Merge pull request #23219 from fengyuentau:add_gelu

fengyuentau · web-flow · commit c2b7c1f13b99 · 2023-02-10T18:03:29.000Z
Add GELU layer for vision transformers

* add gelu and gelu approximation

* drop setKernelParams
diff --git a/modules/dnn/include/opencv2/dnn/all_layers.hpp b/modules/dnn/include/opencv2/dnn/all_layers.hpp
@@ -806,6 +806,18 @@ CV__DNN_INLINE_NS_BEGIN
         static Ptr<SeluLayer> create(const LayerParams &params);
     };
 
+    class CV_EXPORTS GeluLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<GeluLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS GeluApproximationLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<GeluApproximationLayer> create(const LayerParams &params);
+    };
+
     class CV_EXPORTS ThresholdedReluLayer : public ActivationLayer
     {
     public:
diff --git a/modules/dnn/src/init.cpp b/modules/dnn/src/init.cpp
@@ -145,6 +145,8 @@ void initializeLayerFactory()
     CV_DNN_REGISTER_LAYER_CLASS(HardSigmoid,    HardSigmoidLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Selu,           SeluLayer);
     CV_DNN_REGISTER_LAYER_CLASS(ThresholdedRelu,ThresholdedReluLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Gelu,           GeluLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(GeluApproximation, GeluApproximationLayer);
     CV_DNN_REGISTER_LAYER_CLASS(BatchNorm,      BatchNormLayer);
     CV_DNN_REGISTER_LAYER_CLASS(MaxUnpool,      MaxUnpoolLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Dropout,        BlankLayer);
diff --git a/modules/dnn/src/layers/elementwise_layers.cpp b/modules/dnn/src/layers/elementwise_layers.cpp
@@ -837,6 +837,57 @@ struct BaseDefaultFunctor : public BaseFunctor
     static const char* const ocl_kernel_name;
 };
 
+struct GeluFunctor : public BaseDefaultFunctor<GeluFunctor>
+{
+    typedef GeluLayer Layer;
+
+    explicit GeluFunctor() {}
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV;
+    }
+
+    inline float calculate(float x) const
+    {
+        return 0.5f * x * (1.0f + erf(x * M_SQRT1_2));
+    }
+
+    int64 getFLOPSPerElement() const { return 100; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<GeluFunctor>::ocl_kernel_name = "GeluForward";
+
+namespace GeluApproximationConstants
+{
+    static constexpr float sqrt_2_pi = 0.7978845834732056f;
+    static constexpr float coef_sqrt_2_pi = 0.044714998453855515f * sqrt_2_pi;
+}
+
+struct GeluApproximationFunctor : public BaseDefaultFunctor<GeluApproximationFunctor>
+{
+    typedef GeluApproximationLayer Layer;
+
+    explicit GeluApproximationFunctor() {}
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV;
+    }
+
+    inline float calculate(float x) const
+    {
+        return 0.5f * x * (1.f + tanh(x * (GeluApproximationConstants::sqrt_2_pi +
+                                           GeluApproximationConstants::coef_sqrt_2_pi * x * x)));
+    }
+
+    int64 getFLOPSPerElement() const { return 100; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<GeluApproximationFunctor>::ocl_kernel_name = "GeluApproximationForward";
+
 struct TanHFunctor : public BaseDefaultFunctor<TanHFunctor>
 {
     typedef TanHLayer Layer;
@@ -2694,6 +2745,22 @@ Ptr<ReLU6Layer> ReLU6Layer::create(const LayerParams& params)
     return l;
 }
 
+Ptr<GeluLayer> GeluLayer::create(const LayerParams& params)
+{
+    Ptr<GeluLayer> l(new ElementWiseLayer<GeluFunctor>(GeluFunctor()));
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<GeluApproximationLayer> GeluApproximationLayer::create(const LayerParams& params)
+{
+    Ptr<GeluApproximationLayer> l(new ElementWiseLayer<GeluApproximationFunctor>(GeluApproximationFunctor()));
+    l->setParamsFrom(params);
+
+    return l;
+}
+
 Ptr<TanHLayer> TanHLayer::create(const LayerParams& params)
 {
     Ptr<TanHLayer> l(new ElementWiseLayer<TanHFunctor>());
diff --git a/modules/dnn/src/onnx/onnx_graph_simplifier.cpp b/modules/dnn/src/onnx/onnx_graph_simplifier.cpp
@@ -132,6 +132,183 @@ class ONNXGraphWrapper : public ImportGraphWrapper
     opencv_onnx::GraphProto& net;
 };
 
+/*  Fusion for Gelu.
+
+    Graph before fusion:
+           +---------------------------------------------+
+           |                                             |
+        [Input] -> Div[B=sqrt(2)] -> Erf -> Add[B=1] -> Mul -> Mul[B=0.5] -> [Output]
+
+    Graph after fusion:
+        [Input] -> Gelu -> [Output]
+
+*/
+class GeluSubGraph : public Subgraph
+{
+public:
+    GeluSubGraph()
+    {
+        int input = addNodeToMatch("");
+        int div = addNodeToMatch("Div", input, addNodeToMatch("") /* B=sqrt(2) */ );
+        int erf = addNodeToMatch("Erf", div);
+        int add = addNodeToMatch("Add", erf, addNodeToMatch("") /* B=1 */ );
+        int mul = addNodeToMatch("Mul", input, add);
+        addNodeToMatch("Mul", mul, addNodeToMatch("") /* B=0.5 */) ;
+
+        setFusedNode("Gelu", input);
+    }
+
+    static bool isWithInitializer(const std::vector<int>& matchedNodesIds)
+    {
+        // if node.getType() is Constant, Constant nodes are placed between other nodes
+        if (matchedNodesIds[2] - matchedNodesIds[1] != 1)
+            return false;
+        // if Initializer, there is no Constant node between other nodes
+        return true;
+    }
+
+    static float extractConstant(const Ptr<ImportGraphWrapper>& net, int node_id, int input_id, bool withInitializer)
+    {
+        if (withInitializer)
+        {
+            auto onnx_net = net.dynamicCast<ONNXGraphWrapper>();
+            int initializer_id = onnx_net->getInputInitializerId(node_id, input_id);
+            Mat const_mat = onnx_net->getMatFromInitializer(initializer_id);
+            return *const_mat.ptr<float>();
+        } else {
+            const Ptr<ImportNodeWrapper> node = net->getNode(node_id);
+            int constant_id = getInputNodeId(net, node, input_id);
+            Ptr<ImportNodeWrapper> constant_ptr = net->getNode(constant_id);
+            opencv_onnx::NodeProto* constant_node = constant_ptr.dynamicCast<ONNXNodeWrapper>()->node;
+            opencv_onnx::TensorProto constant_proto = constant_node->attribute(0).t();
+            Mat constant_mat = getMatFromTensor(constant_proto);
+            return *constant_mat.ptr<float>();
+        }
+    }
+
+    virtual bool match(const Ptr<ImportGraphWrapper>& net, int nodeId,
+                       std::vector<int>& matchedNodesIds,
+                       std::vector<int>& targetNodesIds) CV_OVERRIDE
+    {
+        if (Subgraph::match(net, nodeId, matchedNodesIds, targetNodesIds))
+        {
+            bool withInitializer = isWithInitializer(matchedNodesIds);
+
+            // Check Div[B=sqrt(2)]
+            float divisor = extractConstant(net, matchedNodesIds[0], 1, withInitializer);
+            if (divisor - M_SQRT2 >= 1e-6)
+                return false;
+
+            // Check Add[B=1]
+            float add_const = extractConstant(net, matchedNodesIds[2], 1, withInitializer);
+            if (add_const - 1.f >= 1e-6)
+                return false;
+
+            // Check Mul[B=0.5]
+            float mul_const = extractConstant(net, matchedNodesIds[4], 1, withInitializer);
+            if (mul_const - 0.5f >= 1e-6)
+                return false;
+
+            return true;
+        }
+        return false;
+    }
+};
+
+/*  Fusion for GeluApproximation.
+
+    Graph before fusion:
+           +--------+------+----------------+------------------------------------+
+           |        |      |                |                                    |
+        [Input] -> Mul -> Mul -> Mul[ ] -> Add -> Mul[ ] -> Tanh -> Add[A=1] -> Mul -> Mul(A=0.5) -> [Output]
+                                    /                  \
+                    A=0.044714998453855515          A=sqrt(2/pie)
+
+    Graph after fusion:
+        [Input] -> GeluApproximation -> [Output]
+
+*/
+class GeluApproximationSubGraph : public Subgraph
+{
+public:
+    GeluApproximationSubGraph()
+    {
+        int input = addNodeToMatch("");
+        int mul0 = addNodeToMatch("Mul", input, input);
+        int mul1 = addNodeToMatch("Mul", input, mul0);
+        int mul2 = addNodeToMatch("Mul", addNodeToMatch("") /* A=0.044714998453855515 */, mul1);
+        int add0 = addNodeToMatch("Add", input, mul2);
+        int mul3 = addNodeToMatch("Mul", addNodeToMatch("") /* A=sqrt(2/pie) */, add0);
+        int tanh = addNodeToMatch("Tanh", mul3);
+        int add1 = addNodeToMatch("Add", addNodeToMatch("") /* A=1 */, tanh);
+        int mul4 = addNodeToMatch("Mul", input, add1);
+        addNodeToMatch("Mul", addNodeToMatch("") /* A=0.5 */, mul4);
+
+        setFusedNode("GeluApproximation", input);
+    }
+
+    static bool isWithInitializer(const std::vector<int>& matchedNodesIds)
+    {
+        // if node.getType() is Constant, Constant nodes are placed between other nodes
+        if (matchedNodesIds[2] - matchedNodesIds[1] != 1)
+            return false;
+        // if Initializer, there is no Constant node between other nodes
+        return true;
+    }
+
+    static float extractConstant(const Ptr<ImportGraphWrapper>& net, int node_id, int input_id, bool withInitializer)
+    {
+        if (withInitializer)
+        {
+            auto onnx_net = net.dynamicCast<ONNXGraphWrapper>();
+            int initializer_id = onnx_net->getInputInitializerId(node_id, input_id);
+            Mat const_mat = onnx_net->getMatFromInitializer(initializer_id);
+            return *const_mat.ptr<float>();
+        } else {
+            const Ptr<ImportNodeWrapper> node = net->getNode(node_id);
+            int constant_id = getInputNodeId(net, node, input_id);
+            Ptr<ImportNodeWrapper> constant_ptr = net->getNode(constant_id);
+            opencv_onnx::NodeProto* constant_node = constant_ptr.dynamicCast<ONNXNodeWrapper>()->node;
+            opencv_onnx::TensorProto constant_proto = constant_node->attribute(0).t();
+            Mat constant_mat = getMatFromTensor(constant_proto);
+            return *constant_mat.ptr<float>();
+        }
+    }
+
+    virtual bool match(const Ptr<ImportGraphWrapper>& net, int nodeId,
+                       std::vector<int>& matchedNodesIds,
+                       std::vector<int>& targetNodesIds) CV_OVERRIDE
+    {
+        if (Subgraph::match(net, nodeId, matchedNodesIds, targetNodesIds))
+        {
+            bool withInitializer = isWithInitializer(matchedNodesIds);
+
+            // Check Mul[A=0.044714998453855515]
+            float coef = extractConstant(net, matchedNodesIds[2], 0, withInitializer);
+            if (coef - 0.044714998453855515 >= 1e-6)
+                return false;
+
+            // Check Mul[A=sqrt(2/pie)]
+            float sqrt_2_pie = extractConstant(net, matchedNodesIds[4], 0, withInitializer);
+            if (sqrt_2_pie - 0.7978845834732056 >= 1e-6)
+                return false;
+
+            // Check Add[A=1]
+            float add_const = extractConstant(net, matchedNodesIds[6], 0, withInitializer);
+            if (add_const - 1.f >= 1e-6)
+                return false;
+
+            // Check Mul[A=0.5]
+            float mul_const = extractConstant(net, matchedNodesIds[8], 0, withInitializer);
+            if (mul_const - 0.5f >= 1e-6)
+                return false;
+
+            return true;
+        }
+        return false;
+    }
+};
+
 class LayerNormSubGraph : public Subgraph
 {
 public:
@@ -904,6 +1081,8 @@ class BatchNormalizationSubgraph2 : public BatchNormalizationSubgraphBase
 void simplifySubgraphs(opencv_onnx::GraphProto& net)
 {
     std::vector<Ptr<Subgraph> > subgraphs;
+    subgraphs.push_back(makePtr<GeluSubGraph>());
+    subgraphs.push_back(makePtr<GeluApproximationSubGraph>());
     subgraphs.push_back(makePtr<LayerNormSubGraph>());
     subgraphs.push_back(makePtr<GatherCastSubgraph>());
     subgraphs.push_back(makePtr<MulCastSubgraph>());
diff --git a/modules/dnn/src/onnx/onnx_importer.cpp b/modules/dnn/src/onnx/onnx_importer.cpp
@@ -4051,7 +4051,8 @@ void ONNXImporter::buildDispatchMap_ONNX_AI(int opset_version)
     std::vector<std::string> simpleLayers{"Acos", "Acosh", "Asin", "Asinh", "Atan", "Atanh", "Ceil", "Celu", "Cos",
                                           "Cosh", "Dropout", "Erf", "Exp", "Floor", "HardSigmoid", "HardSwish",
                                           "Identity", "Log", "Round", "Reciprocal", "Selu", "Sign", "Sigmoid", "Sin", "Sinh", "Softmax",
-                                          "Softplus", "Softsign", "Shrink", "Sqrt", "Tan", "ThresholdedRelu"};
+                                          "Softplus", "Softsign", "Shrink", "Sqrt", "Tan", "ThresholdedRelu", "Gelu",
+                                          "GeluApproximation"};
     for (const auto& name : simpleLayers)
     {
         dispatch[name] = &ONNXImporter::parseSimpleLayers;
diff --git a/modules/dnn/src/opencl/activations.cl b/modules/dnn/src/opencl/activations.cl
@@ -307,6 +307,30 @@ __kernel void ThresholdedReluForward(const int n, __global T* in, __global T* ou
         out[index] = (in[index] > alpha ? in[index] : 0.f);
 }
 
+__kernel void GeluForward(const int n, __global T* in, __global T* out)
+{
+    int index = get_global_id(0);
+    if (index < n)
+    {
+        T x = in[index];
+        out[index] = (T)0.5f * x * ( (T)1.f + erf(x * M_SQRT1_2) );
+    }
+}
+
+__kernel void GeluApproximationForward(const int n, __global T* in, __global T* out)
+{
+    // see GeluApproximationConstants from modules/dnn/src/layers/elementwise_layers.cpp
+    const T sqrt_2_pi = 0.7978845834732056f;
+    const T coef_sqrt_2_pi = 0.044714998453855515f * sqrt_2_pi;
+
+    int index = get_global_id(0);
+    if(index < n)
+    {
+        T x = in[index];
+        out[index] = (T)0.5f * x * ( (T)1.f + tanh(x * (sqrt_2_pi + coef_sqrt_2_pi * x * x)) );
+    }
+}
+
 __kernel void ShrinkForward(const int n, __global T* in, __global T* out,
                             const KERNEL_ARG_DTYPE bias,
                             const KERNEL_ARG_DTYPE lambd)
diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp
@@ -2456,6 +2456,12 @@ TEST_P(Test_ONNX_layers, LayerNormExpanded)
     testONNXModels("layer_norm_expanded_with_initializers");
 }
 
+TEST_P(Test_ONNX_layers, Gelu)
+{
+    testONNXModels("gelu");
+    testONNXModels("gelu_approximation");
+}
+
 INSTANTIATE_TEST_CASE_P(/**/, Test_ONNX_nets, dnnBackendsAndTargets());
 
 }} // namespace
