From 808d9a7bae72bd4de447b88460bae1a9d77d07f8 Mon Sep 17 00:00:00 2001
From: "Jin, Qiao" <89779290+JinBridger@users.noreply.github.com>
Date: Tue, 6 Aug 2024 10:22:33 +0800
Subject: [PATCH] Add MiniCPM-V-2 GPU example (#11699)
* Add MiniCPM-V-2 GPU example
* add example in README.md
* add example in README.md
---
README.md | 2 +
.../Multimodal/MiniCPM-V-2/README.md | 135 ++++++++++++++
.../Multimodal/MiniCPM-V-2/generate.py | 176 ++++++++++++++++++
3 files changed, 313 insertions(+)
create mode 100644 python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2/README.md
create mode 100644 python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2/generate.py
diff --git a/README.md b/README.md
index 240dded6..9542fb78 100644
--- a/README.md
+++ b/README.md
@@ -309,6 +309,8 @@ 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-Llama3-V-2_5 | | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-Llama3-V-2_5) |
## Get Support
- Please report a bug or raise a feature request by opening a [Github Issue](https://github.com/intel-analytics/ipex-llm/issues)
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
new file mode 100644
index 00000000..cc293ab9
--- /dev/null
+++ b/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2/README.md
@@ -0,0 +1,135 @@
+# 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) 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.
+
+## Example: Predict Tokens using `chat()` API
+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.
+### 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
+```
+
+#### 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
+```
+
+### 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 (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?'`.
+- `--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](https://huggingface.co/openbmb/MiniCPM-V-2)
+
+```log
+Inference time: xxxx s
+-------------------- Input --------------------
+http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
+-------------------- Prompt --------------------
+What is in the image?
+-------------------- Output --------------------
+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.
+```
+
+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
new file mode 100644
index 00000000..07b3021e
--- /dev/null
+++ b/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2/generate.py
@@ -0,0 +1,176 @@
+#
+# 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 model')
+ parser.add_argument('--repo-id-or-model-path', type=str, default="openbmb/MiniCPM-V-2",
+ help='The huggingface repo id for the openbmb/MiniCPM-V-2 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="asym_int4",
+ optimize_model=True,
+ trust_remote_code=True,
+ modules_to_not_convert=["vpm", "resampler", "lm_head"],
+ use_cache=True)
+ model = model.float().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/blob/main/README.md
+ msgs = [{'role': 'user', 'content': args.prompt}]
+ st = time.time()
+ res, context, _ = model.chat(
+ image=image,
+ 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)