added minicpm cpu examples (#12027)

* minicpm cpu examples

* add link for minicpm-2

README.md

@@ -317,7 +317,7 @@ Over 50 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaM
 | CodeGeeX2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/codegeex2) | [link](python/llm/example/GPU/HuggingFace/LLM/codegeex2) |
 | MiniCPM | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm) | [link](python/llm/example/GPU/HuggingFace/LLM/minicpm) |
 | MiniCPM-V |  | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V) |
-| MiniCPM-V-2 |  | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2) |
+| MiniCPM-V-2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm-v-2) | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2) |
 | MiniCPM-Llama3-V-2_5 |  | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-Llama3-V-2_5) |
 | MiniCPM-V-2_6 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm-v-2_6) | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6) | 
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm-v-2/README.md

@@ -0,0 +1,101 @@
+# MiniCPM-V-2
+In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM-V-2 models. 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, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
+
+## Example: Predict Tokens using `chat()` API
+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.
+### 1. Install
+We suggest using conda to manage environment:
+
+On Linux:
+
+```bash
+conda create -n llm python=3.11
+conda activate llm
+
+# install ipex-llm with 'all' option
+pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
+pip install torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cpu
+pip install peft timm
+```
+On Windows:
+
+```cmd
+conda create -n llm python=3.11
+conda activate llm
+
+pip install --pre --upgrade ipex-llm[all]
+pip install torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cpu
+pip install peft timm
+```
+
+### 2. Run
+
+- chat without streaming mode:
+  ```
+  python ./chat.py --prompt 'What is in the image?'
+  ```
+- chat in streaming mode:
+  ```
+  python ./chat.py --prompt 'What is in the image?' --stream
+  ```
+
+> [!TIP]
+> For chatting in streaming mode, it is recommended to set the environment variable `PYTHONUNBUFFERED=1`.
+
+
+Arguments info:
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the MiniCPM-V-2 model (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'`.
+- `--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?'`.
+- `--stream`: flag to chat in streaming mode
+
+> **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.
+>
+> Please select the appropriate size of the MiniCPM model based on the capabilities of your machine.
+
+#### 2.1 Client
+On client Windows machine, it is recommended to run directly with full utilization of all cores:
+```cmd
+python ./chat.py 
+```
+
+#### 2.2 Server
+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.
+
+E.g. on Linux,
+```bash
+# set IPEX-LLM env variables
+source ipex-llm-init
+
+# e.g. for a server with 48 cores per socket
+export OMP_NUM_THREADS=48
+numactl -C 0-47 -m 0 python ./chat.py
+```
+
+#### 2.3 Sample Output
+#### [openbmb/MiniCPM-V-2](https://huggingface.co/openbmb/MiniCPM-V-2)
+```log
+Inference time: xxxx s
+-------------------- Input Image --------------------
+http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
+-------------------- Input Prompt --------------------
+What is in the image?
+-------------------- Chat Output --------------------
+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.
+```
+
+```log
+-------------------- Input Image --------------------
+http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
+-------------------- Input Prompt --------------------
+图片里有什么？
+-------------------- Stream Chat Output --------------------
+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。
+```
+
+The sample input image is (which is fetched from [COCO dataset](https://cocodataset.org/#explore?id=264959)):
+
+<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>

python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm-v-2/chat.py

@@ -0,0 +1,102 @@
+#
+# 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.
+#
+
+
+import os
+import time
+import argparse
+import requests
+import torch
+from PIL import Image
+from ipex_llm.transformers import AutoModel
+from transformers import AutoTokenizer
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Predict Tokens using `chat()` API for 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 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('--stream', action='store_true',
+                        help='Whether to chat in streaming mode')
+
+    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
+    model = AutoModel.from_pretrained(model_path,
+                                      load_in_low_bit="asym_int4",
+                                      optimize_model=True,
+                                      trust_remote_code=True,
+                                      use_cache=True,
+                                      torch_dtype=torch.float32,
+                                      modules_to_not_convert=["vpm", "resampler"])
+
+    # Load tokenizer
+    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': args.prompt}]
+
+    if args.stream:
+        res, context, _ = model.chat(
+            image=image,
+            msgs=msgs,
+            context= None,
+            tokenizer=tokenizer,
+            stream=True
+        )
+
+        print('-'*20, 'Input Image', '-'*20)
+        print(image_path)
+        print('-'*20, 'Input Prompt', '-'*20)
+        print(args.prompt)
+        print('-'*20, 'Stream Chat Output', '-'*20)
+        for new_text in res:
+            print(new_text, flush=True, end='')
+    else:
+        st = time.time()
+        res, context, _ = model.chat(
+            image=image,
+            msgs=msgs,
+            context=None,
+            tokenizer=tokenizer,
+        )
+        end = time.time()
+
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Input Image', '-'*20)
+        print(image_path)
+        print('-'*20, 'Input Prompt', '-'*20)
+        print(args.prompt)
+        print('-'*20, 'Chat Output', '-'*20)
+        print(res)

python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm-v-2_6/README.md

@@ -28,7 +28,7 @@ conda activate llm
 
 pip install --pre --upgrade ipex-llm[all]
 pip install torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cpu
-pip install transformers==4.40.0 trl
+pip install transformers==4.41.0 trl
 ```
 
 ### 2. Run