Support directly loading gptq models from huggingface (#9391)
* Support directly loading GPTQ models from huggingface * fix style * fix tests * change example structure * address comments * fix style * address comments
This commit is contained in:
Yang Wang
GitHub
parent
b2b085550b
commit
51d07a9fd8
7 changed files with 430 additions and 10 deletions
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# GPTQ
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This example shows how to directly run 4-bit GPTQ models using BigDL-LLM on Intel CPU. For illustration purposes, we utilize the ["TheBloke/Llama-2-7B-GPTQ"](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GPTQ) as a reference.
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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 Llama2 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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pip install transformers==4.34.0
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BUILD_CUDA_EXT=0 pip install git+https://github.com/PanQiWei/AutoGPTQ.git@1de9ab6
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pip install optimum==0.14.0
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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 Llama2-gptq model (e.g. `TheBloke/Llama-2-7B-GPTQ`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'TheBloke/Llama-2-7B-GPTQ'`.
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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 Llama2 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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#### [TheBloke/Llama-2-7B-GPTQ](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GPTQ)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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### HUMAN:
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What is AI?
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### RESPONSE:
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-------------------- Output --------------------
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### HUMAN:
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What is AI?
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### RESPONSE:
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> AI is a branch of computer science that aims to create intelligent machines that think and act like humans.
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### HUMAN
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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 bigdl.llm.transformers import AutoModelForCausalLM
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from transformers import LlamaTokenizer, GPTQConfig
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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/georgesung/llama2_7b_chat_uncensored#prompt-style
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LLAMA2_PROMPT_FORMAT = """### HUMAN:
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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 Llama2 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="TheBloke/Llama-2-7B-GPTQ",
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help='The huggingface repo id'
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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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torch_dtype=torch.float,
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trust_remote_code=True,)
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# Load tokenizer
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tokenizer = LlamaTokenizer.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 = LLAMA2_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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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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# GPTQ
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This example shows how to directly run 4-bit GPTQ models using BigDL-LLM on Intel GPU. For illustration purposes, we utilize the ["TheBloke/Llama-2-7B-GPTQ"](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GPTQ) as a reference.
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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 Llama2 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[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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pip install transformers==4.34.0
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BUILD_CUDA_EXT=0 pip install git+https://github.com/PanQiWei/AutoGPTQ.git@1de9ab6
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pip install optimum==0.14.0
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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 Llama2-gptq model (e.g. `TheBloke/Llama-2-7B-GPTQ`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'TheBloke/Llama-2-7B-GPTQ'`.
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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 Llama2 model based on the capabilities of your machine.
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#### 2.3 Sample Output
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#### [TheBloke/Llama-2-7B-GPTQ](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GPTQ)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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### HUMAN:
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What is AI?
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### RESPONSE:
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-------------------- Output --------------------
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### HUMAN:
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What is AI?
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### RESPONSE:
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> AI is a branch of computer science that aims to create intelligent machines that think and act like humans.
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### HUMAN
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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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import intel_extension_for_pytorch as ipex
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from bigdl.llm.transformers import AutoModelForCausalLM
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from transformers import LlamaTokenizer, GPTQConfig
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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/georgesung/llama2_7b_chat_uncensored#prompt-style
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LLAMA2_PROMPT_FORMAT = """### HUMAN:
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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 Llama2 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="TheBloke/Llama-2-7B-GPTQ",
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help='The huggingface repo id'
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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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torch_dtype=torch.float,
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trust_remote_code=True,).to("xpu")
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# Load tokenizer
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tokenizer = LlamaTokenizer.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 = LLAMA2_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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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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@ -41,22 +41,38 @@ import torch.nn as nn
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from accelerate import init_empty_weights
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import warnings
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import transformers
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import importlib
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import importlib.util
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from bigdl.llm.ggml.quantize import ggml_tensor_qtype
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from .utils import logger
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from typing import Union
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import numpy as np
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from bigdl.llm.utils.common import invalidInputError
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def is_auto_gptq_available():
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return importlib.util.find_spec("auto_gptq") is not None
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def is_deepspeed_available():
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return importlib.util.find_spec("deepspeed") is not None
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if is_auto_gptq_available():
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from auto_gptq.utils.peft_utils import QuantLinearCuda, QuantLinearCudaOld
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def is_linear_module(module):
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in_features = None
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out_features = None
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mp_group = None
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if isinstance(module, nn.Linear):
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if is_auto_gptq_available() and isinstance(module, QuantLinearCudaOld):
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in_features = module.infeatures
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out_features = module.outfeatures
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mp_group = None
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result = True
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elif isinstance(module, nn.Linear):
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in_features = module.in_features
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out_features = module.out_features
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mp_group = None
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@ -82,6 +98,61 @@ def is_linear_module(module):
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return result, (in_features, out_features, mp_group)
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from bigdl.llm.transformers.low_bit_linear import get_ggml_qk_size
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Q4_1 = get_ggml_qk_size("asym_int4")
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def convert_gptq(module):
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scales = module.scales
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zeros = torch.bitwise_right_shift(
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torch.unsqueeze(module.qzeros, 2).expand(-1, -1, 32 // module.bits),
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module.wf.unsqueeze(0)).to(torch.int16 if module.bits == 8 else torch.int8)
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zeros = torch.bitwise_and(zeros, (2 ** module.bits) - 1)
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zeros = zeros + 1
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zeros = zeros.reshape(scales.shape)
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weight = torch.bitwise_right_shift(
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torch.unsqueeze(module.qweight, 1).expand(-1, 32 // module.bits, -1),
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module.wf.unsqueeze(-1)).to(torch.int8)
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weight = torch.bitwise_and(weight, (2 ** module.bits) - 1)
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weight = weight.reshape(weight.shape[0] * weight.shape[1], weight.shape[2])
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# convert weight to ggml format
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weight = weight.reshape(weight.shape[0]//module.group_size, module.group_size, weight.shape[1])
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weight = weight.permute(2, 0, 1).reshape(weight.shape[2], -1, 2, Q4_1//2)
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weight = weight.transpose(2, 3)
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weight = torch.bitwise_left_shift(weight,
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torch.tensor([0, 4], dtype=torch.int8).reshape(1, 1, 1, 2))
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weight = torch.bitwise_or(weight[:, :, :, 0], weight[:, :, :, 1]).contiguous()
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# convert zeros to ggml format
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zeros = zeros.reshape(-1, 1, zeros.shape[1]).permute(2, 0, 1)\
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.unsqueeze(2)\
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.expand(-1, -1, module.group_size//Q4_1, -1)\
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.reshape(zeros.shape[1], -1, 1)\
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.contiguous().to(torch.float16)
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# convert scales to ggml format
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scales = scales.reshape(-1, 1, scales.shape[1]).permute(2, 0, 1)\
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.unsqueeze(2)\
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.expand(-1, -1, module.group_size//Q4_1, -1)\
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.reshape(scales.shape[-1], -1, 1)\
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.contiguous().to(torch.float16)
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m = -(zeros * scales)
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d = scales
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ggml_weight = torch.cat([d.view(torch.uint8),
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m.view(torch.uint8),
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weight.view(torch.uint8)], dim=-1)
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ggml_weight = ggml_weight.reshape([-1])
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return ggml_weight
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def _replace_with_low_bit_linear(model, qtype, modules_to_not_convert=None,
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current_key_name=None, convert_shape_only=False,
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replace_embedding=False):
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@ -100,7 +171,30 @@ def _replace_with_low_bit_linear(model, qtype, modules_to_not_convert=None,
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in_features, out_features, mp_group = linear_args
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with init_empty_weights():
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new_linear = None
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if qtype != ggml_tensor_qtype["fp16"]:
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if is_auto_gptq_available() and isinstance(module, QuantLinearCudaOld):
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has_bias = module.bias is not None and module.bias.abs().sum() != 0
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new_linear = LowBitLinear(
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in_features,
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out_features,
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qtype=qtype,
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bias=has_bias,
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mp_group=mp_group,
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)
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device_type = module.qweight.data.device.type
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invalidInputError(device_type != "meta",
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"converting from meta device is not supported")
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# Copy the weights
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paramsLowBit = FP4Params(data=convert_gptq(module),
|
||||
requires_grad=False,
|
||||
quantized=True,
|
||||
_shape=(out_features, in_features),
|
||||
convert_shape_only=convert_shape_only,
|
||||
qtype=qtype).to(device_type)
|
||||
new_linear._parameters['weight'] = paramsLowBit
|
||||
if has_bias:
|
||||
new_linear._parameters['bias'] = nn.Parameter(module.bias.data)\
|
||||
.to(device_type)
|
||||
elif qtype != ggml_tensor_qtype["fp16"]:
|
||||
new_linear = LowBitLinear(
|
||||
in_features,
|
||||
out_features,
|
||||
|
|
@ -118,6 +212,9 @@ def _replace_with_low_bit_linear(model, qtype, modules_to_not_convert=None,
|
|||
convert_shape_only=convert_shape_only,
|
||||
qtype=qtype).to(device_type)
|
||||
new_linear._parameters['weight'] = paramsLowBit
|
||||
if module.bias is not None:
|
||||
new_linear._parameters['bias'] = nn.Parameter(module.bias.data)\
|
||||
.to(device_type)
|
||||
else:
|
||||
# only support two size now
|
||||
# may generalize to other sizes
|
||||
|
|
@ -137,13 +234,12 @@ def _replace_with_low_bit_linear(model, qtype, modules_to_not_convert=None,
|
|||
trans_weight = module.weight.data.reshape(m//16, 16, n)
|
||||
trans_weight = trans_weight.transpose(1, 2).contiguous()
|
||||
new_linear._parameters['weight'] = nn.Parameter(trans_weight)
|
||||
if module.bias is not None:
|
||||
new_linear._parameters['bias'] = nn.Parameter(module.bias.data)\
|
||||
.to(device_type)
|
||||
|
||||
# fp16 may generalize to other sizes later
|
||||
if new_linear is not None:
|
||||
if module.bias is not None:
|
||||
new_linear._parameters['bias'] = nn.Parameter(module.bias.data)\
|
||||
.to(device_type)
|
||||
|
||||
model._modules[name] = new_linear
|
||||
has_been_replaced = True
|
||||
# Force requires grad to False to avoid unexpected errors
|
||||
|
|
@ -223,7 +319,8 @@ def ggml_convert_low_bit(model, qtype, optimize_model=True,
|
|||
"an issue on github if you think this is a bug."
|
||||
)
|
||||
elif device == "cpu":
|
||||
model.to(torch.float32)
|
||||
if not (getattr(model, "quantization_method", None) == "gptq"):
|
||||
model.to(torch.float32)
|
||||
elif device == "meta":
|
||||
# Do nothing here for weights are empty.
|
||||
pass
|
||||
|
|
|
|||
|
|
@ -70,6 +70,10 @@ MOFQ4 = ggml_tensor_qtype["mixed_fp4"]
|
|||
MOFQ8 = ggml_tensor_qtype["mixed_fp8"]
|
||||
|
||||
|
||||
def get_ggml_qk_size(qtype: str):
|
||||
return ggml.ggml_qk_size(ggml_tensor_qtype[qtype])
|
||||
|
||||
|
||||
def ggml_convert_qtype(tensor: torch.Tensor, qtype: int,
|
||||
device=None, convert_shape_only=False):
|
||||
QK = ggml.ggml_qk_size(qtype)
|
||||
|
|
|
|||
|
|
@ -22,6 +22,7 @@ from .utils import extract_local_archive_file, \
|
|||
from bigdl.llm.ggml.quantize import ggml_tensor_qtype
|
||||
from bigdl.llm.utils.common import invalidInputError
|
||||
import torch
|
||||
import warnings
|
||||
import copy
|
||||
from .utils import logger
|
||||
|
||||
|
|
@ -30,6 +31,10 @@ def save_low_bit(self, *args, **kwargs):
|
|||
invalidInputError(self.config.to_dict().get("bigdl_transformers_low_bit", False),
|
||||
f"Detected this model is not a low-bit model, please use from_pretrained's"
|
||||
f" load_in_4bit or load_in_low_bit parameter to load a 4-bit model first.")
|
||||
if hasattr(self.config, "quantization_config"):
|
||||
delattr(self.config, "quantization_config")
|
||||
delattr(self.config, "_pre_quantization_dtype")
|
||||
|
||||
self.to('cpu')
|
||||
self.save_pretrained(*args, **kwargs)
|
||||
import json
|
||||
|
|
@ -57,7 +62,9 @@ class _BaseAutoModelClass:
|
|||
|
||||
Three new arguments are added to extend Hugging Face's from_pretrained method as follows:
|
||||
|
||||
:param load_in_4bit: boolean value, True means load linear's weight to symmetric int 4.
|
||||
:param load_in_4bit: boolean value, True means loading linear's weight to symmetric int 4 if
|
||||
the model is a regular fp16/bf16/fp32 model, and to asymmetric int 4
|
||||
if the model is GPTQ model.
|
||||
Default to be False.
|
||||
:param load_in_low_bit: str value, options are sym_int4, asym_int4, sym_int5, asym_int5
|
||||
, sym_int8, nf3, nf4, fp4, fp8 or fp16. sym_int4 means symmetric
|
||||
|
|
@ -70,7 +77,6 @@ class _BaseAutoModelClass:
|
|||
conducting model optimizations. Default to be None.
|
||||
:param replace_embedding: Whether to replace the Embedding layer, may need to set it
|
||||
to `True` when running BigDL-LLM on GPU on Windows. Default to be `False`.
|
||||
|
||||
:return: a model instance
|
||||
"""
|
||||
pretrained_model_name_or_path = kwargs.get("pretrained_model_name_or_path", None) \
|
||||
|
|
@ -87,8 +93,37 @@ class _BaseAutoModelClass:
|
|||
load_in_4bit = kwargs.pop("load_in_4bit", False)
|
||||
load_in_low_bit = kwargs.pop("load_in_low_bit", None)
|
||||
optimize_model = kwargs.pop("optimize_model", True)
|
||||
user_quantization_config = kwargs.pop("quantization_config", None)
|
||||
|
||||
if load_in_4bit or load_in_low_bit:
|
||||
|
||||
if config_dict.get("quantization_config", None) is not None:
|
||||
from bigdl.llm.transformers.low_bit_linear import get_ggml_qk_size
|
||||
q_config = config_dict["quantization_config"]
|
||||
if q_config["quant_method"] == "gptq":
|
||||
invalidInputError(q_config["bits"] == 4,
|
||||
"Only 4-bit gptq is supported in bigdl-llm.")
|
||||
invalidInputError(q_config["desc_act"] is False,
|
||||
"Only desc_act=False is supported in bigdl-llm.")
|
||||
if load_in_low_bit is not None:
|
||||
invalidInputError(load_in_low_bit == "asym_int4",
|
||||
"You can only load gptq model as aysm_int4 low bit type.")
|
||||
|
||||
load_in_low_bit = "asym_int4"
|
||||
if int(q_config["group_size"]) % get_ggml_qk_size(load_in_low_bit) != 0:
|
||||
invalidInputError(False,
|
||||
(f"group_size must be divisible by "
|
||||
f"{get_ggml_qk_size(load_in_low_bit)}."))
|
||||
if user_quantization_config is not None:
|
||||
invalidInputError(user_quantization_config.bits == 4,
|
||||
"Only 4-bit gptq is supported in bigdl-llm.")
|
||||
invalidInputError(user_quantization_config.use_exllama is False,
|
||||
"Only use_exllama=False is supported in bigdl-llm.")
|
||||
else:
|
||||
from transformers import GPTQConfig
|
||||
user_quantization_config = GPTQConfig(bits=4, use_exllama=False)
|
||||
kwargs["quantization_config"] = user_quantization_config
|
||||
|
||||
# load int x-bit
|
||||
kwargs["low_cpu_mem_usage"] = True
|
||||
# set default torch_dtype='auto'
|
||||
|
|
|
|||
Loading…
Reference in a new issue