Add MiniCPM-V cpu example (#11975)
* Add MiniCPM-V cpu example * fix * fix * fix * fix
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Jin, Qiao
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@ -319,7 +319,7 @@ Over 50 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaM
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| MiniCPM-V | | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V) |
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| MiniCPM-V-2 | | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2) |
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| MiniCPM-Llama3-V-2_5 | | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-Llama3-V-2_5) |
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| MiniCPM-V-2_6 | | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6) |
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| MiniCPM-V-2_6 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm-v) | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6) |
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## Get Support
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- Please report a bug or raise a feature request by opening a [Github Issue](https://github.com/intel-analytics/ipex-llm/issues)
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@ -0,0 +1,101 @@
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# MiniCPM-V
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In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM-V models. For illustration purposes, we utilize the [openbmb/MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) as a reference MiniCPM-V model.
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## 0. Requirements
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To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
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## Example: Predict Tokens using `chat()` API
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In the example [chat.py](./chat.py), we show a basic use case for a MiniCPM-V model to predict the next N tokens using `chat()` API, with IPEX-LLM INT4 optimizations.
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### 1. Install
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We suggest using conda to manage environment:
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On Linux:
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```bash
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conda create -n llm python=3.11
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conda activate llm
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# install ipex-llm with 'all' option
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pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
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pip install torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cpu
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pip install transformers==4.40.0 trl
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```
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On Windows:
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```cmd
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conda create -n llm python=3.11
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conda activate llm
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pip install --pre --upgrade ipex-llm[all]
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pip install torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cpu
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pip install transformers==4.40.0 trl
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```
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### 2. Run
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- chat without streaming mode:
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```
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python ./chat.py --prompt 'What is in the image?'
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```
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- chat in streaming mode:
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```
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python ./chat.py --prompt 'What is in the image?' --stream
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```
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> [!TIP]
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> For chatting in streaming mode, it is recommended to set the environment variable `PYTHONUNBUFFERED=1`.
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Arguments info:
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the MiniCPM-V model (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'`.
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- `--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'`.
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- `--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?'`.
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- `--stream`: flag to chat in streaming mode
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> **Note**: When loading the model in 4-bit, IPEX-LLM converts linear layers in the model into INT4 format. In theory, a *X*B model saved in 16-bit will requires approximately 2*X* GB of memory for loading, and ~0.5*X* GB memory for further inference.
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>
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> Please select the appropriate size of the MiniCPM model based on the capabilities of your machine.
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#### 2.1 Client
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On client Windows machine, it is recommended to run directly with full utilization of all cores:
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```cmd
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python ./chat.py
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```
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#### 2.2 Server
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For optimal performance on server, it is recommended to set several environment variables (refer to [here](../README.md#best-known-configuration-on-linux) for more information), and run the example with all the physical cores of a single socket.
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E.g. on Linux,
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```bash
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# set IPEX-LLM env variables
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source ipex-llm-init
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# e.g. for a server with 48 cores per socket
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export OMP_NUM_THREADS=48
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numactl -C 0-47 -m 0 python ./chat.py
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```
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#### 2.3 Sample Output
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#### [openbmb/MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6)
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```log
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Inference time: xxxx s
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-------------------- Input Image --------------------
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http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
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-------------------- Input Prompt --------------------
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What is in the image?
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-------------------- Chat Output --------------------
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The image features a young child holding a white teddy bear dressed in pink. The background includes some red flowers and what appears to be a stone wall.
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```
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```log
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-------------------- Input Image --------------------
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http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
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-------------------- Input Prompt --------------------
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图片里有什么？
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-------------------- Stream Chat Output --------------------
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图片中有一个小女孩，她手里拿着一个穿着粉色裙子的白色小熊玩偶。背景中有红色花朵和石头结构，可能是一个花园或庭院。
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```
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The sample input image is (which is fetched from [COCO dataset](https://cocodataset.org/#explore?id=264959)):
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<a href="http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg"><img width=400px src="http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg" ></a>
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@ -0,0 +1,100 @@
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import time
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import argparse
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import requests
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import torch
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from PIL import Image
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from ipex_llm.transformers import AutoModel
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from transformers import AutoTokenizer
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `chat()` API for MiniCPM-V model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="openbmb/MiniCPM-V-2_6",
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help='The huggingface repo id for the MiniCPM-V model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--image-url-or-path', type=str,
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default='http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg',
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help='The URL or path to the image to infer')
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parser.add_argument('--prompt', type=str, default="What is in the image?",
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help='Prompt to infer')
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parser.add_argument('--stream', action='store_true',
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help='Whether to chat in streaming mode')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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image_path = args.image_url_or_path
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# Load model in 4 bit,
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# which convert the relevant layers in the model into INT4 format
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model = AutoModel.from_pretrained(model_path,
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load_in_low_bit="sym_int4",
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optimize_model=True,
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trust_remote_code=True,
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use_cache=True,
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torch_dtype=torch.float32,
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modules_to_not_convert=["vpm", "resampler"])
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path,
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trust_remote_code=True)
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model.eval()
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query = args.prompt
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if os.path.exists(image_path):
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image = Image.open(image_path).convert('RGB')
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else:
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image = Image.open(requests.get(image_path, stream=True).raw).convert('RGB')
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# Generate predicted tokens
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# here the prompt tuning refers to https://huggingface.co/openbmb/MiniCPM-V-2_6/blob/main/README.md
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msgs = [{'role': 'user', 'content': [image, args.prompt]}]
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if args.stream:
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res = model.chat(
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image=None,
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msgs=msgs,
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tokenizer=tokenizer,
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stream=True
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)
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print('-'*20, 'Input Image', '-'*20)
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print(image_path)
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print('-'*20, 'Input Prompt', '-'*20)
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print(args.prompt)
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print('-'*20, 'Stream Chat Output', '-'*20)
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for new_text in res:
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print(new_text, flush=True, end='')
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else:
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st = time.time()
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res = model.chat(
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image=None,
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msgs=msgs,
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tokenizer=tokenizer,
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)
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end = time.time()
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Input Image', '-'*20)
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print(image_path)
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print('-'*20, 'Input Prompt', '-'*20)
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print(args.prompt)
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print('-'*20, 'Chat Output', '-'*20)
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print(res)
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