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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
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-03 20:24:05 +0000