| /* |
| * 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/ClMatMulLowpNativeMMULKernel.h" |
| |
| #include "arm_compute/core/CL/CLHelpers.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/ITensorPack.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/utils/helpers/AdjustVecSize.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| #include "arm_compute/core/utils/StringUtils.h" |
| |
| #include "src/common/utils/Log.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/gpu/cl/ClCompileContext.h" |
| #include "src/gpu/cl/kernels/helpers/MatMulKernelHelpers.h" |
| #include "support/Cast.h" |
| #include "support/StringSupport.h" |
| #include "utils/TypePrinter.h" |
| |
| namespace arm_compute |
| { |
| namespace opencl |
| { |
| namespace kernels |
| { |
| namespace |
| { |
| // Block size dimensions for the MMUL extension |
| constexpr int mmul_m0 = 4; |
| constexpr int mmul_n0 = 4; |
| constexpr int mmul_k0 = 16; |
| |
| Status validate_matmul_kernel_info(const MatMulKernelInfo &matmul_kernel_info) |
| { |
| const bool adj_lhs = matmul_kernel_info.adj_lhs; |
| 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 != 1) && (m0 != 2) && (m0 != 3) && (m0 != 4) && (m0 != 8) && (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) && (n0 != 2) && (n0 != 3) && (n0 != 4) && (n0 != 8) && (n0 != 16), |
| "Only 1,2,3,4,8,16 are supported for N0"); |
| |
| // Validate K0 |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((k0 != 4), "Only 4 is supported for k0"); |
| |
| // Validate ExportToCLImage |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(matmul_kernel_info.export_rhs_to_cl_image, "Export to CLImage is not supported!"); |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| ClMatMulLowpNativeMMULKernel::ClMatMulLowpNativeMMULKernel() |
| { |
| _type = CLKernelType::GEMM; |
| } |
| |
| Status ClMatMulLowpNativeMMULKernel::validate(const ITensorInfo *lhs, |
| const ITensorInfo *rhs, |
| const ITensorInfo *bias, |
| const ITensorInfo *dst, |
| const MatMulKernelInfo &matmul_kernel_info, |
| const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, 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_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_matmul_kernel_info(matmul_kernel_info)); |
| |
| const TensorShape &lhs_shape = lhs->tensor_shape(); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_matmul_input_shapes(lhs_shape, rhs->tensor_shape(), matmul_kernel_info)); |
| |
| const size_t lhs_k = matmul_kernel_info.adj_lhs ? lhs_shape.y() : lhs_shape.x(); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR((lhs_k % mmul_k0) != 0, "K dimension must be a multiple of %d", mmul_k0); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.activation() != ActivationFunction::IDENTITY), |
| "Activation Function specified is unsupported."); |
| const TensorShape expected_output_shape = |
| misc::shape_calculator::compute_matmul_shape(lhs_shape, rhs->tensor_shape(), matmul_kernel_info); |
| |
| if (dst->total_size() != 0) |
| { |
| const TensorInfo tensor_info_output = dst->clone()->set_tensor_shape(expected_output_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst); |
| } |
| |
| if (bias != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32); |
| ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(expected_output_shape[0] != bias->dimension(0)); |
| } |
| |
| return Status{}; |
| } |
| |
| void ClMatMulLowpNativeMMULKernel::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); |
| ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst, matmul_kernel_info, act_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))); |
| |
| ARM_COMPUTE_UNUSED(compile_context, lhs, rhs, bias, matmul_kernel_info, act_info); |
| CLBuildOptions build_opts; |
| |
| 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 int m0 = std::min(matmul_kernel_info.m0, m); |
| const int n0 = adjust_vec_size(matmul_kernel_info.n0, n); |
| |
| // 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 m0_leftover = m % m0; |
| const unsigned int n0_leftover = n % n0; |
| |
| // Configure kernel window |
| const auto win_config = |
| validate_and_configure_window_for_mmul_kernels(lhs, rhs, dst, matmul_kernel_info, mmul_m0, mmul_n0); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| IClKernel::configure_internal(win_config.second); |
| |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(lhs->data_type())); |
| build_opts.add_option("-DM=" + support::cpp11::to_string(m)); |
| build_opts.add_option("-DN=" + support::cpp11::to_string(n)); |
| build_opts.add_option("-DK=" + support::cpp11::to_string(k)); |
| 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("-DM0_LEFTOVER=" + support::cpp11::to_string(m0_leftover)); |
| build_opts.add_option("-DN0_LEFTOVER=" + support::cpp11::to_string(n0_leftover)); |
| 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_if(bias != nullptr, "-DBIAS"); |
| |
| const UniformQuantizationInfo lqinfo = lhs->quantization_info().uniform(); |
| const UniformQuantizationInfo rqinfo = rhs->quantization_info().uniform(); |
| const UniformQuantizationInfo dqinfo = dst->quantization_info().uniform(); |
| |
| float multiplier = lqinfo.scale * rqinfo.scale / dqinfo.scale; |
| int output_multiplier = 0; |
| int output_shift = 0; |
| arm_compute::quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); |
| |
| build_opts.add_option("-DDST_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); |
| build_opts.add_option("-DDST_SHIFT=" + support::cpp11::to_string(output_shift)); |
| |
| // Note : Offset is not negated, unlike gemmlowp kernels |
| build_opts.add_option("-DLHS_OFFSET=" + support::cpp11::to_string(lqinfo.offset)); |
| build_opts.add_option("-DRHS_OFFSET=" + support::cpp11::to_string(rqinfo.offset)); |
| build_opts.add_option("-DDST_OFFSET=" + support::cpp11::to_string(dqinfo.offset)); |
| |
| std::string kernel_name("mat_mul_native_quantized_mmul"); |
| 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)); |
| |
| // 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(m0); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(n0); |
| } |
| |
| void ClMatMulLowpNativeMMULKernel::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 *lhs = |
| utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0)); |
| const auto *rhs = |
| utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); |
| const auto *bias = utils::cast::polymorphic_downcast<const ICLTensor *>( |
| tensors.get_const_tensor(TensorType::ACL_SRC_2)); // nullptr if bias is not present |
| auto *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; |
| add_3d_tensor_nhw_argument(idx, lhs); |
| add_3d_tensor_nhw_argument(idx, rhs); |
| |
| if (bias != nullptr) |
| { |
| add_3d_tensor_nhw_argument(idx, bias); |
| } |
| add_3d_tensor_nhw_argument(idx, dst); |
| |
| // LWS_x should be multiple of 16 at least. (32, 2) has been chosen to have more work-items on a single core |
| // LWS also enforces the order of execution of the work items which improves cache utilization |
| enqueue(queue, *this, window, cl::NDRange(32, 2), false); |
| } |
| |
| } // namespace kernels |
| } // namespace opencl |
| } // namespace arm_compute |