diff --git a/examples/pytorch/language-modeling/run_fim.py b/examples/pytorch/language-modeling/run_fim.py index f981c97a8e8..a201479ae8f 100644 --- a/examples/pytorch/language-modeling/run_fim.py +++ b/examples/pytorch/language-modeling/run_fim.py @@ -47,7 +47,7 @@ from transformers import ( 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 main(): 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 ), ) diff --git a/examples/pytorch/language-modeling/run_fim_no_trainer.py b/examples/pytorch/language-modeling/run_fim_no_trainer.py index b2cd5ddd129..44d083bc631 100644 --- a/examples/pytorch/language-modeling/run_fim_no_trainer.py +++ b/examples/pytorch/language-modeling/run_fim_no_trainer.py @@ -52,7 +52,7 @@ from transformers import ( 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 diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py index 3a11b3e36b7..d07618a4926 100644 --- a/src/transformers/__init__.py +++ b/src/transformers/__init__.py @@ -1037,7 +1037,6 @@ _import_structure = { "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 @@ if TYPE_CHECKING: 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, diff --git a/src/transformers/image_transforms.py b/src/transformers/image_transforms.py index 1ea163202d1..5b0ba3f9122 100644 --- a/src/transformers/image_transforms.py +++ b/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 diff --git a/src/transformers/utils/__init__.py b/src/transformers/utils/__init__.py index a549af2928d..a427cb18f5b 100644 --- a/src/transformers/utils/__init__.py +++ b/src/transformers/utils/__init__.py @@ -228,7 +228,6 @@ from .import_utils import ( 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, diff --git a/src/transformers/utils/import_utils.py b/src/transformers/utils/import_utils.py index 65a0d1bf374..f98403ded28 100644 --- a/src/transformers/utils/import_utils.py +++ b/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): """ diff --git a/src/transformers/utils/quantization_config.py b/src/transformers/utils/quantization_config.py index 59854401ed3..7128677a79d 100644 --- a/src/transformers/utils/quantization_config.py +++ b/src/transformers/utils/quantization_config.py @@ -682,7 +682,6 @@ class GPTQConfig(QuantizationConfigMixin): 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() @@ -690,7 +689,6 @@ class GPTQConfig(QuantizationConfigMixin): 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 @@ class GPTQConfig(QuantizationConfigMixin): 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 @@ class GPTQConfig(QuantizationConfigMixin): 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 diff --git a/tests/models/seamless_m4t_v2/test_modeling_seamless_m4t_v2.py b/tests/models/seamless_m4t_v2/test_modeling_seamless_m4t_v2.py index c5b10ea34da..2387e5f25ff 100644 --- a/tests/models/seamless_m4t_v2/test_modeling_seamless_m4t_v2.py +++ b/tests/models/seamless_m4t_v2/test_modeling_seamless_m4t_v2.py @@ -592,7 +592,7 @@ class SeamlessM4Tv2ModelWithSpeechInputTest(ModelTesterMixin, unittest.TestCase) # 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