COMPMID-1816: Use parallel reduction on 0 axis in CL ARG_MIN/ARG_MAX
Introducing new CLArgMinMax kernel
Change-Id: I0b8254207cc3859d19ceef9b6429cf5c1c586db0
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2202
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
diff --git a/src/core/CL/kernels/CLArgMinMaxLayerKernel.cpp b/src/core/CL/kernels/CLArgMinMaxLayerKernel.cpp
new file mode 100644
index 0000000..c8e87ba
--- /dev/null
+++ b/src/core/CL/kernels/CLArgMinMaxLayerKernel.cpp
@@ -0,0 +1,283 @@
+/*
+ * Copyright (c) 2019 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/CLArgMinMaxLayerKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/CLValidate.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include "support/ToolchainSupport.h"
+
+namespace arm_compute
+{
+namespace
+{
+constexpr unsigned int vector_size = 16;
+
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *prev_output, const ITensorInfo *output, unsigned int axis, ReductionOperation op)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(op != ReductionOperation::ARG_IDX_MAX && op != ReductionOperation::ARG_IDX_MIN, "Only ARG_IDX_MAX and ARG_IDX_MIN are supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis");
+
+ if(output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32, DataType::S32);
+ }
+ if(prev_output != nullptr && prev_output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(prev_output, 1, DataType::U32, DataType::S32);
+ if(output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(prev_output, output);
+ }
+ }
+
+ return Status{};
+}
+
+std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *prev_output, ITensorInfo *output, unsigned int axis, ReductionOperation op)
+{
+ ARM_COMPUTE_UNUSED(op);
+ // Output tensor auto initialization if not yet initialized
+ TensorShape output_shape{ input->tensor_shape() };
+ output_shape.set(axis, 1);
+ DataType output_data_type = DataType::S32;
+ auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true));
+
+ Window win = calculate_max_window((prev_output != nullptr) ? (*prev_output) : (*input), Steps(vector_size));
+ bool window_changed = false;
+
+ switch(axis)
+ {
+ case 0:
+ {
+ ITensorInfo *input_tensor_access = prev_output != nullptr ? prev_output : input;
+ AccessWindowStatic input_access(input_tensor_access, 0, 0, static_cast<int>(input_tensor_access->dimension(0)), 1);
+ AccessWindowHorizontal output_access(output, 0, 1);
+ window_changed = update_window_and_padding(win, input_access, output_access);
+ output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+ }
+ break;
+ case 1:
+ case 2:
+ case 3:
+ {
+ AccessWindowHorizontal input_access(input, 0, vector_size);
+ AccessWindowHorizontal output_access(output, 0, vector_size);
+ window_changed = update_window_and_padding(win, input_access, output_access);
+ output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+ }
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_tuple(err, win);
+}
+} // namespace
+
+CLArgMinMaxLayerKernel::CLArgMinMaxLayerKernel()
+ : _input(nullptr), _prev_output(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::ARG_IDX_MAX)
+{
+}
+
+void CLArgMinMaxLayerKernel::configure(const ICLTensor *input, const ICLTensor *prev_output, ICLTensor *output, unsigned int axis, ReductionOperation op)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (prev_output != nullptr) ? prev_output->info() : nullptr, output->info(), axis, op));
+ auto win_config = validate_and_configure_window(input->info(), (prev_output != nullptr) ? prev_output->info() : nullptr, output->info(), axis, op);
+ ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));
+
+ _input = input;
+ _prev_output = prev_output;
+ _output = output;
+ _reduction_axis = axis;
+ _op = op;
+
+ // Set build options
+ CLBuildOptions build_opts;
+ const std::string data_type_promoted = get_cl_type_from_data_type(input->info()->data_type());
+
+ build_opts.add_option_if(_prev_output != nullptr, "-DPREV_OUTPUT");
+ build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+ build_opts.add_option("-DDATA_TYPE_PROMOTED=" + data_type_promoted);
+ build_opts.add_option_if(is_data_type_float(input->info()->data_type()), "-DFLOAT_DATA_TYPE");
+ build_opts.add_option_if(op == ReductionOperation::ARG_IDX_MAX, "-DARG_MAX");
+ build_opts.add_option_if(op == ReductionOperation::ARG_IDX_MIN, "-DARG_MIN");
+ build_opts.add_option("-DCOND_DATA_TYPE=" + get_cl_select_type_from_data_type(input->info()->data_type()));
+ build_opts.add_option("-DDATA_TYPE_OUTPUT=" + get_cl_type_from_data_type(output->info()->data_type()));
+
+ // Create kernel
+ cl::NDRange lws_hint = CLKernelLibrary::get().default_ndrange();
+ std::string kernel_axis_name;
+ switch(axis)
+ {
+ case 0:
+ {
+ const ICLTensor *input_for_width = prev_output != nullptr ? _prev_output : _input;
+ build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(input_for_width->info()->dimension(0)));
+
+ kernel_axis_name = "x";
+ lws_hint = create_lws_hint_parallel_implementations(input_for_width->info()->dimension(0), vector_size);
+ }
+ break;
+ case 1:
+ build_opts.add_option("-DHEIGHT=" + support::cpp11::to_string(input->info()->dimension(1)));
+ kernel_axis_name = "y";
+ break;
+ case 2:
+ build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
+ kernel_axis_name = "z";
+ break;
+ case 3:
+ build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
+ build_opts.add_option("-DBATCH=" + support::cpp11::to_string(input->info()->dimension(3)));
+ kernel_axis_name = "w";
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("arg_min_max_" + kernel_axis_name, build_opts.options()));
+
+ // Configure kernel window
+ ICLKernel::configure_internal(std::get<1>(win_config), lws_hint);
+}
+
+Status CLArgMinMaxLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *prev_output, const ITensorInfo *output, unsigned int axis, ReductionOperation op)
+{
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, prev_output, output, axis, op));
+ ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), (prev_output != nullptr) ? prev_output->clone().get() : nullptr, output->clone().get(), axis, op)));
+ return Status{};
+}
+
+void CLArgMinMaxLayerKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+
+ switch(_reduction_axis)
+ {
+ case 0:
+ {
+ // Set out window
+ Window out_window(window);
+ out_window.set(Window::DimX, Window::Dimension(0, 0, 0));
+
+ // Get first input and output slices
+ Window in_slice = window.first_slice_window_2D();
+ Window out_slice = out_window.first_slice_window_2D();
+
+ // Reshape window
+ const unsigned int border_width = ((in_slice.x().end() % vector_size) != 0) ? vector_size - in_slice.x().end() % vector_size : 0;
+ in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start(), in_slice.x().end() + border_width, in_slice.x().step()));
+ const unsigned int num_tensors = _prev_output != nullptr ? 3 : 2;
+
+ // Set local sums buffer
+ unsigned int local_res_size = lws_hint()[0] * _output->info()->element_size();
+ _kernel.setArg(num_arguments_per_2D_tensor() * num_tensors, local_res_size, nullptr);
+ do
+ {
+ unsigned int idx = 0;
+ add_2D_tensor_argument(idx, _input, in_slice);
+ if(_prev_output != nullptr)
+ {
+ add_2D_tensor_argument(idx, _prev_output, in_slice);
+ }
+ add_2D_tensor_argument(idx, _output, out_slice);
+ enqueue(queue, *this, in_slice, lws_hint());
+ }
+ while(window.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice));
+ }
+ break;
+ case 1:
+ {
+ // Get first input and output slices
+ Window window_in{ window };
+ window_in.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), _input->info()->dimension(1)));
+ Window in_slice = window_in.first_slice_window_2D();
+ Window out_slice = window.first_slice_window_2D();
+
+ do
+ {
+ unsigned int idx = 0;
+ add_2D_tensor_argument(idx, _input, in_slice);
+ add_2D_tensor_argument(idx, _output, out_slice);
+ enqueue(queue, *this, in_slice, lws_hint());
+ }
+ while(window_in.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice));
+ }
+ break;
+ case 2:
+ {
+ // Get first input and output slices
+ Window window_in{ window };
+ window_in.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), _input->info()->dimension(2)));
+ Window in_slice = window_in.first_slice_window_3D();
+ Window out_slice = window.first_slice_window_3D();
+
+ do
+ {
+ unsigned int idx = 0;
+ add_3D_tensor_argument(idx, _input, in_slice);
+ add_3D_tensor_argument(idx, _output, out_slice);
+ enqueue(queue, *this, in_slice, lws_hint());
+ }
+ while(window_in.slide_window_slice_3D(in_slice) && window.slide_window_slice_3D(out_slice));
+ }
+ break;
+ case 3:
+ {
+ // Get first input and output slices
+ Window window_in{ window };
+ window_in.set(3, Window::Dimension(0, 1, 1));
+ Window in_slice = window_in.first_slice_window_4D();
+ Window out_slice = window.first_slice_window_4D();
+
+ do
+ {
+ unsigned int idx = 0;
+ add_4D_tensor_argument(idx, _input, in_slice);
+ add_4D_tensor_argument(idx, _output, out_slice);
+ enqueue(queue, *this, in_slice, lws_hint());
+ }
+ while(window_in.slide_window_slice_4D(in_slice) && window.slide_window_slice_4D(out_slice));
+ }
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+}
+} // namespace arm_compute