Remove deprecated code (#37059)

* Remove deprecated code

* fix get_loading_attributes

* fix error

* skip test

---------

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>

Author: 3 people

examples/pytorch/language-modeling/run_fim.py

@@ -47,7 +47,7 @@
     Trainer,
     TrainingArguments,
     default_data_collator,
-    is_torch_tpu_available,
+    is_torch_xla_available,
     set_seed,
 )
 from transformers.integrations import is_deepspeed_zero3_enabled
@@ -525,7 +525,7 @@ def main():
     if torch.cuda.is_availble():
         pad_factor = 8
 
-    elif is_torch_tpu_available():
+    elif is_torch_xla_available(check_is_tpu=True):
         pad_factor = 128
 
     # Add the new tokens to the tokenizer
@@ -795,9 +795,13 @@ def compute_metrics(eval_preds):
         processing_class=tokenizer,
         # Data collator will default to DataCollatorWithPadding, so we change it.
         data_collator=default_data_collator,
-        compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None,
+        compute_metrics=compute_metrics
+        if training_args.do_eval and not is_torch_xla_available(check_is_tpu=True)
+        else None,
         preprocess_logits_for_metrics=(
-            preprocess_logits_for_metrics if training_args.do_eval and not is_torch_tpu_available() else None
+            preprocess_logits_for_metrics
+            if training_args.do_eval and not is_torch_xla_available(check_is_tpu=True)
+            else None
         ),
     )
 

examples/pytorch/language-modeling/run_fim_no_trainer.py

@@ -52,7 +52,7 @@
     SchedulerType,
     default_data_collator,
     get_scheduler,
-    is_torch_tpu_available,
+    is_torch_xla_available,
 )
 from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.utils import check_min_version, send_example_telemetry
@@ -492,7 +492,7 @@ def main():
     if torch.cuda.is_availble():
         pad_factor = 8
 
-    elif is_torch_tpu_available():
+    elif is_torch_xla_available(check_is_tpu=True):
         pad_factor = 128
 
     # Add the new tokens to the tokenizer

src/transformers/__init__.py

@@ -1037,7 +1037,6 @@
         "is_torch_musa_available",
         "is_torch_neuroncore_available",
         "is_torch_npu_available",
-        "is_torch_tpu_available",
         "is_torchvision_available",
         "is_torch_xla_available",
         "is_torch_xpu_available",
@@ -6341,7 +6340,6 @@
         is_torch_musa_available,
         is_torch_neuroncore_available,
         is_torch_npu_available,
-        is_torch_tpu_available,
         is_torch_xla_available,
         is_torch_xpu_available,
         is_torchvision_available,

src/transformers/image_transforms.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import warnings
 from collections.abc import Collection, Iterable
 from math import ceil
 from typing import Optional, Union
@@ -453,7 +452,6 @@ def center_crop(
     size: tuple[int, int],
     data_format: Optional[Union[str, ChannelDimension]] = None,
     input_data_format: Optional[Union[str, ChannelDimension]] = None,
-    return_numpy: Optional[bool] = None,
 ) -> np.ndarray:
     """
     Crops the `image` to the specified `size` using a center crop. Note that if the image is too small to be cropped to
@@ -474,22 +472,11 @@ def center_crop(
                 - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                 - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
             If unset, will use the inferred format of the input image.
-        return_numpy (`bool`, *optional*):
-            Whether or not to return the cropped image as a numpy array. Used for backwards compatibility with the
-            previous ImageFeatureExtractionMixin method.
-                - Unset: will return the same type as the input image.
-                - `True`: will return a numpy array.
-                - `False`: will return a `PIL.Image.Image` object.
     Returns:
         `np.ndarray`: The cropped image.
     """
     requires_backends(center_crop, ["vision"])
 
-    if return_numpy is not None:
-        warnings.warn("return_numpy is deprecated and will be removed in v.4.33", FutureWarning)
-
-    return_numpy = True if return_numpy is None else return_numpy
-
     if not isinstance(image, np.ndarray):
         raise TypeError(f"Input image must be of type np.ndarray, got {type(image)}")
 
@@ -541,9 +528,6 @@ def center_crop(
     new_image = new_image[..., max(0, top) : min(new_height, bottom), max(0, left) : min(new_width, right)]
     new_image = to_channel_dimension_format(new_image, output_data_format, ChannelDimension.FIRST)
 
-    if not return_numpy:
-        new_image = to_pil_image(new_image)
-
     return new_image
 
 

src/transformers/utils/__init__.py

@@ -228,7 +228,6 @@
     is_torch_sdpa_available,
     is_torch_tensorrt_fx_available,
     is_torch_tf32_available,
-    is_torch_tpu_available,
     is_torch_xla_available,
     is_torch_xpu_available,
     is_torchao_available,

src/transformers/utils/import_utils.py

@@ -675,31 +675,6 @@ def is_g2p_en_available():
     return _g2p_en_available
 
 
-@lru_cache()
-def is_torch_tpu_available(check_device=True):
-    "Checks if `torch_xla` is installed and potentially if a TPU is in the environment"
-    warnings.warn(
-        "`is_torch_tpu_available` is deprecated and will be removed in 4.41.0. "
-        "Please use the `is_torch_xla_available` instead.",
-        FutureWarning,
-    )
-
-    if not _torch_available:
-        return False
-    if importlib.util.find_spec("torch_xla") is not None:
-        if check_device:
-            # We need to check if `xla_device` can be found, will raise a RuntimeError if not
-            try:
-                import torch_xla.core.xla_model as xm
-
-                _ = xm.xla_device()
-                return True
-            except RuntimeError:
-                return False
-        return True
-    return False
-
-
 @lru_cache
 def is_torch_xla_available(check_is_tpu=False, check_is_gpu=False):
     """

src/transformers/utils/quantization_config.py

@@ -682,15 +682,13 @@ def __init__(
         self.use_exllama = use_exllama
         self.max_input_length = max_input_length
         self.exllama_config = exllama_config
-        self.disable_exllama = kwargs.pop("disable_exllama", None)
         self.cache_block_outputs = cache_block_outputs
         self.modules_in_block_to_quantize = modules_in_block_to_quantize
         self.post_init()
 
     def get_loading_attributes(self):
         attibutes_dict = copy.deepcopy(self.__dict__)
         loading_attibutes = [
-            "disable_exllama",
             "use_exllama",
             "exllama_config",
             "use_cuda_fp16",
@@ -739,20 +737,9 @@ def post_init(self):
                 self.use_exllama = False
 
         # auto-gptq specific kernel control logic
-        if self.disable_exllama is None and self.use_exllama is None:
+        if self.use_exllama is None:
             # New default behaviour
             self.use_exllama = True
-        elif self.disable_exllama is not None and self.use_exllama is None:
-            # Follow pattern of old config
-            logger.warning(
-                "Using `disable_exllama` is deprecated and will be removed in version 4.37. Use `use_exllama` instead and specify the version with `exllama_config`."
-                "The value of `use_exllama` will be overwritten by `disable_exllama` passed in `GPTQConfig` or stored in your config file."
-            )
-            self.use_exllama = not self.disable_exllama
-            self.disable_exllama = None
-        elif self.disable_exllama is not None and self.use_exllama is not None:
-            # Only happens if user explicitly passes in both arguments
-            raise ValueError("Cannot specify both `disable_exllama` and `use_exllama`. Please use just `use_exllama`")
 
         if self.exllama_config is None:
             self.exllama_config = {"version": ExllamaVersion.ONE}
@@ -809,7 +796,7 @@ def from_dict_optimum(cls, config_dict):
         if "disable_exllama" in config_dict:
             config_dict["use_exllama"] = not config_dict["disable_exllama"]
             # switch to None to not trigger the warning
-            config_dict["disable_exllama"] = None
+            config_dict.pop("disable_exllama")
 
         config = cls(**config_dict)
         return config

tests/models/seamless_m4t_v2/test_modeling_seamless_m4t_v2.py

@@ -592,7 +592,7 @@ def test_attention_outputs(self):
     # TODO: @ydshieh: refer to #34968
     @unittest.skip(reason="Failing on multi-gpu runner")
     def test_retain_grad_hidden_states_attentions(self):
-        pass
+        self.skipTest(reason="Failing on multi-gpu runner")
 
 
 @require_torch