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Diffstat (limited to 'src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_multiplier_quantized.hpp')
-rw-r--r-- | src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_multiplier_quantized.hpp | 251 |
1 files changed, 251 insertions, 0 deletions
diff --git a/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_multiplier_quantized.hpp b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_multiplier_quantized.hpp new file mode 100644 index 0000000000..07ce0d3b55 --- /dev/null +++ b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst_multiplier_quantized.hpp @@ -0,0 +1,251 @@ +/* + * 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 "depthwise_depthfirst_multiplier.hpp" + +namespace arm_conv { +namespace depthwise { + +template <class strategy> +class DepthwiseDepthfirstWithMultiplierQuantized : + public DepthwiseCommon<typename strategy::input_type, + typename strategy::weight_type, + typename strategy::return_type> +{ + using Parent = DepthwiseCommon<typename strategy::input_type, + typename strategy::weight_type, + typename strategy::return_type>; + using TInput = typename strategy::input_type; + using TWeight = typename strategy::weight_type; + using TOutput = typename strategy::return_type; + + const arm_gemm::Requantize32 m_qp; + + size_t sizeof_output_buffer(unsigned int n_channels) const + { + const unsigned int vl = arm_gemm::utils::get_vector_length<typename strategy::return_type>(strategy::vl_type); + const auto rounded_channels = arm_gemm::roundup(n_channels, vl); + return sizeof(typename strategy::return_type) * rounded_channels; + } + + public: + DepthwiseDepthfirstWithMultiplierQuantized(const DepthwiseArgs &args, const arm_gemm::Requantize32 &qp) + : Parent(args), m_qp(qp) + { + } + + DepthwiseDepthfirstWithMultiplierQuantized(DepthwiseDepthfirstWithMultiplierQuantized &) = delete; + DepthwiseDepthfirstWithMultiplierQuantized &operator=(DepthwiseDepthfirstWithMultiplierQuantized &) = delete; + + size_t get_storage_size(void) const override + { + // We produce VL<int32_t> channels at a time, for each of these blocks of + // channels we store a vector of biases, weights (complicated) and + // requantize parameters. + const unsigned int iter_length = + arm_gemm::utils::get_vector_length<int32_t>(strategy::vl_type); + const unsigned int n_iters = + this->m_args.input_channels * arm_gemm::iceildiv(this->m_args.channel_multiplier, iter_length); + + // Compute the cost of storing the weights + const unsigned int n_dots_per_kernel_row = arm_gemm::iceildiv(strategy::kernel_cols, 4u); + + return n_iters * iter_length * ( + sizeof(int32_t) + // Bias + 4 * n_dots_per_kernel_row * strategy::kernel_rows * sizeof(TWeight) + // Weights + 2 * sizeof(int32_t) // Requantisation parameters + ); + } + + // We'll want an optimised version of this, but for now a C++ implementation + // is probably sufficient. + void pack_parameters(void *_buffer, const void *_biases, const void *_weights, size_t ld_weight_col, size_t ld_weight_row) override + { + auto buffer = static_cast<uint8_t *>(_buffer); + auto biases = static_cast<const int32_t *>(_biases); + auto weights = static_cast<const TWeight *>(_weights); + auto requant_muls = m_qp.per_channel_muls; + auto requant_shifts = m_qp.per_channel_right_shifts; + + const unsigned int iter_length = + arm_gemm::utils::get_vector_length<int32_t>(strategy::vl_type); + const unsigned int n_iters_per_input_channel = + arm_gemm::iceildiv(this->m_args.channel_multiplier, iter_length); + + const unsigned int n_dots_per_kernel_row = arm_gemm::iceildiv(strategy::kernel_cols, 4u); + + const size_t iter_stride = iter_length * ( + sizeof(int32_t) + // Bias + 4 * n_dots_per_kernel_row * strategy::kernel_rows * sizeof(int8_t) + // Weights + 2 * sizeof(int32_t) // Requantisation parameters + ); + + ld_weight_col = (ld_weight_col == 0) ? this->m_args.input_channels * this->m_args.channel_multiplier : ld_weight_col; + ld_weight_row = (ld_weight_row == 0) ? this->m_args.kernel_cols * ld_weight_col : ld_weight_row; + + for (unsigned int input_channel = 0; input_channel < this->m_args.input_channels; input_channel++) + { + auto buffer_input_channel = buffer + input_channel * n_iters_per_input_channel * iter_stride; + auto weights_input_channel = weights + input_channel * this->m_args.channel_multiplier; + + for (unsigned int iter = 0; iter < n_iters_per_input_channel; iter++) + { + // Get a pointer to the start of this portion of the buffer; consequently + // derive pointers to the bias, weight and requantisation portions of + // this frame. + auto buffer_base = buffer_input_channel + iter_stride * iter; + auto buffer_biases = reinterpret_cast<int32_t *>(buffer_base); + auto buffer_weights = buffer_base + sizeof(int32_t) * iter_length; + auto buffer_requant_mul = reinterpret_cast<int32_t *>( + buffer_weights + strategy::kernel_rows * n_dots_per_kernel_row * 4 * iter_length); + auto buffer_requant_shift = buffer_requant_mul + iter_length; + auto weights_base = weights_input_channel + iter * iter_length; + + // Hence work through the data for this iteration, on a + // channel-by-channel basis. + const auto this_iter_length = std::min<unsigned int>( + iter_length, this->m_args.channel_multiplier - iter * iter_length + ); + for (unsigned int i = 0; i < this_iter_length; i++) + { + auto weights_channel = weights_base + i; + + // Read the bias value, we modify this as we read the weights. + auto bias_value = biases == nullptr ? 0 : *(biases++); + int32_t elements_sum = 0; + + // Read through the kernel; for each row, marshal together as many dot + // product terms as are required. + for (unsigned int ki = 0; ki < strategy::kernel_rows; ki++) + { + auto buffer_row = buffer_weights + i*4 + ki * 4 * n_dots_per_kernel_row * iter_length; + auto weights_row = weights_channel + ki * ld_weight_row; + + unsigned int kj = 0; + for (; kj < strategy::kernel_cols; kj++) + { + // Determine which element to which we're writing + const auto dot = kj / 4; + const auto elem = kj % 4; + + // Copy the value; include in the sum + const auto val = weights_row[kj * ld_weight_col]; + buffer_row[dot * 4 * iter_length + elem] = val; + elements_sum += val; + } + for (; kj < 4 * n_dots_per_kernel_row; kj++) + { + const auto dot = kj / 4; + const auto elem = kj % 4; + buffer_row[dot * 4 * iter_length + elem] = 0; + } + + buffer_row += 4 * n_dots_per_kernel_row * iter_length; + } + + // Write back the bias and offset values + *(buffer_biases++) = + bias_value - m_qp.a_offset * elements_sum + + strategy::kernel_rows * strategy::kernel_cols * m_qp.a_offset * m_qp.b_offset; + + // Write out the requantisation parameters + *(buffer_requant_mul++) = m_qp.per_channel_requant ? *(requant_muls++) : m_qp.per_layer_mul; + *(buffer_requant_shift++) = m_qp.per_channel_requant ? *(requant_shifts++) : m_qp.per_layer_right_shift; + } + } + } + } + + 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_output_buffer(n_output_channels); + } + + using Parent::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 + + auto executefn = [strat, this] ( + const TInput *const *const inptrs, + TOutput *const *const outptr_array, + const void *const params + ) { + strat.kernel(inptrs, outptr_array, params, this->m_args.channel_multiplier, m_qp); + }; + + // Get working space for this thread + uint8_t *const working_space = static_cast<uint8_t *>(_working_space) + get_working_size(1, input_channels) * thread_id; + + // Determine the stride across blocks of parameters + const unsigned int iter_length = + arm_gemm::utils::get_vector_length<int32_t>(strategy::vl_type); + const unsigned int n_iters_per_input_channel = arm_gemm::iceildiv(this->m_args.channel_multiplier, iter_length); + const unsigned int n_dots_per_kernel_row = arm_gemm::iceildiv(strategy::kernel_cols, 4u); + const size_t param_stride = n_iters_per_input_channel * iter_length * ( + sizeof(int32_t) + // Bias + 4 * n_dots_per_kernel_row * strategy::kernel_rows * sizeof(int8_t) + // Weights + 2 * sizeof(int32_t) // Requantisation parameters + ); + + common::depthwise_multiplier_execute<strategy>( + executefn, m_qp.a_offset, this->m_args, + batches, input_height, input_width, input_channels, padding, + _input, ld_input_col, ld_input_row, ld_input_batch, + parameters, param_stride, + 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 |