[Fluid Clean] remove unfold, deformable_roi_pooling, shard_index, hard_swish,...

[Fluid Clean] remove unfold, deformable_roi_pooling, shard_index, hard_swish, mish, uniform_random, unbind (#48451)

python/paddle/distribution/uniform.py

@@ -23,7 +23,7 @@ from paddle.fluid.framework import (
     _non_static_mode,
     in_dygraph_mode,
 )
-from paddle.fluid.layers import nn, tensor
+from paddle.fluid.layers import tensor
 from paddle.tensor import random
 
 
@@ -187,7 +187,7 @@ class Uniform(distribution.Distribution):
             return output
         else:
             output_shape = shape + batch_shape
-            output = nn.uniform_random(
+            output = paddle.uniform(
                 output_shape, dtype=self.dtype, min=0.0, max=1.0, seed=seed
             ) * (
                 tensor.zeros(output_shape, dtype=self.dtype)

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -1097,7 +1097,6 @@ set_tests_properties(test_bilinear_interp_v2_op PROPERTIES TIMEOUT 120)
 set_tests_properties(test_svd_op PROPERTIES TIMEOUT 80)
 set_tests_properties(test_einsum_op PROPERTIES TIMEOUT 120)
 set_tests_properties(test_qr_op PROPERTIES TIMEOUT 60)
-set_tests_properties(test_deformable_psroi_pooling PROPERTIES TIMEOUT 120)
 set_tests_properties(test_trilinear_interp_v2_op PROPERTIES TIMEOUT 120)
 set_tests_properties(test_imperative_static_runner_mnist PROPERTIES TIMEOUT 120)
 set_tests_properties(test_masked_select_op PROPERTIES TIMEOUT 120)

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_elt_act_fuse_pass.py

@@ -102,7 +102,7 @@ class ElementwiseActivationMkldnnFusePassTest_Add_HardSwish(
 ):
     def set_params(self):
         self.operand = paddle.add
-        self.act = fluid.layers.hard_swish
+        self.act = paddle.nn.functional.hardswish
 
 
 class ElementwiseActivationMkldnnFusePassTest_Add_SQRT(
@@ -202,7 +202,7 @@ class ElementwiseActivationMkldnnFusePassTest_Sub_HardSwish(
 ):
     def set_params(self):
         self.operand = paddle.subtract
-        self.act = fluid.layers.hard_swish
+        self.act = paddle.nn.functional.hardswish
 
 
 class ElementwiseActivationMkldnnFusePassTest_Sub_ABS(
@@ -294,7 +294,7 @@ class ElementwiseActivationMkldnnFusePassTest_Mul_HardSwish(
 ):
     def set_params(self):
         self.operand = paddle.multiply
-        self.act = fluid.layers.hard_swish
+        self.act = paddle.nn.functional.hardswish
 
 
 class ElementwiseActivationMkldnnFusePassTest_Mul_SQRT(

python/paddle/fluid/tests/unittests/ir/inference/test_trt_activation_pass.py

@@ -88,7 +88,7 @@ class TensorRTSubgraphPassSigmoidTest(TensorRTSubgraphPassActivationTest):
 
 class TensorRTSubgraphPassHardSwishTest(TensorRTSubgraphPassActivationTest):
     def append_act(self, x):
-        return fluid.layers.hard_swish(x)
+        return paddle.nn.functional.hardswish(x)
 
 
 class TensorRTSubgraphPassHardSigmoidTest(TensorRTSubgraphPassActivationTest):
@@ -100,7 +100,7 @@ class TensorRTSubgraphPassHardSwishPluginTest(
     TensorRTSubgraphPassActivationTest
 ):
     def append_act(self, x):
-        return fluid.layers.hard_swish(x, threshold=4.0, scale=8.0)
+        return paddle.nn.functional.hardswish(x)
 
 
 class TensorRTSubgraphPassClipTest(TensorRTSubgraphPassActivationTest):
@@ -166,7 +166,7 @@ class TensorRTSubgraphPassMishTest(TensorRTSubgraphPassActivationTest):
         )
 
     def append_act(self, x):
-        return fluid.layers.mish(x)
+        return paddle.nn.functional.mish(x)
 
 
 class TensorRTSubgraphPassMishFp16SerializeTest(
@@ -179,7 +179,7 @@ class TensorRTSubgraphPassMishFp16SerializeTest(
         )
 
     def append_act(self, x):
-        return fluid.layers.mish(x)
+        return paddle.nn.functional.mish(x)
 
 
 class TensorRTSubgraphPassDynamicMishFp16SerializeTest(
@@ -200,7 +200,7 @@ class TensorRTSubgraphPassDynamicMishFp16SerializeTest(
         )
 
     def append_act(self, x):
-        return fluid.layers.mish(x)
+        return paddle.nn.functional.mish(x)
 
 
 class TensorRTSubgraphPassPreluAllTest(TensorRTSubgraphPassActivationTest):

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

@@ -2073,7 +2073,7 @@ class TestHardswishAPI(unittest.TestCase):
     def test_fluid_api(self):
         with fluid.program_guard(fluid.Program()):
             x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
-            out = fluid.layers.hard_swish(x)
+            out = paddle.nn.functional.hardswish(x)
             exe = fluid.Executor(self.place)
             res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
         out_ref = ref_hardswish(self.x_np)
@@ -2081,7 +2081,7 @@ class TestHardswishAPI(unittest.TestCase):
 
         paddle.disable_static(self.place)
         x = paddle.to_tensor(self.x_np)
-        out = paddle.fluid.layers.hard_swish(x)
+        out = paddle.nn.functional.hardswish(x)
         np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
         paddle.enable_static()
 
@@ -3414,7 +3414,7 @@ def ref_mish(x, threshold=20.0):
 class TestMish(TestActivation):
     def setUp(self):
         self.op_type = "mish"
-        self.python_api = paddle.fluid.layers.nn.mish
+        self.python_api = paddle.nn.functional.mish
         self.init_dtype()
         self.init_shape()
 
@@ -3480,7 +3480,7 @@ class TestMishAPI(unittest.TestCase):
         paddle.enable_static()
         with fluid.program_guard(fluid.Program()):
             x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
-            out = fluid.layers.mish(x)
+            out = paddle.nn.functional.mish(x)
             exe = fluid.Executor(self.place)
             res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
         out_ref = ref_mish(self.x_np)

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

@@ -41,15 +41,9 @@ class TestGeneratorSeed(unittest.TestCase):
         gen.manual_seed(111111111)
         st = paddle.get_cuda_rng_state()
 
-        x = fluid.layers.uniform_random(
-            [2, 10], dtype="float32", min=0.0, max=1.0
-        )
-        x_again = fluid.layers.uniform_random(
-            [2, 10], dtype="float32", min=0.0, max=1.0
-        )
-        x_third = fluid.layers.uniform_random(
-            [2, 10], dtype="float32", min=0.0, max=1.0
-        )
+        x = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
+        x_again = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
+        x_third = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
         print("x: {}".format(x.numpy()))
         print("x_again: {}".format(x_again.numpy()))
         x = x + x_again + x_third
@@ -57,15 +51,9 @@ class TestGeneratorSeed(unittest.TestCase):
 
         paddle.set_cuda_rng_state(st)
 
-        x1 = fluid.layers.uniform_random(
-            [2, 10], dtype="float32", min=0.0, max=1.0
-        )
-        x1_again = fluid.layers.uniform_random(
-            [2, 10], dtype="float32", min=0.0, max=1.0
-        )
-        x1_third = fluid.layers.uniform_random(
-            [2, 10], dtype="float32", min=0.0, max=1.0
-        )
+        x1 = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
+        x1_again = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
+        x1_third = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
         x1 = x1 + x1_again + x1_third
         y1 = fluid.layers.dropout(x1, 0.5)
         y_np = y.numpy()
@@ -128,7 +116,7 @@ class TestGeneratorSeed(unittest.TestCase):
         with fluid.program_guard(train_program, startup_program):
             # example 1:
             # attr shape is a list which doesn't contain tensor Variable.
-            x = fluid.layers.uniform_random(shape=[2, 10])
+            x = paddle.uniform(shape=[2, 10])
             result_1 = fluid.layers.fc(
                 input=x,
                 size=10,

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

@@ -408,9 +408,7 @@ class TestDygraphGradientClip(unittest.TestCase):
     def test_gradient_clip(self):
         with fluid.dygraph.guard():
             linear = paddle.nn.Linear(5, 5)
-            inputs = fluid.layers.uniform_random(
-                [16, 5], min=-10, max=10
-            ).astype('float32')
+            inputs = paddle.uniform([16, 5], min=-10, max=10).astype('float32')
             out = linear(fluid.dygraph.to_variable(inputs))
             loss = paddle.mean(out)
             loss.backward()
@@ -552,9 +550,9 @@ class TestDygraphGradientClipFP16(unittest.TestCase):
                     models=model, optimizers=sgd_optimizer, level='O2'
                 )
                 scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
-                inputs = fluid.layers.uniform_random(
-                    [1, 5], min=-10, max=10
-                ).astype('float32')
+                inputs = paddle.uniform([1, 5], min=-10, max=10).astype(
+                    'float32'
+                )
                 with paddle.amp.auto_cast(level='O2'):
                     out = model(fluid.dygraph.to_variable(inputs))
                     loss = paddle.mean(out)
@@ -600,9 +598,7 @@ class TestDygraphGradientClipFP16(unittest.TestCase):
 class TestDygraphGradientClipFP64(unittest.TestCase):
     def test_gradient_clip(self):
         with fluid.dygraph.guard():
-            inputs = fluid.layers.uniform_random(
-                [16, 5], min=-10, max=10
-            ).astype('float32')
+            inputs = paddle.uniform([16, 5], min=-10, max=10).astype('float32')
             linear = paddle.nn.Linear(5, 5)
             out = linear(fluid.dygraph.to_variable(inputs))
             loss = paddle.mean(out)

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

@@ -1843,7 +1843,7 @@ class TestLayer(LayerTest):
     def test_shard_index(self):
         with self.static_graph():
             x = fluid.layers.data(name="label", shape=[4, 1], dtype='int64')
-            shard_label = fluid.layers.shard_index(
+            shard_label = paddle.shard_index(
                 input=x, index_num=20, nshards=2, shard_id=0
             )
 
@@ -2342,7 +2342,7 @@ class TestBook(LayerTest):
             fluid.default_main_program(), fluid.default_startup_program()
         ):
             input = self._get_data(name="input", shape=[16], dtype="float32")
-            out = layers.mish(input, name='mish')
+            out = paddle.nn.functional.mish(input, name='mish')
             return out
 
     def make_cross_entropy(self):
@@ -2794,7 +2794,7 @@ class TestBook(LayerTest):
     def test_unfold(self):
         with self.static_graph():
             x = layers.data(name='x', shape=[3, 20, 20], dtype='float32')
-            out = layers.unfold(x, [3, 3], 1, 1, 1)
+            out = paddle.nn.functional.unfold(x, [3, 3], 1, 1, 1)
             return out
 
     def test_partial_concat(self):
@@ -2809,40 +2809,6 @@ class TestBook(LayerTest):
             )
             return concat1, concat2
 
-    def test_deform_roi_pooling(self):
-        with program_guard(
-            fluid.default_main_program(), fluid.default_startup_program()
-        ):
-            input = layers.data(
-                name='input',
-                shape=[2, 3, 32, 32],
-                dtype='float32',
-                append_batch_size=False,
-            )
-            rois = layers.data(
-                name="rois", shape=[4], dtype='float32', lod_level=1
-            )
-            trans = layers.data(
-                name="trans",
-                shape=[2, 3, 32, 32],
-                dtype='float32',
-                append_batch_size=False,
-            )
-            out = layers.deformable_roi_pooling(
-                input=input,
-                rois=rois,
-                trans=trans,
-                no_trans=False,
-                spatial_scale=1.0,
-                group_size=(1, 1),
-                pooled_height=8,
-                pooled_width=8,
-                part_size=(8, 8),
-                sample_per_part=4,
-                trans_std=0.1,
-            )
-        return out
-
     def test_addmm(self):
         with program_guard(
             fluid.default_main_program(), fluid.default_startup_program()

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

@@ -35,23 +35,17 @@ class TestGeneratorSeed(unittest.TestCase):
         fluid.enable_dygraph()
 
         gen = paddle.seed(12312321111)
-        x = fluid.layers.uniform_random([10], dtype="float32", min=0.0, max=1.0)
+        x = paddle.uniform([10], dtype="float32", min=0.0, max=1.0)
 
         st1 = gen.get_state()
-        x1 = fluid.layers.uniform_random(
-            [10], dtype="float32", min=0.0, max=1.0
-        )
+        x1 = paddle.uniform([10], dtype="float32", min=0.0, max=1.0)
 
         gen.set_state(st1)
         print(gen.get_state())
-        x2 = fluid.layers.uniform_random(
-            [10], dtype="float32", min=0.0, max=1.0
-        )
+        x2 = paddle.uniform([10], dtype="float32", min=0.0, max=1.0)
 
         paddle.seed(12312321111)
-        x3 = fluid.layers.uniform_random(
-            [10], dtype="float32", min=0.0, max=1.0
-        )
+        x3 = paddle.uniform([10], dtype="float32", min=0.0, max=1.0)
 
         x_np = x.numpy()
         x1_np = x1.numpy()
@@ -72,8 +66,8 @@ class TestGeneratorSeed(unittest.TestCase):
         with fluid.program_guard(train_program, startup_program):
             # example 1:
             # attr shape is a list which doesn't contain tensor Variable.
-            result_1 = fluid.layers.uniform_random(shape=[3, 4])
-            result_2 = fluid.layers.uniform_random(shape=[3, 4])
+            result_1 = paddle.uniform(shape=[3, 4])
+            result_2 = paddle.uniform(shape=[3, 4])
 
             exe = fluid.Executor(fluid.CPUPlace())
             exe.run(startup_program)
@@ -102,15 +96,11 @@ class TestGeneratorSeed(unittest.TestCase):
         gen = paddle.seed(111111111)
         st = gen.get_state()
         # x = np.arange(1,101).reshape(2,50).astype("float32")
-        x = fluid.layers.uniform_random(
-            [2, 10], dtype="float32", min=0.0, max=1.0
-        )
+        x = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
         y = fluid.layers.dropout(x, 0.5)
         gen.manual_seed(111111111)
         # gen.set_state(st)
-        x1 = fluid.layers.uniform_random(
-            [2, 10], dtype="float32", min=0.0, max=1.0
-        )
+        x1 = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
         y1 = fluid.layers.dropout(x1, 0.5)
         y_np = y.numpy()
         y1_np = y1.numpy()
@@ -129,7 +119,7 @@ class TestGeneratorSeed(unittest.TestCase):
         with fluid.program_guard(train_program, startup_program):
             # example 1:
             # attr shape is a list which doesn't contain tensor Variable.
-            x_1 = fluid.layers.uniform_random(shape=[2, 10])
+            x_1 = paddle.uniform(shape=[2, 10])
             y_1 = fluid.layers.dropout(x_1, 0.5)
             exe = fluid.Executor(fluid.CPUPlace())
             exe.run(startup_program)
@@ -235,8 +225,8 @@ class TestGeneratorSeed(unittest.TestCase):
         with fluid.program_guard(train_program, startup_program):
             # example 1:
             # attr shape is a list which doesn't contain tensor Variable.
-            result_1 = fluid.layers.uniform_random(shape=[3, 4])
-            result_2 = fluid.layers.uniform_random(shape=[3, 4])
+            result_1 = paddle.uniform(shape=[3, 4])
+            result_2 = paddle.uniform(shape=[3, 4])
 
             exe = fluid.Executor(fluid.CPUPlace())
             exe.run(startup_program)
@@ -384,7 +374,7 @@ class TestGeneratorSeed(unittest.TestCase):
         with fluid.program_guard(train_program, startup_program):
             # example 1:
             # attr shape is a list which doesn't contain tensor Variable.
-            x = fluid.layers.uniform_random(shape=[2, 10])
+            x = paddle.uniform(shape=[2, 10])
             result_1 = fluid.layers.fc(
                 input=x,
                 size=10,

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

@@ -263,7 +263,7 @@ class TestRegularizer(unittest.TestCase):
             regularizer=paddle.regularizer.L1Decay()
         )
         with fluid.program_guard(fluid.Program(), fluid.Program()):
-            x = fluid.layers.uniform_random([2, 2, 3])
+            x = paddle.uniform([2, 2, 3])
             out = fluid.layers.fc(x, 5, param_attr=fc_param_attr)
             loss = paddle.sum(out)
             sgd = fluid.optimizer.SGD(learning_rate=0.1, regularization=l2)

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

@@ -173,7 +173,7 @@ class TestRegularizer(unittest.TestCase):
             regularizer=paddle.regularizer.L1Decay()
         )
         with fluid.program_guard(fluid.Program(), fluid.Program()):
-            x = fluid.layers.uniform_random([2, 2, 3])
+            x = paddle.uniform([2, 2, 3])
             out = fluid.layers.fc(x, 5, param_attr=fc_param_attr)
             loss = paddle.sum(out)
             sgd = fluid.optimizer.SGD(learning_rate=0.1, regularization=l2)

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

@@ -15,6 +15,7 @@
 import unittest
 
 import paddle
+from paddle.fluid.layers.utils import try_set_static_shape_tensor
 
 
 class StaticShapeInferrenceTest(unittest.TestCase):
@@ -24,7 +25,8 @@ class StaticShapeInferrenceTest(unittest.TestCase):
             name="x", shape=[-1, 2], dtype='float32'
         )
         shape = paddle.shape(data)  # shape should be [-1, 2]
-        x = paddle.fluid.layers.uniform_random(shape)
+        x = paddle.uniform(shape)
+        try_set_static_shape_tensor(x, shape)
         self.assertEqual(x.shape, data.shape)
         paddle.disable_static()
 

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

@@ -66,7 +66,7 @@ class TestLayersUnbind(unittest.TestCase):
     def test_layers_unbind(self):
 
         x_1 = fluid.data(shape=[2, 3], dtype='float32', name='x_1')
-        [out_0, out_1] = fluid.layers.unbind(input=x_1, axis=0)
+        [out_0, out_1] = paddle.unbind(input=x_1, axis=0)
         input_1 = np.random.random([2, 3]).astype("float32")
         axis = fluid.data(shape=[1], dtype='int32', name='axis')
         exe = fluid.Executor(place=fluid.CPUPlace())

python/paddle/fluid/tests/unittests/test_uniform_random_bf16_op.py

@@ -162,23 +162,6 @@ class TestUniformRandomOpBF16SelectedRowsWithDiagInit(
         np.testing.assert_allclose(hist, prob, rtol=0, atol=0.01)
 
 
-class TestUniformRandomOpBF16AttrTensorAPI(unittest.TestCase):
-    def test_attr_tensor_API(self):
-        startup_program = fluid.Program()
-        train_program = fluid.Program()
-        with fluid.program_guard(train_program, startup_program):
-            dim_tensor = fluid.layers.fill_constant([1], "int64", 3)
-            ret = fluid.layers.nn.uniform_random(
-                [1, dim_tensor, 2], dtype=np.uint16
-            )
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-
-            exe.run(startup_program)
-            outs = exe.run(train_program, fetch_list=[ret])
-
-
 class TestUniformRandomOpAPISeed(unittest.TestCase):
     def test_attr_tensor_API(self):
         _seed = 10
@@ -189,12 +172,8 @@ class TestUniformRandomOpAPISeed(unittest.TestCase):
             _min = 5
             _max = 10
 
-            ret = fluid.layers.nn.uniform_random(
-                [2, 3, 2], min=_min, max=_max, seed=_seed
-            )
-            ret_2 = fluid.layers.nn.uniform_random(
-                [2, 3, 2], min=_min, max=_max, seed=_seed
-            )
+            ret = paddle.uniform([2, 3, 2], min=_min, max=_max, seed=_seed)
+            ret_2 = paddle.uniform([2, 3, 2], min=_min, max=_max, seed=_seed)
             res = paddle.equal(ret, ret_2)
             place = fluid.CPUPlace()
             exe = fluid.Executor(place)

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

@@ -199,26 +199,18 @@ class TestUniformRandomOpError(unittest.TestCase):
                 x1 = fluid.create_lod_tensor(
                     np.zeros((4, 784)), [[1, 1, 1, 1]], fluid.CPUPlace()
                 )
-                fluid.layers.uniform_random(x1)
+                paddle.uniform(x1)
 
             self.assertRaises(TypeError, test_Variable)
 
             def test_Variable2():
                 x1 = np.zeros((4, 784))
-                fluid.layers.uniform_random(x1)
+                paddle.uniform(x1)
 
             self.assertRaises(TypeError, test_Variable2)
 
-            def test_dtype():
-                x2 = fluid.layers.data(
-                    name='x2', shape=[4, 784], dtype='float32'
-                )
-                fluid.layers.uniform_random(x2, 'int32')
-
-            self.assertRaises(TypeError, test_dtype)
-
             def test_out_dtype():
-                out = fluid.layers.uniform_random(shape=[3, 4], dtype='float64')
+                out = paddle.uniform(shape=[3, 4], dtype='float64')
                 self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP64)
 
             test_out_dtype()
@@ -323,7 +315,7 @@ class TestUniformRandomOp_attr_tensor_API(unittest.TestCase):
         train_program = fluid.Program()
         with fluid.program_guard(train_program, startup_program):
             dim_tensor = fluid.layers.fill_constant([1], "int64", 3)
-            ret = fluid.layers.nn.uniform_random([1, dim_tensor, 2])
+            ret = paddle.uniform([1, dim_tensor, 2])
 
             place = fluid.CPUPlace()
             if fluid.core.is_compiled_with_cuda():
@@ -339,7 +331,7 @@ class TestUniformRandomOp_attr_tensor_API(unittest.TestCase):
         with fluid.program_guard(train_program, startup_program):
             dim_1 = fluid.layers.fill_constant([1], "int64", 3)
             dim_2 = fluid.layers.fill_constant([1], "int32", 2)
-            ret = fluid.layers.nn.uniform_random([1, dim_1, dim_2])
+            ret = paddle.uniform([1, dim_1, dim_2])
 
             place = fluid.CPUPlace()
             if fluid.core.is_compiled_with_cuda():
@@ -354,7 +346,7 @@ class TestUniformRandomOp_attr_tensor_API(unittest.TestCase):
         train_program = fluid.Program()
         with fluid.program_guard(train_program, startup_program):
             shape = fluid.data(name='shape_tensor', shape=[2], dtype="int32")
-            ret = fluid.layers.nn.uniform_random(shape)
+            ret = paddle.uniform(shape)
 
             place = fluid.CPUPlace()
             if fluid.core.is_compiled_with_cuda():
@@ -377,12 +369,8 @@ class TestUniformRandomOp_API_seed(unittest.TestCase):
             _min = 5
             _max = 10
 
-            ret = fluid.layers.nn.uniform_random(
-                [2, 3, 2], min=_min, max=_max, seed=_seed
-            )
-            ret_2 = fluid.layers.nn.uniform_random(
-                [2, 3, 2], min=_min, max=_max, seed=_seed
-            )
+            ret = paddle.uniform([2, 3, 2], min=_min, max=_max, seed=_seed)
+            ret_2 = paddle.uniform([2, 3, 2], min=_min, max=_max, seed=_seed)
             res = paddle.equal(ret, ret_2)
             place = fluid.CPUPlace()
             if fluid.core.is_compiled_with_cuda():
@@ -464,9 +452,7 @@ class TestUniformRandomOpSelectedRowsShapeTensorList(unittest.TestCase):
 class TestUniformRandomDygraphMode(unittest.TestCase):
     def test_check_output(self):
         with fluid.dygraph.guard():
-            x = fluid.layers.uniform_random(
-                [10], dtype="float32", min=0.0, max=1.0
-            )
+            x = paddle.uniform([10], dtype="float32", min=0.0, max=1.0)
             x_np = x.numpy()
             for i in range(10):
                 self.assertTrue((x_np[i] > 0 and x_np[i] < 1.0))
@@ -698,9 +684,7 @@ class TestUniformMinMaxTensor(UnittestBase):
             min_v = paddle.to_tensor([0.1])
             max_v = paddle.to_tensor([0.9])
             y = paddle.uniform([2, 3, 10], min=min_v, max=max_v)
-            z = paddle.fluid.layers.uniform_random(
-                [2, 3, 10], min=min_v, max=max_v
-            )
+            z = paddle.uniform([2, 3, 10], min=min_v, max=max_v)
 
             out = feat + y + z