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/*
* 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 "arm_compute/core/CL/kernels/CLQLSTMLayerNormalizationKernel.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.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
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