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47 lines
2.4 KiB
Markdown
47 lines
2.4 KiB
Markdown
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# EETQ [[eetq]]
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[EETQ](https://github.com/NetEase-FuXi/EETQ) 라이브러리는 NVIDIA GPU에 대해 int8 채널별(per-channel) 가중치 전용 양자화(weight-only quantization)을 지원합니다. 고성능 GEMM 및 GEMV 커널은 FasterTransformer 및 TensorRT-LLM에서 가져왔습니다. 교정(calibration) 데이터셋이 필요 없으며, 모델을 사전에 양자화할 필요도 없습니다. 또한, 채널별 양자화(per-channel quantization) 덕분에 정확도 저하가 미미합니다.
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[릴리스 페이지](https://github.com/NetEase-FuXi/EETQ/releases)에서 eetq를 설치했는지 확인하세요.
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```
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pip install --no-cache-dir https://github.com/NetEase-FuXi/EETQ/releases/download/v1.0.0/EETQ-1.0.0+cu121+torch2.1.2-cp310-cp310-linux_x86_64.whl
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```
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또는 소스 코드 https://github.com/NetEase-FuXi/EETQ 에서 설치할 수 있습니다. EETQ는 CUDA 기능이 8.9 이하이고 7.0 이상이어야 합니다.
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```
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git clone https://github.com/NetEase-FuXi/EETQ.git
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cd EETQ/
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git submodule update --init --recursive
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pip install .
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```
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비양자화 모델은 "from_pretrained"를 통해 양자화할 수 있습니다.
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```py
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from transformers import AutoModelForCausalLM, EetqConfig
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path = "/path/to/model".
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quantization_config = EetqConfig("int8")
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model = AutoModelForCausalLM.from_pretrained(path, device_map="auto", quantization_config=quantization_config)
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```
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양자화된 모델은 "save_pretrained"를 통해 저장할 수 있으며, "from_pretrained"를 통해 다시 사용할 수 있습니다.
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```py
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quant_path = "/path/to/save/quantized/model"
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model.save_pretrained(quant_path)
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model = AutoModelForCausalLM.from_pretrained(quant_path, device_map="auto")
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``` |