Fluid clean move out fill constant (#49511)

* migrate fill_constant to paddle.tensor

* move fill_constant to paddle.tensor and repalce the reference

* add missing fill_constant replacement

* fix typro

* remove unused import fill_constant

* fix zeros import error

* fix circle import

* fix layers.zeros

* fix unitest

* fix unitests

* fix unitest

* use paddle.full replace fill_constant in samplecode

* fix sample code

* recovery xpu test

* recovery xpu test

* fix circle import

* fix utils import error

* fix utils error

* fix circle import

* redo

* fix circle import

* fix prim fill constant import

* fix type error

* fix increase error

* fix test error

* fix fill_constant

python/paddle/common_ops_import.py

@@ -33,7 +33,6 @@ from paddle.fluid.framework import (  # noqa: F401
     in_dygraph_mode,
 )
 from paddle.fluid.layer_helper import LayerHelper  # noqa: F401
-from paddle.fluid.layers import fill_constant  # noqa: F401
 from paddle.fluid.layers.layer_function_generator import (  # noqa: F401
     templatedoc,
 )

python/paddle/distributed/auto_parallel/process_group.py

@@ -17,8 +17,8 @@ from collections import OrderedDict
 import paddle
 from paddle import _legacy_C_ops
 from paddle.framework import core, in_dygraph_mode
+from paddle.tensor import fill_constant
 
-from ...fluid.layers.tensor import fill_constant
 from ..collective import _get_global_env, _new_ring_id
 
 

python/paddle/distributed/fleet/utils/hybrid_parallel_inference.py

@@ -50,9 +50,9 @@ class HybridParallelInferenceHelper:
         # while op pattern
         with paddle.fluid.device_guard(f'{device}:all'):
             # init global cond
-            max_len = layers.fill_constant(shape=[1], dtype="int64", value=10, force_cpu=False)
-            step_idx = layers.fill_constant(shape=[1], dtype="int64", value=0, force_cpu=False)
-            cond_int = layers.fill_constant(shape=[1], dtype="int64", value=0, force_cpu=False, name="cond_int")
+            max_len = paddle.full(shape=[1], dtype="int64", fill_value=10)
+            step_idx = paddle.full(shape=[1], dtype="int64", fill_value=0)
+            cond_int = paddle.full(shape=[1], dtype="int64", fill_value=0, name="cond_int")
             cond = layers.cast(step_idx < max_len, dtype="bool")
             while_op = layers.While(cond, is_test=True)
 
@@ -124,14 +124,14 @@ class HybridParallelInferenceHelper:
                 X = paddle.static.data(name='X', shape=[None, 2], dtype='float32')
 
             with paddle.fluid.device_guard(f'{device}:all'):
-                max_len = layers.fill_constant(
-                    shape=[1], dtype="int64", value=5, force_cpu=False, name="n")
-                step_idx = layers.fill_constant(
-                    shape=[1], dtype="int64", value=0, force_cpu=False, name="i")
+                max_len = paddle.full(
+                    shape=[1], dtype="int64", fill_value=5, name="n")
+                step_idx = paddle.full(
+                    shape=[1], dtype="int64", fill_value=0, name="i")
 
                 data = paddle.tensor.array_write(X, step_idx)
 
-                cond_int = layers.fill_constant(shape=[1], dtype="int64", value=0, force_cpu=False, name="cond_int")
+                cond_int = paddle.full(shape=[1], dtype="int64", fill_value=0, name="cond_int")
                 cond = paddle.less_than(x=step_idx, y=max_len)
                 while_op = layers.While(cond, is_test=True)
 

python/paddle/distributed/passes/auto_parallel_gradient_merge.py

@@ -25,7 +25,6 @@ from paddle.distributed.auto_parallel.utils import (
     set_var_dist_attr,
 )
 from paddle.distributed.fleet.meta_optimizers.common import OP_ROLE_KEY, OpRole
-from paddle.fluid import layers
 from paddle.framework import core
 from paddle.static import device_guard
 
@@ -284,7 +283,7 @@ def _create_cond_block_and_update_optimizer(
 
         # clear gradient_merge_vars
         for param, new_grad in new_params_to_grads:
-            layers.fill_constant(
+            paddle.tensor.fill_constant(
                 shape=new_grad.shape,
                 dtype=new_grad.dtype,
                 value=0.0,

python/paddle/fluid/contrib/layers/metric_op.py

@@ -14,7 +14,7 @@
 """
 Contrib layers just related to metric.
 """
-
+import paddle
 import warnings
 import paddle
 from paddle.fluid.layer_helper import LayerHelper
@@ -79,7 +79,7 @@ def ctr_metric_bundle(input, label, ins_tag_weight=None):
 
     """
     if ins_tag_weight is None:
-        ins_tag_weight = tensor.fill_constant(
+        ins_tag_weight = paddle.tensor.fill_constant(
             shape=[1, 1], dtype="float32", value=1.0
         )
 

python/paddle/fluid/contrib/tests/test_multi_precision_fp16_train.py

@@ -287,10 +287,10 @@ class TestAmpWithNonIterableDataLoader(unittest.TestCase):
                     iterable=False,
                     use_double_buffer=False,
                 )
-                zero_var = fluid.layers.fill_constant(
+                zero_var = paddle.tensor.fill_constant(
                     shape=[1], dtype='int64', value=0
                 )
-                one_var = fluid.layers.fill_constant(
+                one_var = paddle.tensor.fill_constant(
                     shape=[1], dtype='int64', value=1
                 )
                 with fluid.layers.control_flow.Switch() as switch:

python/paddle/fluid/layers/control_flow.py

@@ -15,7 +15,6 @@
 from ..wrapped_decorator import signature_safe_contextmanager
 
 from .layer_function_generator import templatedoc
-from .tensor import fill_constant
 from .. import core
 from ..framework import (
     Program,
@@ -925,11 +924,12 @@ class While:
           .. code-block:: python
 
             import paddle.fluid as fluid
+            import paddle
             import numpy as np
 
-            i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)           # loop counter
+            i = paddle.full(shape=[1], dtype='int64', fill_value=0)           # loop counter
 
-            loop_len = fluid.layers.fill_constant(shape=[1],dtype='int64', value=10)    # loop length
+            loop_len = paddle.full(shape=[1],dtype='int64', fill_value=10)    # loop length
 
             cond = paddle.less_than(x=i, y=loop_len)
             while_op = fluid.layers.While(cond=cond)
@@ -952,11 +952,11 @@ class While:
             import numpy as np
 
             paddle.enable_static()
-            i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
-            loop_len = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
-            one = fluid.layers.fill_constant(shape=[1], dtype='float32', value=1)
+            i = paddle.full(shape=[1], dtype='int64', fill_value=0)
+            loop_len = paddle.full(shape=[1], dtype='int64', fill_value=10)
+            one = paddle.full(shape=[1], dtype='float32', fill_value=1)
             data = fluid.data(name='data', shape=[1], dtype='float32')
-            sums = fluid.layers.fill_constant(shape=[1], dtype='float32', value=0)  # Define the variable to be obtained ouside of While, which name should be different from the variable inside the While to be obtained
+            sums = paddle.full(shape=[1], dtype='float32', fill_value=0)  # Define the variable to be obtained ouside of While, which name should be different from the variable inside the While to be obtained
 
             cond = paddle.less_than(x=i, y=loop_len)
             while_op = fluid.layers.While(cond=cond)
@@ -1537,13 +1537,15 @@ class Switch:
     .. code-block:: python
 
         '''
+        import paddle
+        import paddle.fluid as fluid
         with fluid.layers.Switch() as switch:
             with switch.case(cond1):
-                i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=1)
+                i = paddle.full(shape=[1], dtype='int64', fill_value=1)
             with switch.case(cond2):
-                i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=2)
+                i = paddle.full(shape=[1], dtype='int64', fill_value=2)
             with switch.default():
-                i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
+                i = paddle.full(shape=[1], dtype='int64', fill_value=0)
         '''
 
     Args:
@@ -1561,20 +1563,20 @@ class Switch:
                 dtype='float32',
                 persistable=True,
                 name="learning_rate")
-            zero_var = fluid.layers.fill_constant(
-                shape=[1], dtype='float32', value=0.0)
-            one_var = fluid.layers.fill_constant(
-                shape=[1], dtype='float32', value=1.0)
-            two_var = fluid.layers.fill_constant(
-                shape=[1], dtype='float32', value=2.0)
+            zero_var = paddle.full(
+                shape=[1], dtype='float32', fill_value=0.0)
+            one_var = paddle.full(
+                shape=[1], dtype='float32', fill_value=1.0)
+            two_var = paddle.full(
+                shape=[1], dtype='float32', fill_value=2.0)
 
             global_step = fluid.layers.autoincreased_step_counter(counter_name='@LR_DECAY_COUNTER@', begin=0, step=1)
 
             with fluid.layers.control_flow.Switch() as switch:
                 with switch.case(global_step == zero_var):
-                    fluid.layers.assign(input=one_var, output=lr)
+                    paddle.assign(input=one_var, output=lr)
                 with switch.default():
-                    fluid.layers.assign(input=two_var, output=lr)
+                    paddle.assign(input=two_var, output=lr)
 
             exe = fluid.Executor(fluid.CPUPlace())
             exe.run(fluid.default_startup_program())

python/paddle/fluid/layers/learning_rate_scheduler.py

@@ -352,10 +352,10 @@ def polynomial_decay(
 
             if cycle:
                 div_res = paddle.ceil(global_step / decay_steps)
-                zero_var = tensor.fill_constant(
+                zero_var = paddle.tensor.fill_constant(
                     shape=[1], dtype='float32', value=0.0
                 )
-                one_var = tensor.fill_constant(
+                one_var = paddle.tensor.fill_constant(
                     shape=[1], dtype='float32', value=1.0
                 )
 
@@ -364,7 +364,7 @@ def polynomial_decay(
                         paddle.assign(one_var, output=div_res)
                 decay_steps = decay_steps * div_res
             else:
-                decay_steps_var = tensor.fill_constant(
+                decay_steps_var = paddle.tensor.fill_constant(
                     shape=[1], dtype='float32', value=float(decay_steps)
                 )
                 global_step = paddle.minimum(x=global_step, y=decay_steps_var)
@@ -435,21 +435,21 @@ def piecewise_decay(boundaries, values):
 
             with control_flow.Switch() as switch:
                 for i in range(len(boundaries)):
-                    boundary_val = tensor.fill_constant(
+                    boundary_val = paddle.tensor.fill_constant(
                         shape=[1],
                         dtype='float32',
                         value=float(boundaries[i]),
                         force_cpu=True,
                     )
                     with switch.case(global_step < boundary_val):
-                        tensor.fill_constant(
+                        paddle.tensor.fill_constant(
                             shape=[1],
                             dtype="float32",
                             value=float(values[i]),
                             out=lr,
                         )
                 with switch.default():
-                    tensor.fill_constant(
+                    paddle.tensor.fill_constant(
                         shape=[1],
                         dtype="float32",
                         value=float(values[len(values) - 1]),
@@ -598,7 +598,7 @@ def linear_lr_warmup(learning_rate, warmup_steps, start_lr, end_lr):
                     paddle.assign(decayed_lr, lr)
                 with switch.default():
                     if not isinstance(learning_rate, Variable):
-                        learning_rate = tensor.fill_constant(
+                        learning_rate = paddle.tensor.fill_constant(
                             shape=[1], dtype=dtype, value=float(learning_rate)
                         )
                     paddle.assign(learning_rate, lr)

python/paddle/fluid/layers/nn.py

@@ -41,7 +41,7 @@ from .layer_function_generator import (
     templatedoc,
     _generate_doc_string_,
 )
-from .tensor import fill_constant, zeros
+from .tensor import zeros
 from .. import unique_name
 from .. import core
 from ...utils import deprecated

python/paddle/fluid/layers/tensor.py

@@ -39,142 +39,10 @@ from paddle import _C_ops, _legacy_C_ops
 
 __all__ = [
     'fill_constant_batch_size_like',
-    'fill_constant',
     'zeros',
 ]
 
 
-def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None):
-    """
-
-    This OP creates a Tensor with specified `shape` and `dtype`, and
-    initializes it with a constant specified by `value`.
-
-    The attribute `stop_gradient` of the created Tensor is set to True.
-
-    Args:
-        shape(list|tuple|Tensor): Shape of the output Tensor, the data type of ``shape`` is int32 or int64.
-            If ``shape`` is a list or tuple, the elements of it should be integers or Tensors with shape [1].
-            If ``shape`` is an Tensor, it should be an 1-D Tensor with date type int32 or int64.
-        dtype(np.dtype|str): Data type of the output Tensor which can
-            be float16, float32, float64, uint8, int16, int32, int64.
-        value(bool|float|int|Tensor): The constant value used to initialize
-            the Tensor to be created. If ``value`` is an Tensor, it should be an 1-D Tensor.
-        force_cpu(bool, optional): data should be on CPU if it's true, default value is False.
-        out(Tensor, optional): Optional output which can be any created
-            Tensor that meets the requirements to store the result of operation.
-            if ``out`` is None, a new Tensor will be create to store the result.
-        name(str, optional): The default value is None.  Normally there is no need for user to set this
-            property.  For more information, please refer to :ref:`api_guide_Name`.
-
-    Returns:
-        Tensor: Tensor which is created according to shape and dtype.
-
-    Examples:
-        .. code-block:: python
-
-          import paddle.fluid as fluid
-          # attr shape is a list which doesn't contain  Tensor.
-          data1 = fluid.layers.fill_constant(shape=[2,1], value=0, dtype='int64') # data1=[[0],[0]]
-          data2 = fluid.layers.fill_constant(shape=[2,1], value=5, dtype='int64', out=data1)
-          # data1=[[5], [5]] data2=[[5], [5]]
-
-          # attr shape is a list which contains Tensor.
-          positive_2 = fluid.layers.fill_constant([1], "int32", 2)
-          data3 = fluid.layers.fill_constant(shape=[1, positive_2], dtype='float32', value=1.5) # data3=[[1.5, 1.5]]
-
-          # attr shape is a Tensor.
-          shape = fluid.layers.fill_constant([2], "int32", 2) # shape=[2,2]
-          data4 = fluid.layers.fill_constant(shape=shape, dtype='bool', value=True) # data4=[[True,True],[True,True]]
-
-          # attr value is a Tensor.
-          val = fluid.layers.fill_constant([1], "float32", 2.0) # val=[2.0]
-          data5 = fluid.layers.fill_constant(shape=[2,1], value=val, dtype='float32') #data5=[[2.0],[2.0]]
-    """
-
-    if in_dygraph_mode():
-        place = _current_expected_place()
-        if force_cpu:
-            place = core.CPUPlace()
-        if isinstance(shape, (list, tuple)):
-            shape = paddle.utils.convert_shape_to_list(shape)
-
-        if not isinstance(dtype, core.VarDesc.VarType):
-            dtype = convert_np_dtype_to_dtype_(dtype)
-
-        if out is None:
-            out = _C_ops.full(shape, float(value), dtype, place)
-            out.stop_gradient = True
-            return out
-
-        if out is not None:
-            # final state mode is support out is not None.
-            _C_ops.full_(out, shape, float(value), dtype, place)
-            out.stop_gradient = True
-            return out
-    else:
-        attrs = {'force_cpu': force_cpu}
-        dtype = convert_dtype(dtype)
-        if not isinstance(value, Variable):
-            if dtype in ['uint8', 'int16', 'int32', 'int64']:
-                attrs['str_value'] = str(int(value))
-                attrs['value'] = int(value)
-            else:
-                attrs['str_value'] = str(float(value))
-                attrs['value'] = float(value)
-
-        helper = LayerHelper("fill_constant", **locals())
-        inputs = {}
-        if isinstance(value, Variable):
-            if convert_dtype(value.dtype) != dtype:
-                value = paddle.cast(value, dtype)
-            inputs['ValueTensor'] = value
-
-        paddle.utils.check_shape(shape)
-        check_dtype(
-            dtype,
-            'dtype',
-            [
-                'bool',
-                'float16',
-                'float32',
-                'float64',
-                'uint8',
-                'int16',
-                'int32',
-                'int64',
-                'complex64',
-                'complex128',
-                'uint16',
-            ],
-            'fill_constant',
-        )
-        check_type(shape, 'shape', (Variable, list, tuple), 'fill_constant')
-
-        if out is not None:
-            check_variable_and_dtype(
-                out, 'out', [convert_dtype(dtype)], 'fill_constant'
-            )
-
-        helper = LayerHelper("fill_constant", **locals())
-        paddle.utils.get_shape_tensor_inputs(
-            inputs=inputs, attrs=attrs, shape=shape, op_type='fill_constant'
-        )
-
-        if out is None:
-            out = helper.create_variable_for_type_inference(dtype=dtype)
-        attrs['dtype'] = out.dtype
-        helper.append_op(
-            type='fill_constant',
-            inputs=inputs,
-            outputs={'Out': [out]},
-            attrs=attrs,
-            stop_gradient=True,
-        )
-        out.stop_gradient = True
-        return out
-
-
 @deprecated(since='1.8.0', update_to="paddle.fluid.layers.fill_constant")
 @templatedoc()
 def fill_constant_batch_size_like(
@@ -214,8 +82,9 @@ def fill_constant_batch_size_like(
 
         .. code-block:: python
 
+             import paddle
              import paddle.fluid as fluid
-             like = fluid.layers.fill_constant(shape=[1,2], value=10, dtype='int64') #like=[[10, 10]]
+             like = paddle.full(shape=[1,2], fill_value=10, dtype='int64') #like=[[10, 10]]
              data = fluid.layers.fill_constant_batch_size_like(
                     input=like, shape=[1], value=0, dtype='int64') #like=[[10, 10]] data=[0]
 
@@ -279,10 +148,16 @@ def zeros(shape, dtype, force_cpu=False, name=None):
         .. code-block:: python
 
           import paddle.fluid as fluid
+          import paddle
           data = fluid.layers.zeros(shape=[3, 2], dtype='float32') # [[0., 0.], [0., 0.], [0., 0.]]
 
           # shape is a Tensor
-          shape = fluid.layers.fill_constant(shape=[2], dtype='int32', value=2)
+          shape = paddle.full(shape=[2], dtype='int32', fill_value=2)
           data1 = fluid.layers.zeros(shape=shape, dtype='int32') #[[0, 0], [0, 0]]
     """
-    return fill_constant(value=0.0, **locals())
+    # TODO: remove zeros
+    from paddle.tensor import fill_constant
+
+    return fill_constant(
+        value=0.0, shape=shape, dtype=dtype, force_cpu=force_cpu, name=name
+    )

python/paddle/fluid/optimizer.py

@@ -236,7 +236,7 @@ class Optimizer:
                     None, name='global_step', dtype='int32'
                 )
 
-                tensor.fill_constant(
+                paddle.tensor.fill_constant(
                     [1], "int32", self._learning_rate.step_num, out=var_temp
                 )
 
@@ -7393,11 +7393,11 @@ class LookaheadOptimizer:
             paddle.increment(x=step, value=1.0)
 
             # lookahead
-            zero_var = layers.fill_constant(
+            zero_var = paddle.tensor.fill_constant(
                 shape=[1], dtype='float32', value=0.0
             )
 
-            one_var = layers.fill_constant(
+            one_var = paddle.tensor.fill_constant(
                 shape=[1], dtype='float32', value=1.0
             )
 
@@ -7747,7 +7747,7 @@ class GradientMergeOptimizer:
 
             # clear gradient_merge_vars
             for param, new_grad in new_params_grads:
-                layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=new_grad.shape,
                     dtype=new_grad.dtype,
                     value=0.0,

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

@@ -96,7 +96,9 @@ def init_communicator(
 
     with fluid.program_guard(main_program):
         op_type = "c_allreduce_sum"
-        data = fluid.layers.fill_constant(shape=[1], dtype='float32', value=2.5)
+        data = paddle.tensor.fill_constant(
+            shape=[1], dtype='float32', value=2.5
+        )
         helper = LayerHelper(op_type, **locals())
         helper.append_op(
             type=op_type,

python/paddle/fluid/tests/unittests/auto_parallel/test_while_op_partition.py

@@ -117,11 +117,11 @@ def get_program():
     with fluid.program_guard(train_program, start_program):
 
         # 循环计数器
-        i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
+        i = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=0)
         auto.shard_tensor(i, _g_process_mesh, [None])
 
         # 循环次数
-        loop_len = fluid.layers.fill_constant(
+        loop_len = paddle.tensor.fill_constant(
             shape=[1], dtype='int64', value=epoch_num
         )
         auto.shard_tensor(loop_len, _g_process_mesh, [None])

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

@@ -50,7 +50,7 @@ def net():
     y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
 
     # test int64 value
-    zero = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
+    zero = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=0)
 
     # test float16 value
     fp16_zero = paddle.cast(zero, dtype='float16')

python/paddle/fluid/tests/unittests/collective/collective_scatter_api.py

@@ -16,7 +16,6 @@ from test_collective_api_base import TestCollectiveAPIRunnerBase, runtime_main
 
 import paddle
 import paddle.fluid as fluid
-import paddle.fluid.layers as layers
 
 paddle.enable_static()
 
@@ -32,7 +31,7 @@ class TestCollectiveScatterAPI(TestCollectiveAPIRunnerBase):
                 shape=[10, 1000],
                 dtype='float32',
             )
-            toutdata = layers.fill_constant(
+            toutdata = paddle.tensor.fill_constant(
                 shape=[5, 1000], dtype='float32', value=1.0
             )
             tensor_list = None

python/paddle/fluid/tests/unittests/collective/collective_sendrecv_op_array.py

@@ -41,7 +41,7 @@ class TestCollectiveSendRecv(TestCollectiveRunnerBase):
                 data1 = paddle.assign(np.array([[3, 4, 5]], dtype='float32'))
                 data2 = paddle.assign(np.array([[0, 1, 2]], dtype='float32'))
             tensor_array = paddle.tensor.create_array(dtype='float32')
-            i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
+            i = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=0)
             paddle.tensor.array_write(data1, i, tensor_array)
             paddle.tensor.array_write(data2, i + 1, tensor_array)
             if self.rank == 0:

python/paddle/fluid/tests/unittests/collective/fleet/hybrid_parallel_inference_helper.py

@@ -19,7 +19,6 @@ import numpy as np
 
 import paddle
 import paddle.distributed.fleet as fleet
-import paddle.fluid.layers as layers
 from paddle.distributed.fleet.utils.hybrid_parallel_inference import (
     HybridParallelInferenceHelper,
 )
@@ -66,16 +65,16 @@ class TestHybridParallelInferenceHelperClass(unittest.TestCase):
                 )
 
             with paddle.fluid.device_guard(f'{device}:all'):
-                max_len = layers.fill_constant(
+                max_len = paddle.tensor.fill_constant(
                     shape=[1], dtype="int64", value=2, force_cpu=False, name="n"
                 )
-                step_idx = layers.fill_constant(
+                step_idx = paddle.tensor.fill_constant(
                     shape=[1], dtype="int64", value=0, force_cpu=False, name="i"
                 )
 
                 data = paddle.tensor.array_write(X, step_idx)
 
-                cond_int = layers.fill_constant(
+                cond_int = paddle.tensor.fill_constant(
                     shape=[1],
                     dtype="int64",
                     value=0,

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

@@ -59,7 +59,7 @@ def get_acc(cos_q_nt, cos_q_pt, batch_size):
     cond_3 = paddle.sum(cond)
     acc = paddle.divide(
         cond_3,
-        fluid.layers.fill_constant(
+        paddle.tensor.fill_constant(
             shape=[1], value=batch_size * 1.0, dtype='float64'
         ),
         name="simnet_acc",

python/paddle/fluid/tests/unittests/dygraph_to_static/ifelse_simple_func.py

@@ -60,7 +60,7 @@ def dyfunc_with_if_else2(x, col=100):
         # TODO: Don't support return non-Tensor in Tensor-dependent `if` stament currently.
         #  `x` is Tensor, `col` is not Tensor, and `col` is the return value of `true_fn` after transformed.
         # col = -1
-        col = fluid.layers.fill_constant(shape=[1], value=-1, dtype="int64")
+        col = paddle.tensor.fill_constant(shape=[1], value=-1, dtype="int64")
     if paddle.mean(x).numpy() > x.numpy()[row][col]:
         y = paddle.nn.functional.relu(x)
     else:
@@ -149,7 +149,7 @@ def dyfunc_with_if_else_with_list_geneator(x):
 def nested_if_else(x_v):
     batch_size = 16
     feat_size = x_v.shape[-1]
-    bias = fluid.layers.fill_constant([feat_size], dtype='float32', value=1)
+    bias = paddle.tensor.fill_constant([feat_size], dtype='float32', value=1)
     if x_v.shape[0] != batch_size:
         # TODO: Don't support return non-Tensor in Tensor-dependent `if` stament currently.
         #  `x_v.shape[0]` is not Tensor, and `batch_size` is the return value of `true_fn` after transformed.
@@ -160,14 +160,14 @@ def nested_if_else(x_v):
     # if tensor.shape is [1], now support to compare with numpy.
     if paddle.mean(x_v).numpy() < 0:
         y = x_v + bias
-        w = fluid.layers.fill_constant([feat_size], dtype='float32', value=10)
+        w = paddle.tensor.fill_constant([feat_size], dtype='float32', value=10)
         if y.numpy()[0] < 10:
             tmp = y * w
             y = paddle.nn.functional.relu(tmp)
             if paddle.mean(y).numpy() < batch_size:
                 y = paddle.abs(y)
             else:
-                tmp = fluid.layers.fill_constant(
+                tmp = paddle.tensor.fill_constant(
                     y.shape, dtype='float32', value=-1
                 )
                 y = y - tmp
@@ -185,13 +185,13 @@ def nested_if_else_2(x):
     x_shape_0 = x.shape[0]
     if x_shape_0 < 1:
         if paddle.shape(y).numpy()[0] < 1:
-            res = fluid.layers.fill_constant(
+            res = paddle.tensor.fill_constant(
                 value=2, shape=x.shape, dtype="int32"
             )
             # `z` is a new var here.
             z = y + 1
         else:
-            res = fluid.layers.fill_constant(
+            res = paddle.tensor.fill_constant(
                 value=3, shape=x.shape, dtype="int32"
             )
     else:
@@ -218,13 +218,13 @@ def nested_if_else_3(x):
     else:
         y_shape = paddle.shape(y)
         if y_shape.numpy()[0] < 1:
-            res = fluid.layers.fill_constant(
+            res = paddle.tensor.fill_constant(
                 value=2, shape=x.shape, dtype="int32"
             )
             # `z` is created in above code block.
             z = y + 1
         else:
-            res = fluid.layers.fill_constant(
+            res = paddle.tensor.fill_constant(
                 value=3, shape=x.shape, dtype="int32"
             )
             # `out` is a new var.
@@ -259,14 +259,14 @@ class NetWithControlFlowIf(fluid.dygraph.Layer):
                 )
             )
 
-        self.constant_vars['bias'] = fluid.layers.fill_constant(
+        self.constant_vars['bias'] = paddle.tensor.fill_constant(
             [5], dtype='float32', value=1
         )
         # Control flow `if` statement
         fc_out = self.fc(input)
         if paddle.mean(fc_out).numpy() < 0:
             y = fc_out + self.constant_vars['bias']
-            self.constant_vars['w'] = fluid.layers.fill_constant(
+            self.constant_vars['w'] = paddle.tensor.fill_constant(
                 [5], dtype='float32', value=10
             )
             if y.numpy()[0] < self.alpha:
@@ -277,12 +277,12 @@ class NetWithControlFlowIf(fluid.dygraph.Layer):
                 # Nested `if/else`
                 if y.numpy()[-1] < self.alpha:
                     # Modify variable of class
-                    self.constant_vars['w'] = fluid.layers.fill_constant(
+                    self.constant_vars['w'] = paddle.tensor.fill_constant(
                         [hidden_dim], dtype='float32', value=9
                     )
                     y = paddle.abs(y)
                 else:
-                    tmp = fluid.layers.fill_constant(
+                    tmp = paddle.tensor.fill_constant(
                         y.shape, dtype='float32', value=-1
                     )
                     y = y - tmp

python/paddle/fluid/tests/unittests/dygraph_to_static/simnet_dygraph_model.py

@@ -15,7 +15,6 @@
 from functools import reduce
 
 import paddle
-import paddle.fluid as fluid
 import paddle.fluid.param_attr as attr
 from paddle.common_ops_import import Variable
 from paddle.fluid.dygraph import Layer
@@ -214,7 +213,7 @@ class ConstantLayer:
         shape = list(shape)
         input_shape = paddle.shape(input)
         shape[0] = input_shape[0]
-        constant = fluid.layers.fill_constant(shape, dtype, value)
+        constant = paddle.tensor.fill_constant(shape, dtype, value)
         return constant
 
 

python/paddle/fluid/tests/unittests/dygraph_to_static/simnet_dygraph_model_v2.py

@@ -210,7 +210,7 @@ class ConstantLayer:
         shape = list(shape)
         input_shape = paddle.shape(input)
         shape[0] = input_shape[0]
-        constant = paddle.fluid.layers.fill_constant(shape, dtype, value)
+        constant = paddle.tensor.fill_constant(shape, dtype, value)
         return constant
 
 

python/paddle/fluid/tests/unittests/dygraph_to_static/test_break_continue.py

@@ -62,7 +62,7 @@ def test_continue_in_for_at_end(x):
 
 def test_continue_in_while(x):
     x = fluid.dygraph.to_variable(x)
-    i = fluid.layers.fill_constant(shape=[1], dtype='int32', value=0)
+    i = paddle.tensor.fill_constant(shape=[1], dtype='int32', value=0)
     while i < 10:
         i += 1
         if i > 5:
@@ -94,7 +94,7 @@ def test_break_in_for_at_end(x):
 
 def test_break_in_while(x):
     x = fluid.dygraph.to_variable(x)
-    i = fluid.layers.fill_constant(shape=[1], dtype='int32', value=0)
+    i = paddle.tensor.fill_constant(shape=[1], dtype='int32', value=0)
     while i < 10:
         i += 1
         if i > 5:
@@ -116,8 +116,8 @@ def test_break_continue_in_for(x):
             break
         x += 10086
 
-    a = fluid.layers.fill_constant(shape=[1], dtype='int32', value=0)
-    b = fluid.layers.fill_constant(shape=[1], dtype='int32', value=3)
+    a = paddle.tensor.fill_constant(shape=[1], dtype='int32', value=0)
+    b = paddle.tensor.fill_constant(shape=[1], dtype='int32', value=3)
     # b = 10
     # TODO: add Raise Error and suggestion for usage:
     #   Py for contains break/continue depends on control-flow.
@@ -192,7 +192,7 @@ def test_optim_break_in_for(x):
 
 def test_optim_break_in_while(x):
     x = paddle.to_tensor(x)
-    i = fluid.layers.fill_constant(shape=[1], dtype='int32', value=0)
+    i = paddle.tensor.fill_constant(shape=[1], dtype='int32', value=0)
     while i < 10:
         if i > 5:
             break

python/paddle/fluid/tests/unittests/dygraph_to_static/test_dict.py

@@ -84,12 +84,12 @@ class MainNetWithDict(fluid.dygraph.Layer):
     def forward(self, input, max_len=4):
         input = fluid.dygraph.to_variable(input)
         cache = {
-            "k": fluid.layers.fill_constant(
+            "k": paddle.tensor.fill_constant(
                 shape=[self.batch_size, self.output_size],
                 dtype='float32',
                 value=0,
             ),
-            "v": fluid.layers.fill_constant(
+            "v": paddle.tensor.fill_constant(
                 shape=[self.batch_size, self.output_size],
                 dtype='float32',
                 value=0,

python/paddle/fluid/tests/unittests/dygraph_to_static/test_error.py

@@ -25,7 +25,7 @@ from paddle.jit.dy2static.origin_info import unwrap
 
 
 def inner_func():
-    fluid.layers.fill_constant(shape=[1, 2], value=9, dtype="int")
+    paddle.tensor.fill_constant(shape=[1, 2], value=9, dtype="int")
     return
 
 
@@ -50,7 +50,7 @@ def func_error_in_compile_time_2(x):
 @paddle.jit.to_static
 def func_error_in_runtime(x):
     x = fluid.dygraph.to_variable(x)
-    two = fluid.layers.fill_constant(shape=[1], value=2, dtype="int32")
+    two = paddle.tensor.fill_constant(shape=[1], value=2, dtype="int32")
     x = paddle.reshape(x, shape=[1, two])
     return x
 
@@ -77,7 +77,7 @@ class LayerErrorInCompiletime(fluid.dygraph.Layer):
     )
     def forward(self, x):
         y = self._linear(x)
-        z = fluid.layers.fill_constant(shape=[1, 2], value=9, dtype="int")
+        z = paddle.tensor.fill_constant(shape=[1, 2], value=9, dtype="int")
         out = paddle.mean(y[z])
         return out
 
@@ -101,7 +101,7 @@ class LayerErrorInCompiletime2(fluid.dygraph.Layer):
 @paddle.jit.to_static
 def func_error_in_runtime_with_empty_line(x):
     x = fluid.dygraph.to_variable(x)
-    two = fluid.layers.fill_constant(shape=[1], value=2, dtype="int32")
+    two = paddle.tensor.fill_constant(shape=[1], value=2, dtype="int32")
 
     x = paddle.reshape(x, shape=[1, two])
 
@@ -261,7 +261,7 @@ class TestErrorStaticLayerCallInCompiletime(TestErrorBase):
             'inner_func()',
             'File "{}", line 28, in inner_func'.format(self.filepath),
             'def inner_func():',
-            'fluid.layers.fill_constant(shape=[1, 2], value=9, dtype="int")',
+            'paddle.tensor.fill_constant(shape=[1, 2], value=9, dtype="int")',
             '<--- HERE',
             'return',
         ]
@@ -340,7 +340,7 @@ class TestErrorStaticLayerCallInRuntime(TestErrorStaticLayerCallInCompiletime):
                 self.filepath
             ),
             'x = fluid.dygraph.to_variable(x)',
-            'two = fluid.layers.fill_constant(shape=[1], value=2, dtype="int32")',
+            'two = paddle.tensor.fill_constant(shape=[1], value=2, dtype="int32")',
             'x = paddle.reshape(x, shape=[1, two])',
             '<--- HERE',
             'return x',
@@ -356,7 +356,7 @@ class TestErrorStaticLayerCallInRuntime2(TestErrorStaticLayerCallInRuntime):
             'File "{}", line 106, in func_error_in_runtime_with_empty_line'.format(
                 self.filepath
             ),
-            'two = fluid.layers.fill_constant(shape=[1], value=2, dtype="int32")',
+            'two = paddle.tensor.fill_constant(shape=[1], value=2, dtype="int32")',
             'x = paddle.reshape(x, shape=[1, two])',
             '<--- HERE',
             'return x',
@@ -379,7 +379,7 @@ class TestJitSaveInCompiletime(TestErrorBase):
             'File "{}", line 80, in forward'.format(self.filepath),
             'def forward(self, x):',
             'y = self._linear(x)',
-            'z = fluid.layers.fill_constant(shape=[1, 2], value=9, dtype="int")',
+            'z = paddle.tensor.fill_constant(shape=[1, 2], value=9, dtype="int")',
             '<--- HERE',
             'out = paddle.mean(y[z])',
             'return out',

python/paddle/fluid/tests/unittests/dygraph_to_static/test_for_enumerate.py

@@ -26,7 +26,7 @@ from paddle.static import InputSpec
 # 0. for in range var.numpy()[0]
 @paddle.jit.to_static
 def for_in_range(x):
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     x = fluid.dygraph.to_variable(x)
     for i in range(x.numpy()[0]):
         z = z + i
@@ -36,7 +36,7 @@ def for_in_range(x):
 # 1. for iter list
 @paddle.jit.to_static
 def for_iter_list(x_array):
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     for x in x_array:
         z = z + x
     return z
@@ -45,7 +45,7 @@ def for_iter_list(x_array):
 # 2. for enumerate list
 @paddle.jit.to_static
 def for_enumerate_list(x_array):
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     for i, x in enumerate(x_array):
         z = z + x + i
     return z
@@ -54,7 +54,7 @@ def for_enumerate_list(x_array):
 # 3. for iter var.numpy()
 @paddle.jit.to_static
 def for_iter_var_numpy(x_array):
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     x_array = fluid.dygraph.to_variable(x_array)
     for x in x_array.numpy():
         z = z + x
@@ -64,8 +64,8 @@ def for_iter_var_numpy(x_array):
 # 4. for enumerate var.numpy()
 @paddle.jit.to_static
 def for_enumerate_var_numpy(x_array):
-    y = fluid.layers.fill_constant([1], 'int32', 0)
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    y = paddle.tensor.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     x_array = fluid.dygraph.to_variable(x_array)
     for i, x in enumerate(x_array.numpy()):
         y = y + i
@@ -76,8 +76,8 @@ def for_enumerate_var_numpy(x_array):
 # 5. for enumerate var.numpy() with start
 @paddle.jit.to_static
 def for_enumerate_var_numpy_with_start(x_array):
-    y = fluid.layers.fill_constant([1], 'int32', 0)
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    y = paddle.tensor.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     x_array = fluid.dygraph.to_variable(x_array)
     for i, x in enumerate(x_array.numpy(), 1):
         y = y + i
@@ -88,7 +88,7 @@ def for_enumerate_var_numpy_with_start(x_array):
 # 6. for in range with break
 @paddle.jit.to_static
 def for_in_range_with_break(x):
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     x = fluid.dygraph.to_variable(x)
     for i in range(x.numpy()[0]):
         z = z + i
@@ -100,8 +100,8 @@ def for_in_range_with_break(x):
 # 7. for enumerate var.numpy() with break
 @paddle.jit.to_static
 def for_enumerate_var_numpy_with_break(x_array):
-    y = fluid.layers.fill_constant([1], 'int32', 0)
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    y = paddle.tensor.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     x_array = fluid.dygraph.to_variable(x_array)
     for i, x in enumerate(x_array.numpy()):
         y = y + i
@@ -114,8 +114,8 @@ def for_enumerate_var_numpy_with_break(x_array):
 # 8. for enumerate var.numpy() with continue
 @paddle.jit.to_static
 def for_enumerate_var_numpy_with_continue(x_array):
-    y = fluid.layers.fill_constant([1], 'int32', 0)
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    y = paddle.tensor.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     x_array = fluid.dygraph.to_variable(x_array)
     for i, x in enumerate(x_array.numpy()):
         y = y + i
@@ -128,8 +128,8 @@ def for_enumerate_var_numpy_with_continue(x_array):
 # 9. for enumerate var.numpy() with start & break
 @paddle.jit.to_static
 def for_enumerate_var_numpy_with_start_break(x_array):
-    y = fluid.layers.fill_constant([1], 'int32', 0)
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    y = paddle.tensor.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     x_array = fluid.dygraph.to_variable(x_array)
     for i, x in enumerate(x_array.numpy(), 1):
         y = y + i
@@ -142,8 +142,8 @@ def for_enumerate_var_numpy_with_start_break(x_array):
 # 10. for enumerate var.numpy() with start & continue
 @paddle.jit.to_static
 def for_enumerate_var_numpy_with_start_continue(x_array):
-    y = fluid.layers.fill_constant([1], 'int32', 0)
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    y = paddle.tensor.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     x_array = fluid.dygraph.to_variable(x_array)
     for i, x in enumerate(x_array.numpy(), 1):
         y = y + i
@@ -156,7 +156,7 @@ def for_enumerate_var_numpy_with_start_continue(x_array):
 # 11. for iter var
 @paddle.jit.to_static
 def for_iter_var(x_array):
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     x_array = fluid.dygraph.to_variable(x_array)
 
     for x in x_array:
@@ -167,8 +167,8 @@ def for_iter_var(x_array):
 # 12. for enumerate var
 @paddle.jit.to_static
 def for_enumerate_var(x_array):
-    y = fluid.layers.fill_constant([1], 'int32', 0)
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    y = paddle.tensor.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     x_array = fluid.dygraph.to_variable(x_array)
     for i, x in enumerate(x_array):
         y = y + i
@@ -181,12 +181,12 @@ def for_enumerate_var(x_array):
 def for_iter_var_list(x):
     # 1. prepare data, ref test_list.py
     x = fluid.dygraph.to_variable(x)
-    iter_num = fluid.layers.fill_constant(shape=[1], value=5, dtype="int32")
+    iter_num = paddle.tensor.fill_constant(shape=[1], value=5, dtype="int32")
     a = []
     for i in range(iter_num):
         a.append(x + i)
     # 2. iter list[var]
-    y = fluid.layers.fill_constant([1], 'int32', 0)
+    y = paddle.tensor.fill_constant([1], 'int32', 0)
     for x in a:
         y = y + x
     return y
@@ -197,13 +197,13 @@ def for_iter_var_list(x):
 def for_enumerate_var_list(x):
     # 1. prepare data, ref test_list.py
     x = fluid.dygraph.to_variable(x)
-    iter_num = fluid.layers.fill_constant(shape=[1], value=5, dtype="int32")
+    iter_num = paddle.tensor.fill_constant(shape=[1], value=5, dtype="int32")
     a = []
     for i in range(iter_num):
         a.append(x + i)
     # 2. iter list[var]
-    y = fluid.layers.fill_constant([1], 'int32', 0)
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    y = paddle.tensor.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     for i, x in enumerate(a):
         y = y + i
         z = z + x
@@ -213,7 +213,7 @@ def for_enumerate_var_list(x):
 # 15. for enumerate list[var] with a nested for range
 @paddle.jit.to_static
 def for_enumerate_var_with_nested_range(x_array):
-    x = fluid.layers.fill_constant([1], 'int32', 0)
+    x = paddle.tensor.fill_constant([1], 'int32', 0)
     x_array = fluid.dygraph.to_variable(x_array)
     for i, num in enumerate(x_array):
         for idx in range(num):
@@ -224,7 +224,7 @@ def for_enumerate_var_with_nested_range(x_array):
 # 16. for iter var[idx]
 @paddle.jit.to_static
 def for_iter_var_idx(x_array):
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     x_array = fluid.dygraph.to_variable(x_array)
 
     for x in x_array[0:]:
@@ -306,7 +306,7 @@ class ForwardContainsForLayer(paddle.nn.Layer):
 # 21. for original list
 @paddle.jit.to_static
 def for_original_list():
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     for x in [1, 2, 3]:
         z = z + x
     return z
@@ -315,7 +315,7 @@ def for_original_list():
 # 22. for original tuple
 @paddle.jit.to_static
 def for_original_tuple():
-    z = fluid.layers.fill_constant([1], 'int32', 0)
+    z = paddle.tensor.fill_constant([1], 'int32', 0)
     for x in (1, 2, 3):
         z = z + x
     return z

python/paddle/fluid/tests/unittests/dygraph_to_static/test_ifelse.py

@@ -158,8 +158,8 @@ def dyfunc_ifExp_with_while(x):
         i += 1
         return [i, ten, y]
 
-    i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
-    ten = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
+    i = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=0)
+    ten = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=10)
     i, ten, y = paddle.static.nn.while_loop(cond, body, [i, ten, y])
     return y[0]
 
@@ -180,7 +180,7 @@ def dyfunc_ifExp(x):
     def map_func(func, tensor_list):
         return [func(x) for x in tensor_list]
 
-    i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
+    i = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=0)
     # It will be converted into `layers.cond` as followed.
     # map_func(lambda x: paddle.static.nn.cond(i==1, lambda: x, lambda: add_fn(x), y)
     # `if (Tensor) == 1` is supported in dygraph.

python/paddle/fluid/tests/unittests/dygraph_to_static/test_len.py

@@ -35,7 +35,7 @@ def len_with_tensor(x):
 def len_with_lod_tensor_array(x):
     x = fluid.dygraph.to_variable(x)
 
-    i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
+    i = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=0)
     arr = paddle.tensor.array_write(x, i=i)
     arr_len = len(arr)
 

python/paddle/fluid/tests/unittests/dygraph_to_static/test_list.py

@@ -42,7 +42,7 @@ def test_list_append_in_if(x):
         a.append(x)
     else:
         a.append(
-            fluid.layers.fill_constant(shape=[1, 2], value=9, dtype="int64")
+            paddle.tensor.fill_constant(shape=[1, 2], value=9, dtype="int64")
         )
     # TODO(Aurelius84): Currently, run_program_op doesn't support output LoDTensorArray.
     return a[0]
@@ -51,7 +51,7 @@ def test_list_append_in_if(x):
 def test_list_append_in_for_loop(x, iter_num):
     x = fluid.dygraph.to_variable(x)
     # Use `fill_constant` so that static analysis can analyze the type of iter_num is Tensor
-    iter_num = fluid.layers.fill_constant(
+    iter_num = paddle.tensor.fill_constant(
         shape=[1], value=iter_num, dtype="int32"
     )  # TODO(liym27): Delete it if the type of parameter iter_num can be resolved
     a = []
@@ -88,7 +88,7 @@ def test_list_append_in_for_loop_with_concat(x, iter_num):
     x = fluid.dygraph.to_variable(x)
     a = []
     # Use `fill_constant` so that static analysis can analyze the type of iter_num is Tensor
-    iter_num = fluid.layers.fill_constant(
+    iter_num = paddle.tensor.fill_constant(
         shape=[1], value=iter_num, dtype="int32"
     )  # TODO(liym27): Delete it if the type of parameter iter_num can be resolved
     for i in range(iter_num):
@@ -99,7 +99,7 @@ def test_list_append_in_for_loop_with_concat(x, iter_num):
 
 def test_list_append_in_while_loop(x, iter_num):
     x = fluid.dygraph.to_variable(x)
-    iter_num = fluid.layers.fill_constant(
+    iter_num = paddle.tensor.fill_constant(
         shape=[1], value=iter_num, dtype="int32"
     )
     a = []
@@ -112,7 +112,7 @@ def test_list_append_in_while_loop(x, iter_num):
 
 def test_list_append_in_while_loop_with_stack(x, iter_num):
     x = fluid.dygraph.to_variable(x)
-    iter_num = fluid.layers.fill_constant(
+    iter_num = paddle.tensor.fill_constant(
         shape=[1], value=iter_num, dtype="int32"
     )
     a = []
@@ -159,11 +159,11 @@ def test_list_pop_in_if(x):
     if x.numpy()[0] > 0:
         a.append(x)
         b.append(x + 1)
-        a.append(fluid.layers.fill_constant(shape=[1], value=1, dtype="int64"))
+        a.append(paddle.tensor.fill_constant(shape=[1], value=1, dtype="int64"))
     else:
         a.append(x + 1)
         b.append(x - 1)
-        a.append(fluid.layers.fill_constant(shape=[2], value=2, dtype="int64"))
+        a.append(paddle.tensor.fill_constant(shape=[2], value=2, dtype="int64"))
     item1 = a.pop(1)
     return item1, b[-1]
 
@@ -171,7 +171,7 @@ def test_list_pop_in_if(x):
 def test_list_pop_in_for_loop(x, iter_num):
     x = fluid.dygraph.to_variable(x)
     # Use `fill_constant` so that static analysis can analyze the type of iter_num is Tensor
-    iter_num = fluid.layers.fill_constant(
+    iter_num = paddle.tensor.fill_constant(
         shape=[1], value=iter_num, dtype="int32"
     )  # TODO(liym27): Delete it if the type of parameter iter_num can be resolved
 
@@ -189,7 +189,7 @@ def test_list_pop_in_for_loop(x, iter_num):
 
 def test_list_pop_in_while_loop(x, iter_num):
     x = fluid.dygraph.to_variable(x)
-    iter_num = fluid.layers.fill_constant(
+    iter_num = paddle.tensor.fill_constant(
         shape=[1], value=iter_num, dtype="int32"
     )
     a = []

python/paddle/fluid/tests/unittests/dygraph_to_static/test_loop.py

@@ -89,7 +89,7 @@ def for_loop_dyfunc(max_len):
 
 def for_loop_dyfunc2(max_len):
     # Test case: a variable is used and created in loop, but used before created
-    x = fluid.layers.fill_constant(shape=[1, 2], dtype="int32", value=1)
+    x = paddle.tensor.fill_constant(shape=[1, 2], dtype="int32", value=1)
 
     for i in range(max_len):
         if i > 1:
@@ -97,7 +97,7 @@ def for_loop_dyfunc2(max_len):
         a = 1
         q, _ = x.shape  # test var x.shape only used but not created in loop
 
-    ret = fluid.layers.fill_constant(shape=[1], dtype="int32", value=s + q)
+    ret = paddle.tensor.fill_constant(shape=[1], dtype="int32", value=s + q)
     return ret
 
 
@@ -189,7 +189,7 @@ def for_loop_class_var(max_len):
     foo = Foo()
 
     # Use `to_variable` so that static analysis can analyze the type of X is Tensor
-    max_len = fluid.layers.fill_constant(
+    max_len = paddle.tensor.fill_constant(
         shape=[1], value=max_len, dtype="int32"
     )
 
@@ -206,8 +206,8 @@ def var_create_in_for_loop(max_len):
 
 
 def nested_for_loop_dyfunc():
-    two = fluid.layers.fill_constant(shape=[1], value=2, dtype="int32")
-    three = fluid.layers.fill_constant(shape=[1], value=3, dtype="int32")
+    two = paddle.tensor.fill_constant(shape=[1], value=2, dtype="int32")
+    three = paddle.tensor.fill_constant(shape=[1], value=3, dtype="int32")
     for j in range(two):
         for i in range(10):
             a = 2 + j

python/paddle/fluid/tests/unittests/dygraph_to_static/test_return.py

@@ -69,7 +69,7 @@ def test_return_if_else(x):
 @to_static
 def test_return_in_while(x):
     x = fluid.dygraph.to_variable(x)
-    i = fluid.layers.fill_constant(shape=[1], dtype='int32', value=0)
+    i = paddle.tensor.fill_constant(shape=[1], dtype='int32', value=0)
     while i < 10:
         i += 1
         if i > 5:

python/paddle/fluid/tests/unittests/dygraph_to_static/test_tensor_shape.py

@@ -109,11 +109,11 @@ def dyfunc_with_if_1(x):
         # `res.shape[0]` is transformed into
         #   `paddle.jit.dy2static.convert_var_shape(res)[0]`
         if res.shape[0] > 1:
-            res = fluid.layers.fill_constant(
+            res = paddle.tensor.fill_constant(
                 value=2, shape=x.shape, dtype="int32"
             )
         else:
-            res = fluid.layers.fill_constant(
+            res = paddle.tensor.fill_constant(
                 value=3, shape=x.shape, dtype="int32"
             )
     return res
@@ -125,14 +125,14 @@ def dyfunc_with_if_2(x):
     if len(x.shape) < 1:
         res = x
     else:
-        res = fluid.layers.fill_constant(value=8, shape=x.shape, dtype="int32")
+        res = paddle.tensor.fill_constant(value=8, shape=x.shape, dtype="int32")
 
     return res
 
 
 def dyfunc_with_for_1(x):
     x = fluid.dygraph.to_variable(x)
-    res = fluid.layers.fill_constant(value=0, shape=[1], dtype="int32")
+    res = paddle.tensor.fill_constant(value=0, shape=[1], dtype="int32")
     # `x.shape[0]` is transformed into `paddle.jit.dy2static.convert_var_shape(x)[0]`
     for i in range(x.shape[0]):
         res += 1
@@ -142,7 +142,7 @@ def dyfunc_with_for_1(x):
 def dyfunc_with_for_2(x):
     x = fluid.dygraph.to_variable(x)
     x_shape_0 = x.shape[0]
-    res = fluid.layers.fill_constant(value=0, shape=[1], dtype="int32")
+    res = paddle.tensor.fill_constant(value=0, shape=[1], dtype="int32")
 
     # `x_shape_0` is transformed into `paddle.jit.dy2static.convert_var_shape(x)[0]`
     for i in range(x_shape_0):
@@ -152,7 +152,7 @@ def dyfunc_with_for_2(x):
 
 def dyfunc_with_for_3(x):
     x = fluid.dygraph.to_variable(x)
-    res = fluid.layers.fill_constant(value=0, shape=[1], dtype="int32")
+    res = paddle.tensor.fill_constant(value=0, shape=[1], dtype="int32")
     # `len(x.shape)` is not transformed.
     for i in range(len(x.shape)):
         res += 1
@@ -162,7 +162,7 @@ def dyfunc_with_for_3(x):
 
 def dyfunc_with_while_1(x):
     x = fluid.dygraph.to_variable(x)
-    res = fluid.layers.fill_constant(value=0, shape=[1], dtype="int32")
+    res = paddle.tensor.fill_constant(value=0, shape=[1], dtype="int32")
     # `x.shape[0]` is transformed into `paddle.jit.dy2static.convert_var_shape(x)[0]`
     i = 1
     while i < x.shape[0]:
@@ -174,7 +174,7 @@ def dyfunc_with_while_1(x):
 def dyfunc_with_while_2(x):
     x = fluid.dygraph.to_variable(x)
     x_shape_0 = x.shape[0]
-    res = fluid.layers.fill_constant(value=0, shape=[1], dtype="int32")
+    res = paddle.tensor.fill_constant(value=0, shape=[1], dtype="int32")
     i = 1
     # `x_shape_0` is transformed into `paddle.jit.dy2static.convert_var_shape(x)[0]`
     while i < x_shape_0:
@@ -186,7 +186,7 @@ def dyfunc_with_while_2(x):
 def dyfunc_with_while_3(x):
     x = fluid.dygraph.to_variable(x)
     x_shape = x.shape
-    res = fluid.layers.fill_constant(value=0, shape=[1], dtype="int32")
+    res = paddle.tensor.fill_constant(value=0, shape=[1], dtype="int32")
     i = 1
 
     # `len(x.shape)` is not transformed.
@@ -601,7 +601,7 @@ def dyfunc_with_static_convert_var_shape(x):
     else:
         # Test for correctly to find `batch_size__static_convert_var_shape_suffix_0` in
         # deeply nested scope.
-        res = fluid.layers.fill_constant(
+        res = paddle.tensor.fill_constant(
             value=8, shape=[batch_size], dtype="int32"
         )
 

python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_dygraph_model.py

@@ -16,7 +16,6 @@ import numpy as np
 
 import paddle
 import paddle.fluid as fluid
-import paddle.fluid.layers as layers
 import paddle.nn.functional as F
 from paddle.fluid.dygraph import Layer, to_variable
 from paddle.jit.api import dygraph_to_static_func
@@ -796,7 +795,7 @@ class Transformer(Layer):
         # constant number
         inf = float(1.0 * 1e7)
         max_len = (enc_output.shape[1] + 20) if max_len is None else max_len
-        vocab_size_tensor = layers.fill_constant(
+        vocab_size_tensor = paddle.tensor.fill_constant(
             shape=[1], dtype="int64", value=self.trg_vocab_size
         )
         end_token_tensor = to_variable(
@@ -824,7 +823,7 @@ class Transformer(Layer):
             np.full([batch_size, beam_size], 0, dtype="bool")
         )
 
-        trg_word = layers.fill_constant(
+        trg_word = paddle.tensor.fill_constant(
             shape=[batch_size * beam_size, 1], dtype="int64", value=bos_id
         )
 
@@ -838,12 +837,12 @@ class Transformer(Layer):
         # init states (caches) for transformer, need to be updated according to selected beam
         caches = [
             {
-                "k": layers.fill_constant(
+                "k": paddle.tensor.fill_constant(
                     shape=[batch_size, beam_size, self.n_head, 0, self.d_key],
                     dtype=enc_output.dtype,
                     value=0,
                 ),
-                "v": layers.fill_constant(
+                "v": paddle.tensor.fill_constant(
                     shape=[batch_size, beam_size, self.n_head, 0, self.d_value],
                     dtype=enc_output.dtype,
                     value=0,
@@ -853,7 +852,7 @@ class Transformer(Layer):
         ]
 
         for i in range(paddle.to_tensor(max_len)):
-            trg_pos = layers.fill_constant(
+            trg_pos = paddle.tensor.fill_constant(
                 shape=trg_word.shape, dtype="int64", value=i
             )
             caches = paddle.utils.map_structure(

python/paddle/fluid/tests/unittests/ipu/test_clip_op_ipu.py

@@ -89,10 +89,10 @@ class TestMinMaxTensor(TestBase):
             name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32'
         )
 
-        min = paddle.fluid.layers.fill_constant(
+        min = paddle.tensor.fill_constant(
             name="min", shape=[1], dtype='float32', value=0.1
         )
-        max = paddle.fluid.layers.fill_constant(
+        max = paddle.tensor.fill_constant(
             name="max", shape=[1], dtype='float32', value=3.4
         )
         x = paddle.clip(x, min=min, max=max)
@@ -106,7 +106,7 @@ class TestMinTensor(TestBase):
             name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32'
         )
 
-        min = paddle.fluid.layers.fill_constant(
+        min = paddle.tensor.fill_constant(
             name="min", shape=[1], dtype='float32', value=0.1
         )
         x = paddle.clip(x, min=min)
@@ -120,7 +120,7 @@ class TestMaxTensor(TestBase):
             name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32'
         )
 
-        max = paddle.fluid.layers.fill_constant(
+        max = paddle.tensor.fill_constant(
             name="max", shape=[1], dtype='float32', value=3.4
         )
         x = paddle.clip(x, max=max)
@@ -134,7 +134,7 @@ class TestCombine1(TestBase):
             name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32'
         )
 
-        min = paddle.fluid.layers.fill_constant(
+        min = paddle.tensor.fill_constant(
             name="min", shape=[1], dtype='float32', value=0.1
         )
         x = paddle.clip(x, min=min, max=3.4)
@@ -148,7 +148,7 @@ class TestCombine2(TestBase):
             name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32'
         )
 
-        max = paddle.fluid.layers.fill_constant(
+        max = paddle.tensor.fill_constant(
             name="max", shape=[1], dtype='float32', value=3.4
         )
         x = paddle.clip(x, min=0.1, max=max)
@@ -186,10 +186,10 @@ class TestIntMinMax(TestBase):
         x = paddle.static.data(
             name=self.feed_list[0], shape=self.feed_shape[0], dtype='int32'
         )
-        min = paddle.fluid.layers.fill_constant(
+        min = paddle.tensor.fill_constant(
             name="min", shape=[1], dtype='int32', value=1
         )
-        max = paddle.fluid.layers.fill_constant(
+        max = paddle.tensor.fill_constant(
             name="max", shape=[1], dtype='int32', value=3
         )
         x = paddle.clip(x, min=min, max=max)

python/paddle/fluid/tests/unittests/ipu/test_expand_op_ipu.py

@@ -80,7 +80,7 @@ class TestCase1(TestBase):
         x = paddle.static.data(
             name=self.feed_list[0], shape=self.feed_shape[0], dtype="float32"
         )
-        expand_times = paddle.fluid.layers.fill_constant(
+        expand_times = paddle.tensor.fill_constant(
             shape=[len(self.feed_shape[0])], dtype="int32", value=2
         )
         out = paddle.expand(x, expand_times, **self.attrs)

python/paddle/fluid/tests/unittests/ipu/test_expand_v2_op_ipu.py

@@ -100,7 +100,7 @@ class TestCase4(TestBase):
             'dtype': 'int32',
             'value': 3,
         }
-        y = paddle.fluid.layers.fill_constant(**self.attrs)
+        y = paddle.tensor.fill_constant(**self.attrs)
         out = paddle.expand(x, shape=y)
         self.fetch_list = [out.name]
 

python/paddle/fluid/tests/unittests/ipu/test_fill_constant_op_ipu.py

@@ -46,7 +46,7 @@ class TestBase(IPUOpTest):
 
     @IPUOpTest.static_graph
     def build_model(self):
-        x = paddle.fluid.layers.fill_constant(**self.attrs)
+        x = paddle.tensor.fill_constant(**self.attrs)
         out = paddle.add(x, x)
         self.fetch_list = [out.name]
 

python/paddle/fluid/tests/unittests/ipu/test_pad_op_ipu.py

@@ -73,7 +73,7 @@ class TestCase1(TestBase):
             'dtype': 'int32',
             'value': 2,
         }
-        y = paddle.fluid.layers.fill_constant(**const_attrs)
+        y = paddle.tensor.fill_constant(**const_attrs)
         pad = paddle.nn.functional.pad(x, pad=y)
         self.fetch_list = [pad.name]
 

python/paddle/fluid/tests/unittests/ipu/test_topk_op_ipu.py

@@ -57,7 +57,7 @@ class TestTopKOp(IPUOpTest):
             topk_values, topk_indices = self.op(x, **self.attrs)
         else:
             # !important, popart cannot accept non const tensor
-            K_t = paddle.fluid.layers.fill_constant(
+            K_t = paddle.tensor.fill_constant(
                 shape=[1], dtype='int32', value=self.k, name="in_2"
             )
             topk_values, topk_indices = self.op(x, K_t, **self.attrs)

python/paddle/fluid/tests/unittests/ipu/test_yolo_box_op_ipu.py

@@ -64,7 +64,7 @@ class TestBase(IPUOpTest):
             'dtype': 'int32',
             'value': 6,
         }
-        img_size = paddle.fluid.layers.fill_constant(**attrs)
+        img_size = paddle.tensor.fill_constant(**attrs)
         out = paddle.vision.ops.yolo_box(x=x, img_size=img_size, **self.attrs)
         self.fetch_list = [x.name for x in out]
 

python/paddle/fluid/tests/unittests/ir/test_ir_fusion_group_pass.py

@@ -20,7 +20,6 @@ from pass_test import PassTest
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
-import paddle.fluid.layers as layers
 
 
 class FusionGroupPassTest(PassTest):
@@ -86,7 +85,7 @@ class FusionGroupPassComplicatedTest(FusionGroupPassTest):
         with fluid.program_guard(self.main_program, self.startup_program):
             self.feed_vars = self._prepare_feed_vars([32, 64], dtype, 5, False)
 
-            one = layers.fill_constant(shape=[1], dtype=dtype, value=1.0)
+            one = paddle.tensor.fill_constant(shape=[1], dtype=dtype, value=1.0)
             tmp_0 = one * self.feed_vars[0]
             # subgraph with 9 op nodes
             tmp_1 = tmp_0 * paddle.nn.functional.sigmoid(
@@ -142,7 +141,9 @@ class FusionGroupPassTestCastAndFP16(FusionGroupPassTest):
             tmp_0 = self.feed_vars[0] * self.feed_vars[1]
             tmp_0.stop_gradient = False
             tmp_1 = paddle.cast(tmp_0, dtype="float16")
-            zero = layers.fill_constant(shape=[128], dtype="float16", value=0)
+            zero = paddle.tensor.fill_constant(
+                shape=[128], dtype="float16", value=0
+            )
             # TODO(xreki): fix precision problem when using softmax of float16.
             # tmp_2 = layers.softmax(tmp_1)
             tmp_2 = paddle.add(tmp_1, zero)
@@ -212,7 +213,9 @@ class FusionGroupPassFillConstantTest(FusionGroupPassTest):
 
             tmp_0 = paddle.add(self.feed_vars[0], self.feed_vars[1])
             tmp_0.stop_gradient = False
-            tmp_1 = layers.fill_constant(shape=[2, 2], dtype=dtype, value=2.0)
+            tmp_1 = paddle.tensor.fill_constant(
+                shape=[2, 2], dtype=dtype, value=2.0
+            )
             tmp_2 = paddle.scale(
                 tmp_1, scale=3.0, bias=1.0, bias_after_scale=True
             )

python/paddle/fluid/tests/unittests/ir/test_ir_subgraph_python_interface.py

@@ -16,7 +16,6 @@ import unittest
 
 import paddle
 import paddle.fluid as fluid
-import paddle.fluid.layers as layers
 from paddle.fluid import core
 from paddle.fluid.framework import IrGraph, Program, program_guard
 from paddle.fluid.tests.unittests.op_test import OpTestTool
@@ -55,8 +54,12 @@ class TestQuantizationSubGraph(unittest.TestCase):
             return linear_fc(5)
 
         with program_guard(main_program, startup_program):
-            x = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
-            y = layers.fill_constant(shape=[1], dtype='float32', value=0.23)
+            x = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.1
+            )
+            y = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.23
+            )
             pred = paddle.less_than(y, x)
             out = paddle.static.nn.cond(pred, true_func, false_func)
 

python/paddle/fluid/tests/unittests/mlu/test_expand_v2_op_mlu.py

@@ -235,7 +235,7 @@ class TestExpandV2API(unittest.TestCase):
             name='x', shape=[12, 14], dtype="float32"
         )
 
-        positive_2 = fluid.layers.fill_constant([1], "int32", 12)
+        positive_2 = paddle.tensor.fill_constant([1], "int32", 12)
         expand_shape = paddle.static.data(
             name="expand_shape",
             shape=[2],

python/paddle/fluid/tests/unittests/mlu/test_fill_constant_op_mlu.py

@@ -268,8 +268,8 @@ class TestFillConstantOp2_ValueTensor(OpTest):
 class TestFillConstantAPI(unittest.TestCase):
     def test_api(self):
 
-        positive_2_int32 = fluid.layers.fill_constant([1], "int32", 2)
-        positive_2_int64 = fluid.layers.fill_constant([1], "int64", 2)
+        positive_2_int32 = paddle.tensor.fill_constant([1], "int32", 2)
+        positive_2_int64 = paddle.tensor.fill_constant([1], "int64", 2)
 
         shape_tensor_int32 = fluid.data(
             name="shape_tensor_int32", shape=[2], dtype="int32"
@@ -278,41 +278,41 @@ class TestFillConstantAPI(unittest.TestCase):
             name="shape_tensor_int64", shape=[2], dtype="int64"
         )
 
-        out_1 = fluid.layers.fill_constant(
+        out_1 = paddle.tensor.fill_constant(
             shape=[1, 2], dtype="float32", value=1.1
         )
 
-        out_2 = fluid.layers.fill_constant(
+        out_2 = paddle.tensor.fill_constant(
             shape=[1, positive_2_int32], dtype="float32", value=1.1
         )
 
-        out_3 = fluid.layers.fill_constant(
+        out_3 = paddle.tensor.fill_constant(
             shape=[1, positive_2_int64], dtype="float32", value=1.1
         )
 
-        out_4 = fluid.layers.fill_constant(
+        out_4 = paddle.tensor.fill_constant(
             shape=shape_tensor_int32, dtype="float32", value=1.1
         )
 
-        out_5 = fluid.layers.fill_constant(
+        out_5 = paddle.tensor.fill_constant(
             shape=shape_tensor_int64, dtype="float32", value=1.1
         )
 
-        out_6 = fluid.layers.fill_constant(
+        out_6 = paddle.tensor.fill_constant(
             shape=shape_tensor_int64, dtype=np.float32, value=1.1
         )
 
-        val1 = fluid.layers.fill_constant(
+        val1 = paddle.tensor.fill_constant(
             shape=[1], dtype=np.float32, value=1.1
         )
-        val2 = fluid.layers.fill_constant(
+        val2 = paddle.tensor.fill_constant(
             shape=[1], dtype=np.float64, value=1.1
         )
-        out_7 = fluid.layers.fill_constant(
+        out_7 = paddle.tensor.fill_constant(
             shape=shape_tensor_int64, dtype=np.float32, value=val1
         )
 
-        out_8 = fluid.layers.fill_constant(
+        out_8 = paddle.tensor.fill_constant(
             shape=shape_tensor_int64, dtype=np.float32, value=val2
         )
 
@@ -345,16 +345,16 @@ class TestFillConstantImperative(unittest.TestCase):
             shape = fluid.dygraph.to_variable(data1)
             val = fluid.dygraph.to_variable(data2)
             value = fluid.dygraph.to_variable(data3)
-            res1 = fluid.layers.fill_constant(
+            res1 = paddle.tensor.fill_constant(
                 shape=[1, 2], dtype='float32', value=1.1
             )
-            res2 = fluid.layers.fill_constant(
+            res2 = paddle.tensor.fill_constant(
                 shape=shape, dtype='float32', value=1.1
             )
-            res3 = fluid.layers.fill_constant(
+            res3 = paddle.tensor.fill_constant(
                 shape=shape, dtype='float32', value=val
             )
-            res4 = fluid.layers.fill_constant(
+            res4 = paddle.tensor.fill_constant(
                 shape=shape, dtype='int32', value=value
             )
             assert np.array_equal(
@@ -372,17 +372,17 @@ class TestFillConstantImperative(unittest.TestCase):
 
     def test_nan(self):
         with fluid.dygraph.guard():
-            res = fluid.layers.fill_constant([1], 'float32', np.nan)
+            res = paddle.tensor.fill_constant([1], 'float32', np.nan)
             self.assertTrue(np.isnan(res.numpy().item(0)))
 
     def test_inf(self):
         with fluid.dygraph.guard():
-            res = fluid.layers.fill_constant([1], 'float32', np.inf)
+            res = paddle.tensor.fill_constant([1], 'float32', np.inf)
             self.assertTrue(np.isinf(res.numpy().item(0)))
 
     def test_ninf(self):
         with fluid.dygraph.guard():
-            res = fluid.layers.fill_constant([1], 'float32', np.NINF)
+            res = paddle.tensor.fill_constant([1], 'float32', np.NINF)
             self.assertTrue(np.isinf(res.numpy().item(0)))
             self.assertEqual(np.NINF, res.numpy().item(0))
 
@@ -434,13 +434,13 @@ class TestFillConstantOpError(unittest.TestCase):
 
             # The argument shape's type of fill_constant_op must be list, tuple or Variable.
             def test_shape_type():
-                fluid.layers.fill_constant(shape=1, dtype="float32", value=1)
+                paddle.tensor.fill_constant(shape=1, dtype="float32", value=1)
 
             self.assertRaises(TypeError, test_shape_type)
 
             # The argument shape's size of fill_constant_op must not be 0.
             def test_shape_size():
-                fluid.layers.fill_constant(shape=[], dtype="float32", value=1)
+                paddle.tensor.fill_constant(shape=[], dtype="float32", value=1)
 
             self.assertRaises(AssertionError, test_shape_size)
 
@@ -449,7 +449,7 @@ class TestFillConstantOpError(unittest.TestCase):
                 shape = fluid.data(
                     name="shape_tensor", shape=[2], dtype="float32"
                 )
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=shape, dtype="float32", value=1
                 )
 
@@ -459,7 +459,7 @@ class TestFillConstantOpError(unittest.TestCase):
                 shape = fluid.data(
                     name="shape_tensor_list", shape=[1], dtype="bool"
                 )
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=[shape, 2], dtype="float32", value=1
                 )
 

python/paddle/fluid/tests/unittests/mlu/test_strided_slice_op_mlu.py

@@ -525,8 +525,8 @@ class TestStridedSliceOp_strides_Tensor(OpTest):
 class TestStridedSliceAPI(unittest.TestCase):
     def test_1(self):
         input = np.random.random([3, 4, 5, 6]).astype("float32")
-        minus_1 = fluid.layers.fill_constant([1], "int32", -1)
-        minus_3 = fluid.layers.fill_constant([1], "int32", -3)
+        minus_1 = paddle.tensor.fill_constant([1], "int32", -1)
+        minus_3 = paddle.tensor.fill_constant([1], "int32", -3)
         starts = paddle.static.data(
             name='starts', shape=[3], dtype='int32'
         )

python/paddle/fluid/tests/unittests/mlu/test_tile_op_mlu.py

@@ -234,7 +234,7 @@ class TestTileAPIStatic(unittest.TestCase):
             repeat_times = [2, 2]
             x1 = paddle.static.data(name='x1', shape=[-1, 4], dtype="int32")
             out = paddle.tile(x1, repeat_times)
-            positive_2 = fluid.layers.fill_constant([1], dtype="int32", value=2)
+            positive_2 = paddle.tensor.fill_constant([1], dtype="int32", value=2)
             out2 = paddle.tile(x1, repeat_times=[positive_2, 2])
 
 

python/paddle/fluid/tests/unittests/npu/test_concat_op_npu.py

@@ -171,7 +171,7 @@ class TestConcatAPIWithLoDTensorArray(unittest.TestCase):
             with fluid.program_guard(self.program):
                 input = paddle.assign(self.x)
                 tensor_array = paddle.tensor.create_array(dtype='float32')
-                zero = fluid.layers.fill_constant(
+                zero = paddle.tensor.fill_constant(
                     shape=[1], value=0, dtype="int64"
                 )
 

python/paddle/fluid/tests/unittests/npu/test_expand_v2_op_npu.py

@@ -270,7 +270,7 @@ class TestExpandV2API(unittest.TestCase):
                 dtype="float32",
             )
 
-            positive_2 = fluid.layers.fill_constant([1], "int32", 12)
+            positive_2 = paddle.tensor.fill_constant([1], "int32", 12)
             expand_shape = paddle.static.data(
                 name="expand_shape",
                 shape=[2],

python/paddle/fluid/tests/unittests/npu/test_stack_op_npu.py

@@ -139,7 +139,7 @@ class TestStackAPIWithLoDTensorArray(unittest.TestCase):
         with fluid.program_guard(self.program):
             input = paddle.assign(self.x)
             tensor_array = paddle.tensor.create_array(dtype='float32')
-            zero = fluid.layers.fill_constant(shape=[1], value=0, dtype="int64")
+            zero = paddle.tensor.fill_constant(shape=[1], value=0, dtype="int64")
 
             for i in range(self.iter_num):
                 paddle.tensor.array_write(input, zero + i, tensor_array)
@@ -177,7 +177,7 @@ class TestTensorStackAPIWithLoDTensorArray(unittest.TestCase):
         with fluid.program_guard(self.program):
             input = paddle.assign(self.x)
             tensor_array = paddle.tensor.create_array(dtype='float32')
-            zero = fluid.layers.fill_constant(shape=[1], value=0, dtype="int64")
+            zero = paddle.tensor.fill_constant(shape=[1], value=0, dtype="int64")
 
             for i in range(self.iter_num):
                 paddle.tensor.array_write(input, zero + i, tensor_array)

python/paddle/fluid/tests/unittests/npu/test_strided_slice_op_npu.py

@@ -578,8 +578,8 @@ class TestStridedSliceOp_strides_Tensor(OpTest):
 class TestStridedSliceAPI(unittest.TestCase):
     def test_1(self):
         input = np.random.random([3, 4, 5, 6]).astype("float64")
-        minus_1 = fluid.layers.fill_constant([1], "int32", -1)
-        minus_3 = fluid.layers.fill_constant([1], "int32", -3)
+        minus_1 = paddle.tensor.fill_constant([1], "int32", -1)
+        minus_3 = paddle.tensor.fill_constant([1], "int32", -3)
         starts = paddle.static.data(
             name='starts', shape=[3], dtype='int32'
         )

python/paddle/fluid/tests/unittests/npu/test_while_op_npu.py

@@ -50,15 +50,15 @@ class TestWhileOp(unittest.TestCase):
         i = layers.zeros(shape=[1], dtype='int32')
         i = paddle.cast(i, 'int64')
         i.stop_gradient = True
-        array_len = layers.fill_constant(shape=[1], dtype='int32', value=5)
+        array_len = paddle.tensor.fill_constant(shape=[1], dtype='int32', value=5)
         array_len = paddle.cast(array_len, 'int64')
         array_len.stop_gradient = True
         cond = paddle.ones(shape=[1], dtype='int32')
         cond = paddle.cast(cond, 'bool')
-        j = layers.fill_constant(shape=[1], dtype='int32', value=1)
+        j = paddle.tensor.fill_constant(shape=[1], dtype='int32', value=1)
         j = paddle.cast(j, 'int64')
         j.stop_gradient = True
-        array_len2 = layers.fill_constant(shape=[1], dtype='int32', value=3)
+        array_len2 = paddle.tensor.fill_constant(shape=[1], dtype='int32', value=3)
         array_len2 = paddle.cast(array_len2, 'int64')
         array_len2.stop_gradient = True
         cond2 = paddle.logical_or(x=j, y=array_len2)

python/paddle/fluid/tests/unittests/standalone_executor/test_standalone_controlflow.py

@@ -17,7 +17,6 @@ import unittest
 import numpy as np
 
 import paddle
-import paddle.fluid.layers as layers
 from paddle.fluid import core, framework
 from paddle.fluid.framework import Program, program_guard
 
@@ -42,20 +41,26 @@ class TestCompatibility(unittest.TestCase):
 
     def build_program(self):
         def true_func():
-            return layers.fill_constant(
+            return paddle.tensor.fill_constant(
                 shape=[1, 2], dtype='int32', value=1
-            ), layers.fill_constant(shape=[2, 3], dtype='bool', value=True)
+            ), paddle.tensor.fill_constant(
+                shape=[2, 3], dtype='bool', value=True
+            )
 
         def false_func():
-            return layers.fill_constant(
+            return paddle.tensor.fill_constant(
                 shape=[3, 4], dtype='float32', value=3
-            ), layers.fill_constant(shape=[4, 5], dtype='int64', value=2)
+            ), paddle.tensor.fill_constant(shape=[4, 5], dtype='int64', value=2)
 
         main_program = Program()
         startup_program = Program()
         with program_guard(main_program, startup_program):
-            x = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
-            y = layers.fill_constant(shape=[1], dtype='float32', value=0.23)
+            x = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.1
+            )
+            y = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.23
+            )
             pred = paddle.less_than(x, y)
             out = paddle.static.nn.cond(pred, true_func, false_func)
             # out is a tuple containing 2 tensors

python/paddle/fluid/tests/unittests/standalone_executor/test_standalone_executor.py

@@ -395,7 +395,7 @@ class TestInplaceApiWithDataTransform(unittest.TestCase):
     def test_increment(self):
         if paddle.fluid.core.is_compiled_with_cuda():
             with paddle.fluid.device_guard("gpu:0"):
-                x = paddle.fluid.layers.fill_constant([1], "float32", 0)
+                x = paddle.tensor.fill_constant([1], "float32", 0)
             with paddle.fluid.device_guard("cpu"):
                 x = paddle.increment(x)
             exe = paddle.static.Executor(paddle.CUDAPlace(0))

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

@@ -3045,7 +3045,7 @@ class TestPow_factor_tensor(TestActivation):
             )
 
             factor_1 = 2.0
-            factor_2 = fluid.layers.fill_constant([1], "float32", 3.0)
+            factor_2 = paddle.tensor.fill_constant([1], "float32", 3.0)
             out_1 = paddle.pow(x, factor_1)
             out_2 = paddle.pow(x, factor_2)
             out_4 = paddle.pow(x, factor_1, name='pow_res')

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

@@ -16,7 +16,6 @@ import unittest
 
 import paddle
 import paddle.fluid as fluid
-import paddle.fluid.layers as layers
 from paddle.static.nn.control_flow import Assert
 
 
@@ -31,7 +30,7 @@ class TestAssertOp(unittest.TestCase):
 
     def test_assert_true(self):
         def net_func():
-            condition = layers.fill_constant(
+            condition = paddle.tensor.fill_constant(
                 shape=[1], dtype='bool', value=True
             )
             Assert(condition, [])
@@ -40,7 +39,7 @@ class TestAssertOp(unittest.TestCase):
 
     def test_assert_false(self):
         def net_func():
-            condition = layers.fill_constant(
+            condition = paddle.tensor.fill_constant(
                 shape=[1], dtype='bool', value=False
             )
             Assert(condition)
@@ -50,7 +49,7 @@ class TestAssertOp(unittest.TestCase):
 
     def test_assert_cond_numel_error(self):
         def net_func():
-            condition = layers.fill_constant(
+            condition = paddle.tensor.fill_constant(
                 shape=[1, 2], dtype='bool', value=True
             )
             Assert(condition, [])
@@ -60,8 +59,10 @@ class TestAssertOp(unittest.TestCase):
 
     def test_assert_print_data(self):
         def net_func():
-            zero = layers.fill_constant(shape=[1], dtype='int64', value=0)
-            one = layers.fill_constant(shape=[1], dtype='int64', value=1)
+            zero = paddle.tensor.fill_constant(
+                shape=[1], dtype='int64', value=0
+            )
+            one = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=1)
             condition = paddle.less_than(one, zero)  # False
             Assert(condition, [zero, one])
 
@@ -71,7 +72,9 @@ class TestAssertOp(unittest.TestCase):
 
     def test_assert_summary(self):
         def net_func():
-            x = layers.fill_constant(shape=[10], dtype='float32', value=2.0)
+            x = paddle.tensor.fill_constant(
+                shape=[10], dtype='float32', value=2.0
+            )
             condition = paddle.max(x) < 1.0
             Assert(condition, (x,), 5)
 
@@ -81,7 +84,9 @@ class TestAssertOp(unittest.TestCase):
 
     def test_assert_summary_greater_than_size(self):
         def net_func():
-            x = layers.fill_constant(shape=[2, 3], dtype='float32', value=2.0)
+            x = paddle.tensor.fill_constant(
+                shape=[2, 3], dtype='float32', value=2.0
+            )
             condition = paddle.max(x) < 1.0
             Assert(condition, [x], 10, name="test")
 

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

@@ -76,11 +76,11 @@ class TestAssignOpWithLoDTensorArray(unittest.TestCase):
         with program_guard(main_program):
             x = fluid.data(name='x', shape=[100, 10], dtype='float32')
             x.stop_gradient = False
-            y = fluid.layers.fill_constant(
+            y = paddle.tensor.fill_constant(
                 shape=[100, 10], dtype='float32', value=1
             )
             z = paddle.add(x=x, y=y)
-            i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
+            i = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=0)
             init_array = paddle.tensor.array_write(x=z, i=i)
             array = paddle.assign(init_array)
             sums = paddle.tensor.array_read(array=init_array, i=i)
@@ -129,11 +129,11 @@ class TestAssignOApi(unittest.TestCase):
         with program_guard(main_program):
             x = fluid.data(name='x', shape=[100, 10], dtype='float32')
             x.stop_gradient = False
-            y = fluid.layers.fill_constant(
+            y = paddle.tensor.fill_constant(
                 shape=[100, 10], dtype='float32', value=1
             )
             z = paddle.add(x=x, y=y)
-            i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
+            i = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=0)
             init_array = paddle.tensor.array_write(x=z, i=i)
             array = paddle.assign(init_array)
             sums = paddle.tensor.array_read(array=init_array, i=i)

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

@@ -44,7 +44,7 @@ class TestBroadcastToAPI(unittest.TestCase):
         input = np.random.random([12, 14]).astype("float32")
         x = paddle.static.data(name='x', shape=[12, 14], dtype="float32")
 
-        positive_2 = fluid.layers.fill_constant([1], "int32", 12)
+        positive_2 = paddle.tensor.fill_constant([1], "int32", 12)
         expand_shape = paddle.static.data(
             name="expand_shape",
             shape=[2],
@@ -81,7 +81,7 @@ class TestBroadcastToAPI(unittest.TestCase):
                     name="x", shape=[12, 14], dtype="float16"
                 )
 
-                positive_2 = paddle.fluid.layers.fill_constant([1], "int32", 12)
+                positive_2 = paddle.tensor.fill_constant([1], "int32", 12)
                 expand_shape = paddle.static.data(
                     name="expand_shape",
                     shape=[2],

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

@@ -20,7 +20,6 @@ import numpy as np
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
-import paddle.fluid.layers as layers
 import paddle.fluid.optimizer as optimizer
 from paddle.fluid.backward import append_backward
 from paddle.fluid.framework import Program, program_guard
@@ -31,20 +30,32 @@ paddle.enable_static()
 class TestAPICase(unittest.TestCase):
     def test_return_single_var(self):
         def fn_1():
-            return layers.fill_constant(shape=[4, 2], dtype='int32', value=1)
+            return paddle.tensor.fill_constant(
+                shape=[4, 2], dtype='int32', value=1
+            )
 
         def fn_2():
-            return layers.fill_constant(shape=[4, 2], dtype='int32', value=2)
+            return paddle.tensor.fill_constant(
+                shape=[4, 2], dtype='int32', value=2
+            )
 
         def fn_3():
-            return layers.fill_constant(shape=[4, 3], dtype='int32', value=3)
+            return paddle.tensor.fill_constant(
+                shape=[4, 3], dtype='int32', value=3
+            )
 
         main_program = Program()
         startup_program = Program()
         with program_guard(main_program, startup_program):
-            x = layers.fill_constant(shape=[1], dtype='float32', value=0.3)
-            y = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
-            z = layers.fill_constant(shape=[1], dtype='float32', value=0.2)
+            x = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.3
+            )
+            y = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.1
+            )
+            z = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.2
+            )
             pred_2 = paddle.less_than(x, y)  # false: 0.3 < 0.1
             pred_1 = paddle.less_than(z, x)  # true: 0.2 < 0.3
 
@@ -244,26 +255,32 @@ class TestAPICase(unittest.TestCase):
 
     def test_return_var_tuple(self):
         def fn_1():
-            return layers.fill_constant(
+            return paddle.tensor.fill_constant(
                 shape=[1, 2], dtype='int32', value=1
-            ), layers.fill_constant(shape=[2, 3], dtype='float32', value=2)
+            ), paddle.tensor.fill_constant(
+                shape=[2, 3], dtype='float32', value=2
+            )
 
         def fn_2():
-            return layers.fill_constant(
+            return paddle.tensor.fill_constant(
                 shape=[3, 4], dtype='int32', value=3
-            ), layers.fill_constant(shape=[4, 5], dtype='float32', value=4)
+            ), paddle.tensor.fill_constant(
+                shape=[4, 5], dtype='float32', value=4
+            )
 
         def fn_3():
-            return layers.fill_constant(
+            return paddle.tensor.fill_constant(
                 shape=[5], dtype='int32', value=5
-            ), layers.fill_constant(shape=[5, 6], dtype='float32', value=6)
+            ), paddle.tensor.fill_constant(
+                shape=[5, 6], dtype='float32', value=6
+            )
 
         main_program = Program()
         startup_program = Program()
         with program_guard(main_program, startup_program):
-            x = layers.fill_constant(shape=[1], dtype='float32', value=1)
-            y = layers.fill_constant(shape=[1], dtype='float32', value=1)
-            z = layers.fill_constant(shape=[1], dtype='float32', value=3)
+            x = paddle.tensor.fill_constant(shape=[1], dtype='float32', value=1)
+            y = paddle.tensor.fill_constant(shape=[1], dtype='float32', value=1)
+            z = paddle.tensor.fill_constant(shape=[1], dtype='float32', value=3)
 
             pred_1 = paddle.equal(x, y)  # true
             pred_2 = paddle.equal(x, z)  # false
@@ -291,14 +308,18 @@ class TestAPICase(unittest.TestCase):
 class TestAPICase_Nested(unittest.TestCase):
     def test_nested_case(self):
         def fn_1(x=1):
-            var_5 = layers.fill_constant(shape=[1], dtype='int32', value=5)
-            var_6 = layers.fill_constant(shape=[1], dtype='int32', value=6)
+            var_5 = paddle.tensor.fill_constant(
+                shape=[1], dtype='int32', value=5
+            )
+            var_6 = paddle.tensor.fill_constant(
+                shape=[1], dtype='int32', value=6
+            )
             out = paddle.static.nn.control_flow.case(
                 pred_fn_pairs=[
                     (
                         var_5 < var_6,
                         partial(
-                            layers.fill_constant,
+                            paddle.tensor.fill_constant,
                             shape=[1],
                             dtype='int32',
                             value=x,
@@ -307,7 +328,7 @@ class TestAPICase_Nested(unittest.TestCase):
                     (
                         var_5 == var_6,
                         partial(
-                            layers.fill_constant,
+                            paddle.tensor.fill_constant,
                             shape=[2],
                             dtype='int32',
                             value=x,
@@ -318,15 +339,19 @@ class TestAPICase_Nested(unittest.TestCase):
             return out
 
         def fn_2(x=2):
-            var_5 = layers.fill_constant(shape=[1], dtype='int32', value=5)
-            var_6 = layers.fill_constant(shape=[1], dtype='int32', value=6)
+            var_5 = paddle.tensor.fill_constant(
+                shape=[1], dtype='int32', value=5
+            )
+            var_6 = paddle.tensor.fill_constant(
+                shape=[1], dtype='int32', value=6
+            )
             out = paddle.static.nn.control_flow.case(
                 pred_fn_pairs=[
                     (var_5 < var_6, partial(fn_1, x=x)),
                     (
                         var_5 == var_6,
                         partial(
-                            layers.fill_constant,
+                            paddle.tensor.fill_constant,
                             shape=[2],
                             dtype='int32',
                             value=x,
@@ -337,15 +362,19 @@ class TestAPICase_Nested(unittest.TestCase):
             return out
 
         def fn_3():
-            var_5 = layers.fill_constant(shape=[1], dtype='int32', value=5)
-            var_6 = layers.fill_constant(shape=[1], dtype='int32', value=6)
+            var_5 = paddle.tensor.fill_constant(
+                shape=[1], dtype='int32', value=5
+            )
+            var_6 = paddle.tensor.fill_constant(
+                shape=[1], dtype='int32', value=6
+            )
             out = paddle.static.nn.control_flow.case(
                 pred_fn_pairs=[
                     (var_5 < var_6, partial(fn_2, x=3)),
                     (
                         var_5 == var_6,
                         partial(
-                            layers.fill_constant,
+                            paddle.tensor.fill_constant,
                             shape=[2],
                             dtype='int32',
                             value=7,
@@ -358,9 +387,15 @@ class TestAPICase_Nested(unittest.TestCase):
         main_program = Program()
         startup_program = Program()
         with program_guard(main_program, startup_program):
-            x = layers.fill_constant(shape=[1], dtype='float32', value=0.3)
-            y = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
-            z = layers.fill_constant(shape=[1], dtype='float32', value=0.2)
+            x = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.3
+            )
+            y = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.1
+            )
+            z = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.2
+            )
             pred_2 = paddle.less_than(x, y)  # false: 0.3 < 0.1
             pred_1 = paddle.less_than(z, x)  # true: 0.2 < 0.3
 
@@ -496,13 +531,19 @@ class TestAPICase_Nested(unittest.TestCase):
 class TestAPICase_Error(unittest.TestCase):
     def test_error(self):
         def fn_1():
-            return layers.fill_constant(shape=[4, 2], dtype='int32', value=1)
+            return paddle.tensor.fill_constant(
+                shape=[4, 2], dtype='int32', value=1
+            )
 
         main_program = Program()
         startup_program = Program()
         with program_guard(main_program, startup_program):
-            x = layers.fill_constant(shape=[1], dtype='float32', value=0.23)
-            z = layers.fill_constant(shape=[1], dtype='float32', value=0.2)
+            x = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.23
+            )
+            z = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.2
+            )
             pred_1 = paddle.less_than(z, x)  # true
 
             # The type of 'pred_fn_pairs' in case must be list or tuple
@@ -570,7 +611,7 @@ class TestMutiTask(unittest.TestCase):
 
         switch_id = fluid.data(name='switch_id', shape=[1], dtype='int32')
 
-        one = layers.fill_constant(shape=[1], dtype='int32', value=1)
+        one = paddle.tensor.fill_constant(shape=[1], dtype='int32', value=1)
         adam = optimizer.Adam(learning_rate=0.001)
         adagrad = optimizer.Adagrad(learning_rate=0.001)
 

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

@@ -347,8 +347,8 @@ class TestConcatAPI(unittest.TestCase):
         input_3 = np.random.random([2, 2, 4, 5]).astype("int32")
         x_2 = fluid.data(shape=[2, 1, 4, 5], dtype='int32', name='x_2')
         x_3 = fluid.data(shape=[2, 2, 4, 5], dtype='int32', name='x_3')
-        positive_1_int32 = fluid.layers.fill_constant([1], "int32", 1)
-        positive_1_int64 = fluid.layers.fill_constant([1], "int64", 1)
+        positive_1_int32 = paddle.tensor.fill_constant([1], "int32", 1)
+        positive_1_int64 = paddle.tensor.fill_constant([1], "int64", 1)
         out_1 = paddle.concat([x_2, x_3], axis=1)
         out_2 = paddle.concat([x_2, x_3], axis=positive_1_int32)
         out_3 = paddle.concat([x_2, x_3], axis=positive_1_int64)
@@ -374,9 +374,9 @@ class TestConcatAPI(unittest.TestCase):
         input_3 = np.random.random([2, 2, 4, 5]).astype("int32")
         x_2 = fluid.data(shape=[2, 1, 4, 5], dtype='int32', name='x_2')
         x_3 = fluid.data(shape=[2, 2, 4, 5], dtype='int32', name='x_3')
-        positive_1_int32 = paddle.fluid.layers.fill_constant([1], "int32", 1)
-        positive_1_int64 = paddle.fluid.layers.fill_constant([1], "int64", 1)
-        negative_int64 = paddle.fluid.layers.fill_constant([1], "int64", -3)
+        positive_1_int32 = paddle.tensor.fill_constant([1], "int32", 1)
+        positive_1_int64 = paddle.tensor.fill_constant([1], "int64", 1)
+        negative_int64 = paddle.tensor.fill_constant([1], "int64", -3)
         out_1 = paddle.concat(x=[x_2, x_3], axis=1)
         out_2 = paddle.concat(x=[x_2, x_3], axis=positive_1_int32)
         out_3 = paddle.concat(x=[x_2, x_3], axis=positive_1_int64)
@@ -464,7 +464,7 @@ class TestConcatAPIWithLoDTensorArray(unittest.TestCase):
             with fluid.program_guard(self.program):
                 input = paddle.assign(self.x)
                 tensor_array = paddle.tensor.create_array(dtype='float32')
-                zero = fluid.layers.fill_constant(
+                zero = paddle.tensor.fill_constant(
                     shape=[1], value=0, dtype="int64"
                 )
 

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

@@ -21,7 +21,6 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import paddle.fluid.framework as framework
-import paddle.fluid.layers as layers
 from paddle.fluid.backward import append_backward
 from paddle.fluid.framework import Program, program_guard
 
@@ -42,16 +41,24 @@ class TestCondInputOutput(unittest.TestCase):
         paddle.enable_static()
 
         def true_func():
-            return layers.fill_constant(shape=[2, 3], dtype='int32', value=2)
+            return paddle.tensor.fill_constant(
+                shape=[2, 3], dtype='int32', value=2
+            )
 
         def false_func():
-            return layers.fill_constant(shape=[3, 2], dtype='int32', value=-1)
+            return paddle.tensor.fill_constant(
+                shape=[3, 2], dtype='int32', value=-1
+            )
 
         main_program = Program()
         startup_program = Program()
         with program_guard(main_program, startup_program):
-            x = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
-            y = layers.fill_constant(shape=[1], dtype='float32', value=0.23)
+            x = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.1
+            )
+            y = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.23
+            )
             pred = paddle.less_than(y, x)
             out = paddle.static.nn.cond(pred, true_func, false_func)
             # out is one tensor
@@ -217,19 +224,23 @@ class TestCondInputOutput(unittest.TestCase):
         paddle.enable_static()
 
         def true_func():
-            return layers.fill_constant(
+            return paddle.tensor.fill_constant(
                 shape=[1, 2], dtype='int32', value=1
-            ), layers.fill_constant(shape=[2, 3], dtype='bool', value=True)
+            ), paddle.tensor.fill_constant(
+                shape=[2, 3], dtype='bool', value=True
+            )
 
         def false_func():
-            return layers.fill_constant(
+            return paddle.tensor.fill_constant(
                 shape=[3, 4], dtype='float32', value=3
-            ), layers.fill_constant(shape=[4, 5], dtype='int64', value=2)
+            ), paddle.tensor.fill_constant(shape=[4, 5], dtype='int64', value=2)
 
         main_program = Program()
         startup_program = Program()
         with program_guard(main_program, startup_program):
-            pred = layers.fill_constant(shape=[1], dtype='bool', value=True)
+            pred = paddle.tensor.fill_constant(
+                shape=[1], dtype='bool', value=True
+            )
             out = paddle.static.nn.cond(pred, true_func, false_func)
             # out is a tuple containing 2 tensors
 
@@ -271,7 +282,9 @@ class TestCondInputOutput(unittest.TestCase):
         main_program = Program()
         startup_program = Program()
         with program_guard(main_program, startup_program):
-            a = layers.fill_constant(shape=[3, 2, 1], dtype='int32', value=7)
+            a = paddle.tensor.fill_constant(
+                shape=[3, 2, 1], dtype='int32', value=7
+            )
             i = fluid.data(name="i", shape=[1], dtype='int32')
             pred = (i % 2) == 0
             a = paddle.static.nn.cond(
@@ -346,12 +359,14 @@ class TestCondInputOutput(unittest.TestCase):
             return None
 
         def func_return_one_tensor():
-            return layers.fill_constant(shape=[2, 7], dtype='int32', value=3)
+            return paddle.tensor.fill_constant(
+                shape=[2, 7], dtype='int32', value=3
+            )
 
         def func_return_two_tensors():
-            return layers.fill_constant(
+            return paddle.tensor.fill_constant(
                 shape=[3, 1], dtype='int32', value=7
-            ), layers.fill_constant(shape=[3, 1], dtype='int32', value=8)
+            ), paddle.tensor.fill_constant(shape=[3, 1], dtype='int32', value=8)
 
         main_program = Program()
         startup_program = Program()
@@ -398,11 +413,11 @@ class TestCondInputOutput(unittest.TestCase):
         main_program = fluid.Program()
         startup_program = fluid.Program()
         with fluid.program_guard(main_program, startup_program):
-            a = fluid.layers.fill_constant(
+            a = paddle.tensor.fill_constant(
                 shape=[1], dtype='float32', value=1.23
             )
             a.stop_gradient = False
-            b = fluid.layers.fill_constant(
+            b = paddle.tensor.fill_constant(
                 shape=[1], dtype='float32', value=1.25
             )
             b.stop_gradient = False
@@ -567,11 +582,11 @@ class TestCondNestedControlFlow(unittest.TestCase):
         startup_program = fluid.Program()
 
         with fluid.program_guard(main_program, startup_program):
-            a = fluid.layers.fill_constant(
+            a = paddle.tensor.fill_constant(
                 shape=[1], dtype='float32', value=1.23
             )
             a.stop_gradient = False
-            b = fluid.layers.fill_constant(
+            b = paddle.tensor.fill_constant(
                 shape=[1], dtype='float32', value=1.24
             )
             b.stop_gradient = False

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

@@ -19,7 +19,6 @@ import numpy as np
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
-import paddle.fluid.layers as layers
 from paddle.fluid.backward import append_backward
 from paddle.fluid.executor import Executor
 from paddle.fluid.layers.control_flow import ConditionalBlock
@@ -67,7 +66,7 @@ class TestConditionalBlockOpInferShape(unittest.TestCase):
             step_scope = global_block.create_var(
                 type=core.VarDesc.VarType.STEP_SCOPES
             )
-            cond_var = layers.fill_constant(
+            cond_var = paddle.tensor.fill_constant(
                 shape=[1], dtype='bool', value=False
             )
 

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

@@ -41,7 +41,7 @@ class TestPSMinimize(unittest.TestCase):
             cond_3 = paddle.sum(cond)
             acc = paddle.divide(
                 cond_3,
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=[1], value=batch_size * 1.0, dtype='float64'
                 ),
                 name="simnet_acc",

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

@@ -41,7 +41,7 @@ class TestPSPassWithBow(unittest.TestCase):
             cond_3 = paddle.sum(cond)
             acc = paddle.divide(
                 cond_3,
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=[1], value=batch_size * 1.0, dtype='float64'
                 ),
                 name="simnet_acc",

python/paddle/fluid/tests/unittests/test_dist_fleet_ps11.py

@@ -41,7 +41,7 @@ class TestPSPassWithBow(unittest.TestCase):
             cond_3 = paddle.sum(cond)
             acc = paddle.divide(
                 cond_3,
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=[1], value=batch_size * 1.0, dtype='float64'
                 ),
                 name="simnet_acc",

python/paddle/fluid/tests/unittests/test_dist_fleet_ps12.py

@@ -44,7 +44,7 @@ class TestPSPassWithBow(unittest.TestCase):
             cond_3 = paddle.sum(cond)
             acc = paddle.divide(
                 cond_3,
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=[1], value=batch_size * 1.0, dtype='float64'
                 ),
                 name="simnet_acc",

python/paddle/fluid/tests/unittests/test_dist_fleet_ps13.py

@@ -45,7 +45,7 @@ class TestPSPassWithBow(unittest.TestCase):
             cond_3 = paddle.sum(cond)
             acc = paddle.divide(
                 cond_3,
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=[1], value=batch_size * 1.0, dtype='float64'
                 ),
                 name="simnet_acc",

python/paddle/fluid/tests/unittests/test_dist_fleet_ps2.py

@@ -44,7 +44,7 @@ class TestPSPassWithBow(unittest.TestCase):
             cond_3 = paddle.sum(cond)
             acc = paddle.divide(
                 cond_3,
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=[1], value=batch_size * 1.0, dtype='float64'
                 ),
                 name="simnet_acc",

python/paddle/fluid/tests/unittests/test_dist_fleet_ps3.py

@@ -41,7 +41,7 @@ class TestPSPassWithBow(unittest.TestCase):
             cond_3 = paddle.sum(cond)
             acc = paddle.divide(
                 cond_3,
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=[1], value=batch_size * 1.0, dtype='float64'
                 ),
                 name="simnet_acc",

python/paddle/fluid/tests/unittests/test_dist_fleet_ps4.py

@@ -41,7 +41,7 @@ class TestPSPassWithBow(unittest.TestCase):
             cond_3 = paddle.sum(cond)
             acc = paddle.divide(
                 cond_3,
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=[1], value=batch_size * 1.0, dtype='float64'
                 ),
                 name="simnet_acc",

python/paddle/fluid/tests/unittests/test_dist_fleet_ps5.py

@@ -41,7 +41,7 @@ class TestPSPassWithBow(unittest.TestCase):
             cond_3 = paddle.sum(cond)
             acc = paddle.divide(
                 cond_3,
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=[1], value=batch_size * 1.0, dtype='float64'
                 ),
                 name="simnet_acc",

python/paddle/fluid/tests/unittests/test_dist_fleet_ps6.py

@@ -41,7 +41,7 @@ class TestPSPassWithBow(unittest.TestCase):
             cond_3 = paddle.sum(cond)
             acc = paddle.divide(
                 cond_3,
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=[1], value=batch_size * 1.0, dtype='float64'
                 ),
                 name="simnet_acc",

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

@@ -39,7 +39,7 @@ class TestSPMT(unittest.TestCase):
             cond_3 = paddle.sum(cond)
             acc = paddle.divide(
                 cond_3,
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=[1], value=batch_size * 1.0, dtype='float64'
                 ),
                 name="simnet_acc",

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

@@ -18,7 +18,6 @@ import numpy as np
 
 import paddle
 import paddle.fluid as fluid
-import paddle.fluid.layers as layers
 from paddle.tensor.manipulation import tensor_array_to_tensor
 
 paddle.enable_static()
@@ -33,10 +32,10 @@ def build_and_run_program(place, batch_size, beam_size, stop_gradient=False):
         np.random.rand(batch_size, beam_size, 32).astype("float32")
     )
     indices = fluid.data(shape=[None, beam_size], dtype="int64", name="indices")
-    step_idx = layers.fill_constant(
+    step_idx = paddle.tensor.fill_constant(
         shape=[1], dtype="int64", value=0, force_cpu=True
     )
-    max_len = layers.fill_constant(
+    max_len = paddle.tensor.fill_constant(
         shape=[1], dtype="int64", value=10, force_cpu=True
     )
     cond = paddle.less_than(x=step_idx, y=max_len)

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

@@ -73,14 +73,18 @@ class TestEagerDeletionWhileOpBase(unittest.TestCase):
         i = layers.zeros(shape=[1], dtype='int64')
         i.stop_gradient = True
 
-        array_len = layers.fill_constant(shape=[1], dtype='int64', value=1)
+        array_len = paddle.tensor.fill_constant(
+            shape=[1], dtype='int64', value=1
+        )
         array_len.stop_gradient = True
         cond = paddle.less_than(x=i, y=array_len)
 
-        j = layers.fill_constant(shape=[1], dtype='int64', value=1)
+        j = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=1)
         j.stop_gradient = True
 
-        array_len2 = layers.fill_constant(shape=[1], dtype='int64', value=3)
+        array_len2 = paddle.tensor.fill_constant(
+            shape=[1], dtype='int64', value=3
+        )
         array_len2.stop_gradient = True
         cond2 = paddle.less_than(x=j, y=array_len2)
 

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

@@ -239,8 +239,8 @@ class TestEmptyAPI(unittest.TestCase):
     def test_static_graph(self):
         dtype = 'float64'
 
-        positive_2_int32 = fluid.layers.fill_constant([1], "int32", 3)
-        positive_2_int64 = fluid.layers.fill_constant([1], "int64", 3)
+        positive_2_int32 = paddle.tensor.fill_constant([1], "int32", 3)
+        positive_2_int64 = paddle.tensor.fill_constant([1], "int64", 3)
 
         shape_tensor_int32 = fluid.data(
             name="shape_tensor_int32", shape=[2], dtype="int32"

python/paddle/fluid/tests/unittests/test_expand_v2_op.py

@@ -217,7 +217,7 @@ class TestExpandV2API(unittest.TestCase):
         input = np.random.random([12, 14]).astype("float32")
         x = paddle.static.data(name='x', shape=[12, 14], dtype="float32")
 
-        positive_2 = fluid.layers.fill_constant([1], "int32", 12)
+        positive_2 = paddle.tensor.fill_constant([1], "int32", 12)
         expand_shape = paddle.static.data(
             name="expand_shape",
             shape=[2],

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

@@ -282,8 +282,8 @@ class TestFillConstantOp2_ValueTensor(OpTest):
 class TestFillConstantAPI(unittest.TestCase):
     def test_api(self):
 
-        positive_2_int32 = fluid.layers.fill_constant([1], "int32", 2)
-        positive_2_int64 = fluid.layers.fill_constant([1], "int64", 2)
+        positive_2_int32 = paddle.tensor.fill_constant([1], "int32", 2)
+        positive_2_int64 = paddle.tensor.fill_constant([1], "int64", 2)
 
         shape_tensor_int32 = fluid.data(
             name="shape_tensor_int32", shape=[2], dtype="int32"
@@ -292,41 +292,41 @@ class TestFillConstantAPI(unittest.TestCase):
             name="shape_tensor_int64", shape=[2], dtype="int64"
         )
 
-        out_1 = fluid.layers.fill_constant(
+        out_1 = paddle.tensor.fill_constant(
             shape=[1, 2], dtype="float32", value=1.1
         )
 
-        out_2 = fluid.layers.fill_constant(
+        out_2 = paddle.tensor.fill_constant(
             shape=[1, positive_2_int32], dtype="float32", value=1.1
         )
 
-        out_3 = fluid.layers.fill_constant(
+        out_3 = paddle.tensor.fill_constant(
             shape=[1, positive_2_int64], dtype="float32", value=1.1
         )
 
-        out_4 = fluid.layers.fill_constant(
+        out_4 = paddle.tensor.fill_constant(
             shape=shape_tensor_int32, dtype="float32", value=1.1
         )
 
-        out_5 = fluid.layers.fill_constant(
+        out_5 = paddle.tensor.fill_constant(
             shape=shape_tensor_int64, dtype="float32", value=1.1
         )
 
-        out_6 = fluid.layers.fill_constant(
+        out_6 = paddle.tensor.fill_constant(
             shape=shape_tensor_int64, dtype=np.float32, value=1.1
         )
 
-        val1 = fluid.layers.fill_constant(
+        val1 = paddle.tensor.fill_constant(
             shape=[1], dtype=np.float32, value=1.1
         )
-        val2 = fluid.layers.fill_constant(
+        val2 = paddle.tensor.fill_constant(
             shape=[1], dtype=np.float64, value=1.1
         )
-        out_7 = fluid.layers.fill_constant(
+        out_7 = paddle.tensor.fill_constant(
             shape=shape_tensor_int64, dtype=np.float32, value=val1
         )
 
-        out_8 = fluid.layers.fill_constant(
+        out_8 = paddle.tensor.fill_constant(
             shape=shape_tensor_int64, dtype=np.float32, value=val2
         )
 
@@ -359,16 +359,16 @@ class TestFillConstantImperative(unittest.TestCase):
             shape = fluid.dygraph.to_variable(data1)
             val = fluid.dygraph.to_variable(data2)
             value = fluid.dygraph.to_variable(data3)
-            res1 = fluid.layers.fill_constant(
+            res1 = paddle.tensor.fill_constant(
                 shape=[1, 2], dtype='float32', value=1.1
             )
-            res2 = fluid.layers.fill_constant(
+            res2 = paddle.tensor.fill_constant(
                 shape=shape, dtype='float32', value=1.1
             )
-            res3 = fluid.layers.fill_constant(
+            res3 = paddle.tensor.fill_constant(
                 shape=shape, dtype='float32', value=val
             )
-            res4 = fluid.layers.fill_constant(
+            res4 = paddle.tensor.fill_constant(
                 shape=shape, dtype='int32', value=value
             )
             assert np.array_equal(
@@ -386,17 +386,17 @@ class TestFillConstantImperative(unittest.TestCase):
 
     def test_nan(self):
         with fluid.dygraph.guard():
-            res = fluid.layers.fill_constant([1], 'float32', np.nan)
+            res = paddle.tensor.fill_constant([1], 'float32', np.nan)
             self.assertTrue(np.isnan(res.numpy().item(0)))
 
     def test_inf(self):
         with fluid.dygraph.guard():
-            res = fluid.layers.fill_constant([1], 'float32', np.inf)
+            res = paddle.tensor.fill_constant([1], 'float32', np.inf)
             self.assertTrue(np.isinf(res.numpy().item(0)))
 
     def test_ninf(self):
         with fluid.dygraph.guard():
-            res = fluid.layers.fill_constant([1], 'float32', np.NINF)
+            res = paddle.tensor.fill_constant([1], 'float32', np.NINF)
             self.assertTrue(np.isinf(res.numpy().item(0)))
             self.assertEqual(np.NINF, res.numpy().item(0))
 
@@ -408,7 +408,7 @@ class TestFillConstantOpError(unittest.TestCase):
             x1 = paddle.static.data(name='x1', shape=[-1, 1], dtype="int16")
             self.assertRaises(
                 TypeError,
-                fluid.layers.fill_constant,
+                paddle.tensor.fill_constant,
                 shape=[1],
                 value=5,
                 dtype='uint4',
@@ -416,7 +416,7 @@ class TestFillConstantOpError(unittest.TestCase):
 
             self.assertRaises(
                 TypeError,
-                fluid.layers.fill_constant,
+                paddle.tensor.fill_constant,
                 shape=[1.1],
                 value=5,
                 dtype='float32',
@@ -429,7 +429,7 @@ class TestFillConstantOpError(unittest.TestCase):
 
             self.assertRaises(
                 TypeError,
-                fluid.layers.fill_constant,
+                paddle.tensor.fill_constant,
                 shape=[1],
                 value=5,
                 dtype='float64',
@@ -439,7 +439,7 @@ class TestFillConstantOpError(unittest.TestCase):
             x3 = np.random.randn(100, 100).astype('int32')
             self.assertRaises(
                 TypeError,
-                fluid.layers.fill_constant,
+                paddle.tensor.fill_constant,
                 shape=[100, 100],
                 value=5,
                 dtype='float64',
@@ -448,7 +448,7 @@ class TestFillConstantOpError(unittest.TestCase):
 
             # The argument shape's type of fill_constant_op must be list, tuple or Variable.
             def test_shape_type():
-                fluid.layers.fill_constant(shape=1, dtype="float32", value=1)
+                paddle.tensor.fill_constant(shape=1, dtype="float32", value=1)
 
             self.assertRaises(TypeError, test_shape_type)
 
@@ -457,7 +457,7 @@ class TestFillConstantOpError(unittest.TestCase):
                 shape = fluid.data(
                     name="shape_tensor", shape=[2], dtype="float32"
                 )
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=shape, dtype="float32", value=1
                 )
 
@@ -467,7 +467,7 @@ class TestFillConstantOpError(unittest.TestCase):
                 shape = fluid.data(
                     name="shape_tensor_list", shape=[1], dtype="bool"
                 )
-                fluid.layers.fill_constant(
+                paddle.tensor.fill_constant(
                     shape=[shape, 2], dtype="float32", value=1
                 )
 

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

@@ -24,9 +24,9 @@ from paddle.fluid import Program, program_guard
 # Test python API
 class TestFullAPI(unittest.TestCase):
     def test_api(self):
-        positive_2_int32 = fluid.layers.fill_constant([1], "int32", 2)
+        positive_2_int32 = paddle.tensor.fill_constant([1], "int32", 2)
 
-        positive_2_int64 = fluid.layers.fill_constant([1], "int64", 2)
+        positive_2_int64 = paddle.tensor.fill_constant([1], "int64", 2)
         shape_tensor_int32 = fluid.data(
             name="shape_tensor_int32", shape=[2], dtype="int32"
         )
@@ -57,7 +57,9 @@ class TestFullAPI(unittest.TestCase):
             shape=shape_tensor_int64, dtype=np.float32, fill_value=1.1
         )
 
-        val = fluid.layers.fill_constant(shape=[1], dtype=np.float32, value=1.1)
+        val = paddle.tensor.fill_constant(
+            shape=[1], dtype=np.float32, value=1.1
+        )
         out_7 = paddle.full(
             shape=shape_tensor_int64, dtype=np.float32, fill_value=val
         )
@@ -82,9 +84,11 @@ class TestFullAPI(unittest.TestCase):
 
     def test_api_eager(self):
         with fluid.dygraph.base.guard():
-            positive_2_int32 = fluid.layers.fill_constant([1], "int32", 2)
-            positive_2_int64 = fluid.layers.fill_constant([1], "int64", 2)
-            positive_4_int64 = fluid.layers.fill_constant([1], "int64", 4, True)
+            positive_2_int32 = paddle.tensor.fill_constant([1], "int32", 2)
+            positive_2_int64 = paddle.tensor.fill_constant([1], "int64", 2)
+            positive_4_int64 = paddle.tensor.fill_constant(
+                [1], "int64", 4, True
+            )
 
             out_1 = paddle.full(shape=[1, 2], dtype="float32", fill_value=1.1)
 
@@ -106,7 +110,7 @@ class TestFullAPI(unittest.TestCase):
 
             out_6 = paddle.full(shape=[1, 2], dtype=np.float32, fill_value=1.1)
 
-            val = fluid.layers.fill_constant(
+            val = paddle.tensor.fill_constant(
                 shape=[1], dtype=np.float32, value=1.1
             )
             out_7 = paddle.full(shape=[1, 2], dtype=np.float32, fill_value=val)

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

@@ -21,7 +21,6 @@ from eager_op_test import OpTest
 import paddle
 import paddle.nn.functional as F
 from paddle import tensor
-from paddle.fluid import layers
 from paddle.fluid.framework import default_main_program
 from paddle.incubate.nn import FusedMultiTransformer
 from paddle.incubate.nn.functional import fused_multi_transformer
@@ -862,7 +861,7 @@ class TestFusedMultiTransformerOp(OpTest):
                 assert self.query_length == self.cache_length
                 cache_kv[:] = 0
             else:
-                time_step = layers.fill_constant(
+                time_step = paddle.tensor.fill_constant(
                     shape=[1], dtype="int32", value=0, force_cpu=True
                 )
                 time_step_feed = self.cache_length
@@ -947,7 +946,7 @@ class TestFusedMultiTransformerOp(OpTest):
         for i in range(self.layers):
             if self.has_cache_kv:
                 cache_kvs.append(
-                    layers.fill_constant(
+                    paddle.tensor.fill_constant(
                         shape=cache_kv.shape, dtype=cache_kv.dtype, value=0
                     )
                 )
@@ -955,13 +954,13 @@ class TestFusedMultiTransformerOp(OpTest):
 
             if self.has_pre_cache:
                 cache_kvs.append(
-                    layers.fill_constant(
+                    paddle.tensor.fill_constant(
                         shape=cache_kv.shape, dtype=cache_kv.dtype, value=0
                     )
                 )
                 cache_kvs_feed.append(cache_kv)
                 pre_caches.append(
-                    layers.fill_constant(
+                    paddle.tensor.fill_constant(
                         shape=self.pre_cache_kv.shape,
                         dtype=self.pre_cache_kv.dtype,
                         value=0,

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

@@ -209,9 +209,9 @@ class TestGaussianRandomOp1_ShapeTensor(TestGaussianRandomOp):
 # Test python API
 class TestGaussianRandomAPI(unittest.TestCase):
     def test_api(self):
-        positive_2_int32 = fluid.layers.fill_constant([1], "int32", 2000)
+        positive_2_int32 = paddle.tensor.fill_constant([1], "int32", 2000)
 
-        positive_2_int64 = fluid.layers.fill_constant([1], "int64", 500)
+        positive_2_int64 = paddle.tensor.fill_constant([1], "int64", 500)
         shape_tensor_int32 = fluid.data(
             name="shape_tensor_int32", shape=[2], dtype="int32"
         )

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

@@ -82,7 +82,7 @@ class TestHistogramOpError(unittest.TestCase):
         """Test bins should be greater than or equal to 1."""
 
         def net_func():
-            input_value = paddle.fluid.layers.fill_constant(
+            input_value = paddle.tensor.fill_constant(
                 shape=[3, 4], dtype='float32', value=3.0
             )
             paddle.histogram(input=input_value, bins=-1, min=1, max=5)
@@ -94,7 +94,7 @@ class TestHistogramOpError(unittest.TestCase):
         """Test max must be larger or equal to min."""
 
         def net_func():
-            input_value = paddle.fluid.layers.fill_constant(
+            input_value = paddle.tensor.fill_constant(
                 shape=[3, 4], dtype='float32', value=3.0
             )
             paddle.histogram(input=input_value, bins=1, min=5, max=1)
@@ -106,7 +106,7 @@ class TestHistogramOpError(unittest.TestCase):
         """Test range of min, max is not finite"""
 
         def net_func():
-            input_value = paddle.fluid.layers.fill_constant(
+            input_value = paddle.tensor.fill_constant(
                 shape=[3, 4], dtype='float32', value=3.0
             )
             paddle.histogram(input=input_value, bins=1, min=-np.inf, max=5)

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

@@ -77,7 +77,7 @@ class TestDygraphGAN(unittest.TestCase):
             d_loss_real = paddle.mean(
                 paddle.nn.functional.binary_cross_entropy_with_logits(
                     logit=d_real,
-                    label=fluid.layers.fill_constant(
+                    label=paddle.tensor.fill_constant(
                         shape=[2, 1], dtype='float32', value=1.0
                     ),
                 )
@@ -87,7 +87,7 @@ class TestDygraphGAN(unittest.TestCase):
             d_loss_fake = paddle.mean(
                 paddle.nn.functional.binary_cross_entropy_with_logits(
                     logit=d_fake,
-                    label=fluid.layers.fill_constant(
+                    label=paddle.tensor.fill_constant(
                         shape=[2, 1], dtype='float32', value=0.0
                     ),
                 )
@@ -108,7 +108,7 @@ class TestDygraphGAN(unittest.TestCase):
             g_loss = paddle.mean(
                 paddle.nn.functional.binary_cross_entropy_with_logits(
                     logit=d_fake,
-                    label=fluid.layers.fill_constant(
+                    label=paddle.tensor.fill_constant(
                         shape=[2, 1], dtype='float32', value=1.0
                     ),
                 )

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

@@ -53,20 +53,20 @@ class LeNetDygraph(fluid.dygraph.Layer):
 
 def init_weights(layer):
     if type(layer) == nn.Linear:
-        new_weight = paddle.fluid.layers.fill_constant(
+        new_weight = paddle.tensor.fill_constant(
             layer.weight.shape, layer.weight.dtype, value=0.9
         )
         layer.weight.set_value(new_weight)
-        new_bias = paddle.fluid.layers.fill_constant(
+        new_bias = paddle.tensor.fill_constant(
             layer.bias.shape, layer.bias.dtype, value=-0.1
         )
         layer.bias.set_value(new_bias)
     elif type(layer) == nn.Conv2D:
-        new_weight = paddle.fluid.layers.fill_constant(
+        new_weight = paddle.tensor.fill_constant(
             layer.weight.shape, layer.weight.dtype, value=0.7
         )
         layer.weight.set_value(new_weight)
-        new_bias = paddle.fluid.layers.fill_constant(
+        new_bias = paddle.tensor.fill_constant(
             layer.bias.shape, layer.bias.dtype, value=-0.2
         )
         layer.bias.set_value(new_bias)

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

@@ -212,7 +212,7 @@ class EncoderNet(fluid.dygraph.Layer):
             )
             h_0 = to_variable(h_0)
         else:
-            h_0 = fluid.layers.fill_constant(
+            h_0 = paddle.tensor.fill_constant(
                 shape=[Config.batch_size, rnn_hidden_size],
                 dtype='float32',
                 value=0,

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

@@ -23,7 +23,7 @@ import paddle.fluid as fluid
 class TestIncrement(unittest.TestCase):
     def test_api(self):
         with fluid.program_guard(fluid.Program(), fluid.Program()):
-            input = fluid.layers.fill_constant(
+            input = paddle.tensor.fill_constant(
                 shape=[1], dtype='int64', value=5
             )
             expected_result = np.array([8], dtype='int64')
@@ -45,7 +45,7 @@ class TestInplaceApiWithDataTransform(unittest.TestCase):
         if fluid.core.is_compiled_with_cuda():
             paddle.enable_static()
             with paddle.fluid.device_guard("gpu:0"):
-                x = paddle.fluid.layers.fill_constant([1], "float32", 0)
+                x = paddle.tensor.fill_constant([1], "float32", 0)
             with paddle.fluid.device_guard("cpu"):
                 x = paddle.increment(x)
             exe = paddle.static.Executor(paddle.CUDAPlace(0))

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

@@ -19,7 +19,6 @@ import unittest
 import numpy as np
 
 import paddle
-import paddle.fluid as fluid
 from paddle.fluid import core
 from paddle.fluid.framework import convert_np_dtype_to_dtype_
 from paddle.jit.dy2static.utils import _compatible_non_tensor_spec
@@ -35,7 +34,9 @@ class TestInputSpec(unittest.TestCase):
         self.assertIsNone(tensor_spec.name)
 
     def test_from_tensor(self):
-        x_bool = fluid.layers.fill_constant(shape=[1], dtype='bool', value=True)
+        x_bool = paddle.tensor.fill_constant(
+            shape=[1], dtype='bool', value=True
+        )
         bool_spec = InputSpec.from_tensor(x_bool)
         self.assertEqual(bool_spec.dtype, x_bool.dtype)
         self.assertEqual(list(bool_spec.shape), list(x_bool.shape))

python/paddle/fluid/tests/unittests/test_lambv2_op.py

@@ -18,7 +18,6 @@ import numpy as np
 
 import paddle
 import paddle.fluid as fluid
-import paddle.fluid.layers as layers
 from paddle.fluid import core
 from paddle.fluid.dygraph.base import switch_to_static_graph
 
@@ -39,13 +38,13 @@ class LAMBOptimizer(paddle.optimizer.Lamb):
             self._beta2_pow_acc_str, param_and_grad[0]
         )
 
-        beta_1 = layers.fill_constant(
+        beta_1 = paddle.tensor.fill_constant(
             dtype='float32', shape=[1], value=self._beta1, name='lamb_beta_1'
         )
-        beta_2 = layers.fill_constant(
+        beta_2 = paddle.tensor.fill_constant(
             dtype='float32', shape=[1], value=self._beta2, name='lamb_beta_2'
         )
-        epsilon = layers.fill_constant(
+        epsilon = paddle.tensor.fill_constant(
             dtype='float32', shape=[1], value=self._epsilon, name='epsilon'
         )
 

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

@@ -1030,8 +1030,10 @@ class TestLayer(LayerTest):
 
     def test_while_loop(self):
         with self.static_graph():
-            i = layers.fill_constant(shape=[1], dtype='int64', value=0)
-            ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
+            i = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=0)
+            ten = paddle.tensor.fill_constant(
+                shape=[1], dtype='int64', value=10
+            )
 
             def cond(i):
                 return paddle.less_than(i, ten)
@@ -1043,8 +1045,10 @@ class TestLayer(LayerTest):
             static_ret = self.get_static_graph_result(feed={}, fetch_list=out)
 
         with self.dynamic_graph():
-            i = layers.fill_constant(shape=[1], dtype='int64', value=0)
-            ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
+            i = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=0)
+            ten = paddle.tensor.fill_constant(
+                shape=[1], dtype='int64', value=10
+            )
 
             def cond1(i):
                 return paddle.less_than(i, ten)
@@ -1054,7 +1058,9 @@ class TestLayer(LayerTest):
 
             dy_ret = paddle.static.nn.while_loop(cond1, body1, [i])
             with self.assertRaises(ValueError):
-                j = layers.fill_constant(shape=[1], dtype='int64', value=0)
+                j = paddle.tensor.fill_constant(
+                    shape=[1], dtype='int64', value=0
+                )
 
                 def body2(i):
                     return i + 1, i + 2
@@ -1170,10 +1176,10 @@ class TestLayer(LayerTest):
             return paddle.subtract(a, b)
 
         with self.static_graph():
-            a = fluid.layers.fill_constant(
+            a = paddle.tensor.fill_constant(
                 shape=[1], dtype='float32', value=0.1
             )
-            b = fluid.layers.fill_constant(
+            b = paddle.tensor.fill_constant(
                 shape=[1], dtype='float32', value=0.23
             )
             out = paddle.static.nn.cond(
@@ -1215,18 +1221,30 @@ class TestLayer(LayerTest):
 
     def test_case(self):
         def fn_1():
-            return layers.fill_constant(shape=[1, 2], dtype='float32', value=1)
+            return paddle.tensor.fill_constant(
+                shape=[1, 2], dtype='float32', value=1
+            )
 
         def fn_2():
-            return layers.fill_constant(shape=[2, 2], dtype='int32', value=2)
+            return paddle.tensor.fill_constant(
+                shape=[2, 2], dtype='int32', value=2
+            )
 
         def fn_3():
-            return layers.fill_constant(shape=[3], dtype='int32', value=3)
+            return paddle.tensor.fill_constant(
+                shape=[3], dtype='int32', value=3
+            )
 
         with self.static_graph():
-            x = layers.fill_constant(shape=[1], dtype='float32', value=0.3)
-            y = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
-            z = layers.fill_constant(shape=[1], dtype='float32', value=0.2)
+            x = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.3
+            )
+            y = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.1
+            )
+            z = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.2
+            )
 
             pred_1 = paddle.less_than(z, x)  # true: 0.2 < 0.3
             pred_2 = paddle.less_than(x, y)  # false: 0.3 < 0.1
@@ -1248,9 +1266,15 @@ class TestLayer(LayerTest):
             static_res1, static_res2 = exe.run(fetch_list=[out_1, out_2])
 
         with self.dynamic_graph():
-            x = layers.fill_constant(shape=[1], dtype='float32', value=0.3)
-            y = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
-            z = layers.fill_constant(shape=[1], dtype='float32', value=0.2)
+            x = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.3
+            )
+            y = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.1
+            )
+            z = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.2
+            )
 
             pred_1 = paddle.less_than(z, x)  # true: 0.2 < 0.3
             pred_2 = paddle.less_than(x, y)  # false: 0.3 < 0.1
@@ -1270,17 +1294,27 @@ class TestLayer(LayerTest):
 
     def test_switch_case(self):
         def fn_1():
-            return layers.fill_constant(shape=[1, 2], dtype='float32', value=1)
+            return paddle.tensor.fill_constant(
+                shape=[1, 2], dtype='float32', value=1
+            )
 
         def fn_2():
-            return layers.fill_constant(shape=[2, 2], dtype='int32', value=2)
+            return paddle.tensor.fill_constant(
+                shape=[2, 2], dtype='int32', value=2
+            )
 
         def fn_3():
-            return layers.fill_constant(shape=[3], dtype='int32', value=3)
+            return paddle.tensor.fill_constant(
+                shape=[3], dtype='int32', value=3
+            )
 
         with self.static_graph():
-            index_1 = layers.fill_constant(shape=[1], dtype='int32', value=1)
-            index_2 = layers.fill_constant(shape=[1], dtype='int32', value=2)
+            index_1 = paddle.tensor.fill_constant(
+                shape=[1], dtype='int32', value=1
+            )
+            index_2 = paddle.tensor.fill_constant(
+                shape=[1], dtype='int32', value=2
+            )
 
             out_1 = paddle.static.nn.switch_case(
                 branch_index=index_1,
@@ -1308,8 +1342,12 @@ class TestLayer(LayerTest):
             )
 
         with self.dynamic_graph():
-            index_1 = layers.fill_constant(shape=[1], dtype='int32', value=1)
-            index_2 = layers.fill_constant(shape=[1], dtype='int32', value=2)
+            index_1 = paddle.tensor.fill_constant(
+                shape=[1], dtype='int32', value=1
+            )
+            index_2 = paddle.tensor.fill_constant(
+                shape=[1], dtype='int32', value=2
+            )
 
             out_1 = paddle.static.nn.switch_case(
                 branch_index=index_1,
@@ -1987,9 +2025,15 @@ class TestBook(LayerTest):
             paddle.arange(0, 10, 2, 'int32')
             paddle.arange(0.1, 10.0, 0.2, 'float32')
             paddle.arange(0.1, 10.0, 0.2, 'float64')
-            start = layers.fill_constant(shape=[1], value=0.1, dtype="float32")
-            end = layers.fill_constant(shape=[1], value=10.0, dtype="float32")
-            step = layers.fill_constant(shape=[1], value=0.2, dtype="float32")
+            start = paddle.tensor.fill_constant(
+                shape=[1], value=0.1, dtype="float32"
+            )
+            end = paddle.tensor.fill_constant(
+                shape=[1], value=10.0, dtype="float32"
+            )
+            step = paddle.tensor.fill_constant(
+                shape=[1], value=0.2, dtype="float32"
+            )
             y = paddle.arange(start, end, step, 'float64')
             return y
 
@@ -2088,7 +2132,7 @@ class TestBook(LayerTest):
 
     def test_fill_constant_batch_size_like(self):
         with self.static_graph():
-            like = fluid.layers.fill_constant(
+            like = paddle.tensor.fill_constant(
                 shape=[1, 200], value=10, dtype='int64'
             )
             out = layers.fill_constant_batch_size_like(

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

@@ -26,7 +26,7 @@ from paddle.fluid.executor import Executor
 class TestLoDArrayLength(unittest.TestCase):
     def test_array_length(self):
         tmp = layers.zeros(shape=[10], dtype='int32')
-        i = layers.fill_constant(shape=[1], dtype='int64', value=10)
+        i = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=10)
         arr = paddle.tensor.array_write(tmp, i=i)
         arr_len = paddle.tensor.array_length(arr)
         cpu = core.CPUPlace()

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

@@ -19,7 +19,6 @@ import numpy as np
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
-import paddle.fluid.layers as layers
 import paddle.fluid.optimizer as optimizer
 from paddle.fluid.framework import Program, program_guard
 
@@ -98,7 +97,7 @@ def static(
         sgd = optimizer.SGD(learning_rate=LR)
 
         id = fluid.data('id', [1], 'int32')
-        two = layers.fill_constant([1], 'int32', 2)
+        two = paddle.tensor.fill_constant([1], 'int32', 2)
         mod_two = paddle.remainder(id, two) == 0
 
         if loss_in_switch:

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

@@ -45,7 +45,7 @@ class TestProfiler(unittest.TestCase):
             counter = fluid.layers.zeros(
                 shape=[1], dtype='int64', force_cpu=True
             )
-            until = layers.fill_constant([1], dtype='int64', value=10)
+            until = paddle.tensor.fill_constant([1], dtype='int64', value=10)
             data_arr = paddle.tensor.array_write(hidden1, i)
             cond = paddle.less_than(x=counter, y=until)
             while_op = paddle.static.nn.control_flow.While(cond=cond)

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

@@ -16,7 +16,6 @@ import unittest
 
 import paddle
 import paddle.fluid as fluid
-import paddle.fluid.layers as layers
 
 
 class TestProgramToReadableCode(unittest.TestCase):
@@ -37,14 +36,22 @@ class TestProgramToReadableCode(unittest.TestCase):
 
     def append_cond_op(self, program):
         def true_func():
-            return layers.fill_constant(shape=[2, 3], dtype='int32', value=2)
+            return paddle.tensor.fill_constant(
+                shape=[2, 3], dtype='int32', value=2
+            )
 
         def false_func():
-            return layers.fill_constant(shape=[3, 2], dtype='int32', value=-1)
+            return paddle.tensor.fill_constant(
+                shape=[3, 2], dtype='int32', value=-1
+            )
 
         with fluid.program_guard(program):
-            x = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
-            y = layers.fill_constant(shape=[1], dtype='float32', value=0.23)
+            x = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.1
+            )
+            y = paddle.tensor.fill_constant(
+                shape=[1], dtype='float32', value=0.23
+            )
             pred = paddle.less_than(y, x)
             out = paddle.static.nn.cond(pred, true_func, false_func)
 

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

@@ -72,7 +72,7 @@ def cond_net(use_feed=None):
         avg_loss = paddle.mean(loss, name='mean_softmax_loss')
         return avg_loss
 
-    two = fluid.layers.fill_constant([1], 'int32', 2)
+    two = paddle.tensor.fill_constant([1], 'int32', 2)
     pred = two == 0
     avg_loss = paddle.static.nn.case(
         [(pred, lambda: loss1(prediction, label))],
@@ -106,7 +106,7 @@ def optimization_in_cond_net(with_optimize=False):
         return avg_loss
 
     sgd = fluid.optimizer.SGD(learning_rate=0.1)
-    two = fluid.layers.fill_constant([1], 'int32', 2)
+    two = paddle.tensor.fill_constant([1], 'int32', 2)
     pred = two == 0
     avg_loss = paddle.static.nn.case(
         [(pred, lambda: loss1(sgd, prediction, label, with_optimize))],

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

@@ -42,8 +42,8 @@ class TestRandOpError(unittest.TestCase):
             self.assertRaises(TypeError, test_Variable)
 
             def test_dtype():
-                dim_1 = fluid.layers.fill_constant([1], "int64", 3)
-                dim_2 = fluid.layers.fill_constant([1], "int32", 5)
+                dim_1 = paddle.tensor.fill_constant([1], "int64", 3)
+                dim_2 = paddle.tensor.fill_constant([1], "int32", 5)
                 rand(shape=[dim_1, dim_2], dtype='int32')
 
             self.assertRaises(TypeError, test_dtype)
@@ -64,8 +64,8 @@ class TestRandOp(unittest.TestCase):
             result_0 = rand([3, 4])
             result_1 = rand([3, 4], 'float64')
 
-            dim_1 = fluid.layers.fill_constant([1], "int64", 3)
-            dim_2 = fluid.layers.fill_constant([1], "int32", 5)
+            dim_1 = paddle.tensor.fill_constant([1], "int64", 3)
+            dim_2 = paddle.tensor.fill_constant([1], "int32", 5)
             result_2 = rand(shape=[dim_1, dim_2])
 
             var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
@@ -104,8 +104,8 @@ class TestRandOpForDygraph(unittest.TestCase):
 
             rand([3, 4], 'float64')
 
-            dim_1 = fluid.layers.fill_constant([1], "int64", 3)
-            dim_2 = fluid.layers.fill_constant([1], "int32", 5)
+            dim_1 = paddle.tensor.fill_constant([1], "int64", 3)
+            dim_2 = paddle.tensor.fill_constant([1], "int32", 5)
             rand(shape=[dim_1, dim_2])
 
             var_shape = fluid.dygraph.to_variable(np.array([3, 4]))

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

@@ -126,8 +126,8 @@ class TestRandintAPI(unittest.TestCase):
                 low=-100, high=100, shape=(32, 32, 3), dtype='int64'
             )
             # shape is a tensorlist and dtype is 'float32'
-            dim_1 = paddle.fluid.layers.fill_constant([1], "int64", 32)
-            dim_2 = paddle.fluid.layers.fill_constant([1], "int32", 50)
+            dim_1 = paddle.tensor.fill_constant([1], "int64", 32)
+            dim_2 = paddle.tensor.fill_constant([1], "int32", 50)
             out4 = paddle.randint(
                 low=-100, high=100, shape=[dim_1, 5, dim_2], dtype='int32'
             )

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

@@ -30,8 +30,8 @@ class TestRandnOp(unittest.TestCase):
             x1 = paddle.randn(shape, 'float32')
             x2 = paddle.randn(shape, 'float64')
 
-            dim_1 = paddle.fluid.layers.fill_constant([1], "int64", 20)
-            dim_2 = paddle.fluid.layers.fill_constant([1], "int32", 50)
+            dim_1 = paddle.tensor.fill_constant([1], "int64", 20)
+            dim_2 = paddle.tensor.fill_constant([1], "int32", 50)
             x3 = paddle.randn([dim_1, dim_2, 784])
 
             var_shape = paddle.static.data('X', [2], 'int32')
@@ -66,8 +66,8 @@ class TestRandnOpForDygraph(unittest.TestCase):
         x1 = paddle.randn(shape, 'float32')
         x2 = paddle.randn(shape, 'float64')
 
-        dim_1 = paddle.fluid.layers.fill_constant([1], "int64", 20)
-        dim_2 = paddle.fluid.layers.fill_constant([1], "int32", 50)
+        dim_1 = paddle.tensor.fill_constant([1], "int64", 20)
+        dim_2 = paddle.tensor.fill_constant([1], "int32", 50)
         x3 = paddle.randn(shape=[dim_1, dim_2, 784])
 
         var_shape = paddle.to_tensor(np.array(shape))

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

@@ -327,7 +327,7 @@ class TestReshapeOpBool(TestReshapeOp):
 # Test python API
 class TestReshapeAPI(unittest.TestCase):
     def _set_paddle_api(self):
-        self.fill_constant = paddle.fluid.layers.fill_constant
+        self.fill_constant = paddle.tensor.fill_constant
         self.data = paddle.static.data
         self.to_tensor = paddle.to_tensor
         self._executed_api()

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

@@ -80,7 +80,7 @@ class TestRetainGraph(unittest.TestCase):
             fake_AB = paddle.concat((real_data.detach(), interpolatesv), 1)
             disc_interpolates = netD(fake_AB)
 
-            outs = paddle.fluid.layers.fill_constant(
+            outs = paddle.tensor.fill_constant(
                 disc_interpolates.shape, disc_interpolates.dtype, 1.0
             )
             gradients = paddle.grad(
@@ -125,7 +125,7 @@ class TestRetainGraph(unittest.TestCase):
         fake_AB = paddle.concat((realA, fakeB), 1)
         G_pred_fake = d(fake_AB.detach())
 
-        false_target = paddle.fluid.layers.fill_constant(
+        false_target = paddle.tensor.fill_constant(
             G_pred_fake.shape, 'float32', 0.0
         )
 
@@ -140,7 +140,7 @@ class TestRetainGraph(unittest.TestCase):
         optim_g.clear_gradients()
         fake_AB = paddle.concat((realA, fakeB), 1)
         G_pred_fake = d(fake_AB)
-        true_target = paddle.fluid.layers.fill_constant(
+        true_target = paddle.tensor.fill_constant(
             G_pred_fake.shape, 'float32', 1.0
         )
         loss_g = l1_criterion(fakeB, realB) + gan_criterion(

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

@@ -1306,19 +1306,13 @@ class TestGradientTruncated(unittest.TestCase):
         numel = lambda input_shape: reduce(lambda x, y: x * y, input_shape)
 
         def op1(x):
-            value = paddle.fluid.layers.fill_constant([1], "float32", 1)
+            value = paddle.tensor.fill_constant([1], "float32", 1)
             # test stop_gradient
             value.stop_gradient = True
             x.stop_gradient = False
-            start = paddle.fluid.layers.fill_constant(
-                [1], "int32", 5, force_cpu=True
-            )
-            end = paddle.fluid.layers.fill_constant(
-                [1], "int32", 0, force_cpu=True
-            )
-            step = paddle.fluid.layers.fill_constant(
-                [1], "int32", -2, force_cpu=True
-            )
+            start = paddle.tensor.fill_constant([1], "int32", 5, force_cpu=True)
+            end = paddle.tensor.fill_constant([1], "int32", 0, force_cpu=True)
+            step = paddle.tensor.fill_constant([1], "int32", -2, force_cpu=True)
 
             inputs = {
                 'Input': x,
@@ -1347,7 +1341,7 @@ class TestGradientTruncated(unittest.TestCase):
             return y, value
 
         def op2(x):
-            value = paddle.fluid.layers.fill_constant([1, 3, 2], "float32", 1)
+            value = paddle.tensor.fill_constant([1, 3, 2], "float32", 1)
             # test stop_gradient
             value.stop_gradient = False
             x.stop_gradient = False
@@ -1372,18 +1366,12 @@ class TestGradientTruncated(unittest.TestCase):
             return y, value
 
         def op3(x):
-            value = paddle.fluid.layers.fill_constant([1], "float32", 1)
+            value = paddle.tensor.fill_constant([1], "float32", 1)
             x.stop_gradient = True
             value.stop_gradient = False
-            start = paddle.fluid.layers.fill_constant(
-                [1], "int32", 0, force_cpu=True
-            )
-            end = paddle.fluid.layers.fill_constant(
-                [1], "int32", 5, force_cpu=True
-            )
-            step = paddle.fluid.layers.fill_constant(
-                [1], "int32", 3, force_cpu=True
-            )
+            start = paddle.tensor.fill_constant([1], "int32", 0, force_cpu=True)
+            end = paddle.tensor.fill_constant([1], "int32", 5, force_cpu=True)
+            step = paddle.tensor.fill_constant([1], "int32", 3, force_cpu=True)
 
             inputs = {
                 'Input': x,

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

@@ -196,8 +196,8 @@ class TestSGDOpOptimizeSelectedRows(unittest.TestCase):
 class TestSGDOpWithLargeInput(unittest.TestCase):
     def runTest(self):
         paddle.enable_static()
-        data = fluid.layers.fill_constant(shape=[1], value=128, dtype='int64')
-        label = fluid.layers.fill_constant(
+        data = paddle.tensor.fill_constant(shape=[1], value=128, dtype='int64')
+        label = paddle.tensor.fill_constant(
             shape=[1, 150], value=0.5, dtype='float32'
         )
         emb = paddle.static.nn.embedding(

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

@@ -582,8 +582,8 @@ class TestBF16(OpTest):
 class TestSliceAPI(unittest.TestCase):
     def test_1(self):
         input = np.random.random([3, 4, 5, 6]).astype("float64")
-        minus_1 = fluid.layers.fill_constant([1], "int32", -1)
-        minus_3 = fluid.layers.fill_constant([1], "int64", -3)
+        minus_1 = paddle.tensor.fill_constant([1], "int32", -1)
+        minus_3 = paddle.tensor.fill_constant([1], "int64", -3)
         starts = paddle.static.data(
             name='starts', shape=[1, 3], dtype="float32"
         )
@@ -597,7 +597,7 @@ class TestSliceAPI(unittest.TestCase):
         )
 
         # value_int64 is greater than 2147483647 which is the max of int32
-        value_int64 = fluid.layers.fill_constant([1], "int64", 2147483648)
+        value_int64 = paddle.tensor.fill_constant([1], "int64", 2147483648)
 
         out_1 = paddle.slice(
             x, axes=[0, 1, 2], starts=[-3, 0, 2], ends=[value_int64, 100, -1]
@@ -739,7 +739,7 @@ class TestSliceApiWithLoDTensorArray(unittest.TestCase):
                     slice_arr, axis=self.axis, use_stack=True
                 )
             elif case_num == 3:
-                value_int64 = fluid.layers.fill_constant(
+                value_int64 = paddle.tensor.fill_constant(
                     [1], "int64", 2147483648
                 )
                 self.sliced_arr = slice_arr = arr[self.start : value_int64]