70 lines
2.9 KiB
Markdown
70 lines
2.9 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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# HQQ
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Half-Quadratic Quantization (HQQ) implements on-the-fly quantization via fast robust optimization. It doesn't require calibration data and can be used to quantize any model.
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Please refer to the <a href="https://github.com/mobiusml/hqq/">official package</a> for more details.
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For installation, we recommend you use the following approach to get the latest version and build its corresponding CUDA kernels:
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```
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pip install hqq
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```
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To quantize a model, you need to create an [`HqqConfig`]. There are two ways of doing it:
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``` Python
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from transformers import AutoModelForCausalLM, AutoTokenizer, HqqConfig
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# Method 1: all linear layers will use the same quantization config
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quant_config = HqqConfig(nbits=8, group_size=64, quant_zero=False, quant_scale=False, axis=0) #axis=0 is used by default
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```
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``` Python
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# Method 2: each linear layer with the same tag will use a dedicated quantization config
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q4_config = {'nbits':4, 'group_size':64, 'quant_zero':False, 'quant_scale':False}
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q3_config = {'nbits':3, 'group_size':32, 'quant_zero':False, 'quant_scale':False}
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quant_config = HqqConfig(dynamic_config={
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'self_attn.q_proj':q4_config,
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'self_attn.k_proj':q4_config,
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'self_attn.v_proj':q4_config,
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'self_attn.o_proj':q4_config,
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'mlp.gate_proj':q3_config,
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'mlp.up_proj' :q3_config,
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'mlp.down_proj':q3_config,
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})
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```
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The second approach is especially interesting for quantizing Mixture-of-Experts (MoEs) because the experts are less affected by lower quantization settings.
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Then you simply quantize the model as follows
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``` Python
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model = transformers.AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="cuda",
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quantization_config=quant_config
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)
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```
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## Optimized Runtime
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HQQ supports various backends, including pure Pytorch and custom dequantization CUDA kernels. These backends are suitable for older gpus and peft/QLoRA training.
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For faster inference, HQQ supports 4-bit fused kernels (TorchAO and Marlin), reaching up to 200 tokens/sec on a single 4090.
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For more details on how to use the backends, please refer to https://github.com/mobiusml/hqq/?tab=readme-ov-file#backend
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