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Diffstat (limited to 'src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst.hpp')
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1 files changed, 700 insertions, 0 deletions
diff --git a/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst.hpp b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst.hpp new file mode 100644 index 0000000000..7b00c9a7af --- /dev/null +++ b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst.hpp @@ -0,0 +1,700 @@ +/* + * Copyright (c) 2021-2023 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 "src/core/NEON/kernels/arm_conv/addressing.hpp" +#include "depthwise_strategies_common.hpp" +#include "working_space.hpp" + +#ifdef CYCLE_PROFILING +#include "profiler.hpp" +#endif + +#include <limits> + +namespace arm_conv { +namespace depthwise { + +template <typename TInput, typename TWeight, typename TOutput, typename TAccum, + typename OutputStage> +class DepthwiseDepthfirstStrategyCommon + : public DepthfirstStrategy<TInput, TWeight, TOutput, TAccum, OutputStage> +{ + protected: + unsigned int m_output_rows, m_output_cols; + unsigned int m_kernel_rows, m_kernel_cols; + unsigned int m_stride_rows, m_stride_cols; + + public: + DepthwiseDepthfirstStrategyCommon( + unsigned int output_rows, unsigned int output_cols, + unsigned int kernel_rows, unsigned int kernel_cols, + unsigned int stride_rows=1, unsigned int stride_cols=1 + ) : m_output_rows(output_rows), m_output_cols(output_cols), + m_kernel_rows(kernel_rows), m_kernel_cols(kernel_cols), + m_stride_rows(stride_rows), m_stride_cols(stride_cols) + { + } + + DepthwiseDepthfirstStrategyCommon(unsigned int output_size, unsigned int kernel_size, unsigned int stride=1) + : DepthwiseDepthfirstStrategyCommon(output_size, output_size, kernel_size, kernel_size, stride, stride) + { + } + + virtual ~DepthwiseDepthfirstStrategyCommon() {} + + unsigned int get_output_rows() const override { return m_output_rows; } + unsigned int get_output_cols() const override { return m_output_cols; } + + unsigned int get_kernel_rows() const override { return m_kernel_rows; } + unsigned int get_kernel_cols() const override { return m_kernel_cols; } + + unsigned int get_stride_rows() const override { return m_stride_rows; } + unsigned int get_stride_cols() const override { return m_stride_cols; } +}; + +template <typename TInput, typename TWeight, typename TOutput, typename TAccum, typename OutputStage=typename DefaultOutputStage<TOutput>::Type> +class DepthwiseDepthfirstStrategy : public DepthwiseDepthfirstStrategyCommon<TInput, TWeight, TOutput, TAccum, OutputStage> +{ + using Parent = DepthwiseDepthfirstStrategyCommon<TInput, TWeight, TOutput, TAccum, OutputStage>; + + public: + using Parent::Parent; + + typedef void (*IndirectKernelType)( + const TInput *const *input_ptrs, + TOutput *const *output_ptrs, + const void *params, + unsigned int n_channels, + const TAccum activation_min, + const TAccum activation_max + ); + virtual IndirectKernelType get_indirect_kernel(void) const = 0; + + typedef void (*DirectKernelType)( + const unsigned int n_tile_rows, const unsigned int n_tile_cols, + const TInput *inptr_base, int64_t ld_input_row, int64_t ld_input_col, + TOutput *outptr_base, int64_t ld_output_row, int64_t ld_output_col, + const void *params, unsigned int n_channels, + const TAccum activation_min, + const TAccum activation_max + ); + virtual DirectKernelType get_direct_kernel(void) const = 0; +}; + +template <typename TInput, typename TWeight, typename TOutput> +class DepthwiseDepthfirstStrategy<TInput, TWeight, TOutput, int32_t> +: public DepthwiseDepthfirstStrategyCommon<TInput, TWeight, TOutput, int32_t, arm_gemm::Requantize32> +{ + using Parent = DepthwiseDepthfirstStrategyCommon<TInput, TWeight, TOutput, int32_t, arm_gemm::Requantize32>; + + protected: + interleaves::PackingArguments get_packing_args(void) const + { + return interleaves::PackingArguments( + this->get_kernel_rows(), this->get_kernel_cols(), sizeof(TWeight), + false, sizeof(int32_t), this->uses_premultiply(), // Don't pack the bias + this->get_vl_type(), sizeof(int32_t), this->get_accumulator_depth_vl(), + [this] (unsigned int idx, unsigned int &x, unsigned int &y) -> bool + { return this->get_kernel_packing_point(idx, x, y); } + ); + } + + public: + using Parent::Parent; + + typedef void (*KernelType)( + unsigned int, // n_channels, + const TInput *const *, // inptrs + const TWeight *, // weights + const int32_t *, // bias, + const arm_gemm::Requantize32 &, + const int32_t *, const int32_t *, // requant_muls and requant_shifts + TOutput *const * // outptrs + ); + virtual KernelType get_kernel() const = 0; + + size_t get_storage_size(const DepthwiseArgs &args) const override + { + return interleaves::get_storage_size_generic(get_packing_args(), args); + } + + void pack_parameters( + const DepthwiseArgs &args, void *buffer, + const void *biases, const arm_gemm::Requantize32 &, + const void *weights, size_t ld_weight_col, size_t ld_weight_row + ) const override + { + interleaves::pack_parameters_generic( + get_packing_args(), args, buffer, biases, weights, ld_weight_col, ld_weight_row); + } +}; + +template <typename TInput, typename TWeight, typename TOutput, typename TAccum, typename OutputStage> +class DepthwiseDepthfirstCommon : public DepthfirstDriver<TInput, TWeight, TOutput> +{ + using StratType = DepthwiseDepthfirstStrategyCommon<TInput, TWeight, TOutput, TAccum, OutputStage>; + OutputStage m_os; + + protected: + inline OutputStage &get_output_stage(void) { return m_os; } + inline const OutputStage &get_output_stage(void) const { return m_os; } + + bool uses_intermediate_array() const + { + return this->m_args.channel_multiplier != 1 && this->uses_premultiply(); + } + + virtual void fill_inptr_array(const DepthwiseArgs &args, + const TensorSpec<const TInput *> &input, + const TInput **inptr_array, TInput *input_buffer, + const unsigned int input_i, const unsigned int input_j, + const unsigned int input_pad_top, const unsigned int input_pad_left) const = 0; + + void initialise_inptr_array(const DepthwiseArgs &args, + unsigned int output_channel_start, unsigned int output_channel_end, + const TensorSpec<const TInput *> &input, + const TInput **inptr_array, TInput *input_buffer, TInput *intermediate_buffer, + const unsigned int input_i, const unsigned int input_j, + const unsigned int input_pad_top, const unsigned int input_pad_left, + Tile<TInput> &multiplied_input + ) const + { + // Compute the input pointer array + const auto input_channel_start = output_channel_start / args.channel_multiplier; + + const auto last_valid_row = std::min(input_pad_top + args.input_rows - input_i, this->m_strat->get_input_rows()); + const auto last_valid_col = std::min(input_pad_left + args.input_cols - input_j, this->m_strat->get_input_cols()); + + const auto tile_rows = last_valid_row - input_pad_top; + const auto tile_cols = last_valid_col - input_pad_left; + + const auto tile_channels = output_channel_end - output_channel_start; + + TensorSpec<const TInput *> tile_tensor(0, 0, 0); + if (this->uses_intermediate_array()) { + multiplied_input = Tile<TInput>(intermediate_buffer, tile_rows, tile_cols, tile_channels); + multiplied_input.load_from(input.base, input.ld_row, input.ld_col, + args.input_rows, args.input_cols, + input_i, input_j, args.channel_multiplier); + + tile_tensor = TensorSpec<const TInput *>( + multiplied_input.array, + tile_cols * tile_channels, tile_channels + ); + } else { + tile_tensor = TensorSpec<const TInput *>( + input.base + input_i*input.ld_row + input_j*input.ld_col + input_channel_start, + input.ld_row, input.ld_col + ); + } + + fill_inptr_array(args, + tile_tensor, + inptr_array, input_buffer, + input_i, input_j, + input_pad_top, + input_pad_left + ); + } + + public: + DepthwiseDepthfirstCommon(StratType *const strat, const DepthwiseArgs &args, const OutputStage &os) + : DepthfirstDriver<TInput, TWeight, TOutput>(strat, args), m_os(os) + { + } + + DepthwiseDepthfirstCommon(DepthwiseDepthfirstCommon &) = delete; + DepthwiseDepthfirstCommon &operator=(DepthwiseDepthfirstCommon &) = delete; + + size_t get_storage_size(void) const override + { + return reinterpret_cast<const StratType *>(this->m_strat.get())-> + get_storage_size(this->m_args); + } + + void pack_parameters(void *buffer, const void *biases, const void *weights, size_t ld_weight_col, size_t ld_weight_row) override + { + reinterpret_cast<const StratType *>(this->m_strat.get())-> + pack_parameters(this->m_args, buffer, biases, m_os, weights, ld_weight_col, ld_weight_row); + } +}; + +namespace depthwise_depthfirst { + +/* Workspace Element for an array of input pointers as consumed by the + * specialised depthwise kernels. + */ +template <typename T> +class InputArrayElement +{ + public: + struct Workspace + { + const T **inptr_array; + }; + + template <class OutputStage> + static size_t get_element_size(const WorkspaceArgs<IDepthfirstStrategy, OutputStage> &args) + { + return sizeof(T **) * args.strategy->get_input_rows() * args.strategy->get_input_cols(); + } + + template <class WorkspaceType, class OutputStage> + static void *initialise(WorkspaceType *ws, void *buffer, const WorkspaceArgs<IDepthfirstStrategy, OutputStage> &args) + { + ws->inptr_array = reinterpret_cast<const T**>(buffer); + return reinterpret_cast<char *>(buffer) + get_element_size(args); + } +}; + +template <typename TAccum, typename OutputStage, bool IsDot=false> +struct WorkspaceFinalElement +{ + using Element = ActivationsElement<TAccum, OutputStage>; +}; + +template <> +struct WorkspaceFinalElement<int32_t, arm_gemm::Requantize32, false> +{ + using Element = RequantizationParametersElement; +}; + +template <typename TInput, typename TWeight, typename TOutput, typename TAccum, typename OutputStage> +struct Invoke +{ + constexpr static bool supports_direct_kernel = true; + + template <typename Strat, typename Workspace> + static inline void indirect(const Strat *strat, const Workspace *ws, const OutputStage &, const void *params, const TAccum *, unsigned int n_channels) + { + strat->get_indirect_kernel()( + ws->inptr_array, + ws->outptr_array, + params, n_channels, + ws->activation_min, ws->activation_max + ); + } + + template <typename Strat, typename Workspace> + static void direct( + const Strat *strat, const Workspace *ws, const OutputStage &, + unsigned int n_tile_rows, unsigned int n_tile_cols, + const TInput *inptr, size_t ld_in_row, size_t ld_in_col, + TOutput *outptr, size_t ld_out_row, size_t ld_out_col, + const void *params, unsigned int n_channels + ) + { + strat->get_direct_kernel()( + n_tile_rows, n_tile_cols, + inptr, ld_in_row, ld_in_col, + outptr, ld_out_row, ld_out_col, + params, n_channels, ws->activation_min, ws->activation_max + ); + } +}; + +template <typename TInput, typename TWeight, typename TOutput, typename TAccum> +struct Invoke<TInput, TWeight, TOutput, TAccum, arm_gemm::Requantize32> +{ + constexpr static bool supports_direct_kernel = false; + + template <typename Strat, typename Workspace> + static inline void indirect(const Strat *strat, const Workspace *ws, const arm_gemm::Requantize32 &qp, const void *params, const TAccum *, unsigned int n_channels) + { + strat->get_kernel()( + n_channels, ws->inptr_array, + reinterpret_cast<const TWeight *>(params), ws->bias, + qp, ws->requant_muls, ws->requant_shifts, + ws->outptr_array + ); + } + + template <typename Strat, typename Workspace> + static inline void direct( + const Strat *, const Workspace *, const arm_gemm::Requantize32 &, + unsigned int, unsigned int, // n_tile_rows, n_tile_cols + const TInput *, size_t, size_t, // Input pointer, row stride, column stride + TOutput *, size_t, size_t, // Output pointer, row stride, column stride + const void *, unsigned int // Parameters, number of channels + ) + { + // Do nothing - this should never be reached because entry to it is guarded + // by an `if` on a `constexpr static bool`. + } +}; + +namespace +{ + +template <typename OutputStage> +inline void stash_bias(OutputStage &, const void *) {} + +template <> +inline void stash_bias(arm_gemm::Requantize32 &qp, const void *bias) __attribute__ ((unused)); + +template <> +inline void stash_bias(arm_gemm::Requantize32 &qp, const void *bias) +{ + qp.bias = reinterpret_cast<const int32_t *>(bias); +} + +} + +} // namespace depthwise_depthfirst + +template <typename TInput, + typename TWeight=TInput, + typename TOutput=TInput, + typename TAccum=typename DefaultTAccum<TInput>::Type, + typename OutputStage=typename DefaultOutputStage<TOutput>::Type> +class DepthwiseDepthfirst +: public DepthwiseDepthfirstCommon<TInput, TWeight, TOutput, TAccum, OutputStage> +{ + using StratType = DepthwiseDepthfirstStrategy<TInput, TWeight, TOutput, TAccum>; + using Parent = DepthwiseDepthfirstCommon<TInput, TWeight, TOutput, TAccum, OutputStage>; + using WorkspaceManager = Workspace< + OutputArrayElement<TOutput>, + depthwise_depthfirst::InputArrayElement<TInput>, + InputBufferElement<TInput>, + IntermediateBufferElement<TInput>, + typename depthwise_depthfirst::WorkspaceFinalElement<TAccum, OutputStage>::Element + >; + using WorkingSpace = typename WorkspaceManager::WorkspaceType; + + // We keep a copy of the bias and output stage + const TAccum *m_bias; + + public: + DepthwiseDepthfirst(StratType *const strat, const DepthwiseArgs &args, const OutputStage &os = {}) + : Parent(strat, args, os), m_bias(nullptr) + { + } + + DepthwiseDepthfirst(DepthwiseDepthfirst &) = delete; + DepthwiseDepthfirst &operator=(DepthwiseDepthfirst &) = delete; + + void pack_parameters(void *buffer, const void *biases, const void *weights, size_t ld_weight_col, size_t ld_weight_row) override + { + reinterpret_cast<const StratType *>(this->m_strat.get())->pack_parameters( + this->m_args, buffer, biases, this->get_output_stage(), + weights, ld_weight_col, ld_weight_row + ); + m_bias = reinterpret_cast<const TAccum *>(biases); + depthwise_depthfirst::stash_bias(this->get_output_stage(), biases); + } + + size_t get_working_size_per_thread() const override + { + DepthwiseArgs args(this->m_args); + return WorkspaceManager::get_sizeof_workspace( + WorkspaceArgs<IDepthfirstStrategy, OutputStage>(this->m_strat.get(), args, this->get_output_stage()) + ); + } + + void initialise_working_space(void *buffer) const override + { + DepthwiseArgs args(this->m_args); + WorkspaceManager::initialise( + buffer, WorkspaceArgs<IDepthfirstStrategy, OutputStage>(this->m_strat.get(), args, this->get_output_stage()) + ); + } + + virtual bool supports_direct_padding() const override + { + using Invoker = depthwise_depthfirst::Invoke<TInput, TWeight, TOutput, TAccum, OutputStage>; + return Invoker::supports_direct_kernel && this->uses_intermediate_array(); + } + + protected: + + void fill_inptr_array(const DepthwiseArgs &args, + const TensorSpec<const TInput *> &input, + const TInput **inptr_array, TInput *input_buffer, + const unsigned int input_i, const unsigned int input_j, + const unsigned int input_pad_top, const unsigned int input_pad_left) const override + { + fill_pointer_array<const TInput>( + inptr_array, this->m_strat->get_input_rows(), this->m_strat->get_input_cols(), + input.base, + input.ld_row, input.ld_col, + input_buffer, + input_pad_top, args.input_rows - input_i, + input_pad_left, args.input_cols - input_j + ); + } + + void compute_tile_padded( + const DepthwiseArgs &args, + unsigned int output_i, unsigned int output_j, + unsigned int output_channel_start, unsigned int output_channel_end, + const TensorSpec<const TInput *> &input, + const TensorSpec<TOutput *> &output, + const void *parameters, + void *working_space_raw + ) const override + { + // Get the working space + auto ws = reinterpret_cast<WorkingSpace *>(working_space_raw); + + // Compute the input pointer array + const int ii = static_cast<int>(output_i * args.stride_rows) - args.padding.top; + const auto input_pad_top = static_cast<unsigned int>(ii < 0 ? -ii : 0); + const auto input_i = static_cast<unsigned int>(ii < 0 ? 0 : ii); + + const int ij = static_cast<int>(output_j * args.stride_cols) - args.padding.left; + const auto input_pad_left = static_cast<unsigned int>(ij < 0 ? -ij : 0); + const auto input_j = static_cast<unsigned int>(ij < 0 ? 0 : ij); + + Tile<TInput> multiplied_input; + this->initialise_inptr_array(args, output_channel_start, output_channel_end, input, + ws->inptr_array, ws->input_buffer, ws->intermediate_buffer, + input_i, input_j, input_pad_top, input_pad_left, multiplied_input); + + // Compute the output pointer array + fill_pointer_array( + ws->outptr_array, this->m_strat->get_output_rows(), this->m_strat->get_output_cols(), + output.base + output_i*output.ld_row + output_j*output.ld_col + output_channel_start, + output.ld_row, output.ld_col, + ws->output_buffer, + 0, args.output_rows - output_i, // Top padding, # valid rows + 0, args.output_cols - output_j // Left padding, # valid columns + ); + + // Execute the kernel + depthwise_depthfirst::Invoke<TInput, TWeight, TOutput, TAccum, OutputStage>::indirect( + reinterpret_cast<const StratType *>(this->m_strat.get()), + ws, this->get_output_stage(), parameters, m_bias, output_channel_end - output_channel_start + ); + } + + void compute_row_padded_tile_row( + const DepthwiseArgs &args, + const unsigned int output_i, unsigned int output_j, unsigned int n_tile_cols, + const unsigned int output_channel_start, const unsigned int output_channel_end, + const TensorSpec<const TInput *> &input, + const TensorSpec<TOutput *> &output, + const void *parameters, + void *working_space + ) const override + { + using Invoker = depthwise_depthfirst::Invoke<TInput, TWeight, TOutput, TAccum, OutputStage>; + auto ws = reinterpret_cast<WorkingSpace *>(working_space); + const auto strat = reinterpret_cast<const StratType *>(this->m_strat.get()); + const auto os = this->get_output_stage(); + + // Compute top and bottom padding; hence fill in the initial pointer arrays. + const int ii = static_cast<int>(output_i * args.stride_rows) - args.padding.top; + const auto input_pad_top = static_cast<unsigned int>(ii < 0 ? -ii : 0); + + const auto input_i = static_cast<unsigned int>(ii < 0 ? 0 : ii); + auto input_j = output_j * args.stride_cols - args.padding.left; + + // Valid input rows is the smallest of the input rows that aren't padding for this tile, and the number of rows + // available. + const auto valid_input_rows = std::min(strat->get_input_rows() - input_pad_top, args.input_rows - input_i); + const auto valid_output_rows = std::min(strat->get_output_rows(), args.output_rows - output_i); + + const auto input_point_stride = input.ld_col * this->m_strat->get_output_cols() * args.stride_cols; + const auto output_point_stride = output.ld_col * this->m_strat->get_output_cols(); + + Tile<TInput> multiplied_input; + this->initialise_inptr_array(args, output_channel_start, output_channel_end, input, + ws->inptr_array, ws->input_buffer, ws->intermediate_buffer, + input_i, input_j, input_pad_top, 0, multiplied_input); + + fill_pointer_array( + ws->outptr_array, this->m_strat->get_output_rows(), this->m_strat->get_output_cols(), + output.base + output_i*output.ld_row + output_j*output.ld_col + output_channel_start, + output.ld_row, output.ld_col, + ws->output_buffer, + 0, args.output_rows - output_i, // Top padding, # valid rows + 0, args.output_cols - output_j // Left padding, # valid columns + ); + + for (; n_tile_cols; n_tile_cols--) + { + // Execute the kernel + Invoker::indirect( + strat, ws, os, parameters, m_bias, output_channel_end - output_channel_start + ); + + // Update all unpadded pointers + if (this->uses_intermediate_array()) { + input_j += input_point_stride / input.ld_col; + multiplied_input.load_from(input.base, + input.ld_row, input.ld_col, + args.input_rows, args.input_cols, + input_i, input_j, args.channel_multiplier); + } else { + { + auto ptr = ws->inptr_array + strat->get_input_cols() * input_pad_top; + for (auto n = input_pad_top; n < (valid_input_rows + input_pad_top); n++) + { + for (auto m = 0u; m < strat->get_input_cols(); m++) + { + *(ptr++) += input_point_stride; + } + } + } + } + + { + auto ptr = ws->outptr_array; + for (auto n = 0u; n < valid_output_rows * strat->get_output_cols(); n++) + { + *(ptr++) += output_point_stride; + } + } + } + } + + void compute_tiles_unpadded( + const DepthwiseArgs &args, + unsigned int output_i, const unsigned int output_j, + unsigned int n_tile_rows, unsigned int n_tile_cols, + unsigned int output_channel_start, unsigned int output_channel_end, + const TensorSpec<const TInput *> &input, + const TensorSpec<TOutput *> &output, + const void *parameters, + void *working_space_raw + ) const override + { + using Invoker = depthwise_depthfirst::Invoke<TInput, TWeight, TOutput, TAccum, OutputStage>; + auto ws = reinterpret_cast<WorkingSpace *>(working_space_raw); + const auto strat = reinterpret_cast<const StratType *>(this->m_strat.get()); + const auto os = this->get_output_stage(); + + if (Invoker::supports_direct_kernel) + { + PaddingValues tile_padding = { + args.kernel_cols / 2, + args.kernel_rows / 2, + args.kernel_cols / 2, + args.kernel_rows / 2 + }; + + // If the direct kernel is supported, then use it. + // Compute the base pointers we'll use in the tile. + auto outptr = output.base + output_channel_start + output_i * output.ld_row + output_j * output.ld_col; + const int start_input_i = output_i * args.stride_rows - args.padding.top; + const int start_input_j = output_j * args.stride_cols - args.padding.left; + auto inptr = input.base + output_channel_start + start_input_i * input.ld_row + start_input_j * input.ld_col; + + auto ld_row = input.ld_row; + auto ld_col = input.ld_col; + + const auto tile_rows = this->m_strat->get_output_rows() * args.stride_rows * n_tile_rows + tile_padding.top + tile_padding.bottom; + const auto tile_cols = this->m_strat->get_output_cols() * args.stride_cols * n_tile_cols + tile_padding.left + tile_padding.right; + const auto tile_channels = output_channel_end - output_channel_start; + + Tile<TInput> multiplied_input; + if (this->uses_intermediate_array()) { + multiplied_input = Tile<TInput>(ws->intermediate_buffer, tile_rows, tile_cols, tile_channels); + multiplied_input.load_from(input.base, + input.ld_row, input.ld_col, + args.input_rows, args.input_cols, + start_input_i, start_input_j, args.channel_multiplier); + + ld_row = tile_cols * tile_channels; + ld_col = tile_channels; + inptr = multiplied_input.array; + } + + // Execute the kernel + Invoker::direct( + strat, ws, os, + n_tile_rows, n_tile_cols, + inptr, ld_row, ld_col, + outptr, output.ld_row, output.ld_col, + parameters, output_channel_end - output_channel_start + ); + } + else + { + // Otherwise, we repeatedly call the padded kernel but use our knowledge + // of the tensor structure to avoid recomputing the pointer array. + + const auto n_input_pointers = this->m_strat->get_input_rows() * this->m_strat->get_input_cols(); + const auto input_point_stride = input.ld_col * this->m_strat->get_output_cols() * args.stride_cols; + const auto n_output_pointers = this->m_strat->get_output_rows() * this->m_strat->get_output_cols(); + const auto output_point_stride = output.ld_col * this->m_strat->get_output_cols(); + + // For each tile row, initialise the input and output pointer arrays. For + // each subsequent tile we simply update the pointers. + for (unsigned int tile_i = 0; tile_i < n_tile_rows; tile_i++) + { + const int input_i = static_cast<int>(output_i * args.stride_rows) - args.padding.top; + int input_j = static_cast<int>(output_j * args.stride_cols) - args.padding.left; + + Tile<TInput> multiplied_input; + this->initialise_inptr_array(args, output_channel_start, output_channel_end, input, + ws->inptr_array, ws->input_buffer, ws->intermediate_buffer, + input_i, input_j, 0, 0, multiplied_input); + + // Compute the output pointer array + fill_pointer_array( + ws->outptr_array, this->m_strat->get_output_rows(), this->m_strat->get_output_cols(), + output.base + output_i*output.ld_row + output_j*output.ld_col + output_channel_start, + output.ld_row, output.ld_col, + ws->output_buffer, + 0, args.output_rows, + 0, args.output_cols + ); + + for (unsigned int tile_j = 0; tile_j < n_tile_cols; tile_j++) + { + // Invoke the indirect kernel for this tile + depthwise_depthfirst::Invoke<TInput, TWeight, TOutput, TAccum, OutputStage>::indirect( + strat, ws, os, parameters, m_bias, output_channel_end - output_channel_start + ); + + // Progress the pointers + if (this->uses_intermediate_array()) { + input_j += input_point_stride / input.ld_col; + multiplied_input.load_from(input.base, + input.ld_row, input.ld_col, + args.input_rows, args.input_cols, input_i, input_j, args.channel_multiplier); + } else { + for (auto i = 0u; i < n_input_pointers; i++) + { + ws->inptr_array[i] += input_point_stride; + } + } + + for (auto i = 0u; i < n_output_pointers; i++) + { + ws->outptr_array[i] += output_point_stride; + } + } + + output_i += this->m_strat->get_output_rows(); + } + } + } +}; + +} // namespace depthwise +} // namespace arm_conv |