在文档中统一静态图模式与动态图模式的英文翻译

python/paddle/distributed/fleet/utils/hybrid_parallel_inference.py

@@ -206,7 +206,8 @@ def __init__(
         elif core.is_compiled_with_cuda():
             self._device = "gpu"
         assert self._device, "Only gpu and npu are supported."
-        assert not _non_static_mode(), "Only static graph mode is supported."
+
+        assert not in_dygraph_mode(), "Only static mode is supported."
 
         op_maker = core.op_proto_and_checker_maker
         self._op_role = op_maker.OpRole

python/paddle/tensor/layer_function_generator.py

@@ -334,15 +334,19 @@ def generate_inplace_fn(inplace_op_type):
     origin_op_type = inplace_op_type[:-1]
 
     def func(x, name=None):
-        if in_dygraph_mode() and hasattr(_C_ops, inplace_op_type):
-            op = getattr(_C_ops, inplace_op_type)
-            return op(x)
-        if _non_static_mode():
-            op = getattr(_legacy_C_ops, inplace_op_type)
-            return op(x)
-        warnings.warn(
-            "In static graph mode, {}() is the same as {}() and does not perform inplace operation.".format(
-                inplace_op_type, origin_op_type
+
+        if in_dygraph_mode():
+            if hasattr(_C_ops, inplace_op_type):
+                op = getattr(_C_ops, inplace_op_type)
+                return op(x)
+            else:
+                op = getattr(_legacy_C_ops, inplace_op_type)
+                return op(x)
+        else:
+            warnings.warn(
+                "In static mode, {}() is the same as {}() and does not perform inplace operation.".format(
+                    inplace_op_type, origin_op_type
+                )
             )
             return generate_activation_fn(origin_op_type)(x, name)
 

python/paddle/tensor/manipulation.py

@@ -1138,44 +1138,10 @@ def concat(x, axis=0, name=None):
                     ],
                     'concat',
                 )
-    else:
-        input = [input]
-    check_type(axis, 'axis', (int, Variable), 'concat')
-
-    if isinstance(axis, Variable):
-        check_dtype(
-            axis.dtype,
-            'axis',
-            ['int32', 'int64'],
-            'concat',
-            "The data type of axis must be int32 or int64 when axis is a Tensor",
-        )
-
-    helper = LayerHelper('concat', **locals())
-    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
-
-    if input[0].desc.type() == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
-        # NOTE(liym27): Don't remove this if branch!
-        # This feature is supported for Dynamic-to-Static, because after transformed, the type of inputs[0]
-        # is LOD_TENSOR_ARRAY in some scenarios. And this feature can be used in static graph mode.
-
-        assert len(input) == 1, (
-            "If the elements of 'input' in concat are Variable(LoDTensorArray), "
-            "number of the elements must be 1, but received %s." % len(input)
-        )
-        out_index = helper.create_variable_for_type_inference(dtype="int32")
-        helper.append_op(
-            type='tensor_array_to_tensor',
-            inputs={'X': input[0]},
-            outputs={'Out': [out], 'OutIndex': [out_index]},
-            attrs={'axis': axis, 'use_stack': False},
-        )
-    else:
-        inputs = {'X': input}
-        attrs = {}
-        if isinstance(axis, Variable):
-            axis.stop_gradient = True
-            inputs['AxisTensor'] = axis
+                if x.dtype != input[0].dtype:
+                    raise TypeError(
+                        "All the Tensors in the input must have the same data type."
+                    )
         else:
             input = [input]
         check_type(axis, 'axis', (int, Variable), 'concat')