[docs] Redesign (#31757)
* toctree * not-doctested.txt * collapse sections * feedback * update * rewrite get started sections * fixes * fix * loading models * fix * customize models * share * fix link * contribute part 1 * contribute pt 2 * fix toctree * tokenization pt 1 * Add new model (#32615) * v1 - working version * fix * fix * fix * fix * rename to correct name * fix title * fixup * rename files * fix * add copied from on tests * rename to `FalconMamba` everywhere and fix bugs * fix quantization + accelerate * fix copies * add `torch.compile` support * fix tests * fix tests and add slow tests * copies on config * merge the latest changes * fix tests * add few lines about instruct * Apply suggestions from code review Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix * fix tests --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * "to be not" -> "not to be" (#32636) * "to be not" -> "not to be" * Update sam.md * Update trainer.py * Update modeling_utils.py * Update test_modeling_utils.py * Update test_modeling_utils.py * fix hfoption tag * tokenization pt. 2 * image processor * fix toctree * backbones * feature extractor * fix file name * processor * update not-doctested * update * make style * fix toctree * revision * make fixup * fix toctree * fix * make style * fix hfoption tag * pipeline * pipeline gradio * pipeline web server * add pipeline * fix toctree * not-doctested * prompting * llm optims * fix toctree * fixes * cache * text generation * fix * chat pipeline * chat stuff * xla * torch.compile * cpu inference * toctree * gpu inference * agents and tools * gguf/tiktoken * finetune * toctree * trainer * trainer pt 2 * optims * optimizers * accelerate * parallelism * fsdp * update * distributed cpu * hardware training * gpu training * gpu training 2 * peft * distrib debug * deepspeed 1 * deepspeed 2 * chat toctree * quant pt 1 * quant pt 2 * fix toctree * fix * fix * quant pt 3 * quant pt 4 * serialization * torchscript * scripts * tpu * review * model addition timeline * modular * more reviews * reviews * fix toctree * reviews reviews * continue reviews * more reviews * modular transformers * more review * zamba2 * fix * all frameworks * pytorch * supported model frameworks * flashattention * rm check_table * not-doctested.txt * rm check_support_list.py * feedback * updates/feedback * review * feedback * fix * update * feedback * updates * update --------- Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
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Steven Liu
@@ -14,27 +14,43 @@ rendered properly in your Markdown viewer.
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# HQQ
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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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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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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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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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Install HQQ with the following command to get the latest version and to build its corresponding CUDA kernels.
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```bash
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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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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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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)
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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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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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``` Python
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# Method 2: each linear layer with the same tag will use a dedicated quantization config
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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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@@ -47,23 +63,38 @@ quant_config = HqqConfig(dynamic_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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"meta-llama/Llama-3.1-8B",
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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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</hfoption>
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</hfoptions>
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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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## 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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