Module quantization 

Post-Training Quantization (PTQ) for transformer weight matrices.

This module provides a CPU-first implementation of INT8 quantization for linear layers, following the same “Paradigm B — numerical layers” design used by the moe module.

Architecture

• QuantizedLinear: A weight matrix stored as Array2<i8> with per-channel or per-tensor scale/zero_point. Forward pass dequantizes on the fly then performs f64 matmul (integer-matmul kernel is a future follow-up).
• calibrate_linear: Wraps tensorlogic-scirs-backend’s calibrate_quantization to produce QuantizationParams from a weight matrix, including per-channel calibration.

Example

use ndarray::Array2;
use tensorlogic_trustformers::quantization::{calibrate_linear, QuantizedLinear};
use tensorlogic_scirs_backend::quantization::{QuantizationGranularity, QuantizationType};

let weight = Array2::from_shape_fn((4, 8), |(i, j)| (i * 8 + j) as f64);
let params = calibrate_linear(&weight, QuantizationType::Int8,
                              QuantizationGranularity::PerChannel);
let qlinear = QuantizedLinear::from_fp(&weight, &params).expect("quantize");

let x = Array2::ones((2, 8));
let out = qlinear.forward(&x);
assert_eq!(out.shape(), &[2, 4]);

Re-exports

pub use calibration::calibrate_linear;

pub use linear::QuantizationError;

pub use linear::QuantizedLinear;

Modules

calibration

Calibration helpers for crate::quantization::QuantizedLinear weight matrices.

linear

QuantizedLinear: an INT8 weight matrix with per-channel or per-tensor scale/zero_point.