diff options
Diffstat (limited to 'src/cpu/operators/CpuGemm.cpp')
| -rw-r--r-- | src/cpu/operators/CpuGemm.cpp | 198 |
1 files changed, 124 insertions, 74 deletions
diff --git a/src/cpu/operators/CpuGemm.cpp b/src/cpu/operators/CpuGemm.cpp index 34b845928d..8da166dbef 100644 --- a/src/cpu/operators/CpuGemm.cpp +++ b/src/cpu/operators/CpuGemm.cpp @@ -24,9 +24,10 @@ #include "src/cpu/operators/CpuGemm.h" #include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Validate.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" @@ -57,17 +58,25 @@ cpu::AsmGemmInfo init_assembly_metadata(const GEMMInfo &info) } } // namespace -void CpuGemm::configure(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, ITensorInfo *d, float alpha, float beta, const GEMMInfo &gemm_info) +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; - 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. + const cpu::AsmGemmInfo asm_info = init_assembly_metadata(gemm_info); + const bool is_c_bias = beta == 1 && c != nullptr; + 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; @@ -76,9 +85,12 @@ void CpuGemm::configure(const ITensorInfo *a, const ITensorInfo *b, const ITenso _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()))); + _run_activation = + gemm_info.activation_info().enabled() && + (!run_optimised || + (run_optimised && !cpu::CpuGemmAssemblyDispatch::is_activation_supported(gemm_info.activation_info()))); - if(run_optimised) + if (run_optimised) { const ITensorInfo *c_to_use = is_c_bias ? c : nullptr; _asm_glue = std::make_unique<cpu::CpuGemmAssemblyDispatch>(); @@ -90,10 +102,11 @@ void CpuGemm::configure(const ITensorInfo *a, const ITensorInfo *b, const ITenso _aux_mem[Pretraspose] = asm_mem_req[Pretraspose]; // Scale product by alpha - if(_run_alpha_scale) + 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)); + _alpha_scale_func->configure( + d, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, alpha, 0.f)); } } else @@ -104,7 +117,7 @@ void CpuGemm::configure(const ITensorInfo *a, const ITensorInfo *b, const ITenso _mm_kernel = std::make_unique<cpu::kernels::CpuGemmMatrixMultiplyKernel>(); // Select between GEMV and GEMM - if(_run_vector_matrix_multiplication) + if (_run_vector_matrix_multiplication) { // Configure the matrix multiply kernel _mm_kernel->configure(a, b, gemm_output_to_use, alpha, false); @@ -118,41 +131,50 @@ void CpuGemm::configure(const ITensorInfo *a, const ITensorInfo *b, const ITenso // 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()); + _aux_mem[InterleavedLHS] = + MemoryInfo(offset_int_vec(InterleavedLHS), MemoryLifetime::Temporary, _tmp_a.total_size()); // Configure transpose kernel _transpose_kernel = std::make_unique<cpu::kernels::CpuGemmTranspose1xWKernel>(); _transpose_kernel->configure(b, &_tmp_b); - _aux_mem[TransposedRHS] = MemoryInfo(offset_int_vec(TransposedRHS), MemoryLifetime::Persistent, _tmp_b.total_size()); + _aux_mem[TransposedRHS] = + MemoryInfo(offset_int_vec(TransposedRHS), MemoryLifetime::Persistent, _tmp_b.total_size()); // Configure matrix multiplication kernel _mm_kernel->configure(&_tmp_a, &_tmp_b, gemm_output_to_use, alpha, true, GEMMReshapeInfo(m, n, k)); } - if(_run_bias_addition) + 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()); + _aux_mem[TempResult] = + MemoryInfo(offset_int_vec(TempResult), MemoryLifetime::Temporary, _tmp_d.total_size()); } } // Configure matrix addition kernel - if(_run_addition) + if (_run_addition) { _ma_kernel = std::make_unique<cpu::kernels::CpuGemmMatrixAdditionKernel>(); _ma_kernel->configure(c, d, beta); } // Configure activation - if(_run_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) +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); const bool is_c_bias = beta == 1 && c != nullptr; @@ -162,7 +184,7 @@ Status CpuGemm::validate(const ITensorInfo *a, const ITensorInfo *b, const ITens 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())) + 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, DataType::BFLOAT16); @@ -174,46 +196,54 @@ Status CpuGemm::validate(const ITensorInfo *a, const ITensorInfo *b, const ITens 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->dimension(1) && block_by > 1) + if (a->dimension(0) != b->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()); + 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->dimension(1) = kernel_area * input_channel // a->dimension(0) = b->dimension(1) + kernel_area * input_pad_right const size_t input_pad_right = (dim0_sz - b->dimension(1)) % block_by; const size_t kernel_area = (dim0_sz - b->dimension(1)) / input_pad_right; - ARM_COMPUTE_RETURN_ERROR_ON_MSG((dim0_sz - kernel_area * input_pad_right) != b->dimension(1), "The product AB is defined only if A number of columns and B number of rows are related"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + (dim0_sz - kernel_area * input_pad_right) != b->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->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( + a->dimension(0) != b->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) + if (a->data_type() != DataType::BFLOAT16) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, d); } - if(run_addition) + 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->dimension(0) != c->dimension(0), "The C matrix must have the same number of columns as the matrix B"); + 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->dimension(0) != c->dimension(0), + "The C matrix must have the same number of columns as the matrix B"); } - if(d->total_size() != 0) + 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->dimension(0) != d->dimension(0)); - if(gemm_info.depth_output_gemm3d() != 0) + if (gemm_info.depth_output_gemm3d() != 0) { - if(gemm_info.reinterpret_input_as_3d()) + 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)); @@ -230,15 +260,19 @@ Status CpuGemm::validate(const ITensorInfo *a, const ITensorInfo *b, const ITens } // Check if we need to run the optimized assembly kernel - cpu::AsmGemmInfo asm_info = init_assembly_metadata(gemm_info); - 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) + cpu::AsmGemmInfo asm_info = init_assembly_metadata(gemm_info); + 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"); + 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; @@ -254,7 +288,8 @@ Status CpuGemm::validate(const ITensorInfo *a, const ITensorInfo *b, const ITens 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 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; @@ -263,39 +298,44 @@ Status CpuGemm::validate(const ITensorInfo *a, const ITensorInfo *b, const ITens TensorInfo tmp_b_info{}; TensorInfo tmp_output_info = *d->clone(); - if(run_interleave_transpose) + 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()))); + 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->clone()->set_tensor_shape(compute_transpose1xW_with_element_size_shape(*b, mult_transpose1xW_width))); + auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_transpose1xW_with_element_size_shape( + *b, mult_transpose1xW_width))); ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmTranspose1xWKernel::validate(b, &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)); + 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) + 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) + 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()) + if (activation.enabled()) { ARM_COMPUTE_RETURN_ON_ERROR(cpu::CpuActivation::validate(d, nullptr, activation)); } @@ -312,15 +352,15 @@ void CpuGemm::run(ITensorPack &tensors) auto c = tensors.get_const_tensor(ACL_SRC_2); auto d = tensors.get_tensor(ACL_DST); - if(_asm_glue && _asm_glue->is_configured()) + 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) + if (_run_alpha_scale) { - ITensorPack pack{ { ACL_SRC, d }, { ACL_DST, d } }; + ITensorPack pack{{ACL_SRC, d}, {ACL_DST, d}}; _alpha_scale_func->run(pack); } } @@ -330,18 +370,20 @@ void CpuGemm::run(ITensorPack &tensors) CpuAuxTensorHandler transposed_b(offset_int_vec(TransposedRHS), _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_vector_matrix_multiplication) + ITensorPack mm_pack{{ACL_SRC_0, a}, {ACL_SRC_1, b}, {ACL_DST, (_run_bias_addition) ? temp_d.get() : d}}; + if (!_run_vector_matrix_multiplication) { // 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); + 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); - if(!_reshape_b_only_on_first_run) + if (!_reshape_b_only_on_first_run) { // Run transpose kernel - ITensorPack transpose_pack{ { ACL_SRC, b }, { ACL_DST, transposed_b.get() } }; - NEScheduler::get().schedule_op(_transpose_kernel.get(), Window::DimY, _transpose_kernel->window(), transpose_pack); + ITensorPack transpose_pack{{ACL_SRC, b}, {ACL_DST, transposed_b.get()}}; + NEScheduler::get().schedule_op(_transpose_kernel.get(), Window::DimY, _transpose_kernel->window(), + transpose_pack); } // Use reshaped matrices @@ -349,48 +391,52 @@ void CpuGemm::run(ITensorPack &tensors) mm_pack.add_const_tensor(ACL_SRC_1, transposed_b.get()); } - NEScheduler::get().schedule_op(_mm_kernel.get(), _run_vector_matrix_multiplication ? Window::DimX : Window::DimY, _mm_kernel->window(), mm_pack); + 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) + if (_run_bias_addition) { - ITensorPack pack{ { ACL_SRC_0, temp_d.get() }, { ACL_SRC_1, c }, { ACL_DST, d } }; + 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) + if (_run_addition) { - ITensorPack c_add_pack{ { ACL_SRC, c }, { ACL_DST, d } }; + 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) + if (_run_activation) { - ITensorPack pack{ { ACL_SRC, d }, { ACL_DST, d } }; + ITensorPack pack{{ACL_SRC, d}, {ACL_DST, d}}; _activation_func->run(pack); } } void CpuGemm::prepare(ITensorPack &tensors) { - if(!_is_prepared) + if (!_is_prepared) { - if(_asm_glue && _asm_glue->is_configured()) + if (_asm_glue && _asm_glue->is_configured()) { _asm_glue->prepare(tensors); } - else if(_reshape_b_only_on_first_run && !_run_vector_matrix_multiplication) + else if (_reshape_b_only_on_first_run && !_run_vector_matrix_multiplication) { - const ITensor *b = tensors.get_const_tensor(ACL_SRC_1); - ITensor *b_aux = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(TransposedRHS))); + const ITensor *b = tensors.get_const_tensor(ACL_SRC_1); + ITensor *b_aux = + utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(TransposedRHS))); ARM_COMPUTE_ERROR_ON_NULLPTR(b, b_aux); CpuAuxTensorHandler transposed_b(_tmp_b, *b_aux); - ITensorPack transpose_pack{ { ACL_SRC, b }, { ACL_DST, transposed_b.get() } }; - NEScheduler::get().schedule_op(_transpose_kernel.get(), Window::DimY, _transpose_kernel->window(), transpose_pack); + ITensorPack transpose_pack{{ACL_SRC, b}, {ACL_DST, transposed_b.get()}}; + NEScheduler::get().schedule_op(_transpose_kernel.get(), Window::DimY, _transpose_kernel->window(), + transpose_pack); } _is_prepared = true; } @@ -401,8 +447,12 @@ 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) +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); |