normal: support mean and std tensor; randn = standard_normal (#26367)

python/paddle/__init__.py

@@ -194,7 +194,8 @@ from .tensor.math import clip  #DEFINE_ALIAS
 from .tensor.math import trace  #DEFINE_ALIAS
 from .tensor.math import kron  #DEFINE_ALIAS
 from .tensor.math import prod  #DEFINE_ALIAS
-# from .tensor.random import gaussin        #DEFINE_ALIAS
+from .tensor.random import standard_normal
+from .tensor.random import normal
 from .tensor.random import uniform  #DEFINE_ALIAS
 from .tensor.random import shuffle  #DEFINE_ALIAS
 from .tensor.random import randn  #DEFINE_ALIAS

python/paddle/fluid/layers/nn.py

@@ -10466,6 +10466,7 @@ def uniform_random_batch_size_like(input,
     return out
 
 
+@deprecated(since="2.0.0", update_to="paddle.normal")
 @templatedoc()
 def gaussian_random(shape,
                     mean=0.0,

python/paddle/fluid/tests/unittests/test_normal.py

@@ -0,0 +1,197 @@
+#   Copyright (c) 2020 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.
+
+import unittest
+import numpy as np
+import paddle
+import copy
+
+np.random.seed(10)
+
+
+class TestNormalAPI(unittest.TestCase):
+    def setUp(self):
+        self.mean = 1.0
+        self.std = 0.0
+        self.shape = None
+        self.repeat_num = 1000
+        self.set_attrs()
+        self.dtype = self.get_dtype()
+        self.place=paddle.CUDAPlace(0) \
+            if paddle.fluid.core.is_compiled_with_cuda() \
+            else paddle.CPUPlace()
+
+    def set_attrs(self):
+        self.shape = [8, 12]
+
+    def get_shape(self):
+        if isinstance(self.mean, np.ndarray):
+            shape = self.mean.shape
+        elif isinstance(self.std, np.ndarray):
+            shape = self.std.shape
+        else:
+            shape = self.shape
+        return list(shape)
+
+    def get_dtype(self):
+        if isinstance(self.mean, np.ndarray):
+            return self.mean.dtype
+        elif isinstance(self.std, np.ndarray):
+            return self.std.dtype
+        else:
+            return 'float32'
+
+    def static_api(self):
+        shape = self.get_shape()
+        ret_all_shape = copy.deepcopy(shape)
+        ret_all_shape.insert(0, self.repeat_num)
+        ret_all = np.zeros(ret_all_shape, self.dtype)
+        if isinstance(self.mean, np.ndarray) \
+            and isinstance(self.std, np.ndarray):
+            with paddle.static.program_guard(paddle.static.Program()):
+                mean = paddle.data('Mean', self.mean.shape, self.mean.dtype)
+                std = paddle.data('Std', self.std.shape, self.std.dtype)
+                out = paddle.normal(mean, std, self.shape)
+
+                exe = paddle.static.Executor(self.place)
+                for i in range(self.repeat_num):
+                    ret = exe.run(feed={
+                        'Mean': self.mean,
+                        'Std': self.std.reshape(shape)
+                    },
+                                  fetch_list=[out])
+                    ret_all[i] = ret[0]
+            return ret_all
+        elif isinstance(self.mean, np.ndarray):
+            with paddle.static.program_guard(paddle.static.Program()):
+                mean = paddle.data('Mean', self.mean.shape, self.mean.dtype)
+                out = paddle.normal(mean, self.std, self.shape)
+
+                exe = paddle.static.Executor(self.place)
+                for i in range(self.repeat_num):
+                    ret = exe.run(feed={'Mean': self.mean}, fetch_list=[out])
+                    ret_all[i] = ret[0]
+            return ret_all
+        elif isinstance(self.std, np.ndarray):
+            with paddle.static.program_guard(paddle.static.Program()):
+                std = paddle.data('Std', self.std.shape, self.std.dtype)
+                out = paddle.normal(self.mean, std, self.shape)
+
+                exe = paddle.static.Executor(self.place)
+                for i in range(self.repeat_num):
+                    ret = exe.run(feed={'Std': self.std}, fetch_list=[out])
+                    ret_all[i] = ret[0]
+            return ret_all
+        else:
+            with paddle.static.program_guard(paddle.static.Program()):
+                out = paddle.normal(self.mean, self.std, self.shape)
+
+                exe = paddle.static.Executor(self.place)
+                for i in range(self.repeat_num):
+                    ret = exe.run(fetch_list=[out])
+                    ret_all[i] = ret[0]
+            return ret_all
+
+    def dygraph_api(self):
+        paddle.disable_static(self.place)
+        shape = self.get_shape()
+        ret_all_shape = copy.deepcopy(shape)
+        ret_all_shape.insert(0, self.repeat_num)
+        ret_all = np.zeros(ret_all_shape, self.dtype)
+
+        mean = paddle.to_tensor(self.mean) \
+            if isinstance(self.mean, np.ndarray) else self.mean
+        std = paddle.to_tensor(self.std) \
+            if isinstance(self.std, np.ndarray) else self.std
+        for i in range(self.repeat_num):
+            out = paddle.normal(mean, std, self.shape)
+            ret_all[i] = out.numpy()
+        paddle.enable_static()
+        return ret_all
+
+    def test_api(self):
+        ret_static = self.static_api()
+        ret_dygraph = self.dygraph_api()
+        for ret in [ret_static, ret_dygraph]:
+            shape_ref = self.get_shape()
+            self.assertEqual(shape_ref, list(ret[0].shape))
+
+            ret = ret.flatten().reshape([self.repeat_num, -1])
+            mean = np.mean(ret, axis=0)
+            std = np.std(ret, axis=0)
+            mean_ref=self.mean.reshape([1, -1]) \
+                if isinstance(self.mean, np.ndarray) else self.mean
+            std_ref=self.std.reshape([1, -1]) \
+                if isinstance(self.std, np.ndarray) else self.std
+            self.assertTrue(np.allclose(mean_ref, mean, 0.1, 0.1))
+            self.assertTrue(np.allclose(std_ref, std, 0.1, 0.1))
+
+
+class TestNormalAPI_mean_is_tensor(TestNormalAPI):
+    def set_attrs(self):
+        self.mean = np.random.uniform(-2, -1, [2, 3, 4, 5]).astype('float64')
+
+
+class TestNormalAPI_std_is_tensor(TestNormalAPI):
+    def set_attrs(self):
+        self.std = np.random.uniform(0.7, 1, [2, 3, 17]).astype('float64')
+
+
+class TestNormalAPI_mean_std_are_tensor(TestNormalAPI):
+    def set_attrs(self):
+        self.mean = np.random.uniform(1, 2, [1, 100]).astype('float64')
+        self.std = np.random.uniform(0.5, 1, [1, 100]).astype('float64')
+
+
+class TestNormalAPI_mean_std_are_tensor_with_different_dtype(TestNormalAPI):
+    def set_attrs(self):
+        self.mean = np.random.uniform(1, 2, [100]).astype('float64')
+        self.std = np.random.uniform(1, 2, [100]).astype('float32')
+
+
+class TestNormalAlias(unittest.TestCase):
+    def test_alias(self):
+        paddle.disable_static()
+        shape = [1, 2, 3]
+        out1 = paddle.normal(shape=shape)
+        out2 = paddle.tensor.normal(shape=shape)
+        out3 = paddle.tensor.random.normal(shape=shape)
+        paddle.enable_static()
+
+
+class TestNormalErrors(unittest.TestCase):
+    def test_errors(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            mean = [1, 2, 3]
+            self.assertRaises(TypeError, paddle.normal, mean)
+
+            std = [1, 2, 3]
+            self.assertRaises(TypeError, paddle.normal, std=std)
+
+            mean = paddle.data('Mean', [100], 'int32')
+            self.assertRaises(TypeError, paddle.normal, mean)
+
+            std = paddle.data('Std', [100], 'int32')
+            self.assertRaises(TypeError, paddle.normal, mean=1.0, std=std)
+
+            self.assertRaises(TypeError, paddle.normal, shape=1)
+
+            self.assertRaises(TypeError, paddle.normal, shape=[1.0])
+
+            shape = paddle.data('Shape', [100], 'float32')
+            self.assertRaises(TypeError, paddle.normal, shape=shape)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/tensor/__init__.py

@@ -162,7 +162,8 @@ from .math import clip  #DEFINE_ALIAS
 from .math import trace  #DEFINE_ALIAS
 from .math import kron  #DEFINE_ALIAS
 from .math import prod  #DEFINE_ALIAS
-# from .random import gaussin        #DEFINE_ALIAS
+from .random import standard_normal
+from .random import normal
 from .random import uniform  #DEFINE_ALIAS
 from .random import shuffle  #DEFINE_ALIAS
 from .random import randn  #DEFINE_ALIAS