[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
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# HIGGS
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HIGGS is a 0-shot quantization algorithm that combines Hadamard preprocessing with MSE-Optimal quantization grids to achieve lower quantization error and SOTA performance. You can find more information in the paper [arxiv.org/abs/2411.17525](https://arxiv.org/abs/2411.17525).
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[HIGGS](https://arxiv.org/abs/2411.17525) is a zero-shot quantization algorithm that combines Hadamard preprocessing with MSE-Optimal quantization grids to achieve lower quantization error and state-of-the-art performance.
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Runtime support for HIGGS is implemented through [FLUTE](https://arxiv.org/abs/2407.10960), and its [library](https://github.com/HanGuo97/flute).
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Runtime support for HIGGS is implemented through the [FLUTE](https://github.com/HanGuo97/flute) library. Only the 70B and 405B variants of Llama 3 and Llama 3.0, and the 8B and 27B variants of Gemma 2 are currently supported. HIGGS also doesn't support quantized training and backward passes in general at the moment.
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## Quantization Example
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Run the command below to install FLUTE.
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<hfoptions id="install">
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<hfoption id="CUDA 12.1">
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```bash
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pip install flute-kernel
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```
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</hfoption>
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<hfoption id="CUDA 11.8">
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```bash
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pip install flute-kernel -i https://flute-ai.github.io/whl/cu12.4
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```
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</hfoption>
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</hfoptions>
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Create a [`HiggsConfig`] with the number of bits to quantize a model to.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, HiggsConfig
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@@ -30,37 +49,32 @@ model = AutoModelForCausalLM.from_pretrained(
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quantization_config=HiggsConfig(bits=4),
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
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tokenizer.decode(model.generate(
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**tokenizer("Hi,", return_tensors="pt").to(model.device),
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temperature=0.5,
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top_p=0.80,
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)[0])
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```
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## Pre-quantized models
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> [!TIP]
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> Find models pre-quantized with HIGGS in the official ISTA-DASLab [collection](https://huggingface.co/collections/ISTA-DASLab/higgs-675308e432fd56b7f6dab94e).
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Some pre-quantized models can be found in the [official collection](https://huggingface.co/collections/ISTA-DASLab/higgs-675308e432fd56b7f6dab94e) on Hugging Face Hub.
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## torch.compile
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## Current Limitations
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HIGGS is fully compatible with [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html).
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**Architectures**
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, HiggsConfig
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Currently, FLUTE, and HIGGS by extension, **only support Llama 3 and 3.0 of 8B, 70B and 405B parameters, as well as Gemma-2 9B and 27B**. We're working on allowing to run more diverse models as well as allow arbitrary models by modifying the FLUTE compilation procedure.
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model = AutoModelForCausalLM.from_pretrained(
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"google/gemma-2-9b-it",
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quantization_config=HiggsConfig(bits=4),
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device_map="auto",
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)
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**torch.compile**
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model = torch.compile(model)
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```
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HIGGS is fully compatible with `torch.compile`. Compiling `model.forward`, as described [here](../perf_torch_compile.md), here're the speedups it provides on RTX 4090 for `Llama-3.1-8B-Instruct` (forward passes/sec):
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Refer to the table below for a benchmark of forward passes/sec for Llama-3.1-8B-Instruct on a RTX4090.
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| Batch Size | BF16 (With `torch.compile`) | HIGGS 4bit (No `torch.compile`) | HIGGS 4bit (With `torch.compile`) |
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| Batch Size | BF16 (with `torch.compile`) | HIGGS 4bit (without `torch.compile`) | HIGGS 4bit (with `torch.compile`) |
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|------------|-----------------------------|----------------------------------|-----------------------------------|
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| 1 | 59 | 41 | 124 |
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| 4 | 57 | 42 | 123 |
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| 16 | 56 | 41 | 120 |
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**Quantized training**
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Currently, HIGGS doesn't support quantized training (and backward passes in general). We're working on adding support for it.
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