| /* |
| * Copyright (c) 2021 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_gemm/utils.hpp" |
| |
| #ifdef CYCLE_PROFILING |
| #include "profiler.hpp" |
| #endif |
| |
| namespace arm_conv { |
| namespace depthwise { |
| |
| template <class strategy> |
| class DepthwiseDepthfirstGenericWithMultiplierBase : |
| public DepthwiseCommon<typename strategy::input_type, |
| typename strategy::weight_type, |
| typename strategy::return_type> |
| { |
| protected: |
| |
| using TInput = typename strategy::input_type; |
| using TWeight = typename strategy::weight_type; |
| using TOutput = typename strategy::return_type; |
| using TAccum = typename strategy::bias_type; |
| |
| unsigned int kernel_points(void) const |
| { |
| return this->m_args.kernel_rows * this->m_args.kernel_cols; |
| } |
| |
| unsigned int input_rows(void) const |
| { |
| return (strategy::output_rows() - 1) * this->m_args.stride_rows + this->m_args.kernel_rows; |
| } |
| |
| unsigned int input_cols(void) const |
| { |
| return (strategy::output_cols() - 1) * this->m_args.stride_cols + this->m_args.kernel_cols; |
| } |
| |
| size_t sizeof_inptr_array(void) const |
| { |
| return sizeof(TInput *) * kernel_points() * strategy::output_rows(); |
| } |
| |
| size_t sizeof_input_samples(void) const |
| { |
| // We have a sample for each kernel point, for each point of the output array. |
| return sizeof(TInput) * kernel_points() * |
| strategy::output_rows() * |
| strategy::output_col_regs() * |
| (16 / sizeof(TAccum)); |
| } |
| |
| size_t sizeof_outptr_array(void) const |
| { |
| return sizeof(TOutput *) * strategy::output_rows() * strategy::output_cols(); |
| } |
| |
| size_t sizeof_output_buffer(unsigned int n_channels) const |
| { |
| const unsigned int vl = arm_gemm::utils::get_vector_length<TOutput>(strategy::vl_type); |
| const auto rounded_channels = arm_gemm::roundup(n_channels, vl); |
| return sizeof(TOutput) * rounded_channels; |
| } |
| |
| void pack_weights(TWeight *buffer, const TWeight *weights, size_t ld_weight_col, size_t ld_weight_row) const |
| { |
| const unsigned int vl = arm_gemm::utils::get_vector_length<TAccum>(strategy::vl_type); |
| ld_weight_col = (ld_weight_col == 0) ? this->m_args.channel_multiplier * this->m_args.input_channels : ld_weight_col; |
| ld_weight_row = (ld_weight_row == 0) ? this->m_args.kernel_cols * ld_weight_col : ld_weight_row; |
| |
| for (unsigned int in_c = 0; in_c < this->m_args.input_channels; in_c++) |
| { |
| for (unsigned int n = 0; n < this->m_args.channel_multiplier; n += vl) |
| { |
| const unsigned int out_c = in_c * this->m_args.channel_multiplier + n; |
| const unsigned int todo = std::min(vl, this->m_args.channel_multiplier - n); |
| |
| // Copy each of the weights in turn |
| auto weights_row = weights + out_c; |
| for (unsigned int i = 0; i < this->m_args.kernel_rows; i++) |
| { |
| auto weights_col = weights_row; |
| |
| for (unsigned int j = 0; j < this->m_args.kernel_cols; j++) |
| { |
| for (unsigned int m = 0; m < todo; m++) |
| { |
| buffer[m] = weights_col[m]; |
| } |
| buffer += vl; |
| |
| weights_col += ld_weight_col; |
| } |
| |
| weights_row += ld_weight_row; |
| } |
| } |
| } |
| } |
| |
| void execute_tiles( |
| std::function<void(const TInput **, TOutput **, const TWeight *, unsigned int, unsigned int)> tile_fn, |
| const TInput pad_value, |
| const unsigned int batches, |
| const unsigned int input_height, |
| const unsigned int input_width, |
| const unsigned int input_channels, |
| const PaddingValues &padding, |
| const void *const _input, |
| const size_t ld_input_col, |
| const size_t ld_input_row, |
| const size_t ld_input_batch, |
| const void *const parameters, |
| const unsigned int output_height, |
| const unsigned int output_width, |
| void *const _output, |
| const size_t ld_output_col, |
| const size_t ld_output_row, |
| const size_t ld_output_batch, |
| void *const _working_space, |
| const unsigned int thread_id, |
| const unsigned int n_threads |
| ) const |
| { |
| #ifdef CYCLE_PROFILING |
| arm_gemm::profiler prof; |
| #endif |
| |
| // Determine what portion of the work to do. |
| const unsigned int n_rows_per_thread = arm_gemm::iceildiv(output_height, n_threads); |
| const int start_out_height = std::min(thread_id * n_rows_per_thread, output_height); |
| const int end_out_height = std::min(start_out_height + n_rows_per_thread, output_height); |
| |
| // Need a stride over blocks of parameters |
| const unsigned int vl = arm_gemm::utils::get_vector_length<TAccum>(strategy::vl_type); |
| const unsigned int param_stride = arm_gemm::roundup(this->m_args.channel_multiplier, vl) * kernel_points(); |
| |
| // Cast input and output pointers into the right types |
| const TInput *const inptr = static_cast<const TInput *>(_input); |
| TOutput *const outptr = static_cast<TOutput *>(_output); |
| |
| // Allocate portions of the working space |
| uint8_t *working_space = static_cast<uint8_t *>(_working_space) + |
| get_working_size(thread_id, input_channels); |
| |
| const TInput **inptrs = reinterpret_cast<const TInput **>(working_space); |
| working_space += sizeof_inptr_array(); |
| |
| // To simplify the kernel, we process padded or non-NCHW-ordered input into |
| // a form which can be consumed by the kernel. This data is stored here and |
| // passed into the kernel as an array of N pointers (one per row of the |
| // input). |
| TInput *rearranged_input = reinterpret_cast<TInput *>(working_space); |
| working_space += sizeof_input_samples(); |
| |
| TOutput **outptr_array = reinterpret_cast<TOutput **>(working_space); |
| working_space += sizeof_outptr_array(); |
| |
| TOutput *const output_buffer = reinterpret_cast<TOutput *>(working_space); |
| |
| // TODO Dynamically change the input pointer array in cases where we could |
| // read directly from the input tensor; for now though assume we will |
| // always read from the sample array. |
| { |
| auto my_inptrs = inptrs; |
| auto my_input_samples = rearranged_input; |
| |
| // For each kernel point; for each row of output; for each register of |
| // values containing a QUAD of source values. |
| const unsigned int quad_length = 16 / sizeof(TAccum); |
| |
| for (auto p = 0u; p < kernel_points() * strategy::output_rows(); p++) |
| { |
| *(my_inptrs)++ = my_input_samples; |
| my_input_samples += arm_gemm::roundup(strategy::output_cols(), quad_length); |
| } |
| } |
| |
| // For each output tile, construct the requisite set of pointers and call |
| // into the kernel. |
| for (unsigned int batch = 0; batch < batches; batch++) |
| { |
| // Get batch pointers |
| const auto inptr_batch = inptr + batch * ld_input_batch; |
| const auto outptr_batch = outptr + batch * ld_output_batch; |
| |
| for (int start_out_i = start_out_height; |
| start_out_i < end_out_height; |
| start_out_i += static_cast<int>(strategy::output_rows())) |
| { |
| const int end_out_i = std::min(start_out_i + static_cast<int>(strategy::output_rows()), end_out_height); |
| const int start_in_i = start_out_i * this->m_args.stride_rows - padding.top; |
| const int end_in_i = start_in_i + input_rows(); |
| |
| // Compute top/bottom padding |
| const auto pad_top = static_cast<unsigned int>(-std::min(start_in_i, 0)); |
| const auto pad_bottom = static_cast<unsigned int>(-std::min(static_cast<int>(input_height) - end_in_i, 0)); |
| const unsigned int valid_output_rows = std::min( |
| end_out_i - start_out_i, |
| static_cast<int>(output_height) - start_out_i |
| ); |
| |
| const int pad_rows = pad_top + pad_bottom; |
| |
| for (int start_out_j = 0; start_out_j < static_cast<int>(output_width);) |
| { |
| const int start_in_j = start_out_j * this->m_args.stride_cols - this->m_args.padding.left; |
| const int pad_left = -std::min(0, start_in_j); |
| |
| const int end_out_j = start_out_j + strategy::output_cols(); |
| const int end_in_j = start_in_j + input_cols(); |
| |
| const auto pad_right = static_cast<unsigned int>(-std::min(static_cast<int>(input_width) - end_in_j, 0)); |
| const unsigned int valid_output_cols = std::min( |
| end_out_j - start_out_j, |
| static_cast<int>(output_width) - start_out_j |
| ); |
| |
| const int pad_cols = pad_left + pad_right; |
| |
| // Construct the output pointer array. |
| TOutput **outptr_pos = outptr_array; |
| for (auto i = 0u; i < valid_output_rows; i++) |
| { |
| unsigned int j = 0u; |
| TOutput *colptr = outptr_batch + (start_out_i + i) * ld_output_row + start_out_j * ld_output_col; |
| for (; j < valid_output_cols; j++) |
| { |
| *(outptr_pos++) = colptr; |
| colptr += ld_output_col; |
| } |
| for (; j < strategy::output_cols(); j++) |
| { |
| *(outptr_pos++) = output_buffer; |
| } |
| } |
| for (auto i = valid_output_rows; i < strategy::output_rows(); i++) |
| { |
| for (auto j = 0u; j < strategy::output_cols(); j++) |
| { |
| *(outptr_pos++) = output_buffer; |
| } |
| } |
| |
| start_out_j += strategy::output_cols(); |
| |
| const TWeight *params = static_cast<const TWeight *>(parameters); |
| |
| // Fill the input samples with padding. We can do this outside of |
| // the channel loop, as the position of padding isn't going to |
| // change as a function of channel. |
| for (auto i = 0u; i < kernel_points() * strategy::output_rows() * strategy::output_cols(); i++) |
| { |
| rearranged_input[i] = pad_value; |
| } |
| |
| // Loop over the input channels |
| for (unsigned int in_c = 0; in_c < input_channels; in_c++) |
| { |
| auto inptr_row = inptr_batch + in_c + |
| (start_in_i + pad_top) * ld_input_row + |
| (start_in_j + pad_left) * ld_input_col; |
| |
| // Construct the array of input samples; for each point of the |
| // kernel we provide an input value for each output point. |
| auto input_samples = rearranged_input; |
| for (auto ki = 0u; ki < this->m_args.kernel_rows; ki++) |
| { |
| for (auto kj = 0u; kj < this->m_args.kernel_cols; kj++) |
| { |
| // Copy the pointer for the input samples associated with this |
| // kernel point. Then update the main pointer to account for |
| // this point. |
| auto point_input_samples = input_samples; |
| input_samples += strategy::output_rows() * strategy::output_cols(); |
| |
| int ii = static_cast<int>(ki) - static_cast<int>(pad_top); |
| for (auto oi = 0u; |
| oi < strategy::output_rows() && |
| ii < static_cast<int>(input_rows()) - pad_rows; |
| oi++, ii += this->m_args.stride_rows) |
| { |
| if (0 <= ii) // Fill in values only if this row is in range. |
| { |
| int ij = static_cast<int>(kj) - static_cast<int>(pad_left); |
| for (auto oj = 0u; |
| oj < strategy::output_cols() && |
| ij < static_cast<int>(input_cols()) - pad_cols; |
| oj++, ij += this->m_args.stride_cols) |
| { |
| if (0 <= ij) // Sample if the point is in range. |
| { |
| point_input_samples[oj] = *(inptr_row + ii*ld_input_row + ij*ld_input_col); |
| } |
| } |
| } |
| |
| point_input_samples += strategy::output_cols(); |
| } |
| } |
| } |
| |
| tile_fn(inptrs, outptr_array, params, in_c, in_c*this->m_args.channel_multiplier); |
| |
| // Progress the output pointers |
| TOutput **outptr_pos = outptr_array; |
| for (auto i = 0u; i < strategy::output_rows() * strategy::output_cols(); i++) |
| { |
| outptr_pos[i] += this->m_args.channel_multiplier; |
| } |
| |
| // Progress the pointer into the parameters |
| params += param_stride; |
| } |
| } |
| } |
| } |
| } |
| |
| public: |
| DepthwiseDepthfirstGenericWithMultiplierBase(const DepthwiseArgs &args) : DepthwiseCommon<TInput, TWeight, TOutput>(args) |
| { |
| } |
| |
| DepthwiseDepthfirstGenericWithMultiplierBase(DepthwiseDepthfirstGenericWithMultiplierBase &) = delete; |
| DepthwiseDepthfirstGenericWithMultiplierBase &operator=(DepthwiseDepthfirstGenericWithMultiplierBase &) = delete; |
| |
| size_t get_storage_size(void) const override |
| { |
| const unsigned int vl = arm_gemm::utils::get_vector_length<TAccum>(strategy::vl_type); |
| const auto rounded_channels = this->m_args.input_channels * arm_gemm::roundup(this->m_args.channel_multiplier, vl); |
| return kernel_points() * rounded_channels * sizeof(TWeight); |
| } |
| |
| size_t get_working_size(const unsigned int n_threads, const unsigned int n_channels) const override |
| { |
| const unsigned int n_output_channels = n_channels * this->m_args.channel_multiplier; |
| return n_threads * (sizeof_inptr_array() + |
| sizeof_input_samples() + |
| sizeof_outptr_array() + |
| sizeof_output_buffer(n_output_channels)); |
| } |
| }; |
| |
| template <class strategy> |
| class DepthwiseDepthfirstGenericWithMultiplier : public DepthwiseDepthfirstGenericWithMultiplierBase<strategy> |
| { |
| using TInput = typename strategy::input_type; |
| using TWeight = typename strategy::weight_type; |
| using TOutput = typename strategy::return_type; |
| using TAccum = typename strategy::bias_type; |
| |
| using Parent = DepthwiseDepthfirstGenericWithMultiplierBase<strategy>; |
| |
| const TAccum *m_biases; // Pointer to bias vector |
| |
| public: |
| DepthwiseDepthfirstGenericWithMultiplier(const DepthwiseArgs &args) |
| : Parent(args), m_biases(nullptr) |
| { |
| } |
| |
| DepthwiseDepthfirstGenericWithMultiplier(DepthwiseDepthfirstGenericWithMultiplier &) = delete; |
| DepthwiseDepthfirstGenericWithMultiplier &operator=(DepthwiseDepthfirstGenericWithMultiplier &) = delete; |
| |
| void pack_parameters(void *buffer, const void *biases, const void *weights, size_t ld_weight_col, size_t ld_weight_row) override |
| { |
| m_biases = static_cast<const TAccum *>(biases); |
| Parent::pack_weights(static_cast<TAccum *>(buffer), static_cast<const TWeight *>(weights), ld_weight_col, ld_weight_row); |
| } |
| |
| using DepthwiseDepthfirstGenericWithMultiplierBase<strategy>::execute; |
| void execute( |
| const unsigned int batches, |
| const unsigned int input_height, |
| const unsigned int input_width, |
| const unsigned int input_channels, |
| const PaddingValues &padding, |
| const void *const _input, |
| const size_t ld_input_col, |
| const size_t ld_input_row, |
| const size_t ld_input_batch, |
| const void *const parameters, |
| const unsigned int output_height, |
| const unsigned int output_width, |
| void *const _output, |
| const size_t ld_output_col, |
| const size_t ld_output_row, |
| const size_t ld_output_batch, |
| void *const _working_space, |
| const unsigned int thread_id, |
| const unsigned int n_threads |
| ) const override |
| { |
| strategy strat(this->m_args.cpu_info); |
| #ifdef CYCLE_PROFILING |
| arm_gemm::profiler prof; |
| #endif |
| |
| // Compute activation values |
| TAccum activation_min, activation_max; |
| if (std::numeric_limits<TAccum>::is_integer) |
| { |
| activation_min = std::numeric_limits<TAccum>::min(); |
| activation_max = std::numeric_limits<TAccum>::max(); |
| } |
| else |
| { |
| activation_min = static_cast<TAccum>(-std::numeric_limits<float>::infinity()); |
| activation_max = static_cast<TAccum>(std::numeric_limits<float>::infinity()); |
| } |
| |
| switch (this->m_args.activation.type) |
| { |
| case arm_gemm::Activation::Type::BoundedReLU: |
| activation_max = static_cast<TAccum>(this->m_args.activation.param1); |
| // Fall through |
| case arm_gemm::Activation::Type::ReLU: |
| activation_min = static_cast<TAccum>(0); |
| break; |
| default: |
| break; |
| } |
| |
| // Get a function to call for each point of the output |
| auto tile_fn = [&] (const TInput **inptrs, |
| TOutput **outptrs, |
| const TWeight *weights, |
| const unsigned int, |
| const unsigned int start_output_channel) { |
| #ifdef CYCLE_PROFILING |
| auto p = prof.ScopedProfiler(PROFILE_KERNEL, (unsigned long)(strategy::output_rows() * strategy::output_cols() * this->m_args.channel_multiplier * this->m_args.kernel_rows * this->m_args.kernel_cols)); |
| #endif |
| strat.kernel( |
| inptrs, outptrs, weights, |
| m_biases ? m_biases + start_output_channel : nullptr, |
| this->kernel_points(), this->m_args.channel_multiplier, |
| activation_min, activation_max |
| ); |
| }; |
| |
| Parent::execute_tiles( |
| tile_fn, 0.0f, |
| batches, input_height, input_width, input_channels, padding, |
| _input, ld_input_col, ld_input_row, ld_input_batch, |
| parameters, |
| output_height, output_width, |
| _output, ld_output_col, ld_output_row, ld_output_batch, |
| _working_space, thread_id, n_threads |
| ); |
| } |
| }; |
| |
| } // namespace depthwise |
| } // namespace arm_conv |