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conv_kernel_impl.h
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conv_kernel_impl.h
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/phi/kernels/conv_kernel.h"
#include "paddle/phi/kernels/cpu/conv_util.h"
#include "paddle/phi/kernels/funcs/batch_norm_utils.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/im2col.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/funcs/vol2col.h"
namespace phi {
template <typename T, typename Context>
void ConvKernelImpl(const Context& dev_ctx,
const DenseTensor& input,
const DenseTensor& filter_t,
const std::vector<int>& strides,
const std::vector<int>& paddings_t,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations_t,
const std::string& data_format,
DenseTensor* output) {
std::vector<int> paddings = paddings_t;
std::vector<int> dilations = dilations_t;
DenseTensor filter = filter_t;
// The filter will be reshaped in the calculations,
// so here use an assignment operation,
// that avoids modifying the variable in the Scope.
dev_ctx.template Alloc<T>(output);
const bool channel_last = (data_format == "NHWC" || data_format == "NDHWC");
DenseTensor transformed_input(input.type());
DenseTensor transformed_output(output->type());
if (channel_last) {
ResizeToChannelFirst<Context, T>(dev_ctx, &input, &transformed_input);
TransToChannelFirst<Context, T>(dev_ctx, &input, &transformed_input);
ResizeToChannelFirst<Context, T>(dev_ctx, output, &transformed_output);
} else {
transformed_input = input;
transformed_output = *output;
}
// update padding and dilation
auto trans_in_dims = transformed_input.dims();
auto filter_dims = filter.dims();
DDim in_data_dims = slice_ddim(trans_in_dims, 2, trans_in_dims.size());
DDim filter_data_dims = slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(
&paddings, &dilations, padding_algorithm, in_data_dims, strides, ksize);
const int batch_size = static_cast<int>(transformed_input.dims()[0]);
// filter_shape_vec:
// {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h, k_w}
std::vector<int64_t> filter_shape_vec(vectorize(filter.dims()));
// output_shape_vec:
// {o_n, o_c, o_h, o_w} or {o_n, o_c, o_d, o_h, o_w}
std::vector<int64_t> output_shape_vec(vectorize(transformed_output.dims()));
// use col_shape in the im2col calculation
// col_shape_vec:
// {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h, k_w,
// o_d,o_h, o_w}
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
col_shape_vec[0] = trans_in_dims[1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
}
DDim col_shape(make_ddim(col_shape_vec));
// use col_matrix_shape in the gemm calculation
// size:
// (i_c/g * k_h * k_w, o_h * o_w) or (i_c/g * k_d * k_h * k_w, o_d * o_h *
// o_w)
DDim col_matrix_shape = flatten_to_2d(col_shape, data_dim);
bool is_expand = IsExpand(filter_shape_vec, strides, paddings, dilations);
DenseTensor col;
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
DenseTensor col_matrix;
if (is_expand) {
// col = context.AllocateTmpTensor<T, DeviceContext>(col_shape, dev_ctx);
col.Resize(col_shape);
dev_ctx.template Alloc<T>(&col);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
DDim in_matrix_shape =
slice_ddim(transformed_input.dims(), 1, transformed_input.dims().size());
DDim filter_matrix_shape = {filter.dims()[0],
filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
DDim output_matrix_shape = {
transformed_output.dims()[1],
transformed_output.numel() /
(transformed_output.dims()[0] * transformed_output.dims()[1])};
// convolution operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(transformed_input.dims()[1]) / groups;
int out_step = static_cast<int>(transformed_output.dims()[1]) / groups;
phi::funcs::Im2ColFunctor<phi::funcs::ColFormat::kCFO, Context, T> im2col;
phi::funcs::Vol2ColFunctor<Context, T> vol2col;
auto blas = phi::funcs::GetBlas<Context, T>(dev_ctx);
for (int i = 0; i < batch_size; i++) {
DenseTensor in_batch =
transformed_input.Slice(i, i + 1).Resize(in_matrix_shape);
DenseTensor out_batch =
transformed_output.Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
DenseTensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
im2col(dev_ctx,
in_slice,
dilations,
strides,
std::vector<int>{
paddings[0], paddings[2], paddings[1], paddings[3]},
&col);
} else if (data_dim == 3U) {
vol2col(dev_ctx, in_slice, dilations, strides, paddings, &col);
}
// gemm
DenseTensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
DenseTensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
blas.MatMul(
filter_slice, false, col_matrix, false, T(1.0), &out_slice, T(0.0));
}
}
if (channel_last) {
TransToChannelLast<Context, T>(dev_ctx, &transformed_output, output);
}
}
} // namespace phi