fix unittest timeout (#29820)

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

@@ -0,0 +1,143 @@
+#   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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+import paddle.fluid.core as core
+import gradient_checker
+from decorator_helper import prog_scope
+paddle.enable_static()
+
+
+class TestMulGradCheck(unittest.TestCase):
+    @prog_scope()
+    def func(self, place):
+        prog = fluid.Program()
+        with fluid.program_guard(prog):
+            x = layers.create_parameter(dtype="float64", shape=[2, 8], name='x')
+            y = layers.create_parameter(dtype="float64", shape=[8, 4], name='y')
+            z = layers.mul(x=x, y=y)
+            gradient_checker.grad_check([x, y], z, place=place)
+
+    def test_grad(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+        for p in places:
+            self.func(p)
+
+
+class TestMulDoubleGradCheck(unittest.TestCase):
+    @prog_scope()
+    def func(self, place):
+        # the shape of input variable should be clearly specified, not inlcude -1.
+        x_shape = [7, 11]
+        y_shape = [11, 9]
+        eps = 0.005
+        dtype = np.float64
+
+        x = layers.data('x', x_shape, False, dtype)
+        x.persistable = True
+        y = layers.data('y', y_shape, False, dtype)
+        y.persistable = True
+        out = layers.mul(x, y)
+        x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
+        y_arr = np.random.uniform(-1, 1, y_shape).astype(dtype)
+
+        gradient_checker.double_grad_check(
+            [x, y], out, x_init=[x_arr, y_arr], place=place, eps=eps)
+
+    def test_grad(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+        for p in places:
+            self.func(p)
+
+
+class TestMatmulDoubleGradCheck(unittest.TestCase):
+    def setUp(self):
+        self.init_test()
+
+    def init_test(self):
+        self.x_shape = [2]
+        self.y_shape = [2]
+        self.transpose_x = False
+        self.transpose_y = False
+
+    @prog_scope()
+    def func(self, place):
+        eps = 0.005
+        dtype = np.float64
+        typename = "float64"
+        x = layers.create_parameter(
+            dtype=typename, shape=self.x_shape, name='x')
+        y = layers.create_parameter(
+            dtype=typename, shape=self.y_shape, name='y')
+        out = layers.matmul(
+            x, y, self.transpose_x, self.transpose_y, name='out')
+
+        x_arr = np.random.uniform(-1, 1, self.x_shape).astype(dtype)
+        y_arr = np.random.uniform(-1, 1, self.y_shape).astype(dtype)
+        gradient_checker.double_grad_check(
+            [x, y], out, x_init=[x_arr, y_arr], place=place, eps=eps)
+
+    def test_grad(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+        for p in places:
+            self.func(p)
+
+
+def TestMatmulDoubleGradCheckCase1(TestMatmulDoubleGradCheck):
+    def init_test(self):
+        self.x_shape = [2, 3]
+        self.y_shape = [3, 2]
+        self.transpose_x = True
+        self.transpose_y = True
+
+
+def TestMatmulDoubleGradCheckCase2(TestMatmulDoubleGradCheck):
+    def init_test(self):
+        self.x_shape = [2, 4, 3]
+        self.y_shape = [2, 4, 5]
+        self.transpose_x = True
+        self.transpose_y = False
+
+
+def TestMatmulDoubleGradCheckCase3(TestMatmulDoubleGradCheck):
+    def init_test(self):
+        self.x_shape = [2, 3, 4, 5]
+        self.y_shape = [2, 3, 3, 5]
+        self.transpose_x = False
+        self.transpose_y = True
+
+
+def TestMatmulDoubleGradCheckCase4(TestMatmulDoubleGradCheck):
+    def init_test(self):
+        self.x_shape = [2, 3, 4]
+        self.y_shape = [4, 3]
+        self.transpose_x = False
+        self.transpose_y = False
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -26,24 +26,6 @@ from decorator_helper import prog_scope
 paddle.enable_static()
 
 
-class TestMulGradCheck(unittest.TestCase):
-    @prog_scope()
-    def func(self, place):
-        prog = fluid.Program()
-        with fluid.program_guard(prog):
-            x = layers.create_parameter(dtype="float64", shape=[2, 8], name='x')
-            y = layers.create_parameter(dtype="float64", shape=[8, 4], name='y')
-            z = layers.mul(x=x, y=y)
-            gradient_checker.grad_check([x, y], z, place=place)
-
-    def test_grad(self):
-        places = [fluid.CPUPlace()]
-        if core.is_compiled_with_cuda():
-            places.append(fluid.CUDAPlace(0))
-        for p in places:
-            self.func(p)
-
-
 class TestSliceOpDoubleGradCheck(unittest.TestCase):
     def func(self, place):
         self.config()
@@ -125,66 +107,6 @@ class TestReduceSumWithDimDoubleGradCheck(unittest.TestCase):
             self.func(p)
 
 
-class TestMulDoubleGradCheck(unittest.TestCase):
-    @prog_scope()
-    def func(self, place):
-        # the shape of input variable should be clearly specified, not inlcude -1.
-        x_shape = [7, 11]
-        y_shape = [11, 9]
-        eps = 0.005
-        dtype = np.float64
-
-        x = layers.data('x', x_shape, False, dtype)
-        x.persistable = True
-        y = layers.data('y', y_shape, False, dtype)
-        y.persistable = True
-        out = layers.mul(x, y)
-        x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
-        y_arr = np.random.uniform(-1, 1, y_shape).astype(dtype)
-
-        gradient_checker.double_grad_check(
-            [x, y], out, x_init=[x_arr, y_arr], place=place, eps=eps)
-
-    def test_grad(self):
-        places = [fluid.CPUPlace()]
-        if core.is_compiled_with_cuda():
-            places.append(fluid.CUDAPlace(0))
-        for p in places:
-            self.func(p)
-
-
-class TestMatmulDoubleGradCheck(unittest.TestCase):
-    @prog_scope()
-    def func(self, place):
-        eps = 0.005
-        x_shapes = [[2], [2, 3], [2, 4, 3], [2, 3, 4, 5], [2, 3, 4]]
-        y_shapes = [[2], [3, 2], [2, 4, 5], [2, 3, 3, 5], [4, 3]]
-        transpose_xs = [False, True, True, False, False]
-        transpose_ys = [False, True, False, True, False]
-        dtype = np.float64
-        typename = "float64"
-        for i, (x_shape, y_shape, transpose_x, transpose_y) \
-            in enumerate(zip(x_shapes, y_shapes, transpose_xs, transpose_ys)):
-            x = layers.create_parameter(
-                dtype=typename, shape=x_shape, name='x{}'.format(i))
-            y = layers.create_parameter(
-                dtype=typename, shape=y_shape, name='y{}'.format(i))
-            out = layers.matmul(
-                x, y, transpose_x, transpose_y, name='out{}'.format(i))
-
-            x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
-            y_arr = np.random.uniform(-1, 1, y_shape).astype(dtype)
-            gradient_checker.double_grad_check(
-                [x, y], out, x_init=[x_arr, y_arr], place=place, eps=eps)
-
-    def test_grad(self):
-        places = [fluid.CPUPlace()]
-        if core.is_compiled_with_cuda():
-            places.append(fluid.CUDAPlace(0))
-        for p in places:
-            self.func(p)
-
-
 class TestReshapeDoubleGradCheck(unittest.TestCase):
     @prog_scope()
     def func(self, place):