add input type and dtype check template, and update some APIs check (#21161)

* add input type and dtype check template, and update some APIs check

* refine check template, and update some APIs check in nn.py

* update some APIs check in loss.py

test=develop

python/paddle/fluid/data_feeder.py

@@ -20,6 +20,7 @@ import os
 import six
 from six.moves import zip, range, xrange
 import multiprocessing
+import warnings
 
 from .framework import Variable, default_main_program, _current_expected_place
 from .framework import _cpu_num, _cuda_ids
@@ -64,6 +65,39 @@ def convert_dtype(dtype):
         "int32, int64, uint8]")
 
 
+def check_type_and_dtype(input,
+                         input_name,
+                         expected_type,
+                         expected_dtype,
+                         op_name,
+                         extra_message=''):
+    check_type(input, input_name, expected_type, op_name, extra_message)
+    check_dtype(input.dtype, input_name, expected_dtype, op_name, extra_message)
+
+
+def check_type(input, input_name, expected_type, op_name, extra_message=''):
+    if not isinstance(input, expected_type):
+        raise TypeError(
+            "The type of '%s' in %s must be %s, but received %s. %s" %
+            (input_name, op_name, expected_type, type(input), extra_message))
+
+
+def check_dtype(input_dtype,
+                input_name,
+                expected_dtype,
+                op_name,
+                extra_message=''):
+    if convert_dtype(input_dtype) in ['float16']:
+        warnings.warn(
+            "The data type of '%s' in %s only support float16 in GPU now. %s" %
+            (input_name, op_name, extra_message))
+    if convert_dtype(input_dtype) not in expected_dtype:
+        raise TypeError(
+            "The data type of '%s' in %s must be %s, but received %s. %s" %
+            (input_name, op_name, expected_dtype, convert_dtype(input_dtype),
+             extra_message))
+
+
 class DataToLoDTensorConverter(object):
     def __init__(self, place, lod_level, shape, dtype):
         self.place = place

python/paddle/fluid/input.py

@@ -16,7 +16,7 @@ from __future__ import print_function
 import warnings
 from .framework import Variable, in_dygraph_mode
 from .layer_helper import LayerHelper
-from .data_feeder import convert_dtype
+from .data_feeder import check_type_and_dtype, check_dtype
 
 __all__ = ['one_hot', 'embedding']
 
@@ -233,21 +233,9 @@ def embedding(input,
     """
 
     helper = LayerHelper('embedding', **locals())
-    if not isinstance(input, Variable):
-        raise TypeError(
-            "The type of 'input' in fluid.embedding must be Variable, but received %s"
-            % (type(input)))
-    if convert_dtype(input.dtype) not in ['int64']:
-        raise TypeError(
-            "The data type of 'input' in fluid.embedding must be int64, but received %s."
-            % (convert_dtype(input.dtype)))
-    if convert_dtype(dtype) in ['float16']:
-        warnings.warn(
-            "The 'dtype' of fluid.embedding only support float16 in GPU now.")
-    if convert_dtype(dtype) not in ['float16', 'float32', 'float64']:
-        raise TypeError(
-            "The 'dtype' of fluid.embedding must be float16, float32 or float64, but received %s."
-            % (convert_dtype(dtype)))
+    check_type_and_dtype(input, 'input', Variable, ['int64'], 'fluid.embedding')
+    check_dtype(dtype, 'dtype', ['float16', 'float32', 'float64'],
+                'fluid.embedding')
     remote_prefetch = is_sparse and (not is_distributed)
     if remote_prefetch:
         assert is_sparse is True and is_distributed is False

python/paddle/fluid/layers/layer_function_generator.py

@@ -22,7 +22,7 @@ from six.moves import cStringIO
 from ..proto import framework_pb2
 from ..framework import OpProtoHolder, Variable, core, convert_np_dtype_to_dtype_
 from ..layer_helper import LayerHelper
-from ..data_feeder import convert_dtype
+from ..data_feeder import check_type_and_dtype
 
 __all__ = [
     'deprecated', 'generate_layer_fn', 'generate_activation_fn', 'autodoc',
@@ -251,18 +251,8 @@ def generate_activation_fn(op_type):
 
     def func(x, name=None):
         helper = LayerHelper(op_type, **locals())
-        if not isinstance(x, Variable):
-            raise TypeError(
-                "The type of 'x' in %s must be Variable, but received %s" %
-                (op_type, type(x)))
-        if convert_dtype(x.dtype) in ['float16']:
-            warnings.warn(
-                "The data type of 'x' in %s only support float16 in GPU now." %
-                (op_type))
-        if convert_dtype(x.dtype) not in ['float16', 'float32', 'float64']:
-            raise TypeError(
-                "The data type of 'x' in %s must be float16 (only support on GPU), float32 or float64, but received %s."
-                % (op_type, convert_dtype(x.dtype)))
+        check_type_and_dtype(x, 'x', Variable,
+                             ['float16', 'float32', 'float64'], op_type)
         output = helper.create_variable_for_type_inference(dtype=x.dtype)
         helper.append_op(type=op_type, inputs={"X": x}, outputs={"Out": output})
         return output

python/paddle/fluid/layers/loss.py

@@ -20,7 +20,7 @@ from . import nn
 from .layer_function_generator import templatedoc
 from ..layer_helper import LayerHelper
 from ..framework import Variable
-from ..data_feeder import convert_dtype
+from ..data_feeder import check_type_and_dtype
 from ..param_attr import ParamAttr
 from ..initializer import NumpyArrayInitializer
 
@@ -233,19 +233,8 @@ def cross_entropy(input, label, soft_label=False, ignore_index=kIgnoreIndex):
             predict = fluid.layers.fc(input=x, size=class_num, act='softmax')
             cost = fluid.layers.cross_entropy(input=predict, label=label)
     """
-    if not isinstance(input, Variable):
-        raise TypeError(
-            "The type of 'input' in cross_entropy must be Variable, but received %s"
-            % (type(input)))
-    if convert_dtype(input.dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'input' in cross_entropy only support float16 on GPU now."
-        )
-    if convert_dtype(input.dtype) not in ['float16', 'float32', 'float64']:
-        raise TypeError(
-            "The data type of 'input' in cross_entropy must be float16 or float32 or float64, but received %s."
-            % (convert_dtype(input.dtype)))
-
+    check_type_and_dtype(input, 'input', Variable,
+                         ['float16', 'float32', 'float64'], 'cross_entropy')
     if not soft_label:
         return cross_entropy2(input, label, ignore_index)
     helper = LayerHelper('cross_entropy', **locals())
@@ -674,23 +663,9 @@ def nce(input,
                        custom_dist=dist)
     """
     helper = LayerHelper('nce', **locals())
-
-    if not isinstance(input, Variable):
-        raise TypeError(
-            "The type of 'input' in nce layer must be Variable, but received %s"
-            % (type(input)))
-    if not isinstance(label, Variable):
-        raise TypeError(
-            "The type of 'label' in nce layer must be Variable, but received %s"
-            % (type(label)))
-    if convert_dtype(input.dtype) not in ['float32', 'float64']:
-        raise TypeError(
-            "The data type of 'input' in nce layer must be float32 or float64, but received %s."
-            % (convert_dtype(input.dtype)))
-    if convert_dtype(label.dtype) not in ['int64']:
-        raise TypeError(
-            "The data type of 'label' in nce layer must be int64, but received %s."
-            % (convert_dtype(label.dtype)))
+    check_type_and_dtype(input, 'input', Variable, ['float32', 'float64'],
+                         'nce')
+    check_type_and_dtype(label, 'label', Variable, ['int64'], 'nce')
 
     dim = input.shape[1]
     num_true_class = label.shape[1]

python/paddle/fluid/layers/metric_op.py

@@ -23,7 +23,7 @@ from ..initializer import Normal, Constant
 from ..framework import Variable
 from ..param_attr import ParamAttr
 from . import nn
-from ..data_feeder import convert_dtype
+from ..data_feeder import check_type_and_dtype
 
 __all__ = ['accuracy', 'auc']
 
@@ -72,18 +72,8 @@ def accuracy(input, label, k=1, correct=None, total=None):
             #[array([0.6666667], dtype=float32)]
     """
     helper = LayerHelper("accuracy", **locals())
-    if not isinstance(input, Variable):
-        raise TypeError(
-            "The type of 'input' in accuracy must be Variable, but received %s"
-            % (type(input)))
-    if convert_dtype(input.dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'input' in accuracy only support float16 in GPU now."
-        )
-    if convert_dtype(input.dtype) not in ['float16', 'float32', 'float64']:
-        raise TypeError(
-            "The data type of 'input' in accuracy must be float16 or float32 or float64, but received %s."
-            % (convert_dtype(input.dtype)))
+    check_type_and_dtype(input, 'input', Variable,
+                         ['float16', 'float32', 'float64'], 'accuracy')
     topk_out, topk_indices = nn.topk(input, k=k)
     acc_out = helper.create_variable_for_type_inference(dtype="float32")
     if correct is None:

python/paddle/fluid/layers/nn.py

@@ -34,7 +34,7 @@ from .. import unique_name
 from functools import reduce
 from .. import core
 from ..dygraph import layers
-from ..data_feeder import convert_dtype
+from ..data_feeder import convert_dtype, check_type_and_dtype, check_type, check_dtype
 
 __all__ = [
     'fc',
@@ -301,26 +301,12 @@ def fc(input,
           fc = fluid.layers.fc(input=[data_1, data_2], size=1000, act="tanh")
     """
     helper = LayerHelper("fc", **locals())
+    check_type(input, 'input', (list, tuple, Variable), 'fc')
     if isinstance(input, (list, tuple)):
         for i, input_x in enumerate(input):
-            if not isinstance(input_x, Variable):
-                raise TypeError(
-                    "The type of input[%d] in fc must be Variable, but received %s"
-                    % (i, type(input_x)))
-    else:
-        if not isinstance(input, Variable):
-            raise TypeError(
-                "The type of 'input' in fc must be Variable, but received %s" %
-                (type(input)))
+            check_type(input_x, 'input[' + str(i) + ']', Variable, 'fc')
     dtype = helper.input_dtype()
-    if convert_dtype(dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'input' in fc only support float16 in GPU now.")
-    if convert_dtype(dtype) not in ['float16', 'float32', 'float64']:
-        raise TypeError(
-            "The data type of 'input' in fc must be float16, float32 or float64, but received %s."
-            % (convert_dtype(dtype)))
-
+    check_dtype(dtype, 'input', ['float16', 'float32', 'float64'], 'fc')
     mul_results = []
     for input_var, param_attr in helper.iter_inputs_and_params():
         input_shape = input_var.shape
@@ -470,21 +456,10 @@ def embedding(input,
     """
 
     helper = LayerHelper('embedding', **locals())
-    if not isinstance(input, Variable):
-        raise TypeError(
-            "The type of 'input' in layers.embedding must be Variable, but received %s"
-            % (type(input)))
-    if convert_dtype(input.dtype) not in ['int64']:
-        raise TypeError(
-            "The data type of 'input' in layers.embedding must be int64, but received %s."
-            % (convert_dtype(input.dtype)))
-    if convert_dtype(dtype) in ['float16']:
-        warnings.warn(
-            "The 'dtype' of layers.embedding only support float16 in GPU now.")
-    if convert_dtype(dtype) not in ['float16', 'float32', 'float64']:
-        raise TypeError(
-            "The 'dtype' of layers.embedding must be float16, float32 or float64, but received %s."
-            % (convert_dtype(dtype)))
+    check_type_and_dtype(input, 'input', Variable, ['int64'],
+                         'fluid.layers.embedding')
+    check_dtype(dtype, 'dtype', ['float16', 'float32', 'float64'],
+                'fluid.layers.embedding')
     remote_prefetch = is_sparse and (not is_distributed)
     if remote_prefetch:
         assert is_sparse is True and is_distributed is False
@@ -830,20 +805,8 @@ def dropout(x,
     """
 
     helper = LayerHelper('dropout', **locals())
-
-    if not isinstance(x, Variable):
-        raise TypeError(
-            "The type of 'input' in dropout must be Variable, but received %s" %
-            (type(x)))
-    if convert_dtype(x.dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'input' in dropout only support float16 on GPU now."
-        )
-    if convert_dtype(x.dtype) not in ['float16', 'float32', 'float64']:
-        raise TypeError(
-            "The data type of 'input' in dropout must be float16 or float32 or float64, but received %s."
-            % (convert_dtype(x.dtype)))
-
+    check_type_and_dtype(x, 'x', Variable, ['float16', 'float32', 'float64'],
+                         'dropout')
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
     mask = helper.create_variable_for_type_inference(
         dtype=core.VarDesc.VarType.UINT8, stop_gradient=True)
@@ -1125,18 +1088,8 @@ def softmax(input, use_cudnn=False, name=None, axis=-1):
             print(output)
     """
     helper = LayerHelper('softmax', **locals())
-    if not isinstance(input, Variable):
-        raise TypeError(
-            "The type of 'input' in softmax must be Variable, but received %s" %
-            (type(input)))
-    if convert_dtype(input.dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'input' in softmax only support float16 in GPU now."
-        )
-    if convert_dtype(input.dtype) not in ['float16', 'float32', 'float64']:
-        raise TypeError(
-            "The data type of 'input' in softmax must be float16, float32 or float64, but received %s."
-            % (convert_dtype(input.dtype)))
+    check_type_and_dtype(input, 'input', Variable,
+                         ['float16', 'float32', 'float64'], 'softmax')
 
     dtype = helper.input_dtype()
     softmax_out = helper.create_variable_for_type_inference(dtype)
@@ -1278,19 +1231,8 @@ def conv2d(input,
           conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
     """
 
-    if not isinstance(input, Variable):
-        raise TypeError(
-            "The type of 'input' in conv2d must be Variable, but received %s" %
-            (type(input)))
-    if convert_dtype(input.dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'input' in conv2d only support float16 on GPU now."
-        )
-    if convert_dtype(input.dtype) not in ['float16', 'float32', 'float64']:
-        raise TypeError(
-            "The data type of 'input' in conv2d must be float16 or float32 or float64, but received %s."
-            % (convert_dtype(input.dtype)))
-
+    check_type_and_dtype(input, 'input', Variable,
+                         ['float16', 'float32', 'float64'], 'conv2d')
     num_channels = input.shape[1]
     if not isinstance(use_cudnn, bool):
         raise ValueError("Attr(use_cudnn) should be True or False. Received "
@@ -2518,19 +2460,8 @@ def batch_norm(input,
     assert bias_attr is not False, "bias_attr should not be False in batch_norm."
     helper = LayerHelper('batch_norm', **locals())
 
-    if not isinstance(input, Variable):
-        raise TypeError(
-            "The type of 'input' in batch_norm must be Variable, but received %s"
-            % (type(input)))
-    if convert_dtype(input.dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'input' in batch_norm only support float16 on GPU now."
-        )
-    if convert_dtype(input.dtype) not in ['float16', 'float32', 'float64']:
-        raise TypeError(
-            "The data type of 'input' in batch_norm must be float16 or float32 or float64, but received %s."
-            % (convert_dtype(input.dtype)))
-
+    check_type_and_dtype(input, 'input', Variable,
+                         ['float16', 'float32', 'float64'], 'batch_norm')
     dtype = helper.input_dtype()
     # use fp32 for bn parameter
     if dtype == core.VarDesc.VarType.FP16:
@@ -3786,15 +3717,8 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
 
     """
     helper = LayerHelper('reduce_sum', **locals())
-    if not isinstance(input, Variable):
-        raise TypeError(
-            "The type of 'input' in reduce_sum must be Variable, but received %s"
-            % (type(input)))
-    if convert_dtype(
-            input.dtype) not in ['float32', 'float64', 'int32', 'int64']:
-        raise TypeError(
-            "The data type of 'input' in reduce_sum  must be float32 or float64 or int32 or int64, but received %s."
-            % (convert_dtype(input.dtype)))
+    check_type_and_dtype(input, 'input', Variable,
+                         ['float32', 'float64', 'int32', 'int64'], 'reduce_sum')
     out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
     if dim is not None and not isinstance(dim, list):
         dim = [dim]
@@ -3860,15 +3784,9 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None):
             fluid.layers.reduce_mean(y, dim=[0, 1]) # [4.0, 5.0]
     """
     helper = LayerHelper('reduce_mean', **locals())
-    if not isinstance(input, Variable):
-        raise TypeError(
-            "The type of 'input' in reduce_mean must be Variable, but received %s"
-            % (type(input)))
-    if convert_dtype(
-            input.dtype) not in ['float32', 'float64', 'int32', 'int64']:
-        raise TypeError(
-            "The data type of 'input' in reduce_mean  must be float32 or float64 or int32 or int64, but received %s."
-            % (convert_dtype(input.dtype)))
+    check_type_and_dtype(input, 'input', Variable,
+                         ['float32', 'float64', 'int32', 'int64'],
+                         'reduce_mean')
     out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
     if dim is not None and not isinstance(dim, list):
         dim = [dim]
@@ -4463,20 +4381,8 @@ def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
     def __check_input(x, y):
         var_names = {'x': x, 'y': y}
         for name, val in var_names.items():
-            if not isinstance(val, Variable):
-                raise TypeError(
-                    "The type of %s in matmul must be Variable, but received %s.\n"
-                    % (name, (type(val))))
-            if convert_dtype(val.dtype) in ['float16']:
-                warnings.warn(
-                    "The data type of %s in matmul only support float16 in GPU now."
-                    % name)
-            if convert_dtype(
-                    val.dtype) not in ['float16', 'float32', 'float64']:
-                raise TypeError(
-                    "The data type of %s in matmul must be float16 or float32 or float64, but received %s.\n"
-                    % (name, (convert_dtype(val.dtype))))
-
+            check_type_and_dtype(val, name, Variable,
+                                 ['float16', 'float32', 'float64'], 'matmul')
         x_shape = list(x.shape)
         y_shape = list(y.shape)
         if len(x_shape) == 1:
@@ -4826,20 +4732,10 @@ def transpose(x, perm, name=None):
             #(3L, 2L, 4L)
 
     """
-    if not isinstance(x, Variable):
-        raise TypeError(
-            "The type of Input(x) in transpose must be Variable, but received %s"
-            % (type(x)))
-    if convert_dtype(x.dtype) not in [
-            "float16", "float32", "float64", "int32", "int64"
-    ]:
-        raise TypeError(
-            "The data type of Input(x) in transpose must be one of [float16, float32, float64, int32, int64], but received %s."
-            % (convert_dtype(x.dtype)))
-    if not isinstance(perm, list):
-        raise TypeError(
-            "The type of Input(perm) in transpose must be list, but received %s"
-            % (type(perm)))
+    check_type_and_dtype(x, 'x', Variable,
+                         ['float16', 'float32', 'float64', 'int32', 'int64'],
+                         'transpose')
+    check_type(perm, 'perm', list, 'transpose')
     if len(perm) != len(x.shape):
         raise ValueError(
             "Input(perm) is the permutation of dimensions of Input(x), "
@@ -5431,32 +5327,11 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None):
             reshaped_2 = fluid.layers.reshape(data_2, shape=[dim, 10])
             # the shape of reshaped_2 is [5,10].
     """
-    if not isinstance(x, Variable):
-        raise TypeError(
-            "The type of 'x' in reshape must be Variable, but received %s." %
-            (type(x)))
-
-    if convert_dtype(x.dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'x' in reshape only support float16 in GPU now.")
-
-    if convert_dtype(x.dtype) not in [
-            'float16', 'float32', 'float64', 'int32', 'int64'
-    ]:
-        raise TypeError(
-            "The data type of 'x' in reshape must be float16, float32, float64, int32 or int64, "
-            "but received %s." % (convert_dtype(x.dtype)))
-
-    if not isinstance(shape, (list, tuple, Variable)):
-        raise TypeError(
-            "The type of 'shape' in reshape must be Variable, list or tuple, but "
-            "received %s." % (type(shape)))
-
-    if not isinstance(actual_shape, Variable) and (actual_shape is not None):
-        raise TypeError(
-            "The type of 'actual_shape' in reshape must be Variable "
-            "or None, but received %s." % (type(actual_shape)))
-
+    check_type_and_dtype(x, 'x', Variable,
+                         ['float16', 'float32', 'float64', 'int32', 'int64'],
+                         'reshape')
+    check_type(shape, 'shape', (list, tuple, Variable), 'reshape')
+    check_type(actual_shape, 'actual_shape', (Variable, type(None)), 'reshape')
     helper = LayerHelper("reshape2", **locals())
     inputs = {"X": x}
     attrs = {}
@@ -5592,23 +5467,10 @@ def squeeze(input, axes, name=None):
 
     """
     helper = LayerHelper("squeeze", **locals())
-
-    if not isinstance(input, Variable):
-        raise TypeError(
-            "The type of 'input' in squeeze must be Variable, but received %s" %
-            (type(input)))
-
-    if convert_dtype(input.dtype
-                     ) not in ['float32', 'float64', 'int8', 'int32', 'int64']:
-        raise TypeError(
-            "The data type of 'input' in squeeze must be float32, float64, int8, int32,"
-            "int64, but received %s." % (convert_dtype(input.dtype)))
-
-    if not isinstance(axes, list):
-        raise TypeError(
-            "The type of 'axes' in squeeze must be list, but received %s" %
-            (type(axes)))
-
+    check_type_and_dtype(input, 'input', Variable,
+                         ['float32', 'float64', 'int8', 'int32', 'int64'],
+                         'squeeze')
+    check_type(axes, 'axes', list, 'squeeze')
     out = helper.create_variable_for_type_inference(dtype=input.dtype)
     x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
     helper.append_op(
@@ -8090,27 +7952,16 @@ def crop_tensor(x, shape=None, offsets=None, name=None):
 
     """
     helper = LayerHelper('crop_tensor', **locals())
-
-    if convert_dtype(x.dtype) not in ['float32', 'float64', 'int32', 'int64']:
-        raise TypeError(
-            "Input(x)'s dtype of Op(crop_tensor) must be float32, float64, int32 or int64, "
-            "but received %s." % (convert_dtype(x.dtype)))
-
-    if not (isinstance(shape, list) or isinstance(shape, tuple) or \
-            isinstance(shape, Variable)):
-        raise TypeError(
-            "Attr(shape) of Op(crop_tensor) should be a list, tuple or Variable."
-        )
+    check_type_and_dtype(x, 'x', Variable,
+                         ['float32', 'float64', 'int32', 'int64'],
+                         'crop_tensor')
+    check_type(shape, 'shape', (list, tuple, Variable), 'crop_tensor')
+    check_type(offsets, 'offsets', (list, tuple, Variable, type(None)),
+               'crop_tensor')
 
     if offsets is None:
         offsets = [0] * len(x.shape)
 
-    if not (isinstance(offsets, list) or isinstance(offsets, tuple) or \
-            isinstance(offsets, Variable)):
-        raise TypeError(
-            "Attr(offsets) of Op(crop_tensor) should be a list, tuple or Variable."
-        )
-
     out = helper.create_variable_for_type_inference(x.dtype)
     ipts = {'X': x}
     attrs = {}
@@ -8399,17 +8250,8 @@ def elu(x, alpha=1.0, name=None):
                 # [ 1.          15.6       ]]
     """
     helper = LayerHelper('elu', **locals())
-    if not isinstance(x, Variable):
-        raise TypeError(
-            "The type of 'x' in elu must be Variable, but received %s" %
-            (type(x)))
-    if convert_dtype(x.dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'x' in elu only support float16 in GPU now.")
-    if convert_dtype(x.dtype) not in ['float16', 'float32', 'float64']:
-        raise TypeError(
-            "The data type of 'x' in elu must be float16 (only support on GPU), float32 or float64, but received %s."
-            % (convert_dtype(x.dtype)))
+    check_type_and_dtype(x, 'x', Variable, ['float16', 'float32', 'float64'],
+                         'elu')
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='elu',
@@ -9206,18 +9048,10 @@ def expand(x, expand_times, name=None):
             expanded_2 = fluid.layers.expand(data_2, expand_times=expand_times)
             # the shape of expanded_2 is [48, 56].
     """
-    if not isinstance(x, Variable):
-        raise TypeError(
-            "The type of 'input' in reduce_sum must be Variable, but received %s"
-            % (type(x)))
-    if not isinstance(expand_times, (list, tuple, Variable)):
-        raise ValueError(
-            "Input expand_times must be an Variable, python list or tuple.")
-    if convert_dtype(
-            x.dtype) not in ['bool', 'float32', 'float64', 'int32', 'int64']:
-        raise TypeError(
-            "The data type of input  in expand  must be one of bool float32, float64, int32 or int64, but received %s."
-            % (convert_dtype(x.dtype)))
+    check_type_and_dtype(x, 'x', Variable,
+                         ['bool', 'float32', 'float64', 'int32', 'int64'],
+                         'expand')
+    check_type(expand_times, 'expand_times', (list, tuple, Variable), 'expand')
     if convert_dtype(x.dtype) == 'bool' and x.stop_gradient == True:
         raise ValueError(
             "expand op bool date type must set the stop_gradient to be False")
@@ -10151,34 +9985,12 @@ def _elementwise_op(helper):
 
     assert x is not None, 'x cannot be None in {}'.format(op_type)
     assert y is not None, 'y cannot be None in {}'.format(op_type)
-    if not isinstance(x, Variable):
-        raise TypeError(
-            "The type of 'x' in %s must be Variable, but received %s" %
-            (op_type, type(x)))
-    if not isinstance(y, Variable):
-        raise TypeError(
-            "The type of 'y' in %s must be Variable, but received %s" %
-            (op_type, type(y)))
-    if convert_dtype(x.dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'x' in %s only support float16 on GPU now." %
-            (op_type))
-    if convert_dtype(y.dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'y' in %s only support float16 on GPU now." %
-            (op_type))
-    if convert_dtype(x.dtype) not in [
-            'float16', 'float32', 'float64', 'int32', 'int64'
-    ]:
-        raise TypeError(
-            "The data type of 'x' in %s must be float16 or float32 or float64 or int32 or int64, "
-            "but received %s." % (op_type, convert_dtype(x.dtype)))
-    if convert_dtype(y.dtype) not in [
-            'float16', 'float32', 'float64', 'int32', 'int64'
-    ]:
-        raise TypeError(
-            "The data type of 'y' in %s must be float16 or float32 or float64 or int32 or int64, "
-            "but received %s." % (op_type, convert_dtype(y.dtype)))
+    check_type_and_dtype(x, 'x', Variable,
+                         ['float16', 'float32', 'float64', 'int32', 'int64'],
+                         op_type)
+    check_type_and_dtype(y, 'y', Variable,
+                         ['float16', 'float32', 'float64', 'int32', 'int64'],
+                         op_type)
 
     axis = helper.kwargs.get('axis', -1)
     use_mkldnn = helper.kwargs.get('use_mkldnn', False)
@@ -11170,21 +10982,8 @@ def mean(x, name=None):
     """
 
     helper = LayerHelper("mean", **locals())
-
-    if not isinstance(x, Variable):
-        raise TypeError(
-            "The type of 'x' in mean must be Variable, but received %s.\n" %
-            (type(x)))
-
-    if convert_dtype(x.dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'x' in mean only support float16 in GPU now.")
-
-    if convert_dtype(x.dtype) not in ['float16', 'float32', 'float64']:
-        raise TypeError(
-            "The data type of 'x' in mean must be float16 or float32 or float64, but received %s.\n"
-            % (convert_dtype(x.dtype)))
-
+    check_type_and_dtype(x, 'x', Variable, ['float16', 'float32', 'float64'],
+                         'mean')
     if name is None:
         out = helper.create_variable_for_type_inference(dtype=x.dtype)
     else:
@@ -11265,30 +11064,10 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None):
     """
 
     helper = LayerHelper("mul", **locals())
-
-    if not isinstance(x, Variable):
-        raise TypeError(
-            "The type of 'x' in mul must be Variable, but received %s" %
-            (type(x)))
-    if not isinstance(y, Variable):
-        raise TypeError(
-            "The type of 'y' in mul must be Variable, but received %s" %
-            (type(y)))
-    if convert_dtype(x.dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'x' in mul only support float16 in GPU now.")
-    if convert_dtype(y.dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'y' in mul only support float16 in GPU now.")
-    if convert_dtype(x.dtype) not in ['float16', 'float32', 'float64']:
-        raise TypeError(
-            "The data type of 'x' in mul must be float16, float32 or float64, but received %s."
-            % (convert_dtype(x.dtype)))
-    if convert_dtype(y.dtype) not in ['float16', 'float32', 'float64']:
-        raise TypeError(
-            "The data type of 'y' in mul must be float16, float32 or float64, but received %s."
-            % (convert_dtype(y.dtype)))
-
+    check_type_and_dtype(x, 'x', Variable, ['float16', 'float32', 'float64'],
+                         'mul')
+    check_type_and_dtype(y, 'y', Variable, ['float16', 'float32', 'float64'],
+                         'mul')
     if name is None:
         out = helper.create_variable_for_type_inference(dtype=x.dtype)
     else:
@@ -12746,23 +12525,10 @@ def sign(x):
     """
 
     helper = LayerHelper("sign", **locals())
-
-    if not isinstance(x, Variable):
-        if isinstance(x, np.ndarray):
-            x = assign(x)
-        else:
-            raise TypeError(
-                "The type of 'x' in sign_op must be Variable or numpy.ndarray, but received %s."
-                % (type(x)))
-
-    if convert_dtype(x.dtype) in ['float16']:
-        warnings.warn(
-            "The data type of 'x' in sign_op only support float16 in GPU now.")
-    if convert_dtype(x.dtype) not in ['float16', 'float32', 'float64']:
-        raise TypeError(
-            "The data type of 'x' in sign_op must be float16, float32 or float64, but received %s."
-            % (convert_dtype(x.dtype)))
-
+    check_type(x, 'x', (Variable, np.ndarray), 'sign')
+    if isinstance(x, np.ndarray):
+        x = assign(x)
+    check_dtype(x.dtype, 'x', ['float16', 'float32', 'float64'], 'sign')
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
 
     helper.append_op(type='sign', inputs={'X': [x]}, outputs={'Out': [out]})
@@ -13633,18 +13399,10 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
 
 
     """
-    if not (isinstance(shape, (list, tuple, Variable))):
-        raise TypeError(
-            "Input shape must be a python list,Variable or tuple. But received %s"
-            % (type(shape)))
-
+    check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random')
     if not isinstance(dtype, core.VarDesc.VarType):
         dtype = convert_np_dtype_to_dtype_(dtype)
-
-    if convert_dtype(dtype) not in ['float32', 'float64']:
-        raise TypeError(
-            "The attribute dtype in uniform_random op must be float32 or float64, but received %s."
-            % (convert_dtype(dtype)))
+    check_dtype(dtype, 'dtype', ['float32', 'float64'], 'uniform_random')
 
     def contain_var(one_list):
         for ele in one_list:

python/paddle/fluid/layers/tensor.py

@@ -21,10 +21,9 @@ from ..framework import Variable
 from ..initializer import Constant, force_init_on_cpu
 from ..core import VarDesc
 from .layer_function_generator import templatedoc
-from ..data_feeder import convert_dtype
+from ..data_feeder import check_type_and_dtype, check_type, check_dtype, convert_dtype
 import numpy
 import warnings
-from ..data_feeder import convert_dtype
 
 __all__ = [
     'create_tensor', 'create_parameter', 'create_global_var', 'cast',
@@ -192,17 +191,10 @@ def cast(x, dtype):
             #  [ 0  4]] int32
     """
     helper = LayerHelper('cast', **locals())
-    if not isinstance(x, Variable):
-        raise TypeError(
-            "The type of 'x' in cast must be Variable, but received %s" %
-            (type(x)))
-    if convert_dtype(x.dtype) not in [
-            'bool', 'float16', 'float32', 'float64', 'int32', 'int64', 'uint8'
-    ]:
-        raise TypeError(
-            "The data type of 'x' in cast must be one of [bool, float16, float32, float64, int32, int64, uint8], but received %s."
-            % (convert_dtype(x.dtype)))
-
+    check_type_and_dtype(
+        x, 'x', Variable,
+        ['bool', 'float16', 'float32', 'float64', 'int32', 'int64', 'uint8'],
+        'cast')
     out = helper.create_variable_for_type_inference(dtype=dtype)
     helper.append_op(
         type='cast',
@@ -265,25 +257,11 @@ def concat(input, axis=0, name=None):
             "The type of input in concat should be list, but received %s." %
             (type(input)))
         input = [input]
-    for x in input:
-        if not isinstance(x, Variable):
-            raise TypeError(
-                "The type of x in 'input' in concat must be Variable, but received %s."
-                % (type(x)))
-        if convert_dtype(x.dtype) in ['float16']:
-            warnings.warn(
-                "The data type of x in 'input' in concat only support float16 on GPU now."
-            )
-        if convert_dtype(x.dtype) not in [
-                'float16', 'float32', 'float64', 'int32', 'int64'
-        ]:
-            raise TypeError(
-                "The data type of x in 'input' in concat must be float16(only support on GPU), float32, float64, int32, int64, but received %s."
-                % (convert_dtype(x.dtype)))
-    if not isinstance(axis, (int, Variable)):
-        raise TypeError(
-            "The type of 'axis' in concat must be int or Variable, but "
-            "received %s." % (type(axis)))
+    for id, x in enumerate(input):
+        check_type_and_dtype(
+            x, 'input[' + str(id) + ']', Variable,
+            ['float16', 'float32', 'float64', 'int32', 'int64'], 'concat')
+    check_type(axis, 'axis', (int, Variable), 'concat')
     inputs = {'X': input}
     attrs = {}
     if isinstance(axis, Variable):
@@ -478,14 +456,11 @@ def assign(input, output=None):
           result3 = fluid.layers.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(input, 'input', (Variable, numpy.ndarray), 'assign')
     if isinstance(input, Variable):
-        if convert_dtype(input.dtype) not in [
-                'float32', 'float64', 'int32', 'int64', 'bool'
-        ]:
-            raise TypeError(
-                "When the type of 'input' in assign is Variable, the data "
-                "type of 'input' must be float32, float64, int32, int64 or "
-                "bool, but received %s." % convert_dtype(input.dtype))
+        check_dtype(input.dtype, 'input',
+                    ['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=input.dtype)
@@ -518,9 +493,6 @@ def assign(input, output=None):
                 'shape': list(input.shape),
                 value_name: values
             })
-    else:
-        raise TypeError("The type of 'input' in assign must be Variable or "
-                        "numpy.ndarray, but received %s" % type(input))
 
     return output
 
@@ -570,19 +542,10 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
           data4 = fluid.layers.fill_constant(shape=shape, dtype='bool', value=True) # data4=[[True,True],[True,True]]
     """
     helper = LayerHelper("fill_constant", **locals())
-    if convert_dtype(dtype) not in [
-            'bool', 'float16', 'float32', 'float64', 'int32', 'int64'
-    ]:
-        raise TypeError(
-            "The create data type in fill_constant must be one of 'bool', float16, float32,"
-            "float64, int32 or int64, but received %s." % convert_dtype(
-                (dtype)))
-
-    if not isinstance(shape, (list, tuple, Variable)):
-        raise TypeError(
-            "The type of 'shape' in fill_constant must be Variable, list or tuple, but "
-            "received %s." % (type(shape)))
-
+    check_dtype(dtype, 'create data type',
+                ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
+                'fill_constant')
+    check_type(shape, 'shape', (Variable, list, tuple), 'fill_constant')
     inputs = {}
     attrs = {
         'value': float(value),
@@ -609,12 +572,10 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
         for idx, dim in enumerate(list_shape):
             if isinstance(dim, Variable):
                 dim.stop_gradient = True
-                if convert_dtype(dim.dtype) not in ['int32', 'int64']:
-                    raise TypeError(
-                        "When type of 'shape' in fill_constant is list or tuple, "
-                        "the data type of the element with type Variable must be int32 or int64, "
-                        "but received the data type of shape[%d] is %s." %
-                        (idx, convert_dtype(dim.dtype)))
+                check_dtype(
+                    dim.dtype, 'shape[' + str(idx) + ']', ['int32', 'int64'],
+                    'fill_constant',
+                    '(When type of shape in fill_constant is list or tuple.)')
                 if convert_dtype(dim.dtype) == 'int64':
                     dim = cast(x=dim, dtype='int32')
                 new_shape_tensor.append(dim)
@@ -626,10 +587,8 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
 
     if isinstance(shape, Variable):
         shape.stop_gradient = True
-        if convert_dtype(shape.dtype) not in ['int32', 'int64']:
-            raise TypeError(
-                "When type of 'shape' in fill_constant is Variable, the data type of 'shape' must be int32 or int64, "
-                "but received %s." % (convert_dtype(shape.dtype)))
+        check_dtype(shape.dtype, 'shape', ['int32', 'int64'], 'fill_constant',
+                    '(When type of shape in fill_constant is Variable.)')
         if (convert_dtype(shape.dtype) == 'int64'):
             shape = cast(shape, 'int32')
         inputs["ShapeTensor"] = shape
@@ -644,11 +603,11 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
     if out is None:
         out = helper.create_variable_for_type_inference(dtype=dtype)
     else:
-        if not (convert_dtype(dtype) == convert_dtype(out.dtype)):
-            raise TypeError(
-                "The create data type in op must be same with out type"
-                "but received %s and out dtype %s." % (convert_dtype(
-                    (dtype), convert_dtype(out.dtype))))
+        check_dtype(
+            dtype, 'create data type',
+            convert_dtype(out.dtype), 'fill_constant',
+            '(The create data type in fill_constant must be the same with out data type.)'
+        )
     attrs['dtype'] = out.dtype
     helper.append_op(
         type='fill_constant',
@@ -970,13 +929,9 @@ def zeros(shape, dtype, force_cpu=False):
           import paddle.fluid as fluid
           data = fluid.layers.zeros(shape=[3, 2], dtype='float32') # [[0., 0.], [0., 0.], [0., 0.]]
     """
-    if convert_dtype(dtype) not in [
-            'bool', 'float16', 'float32', 'float64', 'int32', 'int64'
-    ]:
-        raise TypeError(
-            "The create data type in zeros must be one of bool, float16, float32,"
-            " float64, int32 or int64, but received %s." % convert_dtype(
-                (dtype)))
+    check_dtype(dtype, 'create data type',
+                ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
+                'zeros')
     return fill_constant(value=0.0, **locals())
 
 

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

@@ -64,13 +64,13 @@ class TestAccuracyOpError(OpTest):
             # The input type of accuracy_op must be Variable.
             x1 = fluid.create_lod_tensor(
                 np.array([[-1]]), [[1]], fluid.CPUPlace())
-            self.assertRaises(TypeError, fluid.layers.accuracy, x1)
+            label = fluid.layers.data(
+                name='label', shape=[-1, 1], dtype="int32")
+            self.assertRaises(TypeError, fluid.layers.accuracy, x1, label)
             # The input dtype of accuracy_op must be float32 or float64.
             x2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
-            self.assertRaises(TypeError, fluid.layers.accuracy, x2)
+            self.assertRaises(TypeError, fluid.layers.accuracy, x2, label)
             x3 = fluid.layers.data(name='input', shape=[-1, 2], dtype="float16")
-            label = fluid.layers.data(
-                name='label', shape=[-1, 1], dtype="int32")
             fluid.layers.accuracy(input=x3, label=label)
 
 

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

@@ -28,10 +28,9 @@ class TestZerosOpError(OpTest):
     def test_errors(self):
         with program_guard(Program(), Program()):
             # The input dtype of zeros_op must be bool, float16, float32, float64, int32, int64.
-            x1 = fluid.layers.data(name='x1', shape=[4], dtype="int8")
-            self.assertRaises(TypeError, fluid.layers.zeros, x1)
-            x2 = fluid.layers.data(name='x2', shape=[4], dtype="uint8")
-            self.assertRaises(TypeError, fluid.layers.zeros, x2)
+            shape = [4]
+            dtype = "int8"
+            self.assertRaises(TypeError, fluid.layers.zeros, shape, dtype)
 
 
 if __name__ == "__main__":