MegEngine/imperative/python/megengine/module/quantized/batch_matmul_activation.py

from typing import Tuple, Union

import numpy as np

from ... import module as Float
from ...core.tensor import dtype
from ...functional import expand_dims, squeeze
from ...functional.quantized import batch_conv_bias_activation
from ...tensor import Parameter
from ..qat import batch_matmul_activation as QAT
from .module import QuantizedModule


class BatchMatMulActivation(Float.BatchMatMulActivation, QuantizedModule):
    r"""Quantized version of :class:`~.qat.BatchMatMulActivation`."""

    def __init__(
        self,
        batch: int,
        in_features: int,
        out_features: int,
        bias: bool = True,
        nonlinear_mode="identity",
        dtype=None,
        **kwargs
    ):
        super().__init__(batch, in_features, out_features, bias, **kwargs)
        self.output_dtype = dtype

    def calc_bmm_quantized(self, inp):
        inp_scale = dtype.get_scale(inp.dtype)
        w_scale = dtype.get_scale(self.weight.dtype)
        bias_scale = inp_scale * w_scale
        inp = expand_dims(inp, [-1])
        res = batch_conv_bias_activation(
            inp,
            self.weight,
            self.bias.astype(dtype.qint32(bias_scale)),
            dtype=self.output_dtype,
            stride=1,
            padding=0,
            dilation=1,
            groups=1,
            nonlinear_mode=self.nonlinear_mode,
        )
        return squeeze(res, -1)

    @classmethod
    def from_qat_module(cls, qat_module: QAT.BatchMatMulActivation):
        output_dtype = qat_module.get_activation_dtype()
        qbmm = cls(
            qat_module.batch,
            qat_module.in_features,
            qat_module.out_features,
            qat_module.bias is not None,
            dtype=output_dtype,
            name=qat_module.name,
        )
        weight = qat_module.weight.astype(qat_module.get_weight_dtype())
        weight = expand_dims(weight, [-1, -2])
        qbmm.weight = Parameter(weight.numpy(), name=qat_module.weight.name)
        if qat_module.bias is not None:
            bias = qat_module.bias.reshape((1, qbmm.out_features, 1, 1))
            qbmm.bias = Parameter(bias.numpy(), name=qat_module.bias.name)
        else:
            qbmm.bias = Parameter(
                np.zeros((1, qbmm.out_features, 1, 1), dtype=np.float32)
            )
        return qbmm

    def forward(self, inp):
        return self.calc_bmm_quantized(inp)