| /* |
| * Copyright (c) 2017-2020 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/CL/kernels/CLQuantizationLayerKernel.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 "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| #include "src/core/AccessWindowStatic.h" |
| #include "src/core/CL/CLValidate.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| #include "support/StringSupport.h" |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F32, DataType::F16); |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); |
| |
| // Output must always be initialized |
| ARM_COMPUTE_RETURN_ERROR_ON(output->tensor_shape().total_size() == 0); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QASYMM16); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| CLQuantizationLayerKernel::CLQuantizationLayerKernel() |
| : _input(nullptr), _output(nullptr) |
| { |
| } |
| |
| void CLQuantizationLayerKernel::configure(const ICLTensor *input, ICLTensor *output) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), input, output); |
| } |
| |
| void CLQuantizationLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| |
| auto padding_info = get_padding_info({ input, output }); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info())); |
| |
| _input = input; |
| _output = output; |
| |
| const int vec_size_x = 16 / input->info()->element_size(); |
| const int input_width_x = input->info()->tensor_shape().x(); |
| const bool multi_access_x = (input_width_x / vec_size_x > 0); |
| |
| const UniformQuantizationInfo qinfo = output->info()->quantization_info().uniform(); |
| const DataType output_data_type = output->info()->data_type(); |
| |
| float scale_to_apply = qinfo.scale; |
| int32_t offset_to_apply = qinfo.offset; |
| if(is_data_type_quantized_asymmetric(_input->info()->data_type())) |
| { |
| /* |
| * In case of requantization of a quantized input tensor to an output tensor with another quantization |
| * instead of of apply dequantization and then a quantization functions, we just compute new scale and |
| * offset to apply. |
| * |
| * Assuming: |
| * - q_i as input quantized value |
| * - q_o as output quantized value |
| * - z_i as input quantization offset value |
| * - z_o as output quantization offset value |
| * - s_i as input quantization scale value |
| * - s_o as output quantization scale value |
| * - z_n as new quantization offset value |
| * - s_n as new quantization scale value |
| * |
| * q_o = ( q_i - z_i ) * s_i / s_o + z_o |
| * |
| * We can rewrite the formula as: |
| * |
| * q_o = ( q_i * s_i / s_o ) - z_i * s_i / s_o + z_o |
| * |
| * q_o = q_i / s_n + z_n |
| * |
| * Where: |
| * |
| * s_n = s_o / s_i |
| * |
| * z_n = - z_i * s_i / s_o + z_o |
| * |
| */ |
| const UniformQuantizationInfo qinfo_in = _input->info()->quantization_info().uniform(); |
| scale_to_apply /= qinfo_in.scale; |
| // In order to minimize flooring we convert the offset to a float, |
| // then compute the new offset in the float domain, |
| // finally we convert it back as int32_t |
| offset_to_apply -= static_cast<int32_t>(static_cast<float>(qinfo_in.offset) * qinfo_in.scale / qinfo.scale); |
| } |
| |
| // Create kernel |
| CLBuildOptions build_opts; |
| build_opts.add_option_if(is_data_type_float(_input->info()->data_type()), "-DIS_FLOAT"); |
| build_opts.add_option("-DSCALE=" + float_to_string_with_full_precision(scale_to_apply)); |
| build_opts.add_option("-DOFFSET=" + support::cpp11::to_string(offset_to_apply)); |
| build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(vec_size_x)); |
| build_opts.add_option("-DDATA_TYPE_IN=" + get_cl_type_from_data_type(input->info()->data_type())); |
| build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output_data_type)); |
| build_opts.add_option_if(multi_access_x, "-DLAST_ACCESSED_X=" + support::cpp11::to_string(std::max<int>(input_width_x - vec_size_x, 0))); |
| std::pair<int, int> min_max_quant_values = quantization::get_min_max_values_from_quantized_data_type(output_data_type); |
| build_opts.add_option("-DMIN_QUANT_VAL=" + support::cpp11::to_string(min_max_quant_values.first)); |
| build_opts.add_option("-DMAX_QUANT_VAL=" + support::cpp11::to_string(min_max_quant_values.second)); |
| |
| _kernel = create_kernel(compile_context, "quantization_layer", build_opts.options()); |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*input->info(), Steps()); |
| 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); |
| |
| output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape())); |
| |
| ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); |
| } |
| |
| Status CLQuantizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output)); |
| return Status{}; |
| } |
| |
| void CLQuantizationLayerKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); |
| |
| Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), 3); |
| Window slice = window_collapsed.first_slice_window_3D(); |
| |
| do |
| { |
| unsigned int idx = 0; |
| add_3D_tensor_argument(idx, _input, slice); |
| add_3D_tensor_argument(idx, _output, slice); |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| while(window_collapsed.slide_window_slice_3D(slice)); |
| } |
| } // namespace arm_compute |