Fix dropout static when axis != None (#37223) (#37589)

* fix dropout static when axis != None

* update dropout test

* add dropout test

* fix test

* Update test_dropout_op.py

* Update test_dropout_op.py

* fix testcase

* fix testcase

* Update test_dropout_op.py

* fix testcase

* fix testcase

* optimize perf

* add new test

* fix testcase

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

@@ -333,7 +333,7 @@ class TestDropoutFAPI(unittest.TestCase):
 
     def check_static_result(self, place):
         with fluid.program_guard(fluid.Program(), fluid.Program()):
-            input = fluid.data(name="input", shape=[40, 40], dtype="float32")
+            input = fluid.data(name="input", shape=[-1, -1], dtype="float32")
             res1 = paddle.nn.functional.dropout(x=input, p=0., training=False)
             res2 = paddle.nn.functional.dropout(
                 x=input, p=0., axis=0, training=True, mode='upscale_in_train')
@@ -380,7 +380,10 @@ class TestDropoutFAPI(unittest.TestCase):
                 training=False,
                 mode='upscale_in_train')
 
-            in_np = np.random.random([40, 40]).astype("float32")
+            res13 = paddle.nn.functional.dropout(
+                x=input, p=0.7, axis=1, training=True, mode='upscale_in_train')
+
+            in_np = np.ones([40, 40]).astype("float32")
             res_np = in_np
             res_np2 = np.zeros_like(in_np)
 
@@ -398,6 +401,9 @@ class TestDropoutFAPI(unittest.TestCase):
                                feed={"input": in_np},
                                fetch_list=[res10])
             self.assertTrue(np.allclose(fetches2[0], res_np2))
+            fetches3 = exe.run(fluid.default_main_program(),
+                               feed={"input": in_np},
+                               fetch_list=[res13])
 
     def test_static(self):
         for place in self.places:
@@ -471,6 +477,12 @@ class TestDropoutFAPI(unittest.TestCase):
                     axis=(0, 1),
                     training=False,
                     mode='upscale_in_train')
+                res13 = paddle.nn.functional.dropout(
+                    x=input,
+                    p=0.5,
+                    axis=1,
+                    training=True,
+                    mode='upscale_in_train')
 
             res_list = [
                 res1, res2, res3, res4, res5, res6, res7, res8, res9, res11,

python/paddle/nn/functional/common.py

@@ -939,6 +939,8 @@ def dropout(x,
 
             #get mask shape
             input_shape = x.shape
+            if not in_dygraph_mode():
+                input_shape_tensor = paddle.shape(x)
             drop_axes = [axis] if isinstance(axis, int) else list(axis)
             if min(drop_axes) < 0 or max(drop_axes) > len(input_shape) - 1:
                 raise ValueError("axis value should be greater than or equal to 0 and less than dimensions of x:{}, but get axis value:{} " \
@@ -948,8 +950,12 @@ def dropout(x,
                     "length of axis should not be greater than dimensions of x:{}, but get length of axis: {}".
                     format(len(input_shape), len(drop_axes)))
             mask_shape = [1] * len(input_shape)
-            for i in drop_axes:
-                mask_shape[i] = input_shape[i]
+            if not in_dygraph_mode():
+                for i in drop_axes:
+                    mask_shape[i] = input_shape_tensor[i]
+            else:
+                for i in drop_axes:
+                    mask_shape[i] = input_shape[i]
 
             #get mask
             random_tensor = paddle.uniform(