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, 394 insertions, 0 deletions

diff --git a/src/graph/GraphBuilder.cpp b/src/graph/GraphBuilder.cpp
new file mode 100644
index 0000000000..0d1bdc3596
--- /dev/null
+++ b/src/graph/GraphBuilder.cpp
@@ -0,0 +1,394 @@
+/*
+ * Copyright (c) 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/GraphBuilder.h"
+
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/Utils.h"
+#include "arm_compute/graph/algorithms/BFS.h"
+#include "arm_compute/graph/nodes/Nodes.h"
+
+#define CHECK_NODEIDX_PAIR(pair, g) \
+    ARM_COMPUTE_ERROR_ON(((pair).node_id >= (g).nodes().size()) || ((g).node((pair).node_id) == nullptr) || ((pair).index >= (g).node((pair).node_id)->num_outputs()));
+
+namespace arm_compute
+{
+namespace graph
+{
+namespace
+{
+Status set_node_params(Graph &g, NodeID nid, NodeParams &params)
+{
+    INode *node = g.node(nid);
+    ARM_COMPUTE_RETURN_ERROR_ON(!node);
+
+    node->set_common_node_parameters(params);
+
+    return Status{};
+}
+
+Status set_accessor_on_node(Graph &g, NodeID nid, bool is_output, size_t idx, ITensorAccessorUPtr accessor)
+{
+    INode *node = g.node(nid);
+    ARM_COMPUTE_RETURN_ERROR_ON(!node);
+
+    Tensor *tensor = is_output ? node->output(idx) : node->input(idx);
+    ARM_COMPUTE_RETURN_ERROR_ON(!tensor);
+
+    tensor->set_accessor(std::move(accessor));
+
+    return Status{};
+}
+
+NodeID add_const_node_with_name(Graph &g, NodeParams params, const std::string &name, TensorDescriptor desc, ITensorAccessorUPtr accessor)
+{
+    params.name = params.name.empty() ? "" : params.name + name;
+    auto nid    = GraphBuilder::add_const_node(g, params, desc, std::move(accessor));
+    set_node_params(g, nid, params);
+    return nid;
+}
+
+template <typename NT, typename... Args>
+NodeID create_simple_single_input_output_node(Graph &g, NodeParams &params, NodeIdxPair input, Args &&... args)
+{
+    CHECK_NODEIDX_PAIR(input, g);
+
+    NodeID nid = g.add_node<NT>(std::forward<Args>(args)...);
+    g.add_connection(input.node_id, input.index, nid, 0);
+    set_node_params(g, nid, params);
+
+    return nid;
+}
+
+NodeID create_grouped_convolution(Graph &g, NodeParams &params, NodeIdxPair input, NodeID weights, NodeID bias,
+                                  PadStrideInfo conv_info, ConvolutionMethod method, unsigned int num_groups)
+{
+    bool has_bias = (bias != EmptyNodeID);
+
+    // Split input
+    NodeID input_split = GraphBuilder::add_split_node(g, params, input, num_groups, 2);
+
+    // Split weights
+    NodeID weights_split = GraphBuilder::add_split_node(g, params, { weights, 0 }, num_groups, 3);
+
+    // Split bias
+    NodeID bias_split = EmptyNodeID;
+    if(has_bias)
+    {
+        // Split bias
+        bias_split = GraphBuilder::add_split_node(g, params, { bias, 0 }, num_groups, 0);
+    }
+
+    std::vector<NodeIdxPair> convolution_outputs;
+    for(unsigned int i = 0; i < num_groups; ++i)
+    {
+        NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, method);
+        g.add_connection(input_split, i, conv_nid, 0);
+        g.add_connection(weights_split, i, conv_nid, 1);
+        if(has_bias)
+        {
+            g.add_connection(bias_split, i, conv_nid, 2);
+        }
+        set_node_params(g, conv_nid, params);
+        convolution_outputs.push_back({ conv_nid, 0 });
+    }
+
+    // Depth concatenate output
+    return GraphBuilder::add_depth_concatenate_node(g, params, convolution_outputs);
+}
+} // namespace
+
+NodeID GraphBuilder::add_const_node(Graph &g, NodeParams params, TensorDescriptor desc, ITensorAccessorUPtr accessor)
+{
+    auto nid = g.add_node<ConstNode>(desc);
+    set_node_params(g, nid, params);
+    set_accessor_on_node(g, nid, true, 0, std::move(accessor));
+    return nid;
+}
+
+NodeID GraphBuilder::add_input_node(Graph &g, NodeParams params, TensorDescriptor desc, ITensorAccessorUPtr accessor)
+{
+    auto nid = g.add_node<InputNode>(desc);
+    set_node_params(g, nid, params);
+    set_accessor_on_node(g, nid, true, 0, std::move(accessor));
+    return nid;
+}
+
+NodeID GraphBuilder::add_output_node(Graph &g, NodeParams params, NodeIdxPair input, ITensorAccessorUPtr accessor)
+{
+    CHECK_NODEIDX_PAIR(input, g);
+
+    NodeID nid = g.add_node<OutputNode>();
+    g.add_connection(input.node_id, input.index, nid, 0);
+    set_node_params(g, nid, params);
+    set_accessor_on_node(g, nid, false, 0, std::move(accessor));
+
+    return nid;
+}
+
+NodeID GraphBuilder::add_activation_node(Graph &g, NodeParams params, NodeIdxPair input, ActivationLayerInfo act_info)
+{
+    return create_simple_single_input_output_node<ActivationLayerNode>(g, params, input, act_info);
+}
+
+NodeID GraphBuilder::add_batch_normalization_node(Graph &g, NodeParams params, NodeIdxPair input, float epsilon,
+                                                  ITensorAccessorUPtr mean_accessor, ITensorAccessorUPtr var_accessor,
+                                                  ITensorAccessorUPtr beta_accessor, ITensorAccessorUPtr gamma_accessor)
+{
+    CHECK_NODEIDX_PAIR(input, g);
+
+    bool has_beta  = (beta_accessor != nullptr);
+    bool has_gamma = (gamma_accessor != nullptr);
+
+    // Get input tensor descriptor
+    const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]);
+
+    // Calculate Common Descriptor
+    TensorDescriptor common_desc = input_tensor_desc;
+    common_desc.shape            = TensorShape(common_desc.shape.z());
+
+    // Create mean and nodes
+    auto mean_nid = add_const_node_with_name(g, params, "Mean", common_desc, std::move(mean_accessor));
+    auto var_nid  = add_const_node_with_name(g, params, "Variance", common_desc, std::move(var_accessor));
+
+    // Create beta node
+    NodeID beta_nid = EmptyNodeID;
+    if(has_beta)
+    {
+        beta_nid = add_const_node_with_name(g, params, "Beta", common_desc, std::move(beta_accessor));
+    }
+
+    // Create gamma node
+    NodeID gamma_nid = EmptyNodeID;
+    if(has_gamma)
+    {
+        gamma_nid = add_const_node_with_name(g, params, "Gamma", common_desc, std::move(gamma_accessor));
+    }
+
+    // Create batch normalization node and add connections
+    NodeID batch_norm_nid = g.add_node<BatchNormalizationLayerNode>(epsilon);
+    g.add_connection(input.node_id, input.index, batch_norm_nid, 0);
+    g.add_connection(mean_nid, 0, batch_norm_nid, 1);
+    g.add_connection(var_nid, 0, batch_norm_nid, 2);
+    if(has_beta)
+    {
+        g.add_connection(beta_nid, 0, batch_norm_nid, 3);
+    }
+    if(has_gamma)
+    {
+        g.add_connection(gamma_nid, 0, batch_norm_nid, 4);
+    }
+    set_node_params(g, batch_norm_nid, params);
+
+    return batch_norm_nid;
+}
+
+NodeID GraphBuilder::add_convolution_node(Graph &g, NodeParams params, NodeIdxPair input,
+                                          Size2D kernel_spatial_extend, unsigned int depth, PadStrideInfo conv_info,
+                                          unsigned int num_groups, ConvolutionMethod method,
+                                          ITensorAccessorUPtr weights_accessor, ITensorAccessorUPtr bias_accessor)
+{
+    CHECK_NODEIDX_PAIR(input, g);
+    ARM_COMPUTE_ERROR_ON(depth == 0);
+    ARM_COMPUTE_ERROR_ON((kernel_spatial_extend.width == 0) || (kernel_spatial_extend.height == 0));
+
+    bool has_bias = (bias_accessor != nullptr);
+
+    // Get input tensor descriptor
+    const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]);
+
+    // Create weights node
+    TensorDescriptor w_desc = input_tensor_desc;
+    w_desc.shape            = TensorShape(kernel_spatial_extend.width, kernel_spatial_extend.height, w_desc.shape.z() / num_groups, depth);
+    NodeID w_nid            = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor));
+
+    // Create bias nodes
+    NodeID b_nid = EmptyNodeID;
+    if(has_bias)
+    {
+        TensorDescriptor b_desc = input_tensor_desc;
+        b_desc.shape            = TensorShape(depth);
+        b_nid                   = add_const_node_with_name(g, params, "Bias", b_desc, std::move(bias_accessor));
+    }
+
+    if(num_groups == 1)
+    {
+        // Create convolution node and connect
+        NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, method);
+        g.add_connection(input.node_id, input.index, conv_nid, 0);
+        g.add_connection(w_nid, 0, conv_nid, 1);
+        if(has_bias)
+        {
+            g.add_connection(b_nid, 0, conv_nid, 2);
+        }
+        set_node_params(g, conv_nid, params);
+
+        return conv_nid;
+    }
+    else
+    {
+        return create_grouped_convolution(g, params, input, w_nid, b_nid, conv_info, method, num_groups);
+    }
+}
+
+NodeID GraphBuilder::add_depth_concatenate_node(Graph &g, NodeParams params, std::vector<NodeIdxPair> inputs)
+{
+    ARM_COMPUTE_ERROR_ON(inputs.size() == 0);
+
+    NodeID nid = g.add_node<DepthConcatenateLayerNode>(inputs.size());
+
+    unsigned int i = 0;
+    for(const auto &input : inputs)
+    {
+        CHECK_NODEIDX_PAIR(input, g);
+        g.add_connection(input.node_id, input.index, nid, i++);
+    }
+    set_node_params(g, nid, params);
+
+    return nid;
+}
+
+NodeID GraphBuilder::add_depthwise_convolution_node(Graph &g, NodeParams params, NodeIdxPair input, Size2D kernel_spatial_extend, PadStrideInfo conv_info,
+                                                    DepthwiseConvolutionMethod method,
+                                                    ITensorAccessorUPtr weights_accessor, ITensorAccessorUPtr bias_accessor)
+{
+    CHECK_NODEIDX_PAIR(input, g);
+    ARM_COMPUTE_ERROR_ON((kernel_spatial_extend.width == 0) || (kernel_spatial_extend.height == 0));
+
+    bool has_bias = (bias_accessor != nullptr);
+
+    // Get input tensor descriptor
+    const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]);
+
+    // Create weights node
+    TensorDescriptor w_desc = input_tensor_desc;
+    w_desc.shape            = TensorShape(kernel_spatial_extend.width, kernel_spatial_extend.height, w_desc.shape.z());
+    NodeID w_nid            = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor));
+
+    // Create bias nodes
+    NodeID b_nid = EmptyNodeID;
+    if(has_bias)
+    {
+        TensorDescriptor b_desc = input_tensor_desc;
+        b_desc.shape            = TensorShape(b_desc.shape.z());
+        b_nid                   = add_const_node_with_name(g, params, "Bias", b_desc, std::move(bias_accessor));
+    }
+
+    // Create convolution node and connect
+    NodeID conv_nid = g.add_node<DepthwiseConvolutionLayerNode>(conv_info, method);
+    g.add_connection(input.node_id, input.index, conv_nid, 0);
+    g.add_connection(w_nid, 0, conv_nid, 1);
+    if(has_bias)
+    {
+        g.add_connection(b_nid, 0, conv_nid, 2);
+    }
+    set_node_params(g, conv_nid, params);
+
+    return conv_nid;
+}
+
+NodeID GraphBuilder::add_elementwise_node(Graph &g, NodeParams params, NodeIdxPair input0, NodeIdxPair input1, EltwiseOperation operation)
+{
+    CHECK_NODEIDX_PAIR(input0, g);
+    CHECK_NODEIDX_PAIR(input1, g);
+
+    NodeID nid = g.add_node<EltwiseLayerNode>(operation);
+
+    g.add_connection(input0.node_id, input0.index, nid, 0);
+    g.add_connection(input1.node_id, input1.index, nid, 1);
+
+    set_node_params(g, nid, params);
+
+    return nid;
+}
+
+NodeID GraphBuilder::add_flatten_node(Graph &g, NodeParams params, NodeIdxPair input)
+{
+    return create_simple_single_input_output_node<FlattenLayerNode>(g, params, input);
+}
+
+NodeID GraphBuilder::add_fully_connected_layer(Graph &g, NodeParams params, NodeIdxPair input, unsigned int num_outputs,
+                                               ITensorAccessorUPtr weights_accessor, ITensorAccessorUPtr bias_accessor)
+{
+    CHECK_NODEIDX_PAIR(input, g);
+    ARM_COMPUTE_ERROR_ON(num_outputs == 0);
+
+    bool has_bias = (bias_accessor != nullptr);
+
+    // Get input tensor descriptor
+    const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]);
+
+    // Create weights node
+    TensorDescriptor w_desc = input_tensor_desc;
+    w_desc.shape            = FullyConnectedLayerNode::compute_weights_shape(input_tensor_desc.shape, num_outputs);
+    NodeID w_nid            = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor));
+
+    // Create bias nodes
+    NodeID b_nid = EmptyNodeID;
+    if(has_bias)
+    {
+        TensorDescriptor b_desc = input_tensor_desc;
+        b_desc.shape            = TensorShape(num_outputs);
+        b_nid                   = add_const_node_with_name(g, params, "Bias", b_desc, std::move(bias_accessor));
+    }
+
+    // Create convolution node and connect
+    NodeID fc_nid = g.add_node<FullyConnectedLayerNode>(num_outputs);
+    g.add_connection(input.node_id, input.index, fc_nid, 0);
+    g.add_connection(w_nid, 0, fc_nid, 1);
+    if(has_bias)
+    {
+        g.add_connection(b_nid, 0, fc_nid, 2);
+    }
+
+    set_node_params(g, fc_nid, params);
+
+    return fc_nid;
+}
+
+NodeID GraphBuilder::add_normalization_node(Graph &g, NodeParams params, NodeIdxPair input, NormalizationLayerInfo norm_info)
+{
+    return create_simple_single_input_output_node<NormalizationLayerNode>(g, params, input, norm_info);
+}
+
+NodeID GraphBuilder::add_pooling_node(Graph &g, NodeParams params, NodeIdxPair input, PoolingLayerInfo pool_info)
+{
+    return create_simple_single_input_output_node<PoolingLayerNode>(g, params, input, pool_info);
+}
+
+NodeID GraphBuilder::add_reshape_node(Graph &g, NodeParams params, NodeIdxPair input, TensorShape shape)
+{
+    return create_simple_single_input_output_node<ReshapeLayerNode>(g, params, input, shape);
+}
+
+NodeID GraphBuilder::add_softmax_node(Graph &g, NodeParams params, NodeIdxPair input, float beta)
+{
+    return create_simple_single_input_output_node<SoftmaxLayerNode>(g, params, input, beta);
+}
+
+NodeID GraphBuilder::add_split_node(Graph &g, NodeParams params, NodeIdxPair input, unsigned int num_splits, unsigned int axis)
+{
+    return create_simple_single_input_output_node<SplitLayerNode>(g, params, input, num_splits, axis);
+}
+} // namespace graph
+} // namespace arm_compute
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