[Eager] optimize same python api logic (#49473)

* [Eager] optimize same python api logic

* optimize full api

* optimize logic

* optimize logic

python/paddle/fft.py

@@ -1440,12 +1440,12 @@ def fft_c2c(x, n, axis, norm, forward, name):
         _check_fft_n(n)
         s = [n]
         x = _resize_fft_input(x, s, axes)
-    op_type = 'fft_c2c'
 
-    check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], op_type)
     if in_dygraph_mode():
         out = _C_ops.fft_c2c(x, axes, norm, forward)
     else:
+        op_type = 'fft_c2c'
+        check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], op_type)
         inputs = {
             'X': [x],
         }
@@ -1472,12 +1472,13 @@ def fft_r2c(x, n, axis, norm, forward, onesided, name):
         _check_fft_n(n)
         s = [n]
         x = _resize_fft_input(x, s, axes)
-    op_type = 'fft_r2c'
-    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], op_type)
-
     if in_dygraph_mode():
         out = _C_ops.fft_r2c(x, axes, norm, forward, onesided)
     else:
+        op_type = 'fft_r2c'
+        check_variable_and_dtype(
+            x, 'x', ['float16', 'float32', 'float64'], op_type
+        )
         inputs = {
             'X': [x],
         }
@@ -1513,8 +1514,6 @@ def fft_c2r(x, n, axis, norm, forward, name):
         _check_fft_n(n)
         s = [n // 2 + 1]
         x = _resize_fft_input(x, s, axes)
-    op_type = 'fft_c2r'
-    check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], op_type)
 
     if in_dygraph_mode():
         if n is not None:
@@ -1522,6 +1521,8 @@ def fft_c2r(x, n, axis, norm, forward, name):
         else:
             out = _C_ops.fft_c2r(x, axes, norm, forward, 0)
     else:
+        op_type = 'fft_c2r'
+        check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], op_type)
         inputs = {
             'X': [x],
         }
@@ -1572,12 +1573,12 @@ def fftn_c2c(x, s, axes, norm, forward, name):
 
     if s is not None:
         x = _resize_fft_input(x, s, axes)
-    op_type = 'fft_c2c'
-    check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], op_type)
 
     if in_dygraph_mode():
         out = _C_ops.fft_c2c(x, axes, norm, forward)
     else:
+        op_type = 'fft_c2c'
+        check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], op_type)
         inputs = {
             'X': [x],
         }
@@ -1623,12 +1624,13 @@ def fftn_r2c(x, s, axes, norm, forward, onesided, name):
     if s is not None:
         x = _resize_fft_input(x, s, axes)
 
-    op_type = 'fft_r2c'
-    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], op_type)
-
     if in_dygraph_mode():
         out = _C_ops.fft_r2c(x, axes, norm, forward, onesided)
     else:
+        op_type = 'fft_r2c'
+        check_variable_and_dtype(
+            x, 'x', ['float16', 'float32', 'float64'], op_type
+        )
         inputs = {
             'X': [x],
         }
@@ -1686,15 +1688,14 @@ def fftn_c2r(x, s, axes, norm, forward, name):
         fft_input_shape[-1] = fft_input_shape[-1] // 2 + 1
         x = _resize_fft_input(x, fft_input_shape, axes)
 
-    op_type = 'fft_c2r'
-    check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], op_type)
-
     if in_dygraph_mode():
         if s is not None:
             out = _C_ops.fft_c2r(x, axes, norm, forward, s[-1])
         else:
             out = _C_ops.fft_c2r(x, axes, norm, forward, 0)
     else:
+        op_type = 'fft_c2r'
+        check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], op_type)
         inputs = {
             'X': [x],
         }

python/paddle/fluid/layers/tensor.py

@@ -530,16 +530,6 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None):
           data5 = fluid.layers.fill_constant(shape=[2,1], value=val, dtype='float32') #data5=[[2.0],[2.0]]
     """
 
-    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)
-
     if in_dygraph_mode():
         place = _current_expected_place()
         if force_cpu:
@@ -561,6 +551,16 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None):
             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):

python/paddle/tensor/creation.py

@@ -1026,8 +1026,8 @@ def eye(num_rows, num_columns=None, dtype=None, name=None):
     _check_attr(num_rows, "num_rows")
 
     if dtype is None:
-        dtype = 'float32'
-    if not isinstance(dtype, core.VarDesc.VarType):
+        dtype = core.VarDesc.VarType.FP32
+    elif not isinstance(dtype, core.VarDesc.VarType):
         dtype = convert_np_dtype_to_dtype_(dtype)
     if num_columns is not None:
         _check_attr(num_columns, "num_columns")
@@ -1181,14 +1181,6 @@ def arange(start=0, end=None, step=1, dtype=None, name=None):
         end = start
         start = 0
 
-    out_shape = None
-    if (
-        not isinstance(start, Variable)
-        and not isinstance(end, Variable)
-        and not isinstance(step, Variable)
-    ):
-        out_shape = [int(math.ceil((end - start) / step))]
-
     if not isinstance(dtype, core.VarDesc.VarType):
         dtype = convert_np_dtype_to_dtype_(dtype)
 
@@ -1220,6 +1212,13 @@ def arange(start=0, end=None, step=1, dtype=None, name=None):
             'range/arange',
         )
         helper = LayerHelper('range', **locals())
+        out_shape = None
+        if (
+            not isinstance(start, Variable)
+            and not isinstance(end, Variable)
+            and not isinstance(step, Variable)
+        ):
+            out_shape = [int(math.ceil((end - start) / step))]
         out = helper.create_variable_for_type_inference(dtype, shape=out_shape)
         helper.append_op(
             type='range',