COMPMID-2779: Add support for generating synthetic int8 graphs.

Adds SyntheticDataTypeMutator, which is responsible for mutating graphs
to int8 and thus enable performance analysis on a wider range of models.

Change-Id: I9a00f0ae59421ab11952660f5115b5dcd9314aaf
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2418
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>

1 files changed, 261 insertions, 0 deletions

diff --git a/src/graph/mutators/SyntheticDataTypeMutator.cpp b/src/graph/mutators/SyntheticDataTypeMutator.cpp
new file mode 100644
index 0000000000..b318df956e
--- /dev/null
+++ b/src/graph/mutators/SyntheticDataTypeMutator.cpp
@@ -0,0 +1,261 @@
+/*
+ * Copyright (c) 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/graph/mutators/SyntheticDataTypeMutator.h"
+
+#include "arm_compute/graph/GraphBuilder.h"
+#include "arm_compute/graph/ITensorAccessor.h"
+#include "arm_compute/graph/Logger.h"
+#include "arm_compute/graph/Utils.h"
+#include "arm_compute/graph/nodes/Nodes.h"
+
+#include "arm_compute/core/utils/misc/Cast.h"
+
+#include <set>
+
+namespace arm_compute
+{
+namespace graph
+{
+namespace
+{
+/** Empty accessor class */
+class EmptyAccessor final : public graph::ITensorAccessor
+{
+public:
+    /** Default Constructor */
+    EmptyAccessor() = default;
+
+    // Inherited methods overriden:
+    bool access_tensor(ITensor &tensor) override
+    {
+        ARM_COMPUTE_UNUSED(tensor);
+        return true;
+    }
+};
+
+/** Check if the mutation pass can be applied
+ *
+ * @param[in] g Graph the mutation pass need to be applied on
+ *
+ * @return True if the pass can be applied else false
+ */
+bool is_mutation_supported(Graph &g)
+{
+    const std::set<NodeType> unsupported_node_types = { NodeType::DetectionOutputLayer,
+                                                        NodeType::NormalizationLayer,
+                                                        NodeType::PriorBoxLayer
+                                                      };
+
+    for(const auto &utype : unsupported_node_types)
+    {
+        if(!g.nodes(utype).empty())
+        {
+            return false;
+        }
+    }
+    return true;
+}
+
+/** Remove nodes that get optimized out during conversion
+ *
+ * @param[in, out] g Graph to remove the nodes from.
+ */
+void remove_optimized_nodes(Graph &g)
+{
+    const std::set<NodeType> optimized_node_types = { NodeType::BatchNormalizationLayer };
+
+    for(const auto &opt_type : optimized_node_types)
+    {
+        const std::vector<NodeID> opt_nodes_ids = g.nodes(opt_type);
+        for(const auto &node_id : opt_nodes_ids)
+        {
+            INode *node = g.node(node_id);
+
+            // Get input edge
+            Edge *input_edge = node->input_edge(0);
+            ARM_COMPUTE_ERROR_ON(input_edge == nullptr);
+
+            // Get producer node
+            INode       *producer         = input_edge->producer();
+            const EdgeID producer_edge_id = input_edge->producer_idx();
+            ARM_COMPUTE_ERROR_ON(producer == nullptr);
+
+            // Get driving nodes
+            std::vector<NodeIdxPair> driving_nodes = get_driving_nodes(*node);
+
+            // Remove node
+            g.remove_node(node->id());
+
+            // Update connections
+            for(auto &driving_node : driving_nodes)
+            {
+                g.add_connection(producer->id(), producer_edge_id, driving_node.node_id, driving_node.index);
+            }
+        }
+    }
+}
+
+/** Convert tensor meta-data
+ *
+ * @param[in,out] g Graph to convert tensors of.
+ */
+void convert_tensors(Graph &g)
+{
+    auto &tensors = g.tensors();
+    for(auto &tensor : tensors)
+    {
+        if(tensor != nullptr)
+        {
+            tensor->desc().data_type  = DataType::QASYMM8;
+            tensor->desc().quant_info = QuantizationInfo(0.125f, -10);
+        }
+    }
+}
+
+/** Convert special node
+ *
+ * @param[in,out] g                  Graph to convert tensors of.
+ * @param[in]     fnc                Conversion function.
+ * @param[in]     optional_arguments Conversion function arguments.
+ */
+template <typename NT>
+void convert_special_node(Graph &g, std::function<bool(INode *, Tensor *)> const &f)
+{
+    const std::vector<NodeID> nodes_ids = g.nodes(NT::node_type);
+    for(const auto &nodes_id : nodes_ids)
+    {
+        INode *node = arm_compute::utils::cast::polymorphic_downcast<NT *>(g.node(nodes_id));
+        ARM_COMPUTE_ERROR_ON(node == nullptr);
+
+        Tensor *output_tensor = node->output(0);
+        ARM_COMPUTE_ERROR_ON(output_tensor == nullptr);
+
+        f(node, output_tensor);
+    }
+}
+
+/** Converts special tensors
+ *
+ * @param[in,out] g Graph to convert tensors of.
+ */
+void convert_special_tensors(Graph &g)
+{
+    auto softmax_func = [](INode * node, Tensor * tensor)
+    {
+        ARM_COMPUTE_UNUSED(node);
+        tensor->desc().quant_info = QuantizationInfo(1.f / 256.f, 0);
+        return true;
+    };
+
+    auto act_func = [](INode * node, Tensor * tensor)
+    {
+        auto *act_node = arm_compute::utils::cast::polymorphic_downcast<ActivationLayerNode *>(node);
+        if(act_node->activation_info().activation() == ActivationLayerInfo::ActivationFunction::TANH)
+        {
+            tensor->desc().quant_info = QuantizationInfo(1.f / 128.f, 128);
+        }
+        return true;
+    };
+
+    convert_special_node<ActivationLayerNode>(g, act_func);
+    convert_special_node<SoftmaxLayerNode>(g, softmax_func);
+}
+
+/** Handle nodes with bias
+ *
+ * @note Special tensors are for now biases that the data type differ
+ *
+ * @param[in,out] g Graph to convert tensors of.
+ */
+void handle_nodes_with_bias(Graph &g)
+{
+    const std::set<NodeType> special_node_types = { NodeType::ConvolutionLayer,
+                                                    NodeType::DeconvolutionLayer,
+                                                    NodeType::DepthwiseConvolutionLayer,
+                                                    NodeType::FullyConnectedLayer
+                                                  };
+
+    for(const auto &spc_type : special_node_types)
+    {
+        const std::vector<NodeID> scp_nodes_ids = g.nodes(spc_type);
+        for(const auto &node_id : scp_nodes_ids)
+        {
+            INode *node = g.node(node_id);
+            if(node != nullptr)
+            {
+                Tensor *tensor = node->input(2);
+                if(tensor != nullptr)
+                {
+                    tensor->desc().data_type = DataType::S32;
+                }
+                else
+                {
+                    auto params = node->common_node_params();
+                    params.name = params.name.empty() ? "" : params.name + "Bias";
+
+                    TensorDescriptor b_desc = node->input(1)->desc();
+                    auto             depth  = b_desc.shape[get_dimension_idx(b_desc.layout, DataLayoutDimension::BATCHES)];
+                    b_desc.shape            = TensorShape(depth);
+
+                    auto accessor = support::cpp14::make_unique<EmptyAccessor>();
+                    auto b_nid    = GraphBuilder::add_const_node(g, params, b_desc, std::move(accessor));
+                    g.add_connection(b_nid, 0, node_id, 2);
+                }
+            }
+        }
+    }
+}
+} // namespace
+
+const char *SyntheticDataTypeMutator::name()
+{
+    return "SyntheticDataTypeMutator";
+}
+
+IGraphMutator::MutationType SyntheticDataTypeMutator::type() const
+{
+    return IGraphMutator::MutationType::IR;
+}
+
+void SyntheticDataTypeMutator::mutate(Graph &g)
+{
+    if(is_mutation_supported(g))
+    {
+        // Remove nodes that get optimized out (e.g. BatchNorm)
+        remove_optimized_nodes(g);
+
+        // Convert tensor
+        convert_tensors(g);
+        convert_special_tensors(g);
+
+        // Handle special nodes
+        handle_nodes_with_bias(g);
+    }
+    else
+    {
+        ARM_COMPUTE_LOG_GRAPH_VERBOSE("Synthetic data type mutator couldn't be applied" << std::endl);
+    }
+}
+} // namespace graph
+} // namespace arm_compute