/* * 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/ClMatMulLowpNativeKernel.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/QuantizationInfo.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/quantization/AsymmHelpers.h" #include "arm_compute/core/utils/StringUtils.h" #include "src/common/utils/Log.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "src/gpu/cl/ClCompileContext.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{}; } } // namespace ClMatMulLowpNativeKernel::ClMatMulLowpNativeKernel() { _type = CLKernelType::GEMM; } Status ClMatMulLowpNativeKernel::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_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)); ARM_COMPUTE_RETURN_ON_ERROR( validate_matmul_input_shapes(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info)); ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.activation() != ActivationFunction::IDENTITY && act_info.activation() != ActivationFunction::RELU && act_info.activation() != ActivationFunction::LU_BOUNDED_RELU && act_info.activation() != ActivationFunction::BOUNDED_RELU), "Activation Function specified is unsupported."); 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_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 ClMatMulLowpNativeKernel::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); // 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)); 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)); build_opts.add_option_if(bias != nullptr, "-DBIAS"); // Floating point boundaries are quantized prior to being passed as arguments. // Note: We expect the input and output tensors to always adopt a per-tensor quantization approach int a_val{}; int b_val{}; std::tie(b_val, a_val) = get_quantized_activation_min_max(act_info, dst->data_type(), dqinfo); build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val)); build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val)); build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation()))); build_opts.add_option("-DZERO_POINT=" + support::cpp11::to_string(dqinfo.offset)); std::string kernel_name("mat_mul_native_quantized"); 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 const size_t number_of_batches = dst->tensor_shape().total_size() / (m * n); _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(number_of_batches); _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 ClMatMulLowpNativeKernel::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)); 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); 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