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/*
* Copyright (c) Facebook, Inc. and its affiliates.
* All rights reserved.
* This source code is licensed under the BSD-style license found in the
* LICENSE file in the root directory of this source tree.
*/
#pragma once
#include <cstdint>
namespace fbgemm
{
// KERNEL_PROD is the product of all kernels.
// For example, KERNEL_PROD = 9 for 3x3, and 27 for 3x3x3.
template <int KERNEL_PROD>
class PackedDepthWiseConvMatrix
{
public:
// smat in RSG layout
PackedDepthWiseConvMatrix(int K, const std::int8_t *smat);
virtual ~PackedDepthWiseConvMatrix();
const std::int8_t* PackedMat() const {
return pmat_;
}
private:
int K_;
std::int8_t* pmat_;
}; // Packed3x3ConvMatrix
using Packed3x3ConvMatrix = PackedDepthWiseConvMatrix<3 * 3>;
using Packed3x3x3ConvMatrix = PackedDepthWiseConvMatrix<3 * 3 * 3>;
/**
* Depth-wise 3x3 convolution with pad=1 and stride=1 and K a multiple of 8
* @params A The input image in NHWK layout
* @params Bp The pre-packed filter
*/
void depthwise_3x3_pad_1(
int N,
int H,
int W,
int K,
int stride_h,
int stride_w,
std::int32_t A_zero_point, const std::uint8_t* A,
const Packed3x3ConvMatrix& Bp,
std::int32_t* C,
int thread_id = 0, int num_threads = 1);
/**
* Depth-wise 3x3 convolution with pad=1 and stride=1 and K a multiple of 8
* This version is fused with requantization.
*/
void depthwise_3x3_pad_1(
int N, int H, int W, int K,
int stride_h, int stride_w,
std::int32_t A_zero_point, const std::uint8_t* A,
std::int32_t B_zero_point, const Packed3x3ConvMatrix& Bp,
float C_multiplier, std::int32_t C_zero_point,
std::uint8_t* C,
const std::int32_t* col_offsets, const std::int32_t* bias,
int thread_id = 0, int num_threads = 1, bool fuse_relu = false);
/**
* Depth-wise 3x3 convolution with pad=1 and stride=1 and K a multiple of 8
* This version is fused with requantization and uses per-channel quantization.
*/
void depthwise_3x3_per_channel_quantization_pad_1(
int N, int H, int W, int K,
int stride_h, int stride_w,
std::int32_t A_zero_point, const std::uint8_t* A,
const std::int32_t *B_zero_point, const Packed3x3ConvMatrix& Bp,
const float *C_multiplier, std::int32_t C_zero_point,
std::uint8_t* C,
const std::int32_t* col_offsets, const std::int32_t* bias,
int thread_id = 0, int num_threads = 1);
void depthwise_3x3x3_pad_1(
int N,
int T,
int H,
int W,
int K,
int stride_t,
int stride_h,
int stride_w,
std::int32_t A_zero_point, const std::uint8_t* A,
const Packed3x3x3ConvMatrix& Bp,
std::int32_t* C,
int thread_id = 0, int num_threads = 1);
void depthwise_3x3x3_pad_1(
int N, int T, int H, int W, int K,
int stride_t, int stride_h, int stride_w,
std::int32_t A_zero_point, const std::uint8_t* A,
std::int32_t B_zero_point, const Packed3x3x3ConvMatrix& Bp,
float C_multiplier, std::int32_t C_zero_point,
std::uint8_t* C,
const std::int32_t* col_offsets, const std::int32_t* bias,
bool fuse_relu = false, int thread_id = 0, int num_threads = 1);
} // namespace fbgemm
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