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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.
*/
#include <algorithm>
#include <numeric>
#include <vector>
#include <functional>
#include "fbgemm/Fbgemm.h"
namespace fbgemm {
template <int SPATIAL_DIM, typename ACC_T>
bool takeDepthWiseFastPath(const conv_param_t<SPATIAL_DIM>& conv_p) {
// Note: Depthwise convolutions (both 2D and 3D) are optimized for the most
// common case.
return std::is_same<ACC_T, std::int32_t>::value && conv_p.G == conv_p.IC &&
conv_p.G == conv_p.OC && conv_p.G % 8 == 0 &&
std::all_of(
conv_p.stride.begin(),
conv_p.stride.end(),
[](int i) { return i == 1 || i == 2; }) &&
std::all_of(
conv_p.K.begin(), conv_p.K.end(), [](int i) { return i == 3; }) &&
std::all_of(
conv_p.dilation.begin(),
conv_p.dilation.end(),
[](int i) { return i == 1; }) &&
std::all_of(conv_p.pad.begin(), conv_p.pad.end(), [](int i) {
return i == 1;
});
}
template <int SPATIAL_DIM>
bool takePointWiseFastPath(const conv_param_t<SPATIAL_DIM>& conv_p) {
return std::accumulate(conv_p.K.begin(), conv_p.K.end(), 0) == SPATIAL_DIM &&
std::accumulate(conv_p.stride.begin(), conv_p.stride.end(), 0) ==
SPATIAL_DIM &&
std::accumulate(conv_p.dilation.begin(), conv_p.dilation.end(), 0) ==
SPATIAL_DIM &&
std::accumulate(conv_p.pad.begin(), conv_p.pad.end(), 0) == 0;
}
template <int SPATIAL_DIM, typename ACC_T>
optimized_conv_t ConvFastPath(const conv_param_t<SPATIAL_DIM>& conv_p) {
if (takeDepthWiseFastPath<SPATIAL_DIM, ACC_T>(conv_p)) {
return optimized_conv_t::depthwise;
} else if (fbgemmOptimizedGConv<SPATIAL_DIM>(conv_p)) {
return optimized_conv_t::groupwise;
} else if (takePointWiseFastPath<SPATIAL_DIM>(conv_p)) {
return optimized_conv_t::pointwise;
} else {
return optimized_conv_t::im2col;
}
}
template <typename processOutputType, int SPATIAL_DIM, typename ACC_T>
int fbgemmConv(
const conv_param_t<SPATIAL_DIM>& conv_p,
const std::uint8_t* activations,
PackWeightsForConv<SPATIAL_DIM, std::int8_t, ACC_T>& packed_weights,
typename processOutputType::outType* out,
std::int32_t* outBuffer,
processOutputType& outProcess,
int thread_id,
int num_threads,
const BlockingFactors* blocking_params) {
static_assert(
SPATIAL_DIM == 2 || SPATIAL_DIM == 3,
"Only 2D and 3D convolutions are supported");
if (!packed_weights.isPackingCompliant(conv_p)) {
throw std::logic_error(
"[FBGEMM_CONV_ERROR] Prepacked weights can't be used"
" with these convolution parameters!");
}
switch (ConvFastPath<SPATIAL_DIM, ACC_T>(conv_p)) {
case optimized_conv_t::depthwise: {
// 2D and 3D depthwise fast path
// std::cout << "Depthwise fast path" << std::endl;
const std::int32_t* B_zero_point = outProcess.getBZeroPoint();
const float* C_multiplier = outProcess.getCMultiplier();
if (SPATIAL_DIM == 3) {
static_assert(
std::is_same<typename processOutputType::outType, std::uint8_t>::
value,
"For depthwise, only requantized output is supported");
depthwise_3x3x3_pad_1(
conv_p.MB, // mini batch
conv_p.IN_DIM[0], // T
conv_p.IN_DIM[1], // H
conv_p.IN_DIM[2], // W
conv_p.OC, // output channels
conv_p.stride[0], // stride_t
conv_p.stride[1], // stride_h
conv_p.stride[2], // stride_w
outProcess.getAZeroPoint(),
activations,
B_zero_point[0],
*(packed_weights.getPackedWFor3DDW()),
C_multiplier[0],
outProcess.getCZeroPoint(),
out,
outProcess.getColOffsets(),
outProcess.getBias(),
outProcess.RELU_FUSED, // fuse_relu
thread_id,
num_threads);
} else {
depthwise_3x3_pad_1(
conv_p.MB, // mini batch
conv_p.IN_DIM[0], // H
conv_p.IN_DIM[1], // W
conv_p.OC, // output channels
conv_p.stride[0], // stride_h
conv_p.stride[1], // stride_w
outProcess.getAZeroPoint(),
activations,
B_zero_point[0],
*(packed_weights.getPackedWFor2DDW()),
C_multiplier[0],
outProcess.getCZeroPoint(),
out,
outProcess.getColOffsets(),
outProcess.getBias(),
outProcess.RELU_FUSED, // fuse_relu
thread_id,
num_threads);
}
break;
}
case optimized_conv_t::groupwise: {
// optimized groupwise convolution
// std::cout << "Groupwise fast path" << std::endl;
assert(
SPATIAL_DIM == 2 && "Only 2D groupwise convolutions are supported");
std::vector<int32_t> row_offset_buf(
rowOffsetBufferSizeGConv<SPATIAL_DIM>(conv_p));
outProcess.setRowOffsets(row_offset_buf.data());
fbgemmGroupwiseConv(
conv_p,
activations,
outProcess.getAZeroPoint(),
row_offset_buf.data(),
*(packed_weights.getPackedWForGroupwise()),
out,
outBuffer,
outProcess,
thread_id,
num_threads);
break;
}
case optimized_conv_t::pointwise: {
std::vector<int32_t> row_offset_buf(
PackAWithRowOffset<uint8_t>::rowOffsetBufferSize(blocking_params));
int image_dim = std::accumulate(
conv_p.IN_DIM.begin(),
conv_p.IN_DIM.end(),
1,
std::multiplies<int>());
PackAWithRowOffset<uint8_t, ACC_T> packA(
matrix_op_t::NoTranspose,
conv_p.MB * image_dim,
conv_p.IC,
activations,
conv_p.IC,
nullptr,
conv_p.G,
row_offset_buf.data(),
blocking_params);
outProcess.setRowOffsets(row_offset_buf.data());
fbgemmPacked(
packA,
*(packed_weights.getPackedWForPointwise()),
out,
outBuffer,
conv_p.OC,
outProcess,
thread_id,
num_threads,
blocking_params);
break;
}
case optimized_conv_t::im2col: {
// All other convolutions go through im2col-based implementation
// std::cout << "Im2col path" << std::endl;
std::vector<int32_t> row_offset_buf(
PackAWithIm2Col<uint8_t, ACC_T, SPATIAL_DIM>
::rowOffsetBufferSize(blocking_params));
const std::int32_t* b_zero_point = outProcess.getBZeroPoint();
bool b_symmetric = b_zero_point[0] == 0;
PackAWithIm2Col<uint8_t, ACC_T, SPATIAL_DIM> packA(
conv_p,
activations,
nullptr, /* buffer for packed matrix */
outProcess.getAZeroPoint(),
row_offset_buf.data(),
b_symmetric,
blocking_params);
outProcess.setRowOffsets(row_offset_buf.data());
fbgemmPacked(
packA,
*(packed_weights.getPackedWForIm2col()),
out,
outBuffer,
conv_p.OC,
outProcess,
thread_id,
num_threads,
blocking_params);
break;
}
} // switch
return 0;
}
#define INSTANTIATE_BASE(ACC_T, Q_GRAN, RELU, SPATIAL_DIM) \
template int fbgemmConv( \
const conv_param_t<SPATIAL_DIM>& conv_p, \
const std::uint8_t* activations, \
PackWeightsForConv<SPATIAL_DIM, std::int8_t, ACC_T>& packed_weights, \
std::uint8_t* out, \
std::int32_t* outBuffer, \
ReQuantizeOutput<RELU, Q_GRAN>& outProcess, \
int thread_id, \
int num_threads, \
const BlockingFactors* blocking_params);
#define INSTANTIATE_SPATIAL_DIM(ACC_T, Q_GRAN, RELU) \
INSTANTIATE_BASE(ACC_T, Q_GRAN, RELU, 2); \
INSTANTIATE_BASE(ACC_T, Q_GRAN, RELU, 3);
#define INSTANTIATE_RELU(ACC_T, Q_GRAN) \
INSTANTIATE_SPATIAL_DIM(ACC_T, Q_GRAN, true); \
INSTANTIATE_SPATIAL_DIM(ACC_T, Q_GRAN, false);
#define INSTANTIATE_Q_GRANS(ACC_T) \
INSTANTIATE_RELU(ACC_T, QuantizationGranularity::TENSOR); \
INSTANTIATE_RELU(ACC_T, QuantizationGranularity::GROUP); \
INSTANTIATE_RELU(ACC_T, QuantizationGranularity::OUT_CHANNEL);
INSTANTIATE_Q_GRANS(std::int32_t);
#undef INSTANTIATE_Q_GRANS
#undef INSTANTIATE_RELU
#undef INSTANTIATE_SPATIAL_DIM
#undef INSTANTIATE_BASE
template bool takeDepthWiseFastPath<2, std::int32_t>(
const conv_param_t<2>& conv_p);
template bool takeDepthWiseFastPath<3, std::int32_t>(
const conv_param_t<3>& conv_p);
template bool takeDepthWiseFastPath<2, std::int16_t>(
const conv_param_t<2>& conv_p);
template bool takeDepthWiseFastPath<3, std::int16_t>(
const conv_param_t<3>& conv_p);
template optimized_conv_t ConvFastPath<2, std::int32_t>(
const conv_param_t<2>& conv_p);
template optimized_conv_t ConvFastPath<3, std::int32_t>(
const conv_param_t<3>& conv_p);
template optimized_conv_t ConvFastPath<2, std::int16_t>(
const conv_param_t<2>& conv_p);
template optimized_conv_t ConvFastPath<3, std::int16_t>(
const conv_param_t<3>& conv_p);
} // namespace fbgemm
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