COMPMID-1451: Perform CLOutputStage using floats.
Change-Id: Ic8312a5b6790aa7cd4468d42f08d557ad40e9441
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/154570
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp
new file mode 100644
index 0000000..f0096bd
--- /dev/null
+++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.cpp
@@ -0,0 +1,207 @@
+/*
+ * Copyright (c) 2018 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/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+
+#include "support/ToolchainSupport.h"
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
+ int min, int max, unsigned int output_3d_depth)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
+ ARM_COMPUTE_RETURN_ERROR_ON(max > 255);
+ ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max);
+
+ // Check biases if exist
+ if(bias != nullptr)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
+ ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
+ }
+
+ if(output->total_size() != 0)
+ {
+ const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input, output_3d_depth, true);
+ const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(output_shape);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
+ }
+
+ return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output)
+{
+ constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+ // Configure kernel window
+ Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
+
+ AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
+
+ bool window_changed = update_window_and_padding(win,
+ input_access);
+
+ if(output->total_size() != 0)
+ {
+ Window win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
+ AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration);
+ window_changed = window_changed || update_window_and_padding(win_out, output_result_access);
+
+ output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+ }
+
+ if(bias != nullptr)
+ {
+ AccessWindowStatic bias_access(bias, 0, 0, ceil_to_multiple(bias->dimension(0), num_elems_processed_per_iteration), bias->tensor_shape()[1]);
+ window_changed = window_changed || update_window_and_padding(win, bias_access);
+ }
+
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, win);
+}
+} // namespace
+
+class Coordinates;
+} // namespace arm_compute
+
+CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel()
+ : _input(nullptr), _bias(nullptr), _output(nullptr), _reinterpret_as_3d(false)
+{
+}
+
+Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
+ int min, int max, unsigned int output_3d_depth)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max, output_3d_depth));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(),
+ (bias != nullptr) ? bias->clone().get() : nullptr,
+ output->clone().get())
+ .first);
+
+ return Status{};
+}
+
+void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
+ float multiplier, int offset,
+ int min, int max, unsigned int output_3d_depth)
+{
+ // Perform validate step
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+
+ // Output auto inizialitation if not yet initialized
+ const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input->info(), output_3d_depth, true);
+ auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8).set_tensor_shape(output_shape));
+
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(),
+ min, max, output_3d_depth));
+
+ _input = input;
+ _bias = bias;
+ _output = output;
+ _reinterpret_as_3d = output_3d_depth > 1;
+
+ // Set the arguments to pass at compile time
+ CLBuildOptions build_opts;
+ build_opts.add_option("-DREAL_MULTIPLIER=" + float_to_string_with_full_precision(multiplier));
+ build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(offset));
+ build_opts.add_option_if((min != 0) && (min != max), "-DMIN_BOUND=" + support::cpp11::to_string(min));
+ build_opts.add_option_if((max != 255) && (min != max), "-DMAX_BOUND=" + support::cpp11::to_string(max));
+ build_opts.add_option_if(bias != nullptr, "-DADD_BIAS");
+ build_opts.add_option_if(_reinterpret_as_3d, "-DDST_HEIGHT=" + support::cpp11::to_string(input->info()->tensor_shape().y() / output_3d_depth));
+
+ // Create kernel
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemmlowp_output_stage_quantize_down_float", build_opts.options()));
+
+ // Configure kernel window
+ auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info());
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ ICLKernel::configure_internal(win_config.second);
+}
+
+void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
+
+ // Create input window
+ Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
+ Window slice = collapsed.first_slice_window_3D();
+
+ // Setup bias slice
+ unsigned int idx1 = num_arguments_per_3D_tensor();
+ if(_bias != nullptr)
+ {
+ Window biases_slice(slice);
+ biases_slice.set(Window::DimY, Window::Dimension(0, 1, 1));
+ biases_slice.set(Window::DimZ, Window::Dimension(0, 1, 1));
+ add_1D_tensor_argument(idx1, _bias, biases_slice);
+ }
+
+ if(_reinterpret_as_3d)
+ {
+ // Create output window
+ Window window_out;
+ window_out.use_tensor_dimensions(_output->info()->tensor_shape());
+ Window collapsed_out = window_out.collapse_if_possible(window_out, 3);
+ Window slice_out = collapsed.first_slice_window_4D();
+
+ do
+ {
+ unsigned int idx = 0;
+ add_3D_tensor_argument(idx, _input, slice);
+ add_4D_tensor_argument(idx1, _output, slice_out);
+ enqueue(queue, *this, slice);
+ }
+ while(collapsed.slide_window_slice_3D(slice) && collapsed_out.slide_window_slice_4D(slice_out));
+ }
+ else
+ {
+ do
+ {
+ unsigned int idx = 0;
+ add_3D_tensor_argument(idx, _input, slice);
+ add_3D_tensor_argument(idx1, _output, slice);
+ enqueue(queue, *this, slice);
+ }
+ while(collapsed.slide_window_slice_3D(slice));
+ }
+}