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Add awq load support (intel-analytics#9453)
* Support directly loading GPTQ models from huggingface * fix style * fix tests * change example structure * address comments * fix style * init * address comments * add examples * fix style * fix style * fix style * fix style * update * remove * meet comments * fix style --------- Co-authored-by: Yang Wang <[email protected]>
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...llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md
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# AWQ | ||
This example shows how to directly run 4-bit AWQ models using BigDL-LLM on Intel CPU. For illustration purposes, we utilize the ["TheBloke/Llama-2-7B-Chat-AWQ"](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ) as a reference. | ||
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## 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. | ||
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## Example: Predict Tokens using `generate()` API | ||
In the example [generate.py](./generate.py), we show a basic use case for a Llama2 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 | ||
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pip install autoawq==0.1.6 --no-deps | ||
pip install bigdl-llm[all] # install bigdl-llm with 'all' option | ||
pip install transformers==4.35.0 | ||
pip install accelerate==0.24.1 | ||
``` | ||
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### 2. Run | ||
``` | ||
python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT | ||
``` | ||
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||
Arguments info: | ||
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama2-awq model (e.g. `TheBloke/Llama-2-7B-Chat-AWQ`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'TheBloke/Llama-2-7B-Chat-AWQ'`. | ||
- `--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`. | ||
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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. | ||
> | ||
> Please select the appropriate size of the Llama2 model based on the capabilities of your machine. | ||
#### 2.1 Client | ||
On client Windows machine, it is recommended to run directly with full utilization of all cores: | ||
```powershell | ||
python ./generate.py | ||
``` | ||
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#### 2.2 Server | ||
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, | ||
```bash | ||
# set BigDL-Nano env variables | ||
source bigdl-llm-init | ||
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# e.g. for a server with 48 cores per socket | ||
export OMP_NUM_THREADS=48 | ||
numactl -C 0-47 -m 0 python ./generate.py | ||
``` | ||
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#### 2.3 Sample Output | ||
#### ["TheBloke/Llama-2-7B-Chat-AWQ"](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ) | ||
```log | ||
Inference time: xxxx s | ||
-------------------- Prompt -------------------- | ||
### HUMAN: | ||
What is AI? | ||
### RESPONSE: | ||
-------------------- Output -------------------- | ||
### HUMAN: | ||
What is AI? | ||
### RESPONSE: | ||
Artificial intelligence (AI) is the ability of machines to perform tasks that typically require human intelligence, such as learning, problem-solving, decision | ||
``` |
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python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/generate.py
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# | ||
# 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. | ||
# | ||
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import torch | ||
import time | ||
import argparse | ||
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from bigdl.llm.transformers import AutoModelForCausalLM | ||
from transformers import LlamaTokenizer | ||
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# you could tune the prompt based on your own model, | ||
# here the prompt tuning refers to https://huggingface.co/georgesung/llama2_7b_chat_uncensored#prompt-style | ||
LLAMA2_PROMPT_FORMAT = """### HUMAN: | ||
{prompt} | ||
### RESPONSE: | ||
""" | ||
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if __name__ == '__main__': | ||
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model') | ||
parser.add_argument('--repo-id-or-model-path', type=str, default="TheBloke/Llama-2-7B-Chat-AWQ", | ||
help='The huggingface repo id' | ||
', 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') | ||
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args = parser.parse_args() | ||
model_path = args.repo_id_or_model_path | ||
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# 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) | ||
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# Load tokenizer | ||
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True) | ||
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# Generate predicted tokens | ||
with torch.inference_mode(): | ||
prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt) | ||
input_ids = tokenizer.encode(prompt, return_tensors="pt") | ||
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) | ||
end = time.time() | ||
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) |
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...llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md
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# AWQ | ||
This example shows how to directly run 4-bit AWQ models using BigDL-LLM on Intel GPU. For illustration purposes, we utilize the ["TheBloke/Llama-2-7B-Chat-AWQ"](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ) as a reference. | ||
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||
## 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 Llama2 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 | ||
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||
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu | ||
pip install transformers==4.35.0 | ||
pip install autoawq==0.1.6 --no-deps | ||
pip install accelerate==0.24.1 | ||
``` | ||
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### 2. Configures OneAPI environment variables | ||
```bash | ||
source /opt/intel/oneapi/setvars.sh | ||
``` | ||
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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 | ||
export USE_XETLA=OFF | ||
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 | ||
``` | ||
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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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Arguments info: | ||
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama2-awq model (e.g. `TheBloke/Llama-2-7B-Chat-AWQ`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'TheBloke/Llama-2-7B-Chat-AWQ'`. | ||
- `--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 Llama2 model based on the capabilities of your machine. | ||
#### 2.3 Sample Output | ||
#### ["TheBloke/Llama-2-7B-Chat-AWQ"](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ) | ||
```log | ||
Inference time: xxxx s | ||
-------------------- Prompt -------------------- | ||
### HUMAN: | ||
What is AI? | ||
### RESPONSE: | ||
-------------------- Output -------------------- | ||
### HUMAN: | ||
What is AI? | ||
### RESPONSE: | ||
Artificial intelligence (AI) is the ability of machines to perform tasks that typically require human intelligence, such as learning, problem-solving, decision | ||
``` |
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python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/generate.py
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# | ||
# 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. | ||
# | ||
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import torch | ||
import time | ||
import argparse | ||
import intel_extension_for_pytorch as ipex | ||
from bigdl.llm.transformers import AutoModelForCausalLM | ||
from transformers import LlamaTokenizer | ||
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||
# you could tune the prompt based on your own model, | ||
# here the prompt tuning refers to https://huggingface.co/georgesung/llama2_7b_chat_uncensored#prompt-style | ||
LLAMA2_PROMPT_FORMAT = """### HUMAN: | ||
{prompt} | ||
### RESPONSE: | ||
""" | ||
|
||
if __name__ == '__main__': | ||
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model') | ||
parser.add_argument('--repo-id-or-model-path', type=str, default="TheBloke/Llama-2-7B-Chat-AWQ", | ||
help='The huggingface repo id' | ||
', 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') | ||
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args = parser.parse_args() | ||
model_path = args.repo_id_or_model_path | ||
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# 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,).to("xpu") | ||
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# Load tokenizer | ||
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True) | ||
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# Generate predicted tokens | ||
with torch.inference_mode(): | ||
prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt) | ||
input_ids = tokenizer.encode(prompt, return_tensors="pt").to("xpu") | ||
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) | ||
end = time.time() | ||
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) |
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# | ||
# 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. | ||
# | ||
|
||
# This would makes sure Python is aware there is more than one sub-package within bigdl, | ||
# physically located elsewhere. | ||
# Otherwise there would be module not found error in non-pip's setting as Python would | ||
# only search the first bigdl package and end up finding only one sub-package. | ||
|
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# | ||
# 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. | ||
# | ||
# =========================================================================== | ||
# | ||
# This file is copied from | ||
# https://github.com/casper-hansen/AutoAWQ/blob/main/awq/modules/act.py | ||
# | ||
# MIT License | ||
# | ||
# Copyright (c) 2023 MIT HAN Lab | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy | ||
# of this software and associated documentation files (the "Software"), to deal | ||
# in the Software without restriction, including without limitation the rights | ||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
# copies of the Software, and to permit persons to whom the Software is | ||
# furnished to do so, subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in all | ||
# copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
# SOFTWARE. | ||
# | ||
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import torch.nn as nn | ||
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class ScaledActivation(nn.Module): | ||
def __init__(self, module, scales): | ||
super().__init__() | ||
self.act = module | ||
self.scales = nn.Parameter(scales.data) | ||
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def forward(self, x): | ||
return self.act(x) / self.scales.view(1, 1, -1).to(x.device) |
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