| /* |
| * Copyright (c) 2021-2024 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/operators/CpuGemm.h" |
| |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| |
| #include "src/common/utils/Log.h" |
| #include "src/core/CPP/Validate.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/MemoryHelpers.h" |
| #include "src/cpu/utils/CpuAuxTensorHandler.h" |
| |
| using namespace arm_compute::experimental; |
| using namespace arm_compute::misc::shape_calculator; |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| namespace |
| { |
| cpu::AsmGemmInfo init_assembly_metadata(const GEMMInfo &info) |
| { |
| cpu::AsmGemmInfo asm_info; |
| asm_info.method = cpu::AsmConvMethod::Im2Col; |
| asm_info.reinterpret_input_as_3d = info.reinterpret_input_as_3d(); |
| asm_info.depth_output_gemm3d = info.depth_output_gemm3d(); |
| asm_info.activation_info = info.activation_info(); |
| asm_info.fast_mode = info.fast_math(); |
| asm_info.fixed_format = info.fixed_format(); |
| asm_info.weight_format = info.weight_format(); |
| asm_info.accumulate = info.accumulate(); |
| asm_info.transpose_b = |
| info.pretranspose_B(); // The "pretranspose_B" flag here is not the same as the pretranspose_B_array method. The flag here signals to pretranspose_B_array method if we want to perform additional transpose on B before the pretranspose_B_array method |
| |
| return asm_info; |
| } |
| } // namespace |
| |
| void CpuGemm::configure(const ITensorInfo *a, |
| const ITensorInfo *b, |
| const ITensorInfo *c, |
| ITensorInfo *d, |
| float alpha, |
| float beta, |
| const GEMMInfo &gemm_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, d); |
| ARM_COMPUTE_ERROR_THROW_ON(CpuGemm::validate(a, b, c, d, alpha, beta, gemm_info)); |
| ARM_COMPUTE_LOG_PARAMS(a, b, c, d, alpha, beta, gemm_info); |
| |
| const cpu::AsmGemmInfo asm_info = init_assembly_metadata(gemm_info); |
| const bool is_c_bias = beta == 1 && c != nullptr; |
| const bool run_optimised = |
| bool(cpu::CpuGemmAssemblyDispatch::validate(a, b, (is_c_bias) ? c : nullptr, d, asm_info)) && |
| (c == nullptr || beta == 0.f || beta == 1.f) && // Optimized GeMM doesn't support beta coefficient. |
| !(!b->are_values_constant() && |
| b->tensor_shape().z() > 1); // Disable batch matmul as optimized GeMM handles batching differently. |
| |
| // Check if we need to reshape the matrix B only on the first run |
| _is_prepared = false; |
| _reshape_b_only_on_first_run = b->are_values_constant(); |
| _run_vector_matrix_multiplication = a->dimension(1) < 2; |
| _run_alpha_scale = alpha != 1.f; |
| _run_bias_addition = is_c_bias; |
| _run_addition = beta != 0 && beta != 1 && c != nullptr; |
| _run_activation = |
| gemm_info.activation_info().enabled() && |
| (!run_optimised || |
| (run_optimised && !cpu::CpuGemmAssemblyDispatch::is_activation_supported(gemm_info.activation_info()))); |
| |
| if (run_optimised) |
| { |
| _run_interleave_transpose = false; |
| const ITensorInfo *c_to_use = is_c_bias ? c : nullptr; |
| _asm_glue = std::make_unique<cpu::CpuGemmAssemblyDispatch>(); |
| _asm_glue->configure(a, b, c_to_use, d, asm_info); |
| ARM_COMPUTE_ERROR_ON(!_asm_glue->is_configured()); |
| |
| const auto asm_mem_req = _asm_glue->workspace(); |
| for (unsigned int slot = 0; slot < asm_mem_req.size(); ++slot) |
| { |
| _aux_mem[slot] = asm_mem_req[slot]; |
| } |
| |
| // Scale product by alpha |
| if (_run_alpha_scale) |
| { |
| _alpha_scale_func = std::make_unique<cpu::CpuActivation>(); |
| _alpha_scale_func->configure( |
| d, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, alpha, 0.f)); |
| } |
| } |
| else |
| { |
| _run_interleave_transpose = !_run_vector_matrix_multiplication; |
| // Pick output tensor in case bias addition should be performed |
| ITensorInfo *gemm_output_to_use = (_run_bias_addition) ? &_tmp_d : d; |
| // Pick b tensor in case pretranspose should be performed |
| const ITensorInfo *b_to_use = b; |
| |
| _mm_kernel = std::make_unique<cpu::kernels::CpuGemmMatrixMultiplyKernel>(); |
| |
| // Configure rhs pretranspose |
| if (gemm_info.pretranspose_B()) |
| { |
| _pretranspose_b_func = std::make_unique<CpuTranspose>(); |
| _pretranspose_b_func->configure(b_to_use, &_pretransposed_b); |
| MemoryLifetime lifetime; |
| if (_reshape_b_only_on_first_run) |
| { |
| if (_run_interleave_transpose) |
| { |
| // PreTransposedRHS tensor is only used in prepare(), but is then succeeded by Transposed1xWRHS |
| // So PreTransposedRHS can be freed inside prepare() |
| lifetime = MemoryLifetime::Prepare; |
| } |
| else |
| { |
| // PreTransposedRHS tensor is only used in prepare(), but is the final transformation of rhs |
| // So PreTransposedRHS needs to persist beyond prepare() |
| lifetime = MemoryLifetime::Persistent; |
| } |
| } |
| else |
| { |
| // PreTransposedRHS tensor is always used in run() and doesn't need to persist |
| lifetime = MemoryLifetime::Temporary; |
| } |
| _aux_mem[PreTransposedRHS] = |
| MemoryInfo(offset_int_vec(PreTransposedRHS), lifetime, _pretransposed_b.total_size()); |
| b_to_use = &_pretransposed_b; |
| } |
| |
| // Select between GEMV and GEMM |
| if (_run_vector_matrix_multiplication) |
| { |
| // Configure the matrix multiply kernel |
| _mm_kernel->configure(a, b_to_use, gemm_output_to_use, alpha, false); |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR_ON(!_run_interleave_transpose); |
| // Configure interleave kernel |
| _interleave_kernel = std::make_unique<cpu::kernels::CpuGemmInterleave4x4Kernel>(); |
| _interleave_kernel->configure(a, &_tmp_a); |
| _aux_mem[InterleavedLHS] = |
| MemoryInfo(offset_int_vec(InterleavedLHS), MemoryLifetime::Temporary, _tmp_a.total_size()); |
| |
| // Configure rhs transpose1xw kernel |
| _transpose1xW_b_kernel = std::make_unique<cpu::kernels::CpuGemmTranspose1xWKernel>(); |
| _transpose1xW_b_kernel->configure(b_to_use, &_tmp_b); |
| _aux_mem[Transposed1xWRHS] = |
| MemoryInfo(offset_int_vec(Transposed1xWRHS), MemoryLifetime::Persistent, _tmp_b.total_size()); |
| |
| // Use a and b here instead of _tmp_a and _tmp_b because CpuGemmMatrixMultiplyKernel requires the original m,n,k in case of interleaved a and transposed1xw b |
| const int m = a->dimension(1); |
| const int n = b_to_use->dimension(0); |
| const int k = a->dimension(0); |
| |
| // Configure matrix multiplication kernel |
| _mm_kernel->configure(&_tmp_a, &_tmp_b, gemm_output_to_use, alpha, _run_interleave_transpose, |
| GEMMReshapeInfo(m, n, k)); |
| } |
| |
| if (_run_bias_addition) |
| { |
| _add_bias = std::make_unique<cpu::CpuAdd>(); |
| _add_bias->configure(gemm_output_to_use, c, d, ConvertPolicy::SATURATE); |
| _aux_mem[TempResult] = |
| MemoryInfo(offset_int_vec(TempResult), MemoryLifetime::Temporary, _tmp_d.total_size()); |
| } |
| } |
| |
| // Configure matrix addition kernel |
| if (_run_addition) |
| { |
| _ma_kernel = std::make_unique<cpu::kernels::CpuGemmMatrixAdditionKernel>(); |
| _ma_kernel->configure(c, d, beta); |
| } |
| |
| // Configure activation |
| if (_run_activation) |
| { |
| _activation_func = std::make_unique<cpu::CpuActivation>(); |
| _activation_func->configure(d, nullptr, gemm_info.activation_info()); |
| } |
| } |
| |
| Status CpuGemm::validate(const ITensorInfo *a, |
| const ITensorInfo *b, |
| const ITensorInfo *c, |
| const ITensorInfo *d, |
| float alpha, |
| float beta, |
| const GEMMInfo &gemm_info) |
| { |
| ARM_COMPUTE_UNUSED(alpha); |
| // When using accumulation(in place summation), for now, the only supported values for alpha and beta are 1 respectively 0. |
| // Do the appropriate checks before proceeding. |
| if (gemm_info.accumulate()) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(alpha != 1, "Accumulation is not supported when alpha is different from 1"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| (beta != 0 && c != nullptr), |
| "Accumulation is not supported when beta is different from 0 with a non-null bias matrix c"); |
| } |
| |
| const bool is_c_bias = beta == 1 && c != nullptr; |
| const bool run_addition = c != nullptr && beta != 0 && beta != 1; |
| // Check if we should use the pretransposed_b or original b |
| // TODO: COMPMID-6597 |
| // Note that this check should only apply to the non-optimized path. The reason we brought this at the beginning |
| // instead of only for the fallback path is because of the checks performed below, between here and the run_optimised decision |
| // We should simplify this by |
| // 1. Moving the checks between "fix-start" and "fix-end" into their corresponding ops / kernels (e.g. the weights format checks can and should be moved into CpuGemmAssemblyDispatch) |
| // 2. Moving this b_to_use check back into the non-optimized path |
| TensorInfo pretransposed_b = b->clone()->set_tensor_shape(misc::shape_calculator::compute_transposed_shape(*b)); |
| const ITensorInfo *b_to_use = gemm_info.pretranspose_B() ? &pretransposed_b : b; |
| // TODO: COMPMID-6597 fix-start |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(a); |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_BF16_UNSUPPORTED(a); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::BFLOAT16, DataType::F16, DataType::F32); |
| |
| if (is_fixed_format_fast_math(gemm_info.weight_format())) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(a, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(b_to_use, DataType::BFLOAT16); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b_to_use); |
| } |
| |
| const int block_by = arm_compute::block_by(gemm_info.weight_format()); |
| // test if im2col has changed the dimensions that are needed for padding |
| if (a->dimension(0) != b_to_use->dimension(1) && block_by > 1) |
| { |
| // have to verify bias |
| const size_t dim0_sz = a->dimension(0); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| (dim0_sz % block_by) != 0, |
| ("The matrix A number of columns must be a multiple of block_by=" + std::to_string(block_by)).c_str()); |
| // a->dimension(0) = kernel_area * input_channel + kernel_area * input_pad_right |
| // b_to_use->dimension(1) = kernel_area * input_channel |
| // a->dimension(0) = b_to_use->dimension(1) + kernel_area * input_pad_right |
| const size_t input_pad_right = (dim0_sz - b_to_use->dimension(1)) % block_by; |
| const size_t kernel_area = (dim0_sz - b_to_use->dimension(1)) / input_pad_right; |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| (dim0_sz - kernel_area * input_pad_right) != b_to_use->dimension(1), |
| "The product AB is defined only if A number of columns and B number of rows are related"); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| a->dimension(0) != b_to_use->dimension(1), |
| "The product AB is defined only if the number of columns in A is equal to the number of rows in B"); |
| } |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported"); |
| if (a->data_type() != DataType::BFLOAT16) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, d); |
| } |
| |
| if (run_addition) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(gemm_info.depth_output_gemm3d() != 0); |
| ARM_COMPUTE_RETURN_ERROR_ON(gemm_info.reinterpret_input_as_3d()); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(c, d); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != c->dimension(1), |
| "The C matrix must have the same number of rows as the matrix A"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(b_to_use->dimension(0) != c->dimension(0), |
| "The C matrix must have the same number of columns as the matrix B"); |
| } |
| |
| if (d->total_size() != 0) |
| { |
| // For fixed format we are expecting some kind of blocked format for B/RHS so the dimension won't necessarily match the result matrix any more. |
| ARM_COMPUTE_RETURN_ERROR_ON(!gemm_info.fixed_format() && b_to_use->dimension(0) != d->dimension(0)); |
| if (gemm_info.depth_output_gemm3d() != 0) |
| { |
| if (gemm_info.reinterpret_input_as_3d()) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != d->dimension(1)); |
| ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(2) != d->dimension(2)); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != d->dimension(1) * d->dimension(2)); |
| } |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != d->dimension(1)); |
| } |
| } |
| // TODO: COMPMID-6597 fix-end |
| |
| // Check if we need to run the optimized assembly kernel |
| cpu::AsmGemmInfo asm_info = init_assembly_metadata(gemm_info); |
| |
| // Note we use b instead of b_to_use here because asm_info also captures the pretranspose_b() flag |
| // so we pass the original b to CpuGemmAssemblyDispatch |
| const bool run_optimised = |
| bool(cpu::CpuGemmAssemblyDispatch::validate(a, b, is_c_bias ? c : nullptr, d, asm_info)) && |
| (c == nullptr || beta == 0.f || beta == 1.f) && // Optimized GeMM doesn't support beta coefficient. |
| !(!b->are_values_constant() && |
| b->tensor_shape().z() > 1); // Disable batch matmul as optimized GeMM handles batching differently. |
| |
| if (!run_optimised) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.reinterpret_input_as_3d(), |
| "CpuGemm cannot reinterpret the input tensor as 3D"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.depth_output_gemm3d() != 0, |
| "CpuGemm cannot reinterpret the output tensor as 3D"); |
| |
| // Check if the first input tensor is a vector. |
| const bool run_vector_matrix_multiplication = a->dimension(1) < 2; |
| // Check if we need to reshape the matrix A and matrix B |
| const bool run_interleave_transpose = !run_vector_matrix_multiplication; |
| |
| // Arguments used by GEMMReshapeInfo |
| // If we pass the matrix A and matrix B reshaped to CpuGemmMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to GEMMReshapeInfo |
| // in order to know how the matrices have been reshaped |
| const int m = a->dimension(1); |
| const int n = b_to_use->dimension(0); |
| const int k = a->dimension(0); |
| int mult_transpose1xW_width = 1; |
| int mult_interleave4x4_height = 1; |
| |
| const GEMMReshapeInfo reshape_info = GEMMReshapeInfo( |
| m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, gemm_info.depth_output_gemm3d()); |
| |
| const ITensorInfo *matrix_a_info = a; |
| const ITensorInfo *matrix_b_info = b_to_use; |
| |
| TensorInfo tmp_a_info{}; |
| TensorInfo tmp_b_info{}; |
| TensorInfo tmp_output_info = *d->clone(); |
| |
| if (run_interleave_transpose) |
| { |
| matrix_a_info = &tmp_a_info; |
| matrix_b_info = &tmp_b_info; |
| |
| // Validate interleave kernel |
| auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_interleaved_shape( |
| *a, mult_interleave4x4_height, gemm_info.reinterpret_input_as_3d()))); |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmInterleave4x4Kernel::validate(a, &tmp_a_info)); |
| |
| // Validate transpose kernel |
| auto_init_if_empty(tmp_b_info, |
| b_to_use->clone()->set_tensor_shape( |
| compute_transpose1xW_with_element_size_shape(*b_to_use, mult_transpose1xW_width))); |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmTranspose1xWKernel::validate(b_to_use, &tmp_b_info)); |
| } |
| |
| // Validate matrix multiply |
| auto_init_if_empty(tmp_output_info, |
| matrix_a_info->clone()->set_tensor_shape(compute_mm_shape( |
| *matrix_a_info, *matrix_b_info, run_interleave_transpose, reshape_info))); |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmMatrixMultiplyKernel::validate( |
| matrix_a_info, matrix_b_info, &tmp_output_info, alpha, run_interleave_transpose, reshape_info)); |
| |
| if (is_c_bias) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::CpuAdd::validate(&tmp_output_info, c, d, ConvertPolicy::SATURATE)); |
| } |
| } |
| |
| // Validate matrix addition kernel |
| if (run_addition) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmMatrixAdditionKernel::validate(c, d, beta)); |
| } |
| |
| // Validate activation |
| const ActivationLayerInfo &activation = gemm_info.activation_info(); |
| if (activation.enabled()) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::CpuActivation::validate(d, nullptr, activation)); |
| } |
| |
| return Status{}; |
| } |
| |
| void CpuGemm::run(ITensorPack &tensors) |
| { |
| prepare(tensors); |
| |
| auto a = tensors.get_const_tensor(ACL_SRC_0); |
| auto b = tensors.get_const_tensor(ACL_SRC_1); |
| auto c = tensors.get_const_tensor(ACL_SRC_2); |
| auto d = tensors.get_tensor(ACL_DST); |
| |
| if (_asm_glue && _asm_glue->is_configured()) |
| { |
| // Pass c to asm dispatch only if it's the bias tensor |
| ITensorPack asm_pack = tensors; |
| asm_pack.add_const_tensor(ACL_SRC_2, _run_bias_addition ? c : nullptr); |
| _asm_glue->run(asm_pack); |
| if (_run_alpha_scale) |
| { |
| ITensorPack pack{{ACL_SRC, d}, {ACL_DST, d}}; |
| _alpha_scale_func->run(pack); |
| } |
| } |
| else |
| { |
| CpuAuxTensorHandler interleaved_a(offset_int_vec(InterleavedLHS), _tmp_a, tensors, true); |
| CpuAuxTensorHandler pretransposed_b(offset_int_vec(PreTransposedRHS), _pretransposed_b, tensors); |
| CpuAuxTensorHandler transposed1xw_b(offset_int_vec(Transposed1xWRHS), _tmp_b, tensors, true); |
| CpuAuxTensorHandler temp_d(offset_int_vec(TempResult), _tmp_d, tensors, true); |
| |
| ITensorPack mm_pack{{ACL_SRC_0, a}, {ACL_SRC_1, b}, {ACL_DST, (_run_bias_addition) ? temp_d.get() : d}}; |
| |
| if (_run_interleave_transpose) |
| { |
| // Run interleave kernel |
| ITensorPack interleave_pack{{ACL_SRC, a}, {ACL_DST, interleaved_a.get()}}; |
| NEScheduler::get().schedule_op(_interleave_kernel.get(), Window::DimY, _interleave_kernel->window(), |
| interleave_pack); |
| // Use reshaped matrices |
| mm_pack.add_const_tensor(ACL_SRC_0, interleaved_a.get()); |
| } |
| |
| const ITensor *b_to_use = b; |
| if (_pretranspose_b_func) |
| { |
| if (!_reshape_b_only_on_first_run) |
| { |
| // Run pretranspose kernel |
| ITensorPack pretranspose_pack{{ACL_SRC, b_to_use}, {ACL_DST, pretransposed_b.get()}}; |
| _pretranspose_b_func->run(pretranspose_pack); |
| } |
| b_to_use = pretransposed_b.get(); |
| } |
| if (_run_interleave_transpose) |
| { |
| if (!_reshape_b_only_on_first_run) |
| { |
| // Run transpose1xw kernel |
| ITensorPack transpose_pack{{ACL_SRC, b_to_use}, {ACL_DST, transposed1xw_b.get()}}; |
| NEScheduler::get().schedule_op(_transpose1xW_b_kernel.get(), Window::DimY, |
| _transpose1xW_b_kernel->window(), transpose_pack); |
| } |
| b_to_use = transposed1xw_b.get(); |
| } |
| // Use reshaped matrices |
| mm_pack.add_const_tensor(ACL_SRC_1, b_to_use); |
| |
| NEScheduler::get().schedule_op(_mm_kernel.get(), |
| _run_vector_matrix_multiplication ? Window::DimX : Window::DimY, |
| _mm_kernel->window(), mm_pack); |
| |
| // Run bias addition kernel |
| if (_run_bias_addition) |
| { |
| ITensorPack pack{{ACL_SRC_0, temp_d.get()}, {ACL_SRC_1, c}, {ACL_DST, d}}; |
| _add_bias->run(pack); |
| } |
| } |
| |
| // Run matrix addition kernel |
| if (_run_addition) |
| { |
| ITensorPack c_add_pack{{ACL_SRC, c}, {ACL_DST, d}}; |
| NEScheduler::get().schedule_op(_ma_kernel.get(), Window::DimY, _ma_kernel->window(), c_add_pack); |
| } |
| |
| // Run activation function |
| if (_run_activation) |
| { |
| ITensorPack pack{{ACL_SRC, d}, {ACL_DST, d}}; |
| _activation_func->run(pack); |
| } |
| } |
| |
| void CpuGemm::prepare(ITensorPack &tensors) |
| { |
| if (!_is_prepared) |
| { |
| if (_asm_glue && _asm_glue->is_configured()) |
| { |
| _asm_glue->prepare(tensors); |
| } |
| else if (_reshape_b_only_on_first_run) |
| { |
| const ITensor *b = tensors.get_const_tensor(ACL_SRC_1); |
| const ITensor *b_to_use = b; |
| CpuAuxTensorHandler pretransposed_b( |
| offset_int_vec(PreTransposedRHS), _pretransposed_b, tensors, |
| false /*pack_inject: no need to inject into tensors*/, |
| _pretranspose_b_func == |
| nullptr /*bypass_alloc: no need to allocate if _pretranspose_b_func is not run*/); |
| CpuAuxTensorHandler transposed1xw_b(offset_int_vec(Transposed1xWRHS), _tmp_b, tensors, |
| false /*pack_inject*/, !_run_interleave_transpose /*bypass_alloc*/); |
| |
| if (_pretranspose_b_func) |
| { |
| // Run pretranspose kernel |
| ITensorPack pretranspose_pack{{ACL_SRC, b_to_use}, {ACL_DST, pretransposed_b.get()}}; |
| _pretranspose_b_func->run(pretranspose_pack); |
| b_to_use = pretransposed_b.get(); |
| } |
| if (_run_interleave_transpose) |
| { |
| // Run transpose kernel |
| ITensorPack transpose_pack{{ACL_SRC, b_to_use}, {ACL_DST, transposed1xw_b.get()}}; |
| NEScheduler::get().schedule_op(_transpose1xW_b_kernel.get(), Window::DimY, |
| _transpose1xW_b_kernel->window(), transpose_pack); |
| } |
| } |
| _is_prepared = true; |
| } |
| } |
| |
| experimental::MemoryRequirements CpuGemm::workspace() const |
| { |
| return _aux_mem; |
| } |
| |
| Status CpuGemm::has_opt_impl(arm_compute::WeightFormat &expected_weight_format, |
| const ITensorInfo *a, |
| const ITensorInfo *b, |
| const ITensorInfo *c, |
| const ITensorInfo *d, |
| const GEMMInfo &gemm_info) |
| { |
| const cpu::AsmGemmInfo asm_info = init_assembly_metadata(gemm_info); |
| |
| return CpuGemmAssemblyDispatch::has_opt_impl(expected_weight_format, a, b, c, d, asm_info); |
| } |
| |
| bool CpuGemm::isVarWeightsKernel() const |
| { |
| return _asm_glue && _asm_glue->isVarWeightsKernel(); |
| } |
| } // namespace cpu |
| } // namespace arm_compute |