add : files

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,11 +1,13 @@
 ---
-title: SeeForMe Live
-emoji: 😻
-colorFrom: gray
-colorTo: red
+title: SeeForMe Video
+emoji: 🏆
+colorFrom: indigo
+colorTo: yellow
 sdk: gradio
-sdk_version: 5.8.0
-app_file: app.py
+# sdk_version: 5.7.1
+sdk_version: 4.19.2
+# app_file: app.py
+app_file: webcam_gradio_demo.py
 pinned: false
 ---
 

app.py

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+import argparse
+import time
+from threading import Thread
+
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+from moondream.hf import LATEST_REVISION, detect_device
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--cpu", action="store_true")
+args = parser.parse_args()
+
+if args.cpu:
+    device = torch.device("cpu")
+    dtype = torch.float32
+else:
+    device, dtype = detect_device()
+    if device != torch.device("cpu"):
+        print("Using device:", device)
+        print("If you run into issues, pass the `--cpu` flag to this script.")
+        print()
+
+model_id = "vikhyatk/moondream2"
+tokenizer = AutoTokenizer.from_pretrained(model_id, revision=LATEST_REVISION)
+moondream = AutoModelForCausalLM.from_pretrained(
+    model_id, trust_remote_code=True, revision=LATEST_REVISION
+).to(device=device, dtype=dtype)
+moondream.eval()
+
+
+def answer_question(img, prompt):
+    image_embeds = moondream.encode_image(img)
+    streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True)
+    thread = Thread(
+        target=moondream.answer_question,
+        kwargs={
+            "image_embeds": image_embeds,
+            "question": prompt,
+            "tokenizer": tokenizer,
+            "streamer": streamer,
+        },
+    )
+    thread.start()
+
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        yield buffer
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# See For Me")
+
+    gr.HTML(
+        """
+        <style type="text/css">
+            .md_output p {
+                padding-top: 1rem;
+                font-size: 1.2rem !important;
+            }
+        </style>
+        """
+    )
+
+    with gr.Row():
+        prompt = gr.Textbox(
+            label="Prompt",
+            value="What's going on? Respond with a single sentence.",
+            interactive=True,
+        )
+    with gr.Row():
+        img = gr.Image(type="pil", label="Upload an Image", streaming=True)
+        output = gr.Markdown(elem_classes=["md_output"])
+
+    latest_img = None
+    latest_prompt = prompt.value
+
+    @img.change(inputs=[img])
+    def img_change(img):
+        global latest_img
+        latest_img = img
+
+    @prompt.change(inputs=[prompt])
+    def prompt_change(prompt):
+        global latest_prompt
+        latest_prompt = prompt
+
+    @demo.load(outputs=[output])
+    def live_video():
+        while True:
+            if latest_img is None:
+                time.sleep(0.1)
+            else:
+                for text in answer_question(latest_img, latest_prompt):
+                    if len(text) > 0:
+                        yield text
+
+
+demo.queue().launch(debug=True)

batch_generate_example.py

@@ -0,0 +1,35 @@
+from PIL import Image
+from transformers import AutoTokenizer
+
+from moondream.hf import LATEST_REVISION, Moondream, detect_device
+
+device, dtype = detect_device()
+
+model_id = "vikhyatk/moondream2"
+tokenizer = AutoTokenizer.from_pretrained(model_id, revision=LATEST_REVISION)
+moondream = Moondream.from_pretrained(
+    model_id,
+    revision=LATEST_REVISION,
+    torch_dtype=dtype,
+).to(device=device)
+moondream.eval()
+
+image1 = Image.open("assets/demo-1.jpg")
+image2 = Image.open("assets/demo-2.jpg")
+prompts = [
+    "What is the girl doing?",
+    "What color is the girl's hair?",
+    "What is this?",
+    "What is behind the stand?",
+]
+
+answers = moondream.batch_answer(
+    images=[image1, image1, image2, image2],
+    prompts=prompts,
+    tokenizer=tokenizer,
+)
+
+for question, answer in zip(prompts, answers):
+    print(f"Q: {question}")
+    print(f"A: {answer}")
+    print()

clients/python/README.md

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+# Moondream Python Client Library
+
+Python client library for moondream. This library is an alpha preview -- it is
+in an early stage of development, and backward compatibility is not yet
+guaranteed. If you are using this in production, please pin the revision you
+are using.
+
+This library currently offers optimized CPU inference, but will be slower than
+the PyTorch implementation for CUDA and MPS backends. If you are running on a
+Mac with M1/M2/M3 etc. chips, or if you have a GPU available, this library is
+not recommended yet.
+
+## Setup
+
+Install the library using pip:
+
+```bash
+pip install moondream==0.0.2
+```
+
+Then download the model weights:
+
+```bash
+# int8 weights (recommended):
+wget "https://huggingface.co/vikhyatk/moondream2/resolve/client/moondream-latest-int8.bin.gz?download=true" -O - | gunzip > moondream-latest-int8.bin
+# ...or, for fp16 weights (full precision):
+wget "https://huggingface.co/vikhyatk/moondream2/resolve/client/moondream-latest-f16.bin.gz?download=true" -O - | gunzip > moondream-latest-f16.bin
+# ...or, for int4 weights (resource constrained environments):
+wget "https://huggingface.co/vikhyatk/moondream2/resolve/client/moondream-latest-int4.bin.gz?download=true" -O - | gunzip > moondream-latest-int4.bin
+```
+
+## Usage
+
+```python
+import moondream as md
+from PIL import Image
+
+model = md.VL("moondream-latest-int8.bin")
+image = Image.open("path/to/image.jpg").convert("RGB")
+
+# Optional -- encode the image to efficiently run multiple queries on the same
+# image. This is not mandatory, since the model will automatically encode the
+# image if it is not already encoded.
+encoded_image = model.encode_image(image)
+
+# Caption the image.
+caption = model.caption(encoded_image)
+
+# ...or, if you want to stream the output:
+for t in model.caption(encoded_image, stream=True)["caption"]:
+    print(t, end="", flush=True)
+
+# Ask a question about the image.
+question = "How many people are in this image?"
+answer = model.query(encoded_image, question)["answer"]
+
+# ...or again, if you want to stream the output:
+for t in model.query(encoded_image, question, stream=True)["answer"]:
+    print(t, end="", flush=True)
+```

clients/python/moondream/__init__.py

@@ -0,0 +1 @@
+from .vl import VL

clients/python/moondream/preprocess.py

@@ -0,0 +1,63 @@
+from typing import List, Tuple
+
+import numpy as np
+from PIL import Image
+
+
+def im_resize(
+    image: Image.Image,
+    size: Tuple[int, int],
+    resample: int = Image.Resampling.BICUBIC,
+) -> Image.Image:
+    return image.resize(size, resample=resample)
+
+
+def normalize(
+    image: np.ndarray,
+    mean: List[float] = [0.5, 0.5, 0.5],
+    std: List[float] = [0.5, 0.5, 0.5],
+) -> np.ndarray:
+    """
+    Normalize an image array.
+    """
+    return (image - np.array(mean)) / np.array(std)
+
+
+def create_patches(image: Image.Image, image_patch_size=378) -> np.ndarray:
+    """
+    Split the given image into a variable number of patches depending upon its
+    resolution.
+    """
+    # Start off with the global patch.
+    patches = [im_resize(image, (image_patch_size, image_patch_size))]
+
+    # Find the closest resolution template.
+    res_templates = [(1, 2), (2, 1), (2, 2)]
+    im_width, im_height = image.size
+    max_dim = max(im_width, im_height)
+    if max_dim < image_patch_size * 1.4:
+        # If the image is already small, we just do a single patch that is a
+        # duplicate of the global patch. This creates a small amount of
+        # redundant computation now, but it is simpler and future-proofs us
+        # if/when we condition the vision encoder on the patch type.
+        patches.append(patches[0])
+    else:
+        aspect_ratio = im_width / im_height
+        res_template = min(
+            res_templates, key=lambda size: abs((size[1] / size[0]) - aspect_ratio)
+        )
+        # TODO: Actually implement patching... just going to put in the global
+        # patch for now to make progress on other aspects.
+        patches.append(patches[0])
+
+    return np.stack(
+        [
+            normalize(
+                (np.array(patch_img) / 255.0),
+                mean=[0.5, 0.5, 0.5],
+                std=[0.5, 0.5, 0.5],
+            ).transpose(2, 0, 1)
+            for patch_img in patches
+        ],
+        dtype=np.float16,
+    )

clients/python/moondream/vl.py

@@ -0,0 +1,303 @@
+import json
+import os
+import tarfile
+from dataclasses import dataclass
+from io import BytesIO
+from typing import Any, Dict, Generator, List, Optional, TypedDict, Union
+
+import numpy as np
+import onnx
+import onnxruntime as ort
+from PIL import Image
+from tokenizers import Tokenizer
+
+from .preprocess import create_patches
+
+
+@dataclass
+class EncodedImage:
+    pos: int
+    kv_caches: List[np.ndarray]
+
+
+SamplingSettings = TypedDict(
+    "SamplingSettings",
+    {"max_tokens": int},
+    total=False,
+)
+
+CaptionOutput = TypedDict(
+    "CaptionOutput", {"caption": Union[str, Generator[str, None, None]]}
+)
+QueryOutput = TypedDict(
+    "QueryOutput", {"answer": Union[str, Generator[str, None, None]]}
+)
+
+DEFAULT_MAX_TOKENS = 1024
+MIN_SUPPORTED_VERSION = 1
+MAX_SUPPORT_VERSION = 1
+
+
+class Region:
+    pass
+
+
+class VL:
+    def __init__(self, model_path: Optional[str], ort_settings: Dict[str, Any] = {}):
+        """
+        Initialize the Moondream VL (Vision Language) model.
+
+        Args:
+            model_path (str): The path to the model file.
+
+        Returns:
+            None
+        """
+
+        if model_path is None or not os.path.isfile(model_path):
+            raise ValueError("Model path is invalid or file does not exist.")
+
+        if not tarfile.is_tarfile(model_path):
+            raise ValueError(
+                "Model format not recognized. You may need to upgrade the moondream"
+                " package."
+            )
+
+        self.text_decoders = []
+
+        with tarfile.open(model_path, "r:*") as tar:
+            for member in tar.getmembers():
+                name = member.name.split("/")[-1]
+
+                f = tar.extractfile(member)
+                if f is not None:
+                    contents = f.read()
+                else:
+                    continue
+
+                if name == "vision_encoder.onnx":
+                    self.vision_encoder = ort.InferenceSession(contents, **ort_settings)
+                elif name == "vision_projection.onnx":
+                    self.vision_projection = ort.InferenceSession(
+                        contents, **ort_settings
+                    )
+                elif name == "text_encoder.onnx":
+                    self.text_encoder = ort.InferenceSession(contents, **ort_settings)
+                elif "text_decoder" in name and name.endswith(".onnx"):
+                    self.text_decoders.append(
+                        ort.InferenceSession(contents, **ort_settings)
+                    )
+                elif name == "tokenizer.json":
+                    self.tokenizer = Tokenizer.from_buffer(contents)
+                elif name == "initial_kv_caches.npy":
+                    self.initial_kv_caches = [x for x in np.load(BytesIO(contents))]
+                elif name == "config.json":
+                    self.config = json.loads(contents)
+
+        assert self.vision_encoder is not None
+        assert self.vision_projection is not None
+        assert self.text_encoder is not None
+        assert len(self.text_decoders) > 0
+        assert self.tokenizer is not None
+        assert self.initial_kv_caches is not None
+        assert self.config is not None
+
+        if type(self.config) != dict or "model_version" not in self.config:
+            raise ValueError("Model format not recognized.")
+        if (
+            self.config["model_version"] < MIN_SUPPORTED_VERSION
+            or self.config["model_version"] > MAX_SUPPORT_VERSION
+        ):
+            raise ValueError(
+                "Model version not supported. You may need to upgrade the moondream"
+                " package."
+            )
+
+        self.special_tokens = self.config["special_tokens"]
+        self.templates = self.config["templates"]
+
+    def encode_image(self, image: Union[Image.Image, EncodedImage]) -> EncodedImage:
+        """
+        Preprocess the image by running it through the model.
+
+        This method is useful if the user wants to make multiple queries with the same image.
+        The output is not guaranteed to be backward-compatible across version updates,
+        and should not be persisted out of band.
+
+        Args:
+            image (Image.Image): The input image to be encoded.
+
+        Returns:
+            The encoded representation of the image.
+        """
+        if type(image) == EncodedImage:
+            return image
+
+        image_patches = create_patches(image)  # type: ignore
+
+        patch_emb = self.vision_encoder.run(None, {"input": image_patches})[0]
+        patch_emb = np.concatenate([patch_emb[0], patch_emb[1]], axis=-1)
+        patch_emb = np.expand_dims(patch_emb, axis=0)
+        (inputs_embeds,) = self.vision_projection.run(None, {"input": patch_emb})
+
+        kv_caches = self.initial_kv_caches
+        pos = inputs_embeds.shape[-2] + kv_caches[0].shape[-2]
+
+        for i, decoder in enumerate(self.text_decoders):
+            inputs_embeds, kv_cache_update = decoder.run(
+                None,
+                {
+                    "inputs_embeds": inputs_embeds,
+                    "kv_cache": kv_caches[i],
+                },
+            )
+            kv_caches[i] = np.concatenate([kv_caches[i], kv_cache_update], axis=-2)
+        return EncodedImage(pos=pos, kv_caches=kv_caches)
+
+    def _generate(
+        self, hidden: np.ndarray, encoded_image: EncodedImage, max_tokens: int
+    ) -> Generator[str, None, None]:
+        kv_caches = {
+            i: np.zeros(
+                (
+                    *self.initial_kv_caches[0].shape[:-2],
+                    2048,
+                    self.initial_kv_caches[0].shape[-1],
+                ),
+                dtype=np.float16,
+            )
+            for i in range(len(self.text_decoders))
+        }
+        for i, kv_cache in kv_caches.items():
+            kv_cache[:, :, :, :, : encoded_image.pos, :] = encoded_image.kv_caches[i]
+
+        pos = encoded_image.pos
+        generated_tokens = 0
+        while generated_tokens < max_tokens:
+            # Track the original T dimension of the input hidden states, so we can
+            # bind the kv cache update accordingly. We can't check it just-in-time
+            # because the final 'hidden' output is actually the model's logits.
+            og_t = hidden.shape[-2]
+
+            for i, decoder in enumerate(self.text_decoders):
+                hidden, kv_cache_update = decoder.run(
+                    None,
+                    {
+                        "inputs_embeds": hidden,
+                        "kv_cache": kv_caches[i][:, :, :, :, :pos, :],
+                    },
+                )
+                kv_caches[i][:, :, :, :, pos : pos + og_t, :] = kv_cache_update
+
+            next_token = np.argmax(hidden, axis=-1)[0]
+            if next_token == self.special_tokens["eos"]:
+                break
+
+            yield self.tokenizer.decode([next_token])
+            generated_tokens += 1
+            pos += og_t
+            (hidden,) = self.text_encoder.run(None, {"input_ids": [[next_token]]})
+
+    def caption(
+        self,
+        image: Union[Image.Image, EncodedImage],
+        length: str = "normal",
+        stream: bool = False,
+        settings: Optional[SamplingSettings] = None,
+    ) -> CaptionOutput:
+        """
+        Generate a caption for the input image.
+
+        Args:
+            image (Union[Image.Image, EncodedImage]): The input image to be captioned.
+            settings (Optional[SamplingSettings]): Optional settings for the caption generation.
+                If not provided, default settings will be used.
+
+        Returns:
+            str: The caption for the input image.
+        """
+        if "caption" not in self.templates:
+            raise ValueError("Model does not support captioning.")
+        if length not in self.templates["caption"]:
+            raise ValueError(f"Model does not support caption length '{length}'.")
+
+        (input_embeds,) = self.text_encoder.run(
+            None, {"input_ids": [self.templates["caption"][length]]}
+        )
+        if settings is None:
+            settings = {}
+        max_tokens = settings.get("max_tokens", DEFAULT_MAX_TOKENS)
+
+        encoded_image = self.encode_image(image)
+
+        def generator():
+            for t in self._generate(input_embeds, encoded_image, max_tokens):
+                yield t
+
+        if stream:
+            return {"caption": generator()}
+        else:
+            out = ""
+            for t in generator():
+                out += t
+            return {"caption": out}
+
+    def query(
+        self,
+        image: Union[Image.Image, EncodedImage],
+        question: str,
+        stream: bool = False,
+        settings: Optional[SamplingSettings] = None,
+    ) -> QueryOutput:
+        """
+        Generate an answer to the input question about the input image.
+
+        Args:
+            image (Union[Image.Image, EncodedImage]): The input image to be queried.
+            question (str): The question to be answered.
+
+        Returns:
+            str: The answer to the input question about the input image.
+        """
+        if "query" not in self.templates:
+            raise ValueError("Model does not support querying.")
+
+        question_toks = (
+            self.templates["query"]["prefix"]
+            + self.tokenizer.encode(question).ids
+            + self.templates["query"]["suffix"]
+        )
+
+        (input_embeds,) = self.text_encoder.run(None, {"input_ids": [question_toks]})
+        if settings is None:
+            settings = {}
+        max_tokens = settings.get("max_tokens", DEFAULT_MAX_TOKENS)
+
+        encoded_image = self.encode_image(image)
+
+        def generator():
+            for t in self._generate(input_embeds, encoded_image, max_tokens):
+                yield t
+
+        if stream:
+            return {"answer": generator()}
+        else:
+            out = ""
+            for t in generator():
+                out += t
+            return {"answer": out}
+
+    def detect(
+        self, image: Union[Image.Image, EncodedImage], object: str
+    ) -> List[Region]:
+        """
+        Detect and localize the specified object in the input image.
+
+        Args:
+            image (Union[Image.Image, EncodedImage]): The input image to be analyzed.
+            object (str): The object to be detected in the image.
+
+        Returns:
+            List[Region]: A list of Region objects representing the detected instances of the specified object.
+        """
+        return []

clients/python/pyproject.toml

@@ -0,0 +1,24 @@
+[tool.poetry]
+name = "moondream"
+version = "0.0.2"
+description = "Python client library for moondream"
+authors = ["vik <vik@moondream.ai>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.10"
+pillow = "^10.4.0"
+onnxruntime = "^1.19.2"
+numpy = "^2.1.2"
+onnx = "^1.17.0"
+tokenizers = "^0.20.1"
+
+
+[tool.pyright]
+venvPath = "."
+venv = ".venv"
+reportMissingParameterType = false
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

clients/python/scripts/test.py

@@ -0,0 +1,144 @@
+import time
+import tracemalloc
+
+from PIL import Image
+
+import moondream as md
+from moondream.preprocess import create_patches
+
+MODEL_PATH = "../../onnx/out/moondream-latest-int4.bin"
+
+
+class Colors:
+    HEADER = "\033[95m"  # Purple
+    BLUE = "\033[94m"
+    GREEN = "\033[92m"
+    YELLOW = "\033[93m"
+    RED = "\033[91m"
+    ENDC = "\033[0m"
+    BOLD = "\033[1m"
+
+
+def format_memory(memory_mb):
+    """Format memory size with appropriate unit"""
+    if memory_mb < 1024:
+        return f"{memory_mb:.2f} MB"
+    else:
+        return f"{memory_mb/1024:.2f} GB"
+
+
+def print_section(title):
+    """Print a section header with dynamic padding to center the text"""
+    total_width = 65
+    text_length = len(title) + 2  # Add 2 for spaces around title
+    total_padding = total_width - text_length
+    left_padding = total_padding // 2
+    right_padding = total_padding - left_padding
+    print(
+        f"\n{Colors.HEADER}{Colors.BOLD}{'-'*left_padding} {title} {'-'*right_padding}{Colors.ENDC}"
+    )
+
+
+def print_metric(label, value, color=Colors.BLUE):
+    """Print a metric with consistent formatting"""
+    print(f"| {color}{label}{Colors.ENDC}: {value}")
+
+
+def log_memory_and_time(operation_name, start_time, start_memory):
+    """Log memory and time differences for an operation"""
+    end_time = time.time()
+    current_memory = get_memory_usage()
+    time_diff = end_time - start_time
+    memory_diff = current_memory - start_memory
+
+    print("\nStats")
+    print_metric("Time", f"{time_diff:.2f} seconds")
+    print_metric("Memory usage", format_memory(current_memory))
+
+    # Color-code memory increase based on significance
+    color = (
+        Colors.GREEN
+        if memory_diff < 10
+        else Colors.YELLOW if memory_diff < 100 else Colors.RED
+    )
+    print_metric("Memory increase", format_memory(memory_diff), color)
+
+    return end_time, current_memory
+
+
+def get_memory_usage():
+    """Get current memory usage in MB"""
+    current, peak = tracemalloc.get_traced_memory()
+    return current / 1024 / 1024
+
+
+# Start tracking memory
+tracemalloc.start()
+
+# Initial memory measurement
+initial_memory = get_memory_usage()
+print_section("Initial State")
+print_metric("Initial memory usage", format_memory(initial_memory))
+
+# Load image
+print_section("Image Loading")
+start_time = time.time()
+start_memory = get_memory_usage()
+image = Image.open("../../assets/demo-1.jpg")
+log_memory_and_time("Image Loading", start_time, start_memory)
+
+# Initialize model
+print_section("Model Initialization")
+start_time = time.time()
+start_memory = get_memory_usage()
+model = md.VL(MODEL_PATH)
+log_memory_and_time("Model Initialization", start_time, start_memory)
+
+# Encode image
+print_section("Image Encoding")
+start_time = time.time()
+start_memory = get_memory_usage()
+encoded_image = model.encode_image(image)
+log_memory_and_time("Image Encoding", start_time, start_memory)
+
+# Generate caption
+print_section("Caption Generation")
+print(f"{Colors.BOLD}Caption:{Colors.ENDC}", end="", flush=True)
+start_time = time.time()
+start_memory = get_memory_usage()
+tokens = 0
+for tok in model.caption(encoded_image, stream=True)["caption"]:
+    print(tok, end="", flush=True)
+    tokens += 1
+print()
+end_time, end_memory = log_memory_and_time("Caption Stats", start_time, start_memory)
+print_metric("Token generation speed", f"{tokens / (end_time - start_time):.2f} tok/s")
+
+# Generate answer to question
+question = "How many people are in this image? Answer briefly."
+print_section("Question Answering")
+print(f"{Colors.BOLD}Question:{Colors.ENDC} {question}")
+print(f"{Colors.BOLD}Answer:{Colors.ENDC}", end="", flush=True)
+start_time = time.time()
+start_memory = get_memory_usage()
+tokens = 0
+for tok in model.query(encoded_image, question, stream=True)["answer"]:
+    print(tok, end="", flush=True)
+    tokens += 1
+print()
+end_time, end_memory = log_memory_and_time(
+    "Question Answering Stats", start_time, start_memory
+)
+print_metric("Token generation speed", f"{tokens / (end_time - start_time):.2f} tok/s")
+
+# Final summary
+print_section("Final Summary")
+final_memory = get_memory_usage()
+current, peak = tracemalloc.get_traced_memory()
+
+print_metric("Final memory usage", format_memory(final_memory))
+print_metric("Total memory increase", format_memory(final_memory - initial_memory))
+print_metric("Peak memory usage", format_memory(peak / 1024 / 1024))
+
+# Stop tracking memory
+tracemalloc.stop()

gradio_demo.py

@@ -0,0 +1,106 @@
+import argparse
+import re
+from threading import Thread
+
+import gradio as gr
+import torch
+from PIL import ImageDraw
+from torchvision.transforms.v2 import Resize
+from transformers import AutoTokenizer, TextIteratorStreamer
+
+from moondream.hf import LATEST_REVISION, Moondream, detect_device
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--cpu", action="store_true")
+args = parser.parse_args()
+
+if args.cpu:
+    device = torch.device("cpu")
+    dtype = torch.float32
+else:
+    device, dtype = detect_device()
+    if device != torch.device("cpu"):
+        print("Using device:", device)
+        print("If you run into issues, pass the `--cpu` flag to this script.")
+        print()
+
+model_id = "vikhyatk/moondream2"
+tokenizer = AutoTokenizer.from_pretrained(model_id, revision=LATEST_REVISION)
+moondream = Moondream.from_pretrained(
+    model_id, revision=LATEST_REVISION, torch_dtype=dtype
+).to(device=device)
+moondream.eval()
+
+
+def answer_question(img, prompt):
+    image_embeds = moondream.encode_image(img)
+    streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True)
+    thread = Thread(
+        target=moondream.answer_question,
+        kwargs={
+            "image_embeds": image_embeds,
+            "question": prompt,
+            "tokenizer": tokenizer,
+            "streamer": streamer,
+        },
+    )
+    thread.start()
+
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        yield buffer
+
+
+def extract_floats(text):
+    # Regular expression to match an array of four floating point numbers
+    pattern = r"\[\s*(-?\d+\.\d+)\s*,\s*(-?\d+\.\d+)\s*,\s*(-?\d+\.\d+)\s*,\s*(-?\d+\.\d+)\s*\]"
+    match = re.search(pattern, text)
+    if match:
+        # Extract the numbers and convert them to floats
+        return [float(num) for num in match.groups()]
+    return None  # Return None if no match is found
+
+
+def extract_bbox(text):
+    bbox = None
+    if extract_floats(text) is not None:
+        x1, y1, x2, y2 = extract_floats(text)
+        bbox = (x1, y1, x2, y2)
+    return bbox
+
+
+def process_answer(img, answer):
+    if extract_bbox(answer) is not None:
+        x1, y1, x2, y2 = extract_bbox(answer)
+        draw_image = Resize(768)(img)
+        width, height = draw_image.size
+        x1, x2 = int(x1 * width), int(x2 * width)
+        y1, y2 = int(y1 * height), int(y2 * height)
+        bbox = (x1, y1, x2, y2)
+        ImageDraw.Draw(draw_image).rectangle(bbox, outline="red", width=3)
+        return gr.update(visible=True, value=draw_image)
+
+    return gr.update(visible=False, value=None)
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # 🌔 moondream
+        """
+    )
+    with gr.Row():
+        prompt = gr.Textbox(label="Input Prompt", value="Describe this image.", scale=4)
+        submit = gr.Button("Submit")
+    with gr.Row():
+        img = gr.Image(type="pil", label="Upload an Image")
+        with gr.Column():
+            output = gr.Markdown(label="Response")
+            ann = gr.Image(visible=False, label="Annotated Image")
+
+    submit.click(answer_question, [img, prompt], output)
+    prompt.submit(answer_question, [img, prompt], output)
+    output.change(process_answer, [img, output], ann, show_progress=False)
+
+demo.queue().launch(debug=True)

hf_release.py

@@ -0,0 +1,27 @@
+import torch
+
+from moondream.hf import Moondream
+from moondream.hf.configuration_moondream import MoondreamConfig
+
+MoondreamConfig.register_for_auto_class()
+Moondream.register_for_auto_class("AutoModelForCausalLM")
+
+OUT_MODEL = "vikhyatk/moondream-next"
+CKPT_DIRS = []
+
+
+def get_ckpt(filename):
+    ckpts = [torch.load(f"{dir}/{filename}", map_location="cpu") for dir in CKPT_DIRS]
+    avg_ckpt = {key: sum(ckpt[key] for ckpt in ckpts) / len(ckpts) for key in ckpts[0]}
+    return avg_ckpt
+
+
+config = MoondreamConfig()
+model = Moondream(config)
+model.vision_encoder.encoder.load_state_dict(get_ckpt("vision_encoder.final.pt"))
+model.vision_encoder.projection.load_state_dict(get_ckpt("vision_projection.final.pt"))
+model.text_model.load_state_dict(get_ckpt("text_model.final.pt"))
+model.region_model.load_state_dict(get_ckpt("region_model.final.pt"))
+model = model.to(dtype=torch.float16)
+
+model.push_to_hub(OUT_MODEL, config=config)

moondream/eval/docvqa.py

@@ -0,0 +1,45 @@
+import editdistance
+from datasets import load_dataset
+from tqdm import tqdm
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from ..hf import detect_device
+
+MODEL_ID = "vikhyatk/moondream2"
+DEVICE, DTYPE = detect_device()
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+moondream = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True,
+    attn_implementation="flash_attention_2",
+    torch_dtype=DTYPE,
+    device_map={"": DEVICE},
+)
+moondream.eval()
+
+
+def get_anls(s1, s2):
+    s1 = s1.lower().strip()
+    s2 = s2.lower().strip()
+    iou = 1 - editdistance.eval(s1, s2) / max(len(s1), len(s2))
+    anls = iou if iou >= 0.5 else 0.0
+    return anls
+
+
+docvqa_val = load_dataset("vikhyatk/docvqa", split="validation")
+
+scores = []
+for row in tqdm(docvqa_val):
+    image = row["image"]
+    enc_image = moondream.encode_image(image)
+    for qa in row["qa"]:
+        question = qa["question"]
+        answers = qa["answers"]
+        prompt = f"{question}\nAnswer briefly with a single word or phrase."
+
+        model_answer = moondream.answer_question(enc_image, prompt, tokenizer)
+        anls = max(get_anls(model_answer, gt) for gt in answers)
+        scores.append(anls)
+
+print("ANLS:", sum(scores) / len(scores))

moondream/eval/naturalbench.py

@@ -0,0 +1,74 @@
+from datasets import load_dataset
+from tqdm import tqdm
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from ..hf import detect_device
+
+MODEL_ID = "vikhyatk/moondream2"
+DEVICE, DTYPE = detect_device()
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+moondream = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True,
+    attn_implementation="flash_attention_2",
+    torch_dtype=DTYPE,
+    device_map={"": DEVICE},
+)
+moondream.eval()
+
+# Yes, the benchmark test set is stored in the 'train' split...
+dataset = load_dataset("BaiqiL/NaturalBench", split="train")
+
+acc = []
+q_acc = []
+i_acc = []
+g_acc = []
+
+for row in tqdm(dataset):
+    if row["Question_Type"] == "yes_no":
+        suffix = " Answer yes or no."
+    else:
+        suffix = ""
+
+    answers = moondream.batch_answer(
+        images=[row["Image_0"], row["Image_1"], row["Image_0"], row["Image_1"]],
+        prompts=[
+            row["Question_0"] + suffix,
+            row["Question_0"] + suffix,
+            row["Question_1"] + suffix,
+            row["Question_1"] + suffix,
+        ],
+        tokenizer=tokenizer,
+    )
+
+    expected = [
+        row["Image_0_Question_0"],
+        row["Image_1_Question_0"],
+        row["Image_0_Question_1"],
+        row["Image_1_Question_1"],
+    ]
+
+    acc.append(answers[0] == expected[0])
+    acc.append(answers[1] == expected[1])
+    acc.append(answers[2] == expected[2])
+    acc.append(answers[3] == expected[3])
+
+    i_acc.append(answers[0] == expected[0] and answers[2] == expected[2])
+    i_acc.append(answers[1] == expected[1] and answers[3] == expected[3])
+
+    q_acc.append(answers[0] == expected[0] and answers[1] == expected[1])
+    q_acc.append(answers[2] == expected[2] and answers[3] == expected[3])
+
+    g_acc.append(
+        answers[0] == expected[0]
+        and answers[1] == expected[1]
+        and answers[2] == expected[2]
+        and answers[3] == expected[3]
+    )
+
+
+print("Overall Accuracy:", sum(acc) / len(acc))
+print("Image Accuracy:", sum(i_acc) / len(i_acc))
+print("Question Accuracy:", sum(q_acc) / len(q_acc))
+print("Group Accuracy:", sum(g_acc) / len(g_acc))

moondream/eval/pope.py

@@ -0,0 +1,64 @@
+from datasets import load_dataset
+from tqdm import tqdm
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from ..hf import detect_device
+
+MODEL_ID = "vikhyatk/moondream2"
+DEVICE, DTYPE = detect_device()
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+moondream = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True,
+    attn_implementation="flash_attention_2",
+    torch_dtype=DTYPE,
+    device_map={"": DEVICE},
+)
+moondream.eval()
+
+pope_dataset = load_dataset("vikhyatk/POPE", split="test")
+
+stats = {
+    "random": (0, 0),
+    "popular": (0, 0),
+    "adversarial": (0, 0),
+}
+for row in tqdm(pope_dataset):
+    image = row["image"]
+    enc_image = moondream.encode_image(image)
+    for split in ["adversarial", "popular", "random"]:
+        for qa in row[split]:
+            question = qa["question"]
+            answer = qa["answer"]
+            prompt = f"{question}\nAnswer yes or no."
+            model_answer = moondream.answer_question(enc_image, prompt, tokenizer)
+            if model_answer.lower() == answer.lower():
+                stats[split] = (stats[split][0] + 1, stats[split][1] + 1)
+            else:
+                stats[split] = (stats[split][0], stats[split][1] + 1)
+
+print(
+    "Random:",
+    stats["random"][0],
+    "/",
+    stats["random"][1],
+    ":",
+    stats["random"][0] * 100.0 / stats["random"][1],
+)
+print(
+    "Popular:",
+    stats["popular"][0],
+    "/",
+    stats["popular"][1],
+    ":",
+    stats["popular"][0] * 100.0 / stats["popular"][1],
+)
+print(
+    "Adversarial:",
+    stats["adversarial"][0],
+    "/",
+    stats["adversarial"][1],
+    ":",
+    stats["adversarial"][0] * 100.0 / stats["adversarial"][1],
+)

moondream/eval/tallyqa.py

@@ -0,0 +1,72 @@
+# Expects Visual Genome to be downloaded to `data/vg` and the TallyQA test set
+# to be present at `data/tallyqa/test.json`.
+#
+# Steps to download Visual Genome and TallyQA:
+#
+#   mkdir -p data/vg/VG_100K
+#   mkdir -p data/vg/VG_100K_2
+#   mkdir -p data/tallyqa
+#   wget -P data/vg/VG_100K_2/ https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip
+#   wget -P data/vg/VG_100K/ https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip
+#   wget -P data/tallyqa/ https://github.com/manoja328/TallyQA_dataset/raw/master/tallyqa.zip
+#   unzip data/vg/VG_100K_2/images2.zip -d data/vg/
+#   unzip data/vg/VG_100K/images.zip -d data/vg/
+#   unzip data/tallyqa/tallyqa.zip -d data/tallyqa/
+#   rm data/vg/VG_100K_2/images2.zip
+#   rm data/vg/VG_100K/images.zip
+#   rm data/tallyqa/tallyqa.zip
+
+import json
+
+from PIL import Image
+from tqdm import tqdm
+from transformers import AutoTokenizer
+
+from ..hf import Moondream, detect_device
+
+BATCH_SIZE = 16
+DEVICE, DTYPE = detect_device()
+
+model_id = "vikhyatk/moondream2"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = Moondream.from_pretrained(
+    model_id,
+    attn_implementation="flash_attention_2",
+    torch_dtype=DTYPE,
+    device_map={"": DEVICE},
+)
+model.eval()
+
+total = 0
+total_simple = 0
+correct = 0
+correct_simple = 0
+
+# Iterate over tallyqa_test in batches of BATCH_SIZE
+tallyqa_test = json.load(open("data/tallyqa/test.json"))
+for i in tqdm(range(0, len(tallyqa_test), BATCH_SIZE)):
+    batch = tallyqa_test[i : i + BATCH_SIZE]
+
+    images = [Image.open(f"data/vg/{item['image']}") for item in batch]
+    questions = [
+        item["question"] + " Answer in a word or phrase only." for item in batch
+    ]
+
+    answers = model.batch_answer(
+        images=images, prompts=questions, tokenizer=tokenizer, max_new_tokens=10
+    )
+
+    for answer, item in zip(answers, batch):
+        is_simple = item["issimple"]
+        is_correct = 1 if str(item["answer"]) == answer else 0
+
+        total += 1
+        correct += is_correct
+        if is_simple:
+            total_simple += 1
+            correct_simple += is_correct
+
+print(
+    f"Simple: {total_simple}, Correct: {correct_simple}, Accuracy: {correct_simple*100.0/total_simple}"
+)
+print(f"Total: {total}, Correct: {correct}, Accuracy: {correct*100.0/total}")

moondream/hf/__init__.py

@@ -0,0 +1,2 @@
+from .moondream import Moondream
+from .util import LATEST_REVISION, detect_device

moondream/hf/configuration_moondream.py

@@ -0,0 +1,96 @@
+from transformers import PretrainedConfig
+
+
+class PhiConfig(PretrainedConfig):
+    model_type = "phi"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=51200,
+        hidden_size=2048,
+        intermediate_size=8192,
+        num_hidden_layers=24,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attention_dropout=0.0,
+        hidden_act="gelu_new",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        layer_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        partial_rotary_factor=0.5,
+        bos_token_id=1,
+        eos_token_id=2,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.partial_rotary_factor = partial_rotary_factor
+        self._rope_scaling_validation()
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if (
+            rope_scaling_factor is None
+            or not isinstance(rope_scaling_factor, float)
+            or rope_scaling_factor <= 1.0
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}"
+            )
+
+
+class MoondreamConfig(PretrainedConfig):
+    model_type = "moondream1"
+
+    def __init__(self, **kwargs):
+        self.text_config = PhiConfig(**kwargs.pop("text_config", {}))
+        super().__init__(**kwargs)

moondream/hf/fourier_features.py

@@ -0,0 +1,19 @@
+# Adopted from https://github.com/crowsonkb/k-diffusion/blob/transformer-model-v2/k_diffusion/layers.py
+
+import math
+
+import torch
+import torch.nn as nn
+
+
+class FourierFeatures(nn.Module):
+    def __init__(self, in_features, out_features, std=1.0):
+        super().__init__()
+        assert out_features % 2 == 0
+        self.register_buffer(
+            "weight", torch.randn([out_features // 2, in_features]) * std
+        )
+
+    def forward(self, input):
+        f = 2 * math.pi * input @ self.weight.T
+        return torch.cat([f.cos(), f.sin()], dim=-1)

moondream/hf/modeling_phi.py

@@ -0,0 +1,1477 @@
+# coding=utf-8
+# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""PyTorch Phi model."""
+
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from packaging import version
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    get_torch_version,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    is_torchdynamo_compiling,
+    logging,
+    replace_return_docstrings,
+)
+
+from .configuration_moondream import PhiConfig
+
+if is_flash_attn_2_available():
+    from transformers.modeling_flash_attention_utils import _flash_attention_forward
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "PhiConfig"
+
+
+# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position
+def _prepare_4d_causal_attention_mask_with_cache_position(
+    attention_mask: torch.Tensor,
+    sequence_length: int,
+    target_length: int,
+    dtype: torch.dtype,
+    device: torch.device,
+    min_dtype: float,
+    cache_position: torch.Tensor,
+    batch_size: int,
+):
+    """
+    Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+    `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+    Args:
+        attention_mask (`torch.Tensor`):
+            A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
+        sequence_length (`int`):
+            The sequence length being processed.
+        target_length (`int`):
+            The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
+        dtype (`torch.dtype`):
+            The dtype to use for the 4D attention mask.
+        device (`torch.device`):
+            The device to plcae the 4D attention mask on.
+        min_dtype (`float`):
+            The minimum value representable with the dtype `dtype`.
+        cache_position (`torch.Tensor`):
+            Indices depicting the position of the input sequence tokens in the sequence.
+        batch_size (`torch.Tensor`):
+            Batch size.
+    """
+    if attention_mask is not None and attention_mask.dim() == 4:
+        # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+        causal_mask = attention_mask
+    else:
+        causal_mask = torch.full(
+            (sequence_length, target_length),
+            fill_value=min_dtype,
+            dtype=dtype,
+            device=device,
+        )
+        if sequence_length != 1:
+            causal_mask = torch.triu(causal_mask, diagonal=1)
+        causal_mask *= torch.arange(
+            target_length, device=device
+        ) > cache_position.reshape(-1, 1)
+        causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+        if attention_mask is not None:
+            causal_mask = (
+                causal_mask.clone()
+            )  # copy to contiguous memory for in-place edit
+            mask_length = attention_mask.shape[-1]
+            padding_mask = (
+                causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+            )
+            padding_mask = padding_mask == 0
+            causal_mask[:, :, :, :mask_length] = causal_mask[
+                :, :, :, :mask_length
+            ].masked_fill(padding_mask, min_dtype)
+
+    return causal_mask
+
+
+# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Phi
+class PhiRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (
+            self.base
+            ** (
+                torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device)
+                / self.dim
+            )
+        )
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings,
+            device=self.inv_freq.device,
+            dtype=torch.get_default_dtype(),
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=torch.int64
+        ).type_as(self.inv_freq)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# Copied from transformers.models.falcon.modeling_falcon.FalconLinearScalingRotaryEmbedding with Falcon->Phi
+class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
+    """PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+    ):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=torch.int64
+        ).type_as(self.inv_freq)
+        t = t / self.scaling_factor
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.models.falcon.modeling_falcon.FalconDynamicNTKScalingRotaryEmbedding with Falcon->Phi
+class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
+    """PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+    ):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings)
+                - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (
+                base
+                ** (
+                    torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device)
+                    / self.dim
+                )
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=torch.int64
+        ).type_as(self.inv_freq)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.mixtral.modeling_mixtral.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Phi
+class PhiMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.activation_fn = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(
+        batch, num_key_value_heads, n_rep, slen, head_dim
+    )
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class PhiAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: PhiConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.partial_rotary_factor = config.partial_rotary_factor
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.Wqkv = nn.Linear(
+            self.hidden_size, 3 * self.num_heads * self.head_dim, bias=True
+        )
+        self.out_proj = nn.Linear(
+            self.num_heads * self.head_dim, self.hidden_size, bias=True
+        )
+
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = PhiRotaryEmbedding(
+                int(self.partial_rotary_factor * self.head_dim),
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = PhiLinearScalingRotaryEmbedding(
+                    int(self.partial_rotary_factor * self.head_dim),
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = PhiDynamicNTKScalingRotaryEmbedding(
+                    int(self.partial_rotary_factor * self.head_dim),
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states, key_states, value_states = self.Wqkv(hidden_states).chunk(
+            3, dim=-1
+        )
+
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        # Partial rotary embedding
+        query_rot, query_pass = (
+            query_states[..., : self.rotary_emb.dim],
+            query_states[..., self.rotary_emb.dim :],
+        )
+        key_rot, key_pass = (
+            key_states[..., : self.rotary_emb.dim],
+            key_states[..., self.rotary_emb.dim :],
+        )
+        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
+        query_rot, key_rot = apply_rotary_pos_emb(
+            query_rot, key_rot, cos, sin, position_ids
+        )
+
+        # [batch_size, seq_length, num_heads, head_dim]
+        query_states = torch.cat((query_rot, query_pass), dim=-1)
+        key_states = torch.cat((key_rot, key_pass), dim=-1)
+
+        if past_key_value is not None:
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "partial_rotation_size": self.rotary_emb.dim,
+                "cache_position": cache_position,
+            }
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        # Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow
+        attn_weights = torch.matmul(
+            query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights += causal_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(value_states.dtype)
+        attn_weights = nn.functional.dropout(
+            attn_weights, p=self.attention_dropout, training=self.training
+        )
+
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.out_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class PhiFlashAttention2(PhiAttention):
+    """
+    Phi flash attention module. This module inherits from `PhiAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # PhiFlashAttention2 attention does not support output_attentions
+
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states, key_states, value_states = self.Wqkv(hidden_states).chunk(
+            3, dim=-1
+        )
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        # Partial rotary embedding
+        query_rot, query_pass = (
+            query_states[..., : self.rotary_emb.dim],
+            query_states[..., self.rotary_emb.dim :],
+        )
+        key_rot, key_pass = (
+            key_states[..., : self.rotary_emb.dim],
+            key_states[..., self.rotary_emb.dim :],
+        )
+        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
+        query_rot, key_rot = apply_rotary_pos_emb(
+            query_rot, key_rot, cos, sin, position_ids
+        )
+
+        # [batch_size, seq_length, num_heads, head_dim]
+        query_states = torch.cat((query_rot, query_pass), dim=-1)
+        key_states = torch.cat((key_rot, key_pass), dim=-1)
+
+        if past_key_value is not None:
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "partial_rotation_size": self.rotary_emb.dim,
+                "cache_position": cache_position,
+            }
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_dropout = self.attention_dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32.
+
+        if query_states.dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = _flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            position_ids=position_ids,
+            dropout=attn_dropout,
+            softmax_scale=None,
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+            is_causal=self.is_causal,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.out_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class PhiSdpaAttention(PhiAttention):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.require_contiguous_qkv = version.parse(
+            get_torch_version()
+        ) < version.parse("2.2.0")
+
+    """
+    SDPA attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `PhiAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from PhiAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "PhiModel is using PhiSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not "
+                "support `output_attentions=True`. Falling back to the manual attention implementation, but specifying "
+                "the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can "
+                'be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states, key_states, value_states = self.Wqkv(hidden_states).chunk(
+            3, dim=-1
+        )
+
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        # Partial rotary embedding
+        query_rot, query_pass = (
+            query_states[..., : self.rotary_emb.dim],
+            query_states[..., self.rotary_emb.dim :],
+        )
+        key_rot, key_pass = (
+            key_states[..., : self.rotary_emb.dim],
+            key_states[..., self.rotary_emb.dim :],
+        )
+        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
+        query_rot, key_rot = apply_rotary_pos_emb(
+            query_rot, key_rot, cos, sin, position_ids
+        )
+
+        # [batch_size, seq_length, num_heads, head_dim]
+        query_states = torch.cat((query_rot, query_pass), dim=-1)
+        key_states = torch.cat((key_rot, key_pass), dim=-1)
+
+        if past_key_value is not None:
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "partial_rotation_size": self.rotary_emb.dim,
+                "cache_position": cache_position,
+            }
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        causal_mask = attention_mask
+        if attention_mask is not None:
+            causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
+
+        # SDPA with memory-efficient backend is broken in torch==2.1.2 when using non-contiguous inputs and a custom
+        # attn_mask, so we need to call `.contiguous()` here. This was fixed in torch==2.2.0.
+        # Reference: https://github.com/pytorch/pytorch/issues/112577
+        if (
+            self.require_contiguous_qkv
+            and query_states.device.type == "cuda"
+            and attention_mask is not None
+        ):
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+        is_causal = True if causal_mask is None and q_len > 1 else False
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=causal_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            is_causal=is_causal,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+PHI_ATTENTION_CLASSES = {
+    "eager": PhiAttention,
+    "flash_attention_2": PhiFlashAttention2,
+    "sdpa": PhiSdpaAttention,
+}
+
+
+class PhiDecoderLayer(nn.Module):
+    def __init__(self, config: PhiConfig, layer_idx: int):
+        super().__init__()
+        self.mixer = PHI_ATTENTION_CLASSES[config._attn_implementation](
+            config, layer_idx=layer_idx
+        )
+        self.mlp = PhiMLP(config)
+        self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.resid_dropout = nn.Dropout(config.resid_pdrop)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`):
+                input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+                Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
+                `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.ln(hidden_states)
+
+        # Self Attention
+        attn_outputs, self_attn_weights, present_key_value = self.mixer(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+        )
+        attn_outputs = self.resid_dropout(attn_outputs)
+
+        feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
+        hidden_states = attn_outputs + feed_forward_hidden_states + residual
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+PHI_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`PhiConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare Phi Model outputting raw hidden-states without any specific head on top.",
+    PHI_START_DOCSTRING,
+)
+class PhiPreTrainedModel(PreTrainedModel):
+    config_class = PhiConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["PhiDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class Embedding(nn.Module):
+    def __init__(self, config: PhiConfig):
+        super().__init__()
+        self.wte = nn.Embedding(
+            config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
+        )
+
+    def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
+        return self.wte(input_ids)
+
+
+PHI_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
+"""
+
+
+@add_start_docstrings(
+    "The bare Phi Model outputting raw hidden-states without any specific head on top.",
+    PHI_START_DOCSTRING,
+)
+class PhiModel(PhiPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`]
+
+    Args:
+        config: PhiConfig
+    """
+
+    def __init__(self, config: PhiConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embd = Embedding(config)
+        self.embed_dropout = nn.Dropout(config.embd_pdrop)
+        self.h = nn.ModuleList(
+            [
+                PhiDecoderLayer(config, layer_idx)
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self._use_sdpa = config._attn_implementation == "sdpa"
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embd.wte
+
+    def set_input_embeddings(self, value):
+        self.embd.wte = value
+
+    @add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+            )
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        use_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache) and not self.training:
+            use_legacy_cache = True
+            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            logger.warning_once(
+                "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
+                "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)"
+            )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embd(input_ids)
+
+        if cache_position is None:
+            past_seen_tokens = (
+                past_key_values.get_seq_length() if past_key_values is not None else 0
+            )
+            cache_position = torch.arange(
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask,
+            inputs_embeds,
+            cache_position,
+            past_key_values,
+            output_attentions,
+        )
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.h:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    output_attentions,
+                    use_cache,
+                    past_key_values,
+                    cache_position,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = (
+                next_decoder_cache.to_legacy_cache()
+                if use_legacy_cache
+                else next_decoder_cache
+            )
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
+    def _update_causal_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool,
+    ):
+        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
+        # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
+        # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
+        # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
+
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+        past_seen_tokens = (
+            past_key_values.get_seq_length() if past_key_values is not None else 0
+        )
+        using_static_cache = isinstance(past_key_values, StaticCache)
+
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if (
+            self.config._attn_implementation == "sdpa"
+            and not using_static_cache
+            and not output_attentions
+        ):
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                is_training=self.training,
+            ):
+                return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        if using_static_cache:
+            target_length = past_key_values.get_max_length()
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+        causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
+            attention_mask,
+            sequence_length=sequence_length,
+            target_length=target_length,
+            dtype=dtype,
+            device=device,
+            min_dtype=min_dtype,
+            cache_position=cache_position,
+            batch_size=input_tensor.shape[0],
+        )
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type == "cuda"
+            and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            causal_mask = AttentionMaskConverter._unmask_unattended(
+                causal_mask, min_dtype
+            )
+
+        return causal_mask
+
+
+class CausalLMHead(nn.Module):
+    """Causal Language Modeling head. Simplified version."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.linear = nn.Linear(config.hidden_size, config.vocab_size)
+
+    def forward(self, hidden_states):
+        return self.linear(self.ln(hidden_states))
+
+
+class PhiForCausalLM(PhiPreTrainedModel):
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = PhiModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = CausalLMHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
+    def get_input_embeddings(self):
+        return self.transformer.embd.wte
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
+    def set_input_embeddings(self, value):
+        self.transformer.embd.wte = value
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
+    def get_output_embeddings(self):
+        return self.lm_head.linear
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.linear = new_embeddings
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
+    @replace_return_docstrings(
+        output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+    )
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        num_logits_to_keep: int = 0,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+            num_logits_to_keep (`int`, *optional*):
+                Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
+                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, PhiForCausalLM
+
+        >>> model = PhiForCausalLM.from_pretrained("microsoft/phi-1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1")
+
+        >>> prompt = "This is an example script ."
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        'This is an example script .\n\n\n\nfrom typing import List\n\ndef find_most_common_letter(words: List[str'
+        ```"""
+
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.transformer(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
+
+        loss = None
+        if labels is not None:
+            # Upcast to float if we need to compute the loss to avoid potential precision issues
+            logits = logits.float()
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.prepare_inputs_for_generation
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        inputs_embeds=None,
+        past_key_values=None,
+        attention_mask=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        num_logits_to_keep=0,
+        **kwargs,
+    ):
+        assert inputs_embeds is not None, "inputs_embeds is required"
+
+        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
+        if past_key_values is not None:
+            # When doing custom decoding for object detection, we don't update input_ids.
+            # So we will slice `inputs_embeds`` instead.
+            if input_ids.shape[1] == 0:
+                inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :]
+            else:
+                input_ids = input_ids[:, -cache_position.shape[0] :]
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                if input_ids.shape[1] == 0:
+                    position_ids = position_ids[:, -inputs_embeds.shape[1] :]
+                else:
+                    position_ids = position_ids[:, -input_ids.shape[1] :]
+
+                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s
+                # `mode="reduce-overhead`, as otherwise the input `position_ids` would have various
+                # stride during the decoding. Here, simply using `.contiguous()` is not sufficient as
+                # in the batch size = 1 case, `position_ids` is already contiguous but with varying
+                # stride which retriggers a capture.
+                position_ids = position_ids.clone(memory_format=torch.contiguous_format)
+
+        if cache_position[0] == 0:
+            model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
+        else:
+            # The clone here is for the same reason as for `position_ids`.
+            if past_key_values is not None and input_ids.shape[1] == 0:
+                model_inputs = {
+                    "input_ids": None,
+                    "inputs_embeds": inputs_embeds.clone(
+                        memory_format=torch.contiguous_format
+                    ),
+                }
+            else:
+                model_inputs = {
+                    "input_ids": input_ids.clone(memory_format=torch.contiguous_format),
+                    "inputs_embeds": None,
+                }
+
+        if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
+            if model_inputs["inputs_embeds"] is not None:
+                batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
+                device = model_inputs["inputs_embeds"].device
+            else:
+                batch_size, sequence_length = model_inputs["input_ids"].shape
+                device = model_inputs["input_ids"].device
+
+            dtype = self.lm_head.weight.dtype
+            min_dtype = torch.finfo(dtype).min
+
+            attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
+                attention_mask,
+                sequence_length=sequence_length,
+                target_length=past_key_values.get_max_length(),
+                dtype=dtype,
+                device=device,
+                min_dtype=min_dtype,
+                cache_position=cache_position,
+                batch_size=batch_size,
+            )
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+                "num_logits_to_keep": num_logits_to_keep,
+            }
+        )
+        return model_inputs

moondream/hf/moondream.py

@@ -0,0 +1,352 @@
+from typing import List, Literal, Optional
+
+import torch
+from PIL import Image
+from transformers import PreTrainedModel
+
+from .configuration_moondream import MoondreamConfig, PhiConfig
+from .modeling_phi import PhiForCausalLM
+from .region_model import RegionModel
+from .vision_encoder import VisionEncoder
+
+
+class Moondream(PreTrainedModel):
+    config_class = MoondreamConfig
+    _supports_flash_attn_2 = True
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.vision_encoder = VisionEncoder(
+            use_flash_attn=config._attn_implementation == "flash_attention_2"
+        )
+        self.region_model = RegionModel()
+
+        if type(config.text_config) == dict:
+            phi_config = PhiConfig(
+                **config.text_config, attn_implementation=config._attn_implementation
+            )
+        else:
+            phi_config = config.text_config
+        self.text_model = PhiForCausalLM(phi_config)
+
+    @property
+    def device(self):
+        return self.text_model.device
+
+    def encode_image(self, image):
+        with torch.no_grad():
+            return self.vision_encoder(image)
+
+    def input_embeds(self, prompt, image_embeds, tokenizer):
+        def _tokenize(txt):
+            return tokenizer(
+                txt, return_tensors="pt", add_special_tokens=False
+            ).input_ids.to(self.device)
+
+        text_emb = self.text_model.get_input_embeddings()
+
+        # Add BOS token
+        embeds = []
+        embeds.append(
+            text_emb((torch.tensor([[tokenizer.bos_token_id]], device=self.device)))
+        )
+
+        if "<image>" not in prompt:
+            embeds.append(text_emb(_tokenize(prompt)))
+        else:
+            assert prompt.count("<image>") == 1
+            before, after = prompt.split("<image>")
+            if len(before) > 0:
+                embeds.append(text_emb(_tokenize(before)))
+            embeds.append(image_embeds.to(self.device))
+            if len(after) > 0:
+                embeds.append(text_emb(_tokenize(after)))
+
+        return torch.cat(embeds, dim=1)
+
+    def get_input_embeddings(self):
+        return self.text_model.get_input_embeddings()
+
+    def generate(
+        self,
+        image_embeds,
+        prompt,
+        tokenizer,
+        max_new_tokens=128,
+        **kwargs,
+    ):
+        generate_config = {
+            "eos_token_id": tokenizer.eos_token_id,
+            "bos_token_id": tokenizer.bos_token_id,
+            "pad_token_id": tokenizer.bos_token_id,
+            "max_new_tokens": max_new_tokens,
+            **kwargs,
+        }
+
+        with torch.no_grad():
+            inputs_embeds = self.input_embeds(prompt, image_embeds, tokenizer)
+            attention_mask = torch.ones(
+                (inputs_embeds.shape[0], inputs_embeds.shape[1]), device=self.device
+            )
+            output_ids = self.text_model.generate(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                **generate_config,
+            )
+
+        return tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+
+    # Note: Not ready for use yet, intended for September release.
+    def caption(
+        self,
+        images: List[Image.Image],
+        tokenizer,
+        length: Optional[Literal["short"]] = None,
+        **kwargs,
+    ):
+        image_embeds = self.encode_image(images)
+
+        templated_prompts = [
+            f"<image>\n\n{'Short caption' if length == 'short' else 'Caption'}:"
+            for _ in images
+        ]
+        inputs_embeds = torch.stack(
+            [
+                self.input_embeds(prompt, image_embed.unsqueeze(0), tokenizer)[0]
+                for prompt, image_embed in zip(templated_prompts, image_embeds)
+            ]
+        )
+        attention_mask = torch.ones(
+            (inputs_embeds.shape[0], inputs_embeds.shape[1]), device=self.device
+        )
+
+        generate_config = {
+            "eos_token_id": tokenizer.eos_token_id,
+            "bos_token_id": tokenizer.bos_token_id,
+            "pad_token_id": tokenizer.bos_token_id,
+            "repetition_penalty": 1.2,
+            "max_new_tokens": 512,
+            **kwargs,
+        }
+
+        with torch.no_grad():
+            output_ids = self.text_model.generate(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                **generate_config,
+            )
+
+        return [
+            x.strip()
+            for x in tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+        ]
+
+    def answer_question(
+        self,
+        image_embeds,
+        question,
+        tokenizer,
+        chat_history="",
+        result_queue=None,
+        max_new_tokens=256,
+        **kwargs,
+    ):
+        prompt = f"<image>\n\n{chat_history}Question: {question}\n\nAnswer:"
+        answer = self.generate(
+            image_embeds,
+            prompt,
+            tokenizer=tokenizer,
+            max_new_tokens=max_new_tokens,
+            **kwargs,
+        )[0]
+        cleaned_answer = answer.strip()
+
+        # Use the result_queue to pass the result if it is provided
+        if result_queue:
+            result_queue.put(cleaned_answer)
+        else:
+            return cleaned_answer
+
+    def batch_answer(
+        self,
+        images,
+        prompts,
+        tokenizer,
+        **kwargs,
+    ):
+        image_embeds = self.encode_image(images)
+
+        templated_prompts = [
+            f"<image>\n\nQuestion: {prompt}\n\nAnswer:" for prompt in prompts
+        ]
+        prompt_embs = [
+            self.input_embeds(prompt, image_embed.unsqueeze(0), tokenizer)[0]
+            for prompt, image_embed in zip(templated_prompts, image_embeds)
+        ]
+
+        bos_emb = prompt_embs[0][0]
+        max_len = max([p.shape[0] for p in prompt_embs])
+
+        inputs_embeds = torch.cat(
+            [
+                torch.cat([bos_emb.repeat(max_len - p.shape[0], 1), p]).unsqueeze(0)
+                for p in prompt_embs
+            ],
+            dim=0,
+        )
+        attention_mask = torch.cat(
+            [
+                torch.cat(
+                    [
+                        torch.zeros(
+                            1,
+                            max_len - p.shape[0],
+                            device=self.device,
+                            dtype=torch.long,
+                        ),
+                        torch.ones(1, p.shape[0], device=self.device, dtype=torch.long),
+                    ],
+                    dim=1,
+                )
+                for p in prompt_embs
+            ],
+            dim=0,
+        )
+
+        generate_config = {
+            "eos_token_id": tokenizer.eos_token_id,
+            "bos_token_id": tokenizer.bos_token_id,
+            "pad_token_id": tokenizer.bos_token_id,
+            "max_new_tokens": 512,
+            **kwargs,
+        }
+
+        with torch.no_grad():
+            output_ids = self.text_model.generate(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                **generate_config,
+            )
+
+        return [
+            x.strip()
+            for x in tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+        ]
+
+    def detect(
+        self,
+        image: Image.Image,
+        query: str,
+        tokenizer,
+        max_objects=50,
+    ):
+        prompt = f"<image>\n\nDetect: {query}\n\n"
+        image_embeds = self.encode_image(image)
+        inputs_embeds = self.input_embeds(prompt, image_embeds, tokenizer)
+        generate_config = {
+            "eos_token_id": tokenizer.eos_token_id,
+            "bos_token_id": tokenizer.bos_token_id,
+            "pad_token_id": tokenizer.bos_token_id,
+            "max_new_tokens": 1,
+        }
+
+        past_key_values = None
+        generated_boxes = []
+
+        with torch.no_grad():
+            while len(generated_boxes) < max_objects:
+                # x coordinate
+                attention_mask = torch.ones(
+                    (inputs_embeds.shape[0], inputs_embeds.shape[1]), device=self.device
+                )
+                output = self.text_model.generate(
+                    inputs_embeds=inputs_embeds,
+                    past_key_values=past_key_values,
+                    attention_mask=attention_mask,
+                    return_dict_in_generate=True,
+                    output_hidden_states=True,
+                    **generate_config,
+                )
+                if output["sequences"][0][0].item() == tokenizer.eos_token_id:
+                    break
+
+                x_coord_hidden = output["hidden_states"][0][-1][:, -1, :]
+                x_coord_logits = self.region_model.decode_coordinate(x_coord_hidden)
+                x_coord_decoded = (
+                    torch.argmax(x_coord_logits, dim=-1).to(torch.float32) / 1024
+                ).to(torch.float16)
+                x_coord_encoded = self.region_model.encode_coordinate(
+                    x_coord_decoded
+                ).unsqueeze(0)
+                inputs_embeds = torch.cat(
+                    [inputs_embeds, x_coord_encoded.unsqueeze(0)], dim=1
+                )
+                past_key_values = output["past_key_values"]
+
+                # y coordinate
+                attention_mask = torch.ones(
+                    (inputs_embeds.shape[0], inputs_embeds.shape[1]), device=self.device
+                )
+                output = self.text_model.generate(
+                    inputs_embeds=inputs_embeds,
+                    past_key_values=past_key_values,
+                    attention_mask=attention_mask,
+                    return_dict_in_generate=True,
+                    output_hidden_states=True,
+                    **generate_config,
+                )
+                y_coord_hidden = output["hidden_states"][0][-1][:, -1, :]
+                y_coord_logits = self.region_model.decode_coordinate(y_coord_hidden)
+                y_coord_decoded = (
+                    torch.argmax(y_coord_logits, dim=-1).to(torch.float32) / 1024
+                ).to(torch.float16)
+                y_coord_encoded = self.region_model.encode_coordinate(
+                    y_coord_decoded
+                ).unsqueeze(0)
+                inputs_embeds = torch.cat(
+                    [inputs_embeds, y_coord_encoded.unsqueeze(0)], dim=1
+                )
+                past_key_values = output["past_key_values"]
+
+                # size (h and w)
+                attention_mask = torch.ones(
+                    (inputs_embeds.shape[0], inputs_embeds.shape[1]), device=self.device
+                )
+                output = self.text_model.generate(
+                    inputs_embeds=inputs_embeds,
+                    past_key_values=past_key_values,
+                    attention_mask=attention_mask,
+                    return_dict_in_generate=True,
+                    output_hidden_states=True,
+                    **generate_config,
+                )
+                size_hidden = output["hidden_states"][0][-1][:, -1, :]
+                size_logits = self.region_model.decode_size(size_hidden)
+                size_decoded = (
+                    torch.argmax(size_logits, dim=-1).to(torch.float32) / 1024
+                ).to(torch.float16)
+                size_encoded = self.region_model.encode_size(size_decoded)
+                inputs_embeds = torch.cat(
+                    [inputs_embeds, size_encoded.unsqueeze(0)], dim=1
+                )
+                past_key_values = output["past_key_values"]
+
+                x_center = x_coord_decoded[0].item()
+                y_center = y_coord_decoded[0].item()
+                w_center = size_decoded[0][0].item()
+                h_center = size_decoded[0][1].item()
+                x_min = max(x_center - w_center / 2, 0)
+                y_min = max(y_center - h_center / 2, 0)
+                x_max = min(x_center + w_center / 2, 1)
+                y_max = min(y_center + h_center / 2, 1)
+
+                generated_boxes.append(
+                    {
+                        "x_min": x_min,
+                        "y_min": y_min,
+                        "x_max": x_max,
+                        "y_max": y_max,
+                    }
+                )
+
+        return generated_boxes

moondream/hf/region_model.py

@@ -0,0 +1,69 @@
+import torch
+import torch.nn as nn
+
+from .fourier_features import FourierFeatures
+
+
+class MLP(nn.Module):
+
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int = None,
+        out_features: int = None,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features * 4
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = nn.GELU(approximate="tanh")
+        self.fc2 = nn.Linear(hidden_features, out_features)
+
+        torch.nn.init.kaiming_normal_(
+            self.fc1.weight, mode="fan_in", nonlinearity="relu"
+        )
+        torch.nn.init.kaiming_normal_(
+            self.fc2.weight, mode="fan_in", nonlinearity="relu"
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.fc2(x)
+        return x
+
+
+class RegionModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.coordinate_features = FourierFeatures(1, 256)
+        self.coordinate_encoder = nn.Linear(256, 2048)
+        self.size_features = FourierFeatures(2, 512)
+        self.size_encoder = nn.Linear(512, 2048)
+
+        self.coordinate_decoder = MLP(2048, 8192, 1024)
+        self.size_decoder = MLP(2048, 8192, 2048)
+
+    def encode_coordinate(self, coordinate):
+        return self.coordinate_encoder(self.coordinate_features(coordinate))
+
+    def encode_size(self, size):
+        return self.size_encoder(self.size_features(size))
+
+    def decode_coordinate(self, logit):
+        return self.coordinate_decoder(logit)
+
+    def decode_size(self, logit):
+        o = self.size_decoder(logit)
+        return o.view(-1, 2, 1024)
+
+    def encode(self, position, size):
+        c = self.encode_coordinate(position.view(2, 1)).view(2, 2048)
+        return torch.stack([c[0], c[1], self.encode_size(size)], dim=0)
+
+    def decode(self, position_logits, size_logits):
+        return (
+            self.decode_coordinate(position_logits),
+            self.decode_size(size_logits),
+        )

moondream/hf/util.py

@@ -0,0 +1,15 @@
+import torch
+
+LATEST_REVISION = "2024-08-26"
+
+
+def detect_device():
+    """
+    Detects the appropriate device to run on, and return the device and dtype.
+    """
+    if torch.cuda.is_available():
+        return torch.device("cuda"), torch.float16
+    elif torch.backends.mps.is_available():
+        return torch.device("mps"), torch.float16
+    else:
+        return torch.device("cpu"), torch.float32

moondream/hf/vision_encoder.py

@@ -0,0 +1,325 @@
+from typing import Union
+
+import PIL
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from torch import nn
+from torchvision.transforms.v2 import (
+    Compose,
+    InterpolationMode,
+    Normalize,
+    Resize,
+    ToDtype,
+    ToImage,
+)
+from transformers.utils import is_flash_attn_2_available
+
+try:
+    if is_flash_attn_2_available():
+        from flash_attn.modules.mha import FlashSelfAttention
+    else:
+        FlashSelfAttention = None
+except ImportError:
+    FlashSelfAttention = None
+
+
+class Attention(nn.Module):
+
+    def __init__(self, dim, num_heads=16, use_flash_attn=False):
+        super().__init__()
+        assert dim % num_heads == 0, "dim should be divisible by num_heads"
+
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+
+        self.qkv = nn.Linear(dim, dim * 3)
+        self.proj = nn.Linear(dim, dim)
+
+        if use_flash_attn and FlashSelfAttention is not None:
+            self.flash_attn = FlashSelfAttention()
+        else:
+            self.flash_attn = None
+
+        torch.nn.init.kaiming_normal_(
+            self.qkv.weight, mode="fan_in", nonlinearity="relu"
+        )
+        torch.nn.init.kaiming_normal_(
+            self.proj.weight, mode="fan_in", nonlinearity="relu"
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.flash_attn is not None:
+            qkv = self.qkv(x)
+            qkv = rearrange(
+                qkv, "... (three h d) -> ... three h d", three=3, h=self.num_heads
+            )
+            attn_output = self.flash_attn(qkv)
+            output = rearrange(attn_output, "... h d -> ... (h d)")
+            output = self.proj(output)
+            return output
+        else:
+            B, N, C = x.shape
+            qkv = (
+                self.qkv(x)
+                .reshape(B, N, 3, self.num_heads, self.head_dim)
+                .permute(2, 0, 3, 1, 4)
+            )
+            q, k, v = qkv.unbind(0)
+
+            x = F.scaled_dot_product_attention(q, k, v)
+
+            x = x.transpose(1, 2).reshape(B, N, C)
+            x = self.proj(x)
+            return x
+
+
+class VitBlock(nn.Module):
+
+    def __init__(self, embed_dim, use_flash_attn=False):
+        super().__init__()
+        self.attn = Attention(embed_dim, use_flash_attn=use_flash_attn)
+        self.mlp = MLP(embed_dim, 4304)
+        self.norm1 = nn.LayerNorm(embed_dim)
+        self.norm2 = nn.LayerNorm(embed_dim)
+
+    def forward(self, x):
+        x = x + self.attn(self.norm1(x))
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class VisionTransformer(nn.Module):
+
+    def __init__(self, use_flash_attn=False):
+        super().__init__()
+
+        embed_len = 729
+        embed_dim = 1152
+
+        self.patch_embed = LinearPatchEmbedding()
+        self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * 0.02)
+        self.blocks = nn.Sequential(
+            *[VitBlock(embed_dim, use_flash_attn=use_flash_attn) for _ in range(27)]
+        )
+        self.norm = nn.LayerNorm(embed_dim)
+
+    def forward(self, x):
+        x = self.patch_embed(x)
+        x = x + self.pos_embed
+        for block in self.blocks:
+            x = block(x)
+        return self.norm(x)
+
+
+class EncoderWrapper(nn.Module):
+
+    def __init__(self, use_flash_attn=False):
+        super().__init__()
+        self.model = nn.ModuleDict({"visual": VisionTransformer(use_flash_attn)})
+
+    def forward(self, x):
+        return self.model["visual"](x)
+
+
+class LinearPatchEmbedding(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(588, 1152)
+
+    def forward(self, x):
+        b, c, hp1, wp2 = x.shape
+        p1, p2 = 14, 14
+        h, w = hp1 // p1, wp2 // p2
+        x = x.reshape(b, c, h, p1, w, p2)
+        x = x.permute(0, 2, 4, 1, 3, 5)
+        x = x.reshape(b, h * w, c * p1 * p2)
+
+        return self.linear(x)
+
+
+class MLP(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int = None,
+        out_features: int = None,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = nn.GELU(approximate="tanh")
+        self.fc2 = nn.Linear(hidden_features, out_features)
+
+        torch.nn.init.kaiming_normal_(
+            self.fc1.weight, mode="fan_in", nonlinearity="relu"
+        )
+        torch.nn.init.kaiming_normal_(
+            self.fc2.weight, mode="fan_in", nonlinearity="relu"
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.fc2(x)
+        return x
+
+
+class VisionProjection(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        image_embedding_dim = 1152
+        model_dim = 2048
+        hidden_dim = model_dim * 4
+
+        self.mlp = MLP(image_embedding_dim * 2, hidden_dim, model_dim)
+
+    @property
+    def device(self):
+        return self.mlp.fc1.weight.device
+
+    def forward(self, x):
+        return self.mlp(x)
+
+
+def create_patches(image, patch_size=(378, 378)):
+    assert image.dim() == 3, "Image must be in CHW format"
+
+    _, height, width = image.shape  # Channels, Height, Width
+    patch_height, patch_width = patch_size
+
+    if height == patch_height and width == patch_width:
+        return []
+
+    # Iterate over the image and create patches
+    patches = []
+    for i in range(0, height, patch_height):
+        row_patches = []
+        for j in range(0, width, patch_width):
+            patch = image[:, i : i + patch_height, j : j + patch_width]
+            row_patches.append(patch)
+        patches.append(torch.stack(row_patches))
+    return patches
+
+
+class VisionEncoder(nn.Module):
+
+    def __init__(self, use_flash_attn=False):
+        super().__init__()
+
+        self.encoder = EncoderWrapper(use_flash_attn)
+        self.projection = VisionProjection()
+        self.supported_sizes = [(378, 378), (378, 756), (756, 378), (756, 756)]
+
+    @property
+    def device(self):
+        return self.projection.mlp.fc1.weight.device
+
+    @property
+    def dtype(self):
+        return self.projection.mlp.fc1.weight.dtype
+
+    def preprocess(self, image: PIL.Image.Image):
+        width, height = image.size
+        max_dim = max(width, height)
+        if max_dim < 512:
+            im_size = (378, 378)
+        else:
+            aspect_ratio = width / height
+            im_size = min(
+                self.supported_sizes,
+                key=lambda size: (
+                    abs((size[1] / size[0]) - aspect_ratio),
+                    abs(size[0] - width) + abs(size[1] - height),
+                ),
+            )
+
+        return Compose(
+            [
+                Resize(size=im_size, interpolation=InterpolationMode.BICUBIC),
+                ToImage(),
+                ToDtype(torch.float16, scale=True),
+                Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+            ]
+        )(image)
+
+    def forward(
+        self, images: Union[PIL.Image.Image, list[PIL.Image.Image], torch.Tensor]
+    ) -> torch.Tensor:
+        im_list = None
+        if isinstance(images, torch.Tensor):
+            # Input must have dimensions (B, C, H, W)
+            assert (
+                len(images.shape) == 4
+            ), "Tensor input must have dimensions (B, C, H, W)"
+            im_list = list(images)
+        elif isinstance(images, PIL.Image.Image):
+            im_list = [images]
+        elif isinstance(images, list):
+            im_list = images
+        else:
+            raise ValueError(
+                "Input must be a PIL image, list of PIL images, or a tensor"
+            )
+
+        # Preprocess unless the images are already tensors (indicating that
+        # they have already been preprocessed)
+        if not isinstance(im_list[0], torch.Tensor):
+            im_list = [self.preprocess(im.convert("RGB")) for im in im_list]
+
+        patches = [create_patches(im) for im in im_list]
+        flat_patches = [patch for image_patches in patches for patch in image_patches]
+
+        # Images may be variable size, and need to be resized to a common size after
+        # creating patches.
+        resized_images = [
+            F.interpolate(im.unsqueeze(0), size=(378, 378), mode="bilinear")
+            for im in im_list
+        ]
+
+        combined_images = torch.cat([*resized_images, *flat_patches], dim=0)
+        combined_images = combined_images.to(self.device, dtype=self.dtype)
+
+        combined_features = self.encoder(combined_images)
+
+        full_img_features = combined_features[: len(im_list)]
+        patch_features = (
+            combined_features[len(im_list) :].transpose(1, 2).view(-1, 1152, 27, 27)
+        )
+
+        # Reshape patch features back to their original structure
+        reshaped_patch_features = []
+        patch_idx = 0
+        for i, patch_set in enumerate(patches):
+            if len(patch_set) == 0:
+                reshaped_patch_features.append(
+                    full_img_features[i].transpose(0, 1).view(1152, 27, 27)
+                )
+            else:
+                sample_features = []
+                for row_patches in patch_set:
+                    row_len = len(row_patches)
+                    row_features = patch_features[
+                        patch_idx : patch_idx + row_len
+                    ]  # row_len, T, C
+                    row_features = torch.cat(
+                        list(row_features), dim=2
+                    )  # T, C * row_len
+                    patch_idx += row_len
+                    sample_features.append(row_features)
+                sample_features = torch.cat(sample_features, dim=1)
+                sample_features = F.adaptive_avg_pool2d(
+                    sample_features, output_size=(27, 27)
+                )
+                reshaped_patch_features.append(sample_features)
+        reshaped_patch_features = (
+            torch.stack(reshaped_patch_features).view(-1, 1152, 729).transpose(1, 2)
+        )
+
+        final_features = torch.cat([full_img_features, reshaped_patch_features], dim=2)
+
+        return self.projection(final_features)

moondream/torch/layers.py

@@ -0,0 +1,68 @@
+from dataclasses import dataclass
+from typing import Literal
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+
+def gelu_approx(x):
+    return F.gelu(x, approximate="tanh")
+
+
+@dataclass
+class LinearWeights:
+    weight: torch.Tensor
+    bias: torch.Tensor
+
+
+def linear(x: torch.Tensor, w: LinearWeights) -> torch.Tensor:
+    return F.linear(x, w.weight, w.bias)
+
+
+@dataclass
+class LayerNormWeights:
+    weight: torch.Tensor
+    bias: torch.Tensor
+
+
+def layer_norm(x: torch.Tensor, w: LayerNormWeights) -> torch.Tensor:
+    return F.layer_norm(x, w.bias.shape, w.weight, w.bias)
+
+
+@dataclass
+class MLPWeights:
+    fc1: LinearWeights
+    fc2: LinearWeights
+    act: Literal["gelu_approx"] = "gelu_approx"
+
+
+def mlp(x: torch.Tensor, w: MLPWeights) -> torch.Tensor:
+    x = linear(x, w.fc1)
+    if w.act == "gelu_approx":
+        x = gelu_approx(x)
+    else:
+        raise NotImplementedError(f"Activation function {w.act} not implemented.")
+    x = linear(x, w.fc2)
+    return x
+
+
+@dataclass
+class AttentionWeights:
+    qkv: LinearWeights
+    proj: LinearWeights
+    n_heads: int
+
+
+def attn(x: torch.Tensor, w: AttentionWeights) -> torch.Tensor:
+    bsz, q_len, d_model = x.shape
+    n_heads, head_dim = w.n_heads, d_model // w.n_heads
+
+    q, k, v = [
+        t.view(bsz, q_len, n_heads, head_dim).transpose(1, 2)
+        for t in linear(x, w.qkv).chunk(3, dim=-1)
+    ]
+    out = F.scaled_dot_product_attention(q, k, v)
+    out = out.transpose(1, 2).reshape(bsz, q_len, d_model)
+    out = linear(out, w.proj)
+    return out

moondream/torch/rope.py

@@ -0,0 +1,46 @@
+# Ethically sourced from https://github.com/xjdr-alt/entropix
+
+from typing import Tuple
+
+import torch
+
+
+def precompute_freqs_cis(
+    dim: int,
+    end: int,
+    theta: float = 10000.0,
+    use_scaled: bool = False,
+    dtype: torch.dtype = torch.float32,
+) -> torch.Tensor:
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=dtype)[: (dim // 2)] / dim))
+    t = torch.arange(end, dtype=dtype).unsqueeze(1)
+    freqs = t * freqs.unsqueeze(0)
+    freqs = torch.exp(1j * freqs)
+    return torch.stack([freqs.real, freqs.imag], dim=-1)
+
+
+def apply_rotary_emb(
+    x: torch.Tensor,
+    freqs_cis: torch.Tensor,
+    position_ids: torch.Tensor,
+    interleave: bool = False,
+) -> torch.Tensor:
+    rot_dim = freqs_cis.shape[-2] * 2
+    x_rot, x_pass = x[..., :rot_dim], x[..., rot_dim:]
+
+    if interleave:
+        xq_r = x_rot.float().reshape(*x_rot.shape[:-1], -1, 2)[..., 0]
+        xq_i = x_rot.float().reshape(*x_rot.shape[:-1], -1, 2)[..., 1]
+    else:
+        d_q = x_rot.shape[-1] // 2
+        xq_r, xq_i = x_rot[..., :d_q], x_rot[..., d_q:]
+
+    freqs_cos = freqs_cis[..., 0][position_ids, :].unsqueeze(0).unsqueeze(0)
+    freqs_sin = freqs_cis[..., 1][position_ids, :].unsqueeze(0).unsqueeze(0)
+
+    # Complex multiplication: (a + bi) * (c + di) = (ac - bd) + (ad + bc)i
+    xq_out_r = xq_r * freqs_cos - xq_i * freqs_sin
+    xq_out_i = xq_r * freqs_sin + xq_i * freqs_cos
+    xq_out = torch.stack((xq_out_r, xq_out_i), dim=-1).flatten(-2)
+
+    return torch.cat([xq_out.to(x.dtype), x_pass], dim=-1)

moondream/torch/sample.py

@@ -0,0 +1,99 @@
+import argparse
+import json
+import os
+
+import torch
+from PIL import Image
+from transformers import AutoTokenizer
+
+from .rope import precompute_freqs_cis
+from .text import lm_head, text_decoder, text_encoder
+from .vision import encode_image
+from .weights import load_from_pt, load_from_safetensors
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--image", "-i", type=str, required=True)
+    parser.add_argument("--prompt", "-p", type=str, required=True)
+    parser.add_argument("--model", "-m", type=str, required=True)
+    parser.add_argument("--config", "-c", type=str, default="{}")
+    parser.add_argument("--max-tokens", "-t", type=int, default=200)
+    parser.add_argument("--sampler", "-s", type=str, default="greedy")
+    args = parser.parse_args()
+
+    if torch.cuda.is_available():
+        torch.set_default_device("cuda")
+    elif torch.backends.mps.is_available():
+        torch.set_default_device("mps")
+
+    # Load config.
+    config = json.loads(args.config)
+    text_n_heads = config.get("text_n_heads", 32)
+
+    # Load model.
+    model_path = args.model
+    if not os.path.exists(model_path):
+        raise FileNotFoundError(f"Model not found at {model_path}")
+    if model_path.endswith(".pt"):
+        model = load_from_pt(model_path, **config)
+    elif model_path.endswith(".safetensors"):
+        model = load_from_safetensors(model_path, **config)
+    else:
+        raise ValueError(f"Invalid model format: {model_path}")
+
+    # Encode image.
+    image_path = args.image
+    if not os.path.exists(image_path):
+        raise FileNotFoundError(f"Image not found at {image_path}")
+    image = Image.open(image_path)
+    image = image.resize((378, 378))
+    image_tensor = encode_image(image, model.vision)
+
+    # Encode text, and create inputs_embeds.
+    tokenizer = AutoTokenizer.from_pretrained("vikhyatk/moondream2")
+    prompt = f"\n\nQuestion: {args.prompt}\n\nAnswer:"
+    input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"]
+    input_ids = torch.cat([torch.tensor([[tokenizer.eos_token_id]]), input_ids], dim=1)
+    inputs_embeds = text_encoder(input_ids, model.text)
+    inputs_embeds = torch.cat(
+        [
+            inputs_embeds[:, 0:1, :],
+            image_tensor.unsqueeze(0),
+            inputs_embeds[:, 1:, :],
+        ],
+        dim=1,
+    )
+
+    kv_cache = torch.empty(24, 2, 1, text_n_heads, 2048, 64, dtype=torch.float16)
+    freqs_cis = precompute_freqs_cis(32, 2048)
+    pos = 0
+
+    for _ in range(args.max_tokens):
+        with torch.no_grad():
+            hidden, kv_cache_update = text_decoder(
+                inputs_embeds, model.text, kv_cache[:, :, :, :, :pos, :], freqs_cis
+            )
+            logits = lm_head(hidden, model.text)
+            kv_cache[:, :, :, :, pos : pos + kv_cache_update.size(-2), :] = (
+                kv_cache_update
+            )
+            pos += kv_cache_update.size(-2)
+
+            if args.sampler == "multinomial":
+                next_token = torch.multinomial(
+                    torch.softmax(logits, dim=-1), num_samples=1
+                ).squeeze(0)
+            elif args.sampler == "greedy":
+                next_token = torch.argmax(logits, dim=-1)
+            else:
+                raise ValueError(f"Invalid sampler: {args.sampler}")
+
+            if next_token == tokenizer.eos_token_id:
+                print()
+                break
+
+            input_ids = next_token.unsqueeze(0)
+            inputs_embeds = text_encoder(input_ids, model.text)
+
+            output_text = tokenizer.batch_decode(input_ids)[0]
+            print(output_text, end="", flush=True)

moondream/torch/text.py

@@ -0,0 +1,90 @@
+import torch
+from torch.nn import functional as F
+
+from .layers import layer_norm, linear, mlp
+from .rope import apply_rotary_emb, precompute_freqs_cis
+from .weights import AttentionWeights, TextModel, load_from_safetensors
+
+
+def text_encoder(input_ids: torch.Tensor, w: TextModel):
+    return F.embedding(input_ids, w.wte)
+
+
+def attn_mask(pos, seq_len):
+    """
+    Create an attention mask that aligns with the bottom right of the
+    attention matrix. For example, if q_len = 2 and kv_len = 5, we want the
+    following:
+
+         1 1 1 1 0
+         1 1 1 1 1
+
+    and not this, which is what we get by default if we just set is_causal.
+
+         1 0 0 0 0
+         1 1 0 0 0
+    """
+    mask = torch.ones(seq_len, pos + seq_len, dtype=torch.bool)
+    mask[:, pos:] = torch.tril(torch.ones(seq_len, seq_len, dtype=torch.bool))
+    mask = mask.unsqueeze(0).unsqueeze(0)  # Add batch and head dimensions
+    return mask
+
+
+def attn(
+    x: torch.Tensor,
+    w: AttentionWeights,
+    freqs_cis: torch.Tensor,
+    layer_kv_cache: torch.Tensor,
+):
+    bsz, q_len, d_model = x.shape
+    pos = 0 if layer_kv_cache is None else layer_kv_cache.shape[3]
+    n_heads, head_dim = w.n_heads, d_model // w.n_heads
+
+    q, k, v = [
+        t.view(bsz, q_len, n_heads, head_dim).transpose(1, 2)
+        for t in linear(x, w.qkv).chunk(3, dim=-1)
+    ]
+
+    position_ids = torch.arange(pos, pos + q_len, dtype=torch.long)
+    q = apply_rotary_emb(q, freqs_cis, position_ids)
+    k = apply_rotary_emb(k, freqs_cis, position_ids)
+
+    k_, v_ = k, v
+    if layer_kv_cache is not None:
+        k = torch.cat([layer_kv_cache[0], k], dim=2)
+        v = torch.cat([layer_kv_cache[1], v], dim=2)
+
+    out = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask(pos, q_len)).to(
+        # This type conversion isn't needed when running in PyTorch directly, but the
+        # ONNX export runs attention in float32 because the attention mask is cast to
+        # float32.
+        x.dtype
+    )
+    out = out.transpose(1, 2).reshape(bsz, q_len, d_model)
+    out = linear(out, w.proj)
+    return out, torch.stack([k_, v_])
+
+
+def text_decoder(
+    inputs_embeds: torch.Tensor,
+    w: TextModel,
+    kv_cache: torch.Tensor,
+    freqs_cis: torch.Tensor,
+):
+    hidden_BTC = inputs_embeds
+    new_kv_cache = [torch.empty(0)] * len(w.blocks)
+
+    for i, block in enumerate(w.blocks):
+        l_in = layer_norm(hidden_BTC, block.ln)
+        l_attn, new_kv_cache[i] = attn(l_in, block.attn, freqs_cis, kv_cache[i])
+        l_mlp = mlp(l_in, block.mlp)
+        hidden_BTC = hidden_BTC + l_attn + l_mlp
+
+    return hidden_BTC, torch.stack(new_kv_cache)
+
+
+def lm_head(hidden_BTC: torch.Tensor, w: TextModel):
+    hidden_BC = hidden_BTC[:, -1, :]
+    hidden_BC = layer_norm(hidden_BC, w.post_ln)
+    logits = linear(hidden_BC, w.lm_head)
+    return logits

moondream/torch/vision.py

@@ -0,0 +1,104 @@
+from typing import List, Tuple
+
+import torch
+from einops import rearrange
+from PIL import Image
+from torch.nn import functional as F
+from torchvision.transforms.v2 import InterpolationMode
+from torchvision.transforms.v2.functional import normalize
+from torchvision.transforms.v2.functional import resize as tv_resize
+from torchvision.transforms.v2.functional import to_dtype, to_image
+
+from .layers import attn, layer_norm, linear, mlp
+from .weights import VisionModel, load_from_safetensors
+
+
+def im_resize(
+    image: Image.Image,
+    size: List[int],
+    interpolation: InterpolationMode = InterpolationMode.BICUBIC,
+) -> Image.Image:
+    """
+    The 'resize' function from torchvision has bad type signatures.
+    it accepts both PIL images and torch tensors,  but the type signature
+    only allows tensors.
+    """
+    return tv_resize(
+        image,  # type: ignore
+        size,
+        InterpolationMode.BICUBIC,
+    )
+
+
+def create_patches(
+    image: Image.Image, image_patch_size=378
+) -> Tuple[List[Image.Image], Tuple[int, int]]:
+    """
+    Split the given image into a variable number of patches depending upon its
+    resolution.
+    """
+    # Start off with the global patch.
+    patches = [im_resize(image, [image_patch_size, image_patch_size])]
+
+    # Find the closest resolution template.
+    res_templates = [(1, 2), (2, 1), (2, 2)]
+    im_width, im_height = image.size
+    max_dim = max(im_width, im_height)
+    if max_dim < image_patch_size * 1.4:
+        # If the image is already small, we just do a single patch that is a
+        # duplicate of the global patch. This creates a small amount of
+        # redundant computation now, but it is simpler and future-proofs us
+        # if/when we condition the vision encoder on the patch type.
+        res_template = (1, 1)
+        patches.append(patches[0])
+    else:
+        aspect_ratio = im_width / im_height
+        res_template = min(
+            res_templates, key=lambda size: abs((size[1] / size[0]) - aspect_ratio)
+        )
+        # TODO: Actually implement patching... just going to put in the global
+        # patch for now to make progress on other aspects.
+        patches.append(patches[0])
+
+    return patches, res_template
+
+
+def encode_image(image: Image.Image, weights: VisionModel) -> torch.Tensor:
+    patches, res_template = create_patches(image.convert("RGB"))
+    patches = torch.stack(
+        [
+            normalize(
+                to_dtype(to_image(patch), torch.float16, scale=True),
+                mean=[0.5, 0.5, 0.5],
+                std=[0.5, 0.5, 0.5],
+            )
+            for patch in patches
+        ]
+    )
+
+    outputs = vision_encoder(patches, weights)
+
+    # TODO: Merge sub-image patch outputs properly... for now we'll just assume
+    # that the global patch is repeated.
+    assert outputs.shape[0] == 2, "Expected single image patch."
+    outputs = torch.cat([outputs[0], outputs[1]], dim=-1)
+
+    return mlp(outputs, weights.proj_mlp)
+
+
+def vision_encoder(input_BCHW: torch.Tensor, w: VisionModel):
+    x = rearrange(
+        input_BCHW,
+        "b c (h p1) (w p2) -> b (h w) (c p1 p2)",
+        p1=w.patch_size,
+        p2=w.patch_size,
+    )  # B3HW -> B(HxW)(3xP1xP2), aka BTC
+
+    x = linear(x, w.patch_emb)
+    x = x + w.pos_emb
+    for block in w.blocks:
+        x = x + attn(layer_norm(x, block.ln1), block.attn)
+        x = x + mlp(layer_norm(x, block.ln2), block.mlp)
+    x = layer_norm(x, w.post_ln)
+
+    return x

moondream/torch/weights.py

@@ -0,0 +1,216 @@
+import math
+from contextlib import contextmanager
+from dataclasses import dataclass
+from typing import List, Callable
+
+import safetensors
+import torch
+
+from .layers import AttentionWeights, LayerNormWeights, LinearWeights, MLPWeights
+
+
+@dataclass
+class VisionBlock:
+    ln1: LayerNormWeights
+    attn: AttentionWeights
+    ln2: LayerNormWeights
+    mlp: MLPWeights
+
+
+@dataclass
+class VisionModel:
+    patch_size: int
+    patch_emb: LinearWeights
+    pos_emb: torch.Tensor
+    blocks: List[VisionBlock]
+    post_ln: LayerNormWeights
+    proj_mlp: MLPWeights
+
+
+@dataclass
+class TextBlock:
+    ln: LayerNormWeights
+    attn: AttentionWeights
+    mlp: MLPWeights
+
+
+@dataclass
+class TextModel:
+    wte: torch.Tensor
+    blocks: List[TextBlock]
+    post_ln: LayerNormWeights
+    lm_head: LinearWeights
+
+
+@dataclass
+class MoondreamModel:
+    vision: VisionModel
+    text: TextModel
+
+
+@contextmanager
+def safetensors_open(safetensors_file: str):
+    """
+    Simplify interfacing with safetensors files. Eliminates the need to ignore
+    type errors when using the `safe_open` function.
+    """
+    with safetensors.safe_open(
+        safetensors_file, framework="pt"
+    ) as st:  # pyright: ignore
+
+        def get_tensor(name: str) -> torch.Tensor:
+            return st.get_tensor(name)
+
+        yield get_tensor
+
+
+def load_model(
+    get_tensor: Callable[[str], torch.Tensor],
+    vision_blocks: int = 27,
+    text_blocks: int = 24,
+    vision_n_heads: int = 16,
+    text_n_heads: int = 32,
+) -> MoondreamModel:
+    ## Vision encoder
+    prefix = "vision_encoder.encoder.model.visual.patch_embed.linear"
+    patch_emb = LinearWeights(
+        weight=get_tensor(f"{prefix}.weight"), bias=get_tensor(f"{prefix}.bias")
+    )
+    patch_size = int(math.sqrt(patch_emb.weight.shape[1] // 3))
+    pos_emb = get_tensor("vision_encoder.encoder.model.visual.pos_embed")
+    post_ln = LayerNormWeights(
+        weight=get_tensor("vision_encoder.encoder.model.visual.norm.weight"),
+        bias=get_tensor("vision_encoder.encoder.model.visual.norm.bias"),
+    )
+    blocks = []
+    for i in range(vision_blocks):
+        prefix = f"vision_encoder.encoder.model.visual.blocks.{i}"
+        blocks.append(
+            VisionBlock(
+                ln1=LayerNormWeights(
+                    weight=get_tensor(f"{prefix}.norm1.weight"),
+                    bias=get_tensor(f"{prefix}.norm1.bias"),
+                ),
+                attn=AttentionWeights(
+                    qkv=LinearWeights(
+                        weight=get_tensor(f"{prefix}.attn.qkv.weight"),
+                        bias=get_tensor(f"{prefix}.attn.qkv.bias"),
+                    ),
+                    proj=LinearWeights(
+                        weight=get_tensor(f"{prefix}.attn.proj.weight"),
+                        bias=get_tensor(f"{prefix}.attn.proj.bias"),
+                    ),
+                    n_heads=vision_n_heads,
+                ),
+                ln2=LayerNormWeights(
+                    weight=get_tensor(f"{prefix}.norm2.weight"),
+                    bias=get_tensor(f"{prefix}.norm2.bias"),
+                ),
+                mlp=MLPWeights(
+                    fc1=LinearWeights(
+                        weight=get_tensor(f"{prefix}.mlp.fc1.weight"),
+                        bias=get_tensor(f"{prefix}.mlp.fc1.bias"),
+                    ),
+                    fc2=LinearWeights(
+                        weight=get_tensor(f"{prefix}.mlp.fc2.weight"),
+                        bias=get_tensor(f"{prefix}.mlp.fc2.bias"),
+                    ),
+                ),
+            )
+        )
+    proj_mlp = MLPWeights(
+        fc1=LinearWeights(
+            weight=get_tensor("vision_encoder.projection.mlp.fc1.weight"),
+            bias=get_tensor("vision_encoder.projection.mlp.fc1.bias"),
+        ),
+        fc2=LinearWeights(
+            weight=get_tensor("vision_encoder.projection.mlp.fc2.weight"),
+            bias=get_tensor("vision_encoder.projection.mlp.fc2.bias"),
+        ),
+        act="gelu_approx",
+    )
+    vision = VisionModel(
+        patch_size=patch_size,
+        patch_emb=patch_emb,
+        pos_emb=pos_emb,
+        blocks=blocks,
+        post_ln=post_ln,
+        proj_mlp=proj_mlp,
+    )
+
+    ## Text decoder model
+    wte = get_tensor("text_model.transformer.embd.wte.weight")
+    post_ln = LayerNormWeights(
+        weight=get_tensor("text_model.lm_head.ln.weight"),
+        bias=get_tensor("text_model.lm_head.ln.bias"),
+    )
+    lm_head = LinearWeights(
+        weight=get_tensor("text_model.lm_head.linear.weight"),
+        bias=get_tensor("text_model.lm_head.linear.bias"),
+    )
+    blocks = []
+    for i in range(text_blocks):
+        prefix = f"text_model.transformer.h.{i}"
+        blocks.append(
+            TextBlock(
+                ln=LayerNormWeights(
+                    weight=get_tensor(f"{prefix}.ln.weight"),
+                    bias=get_tensor(f"{prefix}.ln.bias"),
+                ),
+                attn=AttentionWeights(
+                    qkv=LinearWeights(
+                        weight=get_tensor(f"{prefix}.mixer.Wqkv.weight"),
+                        bias=get_tensor(f"{prefix}.mixer.Wqkv.bias"),
+                    ),
+                    proj=LinearWeights(
+                        weight=get_tensor(f"{prefix}.mixer.out_proj.weight"),
+                        bias=get_tensor(f"{prefix}.mixer.out_proj.bias"),
+                    ),
+                    n_heads=text_n_heads,
+                ),
+                mlp=MLPWeights(
+                    fc1=LinearWeights(
+                        weight=get_tensor(f"{prefix}.mlp.fc1.weight"),
+                        bias=get_tensor(f"{prefix}.mlp.fc1.bias"),
+                    ),
+                    fc2=LinearWeights(
+                        weight=get_tensor(f"{prefix}.mlp.fc2.weight"),
+                        bias=get_tensor(f"{prefix}.mlp.fc2.bias"),
+                    ),
+                    act="gelu_approx",
+                ),
+            )
+        )
+    text = TextModel(wte=wte, blocks=blocks, post_ln=post_ln, lm_head=lm_head)
+
+    return MoondreamModel(vision=vision, text=text)
+
+
+def load_from_safetensors(
+    safetensors_file: str,
+    vision_blocks: int = 27,
+    text_blocks: int = 24,
+    **kwargs,
+) -> MoondreamModel:
+    with safetensors_open(safetensors_file) as get_tensor:
+        return load_model(get_tensor, vision_blocks, text_blocks, **kwargs)
+
+
+def load_from_pt(
+    pt_file: str,
+    vision_blocks: int = 27,
+    text_blocks: int = 24,
+    **kwargs,
+) -> MoondreamModel:
+    device = str(torch.empty(0).device)
+    tensors = torch.load(pt_file, map_location=device, weights_only=True)
+    tensors = {
+        k.replace("._orig_mod", ""): v.to(dtype=torch.float16)
+        for k, v in tensors.items()
+    }
+    return load_model(lambda x: tensors[x], vision_blocks, text_blocks, **kwargs)
+
+
+if __name__ == "__main__":
+    weights = load_from_safetensors("model.safetensors")
+    print(weights)

notebooks/RepEng.ipynb

@@ -0,0 +1,300 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook shows how to compute control vectors to steer moondream's behavior\n",
+    "in fun and interesting ways. To learn more about control vectors and representation\n",
+    "engineering check out [Theia's blog post on the topic](https://vgel.me/posts/representation-engineering/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "from tqdm import tqdm\n",
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "from sklearn.decomposition import PCA\n",
+    "from IPython.display import display, HTML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenizer = AutoTokenizer.from_pretrained(\"vikhyatk/moondream2\")\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    \"vikhyatk/moondream2\", trust_remote_code=True,\n",
+    "    torch_dtype=torch.float16, device_map={\"\": \"cuda\"}\n",
+    ")\n",
+    "\n",
+    "# We will only be using the images, so it doesn't really matter what\n",
+    "# dataset we use here.\n",
+    "dataset = load_dataset(\"vikhyatk/lnqa\", streaming=True)[\"train\"]\n",
+    "\n",
+    "def hidden_states(enc_img, prompt):\n",
+    "    with torch.no_grad():\n",
+    "        inputs_embeds = model.input_embeds(prompt, enc_img, tokenizer)\n",
+    "        hidden_states = model.text_model.generate(\n",
+    "            inputs_embeds=inputs_embeds,\n",
+    "            max_new_tokens=128,\n",
+    "            pad_token_id=tokenizer.eos_token_id,\n",
+    "            eos_token_id=tokenizer.eos_token_id,\n",
+    "            return_dict_in_generate=True,\n",
+    "            output_hidden_states=True,\n",
+    "            do_sample=True,\n",
+    "            temperature=0.5\n",
+    "        ).hidden_states[1:]\n",
+    "    return [torch.stack([hs.view(-1, 2048) for hs in h[1:]]).cpu() for h in hidden_states]\n",
+    "\n",
+    "class LayerWrapper(torch.nn.Module):\n",
+    "    def __init__(self, og_layer, control_vectors, scale=4.2):\n",
+    "        super().__init__()\n",
+    "        self.og_layer = og_layer\n",
+    "        self.control_vectors = control_vectors\n",
+    "        self.scale = scale\n",
+    "\n",
+    "    def forward(self, *args, **kwargs):\n",
+    "        layer_outputs = self.og_layer(*args, **kwargs)\n",
+    "        layer_outputs = (layer_outputs[0] + self.scale * self.control_vectors, *layer_outputs[1:])\n",
+    "        return layer_outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "negative_prompt = \"<image>\\n\\nQuestion: Describe this image.\\n\\nAnswer:\"\n",
+    "positive_prompt = \"<image>\\n\\nQuestion: What is the meaning of life?\\n\\nAnswer:\"\n",
+    "\n",
+    "# This can be lowered without noticeable loss in quality. Feel free to drop it to\n",
+    "# IMAGES_PER_CONTROL=50 and SAMPLES_PER_IMAGE=2 if it's taking too long.\n",
+    "IMAGES_PER_CONTROL = 200\n",
+    "SAMPLES_PER_IMAGE = 5\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 200/200 [37:09<00:00, 11.15s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This is not very efficient, batching would speed things up a lot.\n",
+    "# But eh, works for a quick demo.\n",
+    "\n",
+    "hs_dataset = [[] for _ in range(24)]\n",
+    "\n",
+    "for i, sample in tqdm(enumerate(dataset), total=IMAGES_PER_CONTROL):\n",
+    "    if i >= IMAGES_PER_CONTROL:\n",
+    "        break\n",
+    "    image = sample[\"image\"]\n",
+    "    enc_img = model.encode_image(image)\n",
+    "    for _ in range(SAMPLES_PER_IMAGE):\n",
+    "        phs = hidden_states(enc_img, positive_prompt)\n",
+    "        nhs = hidden_states(enc_img, negative_prompt)\n",
+    "        t_max = min(len(phs), len(nhs))\n",
+    "        for t in range(t_max):\n",
+    "            phs_t = phs[t]\n",
+    "            nhs_t = nhs[t]\n",
+    "            for j in range(24):\n",
+    "                hs_dataset[j].append(phs_t[j])\n",
+    "                hs_dataset[j].append(nhs_t[j])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 24/24 [02:30<00:00,  6.26s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "control_vectors = []\n",
+    "\n",
+    "for i in tqdm(range(24)):\n",
+    "    layer_hiddens = torch.stack(hs_dataset[i])\n",
+    "\n",
+    "    layer_centers = (layer_hiddens[::2] + layer_hiddens[1::2]) / 2\n",
+    "    relative_layer_hiddens = layer_hiddens\n",
+    "    relative_layer_hiddens[::2] -= layer_centers\n",
+    "    relative_layer_hiddens[1::2] -= layer_centers\n",
+    "\n",
+    "    train = relative_layer_hiddens - relative_layer_hiddens.mean(axis=0, keepdims=True)\n",
+    "    train = train.view(-1, 2048).cpu().numpy()\n",
+    "    pca_model = PCA(n_components=1, whiten=False).fit(train)\n",
+    "    directions = pca_model.components_.astype(np.float32).squeeze(axis=0)\n",
+    "\n",
+    "    projected_hiddens = (layer_hiddens.cpu().numpy() @ directions) / np.linalg.norm(directions)\n",
+    "\n",
+    "    positive_smaller_mean = np.mean(\n",
+    "        [\n",
+    "            projected_hiddens[i] < projected_hiddens[i + 1]\n",
+    "            for i in range(0, len(hs_dataset[i]), 2)\n",
+    "        ]\n",
+    "    )\n",
+    "    positive_larger_mean = np.mean(\n",
+    "        [\n",
+    "            projected_hiddens[i] > projected_hiddens[i + 1]\n",
+    "            for i in range(0, len(hs_dataset[i]), 2)\n",
+    "        ]\n",
+    "    )\n",
+    "    if positive_smaller_mean > positive_larger_mean:  # type: ignore\n",
+    "        directions *= -1\n",
+    "\n",
+    "    control_vectors.append(torch.tensor(directions, device=\"cuda\", dtype=torch.float16))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<table style=\"border: 1px solid black; border-collapse: collapse;\">\n",
+       "    <tr>\n",
+       "        <th style=\"border: 1px solid black; padding: 8px;\">Image</th>\n",
+       "        <th style=\"border: 1px solid black; padding: 8px;\">Text</th>\n",
+       "    </tr>\n",
+       "\n",
+       "    <tr>\n",
+       "        <td style=\"border: 1px solid black; padding: 8px;\">\n",
+       "            <img src=\"../assets/demo-1.jpg\" style=\"width:250px;\">\n",
+       "        </td>\n",
+       "        <td style=\"border: 1px solid black; padding: 8px; text-align: left\">\n",
+       "            <strong>Describe this image.</strong><br><br>\n",
+       "            In this image, we will explore the concept of happiness through an anthropomorphic interpretation. We'll look at it from a spiritual or religious perspective by examining what brings joy and fulfillment to human beings. This can be approached in various ways depending on individual beliefs and experiences.<br><br>To understand happiness, one might consider factors such as personal goals, relationships, health, wealth, spirituality, ethics, and more. It is subjective and varies greatly among individuals based on their values and life experiences. However, many people often seek happiness in different aspects like achieving success, finding inner peace, or living a meaningful life.<br><br>In the context of the provided statements about happiness, some may find happiness in material possessions (e.of), while others may seek happiness through relationships, creativity, or even simple pleasures that bring satisfaction and contentment. Ultimately, happiness is a deeply personal journey for each person to define and pursue according to their own beliefs and values.\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "    \n",
+       "    <tr>\n",
+       "        <td style=\"border: 1px solid black; padding: 8px;\">\n",
+       "            <img src=\"../assets/demo-2.jpg\" style=\"width:250px;\">\n",
+       "        </td>\n",
+       "        <td style=\"border: 1px solid black; padding: 8px; text-align: left\">\n",
+       "            <strong>What is this?</strong><br><br>\n",
+       "            In the context of this image, a server or processor is an essential component for computing and running various applications on computers. A Processor (or CPU) can refer to any device that executes tasks according to specific programming requirements.<br><br>In this particular scenario, we are referring to advanced technologies like virtual machines, artificial intelligence, machine learning, etc., which require powerful computing systems to function effectively.<br><br>For example, in the case of AI research, researchers develop and test theories using sophisticated computer models and simulations. These concepts may involve analyzing vast amounts of data, exploring ethical questions, understanding existence, or even developing new knowledge about life itself.<br><br>In summary, when people talk about \"the meaning\" or \"purpose,\" they often refer to these advanced concepts as well. It's subjective and varies from person to person based on their beliefs, values, and experiences.\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "    \n",
+       "    <tr>\n",
+       "        <td style=\"border: 1px solid black; padding: 8px;\">\n",
+       "            <img src=\"../assets/demo-2.jpg\" style=\"width:250px;\">\n",
+       "        </td>\n",
+       "        <td style=\"border: 1px solid black; padding: 8px; text-align: left\">\n",
+       "            <strong>What color is the couch?</strong><br><br>\n",
+       "            The couch in the image is described as \"black.\" However, without more information or context from different sources, it's difficult to determine its actual color. It could be any of those things like comfort, aesthetics, personal preferences, etc., which can vary among individuals.\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "    </table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prompts = [\n",
+    "    (\"../assets/demo-1.jpg\", \"Describe this image.\"),\n",
+    "    (\"../assets/demo-2.jpg\", \"What is this?\"),\n",
+    "    (\"../assets/demo-2.jpg\", \"What color is the couch?\"),\n",
+    "]\n",
+    "data = []\n",
+    "\n",
+    "def run_model(img_path, prompt, scale=4.2):\n",
+    "    og_h = model.text_model.transformer.h\n",
+    "    model.text_model.transformer.h = torch.nn.ModuleList([\n",
+    "        LayerWrapper(layer, vector, scale) for layer, vector in zip(og_h, control_vectors)\n",
+    "    ])\n",
+    "    answer = model.answer_question(\n",
+    "        model.encode_image(Image.open(img_path)), prompt, tokenizer,\n",
+    "        repetition_penalty=1.2, temperature=0.1, do_sample=True,\n",
+    "        length_penalty=1.2\n",
+    "    )\n",
+    "    model.text_model.transformer.h = og_h\n",
+    "    return answer\n",
+    "\n",
+    "for img_path, prompt in prompts:\n",
+    "    answer = run_model(img_path, prompt)\n",
+    "    data.append({\"prompt\": prompt, \"answer\": answer.replace(\"\\n\", \"<br>\"), \"image\": img_path})\n",
+    "\n",
+    "html_table = \"\"\"\n",
+    "<table style=\"border: 1px solid black; border-collapse: collapse;\">\n",
+    "    <tr>\n",
+    "        <th style=\"border: 1px solid black; padding: 8px;\">Image</th>\n",
+    "        <th style=\"border: 1px solid black; padding: 8px;\">Text</th>\n",
+    "    </tr>\n",
+    "\"\"\"\n",
+    "\n",
+    "for item in data:\n",
+    "    html_table += f\"\"\"\n",
+    "    <tr>\n",
+    "        <td style=\"border: 1px solid black; padding: 8px;\">\n",
+    "            <img src=\"{item['image']}\" style=\"width:250px;\">\n",
+    "        </td>\n",
+    "        <td style=\"border: 1px solid black; padding: 8px; text-align: left\">\n",
+    "            <strong>{item['prompt']}</strong><br><br>\n",
+    "            {item['answer']}\n",
+    "        </td>\n",
+    "    </tr>\n",
+    "    \"\"\"\n",
+    "\n",
+    "html_table += \"</table>\"\n",
+    "\n",
+    "# Display the HTML table\n",
+    "display(HTML(html_table))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

@@ -0,0 +1,8 @@
+accelerate==0.32.1
+huggingface-hub==0.24.0
+Pillow==10.4.0
+torch==2.3.1
+torchvision==0.18.1
+transformers==4.44.0
+einops==0.8.0
+gradio==4.38.1

sample.py

@@ -0,0 +1,84 @@
+import argparse
+from queue import Queue
+from threading import Thread
+
+import torch
+from PIL import Image
+from transformers import AutoTokenizer, TextIteratorStreamer
+
+from moondream.hf import LATEST_REVISION, Moondream, detect_device
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--image", type=str, required=True)
+    parser.add_argument("--prompt", type=str, required=False)
+    parser.add_argument("--caption", action="store_true")
+    parser.add_argument("--cpu", action="store_true")
+    args = parser.parse_args()
+
+    if args.cpu:
+        device = torch.device("cpu")
+        dtype = torch.float32
+    else:
+        device, dtype = detect_device()
+        if device != torch.device("cpu"):
+            print("Using device:", device)
+            print("If you run into issues, pass the `--cpu` flag to this script.")
+            print()
+
+    image_path = args.image
+    prompt = args.prompt
+
+    model_id = "vikhyatk/moondream2"
+    tokenizer = AutoTokenizer.from_pretrained(model_id, revision=LATEST_REVISION)
+    moondream = Moondream.from_pretrained(
+        model_id,
+        revision=LATEST_REVISION,
+        torch_dtype=dtype,
+    ).to(device=device)
+    moondream.eval()
+
+    image = Image.open(image_path)
+
+    if args.caption:
+        print(moondream.caption(images=[image], tokenizer=tokenizer)[0])
+    else:
+        image_embeds = moondream.encode_image(image)
+
+        if prompt is None:
+            chat_history = ""
+
+            while True:
+                question = input("> ")
+
+                result_queue = Queue()
+
+                streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True)
+
+                # Separate direct arguments from keyword arguments
+                thread_args = (image_embeds, question, tokenizer, chat_history)
+                thread_kwargs = {"streamer": streamer, "result_queue": result_queue}
+
+                thread = Thread(
+                    target=moondream.answer_question,
+                    args=thread_args,
+                    kwargs=thread_kwargs,
+                )
+                thread.start()
+
+                buffer = ""
+                for new_text in streamer:
+                    buffer += new_text
+                    if not new_text.endswith("<") and not new_text.endswith("END"):
+                        print(buffer, end="", flush=True)
+                        buffer = ""
+                print(buffer)
+
+                thread.join()
+
+                answer = result_queue.get()
+                chat_history += f"Question: {question}\n\nAnswer: {answer}\n\n"
+        else:
+            print(">", prompt)
+            answer = moondream.answer_question(image_embeds, prompt, tokenizer)
+            print(answer)

webcam_gradio_demo.py

@@ -0,0 +1,101 @@
+import argparse
+import time
+from threading import Thread
+
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+from moondream.hf import LATEST_REVISION, detect_device
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--cpu", action="store_true")
+args = parser.parse_args()
+
+if args.cpu:
+    device = torch.device("cpu")
+    dtype = torch.float32
+else:
+    device, dtype = detect_device()
+    if device != torch.device("cpu"):
+        print("Using device:", device)
+        print("If you run into issues, pass the `--cpu` flag to this script.")
+        print()
+
+model_id = "vikhyatk/moondream2"
+tokenizer = AutoTokenizer.from_pretrained(model_id, revision=LATEST_REVISION)
+moondream = AutoModelForCausalLM.from_pretrained(
+    model_id, trust_remote_code=True, revision=LATEST_REVISION
+).to(device=device, dtype=dtype)
+moondream.eval()
+
+
+def answer_question(img, prompt):
+    image_embeds = moondream.encode_image(img)
+    streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True)
+    thread = Thread(
+        target=moondream.answer_question,
+        kwargs={
+            "image_embeds": image_embeds,
+            "question": prompt,
+            "tokenizer": tokenizer,
+            "streamer": streamer,
+        },
+    )
+    thread.start()
+
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        yield buffer
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# See For Me")
+
+    gr.HTML(
+        """
+        <style type="text/css">
+            .md_output p {
+                padding-top: 1rem;
+                font-size: 1.2rem !important;
+            }
+        </style>
+        """
+    )
+
+    with gr.Row():
+        prompt = gr.Textbox(
+            label="Prompt",
+            value="What's going on? Respond with a single sentence.",
+            interactive=True,
+        )
+    with gr.Row():
+        img = gr.Image(type="pil", label="Upload an Image", streaming=True)
+        output = gr.Markdown(elem_classes=["md_output"])
+
+    latest_img = None
+    latest_prompt = prompt.value
+
+    @img.change(inputs=[img])
+    def img_change(img):
+        global latest_img
+        latest_img = img
+
+    @prompt.change(inputs=[prompt])
+    def prompt_change(prompt):
+        global latest_prompt
+        latest_prompt = prompt
+
+    @demo.load(outputs=[output])
+    def live_video():
+        while True:
+            if latest_img is None:
+                time.sleep(0.1)
+            else:
+                for text in answer_question(latest_img, latest_prompt):
+                    if len(text) > 0:
+                        yield text
+
+
+demo.queue().launch(debug=True, share=True)