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66 lines
2.6 KiB
Markdown
66 lines
2.6 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
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The [Easy & Efficient Quantization for Transformers (EETQ)](https://github.com/NetEase-FuXi/EETQ) library supports int8 weight-only per-channel quantization for NVIDIA GPUs. It uses high-performance GEMM and GEMV kernels from [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) and [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM). The attention layer is optimized with [FlashAttention2](https://github.com/Dao-AILab/flash-attention). No calibration dataset is required, and the model doesn't need to be pre-quantized. Accuracy degradation is negligible owing to the per-channel quantization.
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EETQ further supports fine-tuning with [PEFT](https://huggingface.co/docs/peft).
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Install EETQ from the [release page](https://github.com/NetEase-FuXi/EETQ/releases) or [source code](https://github.com/NetEase-FuXi/EETQ). CUDA 11.4+ is required for EETQ.
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<hfoptions id="install">
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<hfoption id="release page">
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```bash
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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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</hfoption>
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<hfoption id="source code">
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```bash
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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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</hfoption>
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</hfoptions>
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Quantize a model on-the-fly by defining the quantization data type in [`EetqConfig`].
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```py
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from transformers import AutoModelForCausalLM, EetqConfig
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quantization_config = EetqConfig("int8")
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model = AutoModelForCausalLM.from_pretrained(
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"meta-llama/Llama-3.1-8B",
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dtype="auto",
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device_map="auto",
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quantization_config=quantization_config
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)
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```
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Save the quantized model with [`~PreTrainedModel.save_pretrained`] so it can be reused again with [`~PreTrainedModel.from_pretrained`].
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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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```
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