Add CPU and GPU examples of phi-2 (#10014)
* Add CPU and GPU examples of phi-2 * In GPU hf example, updated the readme for Windows GPU supports * In GPU torch example, updated the readme for Windows GPU supports * update the table in BigDL/README.md * update the table in BigDL/python/llm/README.md
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@ -190,6 +190,7 @@ 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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| Phi-2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phi-2) |
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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 +82,7 @@ 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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| Phi-2 | [link](example/CPU/HF-Transformers-AutoModels/Model/phi-2) | [link](example/GPU/HF-Transformers-AutoModels/Model/phi-2) |
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### Working with `bigdl-llm`
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@ -0,0 +1,74 @@
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# phi-2
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In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on phi-2 models. For illustration purposes, we utilize the [microsoft/phi-2](https://huggingface.co/microsoft/phi-2 as a reference phi-2 model.
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> **Note**: If you want to download the Hugging Face *Transformers* model, please refer to [here](https://huggingface.co/docs/hub/models-downloading#using-git).
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>
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> BigDL-LLM optimizes the *Transformers* model in INT4 precision at runtime, and thus no explicit conversion is needed.
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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 phi-2 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 einops # additional package required for phi-2 to conduct generation
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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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> **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 phi-2 model based on the capabilities of your machine.
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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`: str, argument defining the huggingface repo id for the phi-2 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'microsoft/phi-2'`.
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- `--prompt`: str, argument defining the prompt to be inferred (with integrated prompt format for chat). It is default to be `What is AI?`.
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- `--n-predict`: int, argument defining the max number of tokens to predict. It is default to be `32`.
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#### 2.4 Sample Output
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#### [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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Question:What is AI?
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Answer:
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-------------------- Output --------------------
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Question:What is AI?
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Answer: AI stands for Artificial Intelligence. It is a field of computer science that focuses on creating intelligent machines that can perform tasks that would typically require human intelligence.
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```
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@ -0,0 +1,72 @@
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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 bigdl.llm.transformers import AutoModel,AutoModelForCausalLM
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from transformers import AutoTokenizer, GenerationConfig
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# you could tune the prompt based on your own model,
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# here the prompt tuning refers to https://huggingface.co/microsoft/phi-2
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PHI2_PROMPT_FORMAT = " Question:{prompt}\n\n Answer:"
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generation_config = GenerationConfig(use_cache = True)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for phi-2 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="microsoft/phi-2",
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help='The huggingface repo id for the phi-2 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="What is AI?",
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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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# Load model in 4 bit,
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# which convert the relevant layers in the model into INT4 format
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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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# Generate predicted tokens
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with torch.inference_mode():
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prompt = PHI2_PROMPT_FORMAT.format(prompt=args.prompt)
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input_ids = tokenizer.encode(prompt, return_tensors="pt")
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st = time.time()
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# if your selected model is capable of utilizing previous key/value attentions
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# to enhance decoding speed, but has `"use_cache": false` in its model config,
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# it is important to set `use_cache=True` explicitly in the `generate` function
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# to obtain optimal performance with BigDL-LLM INT4 optimizations
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# Note that phi-2 uses GenerationConfig to enable 'use_cache'
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output = model.generate(input_ids, do_sample=False, max_new_tokens=args.n_predict, generation_config = generation_config)
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end = time.time()
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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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(output_str)
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60
python/llm/example/CPU/PyTorch-Models/Model/phi-2/README.md
Normal file
60
python/llm/example/CPU/PyTorch-Models/Model/phi-2/README.md
Normal file
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@ -0,0 +1,60 @@
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# phi-2
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In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate phi-2 models. For illustration purposes, we utilize the [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) as a reference phi-2 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 phi-2 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 einops
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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`: str, argument defining the huggingface repo id for the phi-2 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'microsoft/phi-2'`.
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- `--prompt`: str, argument defining the prompt to be inferred (with integrated prompt format for chat). It is default to be `'What is AI?'`.
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- `--n-predict`: int, argument defining the max number of tokens to predict. It is default to be `32`.
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#### 2.4 Sample Output
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#### [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
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```log
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Inference time: xxxx s
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-------------------- Output --------------------
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Question: What is AI?
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Answer: AI stands for Artificial Intelligence. It is a field of computer science that focuses on creating intelligent machines that can perform tasks that typically require human intelligence.
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```
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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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from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM, GenerationConfig
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from bigdl.llm import optimize_model
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# you could tune the prompt based on your own model,
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# here the prompt tuning refers to https://huggingface.co/microsoft/phi-2
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PHI_2_V1_PROMPT_FORMAT = "Question: {prompt}\n\n Answer:"
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generation_config = GenerationConfig(use_cache = True)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for phi-2 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="microsoft/phi-2",
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help='The huggingface repo id for the phi-2 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="What is AI?",
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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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# Load model
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model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
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# With only one line to enable BigDL-LLM optimization on model
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model = optimize_model(model)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = PHI_2_V1_PROMPT_FORMAT.format(prompt=args.prompt)
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input_ids = tokenizer.encode(prompt, return_tensors="pt")
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st = time.time()
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output = model.generate(input_ids, max_new_tokens=args.n_predict, generation_config = generation_config)
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end = time.time()
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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# phi-2
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In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on phi-2 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) as a reference phi-2 model.
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## 0. Requirements
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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.
|
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|
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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 phi-2 model to predict the next N tokens using `generate()` API, with BigDL-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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```bash
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conda create -n llm python=3.9
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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pip install einops # additional package required for phi-2 to conduct generation
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```
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#### 1.2 Installation on Windows
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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 libuv
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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```
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### 2. Configures OneAPI environment variables
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#### 2.1 Configurations for Linux
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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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#### 2.2 Configurations for Windows
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```cmd
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call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
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```
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> Note: Please make sure you are using **CMD** (**Anaconda Prompt** if using conda) to run the command as PowerShell is not supported.
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### 3. Runtime Configurations
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For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
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#### 3.1 Configurations for Linux
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<details>
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<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
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```bash
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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```
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</details>
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<details>
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<summary>For Intel Data Center GPU Max Series</summary>
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```bash
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export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export ENABLE_SDP_FUSION=1
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```
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> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
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</details>
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#### 3.2 Configurations for Windows
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<details>
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<summary>For Intel iGPU</summary>
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```cmd
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set SYCL_CACHE_PERSISTENT=1
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set BIGDL_LLM_XMX_DISABLED=1
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```
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</details>
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<details>
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<summary>For Intel Arc™ A300-Series or Pro A60</summary>
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```cmd
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set SYCL_CACHE_PERSISTENT=1
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```
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</details>
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<details>
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<summary>For other Intel dGPU Series</summary>
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There is no need to set further environment variables.
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</details>
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|
||||
> 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 AI?'
|
||||
```
|
||||
|
||||
Arguments info:
|
||||
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the phi-2 model (e.g. `microsoft/phi-2`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'microsoft/phi-2'`.
|
||||
- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
|
||||
- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
|
||||
|
||||
#### Sample Output
|
||||
#### [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
|
||||
|
||||
```log
|
||||
Inference time: xxxx s
|
||||
-------------------- Prompt --------------------
|
||||
Question: What is AI?
|
||||
|
||||
Answer:
|
||||
-------------------- Output --------------------
|
||||
Question: What is AI?
|
||||
|
||||
Answer: AI stands for Artificial Intelligence. It is a field of computer science that focuses on creating intelligent machines that can perform tasks that would typically require human intelligence.
|
||||
```
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
#
|
||||
# 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
|
||||
import numpy as np
|
||||
|
||||
from bigdl.llm.transformers import AutoModel,AutoModelForCausalLM
|
||||
from transformers import AutoTokenizer, GenerationConfig
|
||||
|
||||
# you could tune the prompt based on your own model,
|
||||
# here the prompt tuning refers to https://huggingface.co/microsoft/phi-2
|
||||
PHI2_PROMPT_FORMAT = " Question:{prompt}\n\n Answer:"
|
||||
generation_config = GenerationConfig(use_cache = True)
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for phi-2 model')
|
||||
parser.add_argument('--repo-id-or-model-path', type=str, default="microsoft/phi-2",
|
||||
help='The huggingface repo id for the phi-2 model to be downloaded'
|
||||
', or the path to the huggingface checkpoint folder')
|
||||
parser.add_argument('--prompt', type=str, default="What is AI?",
|
||||
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
|
||||
|
||||
# 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 = 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)
|
||||
|
||||
# Generate predicted tokens
|
||||
with torch.inference_mode():
|
||||
prompt = PHI2_PROMPT_FORMAT.format(prompt=args.prompt)
|
||||
input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
|
||||
|
||||
# ipex model needs a warmup, then inference time can be accurate
|
||||
output = model.generate(input_ids,
|
||||
max_new_tokens=args.n_predict,
|
||||
generation_config = generation_config)
|
||||
# start inference
|
||||
st = time.time()
|
||||
# if your selected model is capable of utilizing previous key/value attentions
|
||||
# to enhance decoding speed, but has `"use_cache": false` in its model config,
|
||||
# it is important to set `use_cache=True` explicitly in the `generate` function
|
||||
# to obtain optimal performance with BigDL-LLM INT4 optimizations
|
||||
|
||||
# Note that phi-2 uses GenerationConfig to enable 'use_cache'
|
||||
output = model.generate(input_ids, do_sample=False, max_new_tokens=args.n_predict, generation_config = generation_config)
|
||||
torch.xpu.synchronize()
|
||||
end = time.time()
|
||||
output = output.cpu()
|
||||
output_str = tokenizer.decode(output[0], skip_special_tokens=True)
|
||||
print(f'Inference time: {end-st} s')
|
||||
print('-'*20, 'Prompt', '-'*20)
|
||||
print(prompt)
|
||||
print('-'*20, 'Output', '-'*20)
|
||||
print(output_str)
|
||||
116
python/llm/example/GPU/PyTorch-Models/Model/phi-2/README.md
Normal file
116
python/llm/example/GPU/PyTorch-Models/Model/phi-2/README.md
Normal file
|
|
@ -0,0 +1,116 @@
|
|||
# phi-2
|
||||
In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate phi-2 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) as a reference phi-2 model.
|
||||
|
||||
## Requirements
|
||||
To run these examples with BigDL-LLM, 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-2 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 the Python environment. For more information about conda installation, please refer to [here](https://docs.conda.io/en/latest/miniconda.html#).
|
||||
|
||||
After installing conda, create a Python environment for BigDL-LLM:
|
||||
```bash
|
||||
conda create -n llm python=3.9 # recommend to use Python 3.9
|
||||
conda activate llm
|
||||
|
||||
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
|
||||
pip install einops # additional package required for phi-2 to conduct generation
|
||||
```
|
||||
#### 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
|
||||
```
|
||||
|
||||
### 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 --prompt 'What is AI?'
|
||||
```
|
||||
|
||||
Arguments info:
|
||||
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the phi-2 model (e.g. `microsoft/phi-2`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'microsoft/phi-2'`.
|
||||
- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
|
||||
- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
|
||||
|
||||
#### Sample Output
|
||||
#### [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
|
||||
|
||||
```log
|
||||
Inference time: xxxx s
|
||||
-------------------- Output --------------------
|
||||
Question: What is AI?
|
||||
|
||||
Answer: AI stands for Artificial Intelligence. It is a field of computer science that focuses on creating intelligent machines that can perform tasks that would normally require human intelligence.
|
||||
```
|
||||
|
|
@ -0,0 +1,73 @@
|
|||
#
|
||||
# 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 AutoModel, AutoTokenizer, AutoModelForCausalLM, GenerationConfig
|
||||
from bigdl.llm import optimize_model
|
||||
|
||||
# you could tune the prompt based on your own model,
|
||||
# here the prompt tuning refers to https://huggingface.co/microsoft/phi-2
|
||||
PHI_2_V1_PROMPT_FORMAT = "Question: {prompt}\n\n Answer:"
|
||||
generation_config = GenerationConfig(use_cache = True)
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for phi-2 model')
|
||||
parser.add_argument('--repo-id-or-model-path', type=str, default="microsoft/phi-2",
|
||||
help='The huggingface repo id for the phi-2 model to be downloaded'
|
||||
', or the path to the huggingface checkpoint folder')
|
||||
parser.add_argument('--prompt', type=str, default="What is AI?",
|
||||
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
|
||||
|
||||
# Load model
|
||||
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
|
||||
|
||||
# With only one line to enable BigDL-LLM optimization on model
|
||||
# When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the optimize_model function.
|
||||
# This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU.
|
||||
model = optimize_model(model)
|
||||
model = model.to('xpu')
|
||||
|
||||
# Load tokenizer
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
||||
|
||||
# Generate predicted tokens
|
||||
with torch.inference_mode():
|
||||
prompt = PHI_2_V1_PROMPT_FORMAT.format(prompt=args.prompt)
|
||||
input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
|
||||
|
||||
# ipex model needs a warmup, then inference time can be accurate
|
||||
output = model.generate(input_ids, do_sample=False, max_new_tokens=args.n_predict, generation_config = generation_config)
|
||||
# start inference
|
||||
st = time.time()
|
||||
# Note that phi-2 uses GenerationConfig to enable 'use_cache'
|
||||
output = model.generate(input_ids, do_sample=False, max_new_tokens=args.n_predict, generation_config = generation_config)
|
||||
torch.xpu.synchronize()
|
||||
end = time.time()
|
||||
output = output.cpu()
|
||||
output_str = tokenizer.decode(output[0], skip_special_tokens=True)
|
||||
print(f'Inference time: {end-st} s')
|
||||
print('-'*20, 'Prompt', '-'*20)
|
||||
print(prompt)
|
||||
print('-'*20, 'Output', '-'*20)
|
||||
print(output_str)
|
||||
Loading…
Reference in a new issue