[CPU] NormalizeL2 node migration on nGraph (#13)

a-sidorova · Apr 21, 2021 · ebdc143 · ebdc143
1 parent 3b06850
commit ebdc143
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Showing 19 changed files with 514 additions and 497 deletions.
diff --git a/inference-engine/src/mkldnn_plugin/CMakeLists.txt b/inference-engine/src/mkldnn_plugin/CMakeLists.txt
@@ -42,7 +42,7 @@ set(LAYERS
 #    ${CMAKE_CURRENT_SOURCE_DIR}/nodes/mkldnn_tensoriterator_node.cpp
 #    ${CMAKE_CURRENT_SOURCE_DIR}/nodes/mkldnn_tile_node.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/nodes/mkldnn_mvn_node.cpp
-#    ${CMAKE_CURRENT_SOURCE_DIR}/nodes/mkldnn_normalize_node.cpp
+   ${CMAKE_CURRENT_SOURCE_DIR}/nodes/mkldnn_normalize_node.cpp
 #    ${CMAKE_CURRENT_SOURCE_DIR}/nodes/mkldnn_scatter_update_node.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/nodes/mkldnn_interpolate_node.cpp
 #    ${CMAKE_CURRENT_SOURCE_DIR}/nodes/mkldnn_reduce_node.cpp

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_extension_utils.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_extension_utils.cpp
@@ -209,3 +209,19 @@ InferenceEngine::Precision MKLDNNExtensionUtils::getMaxPrecision(std::vector<Inf
 
     return InferenceEngine::Precision::UNSPECIFIED;
 }
+
+bool MKLDNNExtensionUtils::isPerTensorOrPerChannelBroadcastable(const InferenceEngine::SizeVector &firstInputDims,
+                                                                InferenceEngine::SizeVector secondInputDims) {
+    if (secondInputDims.size() > firstInputDims.size())
+        return false;
+    if (std::accumulate(secondInputDims.begin(), secondInputDims.end(), 1, std::multiplies<size_t>()) == 1)
+        return true;
+    for (size_t i = 0; i < (firstInputDims.size() - secondInputDims.size()); i++) {
+        secondInputDims.insert(secondInputDims.begin(), 1);
+    }
+    for (size_t i = 0; i < secondInputDims.size(); i++) {
+        if ((i == 1 && secondInputDims[i] != firstInputDims[1]) || (i != 1 && secondInputDims[i] != 1))
+            return false;
+    }
+    return true;
+}
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_extension_utils.h b/inference-engine/src/mkldnn_plugin/mkldnn_extension_utils.h
@@ -79,6 +79,7 @@ class MKLDNNExtensionUtils {
     static bool initTensorsAreEqual(const InferenceEngine::TensorDesc &desc1, const InferenceEngine::TensorDesc &desc2);
     static std::string getReorderArgs(const InferenceEngine::TensorDesc &parentDesc, const InferenceEngine::TensorDesc &childDesc);
     static InferenceEngine::Precision getMaxPrecision(std::vector<InferenceEngine::Precision> precisions);
+    static bool isPerTensorOrPerChannelBroadcastable(const InferenceEngine::SizeVector &firstInputDims, InferenceEngine::SizeVector secondInputDims);
 };
 
 }  // namespace MKLDNNPlugin
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
@@ -130,9 +130,8 @@ void MKLDNNGraphOptimizer::ApplyCommonGraphOptimizations(MKLDNNGraph &graph) {
     FuseInterpolateAndSimpleOperation(graph);
     graph.RemoveDroppedNodes();
 
-// TODO [NM]: transformation should be implemented w/o using of CNNLayer
-//    FuseNormalizeAndSimpleOperation(graph);
-//    graph.RemoveDroppedNodes();
+    FuseNormalizeL2AndSimpleOperation(graph);
+    graph.RemoveDroppedNodes();
 
     FuseEltwiseAndSimple(graph);
     graph.RemoveDroppedNodes();
@@ -1428,70 +1427,46 @@ void MKLDNNGraphOptimizer::FuseInterpolateAndSimpleOperation(MKLDNNGraph &graph)
     }
 }
 
-void MKLDNNGraphOptimizer::FuseNormalizeAndSimpleOperation(MKLDNNGraph &graph) {
-//    auto& graphNodes = graph.GetNodes();
-//
-//    auto isSutableParentNode = [](MKLDNNNodePtr node) {
-//        bool isSutableNormalize = node->getType() == Normalize;
-//
-//        if (isSutableNormalize) {
-//            return node->getChildEdges().size() == 1;
-//        } else {
-//            return false;
-//        }
-//    };
-//
-//    auto isSutableChildNode = [&](MKLDNNNodePtr node) {
-//        if (!node->getCnnLayer())
-//            return false;
-//
-//        if (node->getType() == Quantize) {
-//            auto* quantizeNode = dynamic_cast<MKLDNNQuantizeNode*>(node.get());
-//            if (quantizeNode == nullptr)
-//                IE_THROW() << "Cannot get quantize layer " << node->getName();
-//            return !quantizeNode->isBinarization();
-//        } else if (node->getType() == Eltwise) {
-//            auto *eltwiseNode = dynamic_cast<MKLDNNEltwiseNode *>(node.get());
-//            if (eltwiseNode == nullptr)
-//                IE_THROW() << "Cannot get Eltwise node " << node->getName();
-//            return IsOneOf(eltwiseNode->getOpType(), {Relu, Gelu, Elu, Logistic, BoundedRelu, Clamp, Tanh, Swish,
-//                                                      Hswish, Mish, Hsigmoid, Round, Linear, Abs, Square, Sqrt}) ||
-//                    ((eltwiseNode->getOpType() == MulAdd && eltwiseNode->getCnnLayer()->blobs.size() == 2) ||
-//                     (eltwiseNode->getOpType() == Prelu));
-//        }
-//
-//        return false;
-//    };
-//
-//    auto parent = graphNodes.begin();
-//    while (parent != graphNodes.end()) {
-//        auto parentNode = *parent;
-//        if (!isSutableParentNode(parentNode)) {
-//            parent++;
-//            continue;
-//        }
-//
-//        auto childNode = parentNode->getChildEdgeAt(0)->getChild();
-//        if (!isSutableChildNode(childNode)) {
-//            parent++;
-//            continue;
-//        }
-//
-//        parentNode->fuseWith(childNode);
-//
-//        if (childNode->getType() == Quantize || childNode->getType() == Eltwise) {
-//            auto parentEdges = childNode->parentEdges;
-//            for (auto &parentEdge : parentEdges) {
-//                auto p_edge = parentEdge.lock();
-//                if (p_edge->getParent()->getType() == Normalize)
-//                    continue;
-//
-//                removeEdge(graph, p_edge);
-//            }
-//        }
-//
-//        graph.DropNode(childNode);
-//    }
+void MKLDNNGraphOptimizer::FuseNormalizeL2AndSimpleOperation(MKLDNNGraph &graph) {
+    auto& graphNodes = graph.GetNodes();
+
+    auto isSutableParentNode = [](MKLDNNNodePtr node) {
+        if (node->getType() == NormalizeL2) {
+            return node->getChildEdges().size() == 1;
+        } else {
+            return false;
+        }
+    };
+
+    auto parent = graphNodes.begin();
+    while (parent != graphNodes.end()) {
+        auto parentNode = *parent;
+        if (!isSutableParentNode(parentNode)) {
+            parent++;
+            continue;
+        }
+
+        auto childNode = parentNode->getChildEdgeAt(0)->getChild();
+        if (!parentNode->canFuse(childNode)) {
+            parent++;
+            continue;
+        }
+
+        parentNode->fuseWith(childNode);
+
+        if (childNode->getType() == Quantize || childNode->getType() == Eltwise) {
+            auto parentEdges = childNode->parentEdges;
+            for (auto &parentEdge : parentEdges) {
+                auto p_edge = parentEdge.lock();
+                if (p_edge->getParent()->getType() == NormalizeL2)
+                    continue;
+
+                removeEdge(graph, p_edge);
+            }
+        }
+
+        graph.DropNode(childNode);
+    }
 }
 
 void MKLDNNGraphOptimizer::FuseEltwiseAndSimple(MKLDNNGraph &graph) {

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.h b/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.h
@@ -33,7 +33,7 @@ class MKLDNNGraphOptimizer {
     void FuseConvolutionSumAndConvolutionSumActivation(MKLDNNGraph &graph);
     void FuseMVNAndSimpleOperation(MKLDNNGraph &graph);
     void FuseInterpolateAndSimpleOperation(MKLDNNGraph &graph);
-    void FuseNormalizeAndSimpleOperation(MKLDNNGraph &graph);
+    void FuseNormalizeL2AndSimpleOperation(MKLDNNGraph &graph);
 
     void DropDoubleReorders(MKLDNNGraph& graph);
     void DropConvertReorder(MKLDNNGraph& graph);

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
@@ -187,7 +187,7 @@ static const InferenceEngine::details::caseless_unordered_map<std::string, Type>
 //        { "Memory", MemoryOutput },  // for construction from layer ctor
 //        { "Convert", Convert },
         { "MVN", MVN},
-//        { "Normalize", Normalize},
+       { "NormalizeL2", NormalizeL2},
 //        { "ScatterUpdate", ScatterUpdate},
 //        { "ScatterElementsUpdate", ScatterElementsUpdate},
 //        { "ScatterNDUpdate", ScatterNDUpdate},

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_node.h b/inference-engine/src/mkldnn_plugin/mkldnn_node.h
@@ -75,7 +75,7 @@ enum Type {
     TensorIterator,
     Convert,
     MVN,
-    Normalize,
+    NormalizeL2,
     ScatterUpdate,
     ScatterElementsUpdate,
     ScatterNDUpdate,
@@ -238,8 +238,8 @@ static std::string NameFromType(Type type) {
             return "TensorIterator";
         case Convert:
             return "Convert";
-        case Normalize:
-            return "Normalize";
+        case NormalizeL2:
+            return "NormalizeL2";
         case ScatterUpdate:
             return "ScatterUpdate";
         case ScatterElementsUpdate:
@@ -620,6 +620,10 @@ class MKLDNNNode : public InferenceEngine::details::no_copy {
         return algorithm;
     }
 
+    virtual bool canFuse(const MKLDNNNodePtr& node) const {
+        return false;
+    }
+
 protected:
     void setType(Type type) {
         this->type = type;

diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bin_conv_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bin_conv_node.h
@@ -86,7 +86,7 @@ class MKLDNNBinaryConvolutionNode : public MKLDNNNode {
         return false;
     }
     void setPostOps(mkldnn::primitive_attr &attr);
-    bool canFuse(const MKLDNNNodePtr& node) const;
+    bool canFuse(const MKLDNNNodePtr& node) const override;
 
 private:
     bool withSum = false;