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-rw-r--r--src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp241
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diff --git a/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp b/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp
deleted file mode 100644
index 61c0740937..0000000000
--- a/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp
+++ /dev/null
@@ -1,241 +0,0 @@
-/*
- * Copyright (c) 2017-2019 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/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.h"
-
-#include "arm_compute/core/PixelValue.h"
-#include "arm_compute/core/Size2D.h"
-#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/runtime/GLES_COMPUTE/GCScheduler.h"
-
-#include <cmath>
-#include <memory>
-#include <tuple>
-
-using namespace arm_compute;
-
-GCConvolutionLayerReshapeWeights::GCConvolutionLayerReshapeWeights()
- : _weights_reshape_kernel()
-{
-}
-
-void GCConvolutionLayerReshapeWeights::configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
-
- if(biases != nullptr)
- {
- ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(weights->info()->data_type()));
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
- ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
- ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
- }
-
- const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
- const IGCTensor *biases_to_use = (append_biases) ? biases : nullptr;
-
- _weights_reshape_kernel.configure(weights, biases_to_use, output);
-}
-
-void GCConvolutionLayerReshapeWeights::run()
-{
- GCScheduler::get().dispatch(_weights_reshape_kernel);
-}
-
-GCConvolutionLayer::GCConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _mm_gemm(), _output_col2im_kernel(), _fill_border(), _activationlayer_function(), _original_weights(nullptr),
- _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _is_activationlayer_enabled(false), _is_prepared(false)
-{
-}
-
-void GCConvolutionLayer::configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
- ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info()));
-
- _mm_gemm.configure(input, weights, nullptr, output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */));
-}
-
-Status GCConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output)
-{
- // Perform validation step on Matrix multiply function
- GCGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */));
- return Status{};
-}
-
-void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
- const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
- ARM_COMPUTE_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!");
- ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2));
- ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
- ARM_COMPUTE_ERROR_ON(num_groups > 1);
- ARM_COMPUTE_UNUSED(num_groups);
-
- _is_prepared = false;
- _original_weights = weights;
-
- if(biases != nullptr)
- {
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
- ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
- ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
- }
-
- const DataType dt = input->info()->data_type();
-
- // Set the GPU target for im2col and col2im
- _input_im2col_kernel.set_target(GCScheduler::get().get_target());
- _output_col2im_kernel.set_target(GCScheduler::get().get_target());
-
- const bool append_bias = (biases != nullptr);
- const unsigned bias_element = (append_bias) ? 1 : 0;
- const IGCTensor *biases_to_use = (append_bias) ? biases : nullptr;
-
- // Get parameters from conv_info
- unsigned int stride_x = 0;
- unsigned int stride_y = 0;
- std::tie(stride_x, stride_y) = conv_info.stride();
-
- // Get convolved dimensions
- unsigned int conv_w = 0;
- unsigned int conv_h = 0;
-
- const unsigned int kernel_width = weights->info()->dimension(0);
- const unsigned int kernel_height = weights->info()->dimension(1);
- std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
- conv_info, dilation);
-
- unsigned int mat_weights_cols = weights->info()->dimension(3);
- unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
-
- // _weights_reshaped will be auto configured in the kernel.
- // Just append biases and do not transpose 1xW as it will be reshaped in GCGEMM
- _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped);
-
- weights = &_weights_reshaped;
-
- // Create tensor to store im2col reshaped inputs
- const unsigned int mat_input_cols = mat_weights_rows;
- const unsigned int mat_input_rows = conv_w * conv_h;
- TensorShape shape_im2col = input->info()->tensor_shape();
- shape_im2col.set(0, mat_input_cols);
- shape_im2col.set(1, mat_input_rows);
- shape_im2col.set(2, 1);
-
- // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
- TensorInfo im2col_reshaped_info(shape_im2col, 1, dt);
- _input_im2col_reshaped.allocator()->init(im2col_reshaped_info);
- _memory_group.manage(&_input_im2col_reshaped);
-
- // Create GEMM output tensor
- TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
- shape_gemm.set(0, mat_weights_cols);
- shape_gemm.set(1, mat_input_rows);
- const DataType gemm_data_type = dt;
-
- // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
- TensorInfo info_gemm(shape_gemm, 1, gemm_data_type);
- _gemm_output.allocator()->init(info_gemm);
- _memory_group.manage(&_gemm_output);
-
- if(dt == DataType::F16)
- {
- BorderSize border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
- input->info()->extend_padding(border_size);
- _fill_border.configure(input, border_size, BorderMode::CONSTANT, PixelValue()); // for PAD of im2col fp16: consider it as border
- }
- // Configure im2col
- _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation);
-
- // Configure GEMM
- configure_mm(&_input_im2col_reshaped, weights, &_gemm_output);
-
- _input_im2col_reshaped.allocator()->allocate();
-
- // Configure Col2Im
- _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h));
- _gemm_output.allocator()->allocate();
-
- ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
-
- //Configure Activation Layer
- _is_activationlayer_enabled = act_info.enabled();
-
- if(_is_activationlayer_enabled)
- {
- _activationlayer_function.configure(output, nullptr, act_info);
- }
-
- ARM_COMPUTE_UNUSED(weights_info);
-}
-
-void GCConvolutionLayer::run()
-{
- prepare();
-
- MemoryGroupResourceScope scope_mg(_memory_group);
-
- // Run im2col
- GCScheduler::get().dispatch(_fill_border);
- GCScheduler::get().memory_barrier();
- GCScheduler::get().dispatch(_input_im2col_kernel);
-
- // Run gemm on reshaped matrices
- _mm_gemm.run();
- GCScheduler::get().memory_barrier();
-
- // Reshape output matrix
- GCScheduler::get().dispatch(_output_col2im_kernel, false);
- GCScheduler::get().memory_barrier();
-
- // Run Activation Layer
- if(_is_activationlayer_enabled)
- {
- _activationlayer_function.run();
- }
-}
-
-void GCConvolutionLayer::prepare()
-{
- if(!_is_prepared)
- {
- ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
-
- // Run weights reshaping and mark as unused
- _weights_reshaped.allocator()->allocate();
- _reshape_weights.run();
-
- // Mark original weights tensor as unused
- _original_weights->mark_as_unused();
-
- _is_prepared = true;
- }
-}
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