[CodeStyle] Cleanup some useless eager Tensor usage (PaddlePaddle#66303)

python/paddle/amp/grad_scaler.py

@@ -217,7 +217,7 @@ def scale(self, var: Tensor) -> Tensor:
         check_type(
             var,
             "var",
-            (core.eager.Tensor, paddle.pir.Value),
+            (paddle.Tensor, paddle.pir.Value),
             'AmpScaler.scale()',
         )
 

python/paddle/base/dygraph/base.py

@@ -139,7 +139,7 @@ def _convert_into_variable(tensor):
     """
     if paddle.framework.use_pir_api():
         return paddle.pir.core._convert_into_value(tensor)
-    if isinstance(tensor, core.eager.Tensor):
+    if isinstance(tensor, paddle.Tensor):
         # Check whether has been created before.
         new_var = tensor.block._find_var_recursive(tensor.name)
         if new_var is not None:

python/paddle/distributed/fleet/recompute/recompute.py

@@ -79,12 +79,12 @@ def detach_variable(inputs):
 def check_recompute_necessary(inputs):
     necessary_for_each_input = []
     for input_ in inputs:
-        if isinstance(input_, (core.eager.Tensor, paddle.Tensor)):
+        if isinstance(input_, paddle.Tensor):
             necessary_for_each_input.append(input_.stop_gradient)
         elif type(input_) is tuple:
             for i in input_:
                 # traverse all tensors in the tuple
-                if isinstance(i, (core.eager.Tensor, paddle.Tensor)):
+                if isinstance(i, paddle.Tensor):
                     necessary_for_each_input.append(i.stop_gradient)
     if all(necessary_for_each_input):
         logger.warning(

python/paddle/io/dataloader/collate.py

@@ -55,7 +55,7 @@ def default_collate_fn(batch):
     if isinstance(sample, np.ndarray):
         batch = np.stack(batch, axis=0)
         return batch
-    elif isinstance(sample, (paddle.Tensor, core.eager.Tensor)):
+    elif isinstance(sample, paddle.Tensor):
         return paddle.stack(batch, axis=0)
     elif isinstance(sample, numbers.Number):
         batch = np.array(batch)

python/paddle/io/dataloader/dataloader_iter.py

@@ -261,7 +261,7 @@ def _thread_loop(self, legacy_expected_place):
                 # pack as LoDTensorArray
                 array = core.LoDTensorArray()
                 for slot in batch:
-                    if isinstance(slot, (paddle.Tensor, core.eager.Tensor)):
+                    if isinstance(slot, paddle.Tensor):
                         slot = slot.value().get_tensor()
                     elif not isinstance(slot, core.LoDTensor):
                         tmp = core.LoDTensor()
@@ -633,9 +633,7 @@ def _thread_loop(self, legacy_expected_place):
                             # LoDTensor not in shared memory is not
                             # serializable, cannot be create in workers
                             for slot in batch:
-                                if isinstance(
-                                    slot, (paddle.Tensor, core.eager.Tensor)
-                                ):
+                                if isinstance(slot, paddle.Tensor):
                                     slot = slot.get_tensor()
                                 elif not isinstance(slot, core.LoDTensor):
                                     tmp = core.LoDTensor()

python/paddle/metric/metrics.py

@@ -306,7 +306,7 @@ def update(self, correct: Tensor, *args: Any) -> Tensor:
         Return:
             Tensor: the accuracy of current step.
         """
-        if isinstance(correct, (paddle.Tensor, paddle.base.core.eager.Tensor)):
+        if isinstance(correct, paddle.Tensor):
             correct = np.array(correct)
         num_samples = np.prod(np.array(correct.shape[:-1]))
         accs = []
@@ -442,12 +442,12 @@ def update(
                 the shape should keep the same as preds.
                 The data type is 'int32' or 'int64'.
         """
-        if isinstance(preds, (paddle.Tensor, paddle.base.core.eager.Tensor)):
+        if isinstance(preds, paddle.Tensor):
             preds = np.array(preds)
         elif not _is_numpy_(preds):
             raise ValueError("The 'preds' must be a numpy ndarray or Tensor.")
 
-        if isinstance(labels, (paddle.Tensor, paddle.base.core.eager.Tensor)):
+        if isinstance(labels, paddle.Tensor):
             labels = np.array(labels)
         elif not _is_numpy_(labels):
             raise ValueError("The 'labels' must be a numpy ndarray or Tensor.")
@@ -581,12 +581,12 @@ def update(
                 the shape should keep the same as preds.
                 Shape: [batch_size, 1], Dtype: 'int32' or 'int64'.
         """
-        if isinstance(preds, (paddle.Tensor, paddle.base.core.eager.Tensor)):
+        if isinstance(preds, paddle.Tensor):
             preds = np.array(preds)
         elif not _is_numpy_(preds):
             raise ValueError("The 'preds' must be a numpy ndarray or Tensor.")
 
-        if isinstance(labels, (paddle.Tensor, paddle.base.core.eager.Tensor)):
+        if isinstance(labels, paddle.Tensor):
             labels = np.array(labels)
         elif not _is_numpy_(labels):
             raise ValueError("The 'labels' must be a numpy ndarray or Tensor.")
@@ -740,12 +740,12 @@ def update(
                 (batch_size, 1), labels[i] is either o or 1,
                 representing the label of the instance i.
         """
-        if isinstance(labels, (paddle.Tensor, paddle.base.core.eager.Tensor)):
+        if isinstance(labels, paddle.Tensor):
             labels = np.array(labels)
         elif not _is_numpy_(labels):
             raise ValueError("The 'labels' must be a numpy ndarray or Tensor.")
 
-        if isinstance(preds, (paddle.Tensor, paddle.base.core.eager.Tensor)):
+        if isinstance(preds, paddle.Tensor):
             preds = np.array(preds)
         elif not _is_numpy_(preds):
             raise ValueError("The 'preds' must be a numpy ndarray or Tensor.")

python/paddle/optimizer/adamw.py

@@ -211,7 +211,7 @@ def __init__(
             # paddle.Tensor is also iterable, so here we don't check whether
             # the input is iterable, if the input is paddle.Tensor, the
             # list(paddle.Tensor) will be a error value
-            if isinstance(parameters, (paddle.Tensor, core.eager.Tensor)):
+            if isinstance(parameters, paddle.Tensor):
                 raise TypeError(
                     "`parameters` argument given to the optimizer should be "
                     f"an iterable of paddle Tensors, but got argument type is `{type(parameters)}`."

python/paddle/optimizer/optimizer.py

@@ -215,7 +215,7 @@ def __init__(
             # paddle.Tensor is also iterable, so here we don't check whether
             # the input is iterable, if the input is paddle.Tensor, the
             # list(paddle.Tensor) will be a error value
-            if isinstance(parameters, (paddle.Tensor, core.eager.Tensor)):
+            if isinstance(parameters, paddle.Tensor):
                 raise TypeError(
                     "`parameters` argument given to the optimizer should be "
                     f"an iterable of paddle Tensors, but got argument type is `{type(parameters)}`."

python/paddle/static/nn/common.py

@@ -2815,8 +2815,7 @@ def batch_norm(
     if in_dygraph_mode():
         inputs_has_MomentumTensor = False
         attrs_has_momentum = False
-        tmp_tensor_type = core.eager.Tensor
-        if isinstance(momentum, tmp_tensor_type):
+        if isinstance(momentum, paddle.Tensor):
             inputs_has_MomentumTensor = True
         else:
             attrs_has_momentum = True

python/paddle/tensor/creation.py

@@ -1270,7 +1270,7 @@ def eye(
     """
 
     def _check_attr(attr, message):
-        if isinstance(attr, ((Variable, core.eager.Tensor, paddle.pir.Value))):
+        if isinstance(attr, ((Variable, paddle.Tensor, paddle.pir.Value))):
             assert len(attr.shape) == 1 and attr.shape[0] in [1, -1]
         elif not isinstance(attr, int) or attr < 0:
             raise TypeError(f"{message} should be a non-negative int.")

python/paddle/utils/dlpack.py

@@ -59,7 +59,7 @@ def to_dlpack(x: Tensor) -> CapsuleType:
     """
 
     if in_dygraph_mode():
-        if not isinstance(x, (paddle.Tensor, paddle.base.core.eager.Tensor)):
+        if not isinstance(x, paddle.Tensor):
             raise TypeError(
                 "The type of 'x' in to_dlpack must be paddle.Tensor,"
                 f" but received {type(x)}."