| /* |
| * Copyright (c) 2021-2022 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), // 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; } |
| |
| 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>, |
| 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 unsigned int n_input_channels) const override |
| { |
| DepthwiseArgs args(this->m_args); |
| args.input_channels = n_input_channels; |
| return WorkspaceManager::get_sizeof_workspace( |
| WorkspaceArgs<IDepthfirstStrategy, OutputStage>(this->m_strat.get(), args, this->get_output_stage()) |
| ); |
| } |
| |
| void initialise_working_space(void *buffer, unsigned int n_input_channels) const override |
| { |
| DepthwiseArgs args(this->m_args); |
| args.input_channels = n_input_channels; |
| WorkspaceManager::initialise( |
| buffer, WorkspaceArgs<IDepthfirstStrategy, OutputStage>(this->m_strat.get(), args, this->get_output_stage()) |
| ); |
| } |
| |
| protected: |
| void compute_tile_padded( |
| 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 auto input_channel_start = output_channel_start / this->m_args.channel_multiplier; |
| |
| const int ii = static_cast<int>(output_i * this->m_args.stride_rows) - this->m_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 * this->m_args.stride_cols) - this->m_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); |
| |
| fill_pointer_array<const TInput>( |
| ws->inptr_array, this->m_strat->get_input_rows(), this->m_strat->get_input_cols(), |
| input.base + input_i*input.ld_row + input_j*input.ld_col + input_channel_start, |
| input.ld_row, input.ld_col, |
| ws->input_buffer, |
| input_pad_top, this->m_args.input_rows - input_i, |
| input_pad_left, this->m_args.input_cols - input_j |
| ); |
| |
| // 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, this->m_args.output_rows - output_i, // Top padding, # valid rows |
| 0, this->m_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 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 auto input_channel_start = output_channel_start / this->m_args.channel_multiplier; |
| const int ii = static_cast<int>(output_i * this->m_args.stride_rows) - this->m_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 auto input_j = output_j * this->m_args.stride_cols - this->m_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, this->m_args.input_rows - input_i); |
| const auto valid_output_rows = std::min(strat->get_output_rows(), this->m_args.output_rows - output_i); |
| |
| const auto input_point_stride = input.ld_col * this->m_strat->get_output_cols() * this->m_args.stride_cols; |
| const auto output_point_stride = output.ld_col * this->m_strat->get_output_cols(); |
| |
| fill_pointer_array<const TInput>( |
| ws->inptr_array, this->m_strat->get_input_rows(), this->m_strat->get_input_cols(), |
| input.base + input_i*input.ld_row + input_j*input.ld_col + input_channel_start, |
| input.ld_row, input.ld_col, |
| ws->input_buffer, |
| input_pad_top, this->m_args.input_rows - input_i, |
| 0, this->m_args.input_cols - input_j // No left padding |
| ); |
| |
| 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, this->m_args.output_rows - output_i, // Top padding, # valid rows |
| 0, this->m_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 |
| { |
| 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( |
| 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) |
| { |
| // 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 * this->m_args.stride_rows - this->m_args.padding.top; |
| const int start_input_j = output_j * this->m_args.stride_cols - this->m_args.padding.left; |
| auto inptr = input.base + output_channel_start + start_input_i * input.ld_row + start_input_j * input.ld_col; |
| |
| // Execute the kernel |
| Invoker::direct( |
| strat, ws, os, |
| n_tile_rows, n_tile_cols, |
| inptr, input.ld_row, input.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 input_channel_start = output_channel_start / this->m_args.channel_multiplier; |
| |
| 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() * this->m_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 * this->m_args.stride_rows) - this->m_args.padding.top; |
| const int input_j = static_cast<int>(output_j * this->m_args.stride_cols) - this->m_args.padding.left; |
| |
| fill_pointer_array<const TInput>( |
| ws->inptr_array, this->m_strat->get_input_rows(), this->m_strat->get_input_cols(), |
| input.base + input_i*input.ld_row + input_j*input.ld_col + input_channel_start, |
| input.ld_row, input.ld_col, |
| ws->input_buffer, |
| 0, this->m_args.input_rows, |
| 0, this->m_args.input_cols |
| ); |
| |
| // 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, this->m_args.output_rows, |
| 0, this->m_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 |
| 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 |