102 lines
4.0 KiB
Markdown
Executable File
102 lines
4.0 KiB
Markdown
Executable File
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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)](https://github.com/mobiusml/hqq/) supports fast on-the-fly quantization for 8, 4, 3, 2, and even 1-bits. It doesn't require calibration data, and it is compatible with any model modality (LLMs, vision, etc.).
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HQQ further supports fine-tuning with [PEFT](https://huggingface.co/docs/peft) and is fully compatible with [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for even faster inference and training.
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Install HQQ with the following command to get the latest version and to build its corresponding CUDA kernels if you are using a cuda device. It also support Intel XPU with pure pytorch implementation.
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```bash
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pip install hqq
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```
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You can choose to either replace all the linear layers in a model with the same quantization config or dedicate a specific quantization config for specific linear layers.
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<hfoptions id="hqq">
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<hfoption id="replace all layers">
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Quantize a model by creating a [`HqqConfig`] and specifying the `nbits` and `group_size` to replace for all the linear layers ([torch.nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html)) of the model.
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``` py
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, HqqConfig
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quant_config = HqqConfig(nbits=8, group_size=64)
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model = transformers.AutoModelForCausalLM.from_pretrained(
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"meta-llama/Llama-3.1-8B",
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dtype=torch.float16,
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device_map="auto",
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quantization_config=quant_config
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)
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```
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</hfoption>
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<hfoption id="specific layers only">
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Quantize a model by creating a dictionary specifying the `nbits` and `group_size` for the linear layers to quantize. Pass them to [`HqqConfig`] and set which layers to quantize with the config. This approach is especially useful for quantizing mixture-of-experts (MoEs) because they are less affected ly lower quantization settings.
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``` py
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q4_config = {'nbits':4, 'group_size':64}
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q3_config = {'nbits':3, 'group_size':32}
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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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model = transformers.AutoModelForCausalLM.from_pretrained(
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"meta-llama/Llama-3.1-8B",
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dtype=torch.float16,
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device_map="auto",
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quantization_config=quant_config
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)
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```
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</hfoption>
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</hfoptions>
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## Backends
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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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```py
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from hqq.core.quantize import *
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HQQLinear.set_backend(HQQBackend.PYTORCH)
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```
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For faster inference, HQQ supports 4-bit fused kernels (torchao and Marlin) after a model is quantized. These can reach up to 200 tokens/sec on a single 4090. The example below demonstrates enabling the torchao_int4 backend.
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```py
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from hqq.utils.patching import prepare_for_inference
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prepare_for_inference("model", backend="torchao_int4")
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```
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Refer to the [Backend](https://github.com/mobiusml/hqq/#backend) guide for more details.
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## Resources
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Read the [Half-Quadratic Quantization of Large Machine Learning Models](https://mobiusml.github.io/hqq_blog/) blog post for more details about HQQ.
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