| /* |
| * 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<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0)); |
| const ICLTensor *rhs = |
| utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); |
| const ICLTensor *bias = utils::cast::polymorphic_downcast<const ICLTensor *>( |
| tensors.get_const_tensor(TensorType::ACL_SRC_2)); // nullptr if bias is not present |
| ICLTensor *dst = utils::cast::polymorphic_downcast<ICLTensor *>(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 |