move paddle.fluid.layers.tensor.create_tensor to paddle.tensor.creation.create_tensor (#48662)

python/paddle/distribution/distribution.py

@@ -203,7 +203,7 @@ class Distribution:
         dtype = tmp.dtype
         for arg in numpy_args:
             arg_broadcasted, _ = np.broadcast_arrays(arg, tmp)
-            arg_variable = tensor.create_tensor(dtype=dtype)
+            arg_variable = paddle.tensor.create_tensor(dtype=dtype)
             tensor.assign(arg_broadcasted, arg_variable)
             variable_args.append(arg_variable)
 

python/paddle/fluid/layers/tensor.py

@@ -47,7 +47,6 @@ from .utils import check_shape
 from paddle import _C_ops, _legacy_C_ops
 
 __all__ = [
-    'create_tensor',
     'create_global_var',
     'cast',
     'tensor_array_to_tensor',
@@ -62,48 +61,6 @@ __all__ = [
 ]
 
 
-def create_tensor(dtype, name=None, persistable=False):
-    """
-    Create a variable, which will hold a Tensor with data type dtype.
-
-    Args:
-        dtype(string|numpy.dtype): the data type of Tensor to be created, the
-            data type is bool, float16, float32, float64, int8, int16, int32 and int64.
-        name(string, 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`
-        persistable(bool): Set the persistable flag of the create tensor.
-            default value is False.
-
-    Returns:
-        Variable: The tensor to be created according to dtype.
-
-    Examples:
-        .. code-block:: python
-
-          import paddle.fluid as fluid
-          tensor = fluid.layers.create_tensor(dtype='float32')
-    """
-    check_dtype(
-        dtype,
-        'dtype',
-        [
-            'bool',
-            'float16',
-            'float32',
-            'float64',
-            'int8',
-            'int32',
-            'int32',
-            'int64',
-        ],
-        'create_tensor',
-    )
-    helper = LayerHelper("create_tensor", **locals())
-    return helper.create_variable(
-        name=helper.name, dtype=dtype, persistable=persistable
-    )
-
-
 def create_global_var(
     shape, value, dtype, persistable=False, force_cpu=False, name=None
 ):

python/paddle/fluid/tests/test_if_else_op.py

@@ -46,7 +46,7 @@ class TestMNISTIfElseOp(unittest.TestCase):
             cond = paddle.less_than(x=label, y=limit)
             true_image, false_image = split_lod_tensor(input=image, mask=cond)
 
-            true_out = layers.create_tensor(dtype='float32')
+            true_out = paddle.tensor.create_tensor(dtype='float32')
             true_cond = ConditionalBlock([cond])
 
             with true_cond.block():
@@ -54,7 +54,7 @@ class TestMNISTIfElseOp(unittest.TestCase):
                 prob = layers.fc(input=hidden, size=10, act='softmax')
                 layers.assign(input=prob, output=true_out)
 
-            false_out = layers.create_tensor(dtype='float32')
+            false_out = paddle.tensor.create_tensor(dtype='float32')
             false_cond = ConditionalBlock([cond])
 
             with false_cond.block():

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

@@ -38,7 +38,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
         avg_cost = paddle.mean(x=cost)
 
         # Evaluator
-        batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+        batch_size_tensor = paddle.tensor.create_tensor(dtype='int64')
         batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )

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

@@ -105,7 +105,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
 
         # Evaluator
         with fluid.device_guard("gpu:1"):
-            batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+            batch_size_tensor = paddle.tensor.create_tensor(dtype='int64')
             batch_acc = paddle.static.accuracy(
                 input=predict, label=label, total=batch_size_tensor
             )

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

@@ -105,7 +105,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
 
         # Evaluator
         with fluid.device_guard("gpu:1"):
-            batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+            batch_size_tensor = paddle.tensor.create_tensor(dtype='int64')
             batch_acc = paddle.static.accuracy(
                 input=predict, label=label, total=batch_size_tensor
             )

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

@@ -97,7 +97,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
 
         # Evaluator
         with fluid.device_guard("gpu:0"):
-            batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+            batch_size_tensor = paddle.tensor.create_tensor(dtype='int64')
             batch_acc = paddle.static.accuracy(
                 input=predict, label=label, total=batch_size_tensor
             )

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

@@ -81,7 +81,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
         avg_cost = paddle.mean(x=cost)
 
         # Evaluator
-        batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+        batch_size_tensor = paddle.tensor.create_tensor(dtype='int64')
         batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )

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

@@ -83,7 +83,7 @@ class TestFleetMetaOptimizerPrecision(TestDistRunnerBase):
         avg_cost = paddle.mean(x=cost)
 
         # Evaluator
-        batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+        batch_size_tensor = paddle.tensor.create_tensor(dtype='int64')
         batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )

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

@@ -83,7 +83,7 @@ class TestFleetMetaOptimizerFuseAllReducePrecision(TestDistRunnerBase):
         avg_cost = paddle.mean(x=cost)
 
         # Evaluator
-        batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+        batch_size_tensor = paddle.tensor.create_tensor(dtype='int64')
         batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )

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

@@ -82,7 +82,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
         avg_cost = paddle.mean(x=cost)
 
         # Evaluator
-        batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+        batch_size_tensor = paddle.tensor.create_tensor(dtype='int64')
         batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )

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

@@ -47,7 +47,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
         avg_cost = paddle.mean(x=cost)
 
         # Evaluator
-        batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+        batch_size_tensor = paddle.tensor.create_tensor(dtype='int64')
         batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )

python/paddle/fluid/tests/unittests/dist_mnist_fp16_allreduce.py

@@ -41,7 +41,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
         avg_cost = paddle.mean(x=cost)
 
         # Evaluator
-        batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+        batch_size_tensor = paddle.tensor.create_tensor(dtype='int64')
         batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )

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

@@ -38,7 +38,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
         avg_cost = paddle.mean(x=cost)
 
         # Evaluator
-        batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+        batch_size_tensor = paddle.tensor.create_tensor(dtype='int64')
         batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )

python/paddle/fluid/tests/unittests/distribution/test_distribution_categorical.py

@@ -20,7 +20,6 @@ from test_distribution import DistributionNumpy
 import paddle
 from paddle import fluid
 from paddle.distribution import Categorical, Distribution, Normal, Uniform
-from paddle.fluid import layers
 
 np.random.seed(2022)
 
@@ -380,7 +379,7 @@ class DistributionTestError(unittest.TestCase):
         )
 
         value_npdata = np.array([0.8], dtype="float32")
-        value_tensor = layers.create_tensor(dtype="float32")
+        value_tensor = paddle.tensor.create_tensor(dtype="float32")
         self.assertRaises(
             NotImplementedError, distribution.log_prob, value_tensor
         )

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

@@ -93,7 +93,7 @@ class TestAssignApi(unittest.TestCase):
     def test_assign(self):
         main_program = fluid.Program()
         with fluid.program_guard(main_program):
-            x = layers.create_tensor(dtype=self.dtype)
+            x = paddle.tensor.create_tensor(dtype=self.dtype)
             layers.assign(input=self.value, output=x)
 
         exe = fluid.Executor(self.place)

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

@@ -83,7 +83,7 @@ class TestAssignApi(unittest.TestCase):
     def test_assign(self):
         main_program = fluid.Program()
         with fluid.program_guard(main_program):
-            x = layers.create_tensor(dtype=self.dtype)
+            x = paddle.tensor.create_tensor(dtype=self.dtype)
             layers.assign(input=self.value, output=x)
 
         exe = fluid.Executor(self.place)

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

@@ -33,7 +33,7 @@ class ConditionalBlockTest(unittest.TestCase):
             data = layers.data(name='X', shape=[1], dtype='float32')
             data.stop_gradient = False
             cond = ConditionalBlock(inputs=[data])
-            out = layers.create_tensor(dtype='float32')
+            out = paddle.tensor.create_tensor(dtype='float32')
             with cond.block():
                 hidden = layers.fc(input=data, size=10)
                 layers.assign(hidden, out)

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

@@ -77,7 +77,7 @@ def get_model(batch_size):
     avg_cost = paddle.mean(x=cost)
 
     # Evaluator
-    batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+    batch_size_tensor = paddle.tensor.create_tensor(dtype='int64')
     batch_acc = paddle.static.accuracy(
         input=predict, label=label, total=batch_size_tensor
     )

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

@@ -16,6 +16,7 @@ import unittest
 
 import numpy as np
 
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 
@@ -26,7 +27,9 @@ class TestFetchVar(unittest.TestCase):
 
     def test_fetch_var(self):
         self.set_input()
-        x = layers.create_tensor(dtype="int32", persistable=True, name="x")
+        x = paddle.tensor.create_tensor(
+            dtype="int32", persistable=True, name="x"
+        )
         layers.assign(input=self.val, output=x)
         exe = fluid.Executor(fluid.CPUPlace())
         exe.run(fluid.default_main_program(), feed={}, fetch_list=[])

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

@@ -59,7 +59,7 @@ class TestProfiler(unittest.TestCase):
             label = fluid.layers.data(name='y', shape=[1], dtype='int64')
             cost = fluid.layers.cross_entropy(input=predict, label=label)
             avg_cost = paddle.mean(cost)
-            batch_size = fluid.layers.create_tensor(dtype='int64')
+            batch_size = paddle.tensor.create_tensor(dtype='int64')
             batch_acc = paddle.static.accuracy(
                 input=predict, label=label, total=batch_size
             )

python/paddle/fluid/tests/unittests/test_square_error_cost.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.executor import Executor
 
 
@@ -31,8 +30,8 @@ class TestSquareErrorCost(unittest.TestCase):
         sub = input_val - label_val
         np_result = sub * sub
 
-        input_var = layers.create_tensor(dtype="float32", name="input")
-        label_var = layers.create_tensor(dtype="float32", name="label")
+        input_var = paddle.tensor.create_tensor(dtype="float32", name="input")
+        label_var = paddle.tensor.create_tensor(dtype="float32", name="label")
         output = paddle.nn.functional.square_error_cost(
             input=input_var, label=label_var
         )

python/paddle/fluid/tests/unittests/xpu/test_assign_value_op_xpu.py

@@ -94,7 +94,7 @@ class TestAssignApi(unittest.TestCase):
     def test_assign(self):
         main_program = fluid.Program()
         with fluid.program_guard(main_program):
-            x = layers.create_tensor(dtype=self.dtype)
+            x = paddle.tensor.create_tensor(dtype=self.dtype)
             layers.assign(input=self.value, output=x)
 
         exe = fluid.Executor(self.place)

python/paddle/tensor/__init__.py

@@ -19,6 +19,8 @@ from .attribute import shape  # noqa: F401
 from .attribute import real  # noqa: F401
 from .attribute import imag  # noqa: F401
 from .attribute import is_floating_point  # noqa: F401
+from .creation import create_parameter  # noqa: F401
+from .creation import create_tensor  # noqa: F401
 from .creation import to_tensor  # noqa: F401
 from .creation import diag  # noqa: F401
 from .creation import diagflat  # noqa: F401
@@ -289,6 +291,8 @@ from .einsum import einsum  # noqa: F401
 
 # this list used in math_op_patch.py for _binary_creator_
 tensor_method_func = [  # noqa
+    'create_parameter',
+    'create_tensor',
     'matmul',
     'dot',
     'cov',

python/paddle/tensor/creation.py

@@ -100,7 +100,7 @@ def create_parameter(
 
             import paddle
             paddle.enable_static()
-            W = paddle.static.create_parameter(shape=[784, 200], dtype='float32')
+            W = paddle.create_parameter(shape=[784, 200], dtype='float32')
     """
     check_type(shape, 'shape', (list, tuple, np.ndarray), 'create_parameter')
     for item in shape:
@@ -150,6 +150,48 @@ def create_parameter(
     )
 
 
+def create_tensor(dtype, name=None, persistable=False):
+    """
+    Create a variable, which will hold a Tensor with data type dtype.
+
+    Args:
+        dtype(string|numpy.dtype): the data type of Tensor to be created, the
+            data type is bool, float16, float32, float64, int8, int16, int32 and int64.
+        name(string, 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`
+        persistable(bool): Set the persistable flag of the create tensor.
+            default value is False.
+
+    Returns:
+        Variable: The tensor to be created according to dtype.
+
+    Examples:
+        .. code-block:: python
+
+          import paddle
+          tensor = paddle.tensor.create_tensor(dtype='float32')
+    """
+    check_dtype(
+        dtype,
+        'dtype',
+        [
+            'bool',
+            'float16',
+            'float32',
+            'float64',
+            'int8',
+            'int32',
+            'int32',
+            'int64',
+        ],
+        'create_tensor',
+    )
+    helper = LayerHelper("create_tensor", **locals())
+    return helper.create_variable(
+        name=helper.name, dtype=dtype, persistable=persistable
+    )
+
+
 def linspace(start, stop, num, dtype=None, name=None):
     r"""
     Return fixed number of evenly spaced values within a given interval.