[Eager] polish several api (#49589)

python/paddle/nn/functional/activation.py

@@ -494,11 +494,6 @@ def prelu(x, weight, data_format="NCHW", name=None):
             #    [-1.25,  6.  ,  7.  , -2.  ],
             #    [ 6.  ,  7.  ,  8.  ,  9.  ]]]]
     """
-    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'prelu')
-    check_variable_and_dtype(
-        weight, 'weight', ['float16', 'float32', 'float64'], 'prelu'
-    )
-
     assert (
         len(weight.shape) == 1
     ), "The dim count of weight shape should be 1 in prelu()."
@@ -541,6 +536,12 @@ def prelu(x, weight, data_format="NCHW", name=None):
     if in_dygraph_mode():
         return _C_ops.prelu(x, weight, data_format, mode)
     else:
+        check_variable_and_dtype(
+            x, 'x', ['float16', 'float32', 'float64'], 'prelu'
+        )
+        check_variable_and_dtype(
+            weight, 'weight', ['float16', 'float32', 'float64'], 'prelu'
+        )
         helper = LayerHelper('prelu', **locals())
         out = helper.create_variable_for_type_inference(x.dtype)
         helper.append_op(
@@ -622,12 +623,6 @@ def rrelu(x, lower=1.0 / 8.0, upper=1.0 / 3.0, training=True, name=None):
             #   [-1.3766339   6.          7.         -2.3465784 ]
             #   [ 6.          7.          8.          9.        ]]]]
     """
-
-    if not in_dynamic_mode():
-        check_variable_and_dtype(
-            x, 'X', ['float16', 'float32', 'float64'], 'rrelu'
-        )
-
     if not isinstance(lower, float) or not isinstance(upper, float):
         raise TypeError(
             "The lower and upper values must be float type. Received: lower {}, upper {}.".format(
@@ -664,6 +659,9 @@ def rrelu(x, lower=1.0 / 8.0, upper=1.0 / 3.0, training=True, name=None):
         )
         return out
     else:
+        check_variable_and_dtype(
+            x, 'X', ['float16', 'float32', 'float64'], 'rrelu'
+        )
         helper = LayerHelper('rrelu', **locals())
         out = helper.create_variable_for_type_inference(x.dtype)
         noise = helper.create_variable_for_type_inference(dtype=x.dtype)

python/paddle/nn/functional/common.py

@@ -422,9 +422,7 @@ def interpolate(
             return paddle.nn.functional.adaptive_avg_pool2d(x, size)
         elif len(x.shape) == 5:
             return paddle.nn.functional.adaptive_avg_pool3d(x, size)
-
     helper = LayerHelper('{}_interp_v2'.format(resample_type), **locals())
-    dtype = helper.input_dtype(input_param_name='x')
     if len(x.shape) == 3 and data_format not in ['NCW', 'NWC']:
         raise ValueError(
             "Got wrong value for param `data_format`: "
@@ -678,6 +676,9 @@ def interpolate(
             else:
                 out = _legacy_C_ops.bicubic_interp_v2(x, *dy_attr)
         return out
+
+    dtype = helper.input_dtype(input_param_name='x')
+
     out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type='{}_interp_v2'.format(resample_type),

python/paddle/nn/functional/pooling.py

@@ -597,8 +597,6 @@ def max_pool1d(
     """
     """NCL to NCHW"""
     data_format = "NCHW"
-    if not in_dynamic_mode():
-        check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'max_pool1d')
     _check_input(x, 3)
     x = unsqueeze(x, [2])
     kernel_size = [1] + utils.convert_to_list(kernel_size, 1, 'pool_size')
@@ -641,6 +639,7 @@ def max_pool1d(
             return squeeze(pool_out, [2])
 
     else:
+        check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'max_pool1d')
         op_type = 'max_pool2d_with_index' if return_mask else "pool2d"
         helper = LayerHelper(op_type, **locals())
         dtype = helper.input_dtype(input_param_name='x')