Enable cross_entropy operator for a ngraph engine (#15674)

* Enable cross_entropy operator for a ngraph engine
test=develop

* Update tests
test=develop

* Added PADDLE_ENFORCE for the batch_norm operator
test=develop

* Update the message about which format are supported right now
test=develop

paddle/fluid/operators/ngraph/ngraph_bridge.cc

@@ -36,6 +36,8 @@ std::map<std::string,
         {"conv2d_grad", NG_OPS::BuildConv2dGradNode},
         {"batch_norm", NG_OPS::BuildBatchNormNode},
         {"batch_norm_grad", NG_OPS::BuildBatchNormGradNode},
+        {"cross_entropy", NG_OPS::BuildCrossEntropyNode},
+        {"cross_entropy_grad", NG_OPS::BuildCrossEntropyGradNode},
         {"elementwise_add", NG_OPS::BuildElementwiseAddNode},
         {"elementwise_add_grad", NG_OPS::BuildElementwiseAddGradNode},
         {"fill_constant", NG_OPS::BuildFillConstantNode},

paddle/fluid/operators/ngraph/ngraph_ops.h

@@ -26,6 +26,7 @@ limitations under the License. */
 #include "ops/batch_norm_op.h"
 #include "ops/binary_unary_op.h"
 #include "ops/conv2d_op.h"
+#include "ops/cross_entropy_op.h"
 #include "ops/elementwise_add_op.h"
 #include "ops/fill_constant_op.h"
 #include "ops/mean_op.h"

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

@@ -44,6 +44,10 @@ void BuildBatchNormNode(
   const float epsilon = op_attrs.Get<float>("epsilon");
   const float momentum = op_attrs.Get<float>("momentum");
 
+  PADDLE_ENFORCE(
+      data_layout == "NHWC" || data_layout == "NCHW" || data_layout == "NC",
+      "The BatchNorm operator only supports NHWC/NCHW/NC data format");
+
   if (data_layout == "NHWC") {
     x = paddle::platform::Nhwc2Nchw(x);
   }
@@ -110,6 +114,9 @@ void BuildBatchNormGradNode(
                  "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");
+  PADDLE_ENFORCE(
+      data_layout == "NHWC" || data_layout == "NCHW" || data_layout == "NC",
+      "The BatchNorm operator only supports NHWC/NCHW/NC data format");
 
   if (x_shape.size() == 2) {
     x = std::make_shared<ngraph::op::Reshape>(

paddle/fluid/operators/ngraph/ops/cross_entropy_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 <functional>
+#include <string>
+
+#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/platform/ngraph_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace ngraphs {
+
+void BuildCrossEntropyNode(
+    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 x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto label = paddle::platform::GetInputNode(op, "Label", ngb_node_map);
+  auto label_shape = label->get_shape();
+  auto x_shape = x->get_shape();
+  auto label_rank = label_shape.size();
+  auto x_rank = x_shape.size();
+  std::shared_ptr<ngraph::Node> x_2d = x, label_2d = label;
+  auto label_2d_shape = label_shape, x_2d_shape = x_shape;
+
+  if (label_rank > 2) {
+    label_2d_shape = paddle::platform::FlattenTo2d(label_shape, label_rank - 1);
+    label_2d = paddle::platform::NgReshaper(label, label_2d_shape);
+  }
+  if (x_rank > 2) {
+    x_2d_shape = paddle::platform::FlattenTo2d(x_shape, x_rank - 1);
+    x_2d = paddle::platform::NgReshaper(x, x_2d_shape);
+  }
+
+  auto batch_size = x_2d_shape.at(0);
+  auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+  const bool is_soft_label = op_attrs.Get<bool>("soft_label");
+
+  std::shared_ptr<ngraph::Node> node_1_hot = label_2d;
+  if (!is_soft_label) {
+    auto label_1d = paddle::platform::NgReshaper(
+        label_2d, ngraph::Shape{label_2d_shape.at(0)});
+    node_1_hot = std::make_shared<ngraph::op::OneHot>(label_1d, x_2d_shape, 1);
+  }
+  if (x->get_element_type() != node_1_hot->get_element_type()) {
+    node_1_hot = std::make_shared<ngraph::op::Convert>(node_1_hot,
+                                                       x->get_element_type());
+  }
+
+  auto node_log = std::make_shared<ngraph::op::Log>(x_2d);
+  auto high_clip = ngraph::op::Constant::create(node_log->get_element_type(),
+                                                node_log->get_shape(), {1e20});
+  auto low_clip = ngraph::op::Constant::create(node_log->get_element_type(),
+                                               node_log->get_shape(), {-1e20});
+  auto node_min = std::make_shared<ngraph::op::Minimum>(node_log, high_clip);
+  auto node_max = std::make_shared<ngraph::op::Maximum>(node_min, low_clip);
+  auto node_mul = node_1_hot * node_log;
+  auto node_sum =
+      std::make_shared<ngraph::op::Sum>(node_mul, ngraph::AxisSet{1});
+  auto node_neg = std::make_shared<ngraph::op::Negative>(node_sum);
+  auto xe =
+      paddle::platform::NgReshaper(node_neg, ngraph::Shape{batch_size, 1});
+
+  if (!is_soft_label) {
+    auto ignore_index = op_attrs.Get<int>("ignore_index");
+    auto ignore_node = ngraph::op::Constant::create(
+        label->get_element_type(), label_2d_shape, {ignore_index});
+    auto not_equal_node =
+        std::make_shared<ngraph::op::NotEqual>(label_2d, ignore_node);
+    auto mask = std::make_shared<ngraph::op::Convert>(not_equal_node,
+                                                      xe->get_element_type());
+    xe = xe * mask;
+  }
+
+  paddle::platform::SetOutputNode(op, "Y", xe, ngb_node_map);
+}
+
+void BuildCrossEntropyGradNode(
+    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());
+  const bool is_soft_label = op_attrs.Get<bool>("soft_label");
+
+  auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto label = paddle::platform::GetInputNode(op, "Label", ngb_node_map);
+  auto dy = paddle::platform::GetInputNode(op, "Y@GRAD", ngb_node_map);
+  auto x_shape = x->get_shape();
+  auto rank = x_shape.size();
+
+  std::shared_ptr<ngraph::Node> mask;
+  if (!is_soft_label) {
+    auto label_shape = label->get_shape();
+    label_shape.pop_back();
+    label = paddle::platform::NgReshaper(label, label_shape);
+
+    auto ignore_index = op_attrs.Get<int>("ignore_index");
+    auto ignore_node = ngraph::op::Constant::create(
+        label->get_element_type(), label_shape, {ignore_index});
+    auto not_equal_node =
+        std::make_shared<ngraph::op::NotEqual>(label, ignore_node);
+    mask = std::make_shared<ngraph::op::Convert>(not_equal_node,
+                                                 x->get_element_type());
+    mask = std::make_shared<ngraph::op::Broadcast>(mask, x_shape,
+                                                   ngraph::AxisSet{rank - 1});
+
+    label = std::make_shared<ngraph::op::OneHot>(label, x_shape, rank - 1);
+  }
+
+  auto dy_shape = dy->get_shape();
+  dy_shape.pop_back();
+  auto dy_reshape = paddle::platform::NgReshaper(dy, dy_shape);
+  auto dy_bcast = std::make_shared<ngraph::op::Broadcast>(
+      dy_reshape, x_shape, ngraph::AxisSet{rank - 1});
+  if (x->get_element_type() != label->get_element_type()) {
+    label = std::make_shared<ngraph::op::Convert>(label, x->get_element_type());
+  }
+
+  auto xe_grad = -label * dy_bcast / x;
+
+  if (!is_soft_label) {
+    xe_grad = xe_grad * mask;
+  }
+
+  paddle::platform::SetOutputNode(op, "X@GRAD", xe_grad, ngb_node_map);
+}
+}  // namespace ngraphs
+}  // namespace operators
+}  // namespace paddle

python/paddle/fluid/tests/unittests/ngraph/test_cross_entropy_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
+import numpy as np
+import paddle.fluid.core as core
+from paddle.fluid.tests.unittests.op_test import OpTest, randomize_probability
+
+
+class TestCrossEntropyOp(OpTest):
+    """Test cross-entropy with discrete one-hot labels.
+    """
+
+    def setUp(self):
+        self.op_type = "cross_entropy"
+        self.soft_label = False
+        self.ignore_index = -100
+        self.dtype = np.float64
+        self.batch_size = 30
+        self.class_num = 10
+        self._cpu_only = True
+
+        self.init_dtype_type()
+        self.init_attr_type()
+        self.init_bs_class_num()
+        self.init_x()
+        self.init_label()
+        self.get_cross_entropy()
+
+        self.inputs = {"X": self.x, "Label": self.label}
+        self.outputs = {"Y": self.cross_entropy}
+        self.attrs = {
+            "soft_label": self.soft_label,
+            "ignore_index": self.ignore_index
+        }
+
+    def init_x(self):
+        self.x = randomize_probability(
+            self.batch_size, self.class_num, dtype=self.dtype)
+
+    def init_label(self):
+        self.label = np.random.randint(
+            0, self.class_num, (self.batch_size, 1), dtype="int64")
+
+    def get_cross_entropy(self):
+        self.cross_entropy = np.asmatrix(
+            [[-np.log(self.x[i][self.label[i][0]])]
+             for i in range(self.x.shape[0])],
+            dtype="float64")
+
+    def init_attr_type(self):
+        pass
+
+    def init_dtype_type(self):
+        pass
+
+    def init_bs_class_num(self):
+        pass
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["X"], "Y", numeric_grad_delta=0.001)
+
+
+class TestCrossEntropyOp2(TestCrossEntropyOp):
+    """Test cross-entropy with vectorized soft labels.
+    """
+
+    def init_label(self):
+        self.label = np.random.uniform(
+            0.1, 1.0, [self.batch_size, self.class_num]).astype(self.dtype)
+        self.label /= self.label.sum(axis=1, keepdims=True)
+
+    def get_cross_entropy(self):
+        self.cross_entropy = (-self.label * np.log(self.x)).sum(
+            axis=1, keepdims=True).astype(self.dtype)
+
+    def init_attr_type(self):
+        self.soft_label = True
+
+    def init_dtype_type(self):
+        self.dtype = np.float32
+
+    def init_bs_class_num(self):
+        self.batch_size = 5
+        self.class_num = 37
+
+    def test_check_grad(self):
+        self.check_grad(
+            ["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
+
+
+class TestCrossEntropyOp3(TestCrossEntropyOp):
+    """Test cross-entropy with vectorized one-hot representation of labels.
+    """
+
+    def init_label(self):
+        self.label_index = np.random.randint(0, self.class_num,
+                                             (self.batch_size))
+        self.label = np.zeros(self.x.shape).astype(self.dtype)
+        self.label[np.arange(self.batch_size), self.label_index] = 1
+
+    def get_cross_entropy(self):
+        self.cross_entropy = np.asmatrix(
+            [[-np.log(self.x[i][self.label_index[i]])]
+             for i in range(self.x.shape[0])]).astype(self.dtype)
+
+    def init_attr_type(self):
+        self.soft_label = True
+
+    def init_dtype_type(self):
+        self.dtype = np.float32
+
+    def init_bs_class_num(self):
+        self.batch_size = 5
+        self.class_num = 17
+
+    def test_check_grad(self):
+        self.check_grad(
+            ["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
+
+
+class TestCrossEntropyOp4(TestCrossEntropyOp):
+    """Test high rank tensor cross-entropy with discrete one-hot labels.
+    """
+
+    def init_x(self):
+        self.shape = [10, 2, 4]
+        self.ins_num = np.prod(np.array(self.shape))
+        self.X_2d = randomize_probability(self.ins_num,
+                                          self.class_num).astype(self.dtype)
+        self.x = self.X_2d.reshape(self.shape + [self.class_num])
+
+    def init_label(self):
+        self.label_2d = np.random.randint(
+            0, self.class_num, (self.ins_num, 1), dtype="int64")
+        self.label = self.label_2d.reshape(self.shape + [1])
+
+    def get_cross_entropy(self):
+        cross_entropy_2d = np.asmatrix(
+            [[-np.log(self.X_2d[i][self.label_2d[i][0]])]
+             for i in range(self.X_2d.shape[0])]).astype(self.dtype)
+        self.cross_entropy = np.array(cross_entropy_2d).reshape(self.shape +
+                                                                [1])
+
+    def init_attr_type(self):
+        self.soft_label = False
+
+    def init_dtype_type(self):
+        self.dtype = np.float64
+
+    def init_bs_class_num(self):
+        self.class_num = 10
+
+
+class TestCrossEntropyOp5(TestCrossEntropyOp):
+    """Test high rank tensor cross-entropy with vectorized soft labels.
+    """
+
+    def init_x(self):
+        self.shape = [4, 3]
+        self.ins_num = np.prod(np.array(self.shape))
+        self.X_2d = randomize_probability(self.ins_num,
+                                          self.class_num).astype(self.dtype)
+        self.x = self.X_2d.reshape(self.shape + [self.class_num])
+
+    def init_label(self):
+        self.label_2d = np.random.uniform(
+            0.1, 1.0, [self.ins_num, self.class_num]).astype(self.dtype)
+        self.label_2d /= self.label_2d.sum(axis=1, keepdims=True)
+        self.label = self.label_2d.reshape(self.shape + [self.class_num])
+
+    def get_cross_entropy(self):
+        cross_entropy_2d = (-self.label_2d * np.log(self.X_2d)).sum(
+            axis=1, keepdims=True).astype(self.dtype)
+        self.cross_entropy = np.array(cross_entropy_2d).reshape(self.shape +
+                                                                [1])
+
+    def init_attr_type(self):
+        self.soft_label = True
+
+    def init_dtype_type(self):
+        self.dtype = np.float32
+
+    def init_bs_class_num(self):
+        self.class_num = 37
+
+    def test_check_grad(self):
+        self.check_grad(
+            ["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
+
+
+class TestCrossEntropyOp6(TestCrossEntropyOp):
+    """Test high rank tensor cross-entropy with vectorized one-hot representation of labels.
+    """
+
+    def init_x(self):
+        self.shape = [4, 3, 2]
+        self.ins_num = np.prod(np.array(self.shape))
+        self.X_2d = randomize_probability(self.ins_num,
+                                          self.class_num).astype(self.dtype)
+        self.x = self.X_2d.reshape(self.shape + [self.class_num])
+
+    def init_label(self):
+        self.label_index_2d = np.random.randint(
+            0, self.class_num, (self.ins_num), dtype="int64")
+        label_2d = np.zeros(self.X_2d.shape)
+        label_2d[np.arange(self.ins_num), self.label_index_2d] = 1
+        self.label = label_2d.reshape(self.shape + [self.class_num]).astype(
+            self.dtype)
+
+    def get_cross_entropy(self):
+        cross_entropy_2d = np.asmatrix(
+            [[-np.log(self.X_2d[i][self.label_index_2d[i]])]
+             for i in range(self.X_2d.shape[0])])
+        self.cross_entropy = np.array(cross_entropy_2d).reshape(
+            self.shape + [1]).astype(self.dtype)
+
+    def init_attr_type(self):
+        self.soft_label = True
+
+    def init_dtype_type(self):
+        self.dtype = np.float32
+
+    def init_bs_class_num(self):
+        self.class_num = 17
+
+    def test_check_grad(self):
+        self.check_grad(
+            ["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
+
+
+class TestCrossEntropyOp7(TestCrossEntropyOp):
+    """Test cross-entropy with ignore index.
+    """
+
+    def init_label(self):
+        self.label = np.random.randint(
+            0, self.class_num, (self.batch_size, 1), dtype="int64")
+
+    def get_cross_entropy(self):
+        self.cross_entropy = np.asmatrix(
+            [[-np.log(self.x[i][self.label[i][0]])]
+             if self.label[i][0] != self.ignore_index else [0]
+             for i in range(self.x.shape[0])]).astype(self.dtype)
+
+    def init_attr_type(self):
+        self.soft_label = False
+        self.ignore_index = 3
+
+    def init_dtype_type(self):
+        self.dtype = np.float64
+
+    def init_bs_class_num(self):
+        self.batch_size = 30
+        self.class_num = 10
+
+
+if __name__ == "__main__":
+    unittest.main()