MiniCPM-V-2 & MiniCPM-Llama3-V-2_5 example updates (#11988)
* minicpm example updates * --stream
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5 changed files with 143 additions and 60 deletions
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@ -5,7 +5,7 @@ In this directory, you will find examples on how you could apply IPEX-LLM INT4 o
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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.
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## Example: Predict Tokens using `chat()` API
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In the example [generate.py](./generate.py), we show a basic use case for a MiniCPM-Llama3-V-2_5 model to predict the next N tokens using `chat()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
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In the example [chat.py](./chat.py), we show a basic use case for a MiniCPM-Llama3-V-2_5 model to predict the next N tokens using `chat()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
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### 1. Install
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#### 1.1 Installation on Linux
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We suggest using conda to manage environment:
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@ -106,15 +106,20 @@ set SYCL_CACHE_PERSISTENT=1
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> 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.
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### 4. Running examples
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- chat without streaming mode:
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```
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python ./generate.py --prompt 'What is in the image?'
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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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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-Llama3-V-2_5 (e.g. `openbmb/MiniCPM-Llama3-V-2_5`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'openbmb/MiniCPM-Llama3-V-2_5'`.
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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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- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
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- `--stream`: flag to chat in streaming mode
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#### Sample Output
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@ -122,12 +127,21 @@ Arguments info:
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```log
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Inference time: xxxx s
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-------------------- Input --------------------
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-------------------- Input Image --------------------
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http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
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-------------------- Prompt --------------------
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-------------------- Input Prompt --------------------
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What is in the image?
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-------------------- Output --------------------
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The image features a young child holding a white teddy bear. The teddy bear is dressed in a pink outfit. The child appears to be outdoors, with a stone wall and some red flowers in the background.
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-------------------- Chat Output --------------------
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The image features a young child holding a white teddy bear. The teddy bear is dressed in a pink dress with a ribbon on it. The child appears to be smiling and enjoying the moment.
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```
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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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图片里有什么？
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-------------------- Chat Output --------------------
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图片中有一个小孩，手里拿着一个白色的玩具熊。这个孩子看起来很开心，正在微笑并与玩具互动。背景包括红色的花朵和石墙，为这个场景增添了色彩和质感。
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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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@ -14,10 +14,12 @@
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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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@ -33,8 +35,8 @@ if __name__ == '__main__':
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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('--n-predict', type=int, default=32,
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help='Max tokens to predict')
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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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@ -45,11 +47,12 @@ if __name__ == '__main__':
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# When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function.
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# This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU.
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model = AutoModel.from_pretrained(model_path,
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load_in_4bit=True,
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optimize_model=False,
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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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model = model.half().to(device='xpu')
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use_cache=True,
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modules_to_not_convert=["vpm", "resampler"])
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model = model.half().to('xpu')
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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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@ -61,23 +64,45 @@ if __name__ == '__main__':
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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-Llama3-V-2_5/blob/main/README.md
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msgs = [{'role': 'user', 'content': args.prompt}]
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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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# ipex_llm model needs a warmup, then inference time can be accurate
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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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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=image,
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image=None,
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msgs=msgs,
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context=None,
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tokenizer=tokenizer,
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sampling=False,
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temperature=0.7
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)
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torch.xpu.synchronize()
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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', '-'*20)
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print('-'*20, 'Input Image', '-'*20)
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print(image_path)
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print('-'*20, 'Prompt', '-'*20)
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print('-'*20, 'Input Prompt', '-'*20)
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print(args.prompt)
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output_str = res
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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print('-'*20, 'Chat Output', '-'*20)
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print(res)
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@ -5,7 +5,7 @@ In this directory, you will find examples on how you could apply IPEX-LLM INT4 o
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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.
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## Example: Predict Tokens using `chat()` API
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In the example [generate.py](./generate.py), we show a basic use case for a MiniCPM-V-2 model to predict the next N tokens using `chat()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
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In the example [chat.py](./chat.py), we show a basic use case for a MiniCPM-V-2 model to predict the next N tokens using `chat()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
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### 1. Install
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#### 1.1 Installation on Linux
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We suggest using conda to manage environment:
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@ -106,15 +106,20 @@ set SYCL_CACHE_PERSISTENT=1
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> 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.
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### 4. Running examples
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- chat without streaming mode:
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```
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python ./generate.py --prompt 'What is in the image?'
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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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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-2 (e.g. `openbmb/MiniCPM-V-2`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'openbmb/MiniCPM-V-2'`.
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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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- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
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- `--stream`: flag to chat in streaming mode
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#### Sample Output
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@ -122,12 +127,20 @@ Arguments info:
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```log
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Inference time: xxxx s
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-------------------- Input --------------------
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-------------------- Input Image --------------------
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http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
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-------------------- Prompt --------------------
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-------------------- Input Prompt --------------------
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What is in the image?
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-------------------- Output --------------------
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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.
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-------------------- Chat Output --------------------
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In the image, there is a young child holding a teddy bear. The teddy bear is dressed in a pink tutu. The child is also wearing a red and white striped dress. The background of the image features a stone wall and some red flowers.
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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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-------------------- Chat Output --------------------
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图中是一个小女孩，她手里拿着一只粉白相间的泰迪熊。
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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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@ -15,6 +15,7 @@
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#
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from typing import List, Tuple, Optional, Union
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import math
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import timm
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@ -110,6 +111,7 @@ 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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@ -125,8 +127,8 @@ if __name__ == '__main__':
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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('--n-predict', type=int, default=32,
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help='Max tokens to predict')
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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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@ -140,9 +142,9 @@ if __name__ == '__main__':
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load_in_low_bit="asym_int4",
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optimize_model=True,
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trust_remote_code=True,
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modules_to_not_convert=["vpm", "resampler", "lm_head"],
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use_cache=True)
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model = model.half().to(device='xpu')
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use_cache=True,
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modules_to_not_convert=["vpm", "resampler"])
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model = model.half().to('xpu')
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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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@ -156,6 +158,34 @@ if __name__ == '__main__':
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# Generate predicted tokens
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# here the prompt tuning refers to https://huggingface.co/openbmb/MiniCPM-V-2/blob/main/README.md
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msgs = [{'role': 'user', 'content': args.prompt}]
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# ipex_llm model needs a warmup, then inference time can be accurate
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res, context, _ = model.chat(
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image=image,
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msgs=msgs,
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context=None,
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tokenizer=tokenizer,
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sampling=False,
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temperature=0.7
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)
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if args.stream:
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res, context, _ = model.chat(
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image=image,
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msgs=msgs,
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context=None,
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tokenizer=tokenizer,
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sampling=False,
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temperature=0.7
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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, context, _ = model.chat(
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image=image,
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@ -165,12 +195,13 @@ if __name__ == '__main__':
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sampling=False,
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temperature=0.7
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)
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torch.xpu.synchronize()
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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', '-'*20)
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print('-'*20, 'Input Image', '-'*20)
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print(image_path)
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print('-'*20, 'Prompt', '-'*20)
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print('-'*20, 'Input Prompt', '-'*20)
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print(args.prompt)
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output_str = res
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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print('-'*20, 'Chat Output', '-'*20)
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print(res)
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@ -108,11 +108,11 @@ set SYCL_CACHE_PERSISTENT=1
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- chat without streaming mode:
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```
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python ./generate.py --prompt 'What is in the image?'
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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 ./generate.py --prompt 'What is in the image?' --stream
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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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