Replaces xxx_required with requires_backends (#20715)

* Replaces xxx_required with requires_backends

* Fixup

src/transformers/benchmark/benchmark_args.py

@@ -17,7 +17,7 @@
 from dataclasses import dataclass, field
 from typing import Tuple
 
-from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, torch_required
+from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
 from .benchmark_args_utils import BenchmarkArguments
 
 
@@ -76,8 +76,8 @@ class PyTorchBenchmarkArguments(BenchmarkArguments):
     )
 
     @cached_property
-    @torch_required
     def _setup_devices(self) -> Tuple["torch.device", int]:
+        requires_backends(self, ["torch"])
         logger.info("PyTorch: setting up devices")
         if not self.cuda:
             device = torch.device("cpu")
@@ -95,19 +95,19 @@ class PyTorchBenchmarkArguments(BenchmarkArguments):
         return is_torch_tpu_available() and self.tpu
 
     @property
-    @torch_required
     def device_idx(self) -> int:
+        requires_backends(self, ["torch"])
         # TODO(PVP): currently only single GPU is supported
         return torch.cuda.current_device()
 
     @property
-    @torch_required
     def device(self) -> "torch.device":
+        requires_backends(self, ["torch"])
         return self._setup_devices[0]
 
     @property
-    @torch_required
     def n_gpu(self):
+        requires_backends(self, ["torch"])
         return self._setup_devices[1]
 
     @property

src/transformers/benchmark/benchmark_args_tf.py

@@ -17,7 +17,7 @@
 from dataclasses import dataclass, field
 from typing import Tuple
 
-from ..utils import cached_property, is_tf_available, logging, tf_required
+from ..utils import cached_property, is_tf_available, logging, requires_backends
 from .benchmark_args_utils import BenchmarkArguments
 
 
@@ -77,8 +77,8 @@ class TensorFlowBenchmarkArguments(BenchmarkArguments):
     )
 
     @cached_property
-    @tf_required
     def _setup_tpu(self) -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
+        requires_backends(self, ["tf"])
         tpu = None
         if self.tpu:
             try:
@@ -91,8 +91,8 @@ class TensorFlowBenchmarkArguments(BenchmarkArguments):
         return tpu
 
     @cached_property
-    @tf_required
     def _setup_strategy(self) -> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
+        requires_backends(self, ["tf"])
         if self.is_tpu:
             tf.config.experimental_connect_to_cluster(self._setup_tpu)
             tf.tpu.experimental.initialize_tpu_system(self._setup_tpu)
@@ -111,23 +111,23 @@ class TensorFlowBenchmarkArguments(BenchmarkArguments):
         return strategy
 
     @property
-    @tf_required
     def is_tpu(self) -> bool:
+        requires_backends(self, ["tf"])
         return self._setup_tpu is not None
 
     @property
-    @tf_required
     def strategy(self) -> "tf.distribute.Strategy":
+        requires_backends(self, ["tf"])
         return self._setup_strategy
 
     @property
-    @tf_required
     def gpu_list(self):
+        requires_backends(self, ["tf"])
         return tf.config.list_physical_devices("GPU")
 
     @property
-    @tf_required
     def n_gpu(self) -> int:
+        requires_backends(self, ["tf"])
         if self.cuda:
             return len(self.gpu_list)
         return 0

src/transformers/feature_extraction_utils.py

@@ -42,7 +42,7 @@ from .utils import (
     is_torch_device,
     is_torch_dtype,
     logging,
-    torch_required,
+    requires_backends,
 )
 
 
@@ -175,7 +175,6 @@ class BatchFeature(UserDict):
 
         return self
 
-    @torch_required
     def to(self, *args, **kwargs) -> "BatchFeature":
         """
         Send all values to device by calling `v.to(*args, **kwargs)` (PyTorch only). This should support casting in
@@ -190,6 +189,7 @@ class BatchFeature(UserDict):
         Returns:
             [`BatchFeature`]: The same instance after modification.
         """
+        requires_backends(self, ["torch"])
         import torch  # noqa
 
         new_data = {}

src/transformers/file_utils.py

@@ -127,10 +127,8 @@ from .utils import (
     is_vision_available,
     replace_return_docstrings,
     requires_backends,
-    tf_required,
     to_numpy,
     to_py_obj,
     torch_only_method,
-    torch_required,
     torch_version,
 )

src/transformers/tokenization_utils_base.py

@@ -56,8 +56,8 @@ from .utils import (
     is_torch_device,
     is_torch_tensor,
     logging,
+    requires_backends,
     to_py_obj,
-    torch_required,
 )
 
 
@@ -739,7 +739,6 @@ class BatchEncoding(UserDict):
 
         return self
 
-    @torch_required
     def to(self, device: Union[str, "torch.device"]) -> "BatchEncoding":
         """
         Send all values to device by calling `v.to(device)` (PyTorch only).
@@ -750,6 +749,7 @@ class BatchEncoding(UserDict):
         Returns:
             [`BatchEncoding`]: The same instance after modification.
         """
+        requires_backends(self, ["torch"])
 
         # This check catches things like APEX blindly calling "to" on all inputs to a module
         # Otherwise it passes the casts down and casts the LongTensor containing the token idxs

src/transformers/training_args.py

@@ -50,7 +50,6 @@ from .utils import (
     is_torch_tpu_available,
     logging,
     requires_backends,
-    torch_required,
 )
 
 
@@ -1386,8 +1385,8 @@ class TrainingArguments:
         return timedelta(seconds=self.ddp_timeout)
 
     @cached_property
-    @torch_required
     def _setup_devices(self) -> "torch.device":
+        requires_backends(self, ["torch"])
         logger.info("PyTorch: setting up devices")
         if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
             logger.warning(
@@ -1537,15 +1536,14 @@ class TrainingArguments:
         return device
 
     @property
-    @torch_required
     def device(self) -> "torch.device":
         """
         The device used by this process.
         """
+        requires_backends(self, ["torch"])
         return self._setup_devices
 
     @property
-    @torch_required
     def n_gpu(self):
         """
         The number of GPUs used by this process.
@@ -1554,12 +1552,12 @@ class TrainingArguments:
             This will only be greater than one when you have multiple GPUs available but are not using distributed
             training. For distributed training, it will always be 1.
         """
+        requires_backends(self, ["torch"])
         # Make sure `self._n_gpu` is properly setup.
         _ = self._setup_devices
         return self._n_gpu
 
     @property
-    @torch_required
     def parallel_mode(self):
         """
         The current mode used for parallelism if multiple GPUs/TPU cores are available. One of:
@@ -1570,6 +1568,7 @@ class TrainingArguments:
           `torch.nn.DistributedDataParallel`).
         - `ParallelMode.TPU`: several TPU cores.
         """
+        requires_backends(self, ["torch"])
         if is_torch_tpu_available():
             return ParallelMode.TPU
         elif is_sagemaker_mp_enabled():
@@ -1584,11 +1583,12 @@ class TrainingArguments:
             return ParallelMode.NOT_PARALLEL
 
     @property
-    @torch_required
     def world_size(self):
         """
         The number of processes used in parallel.
         """
+        requires_backends(self, ["torch"])
+
         if is_torch_tpu_available():
             return xm.xrt_world_size()
         elif is_sagemaker_mp_enabled():
@@ -1600,11 +1600,11 @@ class TrainingArguments:
         return 1
 
     @property
-    @torch_required
     def process_index(self):
         """
         The index of the current process used.
         """
+        requires_backends(self, ["torch"])
         if is_torch_tpu_available():
             return xm.get_ordinal()
         elif is_sagemaker_mp_enabled():
@@ -1616,11 +1616,11 @@ class TrainingArguments:
         return 0
 
     @property
-    @torch_required
     def local_process_index(self):
         """
         The index of the local process used.
         """
+        requires_backends(self, ["torch"])
         if is_torch_tpu_available():
             return xm.get_local_ordinal()
         elif is_sagemaker_mp_enabled():

src/transformers/training_args_tf.py

@@ -17,7 +17,7 @@ from dataclasses import dataclass, field
 from typing import Optional, Tuple
 
 from .training_args import TrainingArguments
-from .utils import cached_property, is_tf_available, logging, tf_required
+from .utils import cached_property, is_tf_available, logging, requires_backends
 
 
 logger = logging.get_logger(__name__)
@@ -185,8 +185,8 @@ class TFTrainingArguments(TrainingArguments):
     xla: bool = field(default=False, metadata={"help": "Whether to activate the XLA compilation or not"})
 
     @cached_property
-    @tf_required
     def _setup_strategy(self) -> Tuple["tf.distribute.Strategy", int]:
+        requires_backends(self, ["tf"])
         logger.info("Tensorflow: setting up strategy")
 
         gpus = tf.config.list_physical_devices("GPU")
@@ -234,19 +234,19 @@ class TFTrainingArguments(TrainingArguments):
         return strategy
 
     @property
-    @tf_required
     def strategy(self) -> "tf.distribute.Strategy":
         """
         The strategy used for distributed training.
         """
+        requires_backends(self, ["tf"])
         return self._setup_strategy
 
     @property
-    @tf_required
     def n_replicas(self) -> int:
         """
         The number of replicas (CPUs, GPUs or TPU cores) used in this training.
         """
+        requires_backends(self, ["tf"])
         return self._setup_strategy.num_replicas_in_sync
 
     @property
@@ -276,11 +276,11 @@ class TFTrainingArguments(TrainingArguments):
         return per_device_batch_size * self.n_replicas
 
     @property
-    @tf_required
     def n_gpu(self) -> int:
         """
         The number of replicas (CPUs, GPUs or TPU cores) used in this training.
         """
+        requires_backends(self, ["tf"])
         warnings.warn(
             "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.",
             FutureWarning,

src/transformers/utils/__init__.py

@@ -163,9 +163,7 @@ from .import_utils import (
     is_training_run_on_sagemaker,
     is_vision_available,
     requires_backends,
-    tf_required,
     torch_only_method,
-    torch_required,
     torch_version,
 )
 

src/transformers/utils/import_utils.py

@@ -22,7 +22,7 @@ import shutil
 import sys
 import warnings
 from collections import OrderedDict
-from functools import lru_cache, wraps
+from functools import lru_cache
 from itertools import chain
 from types import ModuleType
 from typing import Any
@@ -1039,30 +1039,6 @@ class DummyObject(type):
         requires_backends(cls, cls._backends)
 
 
-def torch_required(func):
-    # Chose a different decorator name than in tests so it's clear they are not the same.
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        if is_torch_available():
-            return func(*args, **kwargs)
-        else:
-            raise ImportError(f"Method `{func.__name__}` requires PyTorch.")
-
-    return wrapper
-
-
-def tf_required(func):
-    # Chose a different decorator name than in tests so it's clear they are not the same.
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        if is_tf_available():
-            return func(*args, **kwargs)
-        else:
-            raise ImportError(f"Method `{func.__name__}` requires TF.")
-
-    return wrapper
-
-
 def is_torch_fx_proxy(x):
     if is_torch_fx_available():
         import torch.fx