Merge pull request #15375 from mozga-intel/mozga-intel/batch_norm_ngraph_operator

Enable batch_norm operator for a ngraph engine

paddle/fluid/operators/ngraph/ngraph_bridge.cc

@@ -34,6 +34,8 @@ std::map<std::string,
         {"accuracy", NG_OPS::BuildAccuracyNode},
         {"conv2d", NG_OPS::BuildConv2dNode},
         {"conv2d_grad", NG_OPS::BuildConv2dGradNode},
+        {"batch_norm", NG_OPS::BuildBatchNormNode},
+        {"batch_norm_grad", NG_OPS::BuildBatchNormGradNode},
         {"elementwise_add", NG_OPS::BuildElementwiseAddNode},
         {"elementwise_add_grad", NG_OPS::BuildElementwiseAddGradNode},
         {"fill_constant", NG_OPS::BuildFillConstantNode},

paddle/fluid/operators/ngraph/ngraph_ops.h

@@ -22,6 +22,7 @@ limitations under the License. */
 #pragma once
 
 #include "ops/accuracy_op.h"
+#include "ops/batch_norm_op.h"
 #include "ops/binary_unary_op.h"
 #include "ops/conv2d_op.h"
 #include "ops/elementwise_add_op.h"

paddle/fluid/operators/ngraph/ops/batch_norm_op.h

+/*Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+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 <string>
+#include <vector>
+
+#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/operators/ngraph/ops/elementwise_node.h"
+#include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"
+#include "paddle/fluid/platform/ngraph_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace ngraphs {
+
+void BuildBatchNormNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+  auto& data_layout = op_attrs.Get<std::string>("data_layout");
+
+  auto bias = paddle::platform::GetInputNode(op, "Bias", ngb_node_map);
+  auto mean = paddle::platform::GetInputNode(op, "Mean", ngb_node_map);
+  auto variance = paddle::platform::GetInputNode(op, "Variance", ngb_node_map);
+  auto scale = paddle::platform::GetInputNode(op, "Scale", ngb_node_map);
+  auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+
+  const bool is_test = op_attrs.Get<bool>("is_test");
+  const float epsilon = op_attrs.Get<float>("epsilon");
+  const float momentum = op_attrs.Get<float>("momentum");
+
+  if (data_layout == "NHWC") {
+    x = paddle::platform::Nhwc2Nchw(x);
+  }
+
+  std::shared_ptr<ngraph::Node> mean_out, saved_mean, saved_variance,
+      variance_out, y;
+
+  if (!is_test) {
+    auto BN = std::make_shared<ngraph::op::BatchNormTraining>(epsilon, scale,
+                                                              bias, x);
+    y = std::make_shared<ngraph::op::GetOutputElement>(BN, 0);
+    saved_mean = std::make_shared<ngraph::op::GetOutputElement>(BN, 1);
+    saved_variance = std::make_shared<ngraph::op::GetOutputElement>(BN, 2);
+
+    mean_out = std::make_shared<ngraph::op::Add>(
+        paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
+            momentum, mean),
+        paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
+            1. - momentum, saved_mean));
+    variance_out = std::make_shared<ngraph::op::Add>(
+        paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
+            momentum, variance),
+        paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
+            1. - momentum, saved_variance));
+
+    if (data_layout == "NHWC") {
+      y = paddle::platform::Nchw2Nhwc(y);
+    }
+
+    paddle::platform::SetOutputNode(op, "MeanOut", mean_out, ngb_node_map);
+    paddle::platform::SetOutputNode(op, "VarianceOut", variance_out,
+                                    ngb_node_map);
+    paddle::platform::SetOutputNode(op, "SavedMean", saved_mean, ngb_node_map);
+    paddle::platform::SetOutputNode(op, "SavedVariance", saved_variance,
+                                    ngb_node_map);
+    paddle::platform::SetOutputNode(op, "Y", y, ngb_node_map);
+  } else {
+    y = std::make_shared<ngraph::op::BatchNormInference>(epsilon, scale, bias,
+                                                         x, mean, variance);
+    paddle::platform::SetOutputNode(op, "Y", y, ngb_node_map);
+  }
+}
+
+void BuildBatchNormGradNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+  auto& data_layout = op_attrs.Get<std::string>("data_layout");
+
+  auto bias = paddle::platform::GetInputNode(op, "Bias", ngb_node_map);
+  auto saved_mean =
+      paddle::platform::GetInputNode(op, "SavedMean", ngb_node_map);
+  auto saved_variance =
+      paddle::platform::GetInputNode(op, "SavedVariance", ngb_node_map);
+  auto scale = paddle::platform::GetInputNode(op, "Scale", ngb_node_map);
+  auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto dy = paddle::platform::GetInputNode(op, "Y@GRAD", ngb_node_map);
+  auto x_shape = x->get_shape();
+  auto dy_shape = dy->get_shape();
+
+  PADDLE_ENFORCE(x_shape.size() == 2 || x_shape.size() == 4,
+                 "BN grap input size needs to be 2 or 4");
+  PADDLE_ENFORCE_EQ(x_shape.size(), dy_shape.size(),
+                    "BN grap input and delta size needs to be equal");
+
+  if (x_shape.size() == 2) {
+    x = std::make_shared<ngraph::op::Reshape>(
+        x, ngraph::AxisVector{0, 1},
+        ngraph::Shape{x_shape.at(0), x_shape.at(1), 1, 1});
+    dy = std::make_shared<ngraph::op::Reshape>(
+        dy, ngraph::AxisVector{0, 1},
+        ngraph::Shape{dy_shape.at(0), dy_shape.at(1), 1, 1});
+  }
+
+  if (data_layout == "NHWC") {
+    x = paddle::platform::Nhwc2Nchw(dy);
+    dy = paddle::platform::Nhwc2Nchw(dy);
+  }
+  const float epsilon = op_attrs.Get<float>("epsilon");
+
+  auto bn_bprop = std::make_shared<ngraph::op::BatchNormTrainingBackprop>(
+      epsilon, scale, bias, x, saved_mean, saved_variance, dy);
+
+  std::shared_ptr<ngraph::Node> dx =
+      std::make_shared<ngraph::op::GetOutputElement>(bn_bprop, 0);
+  auto dscale = std::make_shared<ngraph::op::GetOutputElement>(bn_bprop, 1);
+  auto dbias = std::make_shared<ngraph::op::GetOutputElement>(bn_bprop, 2);
+  paddle::platform::SetOutputNode(op, "Bias@GRAD", dbias, ngb_node_map);
+  paddle::platform::SetOutputNode(op, "Scale@GRAD", dscale, ngb_node_map);
+  if (x_shape.size() == 2) {
+    paddle::platform::SetOutputNode(
+        op, "X@GRAD", paddle::platform::NgReshaper(dx, x_shape), ngb_node_map);
+  } else {
+    if (data_layout == "NHWC") {
+      dx = paddle::platform::Nchw2Nhwc(dx);
+    }
+    paddle::platform::SetOutputNode(op, "X@GRAD", dx, ngb_node_map);
+  }
+}
+}  // namespace ngraphs
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/platform/ngraph_helper.h

@@ -23,6 +23,26 @@ limitations under the License. */
 namespace paddle {
 namespace platform {
 
+std::shared_ptr<ngraph::Node> Nhwc2Nchw(std::shared_ptr<ngraph::Node> in) {
+  auto in_shape = in->get_shape();
+  in_shape[0] = in->get_shape()[0];
+  in_shape[1] = in->get_shape()[3];
+  in_shape[2] = in->get_shape()[1];
+  in_shape[3] = in->get_shape()[2];
+  ngraph::AxisVector axis_vec = {0, 3, 1, 2};
+  return std::make_shared<ngraph::op::Reshape>(in, axis_vec, in_shape);
+}
+
+std::shared_ptr<ngraph::Node> Nchw2Nhwc(std::shared_ptr<ngraph::Node> in) {
+  auto in_shape = in->get_shape();
+  in_shape[0] = in->get_shape()[0];
+  in_shape[1] = in->get_shape()[2];
+  in_shape[2] = in->get_shape()[3];
+  in_shape[3] = in->get_shape()[1];
+  ngraph::AxisVector axis_vec = {0, 2, 3, 1};
+  return std::make_shared<ngraph::op::Reshape>(in, axis_vec, in_shape);
+}
+
 ngraph::Shape FlattenTo2d(ngraph::Shape sh, int num) {
   auto x1 = std::accumulate(std::begin(sh), std::begin(sh) + num, 1,
                             std::multiplies<size_t>());

python/paddle/fluid/tests/unittests/ngraph/test_batch_norm_ngraph_op.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import print_function
+
+import unittest
+from paddle.fluid.tests.unittests.test_batch_norm_op import TestBatchNormOpTraining, TestBatchNormOpInference
+
+
+class TestNGRAPHBatchNormOpTraining(TestBatchNormOpTraining):
+    def init_kernel_type(self):
+        super(TestNGRAPHBatchNormOpTraining, self).init_kernel_type()
+
+
+class TestNGRAPHBatchNormOpInference(TestBatchNormOpInference):
+    def init_kernel_type(self):
+        super(TestNGRAPHBatchNormOpInference, self).init_kernel_type()
+
+
+class TestNGRAPHBatchNormOpWithReluInference(TestBatchNormOpInference):
+    def init_kernel_type(self):
+        super(TestNGRAPHBatchNormOpWithReluInference, self).init_kernel_type()
+
+
+if __name__ == '__main__':
+    unittest.main()