Add Qwen1.5-7B-Chat (#10113)
* add Qwen1.5-7B-Chat * modify Qwen1.5 example * update README * update prompt format * update folder name and example README * add Chinese prompt sample output * update link in README * correct the link * update transformer version
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@ -186,6 +186,7 @@ Over 40 models have been optimized/verified on `bigdl-llm`, including *LLaMA/LLa
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| Bark | [link](python/llm/example/CPU/PyTorch-Models/Model/bark) | [link](python/llm/example/GPU/PyTorch-Models/Model/bark) |
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| SpeechT5 | | [link](python/llm/example/GPU/PyTorch-Models/Model/speech-t5) |
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| Ziya-Coding-34B-v1.0 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/ziya) | |
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| Qwen1.5 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen1.5) |
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***For more details, please refer to the `bigdl-llm` [Document](https://test-bigdl-llm.readthedocs.io/en/main/doc/LLM/index.html), [Readme](python/llm), [Tutorial](https://github.com/intel-analytics/bigdl-llm-tutorial) and [API Doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/LLM/index.html).***
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@ -82,6 +82,8 @@ Over 20 models have been optimized/verified on `bigdl-llm`, including *LLaMA/LLa
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| Bark | [link](example/CPU/PyTorch-Models/Model/bark) | [link](example/GPU/PyTorch-Models/Model/bark) |
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| SpeechT5 | | [link](example/GPU/PyTorch-Models/Model/speech-t5) |
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| Ziya-Coding-34B-v1.0 | [link](example/CPU/HF-Transformers-AutoModels/Model/ziya) | |
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| Qwen1.5 | [link](example/CPU/HF-Transformers-AutoModels/Model/qwen1.5) | [link](example/GPU/HF-Transformers-AutoModels/Model/qwen1.5) |
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### Working with `bigdl-llm`
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@ -0,0 +1,87 @@
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# Qwen1.5
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In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Qwen1.5 models. For illustration purposes, we utilize the [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) as a reference Qwen1.5 model.
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## 0. Requirements
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To run these examples with BigDL-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 `generate()` API
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In the example [generate.py](./generate.py), we show a basic use case for a Qwen model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations.
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### 1. Install
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We suggest using conda to manage environment:
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```bash
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conda create -n llm python=3.9
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conda activate llm
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pip install --pre --upgrade bigdl-llm[all] # install bigdl-llm with 'all' option
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pip install transformers==4.37.0 # install the transformers which support Qwen2
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```
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### 2. Run
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```
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python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
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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 Qwen model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'Qwen/Qwen1.5-7B-Chat'`.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'AI是什么?'`.
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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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> **Note**: When loading the model in 4-bit, BigDL-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 Qwen 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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```powershell
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python ./generate.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 BigDL-LLM env variables
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source bigdl-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 ./generate.py
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```
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#### 2.3 Sample Output
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#### [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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<|im_start|>system
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You are a helpful assistant.<|im_end|>
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<|im_start|>user
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AI是什么？<|im_end|>
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<|im_start|>assistant
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-------------------- Output --------------------
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<|im_start|>system
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You are a helpful assistant.<|im_end|>
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<|im_start|>user
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AI是什么？<|im_end|>
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<|im_start|>assistant
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人工智能（AI）是指计算机科学的一个分支，旨在开发能够执行通常需要人类智能的任务的算法和系统。这些任务包括但不限于理解自然语言、
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```
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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<|im_start|>system
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You are a helpful assistant.<|im_end|>
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<|im_start|>user
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What is AI?<|im_end|>
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<|im_start|>assistant
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-------------------- Output --------------------
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<|im_start|>system
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You are a helpful assistant.<|im_end|>
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<|im_start|>user
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What is AI?<|im_end|>
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<|im_start|>assistant
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AI, or artificial intelligence, refers to the simulation of human intelligence in machines that are designed to perform tasks that typically require human cognition, such as learning, reasoning
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```
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@ -0,0 +1,77 @@
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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 torch
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import time
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import argparse
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import numpy as np
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from transformers import AutoTokenizer
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Qwen1.5-7B-Chat')
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parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen1.5-7B-Chat",
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help='The huggingface repo id for the Qwen1.5 model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--prompt', type=str, default="AI是什么？",
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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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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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from bigdl.llm.transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained(model_path,
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load_in_4bit=True,
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trust_remote_code=True)
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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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prompt = args.prompt
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# Generate predicted tokens
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with torch.inference_mode():
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt")
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st = time.time()
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=args.n_predict
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)
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end = time.time()
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Prompt', '-'*20)
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print(prompt)
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print('-'*20, 'Output', '-'*20)
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print(response)
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# Qwen1.5
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In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate Qwen1.5 models. For illustration purposes, we utilize the [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) as reference Qwen1.5 model.
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## Requirements
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To run these examples with BigDL-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 `generate()` API
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In the example [generate.py](./generate.py), we show a basic use case for a Qwen1.5 model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations.
|
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### 1. Install
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We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://docs.conda.io/en/latest/miniconda.html#).
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After installing conda, create a Python environment for BigDL-LLM:
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```bash
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conda create -n llm python=3.9 # recommend to use Python 3.9
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conda activate llm
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pip install --pre --upgrade bigdl-llm[all] # install the latest bigdl-llm nightly build with 'all' option
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pip install transformers==4.37.0 # install transformers which supports Qwen2
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```
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### 2. Run
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After setting up the Python environment, you could run the example by following steps.
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#### 2.1 Client
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On client Windows machines, it is recommended to run directly with full utilization of all cores:
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```powershell
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python ./generate.py --prompt 'What is AI?'
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```
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More information about arguments can be found in [Arguments Info](#23-arguments-info) section. The expected output can be found in [Sample Output](#24-sample-output) section.
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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 BigDL-LLM env variables
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source bigdl-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 ./generate.py --prompt 'What is AI?'
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```
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More information about arguments can be found in [Arguments Info](#23-arguments-info) section. The expected output can be found in [Sample Output](#24-sample-output) section.
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#### 2.3 Arguments Info
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In the example, several arguments can be passed to satisfy your requirements:
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Qwen1.5 to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'Qwen/Qwen1.5-7B-Chat'`.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'AI是什么？'`.
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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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#### 2.3 Sample Output
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#### [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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<|im_start|>system
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You are a helpful assistant.<|im_end|>
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<|im_start|>user
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AI是什么？<|im_end|>
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<|im_start|>assistant
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-------------------- Output --------------------
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<|im_start|>system
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You are a helpful assistant.<|im_end|>
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<|im_start|>user
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AI是什么？<|im_end|>
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<|im_start|>assistant
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AI（Artificial Intelligence）是指由计算机程序实现的智能，它使机器能够模拟人类的思考、学习和决策过程，从而解决各种复杂
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```
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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<|im_start|>system
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You are a helpful assistant.<|im_end|>
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<|im_start|>user
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What is AI?<|im_end|>
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<|im_start|>assistant
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-------------------- Output --------------------
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<|im_start|>system
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You are a helpful assistant.<|im_end|>
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<|im_start|>user
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What is AI?<|im_end|>
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<|im_start|>assistant
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AI, or artificial intelligence, refers to the simulation of human intelligence in machines that are programmed to think, learn, and make decisions like humans. It involves the
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```
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@ -0,0 +1,78 @@
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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 torch
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import time
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import argparse
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import numpy as np
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from transformers import AutoTokenizer
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Qwen1.5-7B-Chat')
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parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen1.5-7B-Chat",
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help='The huggingface repo id for the Qwen1.5 model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--prompt', type=str, default="AI是什么？",
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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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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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from transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained(model_path,
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trust_remote_code=True)
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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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from bigdl.llm import optimize_model
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model = optimize_model(model)
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prompt = args.prompt
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# Generate predicted tokens
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with torch.inference_mode():
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt")
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st = time.time()
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=args.n_predict
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)
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end = time.time()
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Prompt', '-'*20)
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print(prompt)
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print('-'*20, 'Output', '-'*20)
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print(response)
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|
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@ -0,0 +1,143 @@
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# Qwen1.5
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In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Qwen1.5 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) as a reference InternLM model.
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## 0. Requirements
|
||||
To run these examples with BigDL-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 Qwen1.5 model to predict the next N tokens using `generate()` API, with BigDL-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=3.9
|
||||
conda activate llm
|
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
|
||||
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
|
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pip install transformers==4.37.0 # install transformers which supports Qwen2
|
||||
```
|
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|
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#### 1.2 Installation on Windows
|
||||
We suggest using conda to manage environment:
|
||||
```bash
|
||||
conda create -n llm python=3.9 libuv
|
||||
conda activate llm
|
||||
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
|
||||
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
|
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pip install transformers==4.37.2 # install transformers which supports Qwen2
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```
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### 2. Configures OneAPI environment variables
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#### 2.1 Configurations for Linux
|
||||
```bash
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source /opt/intel/oneapi/setvars.sh
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```
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#### 2.2 Configurations for Windows
|
||||
```cmd
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call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
|
||||
```
|
||||
> Note: Please make sure you are using **CMD** (**Anaconda Prompt** if using conda) to run the command as PowerShell is not supported.
|
||||
### 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
|
||||
```
|
||||
|
||||
</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 ENABLE_SDP_FUSION=1
|
||||
```
|
||||
> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
|
||||
</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™ A300-Series or Pro A60</summary>
|
||||
|
||||
```cmd
|
||||
set SYCL_CACHE_PERSISTENT=1
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
|
||||
<summary>For other Intel dGPU Series</summary>
|
||||
|
||||
There is no need to set further environment variables.
|
||||
|
||||
</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 --repo-id-or-model-path REPO_ID_OR_MODEL_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 Qwen1.5 model (e.g. `Qwen/Qwen1.5-7B-Chat`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'Qwen/Qwen1.5-7B-Chat'`.
|
||||
- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'AI是什么？'`.
|
||||
- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
|
||||
|
||||
#### Sample Output
|
||||
#### [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat)
|
||||
```log
|
||||
Inference time: xxxx s
|
||||
-------------------- Prompt --------------------
|
||||
<|im_start|>system
|
||||
You are a helpful assistant.<|im_end|>
|
||||
<|im_start|>user
|
||||
AI是什么？<|im_end|>
|
||||
<|im_start|>assistant
|
||||
-------------------- Output --------------------
|
||||
<|im_start|>system
|
||||
You are a helpful assistant.<|im_end|>
|
||||
<|im_start|>user
|
||||
AI是什么？<|im_end|>
|
||||
<|im_start|>assistant
|
||||
人工智能（AI）是指通过计算机模拟、延伸和扩展人类智能的学科，其目标是使机器具有学习、推理、感知、理解、交流
|
||||
```
|
||||
|
||||
```log
|
||||
Inference time: xxxx s
|
||||
-------------------- Prompt --------------------
|
||||
<|im_start|>system
|
||||
You are a helpful assistant.<|im_end|>
|
||||
<|im_start|>user
|
||||
What is AI?<|im_end|>
|
||||
<|im_start|>assistant
|
||||
-------------------- Output --------------------
|
||||
<|im_start|>system
|
||||
You are a helpful assistant.<|im_end|>
|
||||
<|im_start|>user
|
||||
What is AI?<|im_end|>
|
||||
<|im_start|>assistant
|
||||
AI, or artificial intelligence, refers to the simulation of human intelligence in machines that are programmed to perform tasks that typically require human cognition, such as learning, reasoning
|
||||
```
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
#
|
||||
# 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 torch
|
||||
import time
|
||||
import argparse
|
||||
|
||||
from transformers import AutoTokenizer
|
||||
from bigdl.llm import optimize_model
|
||||
import intel_extension_for_pytorch as ipex
|
||||
import numpy as np
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Qwen1.5-7B-Chat')
|
||||
parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen1.5-7B-Chat",
|
||||
help='The huggingface repo id for the Qwen1.5 model to be downloaded'
|
||||
', or the path to the huggingface checkpoint folder')
|
||||
parser.add_argument('--prompt', type=str, default="AI是什么？",
|
||||
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
|
||||
|
||||
|
||||
from bigdl.llm.transformers import AutoModelForCausalLM
|
||||
# Load model in 4 bit,
|
||||
# which convert the relevant layers in the model into INT4 format
|
||||
model = AutoModelForCausalLM.from_pretrained(model_path,
|
||||
load_in_4bit=True,
|
||||
trust_remote_code=True)
|
||||
model = model.to("xpu")
|
||||
|
||||
# Load tokenizer
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path,
|
||||
trust_remote_code=True)
|
||||
|
||||
prompt = args.prompt
|
||||
|
||||
# Generate predicted tokens
|
||||
with torch.inference_mode():
|
||||
messages = [
|
||||
{"role": "system", "content": "You are a helpful assistant."},
|
||||
{"role": "user", "content": prompt}
|
||||
]
|
||||
text = tokenizer.apply_chat_template(
|
||||
messages,
|
||||
tokenize=False,
|
||||
add_generation_prompt=True
|
||||
)
|
||||
model_inputs = tokenizer([text], return_tensors="pt").to("xpu")
|
||||
# warmup
|
||||
generated_ids = model.generate(
|
||||
model_inputs.input_ids,
|
||||
max_new_tokens=args.n_predict
|
||||
)
|
||||
|
||||
st = time.time()
|
||||
generated_ids = model.generate(
|
||||
model_inputs.input_ids,
|
||||
max_new_tokens=args.n_predict
|
||||
)
|
||||
torch.xpu.synchronize()
|
||||
end = time.time()
|
||||
generated_ids = generated_ids.cpu()
|
||||
generated_ids = [
|
||||
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
|
||||
]
|
||||
|
||||
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
||||
print(f'Inference time: {end-st} s')
|
||||
print('-'*20, 'Prompt', '-'*20)
|
||||
print(prompt)
|
||||
print('-'*20, 'Output', '-'*20)
|
||||
print(response)
|
||||
143
python/llm/example/GPU/PyTorch-Models/Model/qwen1.5/README.md
Normal file
143
python/llm/example/GPU/PyTorch-Models/Model/qwen1.5/README.md
Normal file
|
|
@ -0,0 +1,143 @@
|
|||
# Qwen1.5
|
||||
In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate Qwen1.5 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) as a reference InternLM model.
|
||||
|
||||
## 0. Requirements
|
||||
To run these examples with BigDL-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 Qwen1.5 model to predict the next N tokens using `generate()` API, with BigDL-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=3.9
|
||||
conda activate llm
|
||||
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
|
||||
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
|
||||
pip install transformers==4.37.0 # install transformers which supports Qwen2
|
||||
```
|
||||
|
||||
#### 1.2 Installation on Windows
|
||||
We suggest using conda to manage environment:
|
||||
```bash
|
||||
conda create -n llm python=3.9 libuv
|
||||
conda activate llm
|
||||
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
|
||||
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
|
||||
pip install transformers==4.37.2 # install transformers which supports Qwen2
|
||||
```
|
||||
|
||||
### 2. Configures OneAPI environment variables
|
||||
#### 2.1 Configurations for Linux
|
||||
```bash
|
||||
source /opt/intel/oneapi/setvars.sh
|
||||
```
|
||||
|
||||
#### 2.2 Configurations for Windows
|
||||
```cmd
|
||||
call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
|
||||
```
|
||||
> Note: Please make sure you are using **CMD** (**Anaconda Prompt** if using conda) to run the command as PowerShell is not supported.
|
||||
### 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
|
||||
```
|
||||
|
||||
</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 ENABLE_SDP_FUSION=1
|
||||
```
|
||||
> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
|
||||
</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™ A300-Series or Pro A60</summary>
|
||||
|
||||
```cmd
|
||||
set SYCL_CACHE_PERSISTENT=1
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
|
||||
<summary>For other Intel dGPU Series</summary>
|
||||
|
||||
There is no need to set further environment variables.
|
||||
|
||||
</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 --repo-id-or-model-path REPO_ID_OR_MODEL_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 Qwen1.5 model (e.g. `Qwen/Qwen1.5-7B-Chat`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'Qwen/Qwen1.5-7B-Chat'`.
|
||||
- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'AI是什么？'`.
|
||||
- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
|
||||
|
||||
#### Sample Output
|
||||
#### [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat)
|
||||
```log
|
||||
Inference time: xxxx s
|
||||
-------------------- Prompt --------------------
|
||||
<|im_start|>system
|
||||
You are a helpful assistant.<|im_end|>
|
||||
<|im_start|>user
|
||||
AI是什么？<|im_end|>
|
||||
<|im_start|>assistant
|
||||
-------------------- Output --------------------
|
||||
<|im_start|>system
|
||||
You are a helpful assistant.<|im_end|>
|
||||
<|im_start|>user
|
||||
AI是什么？<|im_end|>
|
||||
<|im_start|>assistant
|
||||
AI（Artificial Intelligence）是指计算机科学的一个分支，其目标是创建能够理解、学习、推理和自我修正的智能机器。AI系统可以通过
|
||||
```
|
||||
|
||||
```log
|
||||
Inference time: xxxx s
|
||||
-------------------- Prompt --------------------
|
||||
<|im_start|>system
|
||||
You are a helpful assistant.<|im_end|>
|
||||
<|im_start|>user
|
||||
What is AI?<|im_end|>
|
||||
<|im_start|>assistant
|
||||
-------------------- Output --------------------
|
||||
<|im_start|>system
|
||||
You are a helpful assistant.<|im_end|>
|
||||
<|im_start|>user
|
||||
What is AI?<|im_end|>
|
||||
<|im_start|>assistant
|
||||
AI stands for Artificial Intelligence, which is a field of computer science that focuses on creating intelligent machines that can perform tasks that typically require human intelligence, such as learning
|
||||
```
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
#
|
||||
# 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 torch
|
||||
import time
|
||||
import argparse
|
||||
|
||||
from transformers import AutoTokenizer
|
||||
from bigdl.llm import optimize_model
|
||||
import intel_extension_for_pytorch as ipex
|
||||
import numpy as np
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Qwen1.5-7B-Chat')
|
||||
parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen1.5-7B-Chat",
|
||||
help='The huggingface repo id for the Qwen1.5 model to be downloaded'
|
||||
', or the path to the huggingface checkpoint folder')
|
||||
parser.add_argument('--prompt', type=str, default="AI是什么？",
|
||||
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
|
||||
|
||||
|
||||
from transformers import AutoModelForCausalLM
|
||||
from bigdl.llm import optimize_model
|
||||
model = AutoModelForCausalLM.from_pretrained(model_path,
|
||||
trust_remote_code=True,
|
||||
torch_dtype = torch.float16,
|
||||
use_cache=True)
|
||||
model = optimize_model(model)
|
||||
model = model.to("xpu")
|
||||
|
||||
# Load tokenizer
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path,
|
||||
trust_remote_code=True)
|
||||
|
||||
prompt = args.prompt
|
||||
# Generate predicted tokens
|
||||
with torch.inference_mode():
|
||||
messages = [
|
||||
{"role": "system", "content": "You are a helpful assistant."},
|
||||
{"role": "user", "content": prompt}
|
||||
]
|
||||
text = tokenizer.apply_chat_template(
|
||||
messages,
|
||||
tokenize=False,
|
||||
add_generation_prompt=True
|
||||
)
|
||||
model_inputs = tokenizer([text], return_tensors="pt").to("xpu")
|
||||
# warmup
|
||||
generated_ids = model.generate(
|
||||
model_inputs.input_ids,
|
||||
max_new_tokens=args.n_predict
|
||||
)
|
||||
|
||||
st = time.time()
|
||||
generated_ids = model.generate(
|
||||
model_inputs.input_ids,
|
||||
max_new_tokens=args.n_predict
|
||||
)
|
||||
torch.xpu.synchronize()
|
||||
end = time.time()
|
||||
generated_ids = generated_ids.cpu()
|
||||
generated_ids = [
|
||||
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
|
||||
]
|
||||
|
||||
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
||||
print(f'Inference time: {end-st} s')
|
||||
print('-'*20, 'Prompt', '-'*20)
|
||||
print(prompt)
|
||||
print('-'*20, 'Output', '-'*20)
|
||||
print(response)
|
||||
Loading…
Reference in a new issue