src/core/CL/kernels/CLQLSTMLayerNormalizationKernel.cpp - platform/external/ComputeLibrary - Git at Google

 /*
  * Copyright (c) 2020-2021 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/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));

     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)
 {
     _type = CLKernelType::ELEMENTWISE;
 }

 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);
     auto padding_info = get_padding_info({ 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));
     ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
 }

 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
	/*
	* Copyright (c) 2020-2021 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/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));

	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)
	{
	_type = CLKernelType::ELEMENTWISE;
	}

	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);
	auto padding_info = get_padding_info({ 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));
	ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
	}

	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