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Diffstat (limited to 'src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp')
| -rw-r--r-- | src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp | 538 |
1 files changed, 538 insertions, 0 deletions
diff --git a/src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp b/src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp new file mode 100644 index 0000000000..4e934f0f33 --- /dev/null +++ b/src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp @@ -0,0 +1,538 @@ +/* + * Copyright (c) 2017-2021 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/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/CL/OpenCL.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "src/core/AccessWindowStatic.h" +#include "src/core/CL/CLValidate.h" +#include "src/core/helpers/AutoConfiguration.h" +#include "src/core/helpers/WindowHelpers.h" +#include "src/core/utils/helpers/float_ops.h" +#include "support/Cast.h" +#include "support/StringSupport.h" + +namespace arm_compute +{ +namespace opencl +{ +namespace kernels +{ +namespace +{ +using ElementsProcessed = Steps; + +inline Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float beta, + bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src0); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((fp_mixed_precision && (src0->data_type() != DataType::F16)), "Mixed precision floating point is supported only for F16 data"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_interleaved_transposed && reshape_info.reinterpret_input_as_3d(), "The input tensor cannot be reinterpreted as 3D if is_interleaved_transposed is true"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 2 && reshape_info.reinterpret_input_as_3d(), "The src1 tensor cannot have more than 2 dimensions if src0 has to be reinterpreted as 3D"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((reshape_info.reinterpret_input_as_3d() || reshape_info.depth_output_gemm3d() != 0) && (src2 != nullptr) + && (!reshape_info.broadcast_bias()), + "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D"); + + if(!is_interleaved_transposed) + { + ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != src1->dimension(1)); + + if(src2 != nullptr && !(helpers::float_ops::is_zero(beta))) + { + const unsigned int m = reshape_info.reinterpret_input_as_3d() ? src0->dimension(1) * src0->dimension(2) : src0->dimension(1); + const unsigned int n = src1->dimension(0); + const unsigned int src2_dim0 = src2->dimension(0); + const unsigned int src2_dim1 = src2->dimension(1); + + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1); + if(reshape_info.broadcast_bias()) + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix"); + } + } + } + else + { + GEMMRHSMatrixInfo rhs_info; + GEMMLHSMatrixInfo lhs_info; + const auto m = static_cast<unsigned int>(reshape_info.m()); + const auto n = static_cast<unsigned int>(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(); + rhs_info.n0 = max_cl_vector_width / src1->element_size(); + rhs_info.k0 = 1; + rhs_info.h0 = mult_transpose1xW_width; + rhs_info.interleave = false; + rhs_info.transpose = false; + lhs_info.m0 = 4; + lhs_info.k0 = 4; + lhs_info.v0 = mult_interleave4x4_height; + lhs_info.interleave = true; + lhs_info.transpose = true; + + TensorShape tensor_shape0{ src0->tensor_shape() }; + tensor_shape0.set(0, k); + tensor_shape0.set(1, m); + + TensorShape tensor_shape1{ src1->tensor_shape() }; + tensor_shape1.set(0, n); + tensor_shape1.set(1, k); + + const TensorInfo tensor_info0 = src0->clone()->set_tensor_shape(tensor_shape0); + const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1); + + const TensorInfo tensor_info_reshaped0 = src0->clone()->set_tensor_shape(misc::shape_calculator::compute_lhs_reshaped_shape(tensor_info0, lhs_info)); + const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(tensor_info1, rhs_info)); + + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src0, &tensor_info_reshaped0); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1); + + if(src2 != nullptr && !(helpers::float_ops::is_zero(beta))) + { + const unsigned int src2_dim0 = src2->dimension(0); + const unsigned int src2_dim1 = src2->dimension(1); + + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1); + if(reshape_info.broadcast_bias()) + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix"); + } + } + } + + if(dst->total_size() != 0) + { + const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, is_interleaved_transposed, reshape_info)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst); + } + + return Status{}; +} + +inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, + float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, + ElementsProcessed &num_elements_processed) +{ + ARM_COMPUTE_UNUSED(beta); + bool window_changed = false; + Window win{}; + Window win_out{}; + + const DataType data_type = src0->data_type(); + unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0]; + unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1]; + bool reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d(); + bool reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0); + + // In case both input and dst have to be reinterpreted as 3D tensors, + // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. + if(reinterpret_input_as_3d == reinterpret_output_as_3d) + { + reinterpret_input_as_3d = false; + reinterpret_output_as_3d = false; + } + + // dst tensor auto inizialitation if not yet initialized + auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, is_interleaved_transposed, reshape_info))); + + TensorInfo tmp_info(*dst); + + 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 + TensorShape tmp_shape(dst->tensor_shape()); + tmp_shape.collapse(2U, 1U); + tmp_info.set_tensor_shape(tmp_shape); + } + + if(is_interleaved_transposed) + { + // reinterpret_input_as_3d is not supported if is_interleaved_transposed is set + ARM_COMPUTE_ERROR_ON(reshape_info.reinterpret_input_as_3d()); + + // Configure kernel window + num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type); + num_elems_processed_per_iteration_y = 4; + + win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + if(src2 != nullptr) + { + const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x; + + const int bias_processed_per_iteration_y = reshape_info.broadcast_bias() ? 1 : num_elems_processed_per_iteration_y; + + AccessWindowStatic src2_access(src2, 0, 0, + ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x), + ceil_to_multiple(src2->dimension(1), bias_processed_per_iteration_y)); + + window_changed = update_window_and_padding(win, src2_access); // window used by the execute_window_loop + } + } + else // The input tensors have not been reshaped + { + // Special case for 1xN, 2xN, 3xN and 4xN src0 tensor. num_elems_processed_per_iteration_x is set up for the default case. + num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type); + num_elems_processed_per_iteration_y = std::min(static_cast<int>(dst->dimension(1)), 4); + + // Create kernels according to the architecture, data type and input size. + GPUTarget arch_target = get_arch_from_target(gpu_target); + if(arch_target == GPUTarget::BIFROST && data_type == DataType::F32) + { + num_elems_processed_per_iteration_x = (src1->dimension(0) <= 1000 && src0->num_dimensions() == 1) ? 2 : 4; + } + + // Configure window + win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + AccessWindowStatic src0_access(src0, 0, 0, src0->dimension(0), src0->dimension(1)); + AccessWindowStatic src1_access(src1, 0, 0, ceil_to_multiple(src1->dimension(0), num_elems_processed_per_iteration_x), src1->dimension(1)); + AccessWindowStatic dst_access(dst, 0, 0, + dst->dimension(0), + dst->dimension(1)); + + if(src2 != nullptr) + { + const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x; + + AccessWindowStatic src2_access(src2, 0, 0, + ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x), + src2->dimension(1)); + + window_changed = update_window_and_padding(win, src0_access, src1_access, src2_access) || // window used by the execute_window_loop + update_window_and_padding(win_out, dst_access); // window used to update the padding requirements of dst tensor + } + else + { + window_changed = update_window_and_padding(win, src0_access, src1_access) || // window used by the execute_window_loop + update_window_and_padding(win_out, dst_access); // window used to update the padding requirements of dst tensor + } + } + + // Collapse along the Z direction + // This collapse needs to be here in order to tune the Z dimension of LWS + Window collapsed = win; + const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u); + collapsed = win.collapse(win, dimension_to_collapse); + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, collapsed); +} +} // namespace + +ClGemmMatrixMultiplyKernel::ClGemmMatrixMultiplyKernel() +{ + _type = CLKernelType::GEMM; +} + +void ClGemmMatrixMultiplyKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, + float beta, + bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision, const ActivationLayerInfo &activation_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); + + // Perform validate step + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, beta, + is_interleaved_transposed, reshape_info, fp_mixed_precision)); + + auto padding_info = is_interleaved_transposed ? get_padding_info({ src0, src1, dst }) : get_padding_info({ src0, dst }); + + _reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d(); + _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0); + _add_bias = src2 != nullptr; + + // In case both input and dst have to be reinterpreted as 3D tensors, + // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. + if(_reinterpret_input_as_3d == _reinterpret_output_as_3d) + { + _reinterpret_input_as_3d = false; + _reinterpret_output_as_3d = false; + } + + // Check if we need to slide the matrix B + const unsigned int num_dimensions_src0 = _reinterpret_input_as_3d ? src0->num_dimensions() - 1 : src0->num_dimensions(); + + _slide_matrix_b = (src1->num_dimensions() >= num_dimensions_src0); + + const DataType data_type = src0->data_type(); + + // Get target architecture + GPUTarget gpu_target = get_target(); + + ElementsProcessed num_elements_processed{}; + + // Configure kernel window + auto win_config = validate_and_configure_window(src0, src1, src2, dst, beta, is_interleaved_transposed, reshape_info, + gpu_target, num_elements_processed); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + ICLKernel::configure_internal(win_config.second); + + // If _reinterpret_input_as_3d = _reinterpret_output_as_3d = true, both will be turned off (false) + // in which case we will dispatch a batched-GEMM to reduce the complexity of the address calculation within the OpenCL kernel. + // This means that the actual m used by the kernel is given by dst->dimension(1) + const unsigned int internal_m = _reinterpret_output_as_3d ? dst->dimension(1) * dst->dimension(2) : dst->dimension(1); + const unsigned int n = dst->dimension(0); + + const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(1) : src0->dimension(1); + const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(2) : src0->dimension(2); + + const unsigned int m0 = num_elements_processed.y(); + const unsigned int n0 = num_elements_processed.x(); + + // Calculate partial (store instead of load) M0 and partial N0 for the partial blocks at the end of a row/column if any. This is to avoid padding. + const unsigned int partial_store_m0 = internal_m % m0; + const unsigned int partial_store_n0 = n % n0; + + // Create build options + CLBuildOptions build_opts; + + build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha)); + build_opts.add_option_if(src2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta)); + build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA"); + build_opts.add_option_if(reshape_info.broadcast_bias(), "-DBROADCAST_BIAS"); + build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D"); + build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D"); + build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(h_gemm_3d)); + build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(d_gemm_3d)); + build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2))); + build_opts.add_option_if(activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(activation_info.activation()))); + build_opts.add_option_if(activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(activation_info.a())); + build_opts.add_option_if(activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(activation_info.b())); + build_opts.add_option("-DIN1_DIM_X=" + support::cpp11::to_string(src1->dimension(0))); + + const bool is_bifrost = get_arch_from_target(gpu_target) == GPUTarget::BIFROST; + + std::string kernel_name; + if(is_interleaved_transposed) + { + const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width(); + const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height(); + + build_opts.add_option("-DM=" + support::cpp11::to_string(internal_m)); + build_opts.add_option("-DN=" + support::cpp11::to_string(n)); + build_opts.add_option("-DK=" + support::cpp11::to_string(src1->dimension(0) / (n0 * mult_transpose1xW_width))); + build_opts.add_option("-DH0=" + support::cpp11::to_string(mult_transpose1xW_width)); + build_opts.add_option("-DV0=" + support::cpp11::to_string(mult_interleave4x4_height)); + build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0)); + build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0)); + + if(is_data_type_float(data_type) && is_bifrost) + { + kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type)) + "_bifrost"; + } + else + { + kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type)); + if(fp_mixed_precision && data_type == DataType::F16) + { + // currently wider accumulator is only supported for fp16 kernels. + kernel_name += "_acc32"; + } + } + } + else // The input tensors have not been reshaped + { + build_opts.add_option("-DN=" + support::cpp11::to_string(n)); + build_opts.add_option("-DK=" + support::cpp11::to_string(src0->dimension(0))); + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); + build_opts.add_option("-DM0=" + support::cpp11::to_string(m0)); + build_opts.add_option("-DN0=" + support::cpp11::to_string(n0)); + build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0)); + build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0)); + + // Create kernels according to the architecture, data type and input size. + if(is_data_type_float(data_type) && is_bifrost) + { + kernel_name = "gemm_mm_floating_point"; + + if(src0->num_dimensions() != 1) + { + kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost"; + if(fp_mixed_precision && data_type == DataType::F16) + { + // currently wider accumulator is only supported for fp16 kernels. + kernel_name += "_acc32"; + } + } + else if(src1->dimension(0) <= 1000 && data_type == DataType::F32) + { + // The first kernel is optimized for the case of 1000 or less dst elements (e.g. FC8 of AlexNet and VGG-16, and + // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 dst elements (e.g. + // FC6 and FC7 of AlexNet and VGG-16). + kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost_1000"; + } + + // The work-group size equal to the Bifrost quad size has been proved to be optimal for these kernels + // via exhaustive autotuning over a range of representative layer configurations. + set_lws_hint(cl::NDRange(4)); + } + else // (MIDGARD and F32) or (F16) + { + kernel_name = "gemm_mm_floating_point"; + } + } + // Create kernel + _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); + + // Set config_id for enabling LWS tuning + _config_id = "gemm_"; + _config_id += (is_interleaved_transposed ? "reshaped_" : ""); + _config_id += (_add_bias ? "add_bias_" : ""); + _config_id += (reshape_info.broadcast_bias() ? "broadcast_bias_" : ""); + _config_id += (fp_mixed_precision ? "fp_mixed_" : ""); + _config_id += (_reinterpret_input_as_3d ? "3di_" : ""); + _config_id += (_reinterpret_output_as_3d ? "3do_" : ""); + _config_id += lower_string(string_from_data_type(src0->data_type())); + _config_id += "_"; + _config_id += support::cpp11::to_string(dst->dimension(1)); + _config_id += "_"; + _config_id += support::cpp11::to_string(dst->dimension(0)); + _config_id += "_"; + _config_id += support::cpp11::to_string(dst->dimension(2)); + _config_id += "_"; + _config_id += support::cpp11::to_string(dst->dimension(3)); + _config_id += "_"; + _config_id += (is_interleaved_transposed ? support::cpp11::to_string(src1->dimension(0)) : support::cpp11::to_string(src1->dimension(1))); + + ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); +} + +Status ClGemmMatrixMultiplyKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, + bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, bool fp_mixed_precision, const ActivationLayerInfo &activation_info) +{ + // Note: num_elements_processed will be set in validate_and_configure_window() + ElementsProcessed num_elements_processed{}; + ARM_COMPUTE_UNUSED(alpha); + ARM_COMPUTE_UNUSED(activation_info); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, src2, dst, beta, is_interleaved_transposed, reshape_info, fp_mixed_precision)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src0->clone().get(), + src1->clone().get(), + (src2 != nullptr) ? src2->clone().get() : nullptr, + dst->clone().get(), + beta, + is_interleaved_transposed, + reshape_info, + gpu_target, + num_elements_processed) + .first); + + return Status{}; +} + +void ClGemmMatrixMultiplyKernel::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)); + auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); + + ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_ERROR_ON(_add_bias && src2 == nullptr); + + 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); + } + + Window slice = window.first_slice_window_3D(); + Window slice_matrix_b = slice; + + slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1)); + slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1)); + + const unsigned int num_arguments_bias = _add_bias ? num_arguments_per_2D_tensor() + 1 : 0; + + if(_reinterpret_input_as_3d) + { + // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor + const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + num_arguments_bias; + const unsigned int total_cross_plane_pad = src0->info()->padding().top + src0->info()->padding().bottom; + _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad)); + } + + if(_reinterpret_output_as_3d) + { + // Pass bottom paddings to the kernel if the dst has to be reinterpreted as 3D tensor + const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0) + num_arguments_bias; + const unsigned int total_cross_plane_pad = dst->info()->padding().top + dst->info()->padding().bottom; + _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad)); + } + + do + { + Window slice_b = slice; + // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2 + // This scenario can happen when the matrix multiplication is used to perform a convolution operation + if(!_slide_matrix_b) + { + slice_b = slice_matrix_b; + } + + unsigned int idx = 0; + add_2D_tensor_argument(idx, src0, slice); + add_2D_tensor_argument(idx, src1, slice_b); + if(_add_bias) + { + add_2D_tensor_argument(idx, src2, slice); + } + add_2D_tensor_argument(idx, dst, slice); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[2])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[2])); + if(_add_bias) + { + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src2->info()->strides_in_bytes()[2])); + } + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[2])); + enqueue(queue, *this, slice, lws_hint()); + } + while(window.slide_window_slice_3D(slice)); +} +} // namespace kernels +} // namespace opencl +} // namespace arm_compute |