rename _varbase_creator as _create_tensor (#52938)

* rename _varbase_creator as create_tensor

* rename _varbase_creator as create_tensor

python/paddle/common_ops_import.py

@@ -23,9 +23,9 @@ from paddle.fluid.data_feeder import (  # noqa: F401
 from paddle.fluid.framework import (  # noqa: F401
     OpProtoHolder,
     Variable,
+    _create_tensor,
     _dygraph_tracer,
     _non_static_mode,
-    _varbase_creator,
     convert_np_dtype_to_dtype_,
     default_main_program,
     device_guard,

python/paddle/distributed/fleet/layers/mpu/mp_ops.py

@@ -17,7 +17,7 @@ from paddle import _legacy_C_ops
 from paddle.distributed import collective
 from paddle.fluid import core
 from paddle.fluid.data_feeder import check_dtype, check_variable_and_dtype
-from paddle.framework import LayerHelper, _varbase_creator, in_dygraph_mode
+from paddle.framework import LayerHelper, _create_tensor, in_dygraph_mode
 from paddle.nn import Layer
 from paddle.nn.utils import dygraph_utils
 
@@ -447,7 +447,7 @@ def _linear(x, weight, bias=None, name=None):
     Fuction Linear
     """
     if in_dygraph_mode():
-        pre_bias = _varbase_creator(dtype=x.dtype)
+        pre_bias = _create_tensor(dtype=x.dtype)
         _legacy_C_ops.matmul(
             x,
             weight,

python/paddle/distributed/parallel.py

@@ -89,7 +89,7 @@ def _coalesce_tensors(var_groups):
 
 @framework.dygraph_only
 def _reshape_inplace(x, shape):
-    x_shape = framework._varbase_creator(dtype=x.dtype)
+    x_shape = framework._create_tensor(dtype=x.dtype)
     framework._dygraph_tracer().trace_op(
         type="reshape2",
         inputs={'X': x},

python/paddle/fluid/dygraph/math_op_patch.py

@@ -16,9 +16,9 @@ from .. import core
 from ..framework import (
     Variable,
     convert_np_dtype_to_dtype_,
-    _varbase_creator,
     in_dygraph_mode,
 )
+from ..framework import _create_tensor as framework_create_tensor
 from ..layers.layer_function_generator import OpProtoHolder
 from . import no_grad
 from .. import framework
@@ -78,7 +78,7 @@ def monkey_patch_math_varbase():
                 shape, value, dtype, framework._current_expected_place()
             )
         else:
-            out = _varbase_creator(dtype=dtype)
+            out = framework_create_tensor(dtype=dtype)
             out = _legacy_C_ops.fill_constant(
                 out,
                 'dtype',

python/paddle/fluid/framework.py

@@ -1148,7 +1148,7 @@ def _debug_string_(proto, throw_on_error=True):
     return proto.__str__()
 
 
-def _varbase_creator(
+def _create_tensor(
     type=core.VarDesc.VarType.LOD_TENSOR,
     name=None,
     shape=None,
@@ -3836,7 +3836,7 @@ class Block:
 
     def create_var(self, *args, **kwargs):
         if _non_static_mode():
-            var = _varbase_creator(*args, **kwargs)
+            var = _create_tensor(*args, **kwargs)
         else:
             var = Variable(block=self, *args, **kwargs)
             if 'initializer' in kwargs:

python/paddle/fluid/layers/nn.py

@@ -28,7 +28,7 @@ from ..framework import (
     dygraph_only,
     _dygraph_tracer,
     default_main_program,
-    _varbase_creator,
+    _create_tensor,
     static_only,
     _global_flags,
     in_dygraph_mode,

python/paddle/fluid/layers/tensor.py

@@ -20,7 +20,7 @@ from ..layer_helper import LayerHelper
 from ..framework import (
     _current_expected_place,
     convert_np_dtype_to_dtype_,
-    _varbase_creator,
+    _create_tensor,
     in_dygraph_mode,
 )
 from ..framework import Variable

python/paddle/fluid/optimizer.py

@@ -232,7 +232,7 @@ class Optimizer:
 
             if not isinstance(self._learning_rate, _LearningRateEpochDecay):
                 var_tmp = None
-                var_temp = framework._varbase_creator(
+                var_temp = framework._create_tensor(
                     None, name='global_step', dtype='int32'
                 )
 

python/paddle/fluid/regularizer.py

@@ -15,7 +15,7 @@
 import logging
 
 from . import framework
-from .framework import _non_static_mode, _varbase_creator, in_dygraph_mode
+from .framework import _non_static_mode, in_dygraph_mode
 from . import core
 from paddle import _C_ops, _legacy_C_ops
 

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

@@ -197,7 +197,7 @@ class RunProgramOpTest(unittest.TestCase):
 
     def prepare_dygraph_output(self):
         def create_var_base(is_input, name):
-            var = framework._varbase_creator(dtype=None, shape=None, name=name)
+            var = framework._create_tensor(dtype=None, shape=None, name=name)
             var.stop_gradient = False
             return var
 
@@ -210,7 +210,7 @@ class RunProgramOpTest(unittest.TestCase):
         if global_var._in_eager_mode_:
             outputs['OutScope'] = [core.Scope()]
         else:
-            outputs['OutScope'] = framework._varbase_creator(
+            outputs['OutScope'] = framework._create_tensor(
                 type=core.VarDesc.VarType.STEP_SCOPES,
                 name="program_out_scope",
                 persistable=True,

python/paddle/framework/__init__.py

@@ -64,7 +64,7 @@ from ..fluid.framework import dygraph_only  # noqa: F401
 from ..fluid.framework import dygraph_not_support  # noqa: F401
 from ..fluid.framework import (
     convert_np_dtype_to_dtype_,
-    _varbase_creator,
+    _create_tensor,
     OpProtoHolder,
 )  # noqa: F401
 from ..fluid.framework import _dygraph_tracer  # noqa: F401

python/paddle/framework/io.py

@@ -31,10 +31,10 @@ from paddle.fluid.framework import (
     EagerParamBase,
     Program,
     Variable,
+    _create_tensor,
     _current_expected_place,
     _dygraph_tracer,
     _non_static_mode,
-    _varbase_creator,
 )
 
 from .io_utils import (
@@ -133,7 +133,7 @@ def _load_state_dict_from_save_params(model_path):
     # 2. create and load Tensor
     with fluid.dygraph.guard():
         for name in var_name_list:
-            new_var = _varbase_creator(name=name, persistable=True)
+            new_var = _create_tensor(name=name, persistable=True)
             _dygraph_tracer().trace_op(
                 type='load',
                 inputs={},
@@ -458,7 +458,7 @@ def _ndarray_to_tensor(obj, return_numpy):
 
 
 def _lod_tensor2varbase(tensor):
-    return_var = _varbase_creator()
+    return_var = _create_tensor()
     return_var.value().get_tensor().set(tensor, _current_expected_place())
     return return_var
 

python/paddle/jit/translated_layer.py

@@ -650,7 +650,7 @@ def _load_persistable_vars_by_program(
                 persistable=True,
             )
         else:
-            new_var = framework._varbase_creator(
+            new_var = framework._create_tensor(
                 type=each_var.type(),
                 name=each_var.name(),
                 shape=each_var.shape(),
@@ -738,9 +738,7 @@ def _load_persistable_vars(
                 persistable=True,
             )
         else:
-            new_var = framework._varbase_creator(
-                name=new_name, persistable=True
-            )
+            new_var = framework._create_tensor(name=new_name, persistable=True)
 
         new_var.stop_gradient = extra_var_info[name]['stop_gradient']
         load_var_dict[new_name] = new_var

python/paddle/metric/metrics.py

@@ -20,7 +20,7 @@ import paddle
 from paddle import _legacy_C_ops
 
 from ..fluid.data_feeder import check_variable_and_dtype
-from ..fluid.framework import _non_static_mode, _varbase_creator
+from ..fluid.framework import _create_tensor, _non_static_mode
 from ..fluid.layer_helper import LayerHelper
 
 __all__ = []
@@ -804,9 +804,9 @@ def accuracy(input, label, k=1, correct=None, total=None, name=None):
         label = paddle.cast(label, paddle.int64)
     if _non_static_mode():
         if correct is None:
-            correct = _varbase_creator(dtype="int32")
+            correct = _create_tensor(dtype="int32")
         if total is None:
-            total = _varbase_creator(dtype="int32")
+            total = _create_tensor(dtype="int32")
 
         topk_out, topk_indices = paddle.topk(input, k=k)
         _acc, _, _ = _legacy_C_ops.accuracy(

python/paddle/nn/initializer/dirac.py

@@ -205,7 +205,7 @@ class Dirac(Initializer):
 
         if framework.in_dygraph_mode():
             with fluid.dygraph.no_grad():
-                tmp_tensor = framework._varbase_creator()
+                tmp_tensor = framework._create_tensor()
                 _C_ops.assign_value_(
                     tmp_tensor,
                     [len(idx_list)],
@@ -234,7 +234,7 @@ class Dirac(Initializer):
 
         if framework.in_dygraph_mode():
             with fluid.dygraph.no_grad():
-                tmp_tensor = framework._varbase_creator()
+                tmp_tensor = framework._create_tensor()
                 _C_ops.assign_value_(
                     tmp_tensor,
                     [len(value_list)],

python/paddle/nn/quant/quant_layers.py

@@ -17,7 +17,7 @@ import logging
 import paddle
 from paddle import _legacy_C_ops, in_dynamic_mode
 from paddle.fluid.data_feeder import check_variable_and_dtype
-from paddle.fluid.framework import _varbase_creator
+from paddle.fluid.framework import _create_tensor
 from paddle.fluid.log_helper import get_logger
 from paddle.framework import ParamAttr, core
 from paddle.nn import Layer
@@ -87,7 +87,7 @@ class FakeQuantAbsMax(Layer):
     def forward(self, input):
         if in_dynamic_mode():
             attrs = ('bit_length', self._quant_bits)
-            quant_out = _varbase_creator(
+            quant_out = _create_tensor(
                 type=input.type,
                 name=f"{input.name}.quantized.dequantized",
                 shape=input.shape,
@@ -101,7 +101,7 @@ class FakeQuantAbsMax(Layer):
                 )
 
             if not out_scale:
-                out_scale = _varbase_creator(
+                out_scale = _create_tensor(
                     type=core.VarDesc.VarType.LOD_TENSOR,
                     name=self._scale_name,
                     shape=[1],
@@ -210,7 +210,7 @@ class FakeQuantMovingAverageAbsMax(Layer):
                 'is_test',
                 not self.training,
             )
-            quant_out = _varbase_creator(
+            quant_out = _create_tensor(
                 type=input.type,
                 name=f"{input.name}.quantized.dequantized",
                 shape=input.shape,
@@ -322,7 +322,7 @@ class FakeQuantChannelWiseAbsMax(Layer):
                 'quant_axis',
                 self._quant_axis,
             )
-            quant_out = _varbase_creator(
+            quant_out = _create_tensor(
                 type=input.type,
                 name=f"{input.name}.quantized.dequantized",
                 shape=input.shape,
@@ -336,7 +336,7 @@ class FakeQuantChannelWiseAbsMax(Layer):
                     out_scale, op=paddle.distributed.ReduceOp.MAX
                 )
             if out_scale is None:
-                out_scale = _varbase_creator(
+                out_scale = _create_tensor(
                     type=core.VarDesc.VarType.LOD_TENSOR,
                     name=self._scale_name,
                     shape=[self._channel_num],
@@ -441,7 +441,7 @@ class MovingAverageAbsMaxScale(Layer):
                 not self.training,
             )
 
-            quant_out = _varbase_creator(
+            quant_out = _create_tensor(
                 type=input.type,
                 name=f"{input.name}.tmp",
                 shape=input.shape,

python/paddle/nn/utils/transform_parameters.py

@@ -17,8 +17,8 @@ from functools import reduce
 import paddle
 from paddle import _C_ops
 from paddle.fluid.framework import (
+    _create_tensor,
     _dygraph_tracer,
-    _varbase_creator,
     dygraph_only,
     in_dygraph_mode,
 )
@@ -26,7 +26,7 @@ from paddle.fluid.framework import (
 
 # input==output, inplace strategy of reshape has no cost almostly
 def _inplace_reshape_dygraph(x, shape):
-    x_shape = _varbase_creator(dtype='int64')
+    x_shape = _create_tensor(dtype='int64')
     if in_dygraph_mode():
         with paddle.fluid.dygraph.no_grad():
             tmp_out = _C_ops.reshape(x, shape)
@@ -104,7 +104,7 @@ def parameters_to_vector(parameters, name=None):
         origin_shapes.append(param.shape)
         _inplace_reshape_dygraph(param, [-1])
 
-    out = _varbase_creator(dtype=dtype)
+    out = _create_tensor(dtype=dtype)
     if in_dygraph_mode():
         with paddle.fluid.dygraph.no_grad():
             tmp = _C_ops.concat(parameters, 0)

python/paddle/quantization/quanters/abs_max.py

@@ -15,7 +15,7 @@
 import paddle
 from paddle import _legacy_C_ops
 from paddle.fluid.data_feeder import check_variable_and_dtype
-from paddle.fluid.framework import _varbase_creator
+from paddle.fluid.framework import _create_tensor
 from paddle.framework import ParamAttr, core
 from paddle.nn.initializer import Constant
 from paddle.utils import unique_name
@@ -147,7 +147,7 @@ class FakeQuanterWithAbsMaxObserverLayer(BaseQuanter):
             'is_test',
             not self.training,
         )
-        quant_out = _varbase_creator(
+        quant_out = _create_tensor(
             type=input.type,
             name=f"{input.name}.quantized.dequantized",
             shape=input.shape,

python/paddle/static/nn/metric.py

@@ -19,7 +19,7 @@ import numpy as np
 import paddle
 from paddle import _legacy_C_ops
 from paddle.fluid.data_feeder import check_variable_and_dtype
-from paddle.fluid.framework import Variable, _non_static_mode, _varbase_creator
+from paddle.fluid.framework import Variable, _create_tensor, _non_static_mode
 from paddle.fluid.layer_helper import LayerHelper
 from paddle.nn.initializer import ConstantInitializer
 
@@ -74,9 +74,9 @@ def accuracy(input, label, k=1, correct=None, total=None):
     """
     if _non_static_mode():
         if correct is None:
-            correct = _varbase_creator(dtype="int32")
+            correct = _create_tensor(dtype="int32")
         if total is None:
-            total = _varbase_creator(dtype="int32")
+            total = _create_tensor(dtype="int32")
 
         _k = np.array(k).item(0) if isinstance(k, Variable) else k
         topk_out, topk_indices = _legacy_C_ops.top_k_v2(