From d02d5edfa15ba6c04a9986a8a362a945cb38ac31 Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Fri, 22 Jan 2021 09:47:04 +0000 Subject: Integrate improved CPU depthwise convolution kernels * Replace assembly kernels for depthwise convolution with more optimized ones. * Add int8 assembly kernels. * Fix implicit padding on optimized kernels Resolves: COMPMID-3867, COMPMID-4361 Change-Id: I0b0867e05f61be4f368f62190d55e14d0ab3ebf2 Signed-off-by: Michele Di Giorgio Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5622 Tested-by: Arm Jenkins Reviewed-by: Georgios Pinitas --- .../depthwise_depthfirst_generic_multiplier.hpp | 480 +++++++++++++++++++++ 1 file changed, 480 insertions(+) create mode 100644 src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_generic_multiplier.hpp (limited to 'src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_generic_multiplier.hpp') diff --git a/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_generic_multiplier.hpp b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_generic_multiplier.hpp new file mode 100644 index 0000000000..656e4413b2 --- /dev/null +++ b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_generic_multiplier.hpp @@ -0,0 +1,480 @@ +/* + * 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 DepthwiseDepthfirstGenericWithMultiplierBase : + public DepthwiseCommon +{ + 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(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(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 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(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(_input); + TOutput *const outptr = static_cast(_output); + + // Allocate portions of the working space + uint8_t *working_space = static_cast(_working_space) + + get_working_size(thread_id, input_channels); + + const TInput **inptrs = reinterpret_cast(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(working_space); + working_space += sizeof_input_samples(); + + TOutput **outptr_array = reinterpret_cast(working_space); + working_space += sizeof_outptr_array(); + + TOutput *const output_buffer = reinterpret_cast(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(strategy::output_rows())) + { + const int end_out_i = std::min(start_out_i + static_cast(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(-std::min(start_in_i, 0)); + const auto pad_bottom = static_cast(-std::min(static_cast(input_height) - end_in_i, 0)); + const unsigned int valid_output_rows = std::min( + end_out_i - start_out_i, + static_cast(output_height) - start_out_i + ); + + const int pad_rows = pad_top + pad_bottom; + + for (int start_out_j = 0; start_out_j < static_cast(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(-std::min(static_cast(input_width) - end_in_j, 0)); + const unsigned int valid_output_cols = std::min( + end_out_j - start_out_j, + static_cast(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(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(ki) - static_cast(pad_top); + for (auto oi = 0u; + oi < strategy::output_rows() && + ii < static_cast(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(kj) - static_cast(pad_left); + for (auto oj = 0u; + oj < strategy::output_cols() && + ij < static_cast(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(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(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 DepthwiseDepthfirstGenericWithMultiplier : public DepthwiseDepthfirstGenericWithMultiplierBase +{ + 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; + + 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(biases); + Parent::pack_weights(static_cast(buffer), static_cast(weights), ld_weight_col, ld_weight_row); + } + + using DepthwiseDepthfirstGenericWithMultiplierBase::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::is_integer) + { + activation_min = std::numeric_limits::min(); + activation_max = std::numeric_limits::max(); + } + else + { + activation_min = static_cast(-std::numeric_limits::infinity()); + activation_max = static_cast(std::numeric_limits::infinity()); + } + + switch (this->m_args.activation.type) + { + case arm_gemm::Activation::Type::BoundedReLU: + activation_max = static_cast(this->m_args.activation.param1); + // Fall through + case arm_gemm::Activation::Type::ReLU: + activation_min = static_cast(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 -- cgit v1.2.1