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README.md

@@ -1,13 +1,13 @@
 ---
-title: Llava MoE
-emoji: 🔥
+title: MoE LLaVA
+emoji: 🚀
 colorFrom: indigo
-colorTo: yellow
+colorTo: blue
 sdk: gradio
-sdk_version: 4.16.0
+sdk_version: 3.37.0
 app_file: app.py
 pinned: false
 license: apache-2.0
+python_version: 3.11
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+
+import deepspeed
+import os
+os.system('deepspeed --num_gpus=1 moellava/serve/gradio_web_server.py')

cog.yaml

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+# Configuration for Cog ⚙️
+# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md
+
+build:
+  gpu: true
+
+  python_version: "3.11"
+
+  python_packages:
+    - "torch==2.0.1"
+    - "accelerate==0.21.0"
+    - "bitsandbytes==0.41.0"
+    - "deepspeed==0.9.5"
+    - "einops-exts==0.0.4"
+    - "einops==0.6.1"
+    - "gradio==3.35.2"
+    - "gradio_client==0.2.9"
+    - "httpx==0.24.0"
+    - "markdown2==2.4.10"
+    - "numpy==1.26.0"
+    - "peft==0.4.0"
+    - "scikit-learn==1.2.2"
+    - "sentencepiece==0.1.99"
+    - "shortuuid==1.0.11"
+    - "timm==0.6.13"
+    - "tokenizers==0.13.3"
+    - "torch==2.0.1"
+    - "torchvision==0.15.2"
+    - "transformers==4.31.0"
+    - "wandb==0.15.12"
+    - "wavedrom==2.0.3.post3"
+    - "Pygments==2.16.1"
+  run:
+    - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.0.3/pget" && chmod +x /usr/local/bin/pget
+
+# predict.py defines how predictions are run on your model
+predict: "predict.py:Predictor"

predict.py

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+import torch
+
+from moellava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
+from moellava.conversation import conv_templates, SeparatorStyle
+from moellava.model.builder import load_pretrained_model
+from moellava.utils import disable_torch_init
+from moellava.mm_utils import tokenizer_image_token, KeywordsStoppingCriteria
+from transformers.generation.streamers import TextIteratorStreamer
+
+from PIL import Image
+
+import requests
+from io import BytesIO
+
+from cog import BasePredictor, Input, Path, ConcatenateIterator
+import time
+import subprocess
+from threading import Thread
+
+import os
+os.environ["HUGGINGFACE_HUB_CACHE"] = os.getcwd() + "/weights"
+
+# url for the weights mirror
+REPLICATE_WEIGHTS_URL = "https://weights.replicate.delivery/default"
+# files to download from the weights mirrors
+weights = [
+    {
+        "dest": "liuhaotian/llava-v1.5-13b",
+        # git commit hash from huggingface
+        "src": "llava-v1.5-13b/006818fc465ebda4c003c0998674d9141d8d95f8",
+        "files": [
+            "config.json",
+            "generation_config.json",
+            "pytorch_model-00001-of-00003.bin",
+            "pytorch_model-00002-of-00003.bin",
+            "pytorch_model-00003-of-00003.bin",
+            "pytorch_model.bin.index.json",
+            "special_tokens_map.json",
+            "tokenizer.model",
+            "tokenizer_config.json",
+        ]
+    },
+    {
+        "dest": "openai/clip-vit-large-patch14-336",
+        "src": "clip-vit-large-patch14-336/ce19dc912ca5cd21c8a653c79e251e808ccabcd1",
+        "files": [
+            "config.json",
+            "preprocessor_config.json",
+            "pytorch_model.bin"
+        ],
+    }
+]
+
+def download_json(url: str, dest: Path):
+    res = requests.get(url, allow_redirects=True)
+    if res.status_code == 200 and res.content:
+        with dest.open("wb") as f:
+            f.write(res.content)
+    else:
+        print(f"Failed to download {url}. Status code: {res.status_code}")
+
+def download_weights(baseurl: str, basedest: str, files: list[str]):
+    basedest = Path(basedest)
+    start = time.time()
+    print("downloading to: ", basedest)
+    basedest.mkdir(parents=True, exist_ok=True)
+    for f in files:
+        dest = basedest / f
+        url = os.path.join(REPLICATE_WEIGHTS_URL, baseurl, f)
+        if not dest.exists():
+            print("downloading url: ", url)
+            if dest.suffix == ".json":
+                download_json(url, dest)
+            else:
+                subprocess.check_call(["pget", url, str(dest)], close_fds=False)
+    print("downloading took: ", time.time() - start)
+
+class Predictor(BasePredictor):
+    def setup(self) -> None:
+        """Load the model into memory to make running multiple predictions efficient"""
+        for weight in weights:
+            download_weights(weight["src"], weight["dest"], weight["files"])
+        disable_torch_init()
+    
+        self.tokenizer, self.model, self.image_processor, self.context_len = load_pretrained_model("liuhaotian/llava-v1.5-13b", model_name="llava-v1.5-13b", model_base=None, load_8bit=False, load_4bit=False)
+
+    def predict(
+        self,
+        image: Path = Input(description="Input image"),
+        prompt: str = Input(description="Prompt to use for text generation"),
+        top_p: float = Input(description="When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens", ge=0.0, le=1.0, default=1.0),
+        temperature: float = Input(description="Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic", default=0.2, ge=0.0),
+        max_tokens: int = Input(description="Maximum number of tokens to generate. A word is generally 2-3 tokens", default=1024, ge=0),
+    ) -> ConcatenateIterator[str]:
+        """Run a single prediction on the model"""
+    
+        conv_mode = "llava_v1"
+        conv = conv_templates[conv_mode].copy()
+    
+        image_data = load_image(str(image))
+        image_tensor = self.image_processor.preprocess(image_data, return_tensors='pt')['pixel_values'].half().cuda()
+    
+        # loop start
+    
+        # just one turn, always prepend image token
+        inp = DEFAULT_IMAGE_TOKEN + '\n' + prompt
+        conv.append_message(conv.roles[0], inp)
+
+        conv.append_message(conv.roles[1], None)
+        prompt = conv.get_prompt()
+    
+        input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
+        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+        keywords = [stop_str]
+        stopping_criteria = KeywordsStoppingCriteria(keywords, self.tokenizer, input_ids)
+        streamer = TextIteratorStreamer(self.tokenizer, skip_prompt=True, timeout=20.0)
+    
+        with torch.inference_mode():
+            thread = Thread(target=self.model.generate, kwargs=dict(
+                inputs=input_ids,
+                images=image_tensor,
+                do_sample=True,
+                temperature=temperature,
+                top_p=top_p,
+                max_new_tokens=max_tokens,
+                streamer=streamer,
+                use_cache=True,
+                stopping_criteria=[stopping_criteria]))
+            thread.start()
+            # workaround: second-to-last token is always " "
+            # but we want to keep it if it's not the second-to-last token
+            prepend_space = False
+            for new_text in streamer:
+                if new_text == " ":
+                    prepend_space = True
+                    continue
+                if new_text.endswith(stop_str):
+                    new_text = new_text[:-len(stop_str)].strip()
+                    prepend_space = False
+                elif prepend_space:
+                    new_text = " " + new_text
+                    prepend_space = False
+                if len(new_text):
+                    yield new_text
+            if prepend_space:
+                yield " "
+            thread.join()
+    
+
+def load_image(image_file):
+    if image_file.startswith('http') or image_file.startswith('https'):
+        response = requests.get(image_file)
+        image = Image.open(BytesIO(response.content)).convert('RGB')
+    else:
+        image = Image.open(image_file).convert('RGB')
+    return image
+

pyproject.toml

@@ -0,0 +1,37 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "moellava"
+version = "1.0.0"
+description = "Towards GPT-4 like large language and visual assistant."
+readme = "README.md"
+requires-python = ">=3.8"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: Apache Software License",
+]
+dependencies = [
+    "torch==2.0.1", "torchvision==0.15.2",
+    "transformers==4.31.0", "tokenizers>=0.12.1,<0.14", "sentencepiece==0.1.99", "shortuuid",
+    "accelerate==0.21.0", "peft==0.4.0", "bitsandbytes==0.41.0",
+    "pydantic<2,>=1", "markdown2[all]", "numpy", "scikit-learn==1.2.2",
+    "gradio==3.35.2", "gradio_client==0.2.9",
+    "requests", "httpx==0.24.0", "uvicorn", "fastapi",
+    "einops==0.6.1", "einops-exts==0.0.4", "timm==0.6.13",
+    "protobuf==4.24.4", "tiktoken==0.5.2",
+]
+
+[project.optional-dependencies]
+train = ["deepspeed==0.12.6", "ninja", "wandb"]
+
+[project.urls]
+"Homepage" = "https://github.com/PKU-YuanGroup/MoE-LLaVA"
+"Bug Tracker" = "https://github.com/PKU-YuanGroup/MoE-LLaVA/issues"
+
+[tool.setuptools.packages.find]
+exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"]
+
+[tool.wheel]
+exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"]

requirements.txt

@@ -0,0 +1,32 @@
+--extra-index-url https://download.pytorch.org/whl/cu118
+pip==23.3
+einops
+fastapi
+markdown2[all]
+numpy
+requests
+sentencepiece
+tokenizers==0.15.1
+torch==2.0.1
+torchvision==0.15.2
+uvicorn
+wandb
+shortuuid
+httpx==0.24.0
+peft==0.4.0
+transformers==4.37.0
+accelerate==0.21.0
+bitsandbytes==0.41.0
+scikit-learn==1.2.2
+sentencepiece==0.1.99
+einops==0.6.1
+einops-exts==0.0.4
+timm==0.6.13
+decord==0.6.0
+opencv-python==4.7.0.72
+pytorchvideo @ git+https://github.com/facebookresearch/pytorchvideo.git@28fe037d212663c6a24f373b94cc5d478c8c1a1d
+gradio==3.37.0
+gradio_client==0.7.0
+deepspeed==0.12.6
+protobuf==4.24.4
+tiktoken==0.5.2