COMPMID-692 Consistent names for the interfaces
Change-Id: I4b1f3f0da9ff5342c7de7083736fe91871d14e5b
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110351
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3Kernel.cpp
new file mode 100644
index 0000000..003f1f8
--- /dev/null
+++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3Kernel.cpp
@@ -0,0 +1,219 @@
+/*
+ * Copyright (c) 2017 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/CLDepthwiseConvolutionLayer3x3Kernel.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/ICLKernel.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/Utils.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+
+using namespace arm_compute;
+
+namespace
+{
+/** Calculates expected output shape dimension
+ *
+ * @param[in] Input shape
+ *
+ * @return Expected output shape
+ */
+TensorShape get_output_shape(TensorShape input_shape, TensorShape weights_shape, PadStrideInfo conv_info)
+{
+ unsigned int output_width = 0;
+ unsigned int output_height = 0;
+
+ std::tie(output_width, output_height) = scaled_dimensions(input_shape.x(), input_shape.y(), weights_shape.x(), weights_shape.y(), conv_info);
+
+ TensorShape output_shape = input_shape;
+ output_shape.set(0, output_width);
+ output_shape.set(1, output_height);
+
+ return output_shape;
+}
+} // namespace
+
+CLDepthwiseConvolutionLayer3x3Kernel::CLDepthwiseConvolutionLayer3x3Kernel()
+ : _border_size(0), _input(), _output(), _weights(), _biases(), _conv_stride_x(0), _conv_stride_y(0), _conv_pad_left(0), _conv_pad_top(0)
+{
+}
+
+BorderSize CLDepthwiseConvolutionLayer3x3Kernel::border_size() const
+{
+ return _border_size;
+}
+
+void CLDepthwiseConvolutionLayer3x3Kernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info)
+{
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+ ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 3 || weights->info()->dimension(1) != 3);
+
+ if(biases != nullptr)
+ {
+ if(is_data_type_quantized_asymmetric(weights->info()->data_type()))
+ {
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+ }
+ ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(2));
+ ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
+ }
+
+ // Get convolved dimensions
+ TensorShape output_shape = get_output_shape(input->info()->tensor_shape(), weights->info()->tensor_shape(), conv_info);
+
+ // Output auto inizialitation if not yet initialized
+ auto_init_if_empty(*output->info(),
+ output_shape,
+ 1,
+ input->info()->data_type(),
+ input->info()->fixed_point_position(),
+ input->info()->quantization_info());
+
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
+
+ _input = input;
+ _output = output;
+ _weights = weights;
+ _biases = biases;
+ _conv_stride_x = conv_info.stride().first;
+ _conv_stride_y = conv_info.stride().second;
+ _conv_pad_left = conv_info.pad_left();
+ _conv_pad_top = conv_info.pad_top();
+ _border_size = BorderSize(_conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), _conv_pad_left);
+
+ // Set build options
+ ARM_COMPUTE_ERROR_ON(_conv_stride_x < 1 || _conv_stride_x > 3);
+ CLBuildOptions build_opts;
+ build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(_conv_stride_x));
+ build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS");
+
+ // Create kernel
+ std::string kernel_name = is_data_type_quantized_asymmetric(_input->info()->data_type()) ? "depthwise_convolution_3x3_quantized" : "depthwise_convolution_3x3";
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
+
+ // Set static arguments
+ if(is_data_type_quantized_asymmetric(_input->info()->data_type()))
+ {
+ float multiplier = _input->info()->quantization_info().scale * _weights->info()->quantization_info().scale / _output->info()->quantization_info().scale;
+ int output_multiplier = 0;
+ int output_shift = 0;
+ quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+
+ unsigned int idx = 3 * num_arguments_per_3D_tensor() + ((_biases != nullptr) ? num_arguments_per_1D_tensor() : 0);
+
+ _kernel.setArg(idx++, -_input->info()->quantization_info().offset);
+ _kernel.setArg(idx++, -_weights->info()->quantization_info().offset);
+ _kernel.setArg(idx++, _output->info()->quantization_info().offset);
+ _kernel.setArg(idx++, output_multiplier);
+ _kernel.setArg(idx++, output_shift);
+ }
+
+ // Configure the local work size for Bifrost with a value obtained
+ // via exhaustive autotuning for the MobileNets tensor shapes.
+ const GPUTarget gpu_target = get_arch_from_target(get_target());
+ if(gpu_target == GPUTarget::BIFROST)
+ {
+ const size_t width = input->info()->dimension(0);
+ if(width >= 56) // 56 or 112
+ {
+ _lws_hint = cl::NDRange(8, 5, 2);
+ }
+ else if(width >= 14) // 14 or 28
+ {
+ _lws_hint = cl::NDRange(1, 5, 2);
+ }
+ else // 7
+ {
+ _lws_hint = cl::NDRange(1, 1, 2);
+ }
+ }
+
+ // Configure kernel window
+ const unsigned int num_elems_processed_per_iteration = 2;
+ const unsigned int num_elems_written_per_iteration = 2;
+ const unsigned int num_elems_read_per_iteration = 3 + _conv_stride_x;
+ const unsigned int num_rows_read_per_iteration = 3;
+
+ Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
+
+ AccessWindowRectangle input_access(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration, _conv_stride_x, _conv_stride_y);
+ AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+ AccessWindowStatic weights_access(weights->info(), 0, 0, weights->info()->dimension(0), weights->info()->dimension(1));
+
+ update_window_and_padding(win, input_access, weights_access, output_access);
+
+ output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+ ICLKernel::configure(win);
+}
+
+void CLDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, cl::CommandQueue &queue)
+{
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+
+ // Create input window and adjust
+ Window win_in = window;
+ win_in.adjust(Window::DimX, -_conv_pad_left, true);
+ win_in.adjust(Window::DimY, -_conv_pad_top, true);
+ win_in.set_dimension_step(Window::DimX, window.x().step() * _conv_stride_x);
+ win_in.set_dimension_step(Window::DimY, window.y().step() * _conv_stride_y);
+
+ Window slice_in = win_in.first_slice_window_3D();
+ Window slice_out = window.first_slice_window_3D();
+ Window slice_weights = window.first_slice_window_3D();
+ slice_weights.set_dimension_step(Window::DimX, 0);
+ slice_weights.set_dimension_step(Window::DimY, 0);
+
+ // Set biases
+ if(_biases != nullptr)
+ {
+ unsigned int idx = 3 * num_arguments_per_3D_tensor();
+ Window slice_biases;
+ slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape());
+ add_1D_tensor_argument(idx, _biases, slice_biases);
+ }
+
+ do
+ {
+ unsigned int idx = 0;
+ add_3D_tensor_argument(idx, _input, slice_in);
+ add_3D_tensor_argument(idx, _output, slice_out);
+ add_3D_tensor_argument(idx, _weights, slice_weights);
+
+ enqueue(queue, *this, slice_out, _lws_hint);
+ }
+ while(window.slide_window_slice_3D(slice_out) && win_in.slide_window_slice_3D(slice_in));
+}