Add HF and PyTorch example InternLM2 (#10061)
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@ -177,6 +177,7 @@ Over 20 models have been optimized/verified on `bigdl-llm`, including *LLaMA/LLa
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| Yi | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/yi) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/yi) |
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| BlueLM | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/bluelm) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/bluelm) |
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| SOLAR | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/solar) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/solar) |
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| InternLM2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/internlm2) |
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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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@ -75,6 +75,7 @@ Over 20 models have been optimized/verified on `bigdl-llm`, including *LLaMA/LLa
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| Yi | [link](example/CPU/HF-Transformers-AutoModels/Model/yi) | [link](example/GPU/HF-Transformers-AutoModels/Model/yi) |
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| BlueLM | [link](example/CPU/HF-Transformers-AutoModels/Model/bluelm) | [link](example/GPU/HF-Transformers-AutoModels/Model/bluelm) |
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| SOLAR | [link](example/CPU/HF-Transformers-AutoModels/Model/solar) | [link](example/GPU/HF-Transformers-AutoModels/Model/solar) |
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| InternLM2 | [link](example/CPU/HF-Transformers-AutoModels/Model/internlm2) | [link](example/GPU/HF-Transformers-AutoModels/Model/internlm2) |
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### Working with `bigdl-llm`
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@ -0,0 +1,72 @@
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# InternLM2
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In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on InternLM2 models. For illustration purposes, we utilize the [internlm/internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b) as a reference InternLM2 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 InternLM2 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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```
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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 InternLM2 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'internlm/internlm2-chat-7b'`.
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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 InternLM2 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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#### [internlm/internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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<|User|>:AI是什么？
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<|Bot|>:
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-------------------- Output --------------------
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<|User|>:AI是什么？
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<|Bot|>:AI是人工智能的缩写，是计算机科学的一个分支，旨在使计算机能够像人类一样思考、学习和执行任务。AI技术包括机器学习、自然
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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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<|User|>:What is AI?
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<|Bot|>:
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-------------------- Output --------------------
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<|User|>:What is AI?
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<|Bot|>:AI is the ability of machines to perform tasks that would normally require human intelligence, such as perception, reasoning, learning, and decision-making. AI is made possible
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```
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@ -0,0 +1,69 @@
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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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# you could tune the prompt based on your own model,
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# here the prompt tuning refers to https://huggingface.co/internlm/internlm-chat-7b-8k/blob/main/modeling_internlm.py#L768
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INTERNLM_PROMPT_FORMAT = "<|User|>:{prompt}\n<|Bot|>:"
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for InternLM model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="internlm/internlm2-chat-7b",
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help='The huggingface repo id for the InternLM 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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# Generate predicted tokens
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with torch.inference_mode():
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prompt = INTERNLM_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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output = model.generate(input_ids,
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max_new_tokens=args.n_predict)
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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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output_str = output_str.split("<eoa>")[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(output_str)
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# InternLM2
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In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate InternLM2 models. For illustration purposes, we utilize the [internlm/internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b) as reference InternLM2 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 InternLM2 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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```
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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 InternLM2 to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'internlm/internlm2-chat-7b'`.
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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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#### [internlm/internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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<|User|>:AI是什么？
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<|Bot|>:
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-------------------- Output --------------------
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<|User|>:AI是什么？
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<|Bot|>:AI是人工智能的缩写，是计算机科学的一个分支，旨在使计算机能够像人类一样思考、学习和执行任务。AI技术包括机器学习、自然
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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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<|User|>:What is AI?
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<|Bot|>:
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-------------------- Output --------------------
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<|User|>:What is AI?
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<|Bot|>:AI is the ability of machines to perform tasks that would normally require human intelligence, such as perception, reasoning, learning, and decision-making. AI is made possible
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```
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@ -0,0 +1,70 @@
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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 import optimize_model
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from transformers import AutoTokenizer
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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/internlm/internlm-chat-7b-8k/blob/main/modeling_internlm.py#L768
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INTERNLM_PROMPT_FORMAT = "<|User|>:{prompt}\n<|Bot|>:"
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for InternLM model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="internlm/internlm2-chat-7b",
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help='The huggingface repo id for the InternLM 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 import optimize_model
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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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model = optimize_model(model)
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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 = INTERNLM_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,
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max_new_tokens=args.n_predict)
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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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output_str = output_str.split("<eoa>")[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(output_str)
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# InternLM2
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In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on InternLM2 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [internlm/internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b) as a reference InternLM 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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## 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 InternLM2 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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```
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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>
|
||||
|
||||
```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 InternLM2 model (e.g. `internlm/internlm2-chat-7b`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'internlm/internlm2-chat-7b'`.
|
||||
- `--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
|
||||
#### [internlm/internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b)
|
||||
```log
|
||||
Inference time: xxxx s
|
||||
-------------------- Prompt --------------------
|
||||
<|User|>:AI是什么？
|
||||
<|Bot|>:
|
||||
-------------------- Output --------------------
|
||||
<|User|>:AI是什么？
|
||||
<|Bot|>:AI是人工智能的缩写，是计算机科学的一个分支，旨在使计算机能够像人类一样思考、学习和执行任务。AI技术包括机器学习、自然
|
||||
```
|
||||
|
||||
```log
|
||||
Inference time: xxxx s
|
||||
-------------------- Prompt --------------------
|
||||
<|User|>:What is AI?
|
||||
<|Bot|>:
|
||||
-------------------- Output --------------------
|
||||
<|User|>:What is AI?
|
||||
<|Bot|>:AI is the ability of machines to perform tasks that would normally require human intelligence, such as perception, reasoning, learning, and decision-making. AI is made possible
|
||||
```
|
||||
|
|
@ -0,0 +1,88 @@
|
|||
#
|
||||
# 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
|
||||
|
||||
# you could tune the prompt based on your own model,
|
||||
# here the prompt tuning refers to https://huggingface.co/internlm/internlm-chat-7b-8k/blob/main/modeling_internlm.py#L768
|
||||
INTERNLM_PROMPT_FORMAT = "<|User|>:{prompt}\n<|Bot|>:"
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for InternLM model')
|
||||
parser.add_argument('--repo-id-or-model-path', type=str, default="internlm/internlm2-chat-7b",
|
||||
help='The huggingface repo id for the InternLM 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
|
||||
|
||||
# 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.
|
||||
from bigdl.llm.transformers import AutoModelForCausalLM
|
||||
model = AutoModelForCausalLM.from_pretrained(model_path,
|
||||
load_in_4bit=True,
|
||||
trust_remote_code=True,
|
||||
use_cache=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 = INTERNLM_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)
|
||||
|
||||
|
||||
# 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
|
||||
output = model.generate(input_ids,
|
||||
max_new_tokens=args.n_predict)
|
||||
torch.xpu.synchronize()
|
||||
|
||||
end = time.time()
|
||||
output = output.cpu()
|
||||
output_str = tokenizer.decode(output[0], skip_special_tokens=True)
|
||||
output_str = output_str.split("<eoa>")[0]
|
||||
print(f'Inference time: {end-st} s')
|
||||
print('-'*20, 'Prompt', '-'*20)
|
||||
print(prompt)
|
||||
print('-'*20, 'Output', '-'*20)
|
||||
print(output_str)
|
||||
127
python/llm/example/GPU/PyTorch-Models/Model/internlm2/README.md
Normal file
127
python/llm/example/GPU/PyTorch-Models/Model/internlm2/README.md
Normal file
|
|
@ -0,0 +1,127 @@
|
|||
# InternLM2
|
||||
In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on InternLM2 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [internlm/internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b) 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 InternLM2 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
|
||||
```
|
||||
|
||||
#### 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 --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 InternLM2 model (e.g. `internlm/internlm2-chat-7b`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'internlm/internlm2-chat-7b'`.
|
||||
- `--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
|
||||
#### [internlm/internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b)
|
||||
```log
|
||||
Inference time: xxxx s
|
||||
-------------------- Prompt --------------------
|
||||
<|User|>:AI是什么？
|
||||
<|Bot|>:
|
||||
-------------------- Output --------------------
|
||||
<|User|>:AI是什么？
|
||||
<|Bot|>:AI是人工智能的缩写，是计算机科学的一个分支，旨在使计算机能够像人类一样思考、学习和执行任务。AI技术包括机器学习、自然
|
||||
```
|
||||
|
||||
```log
|
||||
Inference time: xxxx s
|
||||
-------------------- Prompt --------------------
|
||||
<|User|>:What is AI?
|
||||
<|Bot|>:
|
||||
-------------------- Output --------------------
|
||||
<|User|>:What is AI?
|
||||
<|Bot|>:AI is the ability of machines to perform tasks that would normally require human intelligence, such as perception, reasoning, learning, and decision-making. AI is made possible
|
||||
```
|
||||
|
|
@ -0,0 +1,80 @@
|
|||
#
|
||||
# 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
|
||||
|
||||
# you could tune the prompt based on your own model,
|
||||
# here the prompt tuning refers to https://huggingface.co/internlm/internlm-chat-7b-8k/blob/main/modeling_internlm.py#L768
|
||||
INTERNLM_PROMPT_FORMAT = "<|User|>:{prompt}\n<|Bot|>:"
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for InternLM model')
|
||||
parser.add_argument('--repo-id-or-model-path', type=str, default="internlm/internlm2-chat-7b",
|
||||
help='The huggingface repo id for the InternLM 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
|
||||
model = AutoModelForCausalLM.from_pretrained(model_path,
|
||||
trust_remote_code=True,
|
||||
use_cache=True)
|
||||
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 = INTERNLM_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)
|
||||
|
||||
|
||||
# start inference
|
||||
st = time.time()
|
||||
|
||||
output = model.generate(input_ids,
|
||||
max_new_tokens=args.n_predict)
|
||||
torch.xpu.synchronize()
|
||||
|
||||
end = time.time()
|
||||
output = output.cpu()
|
||||
output_str = tokenizer.decode(output[0], skip_special_tokens=True)
|
||||
output_str = output_str.split("<eoa>")[0]
|
||||
print(f'Inference time: {end-st} s')
|
||||
print('-'*20, 'Prompt', '-'*20)
|
||||
print(prompt)
|
||||
print('-'*20, 'Output', '-'*20)
|
||||
print(output_str)
|
||||
|
|
@ -829,14 +829,27 @@ def _optimize_post(model, lightweight_bmm=False):
|
|||
modeling_module_name = model.__class__.__module__
|
||||
module = importlib.import_module(modeling_module_name)
|
||||
from bigdl.llm.transformers.models.internlm import internlm_attention_forward
|
||||
convert_forward(model,
|
||||
module.InternLMAttention,
|
||||
internlm_attention_forward
|
||||
)
|
||||
convert_forward(model,
|
||||
module.InternLMRMSNorm,
|
||||
llama_rms_norm_forward
|
||||
)
|
||||
from bigdl.llm.transformers.models.internlm import internlm2_attention_forward
|
||||
try:
|
||||
convert_forward(model,
|
||||
module.InternLM2Attention,
|
||||
internlm2_attention_forward
|
||||
)
|
||||
except:
|
||||
convert_forward(model,
|
||||
module.InternLMAttention,
|
||||
internlm_attention_forward
|
||||
)
|
||||
try:
|
||||
convert_forward(model,
|
||||
module.InternLM2RMSNorm,
|
||||
llama_rms_norm_forward
|
||||
)
|
||||
except:
|
||||
convert_forward(model,
|
||||
module.InternLMRMSNorm,
|
||||
llama_rms_norm_forward
|
||||
)
|
||||
elif model.config.model_type == "qwen":
|
||||
if hasattr(model.config, "visual"):
|
||||
# for Qwen-VL-Chat
|
||||
|
|
|
|||
|
|
@ -175,3 +175,118 @@ def internlm_attention_forward(
|
|||
attn_weights = None
|
||||
|
||||
return attn_output, attn_weights, past_key_value
|
||||
|
||||
|
||||
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
||||
"""
|
||||
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
|
||||
The hidden states go from (batch,
|
||||
num_key_value_heads, seqlen, head_dim) to
|
||||
(batch, num_attention_heads, seqlen, head_dim)
|
||||
"""
|
||||
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
||||
if n_rep == 1:
|
||||
return hidden_states
|
||||
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads,
|
||||
n_rep, slen, head_dim)
|
||||
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
||||
|
||||
|
||||
def internlm2_attention_forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor]=None,
|
||||
position_ids: Optional[torch.LongTensor]=None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]]=None,
|
||||
output_attentions: bool=False,
|
||||
use_cache: bool=False,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
|
||||
qkv_states = self.wqkv(hidden_states)
|
||||
from einops import rearrange
|
||||
qkv_states = rearrange(
|
||||
qkv_states,
|
||||
"b q (h gs d) -> b q h gs d",
|
||||
gs=2 + self.num_key_value_groups,
|
||||
d=self.head_dim,
|
||||
)
|
||||
|
||||
query_states = qkv_states[..., : self.num_key_value_groups, :]
|
||||
query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
|
||||
key_states = qkv_states[..., -2, :]
|
||||
value_states = qkv_states[..., -1, :]
|
||||
|
||||
query_states = query_states.transpose(1, 2)
|
||||
key_states = key_states.transpose(1, 2)
|
||||
value_states = value_states.transpose(1, 2)
|
||||
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
if past_key_value is not None:
|
||||
kv_seq_len += past_key_value[0].shape[-2]
|
||||
if query_states.device.type == "xpu" and not (self.training and query_states.requires_grad):
|
||||
query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
|
||||
key_states,
|
||||
position_ids,
|
||||
"internlm")
|
||||
else:
|
||||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||||
# query_states, key_states = apply_rotary_pos_emb(query_states,
|
||||
# key_states, cos, sin, position_ids)
|
||||
query_states, key_states = apply_rotary_pos_emb(
|
||||
query_states,
|
||||
key_states,
|
||||
cos,
|
||||
sin,
|
||||
position_ids,
|
||||
"internlm")
|
||||
|
||||
if past_key_value is not None:
|
||||
# reuse k, v, self_attention
|
||||
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
||||
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
||||
|
||||
past_key_value = (key_states, value_states) if use_cache else None
|
||||
|
||||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||||
|
||||
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||||
|
||||
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
||||
invalidInputError(
|
||||
False,
|
||||
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, "
|
||||
f"but is {attn_weights.size()}"
|
||||
)
|
||||
|
||||
if attention_mask is not None:
|
||||
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
||||
invalidInputError(
|
||||
False,
|
||||
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, "
|
||||
f"but is {attention_mask.size()}"
|
||||
)
|
||||
attn_weights = attn_weights + attention_mask
|
||||
|
||||
# upcast attention to fp32
|
||||
attn_weights = nn.functional.softmax(attn_weights,
|
||||
dim=-1, dtype=torch.float32).to(query_states.dtype)
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
|
||||
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
||||
invalidInputError(
|
||||
False,
|
||||
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, "
|
||||
f"but is {attn_output.size()}"
|
||||
)
|
||||
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
|
||||
attn_output = self.wo(attn_output)
|
||||
|
||||
if not output_attentions:
|
||||
attn_weights = None
|
||||
|
||||
return attn_output, attn_weights, past_key_value
|
||||
|
|
|
|||
Loading…
Reference in a new issue