diff options
Diffstat (limited to 'src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel.cpp')
| -rw-r--r-- | src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel.cpp | 214 |
1 files changed, 131 insertions, 83 deletions
diff --git a/src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel.cpp b/src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel.cpp index 792c71da76..030c11d069 100644 --- a/src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel.cpp +++ b/src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel.cpp @@ -23,16 +23,15 @@ */ #include "src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel.h" -#include "arm_compute/core/utils/ActivationFunctionUtils.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/utils/ActivationFunctionUtils.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/StringUtils.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" - #include "support/Cast.h" namespace arm_compute @@ -47,39 +46,51 @@ namespace { using ElementsProcessed = Steps; -Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, const GEMMKernelInfo &gemm_info, - const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, - const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) +Status validate_arguments(const ITensorInfo *src0, + const ITensorInfo *src1, + const ITensorInfo *dst, + const GEMMKernelInfo &gemm_info, + const ITensorInfo *vector_sum_col, + const ITensorInfo *vector_sum_row, + const ITensorInfo *bias, + const ITensorInfo *output_multipliers, + const ITensorInfo *output_shifts) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(!arm_matrix_multiply_supported(CLKernelLibrary::get().get_device()), "The extension cl_arm_matrix_multiply is not supported on the target platform"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(!arm_matrix_multiply_supported(CLKernelLibrary::get().get_device()), + "The extension cl_arm_matrix_multiply is not supported on the target platform"); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, + "The number of dimensions for the LHS matrix must be <= 4"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, + "The number of dimensions for the RHS matrix must be <= 3"); const GEMMRHSMatrixInfo rhs_info = gemm_info.rhs_info; const GEMMLHSMatrixInfo lhs_info = gemm_info.lhs_info; const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage; ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_info.k0 != 4 || lhs_info.k0 != 4, "Only 4 is supported as value for k0"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(lhs_info.m0 == 1 || lhs_info.m0 == 2 || lhs_info.m0 == 4), "Only 1,2,4 are supported for m0"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(rhs_info.n0 == 1 || rhs_info.n0 == 4 || rhs_info.n0 == 8), "Only 1,4,8 are supported for n0"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(lhs_info.m0 == 1 || lhs_info.m0 == 2 || lhs_info.m0 == 4), + "Only 1,2,4 are supported for m0"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(rhs_info.n0 == 1 || rhs_info.n0 == 4 || rhs_info.n0 == 8), + "Only 1,4,8 are supported for n0"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_info.export_to_cl_image, "Export to CLImage not supported for quantized GEMM"); const int m = gemm_info.m; const int n = gemm_info.n; const int k = gemm_info.k; - TensorShape tensor_shape1{ src1->tensor_shape() }; + TensorShape tensor_shape1{src1->tensor_shape()}; tensor_shape1.set(0, n); tensor_shape1.set(1, k); - const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1); - const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info)); + const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1); + const TensorInfo tensor_info_reshaped1 = + src1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info)); ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != static_cast<unsigned int>(k)); - if(gemm_info.reinterpret_input_as_3d) + if (gemm_info.reinterpret_input_as_3d) { ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) * src0->dimension(2) != static_cast<unsigned int>(m)); } @@ -90,11 +101,11 @@ Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, cons ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1); const TensorShape expected_dst_shape = compute_mm_shape(*src0, *src1, gemm_info); - if(dst->total_size() != 0) + if (dst->total_size() != 0) { const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(expected_dst_shape); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst); - if(output_stage.type == GEMMLowpOutputStageType::NONE) + if (output_stage.type == GEMMLowpOutputStageType::NONE) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::S32); } @@ -104,38 +115,41 @@ Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, cons } } - if(bias != nullptr) + if (bias != nullptr) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32); ARM_COMPUTE_RETURN_ERROR_ON(expected_dst_shape[0] != bias->dimension(0)); } - ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN) || (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT), + ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN) || + (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT), "Only GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT is supported"); // Checks performed if the dst stage needs to be fused - if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) + if (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) { // If a_offset == 0, vector_sum_col can be a nullptr - if(gemm_info.a_offset != 0) + if (gemm_info.a_offset != 0) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32); ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != expected_dst_shape[0]); } // If b_offset == 0, vector_sum_row can be a nullptr - if(gemm_info.b_offset != 0) + if (gemm_info.b_offset != 0) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32); // Check if mm result is a 3D reinterpretation - const bool reinterpret_as_3d = expected_dst_shape.num_dimensions() > 1 && expected_dst_shape.y() != vector_sum_row->tensor_shape().x(); + const bool reinterpret_as_3d = + expected_dst_shape.num_dimensions() > 1 && expected_dst_shape.y() != vector_sum_row->tensor_shape().x(); // Validate input - ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (expected_dst_shape[1] * expected_dst_shape[2])); + ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != + (expected_dst_shape[1] * expected_dst_shape[2])); ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != expected_dst_shape[1]); - if(expected_dst_shape.num_dimensions() > 1) + if (expected_dst_shape.num_dimensions() > 1) { const unsigned int dst_batch_idx = reinterpret_as_3d ? 3 : 2; @@ -147,30 +161,32 @@ Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, cons ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != collapsed_dst_shape[dst_batch_idx], "vector_sum_row must have the same number of batches of dst tensor"); - if(gemm_info.a_offset != 0) + if (gemm_info.a_offset != 0) { TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape(); vector_sum_col_shape.collapse_from(1); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1], - "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && + vector_sum_col_shape[1] != vector_sum_row_shape[1], + "vector_sum_col tensor must have the same number of batches of " + "vector_sum_row_shape or the number of batches must be set to 1"); } } } - if(dst->total_size() != 0) + if (dst->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON(output_stage.output_data_type != dst->data_type()); } ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound); - if(output_multipliers != nullptr && output_shifts != nullptr) + if (output_multipliers != nullptr && output_shifts != nullptr) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32); ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_shifts, 1, DataType::S32); ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1); - if(output_stage.is_quantized_per_channel) + if (output_stage.is_quantized_per_channel) { ARM_COMPUTE_RETURN_ERROR_ON(expected_dst_shape[0] != output_shifts->dimension(0)); ARM_COMPUTE_RETURN_ERROR_ON(expected_dst_shape[0] != output_multipliers->dimension(0)); @@ -180,9 +196,16 @@ Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, cons return Status{}; } -std::pair<Status, Window> validate_and_configure_window(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst, const GEMMKernelInfo &gemm_info, - ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, ITensorInfo *bias, - ITensorInfo *output_multipliers, ITensorInfo *output_shifts, ElementsProcessed &num_elements_processed) +std::pair<Status, Window> validate_and_configure_window(const ITensorInfo *src0, + const ITensorInfo *src1, + ITensorInfo *dst, + const GEMMKernelInfo &gemm_info, + ITensorInfo *vector_sum_col, + const ITensorInfo *vector_sum_row, + ITensorInfo *bias, + ITensorInfo *output_multipliers, + ITensorInfo *output_shifts, + ElementsProcessed &num_elements_processed) { const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage; @@ -200,9 +223,10 @@ std::pair<Status, Window> validate_and_configure_window(const ITensorInfo *src0, reinterpret_output_as_3d = false; // dst tensor auto initialization if not yet initialized const TensorShape expected_dst_shape = compute_mm_shape(*src0, *src1, gemm_info); - if(output_stage.type != GEMMLowpOutputStageType::NONE) + if (output_stage.type != GEMMLowpOutputStageType::NONE) { - auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(expected_dst_shape).set_data_type(output_stage.output_data_type)); + auto_init_if_empty( + *dst, src0->clone()->set_tensor_shape(expected_dst_shape).set_data_type(output_stage.output_data_type)); } else { @@ -211,7 +235,7 @@ std::pair<Status, Window> validate_and_configure_window(const ITensorInfo *src0, TensorInfo tmp_info(*dst); - if(reinterpret_output_as_3d) + if (reinterpret_output_as_3d) { // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, // the window needs to be constructed on the 2D collapsed version of the tensor @@ -224,11 +248,12 @@ std::pair<Status, Window> validate_and_configure_window(const ITensorInfo *src0, num_elems_processed_per_iteration_x = 1; num_elems_processed_per_iteration_y = 1; - win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + win = + calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) + if (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) { - if(gemm_info.a_offset != 0) + if (gemm_info.a_offset != 0) { AccessWindowHorizontal vector_sum_col_access(vector_sum_col, 0, num_elems_processed_per_iteration_x); window_changed = window_changed || update_window_and_padding(win, vector_sum_col_access); @@ -236,17 +261,19 @@ std::pair<Status, Window> validate_and_configure_window(const ITensorInfo *src0, // No access window needed for vector_sum_row ARM_COMPUTE_UNUSED(vector_sum_row); - if(bias != nullptr) + if (bias != nullptr) { AccessWindowHorizontal bias_access(bias, 0, num_elems_processed_per_iteration_x); window_changed = window_changed || update_window_and_padding(win, bias_access); } - if(output_multipliers != nullptr && output_stage.is_quantized_per_channel) + if (output_multipliers != nullptr && output_stage.is_quantized_per_channel) { - AccessWindowHorizontal output_multipliers_access(output_multipliers, 0, num_elems_processed_per_iteration_x); + AccessWindowHorizontal output_multipliers_access(output_multipliers, 0, + num_elems_processed_per_iteration_x); AccessWindowHorizontal output_shifts_access(output_shifts, 0, num_elems_processed_per_iteration_x); - window_changed = window_changed || update_window_and_padding(win, output_multipliers_access, output_shifts_access); + window_changed = + window_changed || update_window_and_padding(win, output_multipliers_access, output_shifts_access); } } @@ -278,7 +305,8 @@ std::pair<Status, Window> validate_and_configure_window(const ITensorInfo *src0, collapsed.set(Window::DimX, x_dimension); collapsed.set(Window::DimY, y_dimension); - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + Status err = + (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; return std::make_pair(err, collapsed); } } // namespace @@ -288,15 +316,22 @@ ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::ClGemmLowpMatrixMultiplyResha _type = CLKernelType::GEMM; } -void ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::configure(const CLCompileContext &compile_context, const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst, - const GEMMKernelInfo &gemm_info, - ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, ITensorInfo *bias, - ITensorInfo *output_multipliers, ITensorInfo *output_shifts) +void ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::configure(const CLCompileContext &compile_context, + const ITensorInfo *src0, + const ITensorInfo *src1, + ITensorInfo *dst, + const GEMMKernelInfo &gemm_info, + ITensorInfo *vector_sum_col, + const ITensorInfo *vector_sum_row, + ITensorInfo *bias, + ITensorInfo *output_multipliers, + ITensorInfo *output_shifts) { ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, + output_multipliers, output_shifts)); - auto padding_info = get_padding_info({ src0, src1, dst, vector_sum_row }); + auto padding_info = get_padding_info({src0, src1, dst, vector_sum_row}); const GEMMRHSMatrixInfo rhs_info = gemm_info.rhs_info; const GEMMLHSMatrixInfo lhs_info = gemm_info.lhs_info; const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage; @@ -313,7 +348,8 @@ void ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::configure(const CLCompil ElementsProcessed num_elements_processed{}; // Configure kernel window - auto win_config = validate_and_configure_window(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts, num_elements_processed); + auto win_config = validate_and_configure_window(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, + output_multipliers, output_shifts, num_elements_processed); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); @@ -334,18 +370,19 @@ void ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::configure(const CLCompil build_opts.add_option("-DMMUL_M0=" + support::cpp11::to_string(mmul_m0)); build_opts.add_option("-DMMUL_N0=" + support::cpp11::to_string(mmul_n0)); build_opts.add_option("-DMMUL_K0=" + support::cpp11::to_string(mmul_k0)); - build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation()))); + build_opts.add_option("-DACTIVATION_TYPE=" + + lower_string(string_from_activation_func(gemm_info.activation_info.activation()))); build_opts.add_option("-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a())); build_opts.add_option("-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b())); std::string kernel_name("gemmlowp_mm_reshaped_only_rhs_mmul"); - if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) + if (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) { build_opts.add_option("-DFUSED_OUTPUT_STAGE_FIXED_POINT"); _fuse_output_stage = true; // If a_offset == 0, vector_sum_col can be a nullptr - if(a_offset != 0 && vector_sum_col != nullptr) + if (a_offset != 0 && vector_sum_col != nullptr) { build_opts.add_option("-DA_OFFSET=" + support::cpp11::to_string(a_offset)); build_opts.add_option_if(vector_sum_col->tensor_shape().num_dimensions() > 1, "-DSUM_COL_HAS_BATCHES"); @@ -396,42 +433,54 @@ void ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::configure(const CLCompil ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); } -Status ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, const GEMMKernelInfo &gemm_info, - const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, - const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) +Status ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::validate(const ITensorInfo *src0, + const ITensorInfo *src1, + const ITensorInfo *dst, + const GEMMKernelInfo &gemm_info, + const ITensorInfo *vector_sum_col, + const ITensorInfo *vector_sum_row, + const ITensorInfo *bias, + const ITensorInfo *output_multipliers, + const ITensorInfo *output_shifts) { ElementsProcessed num_elements_processed{}; - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src0->clone().get(), - src1->clone().get(), - dst->clone().get(), - gemm_info, - vector_sum_col != nullptr ? vector_sum_col->clone().get() : nullptr, - vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr, - bias != nullptr ? bias->clone().get() : nullptr, - output_multipliers != nullptr ? output_multipliers->clone().get() : nullptr, - output_shifts != nullptr ? output_shifts->clone().get() : nullptr, - num_elements_processed) - .first); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, + output_multipliers, output_shifts)); + ARM_COMPUTE_RETURN_ON_ERROR( + validate_and_configure_window(src0->clone().get(), src1->clone().get(), dst->clone().get(), gemm_info, + vector_sum_col != nullptr ? vector_sum_col->clone().get() : nullptr, + vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr, + bias != nullptr ? bias->clone().get() : nullptr, + output_multipliers != nullptr ? output_multipliers->clone().get() : nullptr, + output_shifts != nullptr ? output_shifts->clone().get() : nullptr, + num_elements_processed) + .first); return Status{}; } -void ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) +void ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::run_op(ITensorPack &tensors, + const Window &window, + cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); - const auto src0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0)); - const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); - const auto src2 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); - const auto vector_sum_col = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_COL_SUM)); - const auto vector_sum_row = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_ROW_SUM)); - auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); + const auto src0 = + utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0)); + const auto src1 = + utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); + const auto src2 = + utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); + const auto vector_sum_col = + utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_COL_SUM)); + const auto vector_sum_row = + utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_ROW_SUM)); + auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); - if(src1->info()->num_dimensions() < 3) + if (src1->info()->num_dimensions() < 3) { // The stride_z for matrix B must be zero if we do not slice ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0); @@ -449,7 +498,7 @@ void ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::run_op(ITensorPack &tens add_3d_tensor_nhw_argument(idx, src1); // Bias buffer (_add_bias == true) - if(src2 != nullptr) + if (src2 != nullptr) { add_3d_tensor_nhw_argument(idx, src2); } @@ -461,21 +510,20 @@ void ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::run_op(ITensorPack &tens _kernel.setArg<cl_int>(idx++, _n); _kernel.setArg<cl_int>(idx++, _k); - if(_fuse_output_stage) + if (_fuse_output_stage) { - if(vector_sum_col != nullptr) + if (vector_sum_col != nullptr) { add_3d_tensor_nhw_argument(idx, vector_sum_col); } - if(vector_sum_row != nullptr) + if (vector_sum_row != nullptr) { add_3d_tensor_nhw_argument(idx, vector_sum_row); } } enqueue(queue, *this, slice, cl::NDRange(32, 2), false); - } - while(window.slide_window_slice_3D(slice)); + } while (window.slide_window_slice_3D(slice)); } } // namespace kernels } // namespace opencl |