COMPMID-631: Merge branches/gles_compute branch

Last commit:
commit b25c5f68042b0c81bf611d59a1bb8535e1c42497
Author: Xinghang Zhou <xinghang.zhou@arm.com>
Date:   Wed Oct 25 18:48:10 2017 +0800

    Synced validation's tolerances of GCSoftmax from cl side

Change-Id: Ibe72054205c1c8721845d679a31af7ed0a7c5cf6
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/93283
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>

1 files changed, 177 insertions, 0 deletions

diff --git a/src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp b/src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp
new file mode 100644
index 0000000000..63cb40e616
--- /dev/null
+++ b/src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp
@@ -0,0 +1,177 @@
+/*
+ * 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/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.h"
+
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/runtime/GLES_COMPUTE/GCScheduler.h"
+#include "support/ToolchainSupport.h"
+
+#include <algorithm>
+
+using namespace arm_compute;
+
+void GCFullyConnectedLayerReshapeWeights::configure(const IGCTensor *input, IGCTensor *output)
+{
+    auto k = arm_compute::support::cpp14::make_unique<GCTransposeKernel>();
+    k->configure(input, output);
+    _kernel = std::move(k);
+}
+
+GCFullyConnectedLayer::GCFullyConnectedLayer()
+    : _im2col_kernel(), _reshape_weights_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(), _reshape_weights_output(), _are_weights_reshaped(true), _is_fc_after_conv(true),
+      _accumulate_biases(false)
+{
+}
+
+void GCFullyConnectedLayer::configure_conv_fc(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output)
+{
+    ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));
+
+    const DataType dt = input->info()->data_type();
+
+    // If the fully connected layer is called after a convolution layer, the input tensor must be linearized
+
+    // Initialize output tensor for im2col
+    TensorShape shape_im2col;
+    shape_im2col.set(0, input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2));
+    shape_im2col.set(1, input->info()->dimension(3));
+    shape_im2col.set(2, input->info()->dimension(4));
+    shape_im2col.set(3, input->info()->dimension(5));
+    _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);
+
+    // Configure matrix multiply kernel
+    _mm_kernel.configure(&_im2col_output, weights, output, 1.0f, false);
+
+    // Allocate the output tensor for im2col once all the configure methods have been called
+    _im2col_output.allocator()->allocate();
+}
+
+void GCFullyConnectedLayer::configure_fc_fc(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output)
+{
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));
+
+    // Configure matrix multiply kernel
+    _mm_kernel.configure(input, weights, output, 1.0f, false);
+}
+
+void GCFullyConnectedLayer::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, bool transpose_weights, bool are_weights_reshaped)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
+    ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 2);
+
+    _are_weights_reshaped = transpose_weights ? are_weights_reshaped : true;
+    _is_fc_after_conv     = true;
+    _accumulate_biases    = false;
+
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+
+        _accumulate_biases = true;
+
+        // Configure accumulate biases kernel
+        _accumulate_biases_kernel.configure(output, biases);
+    }
+
+    // With the Fully Connected layer we can have 4 different cases:
+    //  1) Convolution layer -> Fully Connected layer without batches
+    //  2) Fully Connected layer -> Fully Connected layer without batches
+    //  3) Convolution layer -> Fully Connected layer with batches
+    //  4) Fully Connected layer -> Fully Connected layer with batches
+
+    const IGCTensor *weights_to_use = weights;
+
+    if(!_are_weights_reshaped)
+    {
+        weights_to_use = &_reshape_weights_output;
+
+        // Reshape the weights
+        _reshape_weights_kernel.configure(weights, &_reshape_weights_output);
+    }
+
+    // Check if we have a fully connected layer with batches
+    const bool is_batched_fc_layer = output->info()->dimension(1) > 1;
+
+    if(is_batched_fc_layer)
+    {
+        _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->info()->tensor_shape().cbegin() + 3,
+                                                                                  input->info()->tensor_shape().cend(),
+                                                                                  output->info()->tensor_shape().cbegin() + 1));
+    }
+    else
+    {
+        _is_fc_after_conv = input->info()->num_dimensions() > 1;
+    }
+
+    if(_is_fc_after_conv)
+    {
+        // Fully Connected layer after a Convolution Layer without batches
+        configure_conv_fc(input, weights_to_use, output);
+    }
+    else
+    {
+        // Fully Connected layer after a Fully Connected Layer without batches
+        configure_fc_fc(input, weights_to_use, output);
+    }
+
+    // Allocate the transpose tensor if the are_weights_reshaped flag is false and once all the configure methods have been called
+    if(!_are_weights_reshaped)
+    {
+        // Allocate the tensor for the weights reshaped
+        _reshape_weights_output.allocator()->allocate();
+    }
+}
+
+void GCFullyConnectedLayer::run()
+{
+    // Reshape of the weights (happens only once)
+    if(!_are_weights_reshaped)
+    {
+        _are_weights_reshaped = true;
+        _reshape_weights_kernel.run();
+    }
+
+    // Linearize input if it comes from a convolutional layer
+    if(_is_fc_after_conv)
+    {
+        GCScheduler::get().enqueue(_im2col_kernel, false);
+    }
+
+    GCScheduler::get().sync();
+
+    // Run matrix multiply
+    GCScheduler::get().enqueue(_mm_kernel, !_accumulate_biases);
+
+    // Accumulate biases if provided
+    if(_accumulate_biases)
+    {
+        GCScheduler::get().sync();
+
+        GCScheduler::get().enqueue(_accumulate_biases_kernel);
+    }
+}