/* * Copyright (c) 2018-2019 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. */ #ifndef __ARM_COMPUTE_NEGEMMINTERLEAVEDPREPAREBWRAPPERKERNEL_H__ #define __ARM_COMPUTE_NEGEMMINTERLEAVEDPREPAREBWRAPPERKERNEL_H__ #include "arm_compute/core/Helpers.h" #include "arm_compute/core/ITensor.h" #include "arm_compute/core/NEON/INEKernel.h" #include "arm_compute/core/NEON/kernels/assembly/Helpers.h" #include "arm_compute/core/NEON/kernels/assembly/INEGEMMWrapperKernel.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" namespace arm_compute { /** Unit of work for @ref NEGEMMInterleavedPrepareBWrapperKernel to process */ struct PrepareBWorkload { /** Constructor * * @param[in] offset_b Offset from the start of b's allocation * @param[in] offset_transformed_b Offset from the start of transformed_b's allocation. * @param[in] x0 First value to process along the X dimension (N). * @param[in] xmax Last value to process along the X dimension (N). * @param[in] k0 First value to process along the K dimension. * @param[in] kmax Last value to process along the K dimension. */ PrepareBWorkload(unsigned int offset_b, unsigned int offset_transformed_b, unsigned int x0, unsigned int xmax, unsigned int k0, unsigned int kmax) : _offset_b(offset_b), _offset_transformed_b(offset_transformed_b), _x0(x0), _xmax(xmax), _k0(k0), _kmax(kmax) { } unsigned int _offset_b; /**< Offset from the start of b's allocation.*/ unsigned int _offset_transformed_b; /**< Offset from the start of transformed_b's allocation.*/ unsigned int _x0; /**< First value to process along the X dimension (N). */ unsigned int _xmax; /**< Last value to process along the X dimension (N). */ unsigned int _k0; /**< First value to process along the K dimension. */ unsigned int _kmax; /**< Last value to process along the K dimension. */ }; namespace detail { // Call the lambda function for each workload generated by the passed window. template void for_each_element_in_window(const Window &window, const ITensor *b, ITensor *transformed_b, unsigned int N, unsigned int K, Lambda &&lambda) { unsigned int wl_index = 0; unsigned int num_buffers = 0, reshaped_block_size = 0; if(use_buffer_manager) { num_buffers = transformed_b->info()->tensor_shape()[1]; reshaped_block_size = transformed_b->info()->strides_in_bytes().y(); } unsigned int offset_transformed_b = transformed_b->info()->offset_first_element_in_bytes(); execute_window_loop(window, [&](const Coordinates & coordinates) { const unsigned int x0 = coordinates.x(); const unsigned int k0 = coordinates.y(); const unsigned int multi = coordinates.z(); const unsigned int offset_b = b->info()->offset_element_in_bytes(Coordinates(0, 0, multi)); const unsigned int xmax = std::min(x0 + window.x().step(), N); const unsigned int kmax = std::min(k0 + window.y().step(), K); /* Figure out the size of each block. */ unsigned int x_size = (xmax - x0); unsigned int k_size = (kmax - k0); /* Round sizes up as needed. */ x_size = ceil_to_multiple(x_size, strategy::out_width()); k_size = ceil_to_multiple(k_size, strategy::k_unroll()); lambda(PrepareBWorkload(offset_b, offset_transformed_b, x0, xmax, k0, kmax)); //Each workload represents one block: if(use_buffer_manager) { // Rotate through the BufferManager's buffers: wl_index++; offset_transformed_b = (wl_index % num_buffers) * reshaped_block_size; } else { offset_transformed_b += (x_size * k_size * sizeof(typename strategy::operand_type)); } }); } // Calculate the size of transformed_b: template unsigned int get_B_pretransposed_array_size(unsigned int N, unsigned int K, const BlockSizes &bs, unsigned int multis) { // How many full blocks do N / K contain ? size_t num_full_k = K / bs.k_block; size_t num_full_x = N / bs.x_block; ARM_COMPUTE_ERROR_ON(bs.x_block % strategy::out_width() != 0); ARM_COMPUTE_ERROR_ON(bs.k_block % strategy::k_unroll() != 0); size_t normal_x_size = bs.x_block; size_t normal_k_size = bs.k_block; // Round up the leftovers to be a multiple of the strategy processing size: size_t left_over_x_size = ceil_to_multiple(N % bs.x_block, strategy::out_width()); size_t left_over_k_size = ceil_to_multiple(K % bs.k_block, strategy::k_unroll()); // Calculate the total size of the buffer: size_t total = num_full_k * normal_k_size * (num_full_x * normal_x_size + left_over_x_size); total += left_over_k_size * (left_over_x_size + num_full_x * normal_x_size); total *= multis; return total; } } // namespace detail /** Common interface for the templated wrappers around the B reshape NEON assembly implementations */ class NEGEMMInterleavedPrepareBWrapperKernel : public INEKernel { public: /** Transform the block at the given coordinates * * @param[in] wl Workload to process. * @param[in] info Information about the current thread. */ virtual void transform(const PrepareBWorkload &wl, const ThreadInfo &info) = 0; /** Generate an array of workloads * * @param[out] workloads Container to store the generated workloads. */ virtual void create_workloads(std::vector &workloads) = 0; /** Return the block_sizes used to resape B * * The same block sizes must be used to reshape A and for the matrix multiplication * * @return The block sizes used to reshape B. */ virtual BlockSizes block_sizes() const = 0; // Inherited methods overridden: const char *name() const override { return "NEGEMMInterleavedPrepareBWrapperKernel"; } bool is_parallelisable() const override { return false; // Can't run on arbitrary windows but can be parallelised using an array of workloads } }; /** Equivalent to arm_gemm::GemmInterleaved's strategy::transforms::PrepareB() but using Compute Library types. */ template class NEGEMMInterleavedPrepareBWrapperKernelTemplate : public NEGEMMInterleavedPrepareBWrapperKernel { public: /** Configure the reshape B routine. * * @param[in] b Input matrix B. * @param[out] transformed_b Reshaped matrix B. * @param[in] transpose_b Also transpose B ? * @param[in] ci CPU information * @param[in] params M, N, K sizes. */ void configure(const ITensor *b, ITensor *transformed_b, bool transpose_b, const CPUInfo &ci, const INEGEMMWrapperKernel::Params ¶ms) { const unsigned int multis = b->info()->tensor_shape().z(); _Nsize = b->info()->tensor_shape().x(); _Ksize = b->info()->tensor_shape().y(); _b = b; _transformed_b = transformed_b; _transpose_b = transpose_b; _block_sizes = calculate_block_sizes(ci, params.M, params.N, params.K); auto_init_if_empty(*transformed_b->info(), b->info()->clone()->set_tensor_shape(TensorShape{ detail::get_B_pretransposed_array_size(_Nsize, _Ksize, _block_sizes, multis) })); Window window; window.set(Window::DimX, Window::Dimension(0, ceil_to_multiple(_Nsize, _block_sizes.x_block), _block_sizes.x_block)); window.set(Window::DimY, Window::Dimension(0, ceil_to_multiple(_Ksize, _block_sizes.k_block), _block_sizes.k_block)); window.set(Window::DimZ, Window::Dimension(0, multis)); INEKernel::configure(window); } // Inherited methods overridden: void transform(const PrepareBWorkload &wl, const ThreadInfo &info) override { strategy strat(info.cpu_info); strat.transforms.PrepareB(reinterpret_cast(_transformed_b->buffer() + wl._offset_transformed_b), reinterpret_cast(_b->buffer() + wl._offset_b), _b->info()->strides_in_bytes().y() / sizeof(typename strategy::operand_type), wl._x0, wl._xmax, wl._k0, wl._kmax, _transpose_b); } void create_workloads(std::vector &workloads) override { detail::for_each_element_in_window(window(), _b, _transformed_b, _Nsize, _Ksize, [&workloads](PrepareBWorkload && wl) { workloads.push_back(std::move(wl)); }); } void run(const Window &window, const ThreadInfo &info) override { ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(window, INEKernel::window()); detail::for_each_element_in_window(window, _b, _transformed_b, _Nsize, _Ksize, [&](PrepareBWorkload && wl) { this->transform(wl, info); }); } BlockSizes block_sizes() const override { return _block_sizes; } private: const ITensor *_b { nullptr }; ITensor *_transformed_b{ nullptr }; unsigned int _Nsize{ 0 }; unsigned int _Ksize{ 0 }; bool _transpose_b{ false }; BlockSizes _block_sizes{}; }; } // namespace arm_compute #endif /* __ARM_COMPUTE_NEGEMMINTERLEAVEDPREPAREBWRAPPERKERNEL_H__ */