add CodeShell CPU example (#9345)
* add CodeShell CPU example * fix some problems
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Yining Wang
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@ -161,6 +161,7 @@ Over 20 models have been optimized/verified on `bigdl-llm`, including *LLaMA/LLa
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| Skywork | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/skywork) | |
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| InternLM-XComposer | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm-xcomposer) | |
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| WizardCoder-Python | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/wizardcoder-python) | |
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| CodeShell | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/CodeShell) | |
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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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@ -68,6 +68,7 @@ Over 20 models have been optimized/verified on `bigdl-llm`, including *LLaMA/LLa
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| Skywork | [link](example/CPU/HF-Transformers-AutoModels/Model/skywork) | |
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| InternLM-XComposer | [link](example/CPU/HF-Transformers-AutoModels/Model/internlm-xcomposer) | |
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| WizardCoder-Python | [link](example/CPU/HF-Transformers-AutoModels/Model/wizardcoder-python) | |
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| CodeShell | [link](example/CPU/HF-Transformers-AutoModels/Model/CodeShell) | |
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### Working with `bigdl-llm`
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@ -0,0 +1,79 @@
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# CodeShell-7B
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In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on CodeShell models. For illustration purposes, we utilize the [WisdomShell/CodeShell-7B](https://huggingface.co/WisdomShell/CodeShell-7B) as a reference CodeShell 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 CodeShell 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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> **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 CodeShell 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 'def print_hello_world():'
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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-Nano env variables
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source bigdl-nano-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 'def print_hello_world():'
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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 CodeShell model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'WisdomShell/CodeShell-7B'`.
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- `--prompt`: str, argument defining the prompt to be inferred (with integrated prompt format for code). It is default to be `def print_hello_world():`.
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- `--n-predict`: int, argument defining the max number of tokens to predict. It is default to be `50`.
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#### 2.4 Sample Output
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#### [WisdomShell/CodeShell-7B ](https://huggingface.co/WisdomShell/CodeShell-7B )
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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def print_hello_world():
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-------------------- Output --------------------
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def print_hello_world():
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print("Hello World")
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print_hello_world()
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# Function with parameters
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def print_hello_name(name):
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print("Hello " + name)
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print_hello_name("John")
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print
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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 bigdl.llm.transformers import AutoModelForCausalLM
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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/WisdomShell/CodeShell-7B
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for CodeShell model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="WisdomShell/CodeShell-7B",
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help='The huggingface repo id for the CodeShell 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="def print_hello_world():",
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help='Prompt to infer')
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parser.add_argument('--n-predict', type=int, default=50,
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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 = 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, 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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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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# CodeShell
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In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate CodeShell models. For illustration purposes, we utilize the [WisdomShell/CodeShell-7B](https://huggingface.co/WisdomShell/CodeShell-7B ) as a reference CodeShell 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 CodeShell 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 'def print_hello_world():'
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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-Nano env variables
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source bigdl-nano-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 'def print_hello_world():'
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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 CodeShell model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'WisdomShell/CodeShell-7B'`.
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- `--prompt`: str, argument defining the prompt to be inferred (with integrated prompt format for chat). It is default to be `def print_hello_world():`.
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- `--n-predict`: int, argument defining the max number of tokens to predict. It is default to be `50`.
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#### 2.4 Sample Output
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#### [WisdomShell/CodeShell-7B](https://huggingface.co/WisdomShell/CodeShell-7B)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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def print_hello_world():
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-------------------- Output --------------------
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def print_hello_world():
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print("Hello World")
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print_hello_world()
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# 2.
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def print_hello_world_times(n):
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for i in range(n):
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print("Hello World")
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print
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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 AutoTokenizer, AutoModelForCausalLM
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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/WisdomShell/CodeShell-7B
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for CodeShell model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="WisdomShell/CodeShell-7B",
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help='The huggingface repo id for the CodeShell 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="def print_hello_world():",
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help='Prompt to infer')
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parser.add_argument('--n-predict', type=int, default=50,
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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 = 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)
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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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