qwantize 0.1.0

Optimal block-scaled FP4 quants (NVFP4, MXFP4)

Qwantize

Optimal quantization methods for block-scaled formats.

Formats

• NVFP4 — FP4 E2M1 with FP8 E4M3 scales (block sizes 16, 32)
• MXFP4 — FP4 E2M1 with UE8M0 (power-of-2) scales (block sizes 16, 32)

Methods

Each format supports multiple scale selection strategies:

Method	Description
Naive	Standard heuristic: s = snap(amax / Q_MAX)
SSE-Optimal	Bounded search minimizing sum of squared quantization error
Hessian-Optimal	Bounded search minimizing Hessian-weighted error r^T H r using activations

All methods have both pure-PyTorch (reference) and Triton (GPU-accelerated) implementations.

Install

pip install -e .

Requires PyTorch and Triton (for GPU kernels).

Usage

from qwantize import nvfp4_naive, nvfp4_optimal, nvfp4_dequantize, compute_metrics

# W has shape (..., block_size) where block_size is 16 or 32
W_blocked = W.reshape(M, K // 32, 32)

# Quantize: returns (scales, quants)
scales, quants = nvfp4_optimal(W_blocked, dim=-1)

# Dequantize
W_dq = nvfp4_dequantize(scales, quants, dim=-1)

# Or get dequantized output directly
scales, quants, W_dq = nvfp4_optimal(W_blocked, dim=-1, return_dequant=True)

# Compute metrics: ||Q(W)-W||/||W|| and ||XW_q^T - XW^T||/||XW^T||
metrics = compute_metrics(W, W_dq.reshape(M, K), X)

Triton-accelerated versions:

from qwantize import nvfp4_optimal_triton, nvfp4_optimal_hessian_triton

scales, quants, W_dq = nvfp4_optimal_triton(W_blocked, dim=-1, return_dequant=True)

# Hessian-aware (requires activations X)
scales, quants, W_dq = nvfp4_optimal_hessian_triton(W_blocked, dim=-1, return_dequant=True, X=X)

Benchmarks

Benchmarked on the down_proj weight of the first decoder layer from Qwen3-4B, with activations from WikiText-2 (max_seq_len=512, num_samples=2048).

python bench/full_bench.py

NVFP4 (block size 16)

Method	Weight Error	Output Error	Triton Speedup
Naive	10.05%	6.89%	1.7x
SSE-Optimal	8.74%	6.04%	7.0x
H-Optimal	9.35%	5.31%	1.8x

MXFP4 (block size 16)

Method	Weight Error	Output Error	Triton Speedup
Naive	11.77%	8.48%	1.7x
SSE-Optimal	11.02%	7.67%	33x
H-Optimal	11.10%	7.62%	—

Documentation

Full documentation: qwantize.readthedocs.io

Build locally:

pip install -r docs/requirements.txt
cd docs && make html

Contact

• Author: Ayoub Ghriss, [e-mail]