clone repo

README.md

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+---
+inference: false
+---
+
+<br>
+<br>
+
+# LLaVA Model Card
+
+## Model details
+This is a fork from origianl [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b). This repo added `code/inference.py` and `code/requirements.txt` to provide customize inference script and environment for SageMaker deployment.
+
+**Model type:**
+LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
+It is an auto-regressive language model, based on the transformer architecture.
+
+**Model date:**
+LLaVA-v1.5-13B was trained in September 2023.
+
+**Paper or resources for more information:**
+https://llava-vl.github.io/
+
+## How to Deploy on SageMaker
+
+Following `deploy_llava.ipynb` (full tutorial [here](https://medium.com/@liltom.eth/deploy-llava-1-5-on-amazon-sagemaker-168b2efd2489)) , bundle llava model weights and code into a `model.tar.gz` and upload to S3:
+
+```python
+from sagemaker.s3 import S3Uploader
+
+# upload model.tar.gz to s3
+s3_model_uri = S3Uploader.upload(local_path="./model.tar.gz", desired_s3_uri=f"s3://{sess.default_bucket()}/llava-v1.5-13b")
+
+print(f"model uploaded to: {s3_model_uri}")
+```
+Then use `HuggingfaceModel` to deploy our real-time inference endpoint on SageMaker:
+
+```python
+from sagemaker.huggingface.model import HuggingFaceModel
+
+# create Hugging Face Model Class
+huggingface_model = HuggingFaceModel(
+   model_data=s3_model_uri,      # path to your model and script
+   role=role,                    # iam role with permissions to create an Endpoint
+   transformers_version="4.28.1",  # transformers version used
+   pytorch_version="2.0.0",       # pytorch version used
+   py_version='py310',            # python version used
+   model_server_workers=1
+)
+
+# deploy the endpoint endpoint
+predictor = huggingface_model.deploy(
+    initial_instance_count=1,
+    instance_type="ml.g5.xlarge",
+)
+```
+
+## Inference on SageMaker
+Default `conv_mode` for llava-1.5 is setup as `llava_v1` to process `raw_prompt` into meaningful `prompt`. You can also setup `conv_mode` as `raw` to directly use `raw_prompt`. 
+```python
+data = {
+    "image" : 'https://raw.githubusercontent.com/haotian-liu/LLaVA/main/images/llava_logo.png', 
+    "question" : "Describe the image and color details.",
+    # "max_new_tokens" : 1024,
+    # "temperature" : 0.2,
+    # "conv_mode" : "llava_v1"
+}
+output = predictor.predict(data)
+print(output)
+```
+Or use [SageMakerRuntime](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker-runtime/client/invoke_endpoint.html#invoke-endpoint) to setup endpoint invoking client.
+
+## License
+Llama 2 is licensed under the LLAMA 2 Community License, 
+Copyright (c) Meta Platforms, Inc. All Rights Reserved.
+
+**Where to send questions or comments about the model:**
+https://github.com/haotian-liu/LLaVA/issues
+
+## Intended use
+**Primary intended uses:**
+The primary use of LLaVA is research on large multimodal models and chatbots.
+
+**Primary intended users:**
+The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
+
+## Training dataset
+- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
+- 158K GPT-generated multimodal instruction-following data.
+- 450K academic-task-oriented VQA data mixture.
+- 40K ShareGPT data.
+
+## Evaluation dataset
+A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.

code/inference.py

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+import requests
+from PIL import Image
+from io import BytesIO
+import torch
+from transformers import AutoTokenizer
+
+from llava.model import LlavaLlamaForCausalLM
+from llava.utils import disable_torch_init
+from llava.mm_utils import tokenizer_image_token, KeywordsStoppingCriteria
+
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.constants import (
+    IMAGE_TOKEN_INDEX,
+    DEFAULT_IMAGE_TOKEN,
+    DEFAULT_IM_START_TOKEN,
+    DEFAULT_IM_END_TOKEN,
+)
+
+
+def model_fn(model_dir):
+    kwargs = {"device_map": "auto"}
+    kwargs["torch_dtype"] = torch.float16
+    model = LlavaLlamaForCausalLM.from_pretrained(
+        model_dir, low_cpu_mem_usage=True, **kwargs
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_dir, use_fast=False)
+
+    vision_tower = model.get_vision_tower()
+    if not vision_tower.is_loaded:
+        vision_tower.load_model()
+    vision_tower.to(device="cuda", dtype=torch.float16)
+    image_processor = vision_tower.image_processor
+    return model, tokenizer, image_processor
+
+
+def predict_fn(data, model_and_tokenizer):
+    # unpack model and tokenizer
+    model, tokenizer, image_processor = model_and_tokenizer
+
+    # get prompt & parameters
+    image_file = data.pop("image", data)
+    raw_prompt = data.pop("question", data)
+
+    max_new_tokens = data.pop("max_new_tokens", 1024)
+    temperature = data.pop("temperature", 0.2)
+    conv_mode = data.pop("conv_mode", "llava_v1")
+
+    if conv_mode == "raw":
+        # use raw_prompt as prompt
+        prompt = raw_prompt
+        stop_str = "###"
+    else:
+        conv = conv_templates[conv_mode].copy()
+        roles = conv.roles
+        inp = f"{roles[0]}: {raw_prompt}"
+        inp = (
+            DEFAULT_IM_START_TOKEN
+            + DEFAULT_IMAGE_TOKEN
+            + DEFAULT_IM_END_TOKEN
+            + "\n"
+            + inp
+        )
+        conv.append_message(conv.roles[0], inp)
+        conv.append_message(conv.roles[1], None)
+        prompt = conv.get_prompt()
+        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+
+    if image_file.startswith("http") or image_file.startswith("https"):
+        response = requests.get(image_file)
+        image = Image.open(BytesIO(response.content)).convert("RGB")
+    else:
+        image = Image.open(image_file).convert("RGB")
+
+    disable_torch_init()
+    image_tensor = (
+        image_processor.preprocess(image, return_tensors="pt")["pixel_values"]
+        .half()
+        .cuda()
+    )
+
+    keywords = [stop_str]
+    input_ids = (
+        tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt")
+        .unsqueeze(0)
+        .cuda()
+    )
+    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+    with torch.inference_mode():
+        output_ids = model.generate(
+            input_ids,
+            images=image_tensor,
+            do_sample=True,
+            temperature=temperature,
+            max_new_tokens=max_new_tokens,
+            use_cache=True,
+            stopping_criteria=[stopping_criteria],
+        )
+    outputs = tokenizer.decode(
+        output_ids[0, input_ids.shape[1] :], skip_special_tokens=True
+    ).strip()
+    return outputs

code/requirements.txt

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+llava @ git+https://github.com/haotian-liu/[email protected]

config.json

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+{
+  "_name_or_path": "llava-v1.5-13b",
+  "architectures": [
+    "LlavaLlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "freeze_mm_mlp_adapter": false,
+  "freeze_mm_vision_resampler": false,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "image_aspect_ratio": "pad",
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_length": 4096,
+  "max_position_embeddings": 4096,
+  "mm_hidden_size": 1024,
+  "mm_projector_type": "mlp2x_gelu",
+  "mm_resampler_type": null,
+  "mm_use_im_patch_token": false,
+  "mm_use_im_start_end": false,
+  "mm_vision_select_feature": "patch",
+  "mm_vision_select_layer": -2,
+  "mm_vision_tower": "openai/clip-vit-large-patch14-336",
+  "model_type": "llava",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 40,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.31.0",
+  "tune_mm_mlp_adapter": false,
+  "tune_mm_vision_resampler": false,
+  "unfreeze_mm_vision_tower": false,
+  "use_cache": true,
+  "use_mm_proj": true,
+  "vocab_size": 32000
+}

deploy_llava.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deploy LLaVA on Amazon SageMaker\n",
+    "\n",
+    "Amazon SageMaker is a popular platform for running AI models, and models on huggingface deploy [Hugging Face Transformers](https://github.com/huggingface/transformers) using [Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html) and the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/).\n",
+    "\n",
+    "![llava](https://i.imgur.com/YNVG140.png)\n",
+    "\n",
+    "Install sagemaker sdk:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install sagemaker --upgrade\n",
+    "!pip install -r code/requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bundle llava model weights and code into a `model.tar.gz`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create SageMaker model.tar.gz artifact\n",
+    "!tar -cf model.tar.gz --use-compress-program=pigz *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After we created the `model.tar.gz` archive we can upload it to Amazon S3. We will use the `sagemaker` SDK to upload the model to our sagemaker session bucket.\n",
+    "\n",
+    "Initialize sagemaker session first:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sagemaker\n",
+    "import boto3\n",
+    "sess = sagemaker.Session()\n",
+    "# sagemaker session bucket -> used for uploading data, models and logs\n",
+    "# sagemaker will automatically create this bucket if it not exists\n",
+    "sagemaker_session_bucket=None\n",
+    "if sagemaker_session_bucket is None and sess is not None:\n",
+    "    # set to default bucket if a bucket name is not given\n",
+    "    sagemaker_session_bucket = sess.default_bucket()\n",
+    "\n",
+    "try:\n",
+    "    role = sagemaker.get_execution_role()\n",
+    "except ValueError:\n",
+    "    iam = boto3.client('iam')\n",
+    "    # setup your own rolename in sagemaker\n",
+    "    role = iam.get_role(RoleName='AmazonSageMaker-ExecutionRole-20231008T201275')['Role']['Arn']\n",
+    "\n",
+    "sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)\n",
+    "\n",
+    "print(f\"sagemaker role arn: {role}\")\n",
+    "print(f\"sagemaker bucket: {sess.default_bucket()}\")\n",
+    "print(f\"sagemaker session region: {sess.boto_region_name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Upload the `model.tar.gz` to our sagemaker session bucket:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sagemaker.s3 import S3Uploader\n",
+    "\n",
+    "# upload model.tar.gz to s3\n",
+    "s3_model_uri = S3Uploader.upload(local_path=\"./model.tar.gz\", desired_s3_uri=f\"s3://{sess.default_bucket()}/llava-v1.5-13b\")\n",
+    "\n",
+    "print(f\"model uploaded to: {s3_model_uri}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use `HuggingfaceModel` to create our real-time inference endpoint:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n",
+      "---------------!"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from sagemaker.huggingface.model import HuggingFaceModel\n",
+    "\n",
+    "# create Hugging Face Model Class\n",
+    "huggingface_model = HuggingFaceModel(\n",
+    "   model_data=s3_model_uri,      # path to your model and script\n",
+    "   role=role,                    # iam role with permissions to create an Endpoint\n",
+    "   transformers_version=\"4.28.1\",  # transformers version used\n",
+    "   pytorch_version=\"2.0.0\",       # pytorch version used\n",
+    "   py_version='py310',            # python version used\n",
+    "   model_server_workers=1\n",
+    ")\n",
+    "\n",
+    "# deploy the endpoint endpoint\n",
+    "predictor = huggingface_model.deploy(\n",
+    "    initial_instance_count=1,\n",
+    "    instance_type=\"ml.g5.xlarge\",\n",
+    "    # container_startup_health_check_timeout=600, # increase timeout for large models\n",
+    "    # model_data_download_timeout=600, # increase timeout for large models\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `.deploy()` returns an `HuggingFacePredictor` object which can be used to request inference using the `.predict()` method. Our endpoint expects a `json` with at least `image` and `question` key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The image is a black and white photograph of a man standing in front of a building. The man is wearing a suit and tie, and he appears to be looking at the camera. The building in the background is large and has many windows. The overall atmosphere of the image is formal and professional.\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = {\n",
+    "    \"image\" : 'https://raw.githubusercontent.com/haotian-liu/LLaVA/main/images/llava_logo.png', \n",
+    "    \"question\" : \"Describe the image and color details.\",\n",
+    "    # \"max_new_tokens\" : 1024,\n",
+    "    # \"temperature\" : 0.2,\n",
+    "    # \"stop_str\" : \"###\"\n",
+    "}\n",
+    "\n",
+    "# request\n",
+    "output = predictor.predict(data)\n",
+    "print(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run inference with `llava` special token:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The image features a red toy animal, possibly a horse or a donkey, with a pair of glasses on its face. The toy is made of plastic and has a fire-like appearance, giving it a unique and eye-catching look. The red color of the toy and the glasses on its face create a striking contrast against the background, making it the main focus of the image.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from llava.conversation import conv_templates, SeparatorStyle\n",
+    "from llava.constants import (\n",
+    "DEFAULT_IMAGE_TOKEN,\n",
+    "DEFAULT_IM_START_TOKEN,\n",
+    "DEFAULT_IM_END_TOKEN,\n",
+    ")\n",
+    "def get_prompt(raw_prompt):\n",
+    "    conv_mode = \"llava_v1\"\n",
+    "    conv = conv_templates[conv_mode].copy()\n",
+    "    roles = conv.roles\n",
+    "    inp = f\"{roles[0]}: {raw_prompt}\"\n",
+    "    inp = (\n",
+    "        DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + \"\\n\" + inp\n",
+    "    )\n",
+    "    conv.append_message(conv.roles[0], inp)\n",
+    "    conv.append_message(conv.roles[1], None)\n",
+    "    prompt = conv.get_prompt()\n",
+    "    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2\n",
+    "    return prompt, stop_str\n",
+    "\n",
+    "raw_prompt = \"Describe the image and color details.\"\n",
+    "prompt, stop_str = get_prompt(raw_prompt)\n",
+    "image_path = \"https://raw.githubusercontent.com/haotian-liu/LLaVA/main/images/llava_logo.png\"\n",
+    "data = {\"image\" : image_path, \"question\" : prompt, \"stop_str\" : stop_str}\n",
+    "output = predictor.predict(data)\n",
+    "print(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The inference ` predictor` can also be initilized like with your deployed `endpoint_name` :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Couldn't call 'get_role' to get Role ARN from role name arn:aws:iam::297308036828:root to get Role path.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sagemaker\n",
+    "import boto3\n",
+    "sess = sagemaker.Session()\n",
+    "try:\n",
+    "    role = sagemaker.get_execution_role()\n",
+    "except ValueError:\n",
+    "    iam = boto3.client('iam')\n",
+    "    # setup your own rolename in sagemaker\n",
+    "    role = iam.get_role(RoleName='AmazonSageMaker-ExecutionRole-20231008T201275')['Role']['Arn']\n",
+    "\n",
+    "from sagemaker.huggingface.model import HuggingFacePredictor\n",
+    "# initial the endpoint predictor\n",
+    "predictor2 = HuggingFacePredictor(\n",
+    "    endpoint_name=\"huggingface-pytorch-inference-2023-10-19-05-57-37-847\",\n",
+    "    sagemaker_session=sess\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The image features a small toy animal, resembling a horse or a donkey, with a red and orange color scheme. The toy has a pair of glasses on its face, giving it a unique and quirky appearance. The toy is standing on a gray surface, which provides a contrasting background for the vibrant colors of the toy. The combination of red, orange, and gray creates a visually striking scene.\n"
+     ]
+    }
+   ],
+   "source": [
+    "raw_prompt = \"Describe the image and color details.\"\n",
+    "prompt, stop_str = get_prompt(raw_prompt)\n",
+    "image_path = \"https://raw.githubusercontent.com/haotian-liu/LLaVA/main/images/llava_logo.png\"\n",
+    "data = {\"image\" : image_path, \"question\" : prompt, \"stop_str\" : stop_str}\n",
+    "output = predictor2.predict(data)\n",
+    "print(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To clean up, we can delete the model and endpoint by `delete_endpoint()`or using sagemaker console:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# delete sagemaker endpoint\n",
+    "predictor.delete_endpoint()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llava",
+   "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": "3.10.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

generation_config.json

@@ -0,0 +1,8 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "max_length": 4096,
+  "pad_token_id": 0,
+  "transformers_version": "4.31.0"
+}

pytorch_model.bin.index.json

@@ -0,0 +1,414 @@
+{
+  "metadata": {
+    "total_size": 26094673920
+  },
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special_tokens_map.json

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+  },
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": null,
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}