/* * 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_NEGEMMINTERLEAVEDMATRIXMULTIPLYWRAPPER_H__ #define __ARM_COMPUTE_NEGEMMINTERLEAVEDMATRIXMULTIPLYWRAPPER_H__ #include "arm_compute/core/NEON/kernels/assembly/Helpers.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/ITensor.h" #include "arm_compute/core/NEON/kernels/assembly/INEGEMMWrapperKernel.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" #include "arm_compute/core/WindowIterator.h" namespace arm_compute { class ITensor; /** Unit of work for @ref NEGEMMInterleavedMatrixMultiplyWrapper to process */ struct MatrixMultiplyWorkload { /** Constructor * * @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. * @param[in] multi Multi index. * @param[in] kern_k Number of elements along K actually processed by the kernel. * @param[in] bblocks Number of x_block processed by the kernel. */ MatrixMultiplyWorkload(unsigned int offset_transformed_b, unsigned int x0, unsigned int xmax, unsigned int k0, unsigned int kmax, unsigned int multi, int kern_k, int bblocks) : _offset_transformed_b(offset_transformed_b), _x0(x0), _xmax(xmax), _k0(k0), _kmax(kmax), _multi(multi), _kern_k(kern_k), _bblocks(bblocks) { } 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. */ unsigned int _multi; /**< Multi index. */ int _kern_k; /**< Number of elements along K actually processed by the kernel. */ int _bblocks; /**< Number of x_block processed by the kernel. */ }; /** Common interface for the templated wrappers around the matrix multiply NEON assembly implementations */ class NEGEMMInterleavedMatrixMultiplyWrapper { public: /** Transform the block at the given coordinates * * @param[in] wl Workload to process. * @param[in] info Information about the current thread. * @param[in] batch_window Window containing iteration information for the M and batch dimensions. * @param[in] start_offset Offset relative to the beginning of batch_window to start the processing from. * @param[in] end_offset Offset relative to the beginning of batch_window to stop the processing. */ virtual void transform(const MatrixMultiplyWorkload &wl, const ThreadInfo &info, const Window &batch_window, const Coordinates &start_offset, const Coordinates &end_offset) = 0; /** Generate an array of workloads * * @param[out] workloads Container to store the generated workloads. */ virtual void create_workloads(std::vector &workloads) = 0; /** Default destructor */ virtual ~NEGEMMInterleavedMatrixMultiplyWrapper() = default; }; /** Equivalent to arm_gemm::GemmInterleaved's strategy::kernel() but using Compute Library types. */ template class NEGEMMInterleavedMatrixMultiplyWrapperTemplate : public NEGEMMInterleavedMatrixMultiplyWrapper { public: /** Configure the matrix multiplication: C = alpha * A * B + beta * C * * @param[in] prepared_a Already reshaped matrix A. * @param[in] transformed_b Already reshaped matrix B. * @param[out] tmp_c Temporary buffer to be used to store intermediate results. * @param[in,out] c Result matrix C. * @param[in] block_walker Window containing iteration information for the M and batch dimensions. * @param[in] block_sizes Block sizes to use for the matrix multiplication (A & B must have been reshaped using these same block sizes). * @param[in] params M, N, K sizes. * @param[in] gemm_info GEMM meta-data * @param[in] alpha Alpha value * @param[in] beta Beta value * @param[in] max_num_threads Maximum number of threads that might be used for the calculations. */ void configure(const ITensor *prepared_a, const ITensor *transformed_b, ITensor *tmp_c, ITensor *c, const Window &block_walker, const BlockSizes &block_sizes, const INEGEMMWrapperKernel::Params ¶ms, const GEMMInfo &gemm_info, float alpha, float beta, unsigned int max_num_threads) { _prepared_a = prepared_a; _transformed_b = transformed_b; _tmp_c = tmp_c; _c = c; _block_walker = block_walker; _block_sizes = block_sizes; _params = params; _b_is_pretransposed = gemm_info.pretranpose_B(); _reinterpret_c_as_3d = gemm_info.depth_output_gemm3d() != 0; _alpha = alpha; _beta = beta; auto_init_if_empty(*_tmp_c->info(), c->info()->clone()->set_tensor_shape(TensorShape{ _block_sizes.x_block * strategy::out_height(), max_num_threads })); } // Inherited methods overridden: void transform(const MatrixMultiplyWorkload &wl, const ThreadInfo &info, const Window &batch_window, const Coordinates &start_offset, const Coordinates &end_offset) override { strategy strat(info.cpu_info); TensorAccessor prepared_a(*_prepared_a); TensorAccessor transformed_b(*_transformed_b); TensorAccessor c(*_c); TensorAccessor tmp_c(*_tmp_c); // Handle 3d output re-interpretation if(_reinterpret_c_as_3d) { Strides c_strides_as_3d = _c->info()->strides_in_bytes(); c_strides_as_3d.remove(Window::DimZ); c.set_strides(c_strides_as_3d); } int prev_batch = -1; typename strategy::operand_type *a_ptr = nullptr; auto window_iterator = arm_compute::create_window_iterator(batch_window, start_offset, end_offset, [&](const Coordinates & id) { const unsigned int y = id.x(); const unsigned int batch = id.y(); const unsigned int ymax = std::min(_params.M, y + strategy::out_height()); // If it's the first block of a new batch then reset the pointer to A. if(prev_batch != static_cast(batch)) { const unsigned int first_m = id.x(); a_ptr = prepared_a(0, first_m, batch); prev_batch = batch; } // Call matrix multiply assembly routine to process the block: strat.kernel(a_ptr, transformed_b(wl._offset_transformed_b), tmp_c(0, info.thread_id), 1, wl._bblocks, wl._kern_k); a_ptr += strategy::out_height() * wl._kern_k; // Merge the result with the other blocks' results: strat.transforms.Merge(c(0, 0, batch, wl._multi), tmp_c(0, info.thread_id), c.stride(1), y, ymax, wl._x0, wl._xmax, _alpha, (wl._k0 == 0 ? _beta : static_cast(1))); }); auto on_new_row_size = [&](unsigned int, unsigned int) { //Nothing to do }; window_iterator.iterate_2D(on_new_row_size); } void create_workloads(std::vector &workloads) override { unsigned int offset_transformed_b = 0; unsigned int wl_index = 0; unsigned int num_buffers = 0, reshaped_block_size = 0; if(!_b_is_pretransposed) { num_buffers = _transformed_b->info()->tensor_shape()[1]; reshaped_block_size = _transformed_b->info()->tensor_shape()[0]; } execute_window_loop(_block_walker, [&](const Coordinates & id) { const unsigned int x0 = id.x(); const unsigned int k0 = id.y(); const unsigned int multi = id.z(); const unsigned int xmax = std::min(x0 + _block_walker.x().step(), _params.N); const unsigned int kmax = std::min(k0 + _block_walker.y().step(), _params.K); // Figure out how many "K" the kernel will actually process. const int kern_k = ceil_to_multiple(kmax - k0, strategy::k_unroll()); const int bblocks = DIV_CEIL(xmax - x0, strategy::out_width()); workloads.push_back(MatrixMultiplyWorkload(offset_transformed_b, x0, xmax, k0, kmax, multi, kern_k, bblocks)); if(_b_is_pretransposed) { offset_transformed_b += bblocks * strategy::out_width() * kern_k; } else { // Rotate through the BufferManager's buffers: wl_index++; offset_transformed_b = (wl_index % num_buffers) * reshaped_block_size; } }); } private: const ITensor *_prepared_a { nullptr }; const ITensor *_transformed_b{ nullptr }; ITensor *_tmp_c{ nullptr }; ITensor *_c{ nullptr }; unsigned int _Nsize{ 0 }; unsigned int _Ksize{ 0 }; bool _transpose_b{ false }; BlockSizes _block_sizes{}; INEGEMMWrapperKernel::Params _params{}; Window _block_walker{}; bool _b_is_pretransposed{ false }; bool _reinterpret_c_as_3d{ false }; typename strategy::result_type _alpha{}; typename strategy::result_type _beta{}; }; } // namespace arm_compute #endif /* __ARM_COMPUTE_NEGEMMINTERLEAVEDMATRIXMULTIPLYWRAPPER_H__ */