GRUCell operator.

src/ngraph/CMakeLists.txt

@@ -302,6 +302,8 @@ set (SRC
     op/fused/grn.hpp
     op/fused/group_conv.hpp
     op/fused/group_conv.cpp
+    op/fused/gru_cell.cpp
+    op/fused/gru_cell.hpp
     op/fused/lstm_cell.cpp
     op/fused/lstm_cell.hpp
     op/fused/mvn.cpp

src/ngraph/ngraph.hpp

@@ -102,6 +102,7 @@
 #include "ngraph/op/fused/gemm.hpp"
 #include "ngraph/op/fused/grn.hpp"
 #include "ngraph/op/fused/group_conv.hpp"
+#include "ngraph/op/fused/gru_cell.hpp"
 #include "ngraph/op/fused/hard_sigmoid.hpp"
 #include "ngraph/op/fused/lstm_cell.hpp"
 #include "ngraph/op/fused/mvn.hpp"

src/ngraph/op/fused/gru_cell.hpp

+//*****************************************************************************
+// Copyright 2017-2019 Intel Corporation
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//*****************************************************************************
+
+#pragma once
+
+#include <cstddef>
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "ngraph/node.hpp"
+#include "ngraph/op/fused/rnn_cell_base.hpp"
+#include "ngraph/op/util/activation_functions.hpp"
+#include "ngraph/op/util/fused_op.hpp"
+
+namespace ngraph
+{
+    namespace op
+    {
+        ///
+        /// \brief      Class for GRU cell node.
+        ///
+        /// \note       It follows notation and equations defined as in ONNX standard:
+        ///             https://github.com/onnx/onnx/blob/master/docs/Operators.md#GRU
+        ///
+        ///             Note this class represents only single *cell* and not whole GRU *layer*.
+        ///
+        class GRUCell : public util::FusedOp, public RNNCellBase
+        {
+        public:
+            ///
+            /// \brief      Constructs GRUCell node.
+            ///
+            /// \param[in]  X                 The input tensor with shape: [batch_size, input_size].
+            /// \param[in]  W                 The weight tensor with shape:
+            ///                               [gates_count * hidden_size, input_size].
+            /// \param[in]  R                 The recurrence weight tensor with shape:
+            ///                               [gates_count * hidden_size, hidden_size].
+            /// \param[in]  H_t               The hidden state tensor at current time step with
+            ///                               shape: [batch_size, hidden_size].
+            /// \param[in]  hidden_size  The number of hidden units for recurrent cell.
+            ///
+            GRUCell(const std::shared_ptr<Node>& X,
+                    const std::shared_ptr<Node>& W,
+                    const std::shared_ptr<Node>& R,
+                    const std::shared_ptr<Node>& H_t,
+                    std::size_t hidden_size);
+
+            ///
+            /// \brief      Constructs GRUCell node.
+            ///
+            /// \param[in]  X                 The input tensor with shape: [batch_size, input_size].
+            /// \param[in]  W                 The weight tensor with shape:
+            ///                               [gates_count * hidden_size, input_size].
+            /// \param[in]  R                 The recurrence weight tensor with shape:
+            ///                               [gates_count * hidden_size, hidden_size].
+            /// \param[in]  H_t               The hidden state tensor at current time step with
+            ///                               shape: [batch_size, hidden_size].
+            /// \param[in]  hidden_size       The number of hidden units for recurrent cell.
+            /// \param[in]  activations       The vector of activation functions used inside
+            ///                               recurrent cell.
+            /// \param[in]  activation_alpha  The vector of alpha parameters for activation
+            ///                               functions in order respective to activation list.
+            /// \param[in]  activation_beta   The vector of beta parameters for activation functions
+            ///                               in order respective to activation list.
+            /// \param[in]  clip              The value defining clipping range [-clip, clip] on
+            ///                               input of activation functions.
+            ///
+            GRUCell(const std::shared_ptr<Node>& X,
+                    const std::shared_ptr<Node>& W,
+                    const std::shared_ptr<Node>& R,
+                    const std::shared_ptr<Node>& H_t,
+                    std::size_t hidden_size,
+                    const std::vector<std::string>& activations,
+                    const std::vector<float>& activation_alpha,
+                    const std::vector<float>& activation_beta,
+                    float clip,
+                    bool linear_before_reset);
+
+            ///
+            /// \brief      Constructs GRUCell node.
+            ///
+            /// \param[in]  X                 The input tensor with shape: [batch_size, input_size].
+            /// \param[in]  W                 The weight tensor with shape:
+            ///                               [gates_count * hidden_size, input_size].
+            /// \param[in]  R                 The recurrence weight tensor with shape:
+            ///                               [gates_count * hidden_size, hidden_size].
+            /// \param[in]  H_t               The hidden state tensor at current time step with
+            ///                               shape: [batch_size, hidden_size].
+            /// \param[in]  hidden_size       The number of hidden units for recurrent cell.
+            /// \param[in]  B                 The bias tensor for input gate with shape:
+            ///                               [2 * gates_count * hidden_size].
+            /// \param[in]  activations       The vector of activation functions used inside
+            ///                               recurrent cell.
+            /// \param[in]  activation_alpha  The vector of alpha parameters for activation
+            ///                               functions in order respective to activation list.
+            /// \param[in]  activation_beta   The vector of beta parameters for activation functions
+            ///                               in order respective to activation list.
+            /// \param[in]  clip              The value defining clipping range [-clip, clip] on
+            ///                               input of activation functions.
+            ///
+            GRUCell(const std::shared_ptr<Node>& X,
+                    const std::shared_ptr<Node>& W,
+                    const std::shared_ptr<Node>& R,
+                    const std::shared_ptr<Node>& H_t,
+                    std::size_t hidden_size,
+                    const std::shared_ptr<Node>& B,
+                    const std::vector<std::string>& activations =
+                        std::vector<std::string>{"sigmoid", "tanh"},
+                    const std::vector<float>& activation_alpha = {},
+                    const std::vector<float>& activation_beta = {},
+                    float clip = 0.f,
+                    bool linear_before_reset = false);
+
+            virtual void pre_validate_and_infer_types() override;
+            virtual NodeVector decompose_op() const override;
+            virtual std::shared_ptr<Node>
+                copy_with_new_args(const NodeVector& new_args) const override;
+
+            bool get_linear_before_reset() const { return m_linear_before_reset; }
+        private:
+            ///
+            /// \brief      The input data tensor. Shape: [batch_size, input_size].
+            ///
+            std::shared_ptr<Node> m_X;
+            ///
+            /// \brief      The weight tensor. Shape: [gates_count * hidden_size, input_size].
+            ///
+            std::shared_ptr<Node> m_W;
+            ///
+            /// \brief      The recurrence weight tensor. Shape: [gates_count * hidden_size, hidden_size].
+            ///
+            std::shared_ptr<Node> m_R;
+            ///
+            /// \brief      The hidden state tensor at current time step. Shape: [batch_size, hidden_size].
+            ///
+            std::shared_ptr<Node> m_H_t;
+            ///
+            /// \brief      The bias tensor for the gates. Shape: [2 * gates_count * hidden_size].
+            /// \note       Concatenation of `[Wb[zrh], Rb[zrh]]`.
+            ///
+            std::shared_ptr<Node> m_B;
+            ///
+            /// \brief The Activation function f.
+            ///
+            ActivationFunction m_activation_f;
+            ///
+            /// \brief The Activation function g.
+            ///
+            ActivationFunction m_activation_g;
+
+            static constexpr std::size_t m_gates_count{3};
+            ///
+            /// \brief Control whether or not apply the linear transformation.
+            ///
+            /// \note The linear transformation may be applied when computing the output of hidden gate.
+            ///       It's done before multiplying by the output of the reset gate.
+            ///
+            bool m_linear_before_reset;
+        };
+    }
+}

src/ngraph/op/fused_op_tbl.hpp

@@ -26,6 +26,7 @@ NGRAPH_OP(Elu, ngraph::op)
 NGRAPH_OP(GRN, ngraph::op)
 NGRAPH_OP(Gemm, ngraph::op)
 NGRAPH_OP(GroupConvolution, ngraph::op)
+NGRAPH_OP(GRUCell, ngraph::op)
 NGRAPH_OP(HardSigmoid, ngraph::op)
 NGRAPH_OP(LSTMCell, ngraph::op)
 NGRAPH_OP(MVN, ngraph::op)

src/ngraph/runtime/intelgpu/intelgpu_backend.cpp

@@ -84,6 +84,7 @@
 #include "ngraph/op/fused/gemm.hpp"
 #include "ngraph/op/fused/grn.hpp"
 #include "ngraph/op/fused/group_conv.hpp"
+#include "ngraph/op/fused/gru_cell.hpp"
 #include "ngraph/op/fused/hard_sigmoid.hpp"
 #include "ngraph/op/fused/lstm_cell.hpp"
 #include "ngraph/op/fused/mvn.hpp"
@@ -2061,6 +2062,7 @@ shared_ptr<runtime::Executable>
         case OP_TYPEID::GatherND:
         case OP_TYPEID::GenerateMask:
         case OP_TYPEID::GRN:
+        case OP_TYPEID::GRUCell:
         case OP_TYPEID::HardSigmoid:
         case OP_TYPEID::LSTMCell:
         case OP_TYPEID::MVN:
@@ -2180,6 +2182,7 @@ bool runtime::intelgpu::IntelGPUBackend::is_supported_impl(const Node& node)
     case OP_TYPEID::Elu:
     case OP_TYPEID::Gemm:
     case OP_TYPEID::GRN:
+    case OP_TYPEID::GRUCell:
     case OP_TYPEID::LSTMCell:
     case OP_TYPEID::MVN:
     case OP_TYPEID::Normalize:

src/ngraph/serializer.cpp

@@ -73,6 +73,7 @@
 #include "ngraph/op/fused/gemm.hpp"
 #include "ngraph/op/fused/grn.hpp"
 #include "ngraph/op/fused/group_conv.hpp"
+#include "ngraph/op/fused/gru_cell.hpp"
 #include "ngraph/op/fused/hard_sigmoid.hpp"
 #include "ngraph/op/fused/lstm_cell.hpp"
 #include "ngraph/op/fused/mvn.hpp"
@@ -999,6 +1000,27 @@ static shared_ptr<ngraph::Function>
                 node = make_shared<op::GRN>(args[0], bias);
                 break;
             }
+            case OP_TYPEID::GRUCell:
+            {
+                auto hidden_size = node_js.at("hidden_size").get<size_t>();
+                auto clip = node_js.at("clip").get<float>();
+                auto activations = node_js.at("activations").get<vector<string>>();
+                auto activation_alpha = node_js.at("activation_alpha").get<vector<float>>();
+                auto activation_beta = node_js.at("activation_beta").get<vector<float>>();
+                auto linear_before_reset = node_js.at("linear_before_reset").get<bool>();
+                node = make_shared<op::GRUCell>(args[0],
+                                                args[1],
+                                                args[2],
+                                                args[3],
+                                                hidden_size,
+                                                args[4],
+                                                activations,
+                                                activation_alpha,
+                                                activation_beta,
+                                                clip,
+                                                linear_before_reset);
+                break;
+            }
             case OP_TYPEID::HardSigmoid:
             {
                 auto alpha = node_js.at("alpha").get<float>();
@@ -1989,6 +2011,17 @@ static json write(const Node& n, bool binary_constant_data)
         node["bias"] = tmp->get_bias();
         break;
     }
+    case OP_TYPEID::GRUCell:
+    {
+        auto tmp = dynamic_cast<const op::GRUCell*>(&n);
+        node["hidden_size"] = tmp->get_hidden_size();
+        node["clip"] = tmp->get_clip();
+        node["activations"] = tmp->get_activations();
+        node["activation_alpha"] = tmp->get_activation_alpha();
+        node["activation_beta"] = tmp->get_activation_beta();
+        node["linear_before_reset"] = tmp->get_linear_before_reset();
+        break;
+    }
     case OP_TYPEID::HardSigmoid:
     {
         auto tmp = dynamic_cast<const op::HardSigmoid*>(&n);