remove 5arg BNTraining (#1901)

* remove 5arg BNTraining

* Remove 5-arg BNWithStats from gpu/op.

src/ngraph/op/batch_norm.cpp

@@ -44,18 +44,6 @@ ngraph::op::BatchNormTraining::BatchNormTraining(double eps,
     constructor_validate_and_infer_types();
 }
 
-ngraph::op::BatchNormTraining::BatchNormTraining(double eps,
-                                                 std::shared_ptr<ngraph::Node> gamma,
-                                                 std::shared_ptr<ngraph::Node> beta,
-                                                 std::shared_ptr<ngraph::Node> input,
-                                                 std::shared_ptr<ngraph::Node> mean,
-                                                 std::shared_ptr<ngraph::Node> variance)
-    : Op("BatchNormTraining", check_single_output_args({gamma, beta, input, mean, variance}))
-    , m_epsilon(eps)
-{
-    constructor_validate_and_infer_types();
-}
-
 void ngraph::op::BatchNormInference::validate_and_infer_types()
 {
     element::Type result_et;

src/ngraph/op/batch_norm.hpp

@@ -54,31 +54,6 @@ namespace ngraph
                               std::shared_ptr<Node> beta,
                               std::shared_ptr<Node> input);
 
-            // In this version of BatchNorm:
-            //
-            // MEAN AND VARIANCE: provided by the 'mean' and 'variance' parameters.
-            //
-            // OUTPUT VALUE: a single tensor with the normalized value of 'input'.
-            // mean and variance will also be updated inplace
-            //
-            // AUTODIFF SUPPORT:
-            //   'generate_adjoints(...)' works as expected.
-            //
-            // SHAPE DETAILS:
-            //   gamma:    must have rank 1, with the same span as input's channel axis.
-            //   beta:     must have rank 1, with the same span as input's channel axis.
-            //   input:    must have rank >= 2. The second dimension represents the channel axis and
-            //             must have a span of at least 1.
-            //   mean:     must have rank 1, with the same span as input's channel axis.
-            //   variance: must have rank 1, with the same span as input's channel axis.
-            //   output:   shall have the same shape as 'input'.
-            BatchNormTraining(double eps,
-                              std::shared_ptr<ngraph::Node> gamma,
-                              std::shared_ptr<ngraph::Node> beta,
-                              std::shared_ptr<ngraph::Node> input,
-                              std::shared_ptr<ngraph::Node> mean,
-                              std::shared_ptr<ngraph::Node> variance);
-
             void validate_and_infer_types() override;
 
             double get_eps_value() const { return m_epsilon; }

src/ngraph/runtime/gpu/op/batch_norm.cpp

@@ -32,25 +32,6 @@ ngraph::op::gpu::BatchNormTrainingWithStats::BatchNormTrainingWithStats(
     set_output_type(output_index++, input->get_element_type(), channel_shape);
 }
 
-ngraph::op::gpu::BatchNormTrainingWithStats::BatchNormTrainingWithStats(
-    double eps,
-    std::shared_ptr<ngraph::Node> gamma,
-    std::shared_ptr<ngraph::Node> beta,
-    std::shared_ptr<ngraph::Node> input,
-    std::shared_ptr<ngraph::Node> mean,
-    std::shared_ptr<ngraph::Node> variance,
-    bool training)
-    : ngraph::op::BatchNormTraining(eps, gamma, beta, input, mean, variance)
-{
-    auto output_index = get_output_size();
-    set_output_size(output_index + 2);
-    Shape channel_shape{input->get_shape()[1]};
-    // saved batch mean
-    set_output_type(output_index++, input->get_element_type(), channel_shape);
-    // saved batch inverse variance
-    set_output_type(output_index++, input->get_element_type(), channel_shape);
-}
-
 std::shared_ptr<ngraph::Node> ngraph::op::gpu::BatchNormTrainingWithStats::copy_with_new_args(
     const NodeVector& new_args) const
 {

src/ngraph/runtime/gpu/op/batch_norm.hpp

@@ -39,14 +39,6 @@ namespace ngraph
                                            std::shared_ptr<Node> beta,
                                            std::shared_ptr<Node> input);
 
-                BatchNormTrainingWithStats(double eps,
-                                           std::shared_ptr<ngraph::Node> gamma,
-                                           std::shared_ptr<ngraph::Node> beta,
-                                           std::shared_ptr<ngraph::Node> input,
-                                           std::shared_ptr<ngraph::Node> mean,
-                                           std::shared_ptr<ngraph::Node> variance,
-                                           bool training = false);
-
             protected:
                 virtual std::shared_ptr<Node>
                     copy_with_new_args(const NodeVector& new_args) const override;