flute

FLUTE: Flexible Lookup Table Engine for LUT-quantized LLMs

Update

• February, 2024. HIGGS will appear in NAACL 2025.
• January 9, 2025. Added (very) experimental support for removing specialization on shapes + GPU via auto-tune.
• December 12, 2024. Added support for Hadamard Transform (via HadaCore).
• November 26, 2024. Added support for vector (de)quantization (vector_size=2), as part of HIGGS.
• October 5, 2024. FLUTE will appear in EMNLP 2024 (Findings).
• September 15, 2024. Added experimental support for loading pre-quantized FLUTE models in HuggingFace.
• September 6, 2024. Added (unlearned) NF-quantized LLaMA-3.1 (405B) models: base and instruction tuned.
• August 31, 2024. Added support and example for the Learned Normal Float (NFL) quantization.
• August 26, 2024. Added support for converting bitsandbytes model into FLUTE model.
• August 5, 2024. Added quantized LLaMA-3.1 (8B/70B) models.
• August 2, 2024. Added support for RTX4090.
• July 27, 2024. Added support for LLaMA-3.1 (405B) and tuned BF16 performance. FP16 is still the recommended data type, especially for 3-bit settings.

Installation

Install FLUTE with pip or from source:

# For CUDA 12.1
pip install flute-kernel
# For CUDA 11.8
pip install flute-kernel -i https://flute-ai.github.io/whl/cu118
# For CUDA 12.4
pip install flute-kernel -i https://flute-ai.github.io/whl/cu124

Head over to Getting Started and try it out!

Background

Uniform quantization converts full precision weights to lower-precision intervals of equal size. Lookup table (LUT) quantization is a flexible variant of non-uniform quantization which can map intervals to arbitrary values via a lookup table.

Uniform (Integer) Quantization Lookup Table Quantization

$$\widehat{\mathbf{W}} = \mathtt{float}(\mathbf{Q}) \cdot \mathbf{s}$$

$$\widehat{\mathbf{W}} = \mathtt{tableLookup}(\mathbf{Q}, \mathtt{table}) \cdot \mathbf{s}$$

where $\mathbf{Q}$ denote the quantized weight, $\mathbf{s}$ the (group-wise) scales, and $\widehat{\mathbf{W}}$ the de-quantized weight. Here are some examples of the lookup table suppored in FLUTE.

Examples Notes

int4, int3, int2

recovers uniform/integer quantization

fp4, fp3, fp2

nf4, nf3, nf2

generalizes the nf4 data-format introduced in QLoRA

any arbitrary table

you could even learn it!

New Models Powered by FLUTE

The flexibility of the kernel could lead to new quantization algorithms. As a proof of concept, we are releasing a few models quantized using Learned Normal Float (NFL) --- a simple extension to the nf4 data format introduced in QLoRA. NFL initialized the lookup table and the scales with those from NF quantization. Then, it uses calibration data to learn the scales via straight through estimation for for the gradient with respect to the scales.

Benchmarks

For additional benchmarks, detailed breakdowns, and corresponding instruction-tuned models, please refer to the paper and the model zoo.

LLaMA-3.1

	Wiki PPL	C4 PPL	LLM Eval Avg.		Wiki PPL	C4 PPL	LLM Eval Avg.
LLaMA-3.1 (8B)	6.31	9.60	69.75	LLaMA-3.1 (70B)	2.82	7.18	75.45
+ NFL W4G64	6.24	10.06	69.13	+ NFL W4G64	3.09	7.53	74.84
+ NFL W3G64	7.23	11.83	65.66	+ NFL W3G64	4.29	8.91	72.65

Gemma-2

	Wiki PPL	C4 PPL	LLM Eval Avg.		Wiki PPL	C4 PPL	LLM Eval Avg.
Gemma-2 (9B)	6.88	10.12	73.12	Gemma-2 (27B)	5.70	8.98	75.71
+ NFL W4G64	6.49	10.35	72.50	+ NFL W4G64	5.69	9.31	74.11

Getting Started

FLUTE + vLLM

FLUTE-quantized models (Model Zoo) can be directly served using exisiting frameworks such as vLLM.

- python -m vllm.entrypoints.openai.api_server \
+ python -m flute.integrations.vllm vllm.entrypoints.openai.api_server \
    --model [MODEL] \
    --revision [REVISION] \
    --tensor-parallel-size [TP_SIZE] \
+   --quantization flute

For example, the following commmand runs the FLUTE-quantized LLaMA-3.1 (8B) on a single GPU.

python -m flute.integrations.vllm vllm.entrypoints.openai.api_server \
    --model radi-cho/Meta-Llama-3.1-8B-FLUTE \
    --quantization flute

We can then query the vLLM server as usual.

curl http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "radi-cho/Meta-Llama-3.1-8B-FLUTE",
        "prompt": "San Francisco is a",
        "max_tokens": 7,
        "temperature": 0
    }'

FLUTE + HuggingFace

FLUTE also runs out of the box with HuggingFace and its accelerate extension. This integration is mostly experimental and not optimized. Users sensitive to performance considerations should use the vLLM integration instead.

1. Loading a pre-quantized FLUTE model.

import flute.integrations.huggingface

- model = AutoModelForCausalLM.from_pretrained(
+ model = flute.integrations.huggingface.from_pretrained(
    "radi-cho/Meta-Llama-3.1-8B-FLUTE",
    # all of your favoriate HF flags will be forwarded
    device_map="auto")

1. Loading and quantizing a dense model.

import flute.integrations.base
flute.integrations.base.prepare_model_flute(
    name="model.model.layers",
    module=model.model.layers,  # for LLaMA-3 and Gemma-2
    num_bits=num_bits,
    group_size=group_size,
    fake=False,
    handle_hooks=True)  # for `accelerate` hooks

After this, the model can be used as normal. Please checkout the quantization guide for more information.

Support and Compatibility

Kernel

Description	Supported (via pip)	Supported (build from source)
Input dtypes	torch.float16 torch.bfloat16
Bits	4bit 3bit	2bit
Group Sizes	32 64 128 256	❓
GPUs	A100 A6000 RTX 4090	H100 (unoptimized)

[!WARNING] In the current release, we noticed torch.bfloat16 is slower than torch.float16. This likely because of lack of tuning, and that Ampere GPUs lack a hardware acceleration for bfloat16 vectorized atomic-add.

[!WARNING] We noticed several numerically unstable situations using bits=4, group-size=256, GPU=A100, though this is relatively rare (8 of 9360 test cases failed). We also noticed correctness issues in some situations with bits=4, group-size=256, dtype=bfloat16, GPU=RTX4090 (1 of 52 test cases failed). We will be looking into this, but we suggest avoiding these particular use cases (W4G256) for now.

Models

[!NOTE] As of the current release, the kernel is shape-specialized due to legacy reasons (i.e., we tune tile sizes etc for each matrix shape). Please see the below chart for the supported use cases, as different platform and tensor parallel size changes the matrix shapes. We plan to add supports for a broad range of shapes in the near future. In the meantime, please let us know if you have any specific models in mind and we are happy to add support for them.

Model	Single GPU / Pipeline Parallel	Tensor Parallel
LLaMA-3/3.1 (8B)	✅
LLaMA-3/3.1 (70B)	✅	2 or 4 GPUs
LLaMA-3.1 (405B)	✅	4 or 8 GPUs
Gemma-2 (9B)	✅
Gemma-2 (27B)	✅	2 or 4 GPUs

Model Zoo

[!NOTE] The models we release here are trained on more data and hence different from those in the paper.

[!TIP] The HuggingFace Hub links are for NFL W4G64 quantization by default. To use the NFL W3G64 quantization, add --revision nfl_w3g64.

LLaMA-3.1 (8B)

	Wiki	C4	PIQA	ARC-E	ARC-C	HellaSwag	Wino	Avg.
Unquantized	6.31	9.60	79.16	82.20	52.65	60.71	74.03	69.75
NFL W4G64	6.24	10.06	79.38	81.61	51.54	59.57	73.56	69.13
NFL W3G64	7.23	11.83	77.91	76.98	46.33	56.74	70.32	65.66

LLaMA-3.1 (70B)

	Wiki	C4	PIQA	ARC-E	ARC-C	HellaSwag	Wino	Avg.
Unquantized	2.82	7.18	82.81	85.31	59.64	67.49	82.00	75.45
NFL W4G64	3.09	7.53	83.03	85.52	58.19	67.04	80.43	74.84
NFL W3G64	4.29	8.91	82.04	83.29	54.78	64.99	78.14	72.65

LLaMA-3.1 (405B)

Note that the weights are in the branch nf_w4g64 and thus --revision nf_w4g64 is needed since these are not on the default branch.

LLaMA-3.1 Instruct (8B)

	Wiki	C4
NFL W4G64	6.78	11.11
NFL W3G64	7.73	12.83

LLaMA-3.1 Instruct (70B)

	Wiki	C4
NFL W4G64	4.15	9.18
NFL W3G64	4.74	9.48

LLaMA-3.1 Instruct (405B)

Note that the weights are in the branch nf_w4g64 and thus --revision nf_w4g64 is needed since these are not on the default branch.

LLaMA-3 (8B)

	Wiki	C4	PIQA	ARC-E	ARC-C	HellaSwag	Wino	Avg.
Unquantized	6.1	9.2	79.9	80.1	50.4	60.2	72.8	68.6
NFL W4G64	6.11	9.38	79.33	79.79	49.74	59.22	73.95	68.41
NFL W3G64	7.13	11.06	78.78	76.22	44.37	56.69	70.32	65.28

LLaMA-3 (70B)

	Wiki	C4	PIQA	ARC-E	ARC-C	HellaSwag	Wino	Avg.
Unquantized	2.9	6.9	82.4	86.9	60.3	66.4	80.6	75.3
NFL W4G64	3.03	7.03	82.15	85.98	57.85	66.17	79.79	74.39
NFL W3G64	4.15	8.10	80.74	83.71	55.29	64.05	78.45	72.45

LLaMA-3 Instruct (8B)

	Wiki	C4
NFL W4G64	6.78	10.61
NFL W3G64	7.75	12.28

LLaMA-3 Instruct (70B)

	Wiki	C4
NFL W4G64	3.67	7.95
NFL W3G64	4.90	10.86

[Gemma-