64_t n_embd = llama_model_n_embd(model); + + // allocate embedding output + std::vector emb_unorm(n_embd, 0.0f); + + // sum up all token embeddings + for (int32_t k = n_inst; k < n_toks; k++) { + float * emb = llama_get_embeddings_ith(ctx, k); + for (uint64_t j = 0; j < n_embd; j++) { + emb_unorm[j] += emb[j]; + } + } + + // divide by number of tokens (mean pooling) + { + const uint64_t n_sent = n_toks - n_inst; + + for (uint64_t j = 0; j < n_embd; j++) { + emb_unorm[j] /= n_sent; + } + } + + std::vector emb_norm(emb_unorm.size()); + common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd, 2); + result.push_back(emb_norm); + +#ifdef GRIT_DEBUG + // print out emb_norm + std::printf("embedding %ld: ", i); + for (uint64_t j = 0; j < n_embd; j++) { + std::printf("%.5f ", emb_norm[j]); + } + std::printf("\n\n"); +#endif + } + + llama_batch_free(batch); + + return result; +} + +static std::string generate(llama_context * ctx, llama_sampler * smpl, const std::string & prompt, bool stream) { + std::string result; + + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + llama_token eos_token = llama_vocab_eos(vocab); + + llama_memory_clear(llama_get_memory(ctx), true); + llama_set_causal_attn(ctx, true); + + llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1); + + std::vector inputs = common_tokenize(vocab, prompt, false, true); + int32_t i_current_token = 0; + + while (true) { + common_batch_clear(bat); + { + const int32_t n_inputs = inputs.size(); + + for (int32_t i = 0; i < n_inputs; i++) { + common_batch_add(bat, inputs[i], i_current_token++, { 0 }, i == n_inputs - 1); + } + } + inputs.clear(); + + llama_decode(ctx, bat); + + llama_token token = llama_sampler_sample(smpl, ctx, bat.n_tokens - 1); + + if (token == eos_token) { + break; + } + + std::string piece = common_token_to_piece(ctx, token); + if (stream) { + std::printf("%s", piece.c_str()); + std::fflush(stdout); + } + + inputs.push_back(token); + + result += piece; + } + + if (stream) { + std::printf("\n"); + } + + llama_batch_free(bat); + + return result; +} + +static std::string gritlm_instruction(const std::string & instruction) { + return !instruction.empty() ? "<|user|>\n" + instruction + "\n<|embed|>\n" : "<|embed|>\n"; +} + +int main(int argc, char * argv[]) { + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { + return 1; + } + + common_init(); + + llama_model_params mparams = common_model_params_to_llama(params); + llama_context_params cparams = common_context_params_to_llama(params); + + cparams.embeddings = true; + + llama_backend_init(); + + llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams); + + // create generation context + llama_context * ctx = llama_init_from_model(model, cparams); + + auto sparams = llama_sampler_chain_default_params(); + + sparams.no_perf = false; + + llama_sampler * smpl = llama_sampler_chain_init(sparams); + + llama_sampler_chain_add(smpl, llama_sampler_init_greedy()); + + // ### Embedding/Representation ### + // samples taken from: https://github.com/ContextualAI/gritlm#basic + { + const std::string instruction = "Given a scientific paper title, retrieve the paper's abstract"; + + const std::vector queries = { + "Bitcoin: A Peer-to-Peer Electronic Cash System", + "Generative Representational Instruction Tuning", + }; + + const std::vector documents = { + "A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.", + "All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8X7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm.", + }; + + // No need to add instruction for retrieval documents + const std::vector> d_rep = encode(ctx, documents, gritlm_instruction("")); + const std::vector> q_rep = encode(ctx, queries, gritlm_instruction(instruction)); + + const int n_embd = llama_model_n_embd(model); + + const float cosine_sim_q0_d0 = common_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd); + const float cosine_sim_q0_d1 = common_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd); + const float cosine_sim_q1_d0 = common_embd_similarity_cos(q_rep[1].data(), d_rep[0].data(), n_embd); + const float cosine_sim_q1_d1 = common_embd_similarity_cos(q_rep[1].data(), d_rep[1].data(), n_embd); + + std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0); + std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1); + std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[0].c_str(), cosine_sim_q1_d0); + std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[1].c_str(), cosine_sim_q1_d1); + } + + llama_set_embeddings(ctx, false); + + // ### Generation ### + // GritLM models are not finetuned with system prompts, as you can just include system-like instructions together with your user instruction + { + const std::string prompt = "<|user|>\nPlease write me a poem about my recent hike of Mt. Fuji at midnight in the style of Shakespeare.\n<|assistant|>\n"; + std::string response = generate(ctx, smpl, prompt, true); + } + + llama_sampler_free(smpl); + llama_free(ctx); + llama_model_free(model); + llama_backend_free(); + + return 0; +} diff --git a/llama.cpp/examples/jeopardy/README.md b/llama.cpp/examples/jeopardy/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a47431699c1041c23a32a7169075a950e14ece05 --- /dev/null +++ b/llama.cpp/examples/jeopardy/README.md @@ -0,0 +1,21 @@ +# llama.cpp/example/jeopardy + +This is pretty much just a straight port of aigoopy/llm-jeopardy/ with an added graph viewer. + +The jeopardy test can be used to compare the fact knowledge of different models and compare them to each other. This is in contrast to some other tests, which test logical deduction, creativity, writing skills, etc. + + +Step 1: Open jeopardy.sh and modify the following: +``` +MODEL=(path to your model) +MODEL_NAME=(name of your model) +prefix=(basically, if you use vicuna it's Human: , if you use something else it might be User: , etc) +opts=(add -instruct here if needed for your model, or anything else you want to test out) +``` +Step 2: Run `jeopardy.sh` from the llama.cpp folder + +Step 3: Repeat steps 1 and 2 until you have all the results you need. + +Step 4: Run `graph.py`, and follow the instructions. At the end, it will generate your final graph. + +Note: The Human bar is based off of the full, original 100 sample questions. If you modify the question count or questions, it will not be valid. diff --git a/llama.cpp/examples/jeopardy/graph.py b/llama.cpp/examples/jeopardy/graph.py new file mode 100644 index 0000000000000000000000000000000000000000..431444227f98bf707a265dfc49b97698553bbf02 --- /dev/null +++ b/llama.cpp/examples/jeopardy/graph.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +import matplotlib.pyplot as plt +import os +import csv + +labels = [] +numbers = [] +numEntries = 1 + +rows = [] + + +def bar_chart(numbers, labels, pos): + plt.bar(pos, numbers, color='blue') + plt.xticks(ticks=pos, labels=labels) + plt.title("Jeopardy Results by Model") + plt.xlabel("Model") + plt.ylabel("Questions Correct") + plt.show() + + +def calculatecorrect(): + directory = os.fsencode("./examples/jeopardy/results/") + csv_reader = csv.reader(open("./examples/jeopardy/qasheet.csv", 'rt'), delimiter=',') + for row in csv_reader: + global rows + rows.append(row) + for listing in os.listdir(directory): + filename = os.fsdecode(listing) + if filename.endswith(".txt"): + file = open("./examples/jeopardy/results/" + filename, "rt") + global labels + global numEntries + global numbers + labels.append(filename[:-4]) + numEntries += 1 + i = 1 + totalcorrect = 0 + for line in file.readlines(): + if line.strip() != "------": + print(line) + else: + print("Correct answer: " + rows[i][2] + "\n") + i += 1 + print("Did the AI get the question right? (y/n)") + if input() == "y": + totalcorrect += 1 + numbers.append(totalcorrect) + + +if __name__ == '__main__': + calculatecorrect() + pos = list(range(numEntries)) + labels.append("Human") + numbers.append(48.11) + bar_chart(numbers, labels, pos) + print(labels) + print(numbers) diff --git a/llama.cpp/examples/jeopardy/jeopardy.sh b/llama.cpp/examples/jeopardy/jeopardy.sh new file mode 100644 index 0000000000000000000000000000000000000000..bbba9e9fc7c3e3860a136c49588f619fa140f6d9 --- /dev/null +++ b/llama.cpp/examples/jeopardy/jeopardy.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash +set -e + +MODEL=./models/ggml-vicuna-13b-1.1-q4_0.bin +MODEL_NAME=Vicuna + +# exec options +prefix="Human: " # Ex. Vicuna uses "Human: " +opts="--temp 0 -n 80" # additional flags +nl=' +' +introduction="You will be playing a game of Jeopardy. Simply answer the question in the correct format (Ex. What is Paris, or Who is George Washington)." + +# file options +question_file=./examples/jeopardy/questions.txt +touch ./examples/jeopardy/results/$MODEL_NAME.txt +output_file=./examples/jeopardy/results/$MODEL_NAME.txt + +counter=1 + +echo 'Running' +while IFS= read -r question +do + exe_cmd="./llama-cli -p "\"$prefix$introduction$nl$prefix$question\"" "$opts" -m ""\"$MODEL\""" >> ""\"$output_file\"" + echo $counter + echo "Current Question: $question" + eval "$exe_cmd" + echo -e "\n------" >> $output_file + counter=$((counter+1)) +done < "$question_file" diff --git a/llama.cpp/examples/jeopardy/qasheet.csv b/llama.cpp/examples/jeopardy/qasheet.csv new file mode 100644 index 0000000000000000000000000000000000000000..5dae1b65ee1194622757967428e6f045b76add42 --- /dev/null +++ b/llama.cpp/examples/jeopardy/qasheet.csv @@ -0,0 +1,103 @@ +Index,Original Category,Original Correct Question,Model Prompt +1,The Oscars,Who is John Williams?,Which actor Born in 1932 was the son of a percussionist in the CBS radio orchestra has been nominated for 53 Oscars? +2,English Literature,What is Paradise Lost?,"What work in English Literature says: 'The mind is its own place, & in itself can make a heaven of hell, a hell of heaven. What matter where, if I be still the same'?" +3,Writers’ Lesser-Known Works,Who is Niccolò Machiavelli?,"Known for more philosophical works, he wrote the play 'La Mandragola', in which Florentines are rewarded for immoral actions?" +4,Exploration,What is Easter Island (Rapa Nui)?,"James Cook's account of a 1774 visit where records an object 'near 27 feet long, and upwards of 8 feet over the breast or shoulders'?" +5,The Bill of Rights,What is the Eighth Amendment?,England's 'Bloody Assizes' & a 1685 life sentence for perjury were 2 main origins of which amendment to the U.S. Constitution? +6,Nobel Peace Prize Winners,Who are Nelson Mandela & Desmond Tutu?,"Which nobel peace price winners each lived at times on Vilakazi St. in Soweto , so it claims to be the world's only street home to 2 Nobel Peace Prize winners?" +7,Famous Names,Who is Walt Disney?,"In 1966, the year of who's death did he share plans for an experimental prototype community in Florida?" +8,Geography,What is Colombia?,"Of the 13 nations through which the Equator passes, what is the only one whose coastline borders the Caribbean Sea?" +9,Fashion History,What are rhinestones?,"Which decorative items in fashion history get their name from their origin in the port city of Strasbourg, on the border of France & Germany?" +10,Movies of the ’80s,What is Driving Miss Daisy?,What 1980's movie is based on an off-Broadway play with just 3 characters and won the Best Picture Oscar & the actors in all 3 roles were nominated? +11,Novelists,Who is John Grisham?,"A 2012 book review for which novelist noted subjects that 'sparked his ire': capital punishment, big tobacco & 'the plight of the unjustly convicted'?" +12,20th Century Eponyms,What is the Maginot Line?,"A 1940 headline about what 20th Century Eponym included 'failure', 'liability when it came to offense' & 'stout hearts no match for tanks'?" +13,City History,What is Stockholm?,"Over 700 years after its traditional 1252 founding date, what port city became associated with a psychological response?" +14,Brand Names,What is Jacuzzi?,"The success of what brand has its roots with a hydrotherapy pump its cofounder created for his son, who had arthritis?" +15,American Authors,Who is Washington Irving?,"In a periodical in 1807, what American Author called New York City 'Gotham, Gotham! Most enlightened of cities'?" +16,Symbols,What is “less than”?,What symbol is a rotated V in math and a feeling of some marginalized or underrepresented people in society? +17,Movie Theme Songs,Who is James Bond?,"Monty Norman, the composer of what character's theme, said the staccato riff conveyed sexiness, mystery & ruthlessness?" +18,American Novelists,Who is Joseph Heller?,"What American Novelist served with an airman named Yohannan in World War II & despite what readers might think, he said he enjoyed his service?" +19,Medieval Places,"What is Canterbury, England? (Canterbury Cathedral)","In what Medieval place did one of the participants in an 1170 event say, 'Let us away, knights; he will rise no more'?" +20,Countries of Africa,What is Morocco?,"At one time a province of the Roman Empire, what African country kingdom is known to Arabic scholars as Al-Maghrib Al-Aqsa, 'the far west'?" +21,Statehood,What is Wyoming?,Congress relented in 1890 after what prospective state said it would wait 100 years rather than come in without the women? +22,1980s Movies,What is Raiders of the Lost Ark?,"A writer & producer of what movie said he wanted it to be like a Western or James Bond film, 'only it takes place in the 30s'?" +23,Art Exhibitions,Who is Rembrandt?,In 1898 what's been called the first blockbuster art show was devoted to which artist & put on for Queen Wilhelmina's coronation? +24,Countries of the World,What is Mongolia?,"Part of the largest contiguous land empire during the 1200s & 1300s, today what is the world's second-largest landlocked country?" +25,Literature,What is “Howl”?,A 2006 book was titled 'The Poem That Changed America:' What 'Fifty Years Later'? +26,Invasions,Who is William of Orange?,"Backed by 14,000 troops, who invaded England to restore, in his words, its 'religion, laws, and liberties'?" +27,Landmarks,What is the Eiffel Tower?,"After its completion in the late 19th c., what was landmark was called 'a truly tragic street lamp' & a 'high & skinny pyramid of iron ladders'?" +28,Geographic Name’s the Same,What is Dover?,"The busiest passenger port in the U.K., what shares its name with a capital of one of the original 13 states?" +29,Names in the Bookstore,Who is Peter Mark Roget?,"This man made lists, perhaps to cope with depression; a set of lists he published in 1852 made whose name synonymous with a type of book?" +30,U.S. History,Who is Dr. Samuel Mudd?,"An 1869 presidential pardon was granted to which man, due in part to a plea by the Medical Society of Harford County, Maryland?" +31,American Literature,What is The Things They Carried?,"Letters, pocket knives, C rations & steel helmets are among the tangible items referred to in the title of what American literature modern war classic?" +32,Nonfiction,What is The Communist Manifesto,"What nonfiction book has the line, 'The discovery of America…opened up fresh ground for the rising bourgeoisie'?" +33, a new version was passed 81 years later,Laws in U.S. History,What is the Civil Rights Act?,,,,,,,,,,,,,,,,,,0, 2/3 +34,Names of Myth,Who is Helen of Troy?,"Whose brothers, Castor & Pollux, saved her after Theseus stole her away as a kid; a larger force would seek her later in life?" +35,African Countries,What is Sudan?,"Once Africa's largest country in area, what African Country dropped to third in 2011 when a portion of it declared independence?" +36,The Ancient World,What is Alexandria?,"The ancient writer Galen said books on ships arriving to what city's port were seized, originals kept & copies returned?" +37,Famous Names,Who is Andy Warhol?,"For a special 1970s cookbook, who provided one simple recipe–a can of Campbell's tomato soup & 2 cans of milk?" +38,People & Places,What is Guam?,"Thought to descend from people of Southeast Asia, the Chamorro make up what U.S. territory’s largest ethnic group?" +39,Current World Leaders,What is the Philippines?,"In office from 2022, the president of what country has taken so many foreign trips a play on his name is 'Ferdinand Magellan Jr.'?" +40,Writers & The South,Who is Tennessee Williams?,In 1939 which writer lived on Toulouse Street in the French Quarter & chose the professional name that bonded him to the South? +41,National Parks,What is Yellowstone?,"What National Park is named for a river indigenous people called Mi tse a-da-zi, translated by French-speaking trappers as 'Pierre Jaune'?" +42,Sports,Who are the Harlem Globetrotters?,"In 2010 who introduced the 4-point shot, 35 feet from the basket?" +43,The U.S. Military,What is “Top Gun”?,Losses over Asia in the 1960s led to the establishment of the program known as what at a San Diego naval base in 1969? +44,Art & Science,What is Halley’s Comet?,"A craft that visited what was named for Giotto, based on the story that 680 years earlier, the painter depicted it as the Star of Bethlehem?" +45,Words From World War I,What is “tank”?,"In World War I, 'Cistern' & 'reservoir' were suggested names for what secret invention, but the British preferred this less clumsy monosyllable?" +46,European History,What is Holy Roman Emperor?,"Until 1806, some German nobles included among their honors the title of 'Elector' for their role in selecting this personage?" +47,Theater History,Who is Peter Pan?,"In 1904, wearing a harness, actress Nina Boucicault became the first to play what character onstage?" +48,European Cities,What is Aachen?,"Alphabetically the first German city in encyclopedias, what was also the first one taken by the Allies in World War II?" +49,Word Origins,What is mantra?,This Sanskrit word referring to a spoken word or phrase comes from a word for 'to think'? +50,Inventions,What is barbed wire?,1917's 'Elements of Trench Warfare' said what Old West invention was 'difficult to destroy' & 'difficult to get through'? +51,World War II,What is Schindler’s list?,"Mimi Reinhard, who never learned to type using more than 2 fingers, produced what in World War II with 1,100 names, including hers?" +52, their offspring was the source of this mythical object,Mythology,What is the Golden Fleece? +53,Literature,What is Pride and Prejudice?,"Published in 2011, P.D. James' final novel, 'Death Comes to Pemberley', was a sequel to what novel from 200 years earlier?" +54, only these 2 west of the Mississippi River border each other,U.S. State Names,What are Oregon & Nevada? +55,Word Origins,What is passion?,"Originally relating to a story of suffering, what word now more commonly refers to strong emotion of any kind?" +56,World Cinema,What is La Vie en Rose?,"The 2007 biopic called 'La Môme' in France, meaning 'The Kid', was released in the U.S. under what other French title?" +57,History,What is Santa Maria?,"Returning home in 1493, Columbus stopped in the Azores at an island with what name, also something he'd lost off the Haiti coast?" +58,Landmarks,What is a kremlin?,Pskov & Nizhny Novgorod are 2 of the cities that have a fortress called what? +59,Foreign-Born Authors,Who is Vladimir Nabokov?,In the 1950s the New York Times said what author 'is writing about all lust' & his lecherous narrator 'is all of us'? +60,Astronomy & Geography,What is Capricorn?,"At the winter solstice, the sun is in Sagittarius; it once appeared in what constellation, giving a geographic feature its name?" +61,Television,What is Law & Order?,"Mike Post combined the sound of a slamming jail door, an anvil & 100 men stomping on a floor for what television series that debuted in 1990?" +62,British Landmarks,What is the Tower of London?,"Like Sir Thomas More, 3 16th century English queens are buried at what British location?" +63,Early American History,What are witches?,"In 1692 Increase Mather wrote, 'It were better that ten suspected' of these who 'escape, than that one innocent person … be condemned'?" +64,Geography Mnemonics,What are Arkansas and Louisiana?,"The Geography Mnemonic Mimal, sometimes said to be the silhouette of a chef or elf, stands for Minnesota, Iowa, Missouri, and what other 2 states?" +65,Business Milestones,What is the Ford Model T?,"What was first sold in 1908, at a price equivalent to about $27,000 today?" +66,In The Bookstore,Who is Tom Clancy?,The name of what author dead since 2013 now appears on books written by a former U.S. marshal & a former Apache helicopter pilot? +67,Historic Art,What is the Bayeux Tapestry?,The artwork once known in France as 'la tapisserie de la Reine Mathilde' is better known as what? +68,Pop Stars,Who is Madonna?,In 2022 which pop star became the first woman to have a Billboard Top 10 album in 5 decades starting with the 1980s? +69,Classic Tale Characters,Who is Scheherazade?,"In one 19th century translation, what female classic tale character 'perceived the dawn of day and ceased' speaking nearly 1,000 times?" +70,USA,What is Jack Daniel’s?,"Ironically, though what company founded in the 1860s is Moore County, Tennessee's largest employer, Moore is a dry county?" +71,Historic People,Who was William Bligh?,"After a 1789 event, who wrote, 'My first determination was to seek a supply of…water at Tofoa, & afterwards to sail for Tongataboo'?" +72,The Movies,What is The Godfather?,Laurence Olivier & Ernest Borgnine were considered for the lead role & Sergio Leone to direct for what film that turned 50 in 2022? +73,Continental Geography,What is Colombia?,"Until a 1903 secession, what country's contiguous territory spanned 2 continents?" +74,Foreign-Born Authors,Who is Isabel Allende?,"Early in her career which foreign-born author translated romance novels into Spanish, often changing the dialogue to make the heroines smarter?" +75,Historic Crimes,What is the Mona Lisa?,"Saying it was stolen by Napoleon, self-styled Italian patriot Vincenzo Peruggia took what in 1911?" +76,U.S. Bodies of Water,What is Lake Mead?,"Continuing a downward trend, in July 2022 what US body of water was at 27% capacity, its lowest level since 1937 when it was first being filled?" +77,Gods & Goddesses,Who is Aurora (or Eos)?,"Each morning which goddess began her ride in her chariot across the sky ahead of her brother Sol, or Helios?" +78,America At War,What is the Battle of New Orleans?,"Until the Civil War, the Jan. 8 date of what American battle of dubious military importance but big morale value was a national holiday?" +79,Children’s Books,What is The Velveteen Rabbit?,"Which children's book title character is told 'By the time you are real, most of your hair has been loved off your eyes drop out & you get shabby'?" +80,TV Finales,What is Grace and Frankie?,"In a TV reunion over 40 years in the making, Dolly Parton appeared as an angel named Agnes in the final episode of what comedy in 2022?" +81,American Poems,Who is Evangeline?,"In an 1847 American poem what character sees her town of Grand-Pré burned, but finally reunites with her beau for a kiss before his death?" +82,Famous Names,Who is Banksy?,"In 2001 who published a book called 'Banging Your Head Against a Brick Wall'; in 2002, 'Existencilism'?" +83,Children’s Lit,What is Charlotte’s Web?,The title object of what childrens book 'never looked more beautiful each strand held dozens of bright drops of early morning dew'? +84,Classic Songs,What is “Here Comes Santa Claus”?,The shouts of excited children at a 1946 holiday parade are said to have inspired what perennial classic song favorite? +85,Brand Names,What are Milk Duds?,"Unable to make what candies perfectly round, the confectioner embraced this flawed name for the product?" +86,Countries of the World,What is Italy?,"What country is home to 58 UNESCO World Heritage Sites, more than any other country; the sites include a volcano & a lagoon?" +87,Action Movies,What is Die Hard?,"What action movie's last line is 'If this is their idea of Christmas, I gotta be here for New Years'?" +88,Presidential Facts,Who is Woodrow Wilson?,Only 3 presidents have married while in office— John Tyler was the first & which one was the last? +89,19th Century Americans,Who is Frederick Douglass?,"Demonstrating the dignity & humanity of Black Americans, who sat for 160 known photographs, the most of any American in the 19th century?" +90,Latin Phrases,What is “quid pro quo”?,"Originally, which Latin 3-word phrase referred to when a doctor or apothecary substituted one medicine for another?" +91,1970s Movies,What is Monty Python and the Holy Grail?,The 1975 premiere of what movie comedy advertised free coconuts for the first thousand in the audience? +92,Name’s The Same,What is Manhattan?,"A cocktail, an island & a WWII venture originally called 'Development of Substitute Materials' all bear what name?" +93,U.S. Presidents,Who is Calvin Coolidge?,"Which US President was sworn in twice as President within 2 years, first by his father & then later by a former U.S. President?" +94,Plays,What is The Tempest?,A 1609 story in which an exiled king of Bulgaria creates a sea palace with his magic may have inspired the plot of what play? +95,Landmarks,What is the Berlin Wall?,"In 2009, during a 20th anniversary celebration, what landmark was called 'an edifice of fear. On Nov. 9, it became a place of joy'?" +96,World Capitals,"What is Vienna, Austria?","Among what world capital's nicknames are the 'City of Classical Music' &, possibly in honor of a famous resident from 1860 to 1938, the 'City of Dreams'?" +97,Language & Its Meanings,What is a night owl?,"Now meaning someone with nocturnal habits, what catches a sleeping dove in Shakespeare's 'Lucrece'?" +98,Flags of Our Hemisphere,What is Brazil?,"The stars on what country's flag represent states, 26 of them; unlike the USA's, its 'federal district' gets its own 27th star?" +99,Names in U.S. History,Who is Oliver Brown?,What father was the only man among the 13 plaintiffs in a US class-action case filed in 1951? +100,Children’s Authors,"Who is Sarah? (from Sarah, Plain and Tall)","Reversing the story of what heroine she created, childrens author Patricia Maclachlan was born on the prairie but spent much of her life in New England?" +,,, +TOTALS,,, diff --git a/llama.cpp/examples/jeopardy/questions.txt b/llama.cpp/examples/jeopardy/questions.txt new file mode 100644 index 0000000000000000000000000000000000000000..05a6d1da9b34153ba26c24e5d0e16d22c72e5f3d --- /dev/null +++ b/llama.cpp/examples/jeopardy/questions.txt @@ -0,0 +1,100 @@ +Which man born in 1932 was the son of a percussionist in the CBS radio orchestra has been nominated for 53 Oscars? +What work in English Literature says: 'The mind is its own place, & in itself can make a heaven of hell, a hell of heaven. What matter where, if I be still the same'? +Known for more philosophical works, he wrote the play 'La Mandragola', in which Florentines are rewarded for immoral actions? +James Cook's account of a 1774 visit where records an object 'near 27 feet long, and upwards of 8 feet over the breast or shoulders'? +England's 'Bloody Assizes' & a 1685 life sentence for perjury were 2 main origins of which amendment to the U.S. Constitution? +Which nobel peace price winners each lived at times on Vilakazi St. in Soweto , so it claims to be the world's only street home to 2 Nobel Peace Prize winners? +In 1966, the year of who's death did he share plans for an experimental prototype community in Florida? +Of the 13 nations through which the Equator passes, what is the only one whose coastline borders the Caribbean Sea? +Which decorative items in fashion history get their name from their origin in the port city of Strasbourg, on the border of France & Germany? +What 1980's movie is based on an off-Broadway play with just 3 characters and won the Best Picture Oscar & the actors in all 3 roles were nominated? +A 2012 book review for which novelist noted subjects that 'sparked his ire': capital punishment, big tobacco & 'the plight of the unjustly convicted'? +A 1940 headline about what 20th Century Eponym included 'failure', 'liability when it came to offense' & 'stout hearts no match for tanks'? +Over 700 years after its traditional 1252 founding date, what port city became associated with a psychological response? +The success of what brand has its roots with a hydrotherapy pump its cofounder created for his son, who had arthritis? +In a periodical in 1807, what American Author called New York City 'Gotham, Gotham! Most enlightened of cities'? +What symbol is a rotated V in math and a feeling of some marginalized or underrepresented people in society? +Monty Norman, the composer of what character's theme, said the staccato riff conveyed sexiness, mystery & ruthlessness? +What American Novelist served with an airman named Yohannan in World War II & despite what readers might think, he said he enjoyed his service? +In what Medieval place did one of the participants in an 1170 event say, 'Let us away, knights; he will rise no more'? +At one time a province of the Roman Empire, what African country kingdom is known to Arabic scholars as Al-Maghrib Al-Aqsa, 'the far west'? +Congress relented in 1890 after what prospective state said it would wait 100 years rather than come in without the women? +A writer & producer of what movie said he wanted it to be like a Western or James Bond film, 'only it takes place in the 30s'? +In 1898 what's been called the first blockbuster art show was devoted to which artist & put on for Queen Wilhelmina's coronation? +Part of the largest contiguous land empire during the 1200s & 1300s, today what is the world's second-largest landlocked country? +A 2006 book was titled 'The Poem That Changed America:' What 'Fifty Years Later'? +Backed by 14,000 troops, who invaded England to restore, in his words, its 'religion, laws, and liberties'? +After its completion in the late 19th c., what was landmark was called 'a truly tragic street lamp' & a 'high & skinny pyramid of iron ladders'? +The busiest passenger port in the U.K., what shares its name with a capital of one of the original 13 states? +This man made lists, perhaps to cope with depression; a set of lists he published in 1852 made whose name synonymous with a type of book? +An 1869 presidential pardon was granted to which man, due in part to a plea by the Medical Society of Harford County, Maryland? +Letters, pocket knives, C rations & steel helmets are among the tangible items referred to in the title of what American literature modern war classic? +What nonfiction book has the line, 'The discovery of America…opened up fresh ground for the rising bourgeoisie'? +A radical Republican championed what 1875 act but the Supreme Court struck it down in 1883; a new version was passed 81 years later? +Whose brothers, Castor & Pollux, saved her after Theseus stole her away as a kid; a larger force would seek her later in life? +Once Africa's largest country in area, what African Country dropped to third in 2011 when a portion of it declared independence? +The ancient writer Galen said books on ships arriving to what city's port were seized, originals kept & copies returned? +For a special 1970s cookbook, who provided one simple recipe–a can of Campbell's tomato soup & 2 cans of milk? +Thought to descend from people of Southeast Asia, the Chamorro make up what U.S. territory’s largest ethnic group? +In office from 2022, the president of what country has taken so many foreign trips a play on his name is 'Ferdinand Magellan Jr.'? +In 1939 which writer lived on Toulouse Street in the French Quarter & chose the professional name that bonded him to the South? +What National Park is named for a river indigenous people called Mi tse a-da-zi, translated by French-speaking trappers as 'Pierre Jaune'? +In 2010 who introduced the 4-point shot, 35 feet from the basket? +Losses over Asia in the 1960s led to the establishment of the program known as what at a San Diego naval base in 1969? +A craft that visited what was named for Giotto, based on the story that 680 years earlier, the painter depicted it as the Star of Bethlehem? +In World War I, 'Cistern' & 'reservoir' were suggested names for what secret invention, but the British preferred this less clumsy monosyllable? +Until 1806, some German nobles included among their honors the title of 'Elector' for their role in selecting this personage? +In 1904, wearing a harness, actress Nina Boucicault became the first to play what character onstage? +Alphabetically the first German city in encyclopedias, what was also the first one taken by the Allies in World War II? +This Sanskrit word referring to a spoken word or phrase comes from a word for 'to think'? +1917's 'Elements of Trench Warfare' said what Old West invention was 'difficult to destroy' & 'difficult to get through'? +Mimi Reinhard, who never learned to type using more than 2 fingers, produced what in World War II with 1,100 names, including hers? +Poseidon carried off the maiden Theophane & turned her into a ewe; their offspring was the source of what mythical object? +Published in 2011, P.D. James' final novel, 'Death Comes to Pemberley', was a sequel to what novel from 200 years earlier? +5 U.S. states have 6-letter names; only which 2 west of the Mississippi River border each other? +Originally relating to a story of suffering, what word now more commonly refers to strong emotion of any kind? +The 2007 biopic called 'La Môme' in France, meaning 'The Kid', was released in the U.S. under what other French title? +Returning home in 1493, Columbus stopped in the Azores at an island with what name, also something he'd lost off the Haiti coast? +Pskov & Nizhny Novgorod are 2 of the cities that have a fortress called what? +In the 1950s the New York Times said what author 'is writing about all lust' & his lecherous narrator 'is all of us'? +At the winter solstice, the sun is in Sagittarius; it once appeared in what constellation, giving a geographic feature its name? +Mike Post combined the sound of a slamming jail door, an anvil & 100 men stomping on a floor for what television series that debuted in 1990? +Like Sir Thomas More, 3 16th century English queens are buried at what British location? +In 1692 Increase Mather wrote, 'It were better that ten suspected' of these who 'escape, than that one innocent person be condemned'? +The Geography Mnemonic Mimal, sometimes said to be the silhouette of a chef or elf, stands for Minnesota, Iowa, Missouri, and what other 2 states? +What was first sold in 1908, at a price equivalent to about $27,000 today? +The name of what author dead since 2013 now appears on books written by a former U.S. marshal & a former Apache helicopter pilot? +The artwork once known in France as 'la tapisserie de la Reine Mathilde' is better known as what? +In 2022 which pop star became the first woman to have a Billboard Top 10 album in 5 decades starting with the 1980s? +In one 19th century translation, what female classic tale character 'perceived the dawn of day and ceased' speaking nearly 1,000 times? +Ironically, though what company founded in the 1860s is Moore County, Tennessee's largest employer, Moore is a dry county? +After a 1789 event, who wrote, 'My first determination was to seek a supply of…water at Tofoa, & afterwards to sail for Tongataboo'? +Laurence Olivier & Ernest Borgnine were considered for the lead role & Sergio Leone to direct for what film that turned 50 in 2022? +Until a 1903 secession, what country's contiguous territory spanned 2 continents? +Early in her career which foreign-born author translated romance novels into Spanish, often changing the dialogue to make the heroines smarter? +Saying it was stolen by Napoleon, self-styled Italian patriot Vincenzo Peruggia took what in 1911? +Continuing a downward trend, in July 2022 what US body of water was at 27% capacity, its lowest level since 1937 when it was first being filled? +Each morning which goddess began her ride in her chariot across the sky ahead of her brother Sol, or Helios? +Until the Civil War, the Jan. 8 date of what American battle of dubious military importance but big morale value was a national holiday? +Which children's book title character is told 'By the time you are real, most of your hair has been loved off your eyes drop out & you get shabby'? +In a TV reunion over 40 years in the making, Dolly Parton appeared as an angel named Agnes in the final episode of what comedy in 2022? +In an 1847 American poem what character sees her town of Grand-Pré burned, but finally reunites with her beau for a kiss before his death? +In 2001 who published a book called 'Banging Your Head Against a Brick Wall'; in 2002, 'Existencilism'? +The title object of what childrens book 'never looked more beautiful each strand held dozens of bright drops of early morning dew'? +The shouts of excited children at a 1946 holiday parade are said to have inspired what perennial classic song favorite? +Unable to make what candies perfectly round, the confectioner embraced this flawed name for the product? +What country is home to 58 UNESCO World Heritage Sites, more than any other country; the sites include a volcano & a lagoon? +What action movie's last line is 'If this is their idea of Christmas, I gotta be here for New Years'? +Only 3 presidents have married while in office— John Tyler was the first & which one was the last? +Demonstrating the dignity & humanity of Black Americans, who sat for 160 known photographs, the most of any American in the 19th century? +Originally, which Latin 3-word phrase referred to when a doctor or apothecary substituted one medicine for another? +The 1975 premiere of what movie comedy advertised free coconuts for the first thousand in the audience? +A cocktail, an island & a WWII venture originally called 'Development of Substitute Materials' all bear what name? +Which US President was sworn in twice as President within 2 years, first by his father & then later by a former U.S. President? +A 1609 story in which an exiled king of Bulgaria creates a sea palace with his magic may have inspired the plot of what play? +In 2009, during a 20th anniversary celebration, what landmark was called 'an edifice of fear. On Nov. 9, it became a place of joy'? +Among what world capital's nicknames are the 'City of Classical Music' &, possibly in honor of a famous resident from 1860 to 1938, the 'City of Dreams'? +Now meaning someone with nocturnal habits, what catches a sleeping dove in Shakespeare's 'Lucrece'? +The stars on what country's flag represent states, 26 of them; unlike the USA's, its 'federal district' gets its own 27th star? +What father was the only man among the 13 plaintiffs in a US class-action case filed in 1951? +Reversing the story of what heroine she created, childrens author Patricia Maclachlan was born on the prairie but spent much of her life in New England? diff --git a/llama.cpp/examples/json_schema_pydantic_example.py b/llama.cpp/examples/json_schema_pydantic_example.py new file mode 100644 index 0000000000000000000000000000000000000000..66221425ca121e83b5067769dede329703442340 --- /dev/null +++ b/llama.cpp/examples/json_schema_pydantic_example.py @@ -0,0 +1,82 @@ +# Usage: +#! ./llama-server -m some-model.gguf & +#! pip install pydantic +#! python json_schema_pydantic_example.py + +from pydantic import BaseModel, Field, TypeAdapter +from annotated_types import MinLen +from typing import Annotated, List, Optional +import json, requests + +if True: + + def create_completion(*, response_model=None, endpoint="http://localhost:8080/v1/chat/completions", messages, **kwargs): + ''' + Creates a chat completion using an OpenAI-compatible endpoint w/ JSON schema support + (llama.cpp server, llama-cpp-python, Anyscale / Together...) + + The response_model param takes a type (+ supports Pydantic) and behaves just as w/ Instructor (see below) + ''' + response_format = None + type_adapter = None + + if response_model: + type_adapter = TypeAdapter(response_model) + schema = type_adapter.json_schema() + messages = [{ + "role": "system", + "content": f"You respond in JSON format with the following schema: {json.dumps(schema, indent=2)}" + }] + messages + response_format={"type": "json_object", "schema": schema} + + data = requests.post(endpoint, headers={"Content-Type": "application/json"}, + json=dict(messages=messages, response_format=response_format, **kwargs)).json() + if 'error' in data: + raise Exception(data['error']['message']) + + content = data["choices"][0]["message"]["content"] + return type_adapter.validate_json(content) if type_adapter else content + +else: + + # This alternative branch uses Instructor + OpenAI client lib. + # Instructor support streamed iterable responses, retry & more. + # (see https://python.useinstructor.com/) + #! pip install instructor openai + import instructor, openai + client = instructor.patch( + openai.OpenAI(api_key="123", base_url="http://localhost:8080"), + mode=instructor.Mode.JSON_SCHEMA) + create_completion = client.chat.completions.create + + +if __name__ == '__main__': + + class QAPair(BaseModel): + class Config: + extra = 'forbid' # triggers additionalProperties: false in the JSON schema + question: str + concise_answer: str + justification: str + stars: Annotated[int, Field(ge=1, le=5)] + + class PyramidalSummary(BaseModel): + class Config: + extra = 'forbid' # triggers additionalProperties: false in the JSON schema + title: str + summary: str + question_answers: Annotated[List[QAPair], MinLen(2)] + sub_sections: Optional[Annotated[List['PyramidalSummary'], MinLen(2)]] + + print("# Summary\n", create_completion( + model="...", + response_model=PyramidalSummary, + messages=[{ + "role": "user", + "content": f""" + You are a highly efficient corporate document summarizer. + Create a pyramidal summary of an imaginary internal document about our company processes + (starting high-level, going down to each sub sections). + Keep questions short, and answers even shorter (trivia / quizz style). + """ + }])) diff --git a/llama.cpp/examples/json_schema_to_grammar.py b/llama.cpp/examples/json_schema_to_grammar.py new file mode 100644 index 0000000000000000000000000000000000000000..6c8228e01b1b9511caa03faee56dafe506e7609d --- /dev/null +++ b/llama.cpp/examples/json_schema_to_grammar.py @@ -0,0 +1,814 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import itertools +import json +import re +import sys +from typing import Any, List, Optional, Set, Tuple, Union + +def _build_repetition(item_rule, min_items, max_items, separator_rule=None): + + if max_items == 0: + return "" + + if min_items == 0 and max_items == 1: + return f'{item_rule}?' + + if not separator_rule: + if min_items == 1 and max_items is None: + return f'{item_rule}+' + elif min_items == 0 and max_items is None: + return f'{item_rule}*' + else: + return f'{item_rule}{{{min_items},{max_items if max_items is not None else ""}}}' + + result = item_rule + ' ' + _build_repetition(f'({separator_rule} {item_rule})', min_items - 1 if min_items > 0 else 0, max_items - 1 if max_items is not None else None) + return f'({result})?' if min_items == 0 else result + +def _generate_min_max_int(min_value: Optional[int], max_value: Optional[int], out: list, decimals_left: int = 16, top_level: bool = True): + has_min = min_value != None + has_max = max_value != None + + def digit_range(from_char: str, to_char: str): + out.append("[") + if from_char == to_char: + out.append(from_char) + else: + out.append(from_char) + out.append("-") + out.append(to_char) + out.append("]") + + def more_digits(min_digits: int, max_digits: int): + out.append("[0-9]") + if min_digits == max_digits and min_digits == 1: + return + out.append("{") + out.append(str(min_digits)) + if max_digits != min_digits: + out.append(",") + if max_digits != sys.maxsize: + out.append(str(max_digits)) + out.append("}") + + def uniform_range(from_str: str, to_str: str): + i = 0 + while i < len(from_str) and from_str[i] == to_str[i]: + i += 1 + if i > 0: + out.append("\"") + out.append(from_str[:i]) + out.append("\"") + if i < len(from_str): + if i > 0: + out.append(" ") + sub_len = len(from_str) - i - 1 + if sub_len > 0: + from_sub = from_str[i+1:] + to_sub = to_str[i+1:] + sub_zeros = "0" * sub_len + sub_nines = "9" * sub_len + + to_reached = False + out.append("(") + if from_sub == sub_zeros: + digit_range(from_str[i], chr(ord(to_str[i]) - 1)) + out.append(" ") + more_digits(sub_len, sub_len) + else: + out.append("[") + out.append(from_str[i]) + out.append("] ") + out.append("(") + uniform_range(from_sub, sub_nines) + out.append(")") + if ord(from_str[i]) < ord(to_str[i]) - 1: + out.append(" | ") + if to_sub == sub_nines: + digit_range(chr(ord(from_str[i]) + 1), to_str[i]) + to_reached = True + else: + digit_range(chr(ord(from_str[i]) + 1), chr(ord(to_str[i]) - 1)) + out.append(" ") + more_digits(sub_len, sub_len) + if not to_reached: + out.append(" | ") + digit_range(to_str[i], to_str[i]) + out.append(" ") + uniform_range(sub_zeros, to_sub) + out.append(")") + else: + out.append("[") + out.append(from_str[i]) + out.append("-") + out.append(to_str[i]) + out.append("]") + + if has_min and has_max: + if min_value < 0 and max_value < 0: + out.append("\"-\" (") + _generate_min_max_int(-max_value, -min_value, out, decimals_left, top_level=True) + out.append(")") + return + + if min_value < 0: + out.append("\"-\" (") + _generate_min_max_int(0, -min_value, out, decimals_left, top_level=True) + out.append(") | ") + min_value = 0 + + min_s = str(min_value) + max_s = str(max_value) + min_digits = len(min_s) + max_digits = len(max_s) + + for digits in range(min_digits, max_digits): + uniform_range(min_s, "9" * digits) + min_s = "1" + "0" * digits + out.append(" | ") + uniform_range(min_s, max_s) + return + + less_decimals = max(decimals_left - 1, 1) + + if has_min: + if min_value < 0: + out.append("\"-\" (") + _generate_min_max_int(None, -min_value, out, decimals_left, top_level=False) + out.append(") | [0] | [1-9] ") + more_digits(0, decimals_left - 1) + elif min_value == 0: + if top_level: + out.append("[0] | [1-9] ") + more_digits(0, less_decimals) + else: + more_digits(1, decimals_left) + elif min_value <= 9: + c = str(min_value) + range_start = '1' if top_level else '0' + if c > range_start: + digit_range(range_start, chr(ord(c) - 1)) + out.append(" ") + more_digits(1, less_decimals) + out.append(" | ") + digit_range(c, "9") + out.append(" ") + more_digits(0, less_decimals) + else: + min_s = str(min_value) + length = len(min_s) + c = min_s[0] + + if c > "1": + digit_range("1" if top_level else "0", chr(ord(c) - 1)) + out.append(" ") + more_digits(length, less_decimals) + out.append(" | ") + digit_range(c, c) + out.append(" (") + _generate_min_max_int(int(min_s[1:]), None, out, less_decimals, top_level=False) + out.append(")") + if c < "9": + out.append(" | ") + digit_range(chr(ord(c) + 1), "9") + out.append(" ") + more_digits(length - 1, less_decimals) + return + + if has_max: + if max_value >= 0: + if top_level: + out.append("\"-\" [1-9] ") + more_digits(0, less_decimals) + out.append(" | ") + _generate_min_max_int(0, max_value, out, decimals_left, top_level=True) + else: + out.append("\"-\" (") + _generate_min_max_int(-max_value, None, out, decimals_left, top_level=False) + out.append(")") + return + + raise RuntimeError("At least one of min_value or max_value must be set") + +class BuiltinRule: + def __init__(self, content: str, deps: list | None = None): + self.content = content + self.deps = deps or [] + +# Constraining spaces to prevent model "running away". +SPACE_RULE = '| " " | "\\n"{1,2} [ \\t]{0,20}' + +PRIMITIVE_RULES = { + 'boolean' : BuiltinRule('("true" | "false") space', []), + 'decimal-part' : BuiltinRule('[0-9]{1,16}', []), + 'integral-part': BuiltinRule('[0] | [1-9] [0-9]{0,15}', []), + 'number' : BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']), + 'integer' : BuiltinRule('("-"? integral-part) space', ['integral-part']), + 'value' : BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']), + 'object' : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']), + 'array' : BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']), + 'uuid' : BuiltinRule(r'"\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\"" space', []), + 'char' : BuiltinRule(r'[^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})', []), + 'string' : BuiltinRule(r'"\"" char* "\"" space', ['char']), + 'null' : BuiltinRule('"null" space', []), +} + +# TODO: support "uri", "email" string formats +STRING_FORMAT_RULES = { + 'date' : BuiltinRule('[0-9]{4} "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []), + 'time' : BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []), + 'date-time' : BuiltinRule('date "T" time', ['date', 'time']), + 'date-string' : BuiltinRule('"\\"" date "\\"" space', ['date']), + 'time-string' : BuiltinRule('"\\"" time "\\"" space', ['time']), + 'date-time-string': BuiltinRule('"\\"" date-time "\\"" space', ['date-time']), +} + +DOTALL = '[\\U00000000-\\U0010FFFF]' +DOT = '[^\\x0A\\x0D]' + +RESERVED_NAMES = set(["root", "dot", *PRIMITIVE_RULES.keys(), *STRING_FORMAT_RULES.keys()]) + +INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+') +GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]') +GRAMMAR_RANGE_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"\]\-\\]') +GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]'} + +NON_LITERAL_SET = set('|.()[]{}*+?') +ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set('^$.[]()|{}*+?') + + +class SchemaConverter: + def __init__(self, *, prop_order, allow_fetch, dotall, raw_pattern): + self._prop_order = prop_order + self._allow_fetch = allow_fetch + self._dotall = dotall + self._raw_pattern = raw_pattern + self._rules = { + 'space': SPACE_RULE, + } + self._refs = {} + self._refs_being_resolved = set() + + def _format_literal(self, literal): + escaped = GRAMMAR_LITERAL_ESCAPE_RE.sub( + lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)) or m.group(0), literal + ) + return f'"{escaped}"' + + def not_literal(self, literal: str, dotall: bool = True, maybe_escaped_underscores = False) -> str: + ''' + not_literal('a') -> '[^a]' + not_literal('abc') -> '([^a] | "a" ([^b] | "b" ([^c])?)?)?' + ''' + assert len(literal) > 0, 'Empty literal not supported' + def recurse(i: int): + c = literal[i] + if maybe_escaped_underscores and c == '_': + yield f'[^{c}\\\\]' + yield ' | ' + yield f'"\\\\"? "{c}"' + else: + yield f'[^{c}]' + if i < len(literal) - 1: + yield ' | ' + yield self._format_literal(c) + yield ' (' + yield from recurse(i + 1) + yield ')?' + + return ''.join(('(', *recurse(0), ')')) + + def _not_strings(self, strings): + class TrieNode: + def __init__(self): + self.children = {} + self.is_end_of_string = False + + def insert(self, string): + node = self + for c in string: + node = node.children.setdefault(c, TrieNode()) + node.is_end_of_string = True + + trie = TrieNode() + for s in strings: + trie.insert(s) + + char_rule = self._add_primitive('char', PRIMITIVE_RULES['char']) + out = ['["] ( '] + + def visit(node): + rejects = [] + first = True + for c in sorted(node.children.keys()): + child = node.children[c] + rejects.append(c) + if first: + first = False + else: + out.append(' | ') + out.append(f'[{c}]') + if child.children: + out.append(f' (') + visit(child) + out.append(')') + elif child.is_end_of_string: + out.append(f' {char_rule}+') + if node.children: + if not first: + out.append(' | ') + out.append(f'[^"{"".join(rejects)}] {char_rule}*') + visit(trie) + + out.append(f' ){"" if trie.is_end_of_string else "?"} ["] space') + return ''.join(out) + + def _add_rule(self, name, rule): + esc_name = INVALID_RULE_CHARS_RE.sub('-', name) + if esc_name not in self._rules or self._rules[esc_name] == rule: + key = esc_name + else: + i = 0 + while f'{esc_name}{i}' in self._rules and self._rules[f'{esc_name}{i}'] != rule: + i += 1 + key = f'{esc_name}{i}' + self._rules[key] = rule + return key + + def resolve_refs(self, schema: dict, url: str): + ''' + Resolves all $ref fields in the given schema, fetching any remote schemas, + replacing $ref with absolute reference URL and populating self._refs with the + respective referenced (sub)schema dictionaries. + ''' + def visit(n: dict): + if isinstance(n, list): + return [visit(x) for x in n] + elif isinstance(n, dict): + ref = n.get('$ref') + if ref is not None and ref not in self._refs: + if ref.startswith('https://'): + assert self._allow_fetch, 'Fetching remote schemas is not allowed (use --allow-fetch for force)' + import requests + + frag_split = ref.split('#') + base_url = frag_split[0] + + target = self._refs.get(base_url) + if target is None: + target = self.resolve_refs(requests.get(ref).json(), base_url) + self._refs[base_url] = target + + if len(frag_split) == 1 or frag_split[-1] == '': + return target + elif ref.startswith('#/'): + target = schema + ref = f'{url}{ref}' + n['$ref'] = ref + else: + raise ValueError(f'Unsupported ref {ref}') + + for sel in ref.split('#')[-1].split('/')[1:]: + assert target is not None and sel in target, f'Error resolving ref {ref}: {sel} not in {target}' + target = target[sel] + + self._refs[ref] = target + else: + for v in n.values(): + visit(v) + + return n + return visit(schema) + + def _generate_union_rule(self, name, alt_schemas): + return ' | '.join(( + self.visit(alt_schema, f'{name}{"-" if name else "alternative-"}{i}') + for i, alt_schema in enumerate(alt_schemas) + )) + + def _visit_pattern(self, pattern, name): + ''' + Transforms a regular expression pattern into a GBNF rule. + + Input: https://json-schema.org/understanding-json-schema/reference/regular_expressions + Output: https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md + + Unsupported features: negative/positive lookaheads, greedy/non-greedy modifiers. + + Mostly a 1:1 translation, except for {x} / {x,} / {x,y} quantifiers for which + we define sub-rules to keep the output lean. + ''' + + assert pattern.startswith('^') and pattern.endswith('$'), 'Pattern must start with "^" and end with "$"' + pattern = pattern[1:-1] + sub_rule_ids = {} + + i = 0 + length = len(pattern) + + def to_rule(s: tuple[str, bool]) -> str: + (txt, is_literal) = s + return "\"" + txt + "\"" if is_literal else txt + + def transform() -> tuple[str, bool]: + ''' + Parse a unit at index i (advancing it), and return its string representation + whether it's a literal. + ''' + nonlocal i + nonlocal pattern + nonlocal sub_rule_ids + + start = i + # For each component of this sequence, store its string representation and whether it's a literal. + # We only need a flat structure here to apply repetition operators to the last item, and + # to merge literals at the and (we're parsing grouped ( sequences ) recursively and don't treat '|' specially + # (GBNF's syntax is luckily very close to regular expressions!) + seq: list[tuple[str, bool]] = [] + + def get_dot(): + if self._dotall: + rule = DOTALL + else: + # Accept any character... except \n and \r line break chars (\x0A and \xOD) + rule = DOT + return self._add_rule(f'dot', rule) + + def join_seq(): + nonlocal seq + ret = [] + for is_literal, g in itertools.groupby(seq, lambda x: x[1]): + if is_literal: + ret.append((''.join(x[0] for x in g), True)) + else: + ret.extend(g) + if len(ret) == 1: + return ret[0] + return (' '.join(to_rule(x) for x in seq), False) + + while i < length: + c = pattern[i] + if c == '.': + seq.append((get_dot(), False)) + i += 1 + elif c == '(': + i += 1 + if i < length: + assert pattern[i] != '?', f'Unsupported pattern syntax "{pattern[i]}" at index {i} of /{pattern}/' + seq.append((f'({to_rule(transform())})', False)) + elif c == ')': + i += 1 + assert start > 0 and pattern[start-1] == '(', f'Unbalanced parentheses; start = {start}, i = {i}, pattern = {pattern}' + return join_seq() + elif c == '[': + square_brackets = c + i += 1 + while i < length and pattern[i] != ']': + if pattern[i] == '\\': + square_brackets += pattern[i:i+2] + i += 2 + else: + square_brackets += pattern[i] + i += 1 + assert i < length, f'Unbalanced square brackets; start = {start}, i = {i}, pattern = {pattern}' + square_brackets += ']' + i += 1 + seq.append((square_brackets, False)) + elif c == '|': + seq.append(('|', False)) + i += 1 + elif c in ('*', '+', '?'): + seq[-1] = (to_rule(seq[-1]) + c, False) + i += 1 + elif c == '{': + curly_brackets = c + i += 1 + while i < length and pattern[i] != '}': + curly_brackets += pattern[i] + i += 1 + assert i < length, f'Unbalanced curly brackets; start = {start}, i = {i}, pattern = {pattern}' + curly_brackets += '}' + i += 1 + nums = [s.strip() for s in curly_brackets[1:-1].split(',')] + min_times = 0 + max_times = None + try: + if len(nums) == 1: + min_times = int(nums[0]) + max_times = min_times + else: + assert len(nums) == 2 + min_times = int(nums[0]) if nums[0] else 0 + max_times = int(nums[1]) if nums[1] else None + except ValueError: + raise ValueError(f'Invalid quantifier {curly_brackets} in /{pattern}/') + + (sub, sub_is_literal) = seq[-1] + + if not sub_is_literal: + id = sub_rule_ids.get(sub) + if id is None: + id = self._add_rule(f'{name}-{len(sub_rule_ids) + 1}', sub) + sub_rule_ids[sub] = id + sub = id + + seq[-1] = (_build_repetition(f'"{sub}"' if sub_is_literal else sub, min_times, max_times), False) + else: + literal = '' + while i < length: + if pattern[i] == '\\' and i < length - 1: + next = pattern[i + 1] + if next in ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS: + i += 1 + literal += pattern[i] + i += 1 + else: + literal += pattern[i:i+2] + i += 2 + elif pattern[i] == '"' and not self._raw_pattern: + literal += '\\"' + i += 1 + elif pattern[i] not in NON_LITERAL_SET and \ + (i == length - 1 or literal == '' or pattern[i+1] == '.' or pattern[i+1] not in NON_LITERAL_SET): + literal += pattern[i] + i += 1 + else: + break + if literal: + seq.append((literal, True)) + + return join_seq() + + return self._add_rule( + name, + to_rule(transform()) if self._raw_pattern \ + else "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space") + + + def _resolve_ref(self, ref): + ref_name = ref.split('/')[-1] + if ref_name not in self._rules and ref not in self._refs_being_resolved: + self._refs_being_resolved.add(ref) + resolved = self._refs[ref] + ref_name = self.visit(resolved, ref_name) + self._refs_being_resolved.remove(ref) + return ref_name + + def _generate_constant_rule(self, value): + return self._format_literal(json.dumps(value)) + + def visit(self, schema, name): + schema_type = schema.get('type') + schema_format = schema.get('format') + rule_name = name + '-' if name in RESERVED_NAMES else name or 'root' + + if (ref := schema.get('$ref')) is not None: + return self._add_rule(rule_name, self._resolve_ref(ref)) + + elif 'oneOf' in schema or 'anyOf' in schema: + return self._add_rule(rule_name, self._generate_union_rule(name, schema.get('oneOf') or schema['anyOf'])) + + elif isinstance(schema_type, list): + return self._add_rule(rule_name, self._generate_union_rule(name, [{**schema, 'type': t} for t in schema_type])) + + elif 'const' in schema: + return self._add_rule(rule_name, self._generate_constant_rule(schema['const']) + ' space') + + elif 'enum' in schema: + rule = '(' + ' | '.join((self._generate_constant_rule(v) for v in schema['enum'])) + ') space' + return self._add_rule(rule_name, rule) + + elif schema_type in (None, 'object') and \ + ('properties' in schema or \ + ('additionalProperties' in schema and schema['additionalProperties'] is not True)): + required = set(schema.get('required', [])) + properties = list(schema.get('properties', {}).items()) + return self._add_rule(rule_name, self._build_object_rule(properties, required, name, schema.get('additionalProperties'))) + + elif schema_type in (None, 'object') and 'allOf' in schema: + required = set() + properties = [] + hybrid_name = name + def add_component(comp_schema, is_required): + if (ref := comp_schema.get('$ref')) is not None: + comp_schema = self._refs[ref] + + if 'properties' in comp_schema: + for prop_name, prop_schema in comp_schema['properties'].items(): + properties.append((prop_name, prop_schema)) + if is_required: + required.add(prop_name) + + for t in schema['allOf']: + if 'anyOf' in t: + for tt in t['anyOf']: + add_component(tt, is_required=False) + else: + add_component(t, is_required=True) + + return self._add_rule(rule_name, self._build_object_rule(properties, required, hybrid_name, additional_properties=None)) + + elif schema_type in (None, 'array') and ('items' in schema or 'prefixItems' in schema): + items = schema.get('items') or schema['prefixItems'] + if isinstance(items, list): + return self._add_rule( + rule_name, + '"[" space ' + + ' "," space '.join( + self.visit(item, f'{name}{"-" if name else ""}tuple-{i}') + for i, item in enumerate(items)) + + ' "]" space') + else: + item_rule_name = self.visit(items, f'{name}{"-" if name else ""}item') + min_items = schema.get("minItems", 0) + max_items = schema.get("maxItems") + return self._add_rule(rule_name, '"[" space ' + _build_repetition(item_rule_name, min_items, max_items, separator_rule='"," space') + ' "]" space') + + elif schema_type in (None, 'string') and 'pattern' in schema: + return self._visit_pattern(schema['pattern'], rule_name) + + elif schema_type in (None, 'string') and re.match(r'^uuid[1-5]?$', schema_format or ''): + return self._add_primitive( + 'root' if rule_name == 'root' else schema_format, + PRIMITIVE_RULES['uuid'] + ) + + elif schema_type in (None, 'string') and f'{schema_format}-string' in STRING_FORMAT_RULES: + prim_name = f'{schema_format}-string' + return self._add_rule(rule_name, self._add_primitive(prim_name, STRING_FORMAT_RULES[prim_name])) + + elif schema_type == 'string' and ('minLength' in schema or 'maxLength' in schema): + char_rule = self._add_primitive('char', PRIMITIVE_RULES['char']) + min_len = schema.get('minLength', 0) + max_len = schema.get('maxLength') + + return self._add_rule(rule_name, r'"\"" ' + _build_repetition(char_rule, min_len, max_len) + r' "\"" space') + + elif schema_type in (None, 'integer') and \ + ('minimum' in schema or 'exclusiveMinimum' in schema or 'maximum' in schema or 'exclusiveMaximum' in schema): + min_value = None + max_value = None + if 'minimum' in schema: + min_value = schema['minimum'] + elif 'exclusiveMinimum' in schema: + min_value = schema['exclusiveMinimum'] + 1 + if 'maximum' in schema: + max_value = schema['maximum'] + elif 'exclusiveMaximum' in schema: + max_value = schema['exclusiveMaximum'] - 1 + + out = ["("] + _generate_min_max_int(min_value, max_value, out) + out.append(") space") + return self._add_rule(rule_name, ''.join(out)) + + elif (schema_type == 'object') or (len(schema) == 0): + return self._add_rule(rule_name, self._add_primitive('object', PRIMITIVE_RULES['object'])) + + else: + assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}' + # TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero + return self._add_primitive('root' if rule_name == 'root' else schema_type, PRIMITIVE_RULES[schema_type]) + + def _add_primitive(self, name: str, rule: BuiltinRule): + n = self._add_rule(name, rule.content) + + for dep in rule.deps: + dep_rule = PRIMITIVE_RULES.get(dep) or STRING_FORMAT_RULES.get(dep) + assert dep_rule, f'Rule {dep} not known' + if dep not in self._rules: + self._add_primitive(dep, dep_rule) + return n + + def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[str], name: str, additional_properties: Optional[Union[bool, Any]]): + prop_order = self._prop_order + # sort by position in prop_order (if specified) then by original order + sorted_props = [kv[0] for _, kv in sorted(enumerate(properties), key=lambda ikv: (prop_order.get(ikv[1][0], len(prop_order)), ikv[0]))] + + prop_kv_rule_names = {} + for prop_name, prop_schema in properties: + prop_rule_name = self.visit(prop_schema, f'{name}{"-" if name else ""}{prop_name}') + prop_kv_rule_names[prop_name] = self._add_rule( + f'{name}{"-" if name else ""}{prop_name}-kv', + fr'{self._format_literal(json.dumps(prop_name))} space ":" space {prop_rule_name}' + ) + required_props = [k for k in sorted_props if k in required] + optional_props = [k for k in sorted_props if k not in required] + + if additional_properties is not None and additional_properties != False: + sub_name = f'{name}{"-" if name else ""}additional' + value_rule = self.visit(additional_properties, f'{sub_name}-value') if isinstance(additional_properties, dict) else \ + self._add_primitive('value', PRIMITIVE_RULES['value']) + key_rule = self._add_primitive('string', PRIMITIVE_RULES['string']) if not sorted_props \ + else self._add_rule(f'{sub_name}-k', self._not_strings(sorted_props)) + + prop_kv_rule_names["*"] = self._add_rule( + f'{sub_name}-kv', + f'{key_rule} ":" space {value_rule}' + ) + optional_props.append("*") + + rule = '"{" space ' + rule += ' "," space '.join(prop_kv_rule_names[k] for k in required_props) + + if optional_props: + rule += ' (' + if required_props: + rule += ' "," space ( ' + + def get_recursive_refs(ks, first_is_optional): + [k, *rest] = ks + kv_rule_name = prop_kv_rule_names[k] + comma_ref = f'( "," space {kv_rule_name} )' + if first_is_optional: + res = comma_ref + ('*' if k == '*' else '?') + else: + res = kv_rule_name + (' ' + comma_ref + "*" if k == '*' else '') + if len(rest) > 0: + res += ' ' + self._add_rule( + f'{name}{"-" if name else ""}{k}-rest', + get_recursive_refs(rest, first_is_optional=True) + ) + return res + + rule += ' | '.join( + get_recursive_refs(optional_props[i:], first_is_optional=False) + for i in range(len(optional_props)) + ) + if required_props: + rule += ' )' + rule += ' )?' + + rule += ' "}" space' + + return rule + + def format_grammar(self): + return '\n'.join( + f'{name} ::= {rule}' + for name, rule in sorted(self._rules.items(), key=lambda kv: kv[0]) + ) + + +def main(args_in = None): + parser = argparse.ArgumentParser( + description=''' + Generates a grammar (suitable for use in ./llama-cli) that produces JSON conforming to a + given JSON schema. Only a subset of JSON schema features are supported; more may be + added in the future. + ''', + ) + parser.add_argument( + '--prop-order', + default=[], + type=lambda s: s.split(','), + help=''' + comma-separated property names defining the order of precedence for object properties; + properties not specified here are given lower precedence than those that are, and + are kept in their original order from the schema. Required properties are always + given precedence over optional properties. + ''' + ) + parser.add_argument( + '--allow-fetch', + action='store_true', + default=False, + help='Whether to allow fetching referenced schemas over HTTPS') + parser.add_argument( + '--dotall', + action='store_true', + default=False, + help='Whether to treat dot (".") as matching all chars including line breaks in regular expression patterns') + parser.add_argument( + '--raw-pattern', + action='store_true', + default=False, + help='Treats string patterns as raw patterns w/o quotes (or quote escapes)') + + parser.add_argument('schema', help='file containing JSON schema ("-" for stdin)') + args = parser.parse_args(args_in) + + if args.schema.startswith('https://'): + url = args.schema + import requests + schema = requests.get(url).json() + elif args.schema == '-': + url = 'stdin' + schema = json.load(sys.stdin) + else: + url = f'file://{args.schema}' + with open(args.schema) as f: + schema = json.load(f) + converter = SchemaConverter( + prop_order={name: idx for idx, name in enumerate(args.prop_order)}, + allow_fetch=args.allow_fetch, + dotall=args.dotall, + raw_pattern=args.raw_pattern) + schema = converter.resolve_refs(schema, url) + converter.visit(schema, '') + print(converter.format_grammar()) + + +if __name__ == '__main__': + main() diff --git a/llama.cpp/examples/llama.android/.gitignore b/llama.cpp/examples/llama.android/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..0c1aed6e09109130dc54340b6df436d9ab0706a8 --- /dev/null +++ b/llama.cpp/examples/llama.android/.gitignore @@ -0,0 +1,33 @@ +# Gradle files +.gradle/ +build/ + +# Local configuration file (sdk path, etc) +local.properties + +# Log/OS Files +*.log + +# Android Studio generated files and folders +captures/ +.externalNativeBuild/ +.cxx/ +*.apk +output.json + +# IntelliJ +*.iml +.idea/ +misc.xml +deploymentTargetDropDown.xml +render.experimental.xml + +# Keystore files +*.jks +*.keystore + +# Google Services (e.g. APIs or Firebase) +google-services.json + +# Android Profiling +*.hprof diff --git a/llama.cpp/examples/llama.android/README.md b/llama.cpp/examples/llama.android/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/llama.cpp/examples/llama.android/app/.gitignore b/llama.cpp/examples/llama.android/app/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..3543521e9fef8e7322940a87c2b45dd0061b0f45 --- /dev/null +++ b/llama.cpp/examples/llama.android/app/.gitignore @@ -0,0 +1 @@ +/build diff --git a/llama.cpp/examples/llama.android/app/build.gradle.kts b/llama.cpp/examples/llama.android/app/build.gradle.kts new file mode 100644 index 0000000000000000000000000000000000000000..cddf21f278962d17d50af5bd41a6dcefa20c6cec --- /dev/null +++ b/llama.cpp/examples/llama.android/app/build.gradle.kts @@ -0,0 +1,65 @@ +plugins { + id("com.android.application") + id("org.jetbrains.kotlin.android") +} + +android { + namespace = "com.example.llama" + compileSdk = 34 + + defaultConfig { + applicationId = "com.example.llama" + minSdk = 33 + targetSdk = 34 + versionCode = 1 + versionName = "1.0" + + testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner" + vectorDrawables { + useSupportLibrary = true + } + } + + buildTypes { + release { + isMinifyEnabled = false + proguardFiles( + getDefaultProguardFile("proguard-android-optimize.txt"), + "proguard-rules.pro" + ) + } + } + compileOptions { + sourceCompatibility = JavaVersion.VERSION_1_8 + targetCompatibility = JavaVersion.VERSION_1_8 + } + kotlinOptions { + jvmTarget = "1.8" + } + buildFeatures { + compose = true + } + composeOptions { + kotlinCompilerExtensionVersion = "1.5.1" + } +} + +dependencies { + + implementation("androidx.core:core-ktx:1.12.0") + implementation("androidx.lifecycle:lifecycle-runtime-ktx:2.6.2") + implementation("androidx.activity:activity-compose:1.8.2") + implementation(platform("androidx.compose:compose-bom:2023.08.00")) + implementation("androidx.compose.ui:ui") + implementation("androidx.compose.ui:ui-graphics") + implementation("androidx.compose.ui:ui-tooling-preview") + implementation("androidx.compose.material3:material3") + implementation(project(":llama")) + testImplementation("junit:junit:4.13.2") + androidTestImplementation("androidx.test.ext:junit:1.1.5") + androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1") + androidTestImplementation(platform("androidx.compose:compose-bom:2023.08.00")) + androidTestImplementation("androidx.compose.ui:ui-test-junit4") + debugImplementation("androidx.compose.ui:ui-tooling") + debugImplementation("androidx.compose.ui:ui-test-manifest") +} diff --git a/llama.cpp/examples/llama.android/app/proguard-rules.pro b/llama.cpp/examples/llama.android/app/proguard-rules.pro new file mode 100644 index 0000000000000000000000000000000000000000..6e7ffa997ebe9c6370c559dda6ddfcd0d9d7f74c --- /dev/null +++ b/llama.cpp/examples/llama.android/app/proguard-rules.pro @@ -0,0 +1,21 @@ +# Add project specific ProGuard rules here. +# You can control the set of applied configuration files using the +# proguardFiles setting in build.gradle. +# +# For more details, see +# http://developer.android.com/guide/developing/tools/proguard.html + +# If your project uses WebView with JS, uncomment the following +# and specify the fully qualified class name to the JavaScript interface +# class: +#-keepclassmembers class fqcn.of.javascript.interface.for.webview { +# public *; +#} + +# Uncomment this to preserve the line number information for +# debugging stack traces. +#-keepattributes SourceFile,LineNumberTable + +# If you keep the line number information, uncomment this to +# hide the original source file name. +#-renamesourcefileattribute SourceFile diff --git a/llama.cpp/examples/llama.android/app/src/main/AndroidManifest.xml b/llama.cpp/examples/llama.android/app/src/main/AndroidManifest.xml new file mode 100644 index 0000000000000000000000000000000000000000..6ed2c235524e90ba04ee17fc999e2b157aa0034d --- /dev/null +++ b/llama.cpp/examples/llama.android/app/src/main/AndroidManifest.xml @@ -0,0 +1,30 @@ + + + + + + + + + + + + + + + + + diff --git a/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt b/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt new file mode 100644 index 0000000000000000000000000000000000000000..50ee1f724353da7a11d565e070ac23eefd794300 --- /dev/null +++ b/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt @@ -0,0 +1,119 @@ +package com.example.llama + +import android.app.DownloadManager +import android.net.Uri +import android.util.Log +import androidx.compose.material3.Button +import androidx.compose.material3.Text +import androidx.compose.runtime.Composable +import androidx.compose.runtime.getValue +import androidx.compose.runtime.mutableDoubleStateOf +import androidx.compose.runtime.mutableStateOf +import androidx.compose.runtime.remember +import androidx.compose.runtime.rememberCoroutineScope +import androidx.compose.runtime.setValue +import androidx.core.database.getLongOrNull +import androidx.core.net.toUri +import kotlinx.coroutines.delay +import kotlinx.coroutines.launch +import java.io.File + +data class Downloadable(val name: String, val source: Uri, val destination: File) { + companion object { + @JvmStatic + private val tag: String? = this::class.qualifiedName + + sealed interface State + data object Ready: State + data class Downloading(val id: Long): State + data class Downloaded(val downloadable: Downloadable): State + data class Error(val message: String): State + + @JvmStatic + @Composable + fun Button(viewModel: MainViewModel, dm: DownloadManager, item: Downloadable) { + var status: State by remember { + mutableStateOf( + if (item.destination.exists()) Downloaded(item) + else Ready + ) + } + var progress by remember { mutableDoubleStateOf(0.0) } + + val coroutineScope = rememberCoroutineScope() + + suspend fun waitForDownload(result: Downloading, item: Downloadable): State { + while (true) { + val cursor = dm.query(DownloadManager.Query().setFilterById(result.id)) + + if (cursor == null) { + Log.e(tag, "dm.query() returned null") + return Error("dm.query() returned null") + } + + if (!cursor.moveToFirst() || cursor.count < 1) { + cursor.close() + Log.i(tag, "cursor.moveToFirst() returned false or cursor.count < 1, download canceled?") + return Ready + } + + val pix = cursor.getColumnIndex(DownloadManager.COLUMN_BYTES_DOWNLOADED_SO_FAR) + val tix = cursor.getColumnIndex(DownloadManager.COLUMN_TOTAL_SIZE_BYTES) + val sofar = cursor.getLongOrNull(pix) ?: 0 + val total = cursor.getLongOrNull(tix) ?: 1 + cursor.close() + + if (sofar == total) { + return Downloaded(item) + } + + progress = (sofar * 1.0) / total + + delay(1000L) + } + } + + fun onClick() { + when (val s = status) { + is Downloaded -> { + viewModel.load(item.destination.path) + } + + is Downloading -> { + coroutineScope.launch { + status = waitForDownload(s, item) + } + } + + else -> { + item.destination.delete() + + val request = DownloadManager.Request(item.source).apply { + setTitle("Downloading model") + setDescription("Downloading model: ${item.name}") + setAllowedNetworkTypes(DownloadManager.Request.NETWORK_WIFI) + setDestinationUri(item.destination.toUri()) + } + + viewModel.log("Saving ${item.name} to ${item.destination.path}") + Log.i(tag, "Saving ${item.name} to ${item.destination.path}") + + val id = dm.enqueue(request) + status = Downloading(id) + onClick() + } + } + } + + Button(onClick = { onClick() }, enabled = status !is Downloading) { + when (status) { + is Downloading -> Text(text = "Downloading ${(progress * 100).toInt()}%") + is Downloaded -> Text("Load ${item.name}") + is Ready -> Text("Download ${item.name}") + is Error -> Text("Download ${item.name}") + } + } + } + + } +} diff --git a/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt b/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt new file mode 100644 index 0000000000000000000000000000000000000000..fa99328d243a4587debdd5e78d1ecda2f3f0aa79 --- /dev/null +++ b/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt @@ -0,0 +1,154 @@ +package com.example.llama + +import android.app.ActivityManager +import android.app.DownloadManager +import android.content.ClipData +import android.content.ClipboardManager +import android.net.Uri +import android.os.Bundle +import android.os.StrictMode +import android.os.StrictMode.VmPolicy +import android.text.format.Formatter +import androidx.activity.ComponentActivity +import androidx.activity.compose.setContent +import androidx.activity.viewModels +import androidx.compose.foundation.layout.Box +import androidx.compose.foundation.layout.Column +import androidx.compose.foundation.layout.Row +import androidx.compose.foundation.layout.fillMaxSize +import androidx.compose.foundation.layout.padding +import androidx.compose.foundation.lazy.LazyColumn +import androidx.compose.foundation.lazy.items +import androidx.compose.foundation.lazy.rememberLazyListState +import androidx.compose.material3.Button +import androidx.compose.material3.LocalContentColor +import androidx.compose.material3.MaterialTheme +import androidx.compose.material3.OutlinedTextField +import androidx.compose.material3.Surface +import androidx.compose.material3.Text +import androidx.compose.runtime.Composable +import androidx.compose.ui.Modifier +import androidx.compose.ui.unit.dp +import androidx.core.content.getSystemService +import com.example.llama.ui.theme.LlamaAndroidTheme +import java.io.File + +class MainActivity( + activityManager: ActivityManager? = null, + downloadManager: DownloadManager? = null, + clipboardManager: ClipboardManager? = null, +): ComponentActivity() { + private val tag: String? = this::class.simpleName + + private val activityManager by lazy { activityManager ?: getSystemService()!! } + private val downloadManager by lazy { downloadManager ?: getSystemService()!! } + private val clipboardManager by lazy { clipboardManager ?: getSystemService()!! } + + private val viewModel: MainViewModel by viewModels() + + // Get a MemoryInfo object for the device's current memory status. + private fun availableMemory(): ActivityManager.MemoryInfo { + return ActivityManager.MemoryInfo().also { memoryInfo -> + activityManager.getMemoryInfo(memoryInfo) + } + } + + override fun onCreate(savedInstanceState: Bundle?) { + super.onCreate(savedInstanceState) + + StrictMode.setVmPolicy( + VmPolicy.Builder(StrictMode.getVmPolicy()) + .detectLeakedClosableObjects() + .build() + ) + + val free = Formatter.formatFileSize(this, availableMemory().availMem) + val total = Formatter.formatFileSize(this, availableMemory().totalMem) + + viewModel.log("Current memory: $free / $total") + viewModel.log("Downloads directory: ${getExternalFilesDir(null)}") + + val extFilesDir = getExternalFilesDir(null) + + val models = listOf( + Downloadable( + "Phi-2 7B (Q4_0, 1.6 GiB)", + Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true"), + File(extFilesDir, "phi-2-q4_0.gguf"), + ), + Downloadable( + "TinyLlama 1.1B (f16, 2.2 GiB)", + Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true"), + File(extFilesDir, "tinyllama-1.1-f16.gguf"), + ), + Downloadable( + "Phi 2 DPO (Q3_K_M, 1.48 GiB)", + Uri.parse("https://huggingface.co/TheBloke/phi-2-dpo-GGUF/resolve/main/phi-2-dpo.Q3_K_M.gguf?download=true"), + File(extFilesDir, "phi-2-dpo.Q3_K_M.gguf") + ), + ) + + setContent { + LlamaAndroidTheme { + // A surface container using the 'background' color from the theme + Surface( + modifier = Modifier.fillMaxSize(), + color = MaterialTheme.colorScheme.background + ) { + MainCompose( + viewModel, + clipboardManager, + downloadManager, + models, + ) + } + + } + } + } +} + +@Composable +fun MainCompose( + viewModel: MainViewModel, + clipboard: ClipboardManager, + dm: DownloadManager, + models: List +) { + Column { + val scrollState = rememberLazyListState() + + Box(modifier = Modifier.weight(1f)) { + LazyColumn(state = scrollState) { + items(viewModel.messages) { + Text( + it, + style = MaterialTheme.typography.bodyLarge.copy(color = LocalContentColor.current), + modifier = Modifier.padding(16.dp) + ) + } + } + } + OutlinedTextField( + value = viewModel.message, + onValueChange = { viewModel.updateMessage(it) }, + label = { Text("Message") }, + ) + Row { + Button({ viewModel.send() }) { Text("Send") } + Button({ viewModel.bench(8, 4, 1) }) { Text("Bench") } + Button({ viewModel.clear() }) { Text("Clear") } + Button({ + viewModel.messages.joinToString("\n").let { + clipboard.setPrimaryClip(ClipData.newPlainText("", it)) + } + }) { Text("Copy") } + } + + Column { + for (model in models) { + Downloadable.Button(viewModel, dm, model) + } + } + } +} diff --git a/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt b/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt new file mode 100644 index 0000000000000000000000000000000000000000..48cf79f5ef29c27733eeb1fa088b385ae98082a2 --- /dev/null +++ b/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt @@ -0,0 +1,105 @@ +package com.example.llama + +import android.llama.cpp.LLamaAndroid +import android.util.Log +import androidx.compose.runtime.getValue +import androidx.compose.runtime.mutableStateOf +import androidx.compose.runtime.setValue +import androidx.lifecycle.ViewModel +import androidx.lifecycle.viewModelScope +import kotlinx.coroutines.flow.catch +import kotlinx.coroutines.launch + +class MainViewModel(private val llamaAndroid: LLamaAndroid = LLamaAndroid.instance()): ViewModel() { + companion object { + @JvmStatic + private val NanosPerSecond = 1_000_000_000.0 + } + + private val tag: String? = this::class.simpleName + + var messages by mutableStateOf(listOf("Initializing...")) + private set + + var message by mutableStateOf("") + private set + + override fun onCleared() { + super.onCleared() + + viewModelScope.launch { + try { + llamaAndroid.unload() + } catch (exc: IllegalStateException) { + messages += exc.message!! + } + } + } + + fun send() { + val text = message + message = "" + + // Add to messages console. + messages += text + messages += "" + + viewModelScope.launch { + llamaAndroid.send(text) + .catch { + Log.e(tag, "send() failed", it) + messages += it.message!! + } + .collect { messages = messages.dropLast(1) + (messages.last() + it) } + } + } + + fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1) { + viewModelScope.launch { + try { + val start = System.nanoTime() + val warmupResult = llamaAndroid.bench(pp, tg, pl, nr) + val end = System.nanoTime() + + messages += warmupResult + + val warmup = (end - start).toDouble() / NanosPerSecond + messages += "Warm up time: $warmup seconds, please wait..." + + if (warmup > 5.0) { + messages += "Warm up took too long, aborting benchmark" + return@launch + } + + messages += llamaAndroid.bench(512, 128, 1, 3) + } catch (exc: IllegalStateException) { + Log.e(tag, "bench() failed", exc) + messages += exc.message!! + } + } + } + + fun load(pathToModel: String) { + viewModelScope.launch { + try { + llamaAndroid.load(pathToModel) + messages += "Loaded $pathToModel" + } catch (exc: IllegalStateException) { + Log.e(tag, "load() failed", exc) + messages += exc.message!! + } + } + } + + fun updateMessage(newMessage: String) { + message = newMessage + } + + fun clear() { + messages = listOf() + } + + fun log(message: String) { + messages += message + } +} diff --git a/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt b/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt new file mode 100644 index 0000000000000000000000000000000000000000..0161aa7156f580b32878fe9429d42b9818882699 --- /dev/null +++ b/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt @@ -0,0 +1,11 @@ +package com.example.llama.ui.theme + +import androidx.compose.ui.graphics.Color + +val Purple80 = Color(0xFFD0BCFF) +val PurpleGrey80 = Color(0xFFCCC2DC) +val Pink80 = Color(0xFFEFB8C8) + +val Purple40 = Color(0xFF6650a4) +val PurpleGrey40 = Color(0xFF625b71) +val Pink40 = Color(0xFF7D5260) diff --git a/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt b/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt new file mode 100644 index 0000000000000000000000000000000000000000..b98a09529f4de5b734c344fa47f97b171c2706a0 --- /dev/null +++ b/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt @@ -0,0 +1,70 @@ +package com.example.llama.ui.theme + +import android.app.Activity +import android.os.Build +import androidx.compose.foundation.isSystemInDarkTheme +import androidx.compose.material3.MaterialTheme +import androidx.compose.material3.darkColorScheme +import androidx.compose.material3.dynamicDarkColorScheme +import androidx.compose.material3.dynamicLightColorScheme +import androidx.compose.material3.lightColorScheme +import androidx.compose.runtime.Composable +import androidx.compose.runtime.SideEffect +import androidx.compose.ui.graphics.toArgb +import androidx.compose.ui.platform.LocalContext +import androidx.compose.ui.platform.LocalView +import androidx.core.view.WindowCompat + +private val DarkColorScheme = darkColorScheme( + primary = Purple80, + secondary = PurpleGrey80, + tertiary = Pink80 +) + +private val LightColorScheme = lightColorScheme( + primary = Purple40, + secondary = PurpleGrey40, + tertiary = Pink40 + + /* Other default colors to override + background = Color(0xFFFFFBFE), + surface = Color(0xFFFFFBFE), + onPrimary = Color.White, + onSecondary = Color.White, + onTertiary = Color.White, + onBackground = Color(0xFF1C1B1F), + onSurface = Color(0xFF1C1B1F), + */ +) + +@Composable +fun LlamaAndroidTheme( + darkTheme: Boolean = isSystemInDarkTheme(), + // Dynamic color is available on Android 12+ + dynamicColor: Boolean = true, + content: @Composable () -> Unit +) { + val colorScheme = when { + dynamicColor && Build.VERSION.SDK_INT >= Build.VERSION_CODES.S -> { + val context = LocalContext.current + if (darkTheme) dynamicDarkColorScheme(context) else dynamicLightColorScheme(context) + } + + darkTheme -> DarkColorScheme + else -> LightColorScheme + } + val view = LocalView.current + if (!view.isInEditMode) { + SideEffect { + val window = (view.context as Activity).window + window.statusBarColor = colorScheme.primary.toArgb() + WindowCompat.getInsetsController(window, view).isAppearanceLightStatusBars = darkTheme + } + } + + MaterialTheme( + colorScheme = colorScheme, + typography = Typography, + content = content + ) +} diff --git a/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt b/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt new file mode 100644 index 0000000000000000000000000000000000000000..41a566c0d9a40e4797b71e08fae000efcf2fec08 --- /dev/null +++ b/llama.cpp/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt @@ -0,0 +1,34 @@ +package com.example.llama.ui.theme + +import androidx.compose.material3.Typography +import androidx.compose.ui.text.TextStyle +import androidx.compose.ui.text.font.FontFamily +import androidx.compose.ui.text.font.FontWeight +import androidx.compose.ui.unit.sp + +// Set of Material typography styles to start with +val Typography = Typography( + bodyLarge = TextStyle( + fontFamily = FontFamily.Default, + fontWeight = FontWeight.Normal, + fontSize = 16.sp, + lineHeight = 24.sp, + letterSpacing = 0.5.sp + ) + /* Other default text styles to override + titleLarge = TextStyle( + fontFamily = FontFamily.Default, + fontWeight = FontWeight.Normal, + fontSize = 22.sp, + lineHeight = 28.sp, + letterSpacing = 0.sp + ), + labelSmall = TextStyle( + fontFamily = FontFamily.Default, + fontWeight = FontWeight.Medium, + fontSize = 11.sp, + lineHeight = 16.sp, + letterSpacing = 0.5.sp + ) + */ +) diff --git a/llama.cpp/examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml b/llama.cpp/examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml new file mode 100644 index 0000000000000000000000000000000000000000..a4f78de59d0a22ed75afff51c930861ecb8d36b6 --- /dev/null +++ b/llama.cpp/examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml @@ -0,0 +1,170 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/llama.cpp/examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml b/llama.cpp/examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml new file mode 100644 index 0000000000000000000000000000000000000000..93090791529cd81c36ba9217441700e159564042 --- /dev/null +++ b/llama.cpp/examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml @@ -0,0 +1,30 @@ + + + + + + + + + + + diff --git a/llama.cpp/examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher.xml b/llama.cpp/examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher.xml new file mode 100644 index 0000000000000000000000000000000000000000..3314280a8998ee94a7cc0c724cf53d25ace19abb --- /dev/null +++ b/llama.cpp/examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher.xml @@ -0,0 +1,6 @@ + + + + + + diff --git a/llama.cpp/examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher_round.xml b/llama.cpp/examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher_round.xml new file mode 100644 index 0000000000000000000000000000000000000000..3314280a8998ee94a7cc0c724cf53d25ace19abb --- /dev/null +++ b/llama.cpp/examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher_round.xml @@ -0,0 +1,6 @@ + + + + + + diff --git a/llama.cpp/examples/llama.android/app/src/main/res/mipmap-hdpi/ic_launcher.webp b/llama.cpp/examples/llama.android/app/src/main/res/mipmap-hdpi/ic_launcher.webp new file mode 100644 index 0000000000000000000000000000000000000000..c209e78ecd372343283f4157dcfd918ec5165bb3 Binary files /dev/null and b/llama.cpp/examples/llama.android/app/src/main/res/mipmap-hdpi/ic_launcher.webp differ diff --git 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0000000000000000000000000000000000000000..2e750d7c6c7c2de180bbd2acb9f4ec3f457619ff --- /dev/null +++ b/llama.cpp/examples/llama.android/app/src/main/res/values/colors.xml @@ -0,0 +1,10 @@ + + + #FFBB86FC + #FF6200EE + #FF3700B3 + #FF03DAC5 + #FF018786 + #FF000000 + #FFFFFFFF + diff --git a/llama.cpp/examples/llama.android/app/src/main/res/values/strings.xml b/llama.cpp/examples/llama.android/app/src/main/res/values/strings.xml new file mode 100644 index 0000000000000000000000000000000000000000..964d36adbdc173a8f374c4ed2e350cd400f9ab5f --- /dev/null +++ b/llama.cpp/examples/llama.android/app/src/main/res/values/strings.xml @@ -0,0 +1,3 @@ + + LlamaAndroid + diff --git a/llama.cpp/examples/llama.android/app/src/main/res/values/themes.xml b/llama.cpp/examples/llama.android/app/src/main/res/values/themes.xml new file mode 100644 index 0000000000000000000000000000000000000000..6ebc5d32cca52edf5ecb22780fd10a5bcf326346 --- /dev/null +++ b/llama.cpp/examples/llama.android/app/src/main/res/values/themes.xml @@ -0,0 +1,5 @@ + + + +