| /* |
| * Copyright (c) 2017-2022 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. |
| */ |
| #include "src/cpu/kernels/CpuGemmMatrixMultiplyKernel.h" |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "src/core/CPP/Validate.h" |
| #include "src/core/common/Registrars.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| #include "src/cpu/kernels/gemm_matrix_mul/list.h" |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| namespace kernels |
| { |
| namespace |
| { |
| static const std::vector<CpuGemmMatrixMultiplyKernel::GemmMatrixMulKernel> available_kernels = |
| { |
| { |
| "neon_fp32_gemm_matrix_mul", |
| [](const DataTypeISASelectorData & data) |
| { |
| return (data.dt == DataType::F32); |
| }, |
| REGISTER_FP32_NEON(neon_fp32_gemm_matrix_mul) |
| }, |
| { |
| "neon_fp16_gemm_matrix_mul", |
| [](const DataTypeISASelectorData & data) |
| { |
| return (data.dt == DataType::F16) && data.isa.fp16; |
| }, |
| REGISTER_FP16_NEON(neon_fp16_gemm_matrix_mul) |
| }, |
| }; |
| |
| inline Status validate_arguments(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info) |
| { |
| ARM_COMPUTE_UNUSED(alpha); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(lhs); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs, dst); |
| |
| if(!is_interleaved) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(lhs->dimension(0) != rhs->dimension(1)); |
| |
| if(dst->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(rhs->dimension(0) != dst->dimension(0)); |
| ARM_COMPUTE_RETURN_ERROR_ON(lhs->dimension(1) != dst->dimension(1)); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst); |
| } |
| } |
| else |
| { |
| const int m = reshape_info.m(); |
| const int n = reshape_info.n(); |
| const int k = reshape_info.k(); |
| const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width(); |
| const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height(); |
| |
| /* Interleave */ |
| TensorShape tensor_shape0{ lhs->tensor_shape() }; |
| tensor_shape0.set(0, k); |
| tensor_shape0.set(1, m); |
| |
| const TensorInfo tensor_info0 = lhs->clone()->set_tensor_shape(tensor_shape0); |
| const TensorInfo tensor_info_reshaped0 = lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_interleaved_shape(tensor_info0, mult_interleave4x4_height)); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(lhs, &tensor_info_reshaped0); |
| |
| if(n != 0) /* Transpose */ |
| { |
| TensorShape tensor_shape1{ rhs->tensor_shape() }; |
| tensor_shape1.set(0, n); |
| tensor_shape1.set(1, k); |
| |
| const TensorInfo tensor_info1 = rhs->clone()->set_tensor_shape(tensor_shape1); |
| const TensorInfo tensor_info_reshaped1 = rhs->clone()->set_tensor_shape(misc::shape_calculator::compute_transpose1xW_with_element_size_shape(tensor_info1, mult_transpose1xW_width)); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(rhs, &tensor_info_reshaped1); |
| } |
| |
| if(dst->total_size() != 0) |
| { |
| if(n != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(dst->dimension(0) != static_cast<size_t>(n)); |
| } |
| ARM_COMPUTE_RETURN_ERROR_ON(dst->dimension(1) != static_cast<size_t>(m)); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst); |
| } |
| } |
| |
| return Status{}; |
| } |
| |
| } // namespace |
| |
| void CpuGemmMatrixMultiplyKernel::configure(const ITensorInfo *lhs, const ITensorInfo *rhs, ITensorInfo *dst, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst); |
| |
| // dst tensor auto inizialitation if not yet initialized |
| TensorShape tensor_shape{ lhs->tensor_shape() }; |
| tensor_shape.set(0, is_interleaved ? reshape_info.n() : rhs->dimension(0)); |
| tensor_shape.set(1, is_interleaved ? reshape_info.m() : lhs->dimension(1)); |
| |
| auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(tensor_shape)); |
| |
| // Perform validate step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(lhs, rhs, dst, alpha, is_interleaved, reshape_info)); |
| |
| _alpha = alpha; |
| |
| // Configure kernel window |
| Window win{}; |
| |
| // Check if the dst tensor is a vector. If so,the kernel runs the vector-matrix multiplication |
| const bool is_dst_vector = (dst->dimension(1) == 1); |
| if(is_dst_vector) |
| { |
| const unsigned int num_elems_processed_per_iteration_x = (lhs->data_type() == DataType::F32) ? 16 : 32; |
| |
| win = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x)); |
| } |
| else |
| { |
| constexpr unsigned int num_elems_processed_per_iteration_x = 8; |
| constexpr unsigned int num_elems_processed_per_iteration_y = 4; |
| |
| win = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); |
| } |
| |
| const auto uk = CpuGemmMatrixMultiplyKernel::get_implementation(DataTypeISASelectorData{ lhs->data_type(), CPUInfo::get().get_isa() }); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(uk); |
| _func = uk->ukernel; |
| |
| ICPPKernel::configure(win); |
| } |
| |
| Status CpuGemmMatrixMultiplyKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, float alpha, bool is_interleaved, |
| const GEMMReshapeInfo &reshape_info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(lhs, rhs, dst, alpha, is_interleaved, reshape_info)); |
| |
| return Status{}; |
| } |
| |
| void CpuGemmMatrixMultiplyKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(tensors.empty()); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr); |
| |
| const ITensor *lhs = tensors.get_const_tensor(TensorType::ACL_SRC_0); |
| const ITensor *rhs = tensors.get_const_tensor(TensorType::ACL_SRC_1); |
| ITensor *dst = tensors.get_tensor(TensorType::ACL_DST); |
| |
| const bool is_dst_vector = (dst->info()->dimension(1) == 1); |
| (*_func)(lhs, rhs, dst, window, info, _alpha, is_dst_vector); |
| } |
| |
| const char *CpuGemmMatrixMultiplyKernel::name() const |
| { |
| return "CpuGemmMatrixMultiplyKernel"; |
| } |
| |
| const std::vector<CpuGemmMatrixMultiplyKernel::GemmMatrixMulKernel> &CpuGemmMatrixMultiplyKernel::get_available_kernels() |
| { |
| return available_kernels; |
| } |
| } // namespace kernels |
| } // namespace cpu |
| } // namespace arm_compute |