add layer_norm

paddle/operators/layer_norm_op.h

@@ -0,0 +1,35 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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 "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class LayerNormKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override;
+};
+
+template <typename DeviceContext, typename T>
+class LayerNormGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override;
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/v2/fluid/tests/test_layer_norm_op.py

@@ -0,0 +1,81 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
+
+import unittest
+import numpy as np
+
+from op_test import OpTest
+
+
+def layer_norm_naive(x, scale, beta, epsilon):
+    n, c, h, w = x.shape
+    mean = np.mean(x, axis=(1, 2, 3))
+    var = np.var(x, axis=(1, 2, 3)) + epsilon
+    output = scale * np.divide((x - mean.reshape([n, 1, 1, 1])),
+                               (np.sqrt(var)).reshape([n, 1, 1, 1])) + beta
+    return output, mean, var
+
+
+class TestLayerNormdOp(OpTest):
+    def setUp(self):
+        self.init_test_case()
+
+        input = np.random.random(self.input_size).astype("float32")
+        self.inputs = {
+            'X': input,
+            'Scale': np.array([self.scale]).astype("float32"),
+            'Bias': np.array([self.bias]).astype("float32")
+        }
+        output, mean, var = layer_norm_naive(input, self.scale, self.bias,
+                                             self.epsilon)
+        self.outputs = {'Y': output, 'Mean': mean, 'Variance': var}
+
+    def test_check_output(self):
+        self.check_output()
+
+    # def test_check_grad(self):
+    #     self.check_grad(
+    #         ['Scale', 'Bias', 'X'], ['Y', 'Mean', 'Variance'],
+    #         max_relative_error=0.02)
+
+    def test_check_grad_no_x(self):
+        self.check_grad(
+            ['Scale', 'Bias'], ['Y', 'Mean', 'Variance'],
+            max_relative_error=0.02,
+            no_grad_set=set(['X']))
+
+    # def test_check_grad_no_scale(self):
+    #     self.check_grad(
+    #         ['Bias','X'],
+    #         'Y',
+    #         max_relative_error=0.02,
+    #         no_grad_set=set(['Scale']))
+    #
+    # def test_check_grad_no_bias(self):
+    #     self.check_grad(
+    #         ['Scale','X'],
+    #         'Y',
+    #         max_relative_error=0.02,
+    #         no_grad_set=set(['Bias']))
+
+    def init_test_case(self):
+        self.op_type = "layer_norm"
+        self.input_size = [2, 3, 4, 5]
+        self.scale = 0.21
+        self.bias = 0.1
+        self.epsilon = 0.00001
+
+
+if __name__ == '__main__':
+    unittest.main()