| /* |
| * Copyright (c) 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/CLQLSTMLayerNormalizationKernel.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| #include "support/StringSupport.h" |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| QuantizationInfo compute_output_qinfo() |
| { |
| return QuantizationInfo(1.f / 4096); |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input); |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output, *input); |
| output->set_quantization_info(compute_output_qinfo()); |
| |
| const uint32_t temp_num_elems_processed_per_iteration = max_cl_vector_width / input->element_size(); |
| /* If width is less then step, then make step same as width to avoid global size being step instead of actual width. */ |
| /* Or we should fix in arm_compute::enqueue() or arm_compute::calculate_max_window(). */ |
| const uint32_t num_elems_processed_per_iteration = (input->dimension(0) < temp_num_elems_processed_per_iteration) ? input->dimension(0) : temp_num_elems_processed_per_iteration; |
| |
| // This kernel doesn't need padding |
| Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); |
| output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape())); |
| |
| return std::make_pair(Status{}, win); |
| } |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *weight, const ITensorInfo *bias) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weight, bias, output); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 2, "Input tensor cannot have more than 2 dimensions"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weight->num_dimensions() > 1, "Weight tensor cannot have more than 1 dimensions"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(bias->num_dimensions() > 1, "Bias tensor cannot have more than 1 dimensions"); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QSYMM16); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weight, 1, DataType::QSYMM16); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape().x() != weight->tensor_shape().x()); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(weight, bias); |
| |
| // Checks performed when output is configured |
| if(output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| return Status{}; |
| } |
| } // namespace |
| |
| CLQLSTMLayerNormalizationKernel::CLQLSTMLayerNormalizationKernel() |
| : _input(nullptr), _weight(nullptr), _bias(nullptr), _output(nullptr) |
| { |
| } |
| |
| void CLQLSTMLayerNormalizationKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const ICLTensor *weight, const ICLTensor *bias) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weight, bias, output); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), weight->info(), bias->info())); |
| |
| _input = input; |
| _weight = weight; |
| _bias = bias; |
| _output = output; |
| |
| const uint32_t num_elems_processed_per_iteration = max_cl_vector_width / input->info()->element_size(); |
| |
| int32_t output_multiplier{}; |
| int32_t output_shift{}; |
| const UniformQuantizationInfo quan_info = _weight->info()->quantization_info().uniform(); |
| const Status status = quantization::calculate_quantized_multiplier(quan_info.scale, &output_multiplier, &output_shift); |
| output_shift *= -1; |
| |
| // Set build options |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); |
| build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)); |
| build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(input->info()->dimension(0))); |
| build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); |
| build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift)); |
| build_opts.add_option("-DMIN_BOUND=" + support::cpp11::to_string(std::get<0>(quantization::get_min_max_values_from_quantized_data_type(input->info()->data_type())))); |
| build_opts.add_option("-DMAX_BOUND=" + support::cpp11::to_string(std::get<1>(quantization::get_min_max_values_from_quantized_data_type(input->info()->data_type())))); |
| |
| // Create kernel |
| _kernel = create_kernel(compile_context, "qlstm_layer_normalization", build_opts.options()); |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), output->info()); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| ICLKernel::configure_internal(win_config.second); |
| |
| // Set config_id for enabling LWS tuning |
| _config_id = "qlstm_layer_normalization_"; |
| _config_id += lower_string(string_from_data_type(input->info()->data_type())); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(input->info()->dimension(0)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(input->info()->dimension(1)); |
| } |
| |
| void CLQLSTMLayerNormalizationKernel::configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *weight, const ICLTensor *bias) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), input, output, weight, bias); |
| } |
| |
| Status CLQLSTMLayerNormalizationKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *weight, const ITensorInfo *bias) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, weight, bias)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first); |
| return Status{}; |
| } |
| |
| void CLQLSTMLayerNormalizationKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); |
| |
| Window slice = window.first_slice_window_2D(); |
| // Set slice step equal to width to force gws[0] to 1, as each thread normalizes across all rows |
| slice.set_dimension_step(Window::DimX, _input->info()->dimension(0)); |
| |
| Window weight_window; |
| Window weight_slice; |
| |
| weight_window.use_tensor_dimensions(_weight->info()->tensor_shape()); |
| weight_slice = weight_window.first_slice_window_1D(); |
| |
| do |
| { |
| unsigned int idx = 0; |
| add_2D_tensor_argument(idx, _input, slice); |
| add_1D_tensor_argument(idx, _weight, weight_slice); |
| add_1D_tensor_argument(idx, _bias, weight_slice); |
| add_2D_tensor_argument(idx, _output, slice); |
| |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| while(window.slide_window_slice_2D(slice)); |
| } |
| } // namespace arm_compute |