From adfbb9124ab02e10a2128a4015f1d476be3b60ce Mon Sep 17 00:00:00 2001 From: Jinhe Date: Fri, 16 Aug 2024 14:48:56 +0800 Subject: [PATCH] Reorganize MiniCPM-V-2_6 example & update others MiniCPM-V-2 exmaples (#11815) * model to fp16 & 2_6 reorganize * revisions * revisions * half * deleted transformer version requirements * deleted transformer version requirements --------- Co-authored-by: ivy-lv11 --- .../GPU/HuggingFace/LLM/codellama/readme.md | 4 - .../GPU/HuggingFace/LLM/deciLM-7b/README.md | 4 - .../GPU/HuggingFace/LLM/internlm2/README.md | 4 +- .../MiniCPM-Llama3-V-2_5/generate.py | 3 +- .../Multimodal/MiniCPM-V-2/README.md | 16 +- .../Multimodal/MiniCPM-V-2/generate.py | 6 +- .../Multimodal/MiniCPM-V-2_6/README.md | 143 ++++++++++++++ .../Multimodal/MiniCPM-V-2_6/generate.py | 175 ++++++++++++++++++ 8 files changed, 325 insertions(+), 30 deletions(-) create mode 100644 python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6/README.md create mode 100644 python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6/generate.py diff --git a/python/llm/example/GPU/HuggingFace/LLM/codellama/readme.md b/python/llm/example/GPU/HuggingFace/LLM/codellama/readme.md index f977a09b..40cf921c 100644 --- a/python/llm/example/GPU/HuggingFace/LLM/codellama/readme.md +++ b/python/llm/example/GPU/HuggingFace/LLM/codellama/readme.md @@ -14,8 +14,6 @@ conda create -n llm python=3.11 conda activate llm # below command will install intel_extension_for_pytorch==2.1.10+xpu as default pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ - -pip install transformers==4.34.1 # CodeLlamaTokenizer is supported in higher version of transformers ``` #### 1.2 Installation on Windows @@ -26,8 +24,6 @@ conda activate llm # below command will install intel_extension_for_pytorch==2.1.10+xpu as default pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ - -pip install transformers==4.34.1 # CodeLlamaTokenizer is supported in higher version of transformers ``` ### 2. Configures OneAPI environment variables for Linux diff --git a/python/llm/example/GPU/HuggingFace/LLM/deciLM-7b/README.md b/python/llm/example/GPU/HuggingFace/LLM/deciLM-7b/README.md index 885cf792..728d534b 100644 --- a/python/llm/example/GPU/HuggingFace/LLM/deciLM-7b/README.md +++ b/python/llm/example/GPU/HuggingFace/LLM/deciLM-7b/README.md @@ -14,8 +14,6 @@ conda create -n llm python=3.11 conda activate llm # below command will install intel_extension_for_pytorch==2.1.10+xpu as default pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ - -pip install transformers==4.35.2 # required by DeciLM-7B ``` #### 1.2 Installation on Windows @@ -26,8 +24,6 @@ conda activate llm # below command will install intel_extension_for_pytorch==2.1.10+xpu as default pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ - -pip install transformers==4.35.2 # required by DeciLM-7B ``` ### 2. Configures OneAPI environment variables for Linux diff --git a/python/llm/example/GPU/HuggingFace/LLM/internlm2/README.md b/python/llm/example/GPU/HuggingFace/LLM/internlm2/README.md index 5c4c1771..97e6c027 100644 --- a/python/llm/example/GPU/HuggingFace/LLM/internlm2/README.md +++ b/python/llm/example/GPU/HuggingFace/LLM/internlm2/README.md @@ -14,7 +14,7 @@ conda create -n llm python=3.11 conda activate llm # below command will install intel_extension_for_pytorch==2.1.10+xpu as default pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ -pip install transformers==3.38.0 +pip install transformers==4.38.0 pip install einops pip install huggingface_hub ``` @@ -27,7 +27,7 @@ conda activate llm # below command will install intel_extension_for_pytorch==2.1.10+xpu as default pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ -pip install transformers==3.38.0 +pip install transformers==4.38.0 pip install einops pip install huggingface_hub ``` diff --git a/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-Llama3-V-2_5/generate.py b/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-Llama3-V-2_5/generate.py index e1bde9ee..fe5ab5e1 100644 --- a/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-Llama3-V-2_5/generate.py +++ b/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-Llama3-V-2_5/generate.py @@ -48,9 +48,8 @@ if __name__ == '__main__': load_in_4bit=True, optimize_model=False, trust_remote_code=True, - modules_to_not_convert=["vpm", "resampler"], use_cache=True) - model = model.float().to(device='xpu') + model = model.half().to(device='xpu') tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model.eval() diff --git a/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2/README.md b/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2/README.md index b79adf3b..da5f9400 100644 --- a/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2/README.md +++ b/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2/README.md @@ -1,5 +1,5 @@ # MiniCPM-V-2 -In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM-V-2 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [openbmb/MiniCPM-V-2](https://huggingface.co/openbmb/MiniCPM-V-2) and [openbmb/MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) as reference MiniCPM-V-2 models. +In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM-V-2 model on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [openbmb/MiniCPM-V-2](https://huggingface.co/openbmb/MiniCPM-V-2) as a reference MiniCPM-V-2 model. ## 0. Requirements To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information. @@ -27,7 +27,7 @@ conda activate llm # below command will install intel_extension_for_pytorch==2.1.10+xpu as default pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ -pip install timm peft transformers==4.40.0 trl +pip install timm peft ``` ### 2. Configures OneAPI environment variables for Linux @@ -130,18 +130,6 @@ What is in the image? In the image, there is a young child holding a teddy bear. The teddy bear appears to be dressed in a pink tutu. The child is also wearing a red and white striped dress. The background of the image includes a stone wall and some red flowers. ``` -#### [openbmb/MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) - -```log -Inference time: 3.102498769760132 s --------------------- Input -------------------- -http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg --------------------- Prompt -------------------- -What is in the image? --------------------- Output -------------------- -The image features a young child holding a white teddy bear with a pink tutu. The child is wearing a striped dress and is standing in front of a stone wall with some red flowers in the background. -``` - The sample input image is (which is fetched from [COCO dataset](https://cocodataset.org/#explore?id=264959)): diff --git a/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2/generate.py b/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2/generate.py index 52cc5450..91ae81d2 100644 --- a/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2/generate.py +++ b/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2/generate.py @@ -142,7 +142,7 @@ if __name__ == '__main__': trust_remote_code=True, modules_to_not_convert=["vpm", "resampler", "lm_head"], use_cache=True) - model = model.float().to(device='xpu') + model = model.half().to(device='xpu') tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model.eval() @@ -157,7 +157,7 @@ if __name__ == '__main__': # here the prompt tuning refers to https://huggingface.co/openbmb/MiniCPM-V-2/blob/main/README.md msgs = [{'role': 'user', 'content': args.prompt}] st = time.time() - res = model.chat( + res, context, _ = model.chat( image=image, msgs=msgs, context=None, @@ -165,8 +165,6 @@ if __name__ == '__main__': sampling=False, temperature=0.7 ) - if model.config._name_or_path.endswith("2"): - res, context, _ = res end = time.time() print(f'Inference time: {end-st} s') print('-'*20, 'Input', '-'*20) diff --git a/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6/README.md b/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6/README.md new file mode 100644 index 00000000..64192eb8 --- /dev/null +++ b/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6/README.md @@ -0,0 +1,143 @@ +# MiniCPM-V-2_6 +In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM-V-2_6 model on [Intel GPUs](../../../README.md). For illustration purposes, we utilize [openbmb/MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) as reference MiniCPM-V-2_6 model. + +## 0. Requirements +To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information. + +## Example: Predict Tokens using `chat()` API +In the example [generate.py](./generate.py), we show a basic use case for a MiniCPM-V-2_6 model to predict the next N tokens using `chat()` API, with IPEX-LLM INT4 optimizations on Intel GPUs. +### 1. Install +#### 1.1 Installation on Linux +We suggest using conda to manage environment: +```bash +conda create -n llm python=3.11 +conda activate llm +# below command will install intel_extension_for_pytorch==2.1.10+xpu as default +pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ + +pip install timm peft transformers==4.40.0 trl +``` + +#### 1.2 Installation on Windows +We suggest using conda to manage environment: +```bash +conda create -n llm python=3.11 libuv +conda activate llm + +# below command will install intel_extension_for_pytorch==2.1.10+xpu as default +pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ + +pip install timm peft transformers==4.40.0 trl +``` + +### 2. Configures OneAPI environment variables for Linux + +> [!NOTE] +> Skip this step if you are running on Windows. + +This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI. + +```bash +source /opt/intel/oneapi/setvars.sh +``` + +### 3. Runtime Configurations +For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device. +#### 3.1 Configurations for Linux +
+ +For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series + +```bash +export USE_XETLA=OFF +export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 +export SYCL_CACHE_PERSISTENT=1 +``` + +
+ +
+ +For Intel Data Center GPU Max Series + +```bash +export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so +export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 +export SYCL_CACHE_PERSISTENT=1 +export ENABLE_SDP_FUSION=1 +``` +> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`. +
+ +
+ +For Intel iGPU + +```bash +export SYCL_CACHE_PERSISTENT=1 +export BIGDL_LLM_XMX_DISABLED=1 +``` + +
+ +#### 3.2 Configurations for Windows +
+ +For Intel iGPU + +```cmd +set SYCL_CACHE_PERSISTENT=1 +set BIGDL_LLM_XMX_DISABLED=1 +``` + +
+ +
+ +For Intel Arc™ A-Series Graphics + +```cmd +set SYCL_CACHE_PERSISTENT=1 +``` + +
+ +> [!NOTE] +> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile. +### 4. Running examples + +``` +python ./generate.py --prompt 'What is in the image?' +``` + +Arguments info: +- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the MiniCPM-V-2_6 (e.g. `openbmb/MiniCPM-V-2_6`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'openbmb/MiniCPM-V-2_6'`. +- `--image-url-or-path IMAGE_URL_OR_PATH`: argument defining the image to be infered. It is default to be `'http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg'`. +- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is in the image?'`. +- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`. + +#### Sample Output + +#### [openbmb/MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) + +```log +Inference time: xxxx s +-------------------- Input -------------------- +http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg +-------------------- Prompt -------------------- +What is in the image? +-------------------- Output -------------------- +The image features a young child holding a white teddy bear with a pink tutu. The child is wearing a striped dress and is standing in front of a stone wall with some red flowers in the background. +``` +```log +Inference time: xxxx s +-------------------- Input -------------------- +http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg +-------------------- Prompt -------------------- +图片里有什么？ +-------------------- Output -------------------- +这幅图片展示了一个年幼的孩子，可能是一个蹒跚学步的幼儿，手里拿着一个毛绒玩具熊。孩子穿着一件条纹连衣裙，主要颜色是粉红色和白色。毛绒熊是白色的，戴着一条粉色的蝴蝶结围裙。背景中有红色的花朵，暗示着室外的环境，可能是一个花园或公园。 +``` +The sample input image is (which is fetched from [COCO dataset](https://cocodataset.org/#explore?id=264959)): + + diff --git a/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6/generate.py b/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6/generate.py new file mode 100644 index 00000000..85f42060 --- /dev/null +++ b/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6/generate.py @@ -0,0 +1,175 @@ +# +# Copyright 2016 The BigDL Authors. +# +# 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. +# + + +from typing import List, Tuple, Optional, Union +import math +import timm +import torch +import torch.nn.functional as F + +# patched: `timm` has limited support for XPU backend, so we need to use CPU as a workaround +def resample_abs_pos_embed( + posemb: torch.Tensor, + new_size: List[int], + old_size: Optional[List[int]] = None, + num_prefix_tokens: int = 1, + interpolation: str = 'bicubic', + antialias: bool = True, + verbose: bool = False, +): + # sort out sizes, assume square if old size not provided + num_pos_tokens = posemb.shape[1] + num_new_tokens = new_size[0] * new_size[1] + num_prefix_tokens + if num_new_tokens == num_pos_tokens and new_size[0] == new_size[1]: + return posemb + + if old_size is None: + hw = int(math.sqrt(num_pos_tokens - num_prefix_tokens)) + old_size = hw, hw + + if num_prefix_tokens: + posemb_prefix, posemb = posemb[:, :num_prefix_tokens], posemb[:, num_prefix_tokens:] + else: + posemb_prefix, posemb = None, posemb + + # do the interpolation + embed_dim = posemb.shape[-1] + orig_dtype = posemb.dtype + posemb = posemb.float() # interpolate needs float32 + posemb = posemb.reshape(1, old_size[0], old_size[1], -1).permute(0, 3, 1, 2) + #posemb = F.interpolate(posemb, size=new_size, mode=interpolation, antialias=antialias) + posemb = F.interpolate(posemb.to("cpu"), size=new_size, mode=interpolation, antialias=antialias).to(posemb.device) + posemb = posemb.permute(0, 2, 3, 1).reshape(1, -1, embed_dim) + posemb = posemb.to(orig_dtype) + + # add back extra (class, etc) prefix tokens + if posemb_prefix is not None: + posemb = torch.cat([posemb_prefix, posemb], dim=1) + + if not torch.jit.is_scripting() and verbose: + _logger.info(f'Resized position embedding: {old_size} to {new_size}.') + + return posemb + + +def _pos_embed(self, x: torch.Tensor) -> torch.Tensor: + if self.pos_embed is None: + return x.view(x.shape[0], -1, x.shape[-1]) + + if self.dynamic_img_size: + B, H, W, C = x.shape + pos_embed = resample_abs_pos_embed( + self.pos_embed, + (H, W), + num_prefix_tokens=0 if self.no_embed_class else self.num_prefix_tokens, + ) + x = x.view(B, -1, C) + else: + pos_embed = self.pos_embed + + to_cat = [] + if self.cls_token is not None: + to_cat.append(self.cls_token.expand(x.shape[0], -1, -1)) + if self.reg_token is not None: + to_cat.append(self.reg_token.expand(x.shape[0], -1, -1)) + + if self.no_embed_class: + # deit-3, updated JAX (big vision) + # position embedding does not overlap with class token, add then concat + x = x + pos_embed + if to_cat: + x = torch.cat(to_cat + [x], dim=1) + else: + # original timm, JAX, and deit vit impl + # pos_embed has entry for class token, concat then add + if to_cat: + x = torch.cat(to_cat + [x], dim=1) + x = x + pos_embed + + return self.pos_drop(x) + + +setattr(timm.models.VisionTransformer, "_pos_embed", _pos_embed) + + +import os +import time +import argparse +import requests +from PIL import Image +from ipex_llm.transformers import AutoModel +from transformers import AutoTokenizer + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for openbmb/MiniCPM-V-2_6 model') + parser.add_argument('--repo-id-or-model-path', type=str, default="openbmb/MiniCPM-V-2_6", + help='The huggingface repo id for the openbmb/MiniCPM-V-2_6 model to be downloaded' + ', or the path to the huggingface checkpoint folder') + parser.add_argument('--image-url-or-path', type=str, + default='http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg', + help='The URL or path to the image to infer') + parser.add_argument('--prompt', type=str, default="What is in the image?", + help='Prompt to infer') + parser.add_argument('--n-predict', type=int, default=32, + help='Max tokens to predict') + + args = parser.parse_args() + model_path = args.repo_id_or_model_path + image_path = args.image_url_or_path + + # Load model in 4 bit, + # which convert the relevant layers in the model into INT4 format + # When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function. + # This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU. + model = AutoModel.from_pretrained(model_path, + load_in_low_bit="sym_int4", + optimize_model=True, + trust_remote_code=True, + use_cache=True) + model = model.half().to(device='xpu') + tokenizer = AutoTokenizer.from_pretrained(model_path, + trust_remote_code=True) + model.eval() + + query = args.prompt + if os.path.exists(image_path): + image = Image.open(image_path).convert('RGB') + else: + image = Image.open(requests.get(image_path, stream=True).raw).convert('RGB') + + # Generate predicted tokens + # here the prompt tuning refers to https://huggingface.co/openbmb/MiniCPM-V-2_6/blob/main/README.md + msgs = [{'role': 'user', 'content': [image, args.prompt]}] + st = time.time() + res = model.chat( + image=None, + msgs=msgs, + context=None, + tokenizer=tokenizer, + sampling=False, + temperature=0.7 + ) + end = time.time() + print(f'Inference time: {end-st} s') + print('-'*20, 'Input', '-'*20) + print(image_path) + print('-'*20, 'Prompt', '-'*20) + print(args.prompt) + output_str = res + print('-'*20, 'Output', '-'*20) + print(output_str)