/* * Copyright (c) 2023 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/ClMatMulNativeKernel.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/ITensorPack.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/utils/ActivationFunctionUtils.h" #include "arm_compute/core/utils/helpers/AdjustVecSize.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/StringUtils.h" #include "src/common/utils/Log.h" #include "src/core/CL/CLUtils.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "src/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include "src/gpu/cl/kernels/helpers/MatMulKernelHelpers.h" #include "support/Cast.h" #include "support/StringSupport.h" namespace arm_compute { namespace opencl { namespace kernels { namespace { Status validate_matmul_kernel_info(const MatMulKernelInfo &matmul_kernel_info) { const bool adj_lhs = matmul_kernel_info.adj_lhs; const bool adj_rhs = matmul_kernel_info.adj_rhs; const int m0 = matmul_kernel_info.m0; const int n0 = matmul_kernel_info.n0; const int k0 = matmul_kernel_info.k0; // Validate M0 ARM_COMPUTE_RETURN_ERROR_ON_MSG(m0 < 1, "Only positive integers are supported for M0"); if (adj_lhs) { ARM_COMPUTE_RETURN_ERROR_ON_MSG(((m0 & (m0 - 1)) && (m0 != 3)) || (m0 > 16), "Only 1,2,3,4,8,16 are supported for M0 for Lhs transposed"); } // Validate N0 ARM_COMPUTE_RETURN_ERROR_ON_MSG(n0 < 1, "Only positive integers are supported for N0"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(((n0 & (n0 - 1)) && (n0 != 3)) || (n0 > 16), "Only 1,2,3,4,8,16 are supported for N0"); // Validate K0 ARM_COMPUTE_RETURN_ERROR_ON_MSG(k0 < 1, "Only positive integers are supported for K0"); if (!adj_lhs || adj_rhs) { ARM_COMPUTE_RETURN_ERROR_ON_MSG(((k0 & (k0 - 1)) && (k0 != 3)) || (k0 > 16), "Only 1,2,3,4,8,16 are supported for K0"); } return Status{}; } Status validate_export_to_cl_image(const ITensorInfo *rhs, const MatMulKernelInfo &matmul_kernel_info) { ARM_COMPUTE_RETURN_ERROR_ON(matmul_kernel_info.export_rhs_to_cl_image && rhs->lock_paddings()); if (matmul_kernel_info.export_rhs_to_cl_image) { if (matmul_kernel_info.adj_rhs) { const int k0 = matmul_kernel_info.k0; ARM_COMPUTE_RETURN_ERROR_ON_MSG(k0 != 4 && k0 != 8 && k0 != 16, "K0 can only be: 4, 8, and 16 for Rhs transposed"); } else { const int n0 = matmul_kernel_info.n0; ARM_COMPUTE_RETURN_ERROR_ON_MSG(n0 != 4 && n0 != 8 && n0 != 16, "N0 can only be: 4, 8, and 16 for Rhs non-transposed"); } ARM_COMPUTE_RETURN_ERROR_ON_MSG(!export_to_cl_image(rhs), "Export to CLImage is not supported for this device/configuration"); } return Status{}; } } // namespace ClMatMulNativeKernel::ClMatMulNativeKernel() { _type = CLKernelType::GEMM; } Status ClMatMulNativeKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info, const ActivationLayerInfo &act_info) { ARM_COMPUTE_UNUSED(act_info); ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs); ARM_COMPUTE_RETURN_ON_ERROR(validate_matmul_kernel_info(matmul_kernel_info)); ARM_COMPUTE_RETURN_ON_ERROR( validate_matmul_input_shapes(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info)); ARM_COMPUTE_RETURN_ON_ERROR(validate_export_to_cl_image(rhs, matmul_kernel_info)); const TensorShape expected_output_shape = misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info); if (dst->total_size() != 0) { const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(expected_output_shape); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst); } if (bias != nullptr) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(bias, lhs); ARM_COMPUTE_RETURN_ERROR_ON_MSG((bias->num_dimensions() > 1), "Multi dimensional bias is unsupported."); ARM_COMPUTE_RETURN_ERROR_ON_MSG(bias->dimension(0) != expected_output_shape[0], "First dimension of bias and output tensors must match."); } return Status{}; } void ClMatMulNativeKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info, const ActivationLayerInfo &act_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst, &compile_context, &matmul_kernel_info); ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst, matmul_kernel_info); ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, bias, dst, matmul_kernel_info)); // dst tensor auto initialization if not yet initialized auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape( lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info))); const int m = dst->dimension(1); const int n = dst->dimension(0); const int k = matmul_kernel_info.adj_lhs ? lhs->tensor_shape().y() : lhs->tensor_shape().x(); const bool adj_lhs = matmul_kernel_info.adj_lhs; int m0 = adj_lhs ? adjust_vec_size(matmul_kernel_info.m0, m) : std::min(matmul_kernel_info.m0, m); int n0 = adjust_vec_size(matmul_kernel_info.n0, n); _export_rhs_to_cl_image = matmul_kernel_info.export_rhs_to_cl_image && !rhs->lock_paddings(); // Configure kernel window Window win = calculate_max_window(*dst, Steps(n0, m0)); win = win.collapse(win, Window::DimZ); IClKernel::configure_internal(win); // 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 = m % m0; const unsigned int partial_store_n0 = n % n0; CLBuildOptions build_opts; build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(lhs->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("-DK0=" + support::cpp11::to_string(matmul_kernel_info.k0)); 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)); build_opts.add_option("-DK=" + support::cpp11::to_string(k)); build_opts.add_option_if(bias != nullptr, "-DBIAS"); build_opts.add_option_if_else(_export_rhs_to_cl_image, "-DRHS_TENSOR_TYPE=IMAGE", "-DRHS_TENSOR_TYPE=BUFFER"); // Define values for activation function build_opts.add_option(("-DA_VAL=" + float_to_string_with_full_precision(act_info.a()))); build_opts.add_option(("-DB_VAL=" + float_to_string_with_full_precision(act_info.b()))); build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation()))); std::string kernel_name("mat_mul_native"); kernel_name += matmul_kernel_info.adj_lhs ? "_t" : "_nt"; kernel_name += matmul_kernel_info.adj_rhs ? "_t" : "_nt"; // A macro guard to compile ONLY the kernel of interest build_opts.add_option("-D" + upper_string(kernel_name)); if (_export_rhs_to_cl_image) { gemm::update_padding_for_cl_image(rhs); } // Create kernel _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); // Set config_id for enabling LWS tuning _config_id = kernel_name; _config_id += "_"; _config_id += lower_string(string_from_data_type(lhs->data_type())); _config_id += "_"; _config_id += support::cpp11::to_string(m); _config_id += "_"; _config_id += support::cpp11::to_string(n); _config_id += "_"; _config_id += support::cpp11::to_string(k); _config_id += "_"; _config_id += support::cpp11::to_string(dst->dimension(2)); _config_id += "_"; _config_id += support::cpp11::to_string(_export_rhs_to_cl_image); _config_id += "_"; _config_id += support::cpp11::to_string(m0); _config_id += "_"; _config_id += support::cpp11::to_string(n0); _config_id += "_"; _config_id += support::cpp11::to_string(matmul_kernel_info.k0); } void ClMatMulNativeKernel::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 ICLTensor *lhs = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_SRC_0)); const ICLTensor *rhs = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_SRC_1)); const ICLTensor *bias = utils::cast::polymorphic_downcast( tensors.get_const_tensor(TensorType::ACL_SRC_2)); // nullptr if bias is not present ICLTensor *dst = utils::cast::polymorphic_downcast(tensors.get_tensor(TensorType::ACL_DST)); ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst); ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst); unsigned int idx = 0; Window window_collapsed = window.collapse(ICLKernel::window(), Window::DimZ); add_3d_tensor_nhw_argument(idx, lhs); cl::Image2D rhs_cl_image; if (_export_rhs_to_cl_image) { const size_t image_w = rhs->info()->dimension(0) / 4; const size_t image_h = rhs->info()->tensor_shape().total_size() / rhs->info()->dimension(0); const TensorShape shape2d(image_w, image_h); const size_t image_row_pitch = rhs->info()->strides_in_bytes()[1]; // Export cl_buffer to cl_image rhs_cl_image = create_image2d_from_buffer(CLKernelLibrary::get().context(), rhs->cl_buffer(), shape2d, rhs->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly); _kernel.setArg(idx++, rhs_cl_image); } add_3d_tensor_nhw_argument(idx, rhs); if (bias != nullptr) { add_3d_tensor_nhw_argument(idx, bias); } add_3d_tensor_nhw_argument(idx, dst); enqueue(queue, *this, window_collapsed, lws_hint()); } } // namespace kernels } // namespace opencl } // namespace arm_compute