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Add phi-3-vision example (#11156)

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* Add phi-3-vision example (HF-Automodels)

* fix

* fix

* fix

* Add phi-3-vision CPU example (HF-Automodels)

* add in readme

* fix

* fix

* fix

* fix

* use fp8 for gpu example

* remove eval
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README.md
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@ -198,6 +198,7 @@ Over 50 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaM
| Ziya-Coding-34B-v1.0 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/ziya) | |
| Phi-2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phi-2) |
| Phi-3 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-3) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phi-3) |
| Phi-3-vision | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-3-vision) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phi-3-vision) |
| Yuan2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/yuan2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/yuan2) |
| Gemma | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/gemma) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/gemma) |
| DeciLM-7B | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/deciLM-7b) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/deciLM-7b) |

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docs/readthedocs/source/index.rst
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@ -555,6 +555,13 @@ Verified Models
<td>
<a href="https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/phi-3">link</a></td>
</tr>
<tr>
<td>Phi-3-vision</td>
<td>
<a href="https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-3-vision">link</a></td>
<td>
<a href="https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/phi-3-vision">link</a></td>
</tr>
<tr>
<td>Yuan2</td>
<td>

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# phi-3-vision
In this directory, you will find examples on how you could apply IPEX-LLM INT8 optimizations on phi-3-vision models. For illustration purposes, we utilize the [microsoft/Phi-3-vision-128k-instruct](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) as a reference phi-3-vision 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 `generate()` API
In the example [generate.py](./generate.py), we show a basic use case for a phi-3-vision model to predict the next N tokens using `generate()` API, with IPEX-LLM INT8 optimizations.
### 1. Install
We suggest using conda to manage environment:
On Linux:
```bash
conda create -n llm python=3.11 # recommend to use 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 pillow torchvision
pip install transformers==4.37.0
```
On Windows:
```cmd
conda create -n llm python=3.11
conda activate llm
pip install --pre --upgrade ipex-llm[all]
pip install pillow torchvision
pip install transformers==4.37.0
```
### 2. Run
```
python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --image-url-or-path IMAGE_URL_OR_PATH --prompt PROMPT --n-predict N_PREDICT
```
Arguments Info:
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the phi-3-vision model (e.g. `microsoft/Phi-3-vision-128k-instruct`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'microsoft/Phi-3-vision-128k-instruct'`.
- `--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`.
> **Note**: When loading the model in 8-bit, IPEX-LLM converts linear layers in the model into INT8 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 phi-3-vision model based on the capabilities of your machine.
#### 2.1 Client
On client Windows machines, it is recommended to run directly with full utilization of all cores:
```cmd
python ./generate.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 ./generate.py
```
#### 2.3 Sample Output
#### [microsoft/Phi-3-vision-128k-instruct](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct)
```log
Inference time: xxxx s
-------------------- Prompt --------------------
Message: [{'role': 'user', 'content': '<|image_1|>\nWhat is in the image?'}]
Image link/path: http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
-------------------- Output --------------------
What is in the image?
The image shows a child holding a white teddy bear dressed in a pink dress.
```
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>

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#
# 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 torch
import argparse
import requests
from PIL import Image
from ipex_llm.transformers import AutoModelForCausalLM
from transformers import AutoProcessor
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for phi-3 model')
parser.add_argument('--repo-id-or-model-path', type=str, default="microsoft/Phi-3-vision-128k-instruct",
help='The huggingface repo id for the phi-3-vision 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 INT8,
# which convert the relevant layers in the model into INT8 format
# We here use INT8 instead of INT4 for better output
# `_attn_implementation="eager"` is required for phi-3-vision
# `modules_to_not_convert=["vision_embed_tokens"]` is for acceleration and is optional
model = AutoModelForCausalLM.from_pretrained(model_path,
trust_remote_code=True,
load_in_low_bit="sym_int8",
_attn_implementation="eager",
modules_to_not_convert=["vision_embed_tokens"])
# Load processor
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
# here the message formatting refers to https://huggingface.co/microsoft/Phi-3-vision-128k-instruct#sample-inference-code
messages = [
{"role": "user", "content": "<|image_1|>\n{prompt}".format(prompt=args.prompt)},
]
prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
if os.path.exists(image_path):
image = Image.open(image_path)
else:
image = Image.open(requests.get(image_path, stream=True).raw)
# Generate predicted tokens
with torch.inference_mode():
inputs = processor(prompt, [image], return_tensors="pt")
st = time.time()
output = model.generate(**inputs,
eos_token_id=processor.tokenizer.eos_token_id,
num_beams=1,
do_sample=False,
max_new_tokens=args.n_predict,
temperature=0.0)
end = time.time()
print(f'Inference time: {end-st} s')
output_str = processor.decode(output[0],
skip_special_tokens=True,
clean_up_tokenization_spaces=False)
print('-'*20, 'Prompt', '-'*20)
print(f'Message: {messages}')
print(f'Image link/path: {image_path}')
print('-'*20, 'Output', '-'*20)
print(output_str)

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# phi-3-vision
In this directory, you will find examples on how you could apply IPEX-LLM FP8 optimizations on phi-3-vision models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [microsoft/Phi-3-vision-128k-instruct](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) as a reference phi-3-vision 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 `generate()` API
In the example [generate.py](./generate.py), we show a basic use case for a phi-3-vision model to predict the next N tokens using `generate()` API, with IPEX-LLM FP8 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 transformers==4.37.0
```
#### 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 transformers==4.37.0
```
### 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
<details>
<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
```bash
export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export SYCL_CACHE_PERSISTENT=1
```
</details>
<details>
<summary>For Intel Data Center GPU Max Series</summary>
```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`.
</details>
<details>
<summary>For Intel iGPU</summary>
```bash
export SYCL_CACHE_PERSISTENT=1
export BIGDL_LLM_XMX_DISABLED=1
```
</details>
#### 3.2 Configurations for Windows
<details>
<summary>For Intel iGPU</summary>
```cmd
set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=1
```
</details>
<details>
<summary>For Intel Arc™ A-Series Graphics</summary>
```cmd
set SYCL_CACHE_PERSISTENT=1
```
</details>
> [!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 phi-3-vision model (e.g. `microsoft/Phi-3-vision-128k-instruct`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'microsoft/Phi-3-vision-128k-instruct'`.
- `--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
#### [microsoft/Phi-3-vision-128k-instruct](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct)
```log
Inference time: xxxx s
-------------------- Prompt --------------------
Message: [{'role': 'user', 'content': '<|image_1|>\nWhat is in the image?'}]
Image link/path: http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
-------------------- Output --------------------
What is in the image?
The image shows a young girl holding a white teddy bear. She is wearing a pink dress with a heart on it. The background includes a stone
```
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>

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#
# 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 torch
import argparse
import requests
from PIL import Image
from ipex_llm.transformers import AutoModelForCausalLM
from transformers import AutoProcessor
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for phi-3 model')
parser.add_argument('--repo-id-or-model-path', type=str, default="microsoft/Phi-3-vision-128k-instruct",
help='The huggingface repo id for the phi-3-vision 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 FP8,
# which convert the relevant layers in the model into FP8 format
# We here use FP8 instead of INT4 for better output
# You could also try `'sym_int4'` for INT4, `'sym_int8'` for INT8 and `'fp6'` for FP6
# `_attn_implementation="eager"` is required for phi-3-vision
# `modules_to_not_convert=["vision_embed_tokens"]` and `model = model.half()` are for acceleration and are optional
# 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 = AutoModelForCausalLM.from_pretrained(model_path,
trust_remote_code=True,
load_in_low_bit="fp8",
_attn_implementation="eager",
modules_to_not_convert=["vision_embed_tokens"])
model = model.half().to('xpu')
# Load processor
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
# here the message formatting refers to https://huggingface.co/microsoft/Phi-3-vision-128k-instruct#sample-inference-code
messages = [
{"role": "user", "content": "<|image_1|>\n{prompt}".format(prompt=args.prompt)},
]
prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
if os.path.exists(image_path):
image = Image.open(image_path)
else:
image = Image.open(requests.get(image_path, stream=True).raw)
# Generate predicted tokens
with torch.inference_mode():
# ipex_llm model needs a warmup, then inference time can be accurate
inputs = processor(prompt, [image], return_tensors="pt")
inputs = inputs.to('xpu')
output = model.generate(**inputs,
eos_token_id=processor.tokenizer.eos_token_id,
num_beams=1,
do_sample=False,
max_new_tokens=args.n_predict,
temperature=0.0)
# start inference
st = time.time()
inputs = processor(prompt, [image], return_tensors="pt")
inputs = inputs.to('xpu')
output = model.generate(**inputs,
eos_token_id=processor.tokenizer.eos_token_id,
num_beams=1,
do_sample=False,
max_new_tokens=args.n_predict,
temperature=0.0)
end = time.time()
print(f'Inference time: {end-st} s')
output_str = processor.decode(output[0],
skip_special_tokens=True,
clean_up_tokenization_spaces=False)
print('-'*20, 'Prompt', '-'*20)
print(f'Message: {messages}')
print(f'Image link/path: {image_path}')
print('-'*20, 'Output', '-'*20)
print(output_str)