In creation.assgin, reuse implamention code of layers.tensor.assign to avoid maintain two code (#30227)

python/paddle/tensor/creation.py

@@ -15,6 +15,7 @@
 from __future__ import print_function
 import numpy as np
 
+from ..fluid.layers import tensor
 from ..fluid.framework import Variable
 from ..fluid.framework import unique_name
 from ..fluid.framework import _current_expected_place, _get_paddle_place
@@ -1057,46 +1058,5 @@ def assign(x, output=None):
           result2 = paddle.assign(data)  # result2 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
           result3 = paddle.assign(np.array([[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]], dtype='float32')) # result3 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
     """
-    helper = LayerHelper('assign', **locals())
     check_type(x, 'x', (Variable, numpy.ndarray), 'assign')
-    if isinstance(x, Variable):
-        check_dtype(
-            x.dtype, 'x',
-            ['float16', 'float32', 'float64', 'int32', 'int64', 'bool'],
-            'assign', '(When the type of input in assign is Variable.)')
-        if output is None:
-            output = helper.create_variable_for_type_inference(dtype=x.dtype)
-        helper.append_op(
-            type='assign', inputs={'X': [x]}, outputs={'Out': [output]})
-    elif isinstance(x, numpy.ndarray):
-        dtype = convert_np_dtype_to_dtype_(x.dtype)
-        if dtype == VarDesc.VarType.BOOL:
-            value_name = "bool_values"
-            values = [bool(v) for v in x.flat]
-        elif dtype == VarDesc.VarType.FP32:
-            value_name = "fp32_values"
-            values = [float(v) for v in x.flat]
-        elif dtype == VarDesc.VarType.INT32:
-            value_name = "int32_values"
-            values = [int(v) for v in x.flat]
-        elif dtype == VarDesc.VarType.INT64:
-            value_name = "int64_values"
-            values = [int(v) for v in x.flat]
-        else:
-            raise TypeError(
-                "When the type of 'x' in assign is numpy.ndarray, "
-                "the data type of 'x' must be bool, float32, int32 or int64, but "
-                "received %s." % convert_dtype(dtype))
-        if x.size > 1024 * 1024:
-            raise ValueError("The size of input is too big. Please consider "
-                             "saving it to file and 'load_op' to load it")
-        if output is None:
-            output = helper.create_variable_for_type_inference(dtype=x.dtype)
-        helper.append_op(
-            type='assign_value',
-            outputs={'Out': [output]},
-            attrs={'dtype': dtype,
-                   'shape': list(x.shape),
-                   value_name: values})
-
-    return output
+    return tensor.assign(x, output)