COMPMID-797: Switch to new graph.

- Cleaned up build system

Change-Id: If2faa27ee5b31fa8b972836960ab3ef671059c8d
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/126435
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>

1 files changed, 0 insertions, 363 deletions

diff --git a/src/graph/nodes/ConvolutionLayer.cpp b/src/graph/nodes/ConvolutionLayer.cpp
deleted file mode 100644
index 5b3a84a4ad..0000000000
--- a/src/graph/nodes/ConvolutionLayer.cpp
+++ /dev/null
@@ -1,363 +0,0 @@
-/*
- * 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/graph/nodes/ConvolutionLayer.h"
-
-#include "arm_compute/graph/Error.h"
-#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h"
-#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h"
-#include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h"
-#include "arm_compute/runtime/IFunction.h"
-#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"
-#include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h"
-#include "arm_compute/runtime/NEON/functions/NEWinogradLayer.h"
-#include "support/ToolchainSupport.h"
-#include "utils/GraphTypePrinter.h"
-#include "utils/TypePrinter.h"
-
-#include <tuple>
-#include <vector>
-
-using namespace arm_compute::graph;
-
-namespace
-{
-/** Calculates the output shaped of the convolution layer
- *
- * @param[in] input_shape   Input tensor shape
- * @param[in] weights_shape Weights shape
- * @param[in] conv_info     Convolution information (padding, stride, etc.)
- *
- * @return The expected output tensor shape
- */
-TensorShape calculate_convolution_layer_output_shape(const TensorShape &input_shape, const TensorShape &weights_shape, const PadStrideInfo &conv_info)
-{
-    unsigned int output_width  = 0;
-    unsigned int output_height = 0;
-
-    // Get output width and height
-    std::tie(output_width, output_height) = arm_compute::scaled_dimensions(input_shape.x(), input_shape.y(), weights_shape.x(), weights_shape.y(), conv_info);
-
-    // Create output shape
-    TensorShape output_shape = input_shape;
-    output_shape.set(0, output_width);
-    output_shape.set(1, output_height);
-    output_shape.set(2, weights_shape[3]);
-
-    return output_shape;
-}
-
-// Instantiate GEMM based convolution layer
-template <typename ConvolutionType, typename TensorType, TargetHint target_hint>
-std::unique_ptr<arm_compute::IFunction> instantiate_function(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output,
-                                                             const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
-{
-    auto conv = arm_compute::support::cpp14::make_unique<ConvolutionType>();
-    conv->configure(
-        dynamic_cast<TensorType *>(input),
-        dynamic_cast<TensorType *>(weights),
-        dynamic_cast<TensorType *>(biases),
-        dynamic_cast<TensorType *>(output),
-        conv_info, weights_info);
-    return std::move(conv);
-}
-
-// Instantiate direct convolution layer
-template <typename ConvolutionType, typename TensorType, TargetHint target_hint>
-std::unique_ptr<arm_compute::IFunction> instantiate_direct_function(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output,
-                                                                    const PadStrideInfo &conv_info)
-{
-    auto conv = arm_compute::support::cpp14::make_unique<ConvolutionType>();
-    conv->configure(
-        dynamic_cast<TensorType *>(input),
-        dynamic_cast<TensorType *>(weights),
-        dynamic_cast<TensorType *>(biases),
-        dynamic_cast<TensorType *>(output),
-        conv_info);
-    return std::move(conv);
-}
-
-template <TargetHint                    target_hint>
-std::unique_ptr<arm_compute::IFunction> instantiate(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output,
-                                                    const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
-                                                    ConvolutionMethodHint conv_method);
-
-template <>
-std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output,
-                                                                        const PadStrideInfo &conv_info,
-                                                                        const WeightsInfo    &weights_info,
-                                                                        ConvolutionMethodHint conv_method)
-{
-    if((conv_method == ConvolutionMethodHint::WINOGRAD)
-       && arm_compute::CLWinogradConvolutionLayer::validate(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), conv_info)) // NOLINT
-    {
-        ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLWinogradConvolutionLayer");
-        return instantiate_direct_function<arm_compute::CLWinogradConvolutionLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, weights, biases, output, conv_info);
-    }
-    else if((conv_method == ConvolutionMethodHint::DIRECT)
-            && arm_compute::CLDirectConvolutionLayer::validate(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), conv_info)) // NOLINT
-    {
-        ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLDirectConvolutionLayer");
-        return instantiate_direct_function<arm_compute::CLDirectConvolutionLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, weights, biases, output, conv_info);
-    }
-    else
-    {
-        ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLConvolutionLayer");
-        return instantiate_function<arm_compute::CLConvolutionLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, weights, biases, output, conv_info, weights_info);
-    }
-}
-
-template <>
-std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output,
-                                                                      const PadStrideInfo &conv_info,
-                                                                      const WeightsInfo    &weights_info,
-                                                                      ConvolutionMethodHint conv_method)
-{
-    const unsigned int kernel_size_x = weights->info()->tensor_shape().x();
-    const unsigned int kernel_size_y = weights->info()->tensor_shape().y();
-    const unsigned int conv_stride_x = conv_info.stride().first;
-    const unsigned int conv_stride_y = conv_info.stride().second;
-
-    bool is_square_kernel = (kernel_size_x == kernel_size_y);
-    bool has_same_stride  = (conv_stride_x == conv_stride_y);
-
-    // TODO (COMPID-765) : Winograd should have a validate function
-    if(conv_method == ConvolutionMethodHint::WINOGRAD && is_square_kernel && ((kernel_size_x == 3) || (kernel_size_x == 5)) && has_same_stride && (conv_info.stride().first == 1))
-    {
-        ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEWinogradConvolutionLayer");
-        return instantiate_direct_function<arm_compute::NEWinogradLayer, arm_compute::ITensor, TargetHint::NEON>(input, weights, biases, output, conv_info);
-    }
-    else if((conv_method == ConvolutionMethodHint::DIRECT)
-            && arm_compute::NEDirectConvolutionLayer::validate(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), conv_info)) // NOLINT
-    {
-        ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEDirectConvolutionLayer");
-        return instantiate_direct_function<arm_compute::NEDirectConvolutionLayer, arm_compute::ITensor, TargetHint::NEON>(input, weights, biases, output, conv_info);
-    }
-    else
-    {
-        ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEConvolutionLayer");
-        return instantiate_function<arm_compute::NEConvolutionLayer, arm_compute::ITensor, TargetHint::NEON>(input, weights, biases, output, conv_info, weights_info);
-    }
-}
-} // namespace
-
-/** Grouped Convolution function */
-class GroupedConvolutionFunction final : public arm_compute::IFunction
-{
-public:
-    /** Default Constructor */
-    GroupedConvolutionFunction() = default;
-    /** Default Destructor */
-    ~GroupedConvolutionFunction() final = default;
-    /** Prevent instances from being copy constructed */
-    GroupedConvolutionFunction(const GroupedConvolutionFunction &) = delete;
-    /** Prevent instances from being copy assigned */
-    GroupedConvolutionFunction &operator=(const GroupedConvolutionFunction &) = delete;
-    /** Allow instances to be move constructed */
-    GroupedConvolutionFunction(GroupedConvolutionFunction &&) noexcept = default;
-    /** Allow instances to be move assigned */
-    GroupedConvolutionFunction &operator=(GroupedConvolutionFunction &&) noexcept = default;
-    /** Adds a convolution
-     *
-     * @param convolution Convolution function to add
-     */
-    void add_convolution_function(std::unique_ptr<IFunction> convolution) // NOLINT
-    {
-        _convolutions.emplace_back(std::move(convolution));
-    }
-
-    // Inherited methods overridden:
-    void run() override
-    {
-        for(auto &c : _convolutions)
-        {
-            c->run();
-        }
-    }
-
-private:
-    std::vector<std::unique_ptr<IFunction>> _convolutions{};
-};
-
-std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output)
-{
-    ARM_COMPUTE_ERROR_ON_UNALLOCATED_TENSOR_OBJECT(input, output);
-
-    arm_compute::ITensor *in  = input->tensor();
-    arm_compute::ITensor *out = output->tensor();
-
-    // Set weights and biases info
-    if(_weights.tensor() == nullptr)
-    {
-        TensorInfo info = TensorInfo(TensorShape(_conv_width, _conv_height, in->info()->dimension(2) / _num_groups, _ofm),
-                                     in->info()->num_channels(),
-                                     in->info()->data_type(),
-                                     in->info()->fixed_point_position());
-        info.set_quantization_info(_weights_quant_info);
-        _weights.set_info(std::move(info));
-    }
-    if(_biases.has_accessor() && _biases.tensor() == nullptr)
-    {
-        DataType dt = in->info()->data_type();
-        _biases.set_info(TensorInfo(TensorShape(_ofm), in->info()->num_channels(), is_data_type_quantized_asymmetric(dt) ? DataType::S32 : dt, in->info()->fixed_point_position()));
-    }
-
-    std::unique_ptr<arm_compute::IFunction> func;
-    _target_hint                                 = ctx.hints().target_hint();
-    const ConvolutionMethodHint conv_method_hint = ctx.hints().convolution_method_hint();
-
-    // Check if the weights and biases are loaded
-    bool weights_are_loaded = _weights.tensor() != nullptr;
-    bool biases_are_loaded  = _biases.has_accessor() ? _biases.tensor() != nullptr : true;
-
-    // Set bias and weights target
-    _weights.set_target(_target_hint);
-    if(_biases.has_accessor())
-    {
-        _biases.set_target(_target_hint);
-    }
-
-    // Calculate output shape
-    TensorShape output_shape = calculate_convolution_layer_output_shape(in->info()->tensor_shape(), _weights.info().tensor_shape(), _conv_info);
-
-    // Output auto inizialitation if not yet initialized
-    arm_compute::auto_init_if_empty(*out->info(), output_shape, 1, in->info()->data_type(), in->info()->fixed_point_position(),
-                                    (_out_quant_info.empty()) ? in->info()->quantization_info() : _out_quant_info);
-
-    // Create appropriate convolution function
-    if(_num_groups == 1)
-    {
-        func = instantiate_convolution(in, out, conv_method_hint);
-    }
-    else
-    {
-        func = instantiate_grouped_convolution(in, out, conv_method_hint);
-    }
-
-    // Fill weights
-    if(!weights_are_loaded)
-    {
-        _weights.allocate_and_fill_if_needed();
-    }
-    // Fill biases
-    if(!biases_are_loaded)
-    {
-        _biases.allocate_and_fill_if_needed();
-    }
-
-    ARM_COMPUTE_LOG_GRAPH_INFO(" Data Type: " << in->info()->data_type()
-                               << " Input Shape: " << in->info()->tensor_shape()
-                               << " Weights shape: " << _weights.info().tensor_shape()
-                               << " Biases Shape: " << _biases.info().tensor_shape()
-                               << " Output Shape: " << out->info()->tensor_shape()
-                               << " PadStrideInfo: " << _conv_info
-                               << " Groups: " << _num_groups
-                               << " WeightsInfo: " << _weights_info
-                               << std::endl);
-
-    return func;
-}
-
-std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_convolution(ITensor *input, ITensor *output, ConvolutionMethodHint conv_method_hint)
-{
-    std::unique_ptr<arm_compute::IFunction> func;
-    if(_target_hint == TargetHint::OPENCL)
-    {
-        func = instantiate<TargetHint::OPENCL>(input, _weights.tensor(), _biases.tensor(), output, _conv_info, _weights_info, conv_method_hint);
-    }
-    else
-    {
-        func = instantiate<TargetHint::NEON>(input, _weights.tensor(), _biases.tensor(), output, _conv_info, _weights_info, conv_method_hint);
-    }
-    return func;
-}
-
-std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_grouped_convolution(ITensor *input, ITensor *output, ConvolutionMethodHint conv_method_hint)
-{
-    // Get tensor shapes
-    TensorShape input_shape   = input->info()->tensor_shape();
-    TensorShape output_shape  = output->info()->tensor_shape();
-    TensorShape weights_shape = _weights.info().tensor_shape();
-    TensorShape biases_shape  = _biases.info().tensor_shape();
-
-    ARM_COMPUTE_ERROR_ON_MSG((input_shape.z() % _num_groups) != 0, "Input depth not multiple of the number of groups!");
-    ARM_COMPUTE_ERROR_ON_MSG((output_shape.z() % _num_groups) != 0, "Output depth not multiple of the number of groups!");
-    ARM_COMPUTE_ERROR_ON_MSG((weights_shape[3] % _num_groups) != 0, "Number of kernels not multiple of the number of groups!");
-    ARM_COMPUTE_ERROR_ON_MSG((biases_shape.x() % _num_groups) != 0, "Biases not multiple of the number of groups!");
-
-    // Create a grouped convolution function
-    auto grouped_conv = arm_compute::support::cpp14::make_unique<GroupedConvolutionFunction>();
-
-    // Create sub-tensors vectors
-    _is = arm_compute::support::cpp14::make_unique<SubTensor[]>(_num_groups);
-    _os = arm_compute::support::cpp14::make_unique<SubTensor[]>(_num_groups);
-    _ws = arm_compute::support::cpp14::make_unique<SubTensor[]>(_num_groups);
-    _bs = arm_compute::support::cpp14::make_unique<SubTensor[]>(_num_groups);
-
-    // Calculate sub-tensor splits
-    const int input_split   = input_shape.z() / _num_groups;
-    const int output_split  = output_shape.z() / _num_groups;
-    const int weights_split = weights_shape[3] / _num_groups;
-    const int biases_split  = biases_shape.x() / _num_groups;
-
-    // Calculate sub-tensor shapes
-    input_shape.set(2, input_split);
-    output_shape.set(2, output_split);
-    weights_shape.set(3, weights_split);
-    biases_shape.set(0, biases_split);
-
-    // Configure sub-tensors
-    for(int i = 0; i < static_cast<int>(_num_groups); ++i)
-    {
-        // Create convolution function
-        std::unique_ptr<arm_compute::IFunction> func;
-
-        // Calculate sub-tensors starting coordinates
-        Coordinates input_coord(0, 0, input_split * i);
-        Coordinates output_coord(0, 0, output_split * i);
-        Coordinates weights_coord(0, 0, 0, weights_split * i);
-        Coordinates biases_coord(biases_split * i);
-
-        // Create sub-tensors for input, output, weights and bias
-        auto hint_to_use = (_target_hint == TargetHint::OPENCL) ? TargetHint::OPENCL : TargetHint::NEON;
-        _is[i]           = SubTensor(input, input_shape, input_coord, hint_to_use);
-        _os[i]           = SubTensor(output, output_shape, output_coord, hint_to_use);
-        _ws[i]           = SubTensor(_weights.tensor(), weights_shape, weights_coord, hint_to_use);
-        _bs[i]           = SubTensor(_biases.tensor(), biases_shape, biases_coord, hint_to_use);
-
-        // Instantiate convolution function
-        if(_target_hint == TargetHint::OPENCL)
-        {
-            func = instantiate<TargetHint::OPENCL>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint);
-        }
-        else
-        {
-            func = instantiate<TargetHint::NEON>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint);
-        }
-
-        // Add convolution function to the list of convolutions for the grouped convolution
-        grouped_conv->add_convolution_function(std::move(func));
-    }
-
-    return std::move(grouped_conv);
-}