APPBROWSER-377: GCConvoutionLayer support for FP16

Change-Id: I801b5e393a16a9f92c062826e6fcfd5982ca7bb3
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/116584
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

2 files changed, 287 insertions, 2 deletions

diff --git a/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp b/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp
new file mode 100644
index 0000000000..5689722340
--- /dev/null
+++ b/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp
@@ -0,0 +1,285 @@
+/*
+ * Copyright (c) 2017-2018 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/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(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
+{
+}
+
+void GCConvolutionLayerReshapeWeights::configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, bool transpose1xW)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
+    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 unsigned   bias_element  = (append_biases) ? 1 : 0;
+    const IGCTensor *biases_to_use = (append_biases) ? biases : nullptr;
+
+    _transpose1xW = transpose1xW;
+
+    if(transpose1xW)
+    {
+        // Create tensor to store the reshaped weights
+        const unsigned int mat_weights_cols = weights->info()->dimension(3);
+        const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
+        TensorShape        shape_wr(mat_weights_cols, mat_weights_rows);
+        const DataType     dt                   = weights->info()->data_type();
+        const int          fixed_point_position = weights->info()->fixed_point_position();
+        TensorInfo         info_wr(shape_wr, 1, dt, fixed_point_position);
+
+        _weights_reshaped.allocator()->init(info_wr);
+        _weights_reshape_kernel.configure(weights, biases_to_use, &_weights_reshaped);
+        _weights_transposed_kernel.configure(&_weights_reshaped, output);
+        _weights_reshaped.allocator()->allocate();
+    }
+    else
+    {
+        _weights_reshape_kernel.configure(weights, biases_to_use, output);
+    }
+}
+
+void GCConvolutionLayerReshapeWeights::run()
+{
+    GCScheduler::get().dispatch(_weights_reshape_kernel);
+    if(_transpose1xW)
+    {
+        GCScheduler::get().dispatch(_weights_transposed_kernel);
+    }
+}
+
+GCConvolutionLayer::GCConvolutionLayer()
+    : _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _fill_border(), _input_im2col_reshaped(), _input_interleaved_reshaped(),
+      _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
+{
+}
+
+void GCConvolutionLayer::configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output, bool is_interleaved_transposed)
+{
+    _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed);
+}
+
+void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+{
+    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(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
+    ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
+
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3));
+        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
+    }
+
+    const DataType dt = input->info()->data_type();
+
+    _append_bias          = (biases != nullptr);
+    _are_weights_reshaped = weights_info.are_reshaped();
+
+    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  = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0);
+    const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : 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);
+
+    // Check if its a "fully connected" convolution
+    _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+    const bool run_interleaved      = (!_is_fully_connected_convolution);
+
+    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;
+
+    // Reshape weights if needed
+    if(_are_weights_reshaped)
+    {
+        if(_is_fully_connected_convolution)
+        {
+            mat_weights_cols = weights->info()->dimension(0);
+            mat_weights_rows = weights->info()->dimension(1);
+        }
+        else
+        {
+            mat_weights_cols                         = weights_info.num_kernels();
+            const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4;
+            mat_weights_rows                         = quarter_reshaped_cols + bias_element;
+        }
+    }
+    else
+    {
+        if(_is_fully_connected_convolution)
+        {
+            // Create tensor to store the reshaped weights
+            int num_elems_read_per_iteration_x = 1;
+            if(dt == DataType::F16)
+            {
+                num_elems_read_per_iteration_x = 2;
+            }
+            TensorShape shape_wr((ceil_to_multiple(mat_weights_cols, num_elems_read_per_iteration_x)), mat_weights_rows);
+            _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wr));
+            _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false /* 1xW transpose */);
+        }
+        else
+        {
+            // Create tensor to store transposed weights
+            const float transpose_width = 16.0f / input->info()->element_size();
+            TensorShape shape_wt(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
+            _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wt));
+            _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, true /* 1xW transpose */);
+        }
+        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->info()->fixed_point_position());
+    _input_im2col_reshaped.allocator()->init(im2col_reshaped_info);
+
+    // Create tensor (interleave) to prepare input tensor for GEMM
+    if(run_interleaved)
+    {
+        TensorShape shape_interleaved = shape_im2col;
+        shape_interleaved.set(0, shape_interleaved.x() * 4);
+        shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
+
+        // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
+        TensorInfo interleaved_info(shape_interleaved, 1, dt, input->info()->fixed_point_position());
+        _input_interleaved_reshaped.allocator()->init(interleaved_info);
+    }
+
+    // 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, input->info()->fixed_point_position());
+    _gemm_output.allocator()->init(info_gemm);
+
+    // Configure kernels
+    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(0)); // for PAD of im2col fp16: consider it as border
+    }
+    _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias);
+
+    // Configure matrix multiply
+    if(run_interleaved)
+    {
+        _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
+        configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output);
+        _input_interleaved_reshaped.allocator()->allocate();
+    }
+    else
+    {
+        configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, false);
+    }
+    _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");
+
+    // Allocate intermediate tensor
+    if(!_are_weights_reshaped)
+    {
+        _weights_reshaped.allocator()->allocate();
+    }
+}
+
+void GCConvolutionLayer::run()
+{
+    // Run weights reshaping (Runs once for every configure)
+    if(!_are_weights_reshaped)
+    {
+        _are_weights_reshaped = true;
+        _reshape_weights.run();
+    }
+
+    // Run im2col
+    GCScheduler::get().dispatch(_fill_border);
+    GCScheduler::get().memory_barrier();
+    GCScheduler::get().dispatch(_input_im2col_kernel);
+
+    if(!_is_fully_connected_convolution)
+    {
+        GCScheduler::get().memory_barrier();
+        // Run interleave4x4
+        GCScheduler::get().dispatch(_input_interleave_kernel);
+    }
+
+    GCScheduler::get().memory_barrier();
+    // Runs matrix multiply on reshaped matrices
+    GCScheduler::get().dispatch(_mm_kernel);
+
+    GCScheduler::get().memory_barrier();
+    // Reshape output matrix
+    GCScheduler::get().dispatch(_output_col2im_kernel, false);
+}
diff --git a/src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp b/src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp
index 041622d255..9e4f0f6c95 100644
--- a/src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp
+++ b/src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -61,7 +61,7 @@ void GCFullyConnectedLayer::configure_conv_fc(const IGCTensor *input, const IGCT
     _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt));
 
     // Configure im2col kernel
-    _im2col_kernel.configure(input, &_im2col_output, std::make_pair(1, 1), PadStrideInfo(1, 1, 0, 0), false);
+    _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
 
     // Configure matrix multiply kernel
     _mm_kernel.configure(&_im2col_output, weights, output, 1.0f, false);