/* * 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/core/gpu/cl/kernels/ClDequantizationKernel.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/TensorInfo.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "src/core/CL/CLValidate.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "support/Cast.h" #include "support/StringSupport.h" namespace arm_compute { namespace opencl { namespace kernels { namespace { Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8, DataType::QSYMM16); if(dst->tensor_shape().total_size() > 0) { ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(dst); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src, dst); } return Status{}; } } // namespace ClDequantizationKernel::ClDequantizationKernel() { } void ClDequantizationKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst) { ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); // Output tensor auto initialization if not yet initialized auto_init_if_empty(*dst, src->tensor_shape(), 1, DataType::F32); auto padding_info = get_padding_info({ src, dst }); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst)); const int vec_size_x = 16 / dst->element_size(); const int output_width_x = dst->tensor_shape().x(); const bool multi_access_x = (output_width_x / vec_size_x > 0); const bool is_quantized_per_channel = is_data_type_quantized_per_channel(src->data_type()); std::string kernel_name = "dequantization_layer"; // Create kernel CLBuildOptions build_opts; if(!is_quantized_per_channel) { const UniformQuantizationInfo qinfo = src->quantization_info().uniform(); const int qoffset = is_data_type_quantized_asymmetric(src->data_type()) ? qinfo.offset : 0; build_opts.add_option("-DSCALE=" + float_to_string_with_full_precision(qinfo.scale)); build_opts.add_option("-DOFFSET=" + support::cpp11::to_string(qoffset)); } else { kernel_name += "_per_channel"; kernel_name += src->data_layout() == DataLayout::NCHW ? "_nchw" : "_nhwc"; } build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(vec_size_x)); build_opts.add_option("-DDATA_TYPE_SRC=" + get_cl_type_from_data_type(src->data_type())); build_opts.add_option("-DDATA_TYPE_DST=" + get_cl_type_from_data_type(dst->data_type())); build_opts.add_option_if(multi_access_x, "-DLAST_ACCESSED_X=" + support::cpp11::to_string(std::max(output_width_x - vec_size_x, 0))); // Create kernel name _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); // Configure kernel window Window win = calculate_max_window(*dst); if(multi_access_x) { win.set(Window::DimX, Window::Dimension(win.x().start(), ceil_to_multiple(win.x().end(), vec_size_x), vec_size_x)); } ICLKernel::configure_internal(win); ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); } Status ClDequantizationKernel::validate(const ITensorInfo *src, const ITensorInfo *dst) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst)); return Status{}; } void ClDequantizationKernel::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); auto src = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_SRC)); auto dst = utils::cast::polymorphic_downcast(tensors.get_tensor(TensorType::ACL_DST)); const bool is_quantized_per_channel = is_data_type_quantized_per_channel(src->info()->data_type()); // Collapse windo Window new_window = is_quantized_per_channel ? window.collapse_if_possible(ICLKernel::window(), 4) : window.collapse_if_possible(ICLKernel::window(), 3); Window slice = new_window.first_slice_window_3D(); if(is_quantized_per_channel) { unsigned int idx = num_arguments_per_3D_tensor() * 2; //Skip the input and output parameters _kernel.setArg(idx++, src->quantization().scale->cl_buffer()); } do { unsigned int idx = 0; add_3D_tensor_argument(idx, src, slice); add_3D_tensor_argument(idx, dst, slice); enqueue(queue, *this, slice, lws_hint()); } while(new_window.slide_window_slice_3D(slice)); } } // namespace kernels } // namespace opencl } // namespace arm_compute