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/*
* Copyright (c) 2022-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#pragma once
#include "arm_gemm.hpp"
#include <cstddef>
namespace arm_conv
{
struct Shape2D
{
unsigned int rows, cols;
};
struct ConvolutionArgs
{
unsigned int n_batches;
Shape2D input_shape;
unsigned int n_input_channels;
unsigned int pad_top, pad_left;
Shape2D output_shape;
unsigned int n_output_channels;
Shape2D kernel_shape;
arm_gemm::Activation activation;
ConvolutionArgs(unsigned int n_batches,
const Shape2D &input_shape,
unsigned int n_input_channels,
unsigned int pad_top,
unsigned int pad_left,
const Shape2D &output_shape,
unsigned int n_output_channels,
const Shape2D kernel_shape,
const arm_gemm::Activation &activation = {})
: n_batches(n_batches),
input_shape(input_shape),
n_input_channels(n_input_channels),
pad_top(pad_top),
pad_left(pad_left),
output_shape(output_shape),
n_output_channels(n_output_channels),
kernel_shape(kernel_shape),
activation(activation)
{
}
};
namespace winograd
{
/* Constrain the selected Winograd implementation.
*/
struct WinogradConfig
{
unsigned int output_rows = 0, output_cols = 0;
std::string input_transform_filter = "";
std::string output_transform_filter = "";
std::string weight_transform_filter = "";
};
/* Struct describing (suggested) memory layout within the Winograd domain.
*/
struct WinogradDomainSpec
{
size_t weight_matrix_size_bytes, input_matrix_size_bytes, output_matrix_size_bytes;
size_t weight_ld_matrix, weight_ld_row;
size_t input_ld_batch, input_ld_matrix, input_ld_row;
size_t output_ld_batch, output_ld_matrix, output_ld_row;
};
class ITransformCommon
{
public:
virtual ~ITransformCommon() = default;
// Get the name of the transform
virtual const std::string &get_name(void) const = 0;
};
namespace weight_transform
{
class ITransform : public ITransformCommon
{
public:
~ITransform() = default;
virtual unsigned int get_kernel_rows(void) const = 0;
virtual unsigned int get_kernel_cols(void) const = 0;
virtual unsigned int get_transformed_tile_rows(void) const = 0;
virtual unsigned int get_transformed_tile_cols(void) const = 0;
void execute(const ConvolutionArgs &args,
const void *inptr,
size_t ld_in_row,
size_t ld_in_col,
size_t ld_input_channel,
void *outptr,
const WinogradDomainSpec &wds,
unsigned int thread_id,
unsigned int n_threads) const
{
this->execute(args, inptr, ld_in_row, ld_in_col, ld_input_channel, outptr, wds.weight_ld_matrix,
wds.weight_ld_row, thread_id, n_threads);
}
virtual void execute(const ConvolutionArgs &args,
const void *inptr,
size_t ld_in_row,
size_t ld_in_col,
size_t ld_input_channel,
void *outptr,
size_t ld_out_matrix,
size_t ld_out_row,
unsigned int thread_id,
unsigned int n_threads) const = 0;
};
} // namespace weight_transform
namespace input_transform
{
class ITransform : public ITransformCommon
{
public:
~ITransform() = default;
virtual unsigned int get_input_rows(void) const = 0;
virtual unsigned int get_input_cols(void) const = 0;
virtual size_t get_working_space_size(const ConvolutionArgs &args, unsigned int n_threads) const = 0;
void execute(const ConvolutionArgs &args,
const void *inptr,
size_t ld_in_batch,
size_t ld_in_row,
size_t ld_in_col,
void *outptr,
const WinogradDomainSpec &wds,
void *working_space,
unsigned int thread_id,
unsigned int n_threads) const
{
this->execute(args, inptr, ld_in_batch, ld_in_row, ld_in_col, outptr, wds.input_ld_batch, wds.input_ld_matrix,
wds.input_ld_row, working_space, thread_id, n_threads);
}
virtual void execute(const ConvolutionArgs &args,
const void *inptr,
size_t ld_in_batch,
size_t ld_in_row,
size_t ld_in_col,
void *outptr,
size_t ld_out_batch,
size_t ld_out_matrix,
size_t ld_out_row,
void *working_space,
unsigned int thread_id,
unsigned int n_threads) const = 0;
};
} // namespace input_transform
namespace output_transform
{
class ITransform : public ITransformCommon
{
public:
~ITransform() = default;
virtual unsigned int get_input_rows(void) const = 0;
virtual unsigned int get_input_cols(void) const = 0;
virtual unsigned int get_output_rows(void) const = 0;
virtual unsigned int get_output_cols(void) const = 0;
virtual unsigned int get_kernel_rows(void) const = 0;
virtual unsigned int get_kernel_cols(void) const = 0;
virtual size_t get_working_space_size(const ConvolutionArgs &args, unsigned int n_threads) const = 0;
void execute(const ConvolutionArgs &args,
const void *inptr,
const WinogradDomainSpec &wds,
const void *bias,
void *outptr,
size_t ld_out_batch,
size_t ld_out_row,
size_t ld_out_col,
void *working_space,
unsigned int thread_id,
unsigned int n_threads) const
{
this->execute(args, inptr, wds.output_ld_batch, wds.output_ld_matrix, wds.output_ld_row, bias, outptr,
ld_out_batch, ld_out_row, ld_out_col, working_space, thread_id, n_threads);
}
virtual void execute(const ConvolutionArgs &args,
const void *inptr,
size_t ld_in_batch,
size_t ld_in_matrix,
size_t ld_in_row,
const void *bias,
void *outptr,
size_t ld_out_batch,
size_t ld_out_row,
size_t ld_out_col,
void *working_space,
unsigned int thread_id,
unsigned int n_threads) const = 0;
};
} // namespace output_transform
struct WinogradImpl
{
const output_transform::ITransform *output_transform = nullptr;
const weight_transform::ITransform *weight_transform = nullptr;
const input_transform::ITransform *input_transform = nullptr;
std::unique_ptr<arm_gemm::GemmArgs> gemm_args;
WinogradDomainSpec winograd_spec;
};
/* Get pointers to Winograd transforms for the given convolution problem.
*
* Assigns to the pointers in the `dest` struct and returns true or false to
* indicate whether the given problem can be executed or not.
*/
template <typename TIn,
typename TWeight = TIn,
typename TOut = TIn,
typename TWinogradIn = TIn,
typename TWinogradOut = TOut>
bool get_implementation(WinogradImpl &dest, // Destination for the selected implementation
const CPUInfo *,
const ConvolutionArgs &,
int max_threads,
bool fast_mode,
const WinogradConfig *,
const arm_gemm::GemmConfig *);
} // namespace winograd
} // namespace arm_conv
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