add LLM example of aquila on GPU (#9056)
* aquila, dolly-v1, dolly-v2, vacuna
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Guoqiong Song
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# Aquila
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In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Aquila models. For illustration purposes, we utilize the [BAAI/AquilaChat-7B](https://huggingface.co/BAAI/AquilaChat-7B) as a reference Aquila 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 Aquila 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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# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
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# you can install specific ipex/torch version for your need
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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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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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### 3. Run
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For optimal performance on Arc, it is recommended to set several environment variables.
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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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```
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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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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 Aquila model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'BAAI/AquilaChat-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 `'AI是什么？'`.
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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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#### Sample Output
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#### [BAAI/AquilaChat-7B](https://huggingface.co/BAAI/AquilaChat-7B)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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Human: AI是什么？###Assistant:
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-------------------- Output --------------------
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Human: AI是什么？###Assistant: AI是人工智能的缩写。人工智能是一种技术，旨在使计算机能够像人类一样思考、学习和执行任务。AI包括许多不同的技术和方法，例如机器
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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 intel_extension_for_pytorch as ipex
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import time
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import argparse
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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/BAAI/AquilaChat-7B/blob/13577616fd4ff0d21c5735a88d350a68dae120e5/cyg_conversation.py
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AQUILA_PROMPT_FORMAT = "Human: {prompt}###Assistant:"
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(
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description='Predict Tokens using `generate()` API for Aquila model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="BAAI/AquilaChat-7B",
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help='The huggingface repo id for the Aquila 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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# 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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model = model.to('xpu')
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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 = AQUILA_PROMPT_FORMAT.format(prompt=args.prompt)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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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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torch.xpu.synchronize()
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end = time.time()
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output = output.cpu()
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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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# Dolly v1
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In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Dolly v1 models. For illustration purposes, we utilize the [databricks/dolly-v1-6b](https://huggingface.co/databricks/dolly-v1-6b) as a reference Dolly v1 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 Dolly v1 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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# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
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# you can install specific ipex/torch version for your need
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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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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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### 3. Run
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For optimal performance on Arc, it is recommended to set several environment variables.
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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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```
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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 Dolly v1 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'databricks/dolly-v1-6b'`.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
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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 Dolly v1 model based on the capabilities of your machine.
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#### Sample Output
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#### [databricks/dolly-v1-6b](https://huggingface.co/databricks/dolly-v1-6b)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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What is AI?
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### Response:
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-------------------- Output --------------------
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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What is AI?
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### Response:
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AI is an umbrella term for a variety of technologies that enable computers to think and act like humans. AI can be used to automate tasks, analyze data, and
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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 intel_extension_for_pytorch as ipex
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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/databricks/dolly-v1-6b#generate-text
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DOLLY_V1_PROMPT_FORMAT = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{prompt}
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### Response:
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"""
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Dolly v1 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="databricks/dolly-v1-6b",
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help='The huggingface repo id for the Dolly v1 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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model = model.to('xpu')
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = DOLLY_V1_PROMPT_FORMAT.format(prompt=args.prompt)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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end_key_token_id=tokenizer.encode("### End")[0]
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st = time.time()
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# enabling `use_cache=True` allows the model to utilize the previous
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# key/values attentions to speed up decoding;
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# to obtain optimal performance with BigDL-LLM INT4 optimizations,
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# it is important to set use_cache=True for Dolly v1 models
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output = model.generate(input_ids,
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use_cache=True,
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max_new_tokens=args.n_predict,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=end_key_token_id)
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torch.xpu.synchronize()
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end = time.time()
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output = output.cpu()
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end_token_position = None
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end_token_positions = np.where(output[0] == end_key_token_id)[0]
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if len(end_token_positions) > 0:
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end_token_position = end_token_positions[0]
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output_str = tokenizer.decode(output[0][:end_token_position], skip_special_tokens=False)
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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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# Dolly v2
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In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Dolly v2 models. For illustration purposes, we utilize the [databricks/dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b) as a reference Dolly v2 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 Dolly v2 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 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 Dolly v2 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'databricks/dolly-v2-12b'`.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
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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 Dolly v2 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-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
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```
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#### 2.3 Sample Output
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#### [databricks/dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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What is AI?
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### Response:
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-------------------- Output --------------------
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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What is AI?
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### Response:
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Artificial Intelligence (AI) is the area of computer science concerned with building machines that can perform tasks normally associated with human intelligence, such as reasoning, learning,
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```
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@ -0,0 +1,86 @@
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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.
|
||||
# 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.
|
||||
#
|
||||
|
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import torch
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import intel_extension_for_pytorch as ipex
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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/databricks/dolly-v2-12b/blob/main/instruct_pipeline.py#L15
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DOLLY_V2_PROMPT_FORMAT = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{prompt}
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### Response:
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"""
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Dolly v2 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="databricks/dolly-v2-12b",
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help='The huggingface repo id for the Dolly v2 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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|
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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
|
||||
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 = DOLLY_V2_PROMPT_FORMAT.format(prompt=args.prompt)
|
||||
input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
|
||||
end_key_token_id=tokenizer.encode("### End")[0]
|
||||
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,
|
||||
pad_token_id=tokenizer.pad_token_id,
|
||||
eos_token_id=end_key_token_id)
|
||||
torch.xpu.synchronize()
|
||||
end = time.time()
|
||||
output = output.cpu()
|
||||
end_token_position = None
|
||||
end_token_positions = np.where(output[0] == end_key_token_id)[0]
|
||||
if len(end_token_positions) > 0:
|
||||
end_token_position = end_token_positions[0]
|
||||
output_str = tokenizer.decode(output[0][:end_token_position], skip_special_tokens=False)
|
||||
print(f'Inference time: {end-st} s')
|
||||
print('-'*20, 'Prompt', '-'*20)
|
||||
print(prompt)
|
||||
print('-'*20, 'Output', '-'*20)
|
||||
print(output_str)
|
||||
|
|
@ -0,0 +1,76 @@
|
|||
# Vicuna
|
||||
In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Vicuna models. For illustration purposes, we utilize the [lmsys/vicuna-13b-v1.3](https://huggingface.co/lmsys/vicuna-13b-v1.3) and [eachadea/vicuna-7b-1.1](https://huggingface.co/eachadea/vicuna-7b-1.1) as reference Vicuna models.
|
||||
|
||||
## 0. Requirements
|
||||
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.
|
||||
|
||||
## Example: Predict Tokens using `generate()` API
|
||||
In the example [generate.py](./generate.py), we show a basic use case for a Vicuna model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations.
|
||||
|
||||
|
||||
### 1. Install
|
||||
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==2.0.110+xpu as default
|
||||
# you can install specific ipex/torch version for your need
|
||||
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
|
||||
```
|
||||
### 2. Configures OneAPI environment variables
|
||||
```bash
|
||||
source /opt/intel/oneapi/setvars.sh
|
||||
```
|
||||
|
||||
### 3. Run
|
||||
|
||||
For optimal performance on Arc, it is recommended to set several environment variables.
|
||||
|
||||
```bash
|
||||
export USE_XETLA=OFF
|
||||
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
|
||||
```
|
||||
|
||||
```
|
||||
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 Vicuna model (e.g. `lmsys/vicuna-13b-v1.3` and `eachadea/vicuna-7b-1.1`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'lmsys/vicuna-13b-v1.3'`.
|
||||
- `--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`.
|
||||
|
||||
> **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.
|
||||
>
|
||||
> Please select the appropriate size of the Vicuna model based on the capabilities of your machine.
|
||||
|
||||
#### Sample Output
|
||||
#### [lmsys/vicuna-13b-v1.3](https://huggingface.co/lmsys/vicuna-13b-v1.3)
|
||||
```log
|
||||
Inference time: xxxx s
|
||||
-------------------- Prompt --------------------
|
||||
### Human:
|
||||
What is AI?
|
||||
### Assistant:
|
||||
|
||||
-------------------- Output --------------------
|
||||
### Human:
|
||||
What is AI?
|
||||
### Assistant:
|
||||
AI, or Artificial Intelligence, refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception,
|
||||
```
|
||||
|
||||
#### [eachadea/vicuna-7b-1.1](https://huggingface.co/eachadea/vicuna-7b-1.1)
|
||||
```log
|
||||
Inference time: xxxx s
|
||||
-------------------- Prompt --------------------
|
||||
### Human:
|
||||
What is AI?
|
||||
### Assistant:
|
||||
|
||||
-------------------- Output --------------------
|
||||
### Human:
|
||||
What is AI?
|
||||
### Assistant:
|
||||
AI, or artificial intelligence, refers to the ability of a machine or computer program to mimic human intelligence and perform tasks that would normally require human intelligence to
|
||||
```
|
||||
|
|
@ -0,0 +1,71 @@
|
|||
#
|
||||
# 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 intel_extension_for_pytorch as ipex
|
||||
import time
|
||||
import argparse
|
||||
|
||||
from bigdl.llm.transformers import AutoModelForCausalLM
|
||||
from transformers import LlamaTokenizer
|
||||
|
||||
# you could tune the prompt based on your own model,
|
||||
# here the prompt tuning refers to https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md#example-prompt-weights-v0
|
||||
Vicuna_PROMPT_FORMAT = "### Human:\n{prompt} \n ### Assistant:\n"
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Vicuna model')
|
||||
parser.add_argument('--repo-id-or-model-path', type=str, default="lmsys/vicuna-13b-v1.3",
|
||||
help='The huggingface repo id for the Vicuna (e.g. `lmsys/vicuna-13b-v1.3` and `eachadea/vicuna-7b-1.1`) 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
|
||||
model = AutoModelForCausalLM.from_pretrained(model_path,
|
||||
load_in_4bit=True)
|
||||
model = model.to('xpu')
|
||||
|
||||
# Load tokenizer
|
||||
tokenizer = LlamaTokenizer.from_pretrained(model_path)
|
||||
|
||||
# Generate predicted tokens
|
||||
with torch.inference_mode():
|
||||
prompt = Vicuna_PROMPT_FORMAT.format(prompt=args.prompt)
|
||||
input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
|
||||
st = time.time()
|
||||
# enabling `use_cache=True` allows the model to utilize the previous
|
||||
# key/values attentions to speed up decoding;
|
||||
# to obtain optimal performance with BigDL-LLM INT4 optimizations,
|
||||
# it is important to set use_cache=True for vicuna-v1.3 models
|
||||
output = model.generate(input_ids,
|
||||
use_cache=True,
|
||||
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)
|
||||
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