From c9035e453799adf897a000953d4b83ff764263cc Mon Sep 17 00:00:00 2001 From: Stas Bekman Date: Wed, 7 Apr 2021 06:20:06 -0700 Subject: [PATCH] fix: The 'warn' method is deprecated (#11105) * The 'warn' method is deprecated * fix test --- examples/language-modeling/run_clm.py | 4 ++-- examples/language-modeling/run_clm_no_trainer.py | 4 ++-- examples/language-modeling/run_mlm.py | 4 ++-- examples/language-modeling/run_mlm_no_trainer.py | 4 ++-- examples/language-modeling/run_plm.py | 2 +- examples/legacy/question-answering/run_squad.py | 2 +- examples/legacy/seq2seq/seq2seq_trainer.py | 4 ++-- examples/multiple-choice/run_swag.py | 4 ++-- examples/question-answering/run_qa.py | 2 +- examples/question-answering/run_qa_beam_search.py | 2 +- .../question-answering/run_qa_beam_search_no_trainer.py | 2 +- examples/question-answering/run_qa_no_trainer.py | 2 +- examples/question-answering/run_tf_squad.py | 2 +- .../movement-pruning/masked_run_squad.py | 2 +- examples/seq2seq/run_summarization.py | 2 +- examples/seq2seq/run_translation.py | 2 +- examples/text-classification/run_glue.py | 4 ++-- examples/text-classification/run_glue_no_trainer.py | 2 +- src/transformers/configuration_utils.py | 2 +- src/transformers/data/datasets/squad.py | 2 +- src/transformers/file_utils.py | 2 +- src/transformers/integrations.py | 2 +- src/transformers/modeling_tf_utils.py | 8 +++++--- src/transformers/models/auto/tokenization_auto.py | 2 +- src/transformers/models/bart/modeling_bart.py | 2 +- src/transformers/models/bert/modeling_bert.py | 2 +- .../models/bert_generation/modeling_bert_generation.py | 2 +- src/transformers/models/big_bird/modeling_big_bird.py | 2 +- src/transformers/models/blenderbot/modeling_blenderbot.py | 2 +- .../models/blenderbot_small/modeling_blenderbot_small.py | 2 +- src/transformers/models/electra/modeling_electra.py | 2 +- src/transformers/models/gpt2/modeling_gpt2.py | 2 +- src/transformers/models/gpt_neo/modeling_gpt_neo.py | 2 +- src/transformers/models/layoutlm/modeling_layoutlm.py | 2 +- src/transformers/models/led/modeling_led.py | 2 +- src/transformers/models/m2m_100/modeling_m2m_100.py | 2 +- src/transformers/models/marian/modeling_marian.py | 2 +- src/transformers/models/mbart/modeling_mbart.py | 2 +- src/transformers/models/pegasus/modeling_pegasus.py | 2 +- src/transformers/models/prophetnet/modeling_prophetnet.py | 2 +- src/transformers/models/roberta/modeling_roberta.py | 2 +- .../models/speech_to_text/modeling_speech_to_text.py | 2 +- ...ert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py | 2 +- src/transformers/models/xlm/modeling_tf_xlm.py | 2 +- src/transformers/models/xlm/modeling_xlm.py | 2 +- src/transformers/pipelines/zero_shot_classification.py | 2 +- src/transformers/trainer_callback.py | 4 ++-- src/transformers/trainer_pt_utils.py | 4 ++-- .../modeling_{{cookiecutter.lowercase_modelname}}.py | 4 ++-- .../scripts/pytorch/run_glue_model_parallelism.py | 4 ++-- tests/test_logging.py | 4 ++-- tests/test_trainer_callback.py | 2 +- 52 files changed, 68 insertions(+), 66 deletions(-) diff --git a/examples/language-modeling/run_clm.py b/examples/language-modeling/run_clm.py index 4635703b9d..7f21548efd 100755 --- a/examples/language-modeling/run_clm.py +++ b/examples/language-modeling/run_clm.py @@ -330,14 +330,14 @@ def main(): if data_args.block_size is None: block_size = tokenizer.model_max_length if block_size > 1024: - logger.warn( + logger.warning( f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " "Picking 1024 instead. You can change that default value by passing --block_size xxx." ) block_size = 1024 else: if data_args.block_size > tokenizer.model_max_length: - logger.warn( + logger.warning( f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model" f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}." ) diff --git a/examples/language-modeling/run_clm_no_trainer.py b/examples/language-modeling/run_clm_no_trainer.py index 559501dd75..70fabd31df 100755 --- a/examples/language-modeling/run_clm_no_trainer.py +++ b/examples/language-modeling/run_clm_no_trainer.py @@ -305,14 +305,14 @@ def main(): if args.block_size is None: block_size = tokenizer.model_max_length if block_size > 1024: - logger.warn( + logger.warning( f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " "Picking 1024 instead. You can change that default value by passing --block_size xxx." ) block_size = 1024 else: if args.block_size > tokenizer.model_max_length: - logger.warn( + logger.warning( f"The block_size passed ({args.block_size}) is larger than the maximum length for the model" f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}." ) diff --git a/examples/language-modeling/run_mlm.py b/examples/language-modeling/run_mlm.py index f3c2c45fb6..4fd3c4f217 100755 --- a/examples/language-modeling/run_mlm.py +++ b/examples/language-modeling/run_mlm.py @@ -324,14 +324,14 @@ def main(): if data_args.max_seq_length is None: max_seq_length = tokenizer.model_max_length if max_seq_length > 1024: - logger.warn( + logger.warning( f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx." ) max_seq_length = 1024 else: if data_args.max_seq_length > tokenizer.model_max_length: - logger.warn( + logger.warning( f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) diff --git a/examples/language-modeling/run_mlm_no_trainer.py b/examples/language-modeling/run_mlm_no_trainer.py index 71a3bbe0c5..1cf1c242ab 100755 --- a/examples/language-modeling/run_mlm_no_trainer.py +++ b/examples/language-modeling/run_mlm_no_trainer.py @@ -308,14 +308,14 @@ def main(): if args.max_seq_length is None: max_seq_length = tokenizer.model_max_length if max_seq_length > 1024: - logger.warn( + logger.warning( f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx." ) max_seq_length = 1024 else: if args.max_seq_length > tokenizer.model_max_length: - logger.warn( + logger.warning( f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) diff --git a/examples/language-modeling/run_plm.py b/examples/language-modeling/run_plm.py index 3d21d20303..f5c9c47b72 100755 --- a/examples/language-modeling/run_plm.py +++ b/examples/language-modeling/run_plm.py @@ -319,7 +319,7 @@ def main(): text_column_name = "text" if "text" in column_names else column_names[0] if data_args.max_seq_length > tokenizer.model_max_length: - logger.warn( + logger.warning( f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) diff --git a/examples/legacy/question-answering/run_squad.py b/examples/legacy/question-answering/run_squad.py index ff693ad24d..84986eff6f 100644 --- a/examples/legacy/question-answering/run_squad.py +++ b/examples/legacy/question-answering/run_squad.py @@ -436,7 +436,7 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.") if args.version_2_with_negative: - logger.warn("tensorflow_datasets does not handle version 2 of SQuAD.") + logger.warning("tensorflow_datasets does not handle version 2 of SQuAD.") tfds_examples = tfds.load("squad") examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate) diff --git a/examples/legacy/seq2seq/seq2seq_trainer.py b/examples/legacy/seq2seq/seq2seq_trainer.py index cba3e958e9..075e9f728b 100644 --- a/examples/legacy/seq2seq/seq2seq_trainer.py +++ b/examples/legacy/seq2seq/seq2seq_trainer.py @@ -73,7 +73,7 @@ class Seq2SeqTrainer(Trainer): ), "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss calculation or doing label smoothing." if self.config.pad_token_id is None and self.config.eos_token_id is not None: - logger.warn( + logger.warning( f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for padding.." ) @@ -127,7 +127,7 @@ class Seq2SeqTrainer(Trainer): if self.lr_scheduler is None: self.lr_scheduler = self._get_lr_scheduler(num_training_steps) else: # ignoring --lr_scheduler - logger.warn("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.") + logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.") def _get_lr_scheduler(self, num_training_steps): schedule_func = arg_to_scheduler[self.args.lr_scheduler] diff --git a/examples/multiple-choice/run_swag.py b/examples/multiple-choice/run_swag.py index a4bd29aea0..04ad05affd 100755 --- a/examples/multiple-choice/run_swag.py +++ b/examples/multiple-choice/run_swag.py @@ -310,14 +310,14 @@ def main(): if data_args.max_seq_length is None: max_seq_length = tokenizer.model_max_length if max_seq_length > 1024: - logger.warn( + logger.warning( f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx." ) max_seq_length = 1024 else: if data_args.max_seq_length > tokenizer.model_max_length: - logger.warn( + logger.warning( f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) diff --git a/examples/question-answering/run_qa.py b/examples/question-answering/run_qa.py index 0fec278378..fa76110b51 100755 --- a/examples/question-answering/run_qa.py +++ b/examples/question-answering/run_qa.py @@ -324,7 +324,7 @@ def main(): pad_on_right = tokenizer.padding_side == "right" if data_args.max_seq_length > tokenizer.model_max_length: - logger.warn( + logger.warning( f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) diff --git a/examples/question-answering/run_qa_beam_search.py b/examples/question-answering/run_qa_beam_search.py index e0bf5f96cb..7a6d0b5bb4 100755 --- a/examples/question-answering/run_qa_beam_search.py +++ b/examples/question-answering/run_qa_beam_search.py @@ -313,7 +313,7 @@ def main(): pad_on_right = tokenizer.padding_side == "right" if data_args.max_seq_length > tokenizer.model_max_length: - logger.warn( + logger.warning( f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) diff --git a/examples/question-answering/run_qa_beam_search_no_trainer.py b/examples/question-answering/run_qa_beam_search_no_trainer.py index 15a6269eb1..ca0d60c0f8 100644 --- a/examples/question-answering/run_qa_beam_search_no_trainer.py +++ b/examples/question-answering/run_qa_beam_search_no_trainer.py @@ -291,7 +291,7 @@ def main(): pad_on_right = tokenizer.padding_side == "right" if args.max_seq_length > tokenizer.model_max_length: - logger.warn( + logger.warning( f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) diff --git a/examples/question-answering/run_qa_no_trainer.py b/examples/question-answering/run_qa_no_trainer.py index e8e4e3a33a..7a8b2215be 100755 --- a/examples/question-answering/run_qa_no_trainer.py +++ b/examples/question-answering/run_qa_no_trainer.py @@ -343,7 +343,7 @@ def main(): pad_on_right = tokenizer.padding_side == "right" if args.max_seq_length > tokenizer.model_max_length: - logger.warn( + logger.warning( f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) diff --git a/examples/question-answering/run_tf_squad.py b/examples/question-answering/run_tf_squad.py index 0cad705433..20723f70e8 100755 --- a/examples/question-answering/run_tf_squad.py +++ b/examples/question-answering/run_tf_squad.py @@ -181,7 +181,7 @@ def main(): # Get datasets if data_args.use_tfds: if data_args.version_2_with_negative: - logger.warn("tensorflow_datasets does not handle version 2 of SQuAD. Switch to version 1 automatically") + logger.warning("tensorflow_datasets does not handle version 2 of SQuAD. Switch to version 1 automatically") try: import tensorflow_datasets as tfds diff --git a/examples/research_projects/movement-pruning/masked_run_squad.py b/examples/research_projects/movement-pruning/masked_run_squad.py index 979649a6be..9fd219c089 100644 --- a/examples/research_projects/movement-pruning/masked_run_squad.py +++ b/examples/research_projects/movement-pruning/masked_run_squad.py @@ -629,7 +629,7 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.") if args.version_2_with_negative: - logger.warn("tensorflow_datasets does not handle version 2 of SQuAD.") + logger.warning("tensorflow_datasets does not handle version 2 of SQuAD.") tfds_examples = tfds.load("squad") examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate) diff --git a/examples/seq2seq/run_summarization.py b/examples/seq2seq/run_summarization.py index bc37c4385d..811c5a5242 100755 --- a/examples/seq2seq/run_summarization.py +++ b/examples/seq2seq/run_summarization.py @@ -394,7 +394,7 @@ def main(): padding = "max_length" if data_args.pad_to_max_length else False if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"): - logger.warn( + logger.warning( "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for" f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory" ) diff --git a/examples/seq2seq/run_translation.py b/examples/seq2seq/run_translation.py index a271a86379..dab84d5915 100755 --- a/examples/seq2seq/run_translation.py +++ b/examples/seq2seq/run_translation.py @@ -367,7 +367,7 @@ def main(): padding = "max_length" if data_args.pad_to_max_length else False if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"): - logger.warn( + logger.warning( "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for" f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory" ) diff --git a/examples/text-classification/run_glue.py b/examples/text-classification/run_glue.py index 9dfcedd785..94b52a4bd0 100755 --- a/examples/text-classification/run_glue.py +++ b/examples/text-classification/run_glue.py @@ -351,7 +351,7 @@ def main(): if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)): label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)} else: - logger.warn( + logger.warning( "Your model seems to have been trained with labels, but they don't match the dataset: ", f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}." "\nIgnoring the model labels as a result.", @@ -360,7 +360,7 @@ def main(): label_to_id = {v: i for i, v in enumerate(label_list)} if data_args.max_seq_length > tokenizer.model_max_length: - logger.warn( + logger.warning( f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) diff --git a/examples/text-classification/run_glue_no_trainer.py b/examples/text-classification/run_glue_no_trainer.py index f02fc0757c..646d6e93f6 100644 --- a/examples/text-classification/run_glue_no_trainer.py +++ b/examples/text-classification/run_glue_no_trainer.py @@ -274,7 +274,7 @@ def main(): ) label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)} else: - logger.warn( + logger.warning( "Your model seems to have been trained with labels, but they don't match the dataset: ", f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}." "\nIgnoring the model labels as a result.", diff --git a/src/transformers/configuration_utils.py b/src/transformers/configuration_utils.py index 9aa2440ce9..ad517ba154 100755 --- a/src/transformers/configuration_utils.py +++ b/src/transformers/configuration_utils.py @@ -262,7 +262,7 @@ class PretrainedConfig(object): # TPU arguments if kwargs.pop("xla_device", None) is not None: - logger.warn( + logger.warning( "The `xla_device` argument has been deprecated in v4.4.0 of Transformers. It is ignored and you can " "safely remove it from your `config.json` file." ) diff --git a/src/transformers/data/datasets/squad.py b/src/transformers/data/datasets/squad.py index 00f433e4a3..9665fb25c2 100644 --- a/src/transformers/data/datasets/squad.py +++ b/src/transformers/data/datasets/squad.py @@ -152,7 +152,7 @@ class SquadDataset(Dataset): ) if self.dataset is None or self.examples is None: - logger.warn( + logger.warning( f"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in future run" ) else: diff --git a/src/transformers/file_utils.py b/src/transformers/file_utils.py index bba9afc3a4..59db34521f 100644 --- a/src/transformers/file_utils.py +++ b/src/transformers/file_utils.py @@ -194,7 +194,7 @@ if ( and "PYTORCH_TRANSFORMERS_CACHE" not in os.environ and "TRANSFORMERS_CACHE" not in os.environ ): - logger.warn( + logger.warning( "In Transformers v4.0.0, the default path to cache downloaded models changed from " "'~/.cache/torch/transformers' to '~/.cache/huggingface/transformers'. Since you don't seem to have overridden " "and '~/.cache/torch/transformers' is a directory that exists, we're moving it to " diff --git a/src/transformers/integrations.py b/src/transformers/integrations.py index 57336f8fe7..d7e330421f 100644 --- a/src/transformers/integrations.py +++ b/src/transformers/integrations.py @@ -54,7 +54,7 @@ from .trainer_utils import PREFIX_CHECKPOINT_DIR, BestRun, IntervalStrategy # n def is_wandb_available(): # any value of WANDB_DISABLED disables wandb if os.getenv("WANDB_DISABLED", "").upper() in ENV_VARS_TRUE_VALUES: - logger.warn( + logger.warning( "Using the `WAND_DISABLED` environment variable is deprecated and will be removed in v5. Use the " "--report_to flag to control the integrations used for logging result (for instance --report_to none)." ) diff --git a/src/transformers/modeling_tf_utils.py b/src/transformers/modeling_tf_utils.py index 36e2b403b4..3eec82e0db 100644 --- a/src/transformers/modeling_tf_utils.py +++ b/src/transformers/modeling_tf_utils.py @@ -290,7 +290,7 @@ def booleans_processing(config, **kwargs): or kwargs["output_hidden_states"] is not None or ("use_cache" in kwargs and kwargs["use_cache"] is not None) ): - tf_logger.warn( + tf_logger.warning( "The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model." "They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`)." ) @@ -299,7 +299,9 @@ def booleans_processing(config, **kwargs): final_booleans["output_hidden_states"] = config.output_hidden_states if kwargs["return_dict"] is not None: - tf_logger.warn("The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.") + tf_logger.warning( + "The parameter `return_dict` cannot be set in graph mode and will always be set to `True`." + ) final_booleans["return_dict"] = True if "use_cache" in kwargs: @@ -398,7 +400,7 @@ def input_processing(func, config, input_ids, **kwargs): if isinstance(v, allowed_types) or v is None: output[k] = v elif k not in parameter_names and "args" not in parameter_names: - logger.warn( + logger.warning( f"The parameter {k} does not belongs to the parameter list {parameter_names} and will be ignored." ) continue diff --git a/src/transformers/models/auto/tokenization_auto.py b/src/transformers/models/auto/tokenization_auto.py index c4f28a43d0..212c32cb4a 100644 --- a/src/transformers/models/auto/tokenization_auto.py +++ b/src/transformers/models/auto/tokenization_auto.py @@ -409,7 +409,7 @@ class AutoTokenizer: # if model is an encoder decoder, the encoder tokenizer class is used by default if isinstance(config, EncoderDecoderConfig): if type(config.decoder) is not type(config.encoder): # noqa: E721 - logger.warn( + logger.warning( f"The encoder model config class: {config.encoder.__class__} is different from the decoder model " f"config class: {config.decoder.__class}. It is not recommended to use the " "`AutoTokenizer.from_pretrained()` method in this case. Please use the encoder and decoder " diff --git a/src/transformers/models/bart/modeling_bart.py b/src/transformers/models/bart/modeling_bart.py index 144b61324a..e5693604f8 100755 --- a/src/transformers/models/bart/modeling_bart.py +++ b/src/transformers/models/bart/modeling_bart.py @@ -1011,7 +1011,7 @@ class BartDecoder(BartPretrainedModel): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/bert/modeling_bert.py b/src/transformers/models/bert/modeling_bert.py index 370af8b47f..a1176f3a4a 100755 --- a/src/transformers/models/bert/modeling_bert.py +++ b/src/transformers/models/bert/modeling_bert.py @@ -544,7 +544,7 @@ class BertEncoder(nn.Module): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/bert_generation/modeling_bert_generation.py b/src/transformers/models/bert_generation/modeling_bert_generation.py index 57ec9345b5..6f366c7f42 100755 --- a/src/transformers/models/bert_generation/modeling_bert_generation.py +++ b/src/transformers/models/bert_generation/modeling_bert_generation.py @@ -450,7 +450,7 @@ class BertGenerationDecoder(BertGenerationPreTrainedModel): super().__init__(config) if not config.is_decoder: - logger.warn("If you want to use `BertGenerationDecoder` as a standalone, add `is_decoder=True.`") + logger.warning("If you want to use `BertGenerationDecoder` as a standalone, add `is_decoder=True.`") self.bert = BertGenerationEncoder(config) self.lm_head = BertGenerationOnlyLMHead(config) diff --git a/src/transformers/models/big_bird/modeling_big_bird.py b/src/transformers/models/big_bird/modeling_big_bird.py index f7fd54b946..5b5d96b4e9 100755 --- a/src/transformers/models/big_bird/modeling_big_bird.py +++ b/src/transformers/models/big_bird/modeling_big_bird.py @@ -1586,7 +1586,7 @@ class BigBirdEncoder(nn.Module): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/blenderbot/modeling_blenderbot.py b/src/transformers/models/blenderbot/modeling_blenderbot.py index abe83d0181..e8f6124e21 100755 --- a/src/transformers/models/blenderbot/modeling_blenderbot.py +++ b/src/transformers/models/blenderbot/modeling_blenderbot.py @@ -973,7 +973,7 @@ class BlenderbotDecoder(BlenderbotPreTrainedModel): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py b/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py index 372520bb7a..5bbedbc55f 100755 --- a/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py +++ b/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py @@ -974,7 +974,7 @@ class BlenderbotSmallDecoder(BlenderbotSmallPreTrainedModel): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/electra/modeling_electra.py b/src/transformers/models/electra/modeling_electra.py index 913d269ad5..8f77289fe5 100644 --- a/src/transformers/models/electra/modeling_electra.py +++ b/src/transformers/models/electra/modeling_electra.py @@ -541,7 +541,7 @@ class ElectraEncoder(nn.Module): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/gpt2/modeling_gpt2.py b/src/transformers/models/gpt2/modeling_gpt2.py index 2a8fb28162..881b17b2d8 100644 --- a/src/transformers/models/gpt2/modeling_gpt2.py +++ b/src/transformers/models/gpt2/modeling_gpt2.py @@ -726,7 +726,7 @@ class GPT2Model(GPT2PreTrainedModel): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/gpt_neo/modeling_gpt_neo.py b/src/transformers/models/gpt_neo/modeling_gpt_neo.py index ddda96da63..5808601d6b 100755 --- a/src/transformers/models/gpt_neo/modeling_gpt_neo.py +++ b/src/transformers/models/gpt_neo/modeling_gpt_neo.py @@ -823,7 +823,7 @@ class GPTNeoModel(GPTNeoPreTrainedModel): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/layoutlm/modeling_layoutlm.py b/src/transformers/models/layoutlm/modeling_layoutlm.py index 3211d6a0f2..bce2ddd275 100644 --- a/src/transformers/models/layoutlm/modeling_layoutlm.py +++ b/src/transformers/models/layoutlm/modeling_layoutlm.py @@ -470,7 +470,7 @@ class LayoutLMEncoder(nn.Module): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/led/modeling_led.py b/src/transformers/models/led/modeling_led.py index 38da6e3bdc..eecfcc27f6 100755 --- a/src/transformers/models/led/modeling_led.py +++ b/src/transformers/models/led/modeling_led.py @@ -2070,7 +2070,7 @@ class LEDDecoder(LEDPreTrainedModel): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/m2m_100/modeling_m2m_100.py b/src/transformers/models/m2m_100/modeling_m2m_100.py index 2ef53d8f2b..940ae65156 100755 --- a/src/transformers/models/m2m_100/modeling_m2m_100.py +++ b/src/transformers/models/m2m_100/modeling_m2m_100.py @@ -968,7 +968,7 @@ class M2M100Decoder(M2M100PreTrainedModel): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/marian/modeling_marian.py b/src/transformers/models/marian/modeling_marian.py index 0548373a05..7da158680f 100755 --- a/src/transformers/models/marian/modeling_marian.py +++ b/src/transformers/models/marian/modeling_marian.py @@ -981,7 +981,7 @@ class MarianDecoder(MarianPreTrainedModel): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/mbart/modeling_mbart.py b/src/transformers/models/mbart/modeling_mbart.py index 61763cc38c..40be2149e6 100755 --- a/src/transformers/models/mbart/modeling_mbart.py +++ b/src/transformers/models/mbart/modeling_mbart.py @@ -1020,7 +1020,7 @@ class MBartDecoder(MBartPreTrainedModel): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/pegasus/modeling_pegasus.py b/src/transformers/models/pegasus/modeling_pegasus.py index 5cbbd31080..c46582f70b 100755 --- a/src/transformers/models/pegasus/modeling_pegasus.py +++ b/src/transformers/models/pegasus/modeling_pegasus.py @@ -987,7 +987,7 @@ class PegasusDecoder(PegasusPreTrainedModel): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/prophetnet/modeling_prophetnet.py b/src/transformers/models/prophetnet/modeling_prophetnet.py index 03aac1bd89..3b369c3373 100644 --- a/src/transformers/models/prophetnet/modeling_prophetnet.py +++ b/src/transformers/models/prophetnet/modeling_prophetnet.py @@ -1475,7 +1475,7 @@ class ProphetNetDecoder(ProphetNetPreTrainedModel): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/roberta/modeling_roberta.py b/src/transformers/models/roberta/modeling_roberta.py index 88155f76de..f7a73b336c 100644 --- a/src/transformers/models/roberta/modeling_roberta.py +++ b/src/transformers/models/roberta/modeling_roberta.py @@ -484,7 +484,7 @@ class RobertaEncoder(nn.Module): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) diff --git a/src/transformers/models/speech_to_text/modeling_speech_to_text.py b/src/transformers/models/speech_to_text/modeling_speech_to_text.py index 1c3c6f0011..6afb3f6791 100755 --- a/src/transformers/models/speech_to_text/modeling_speech_to_text.py +++ b/src/transformers/models/speech_to_text/modeling_speech_to_text.py @@ -1015,7 +1015,7 @@ class Speech2TextDecoder(Speech2TextPreTrainedModel): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache = True` is incompatible with `config.gradient_checkpointing = True`. Setting `use_cache = False`..." ) use_cache = False diff --git a/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py b/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py index d386d8b7bf..02be2b8ec7 100644 --- a/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py +++ b/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py @@ -111,7 +111,7 @@ def recursively_load_weights(fairseq_model, hf_model, is_finetuned): if not is_used: unused_weights.append(name) - logger.warn(f"Unused weights: {unused_weights}") + logger.warning(f"Unused weights: {unused_weights}") def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm): diff --git a/src/transformers/models/xlm/modeling_tf_xlm.py b/src/transformers/models/xlm/modeling_tf_xlm.py index f2989ffa56..6bac6f597c 100644 --- a/src/transformers/models/xlm/modeling_tf_xlm.py +++ b/src/transformers/models/xlm/modeling_tf_xlm.py @@ -1140,7 +1140,7 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss): ) if inputs["lengths"] is not None: - logger.warn( + logger.warning( "The `lengths` parameter cannot be used with the XLM multiple choice models. Please use the " "attention mask instead.", ) diff --git a/src/transformers/models/xlm/modeling_xlm.py b/src/transformers/models/xlm/modeling_xlm.py index 3ccd63ee97..a4a6c0dd08 100755 --- a/src/transformers/models/xlm/modeling_xlm.py +++ b/src/transformers/models/xlm/modeling_xlm.py @@ -1232,7 +1232,7 @@ class XLMForMultipleChoice(XLMPreTrainedModel): ) if lengths is not None: - logger.warn( + logger.warning( "The `lengths` parameter cannot be used with the XLM multiple choice models. Please use the " "attention mask instead." ) diff --git a/src/transformers/pipelines/zero_shot_classification.py b/src/transformers/pipelines/zero_shot_classification.py index 24e99072b6..dd66fb9587 100644 --- a/src/transformers/pipelines/zero_shot_classification.py +++ b/src/transformers/pipelines/zero_shot_classification.py @@ -142,7 +142,7 @@ class ZeroShotClassificationPipeline(Pipeline): """ if "multi_class" in kwargs and kwargs["multi_class"] is not None: multi_label = kwargs.pop("multi_class") - logger.warn( + logger.warning( "The `multi_class` argument has been deprecated and renamed to `multi_label`. " "`multi_class` will be removed in a future version of Transformers." ) diff --git a/src/transformers/trainer_callback.py b/src/transformers/trainer_callback.py index 9409f8aaf6..151dbf52a0 100644 --- a/src/transformers/trainer_callback.py +++ b/src/transformers/trainer_callback.py @@ -289,7 +289,7 @@ class CallbackHandler(TrainerCallback): self.eval_dataloader = None if not any(isinstance(cb, DefaultFlowCallback) for cb in self.callbacks): - logger.warn( + logger.warning( "The Trainer will not work properly if you don't have a `DefaultFlowCallback` in its callbacks. You\n" + "should add one before training with `trainer.add_callback(DefaultFlowCallback). The current list of" + "callbacks is\n:" @@ -300,7 +300,7 @@ class CallbackHandler(TrainerCallback): cb = callback() if isinstance(callback, type) else callback cb_class = callback if isinstance(callback, type) else callback.__class__ if cb_class in [c.__class__ for c in self.callbacks]: - logger.warn( + logger.warning( f"You are adding a {cb_class} to the callbacks of this Trainer, but there is already one. The current" + "list of callbacks is\n:" + self.callback_list diff --git a/src/transformers/trainer_pt_utils.py b/src/transformers/trainer_pt_utils.py index eedbb616fe..0d3fe6407c 100644 --- a/src/transformers/trainer_pt_utils.py +++ b/src/transformers/trainer_pt_utils.py @@ -391,7 +391,7 @@ class DistributedTensorGatherer: if self._storage is None: return if self._offsets[0] != self.process_length: - logger.warn("Not all data has been set. Are you sure you passed all values?") + logger.warning("Not all data has been set. Are you sure you passed all values?") return nested_truncate(self._storage, self.num_samples) @@ -589,7 +589,7 @@ def _get_learning_rate(self): last_lr = self.lr_scheduler.get_last_lr()[0] except AssertionError as e: if "need to call step" in str(e): - logger.warn("tried to get lr value before scheduler/optimizer started stepping, returning lr=0") + logger.warning("tried to get lr value before scheduler/optimizer started stepping, returning lr=0") last_lr = 0 else: raise diff --git a/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py b/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py index e8e0d56a4d..005328b06d 100755 --- a/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py +++ b/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py @@ -531,7 +531,7 @@ class {{cookiecutter.camelcase_modelname}}Encoder(nn.Module): if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn( + logger.warning( "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " "`use_cache=False`..." ) @@ -2512,7 +2512,7 @@ class {{cookiecutter.camelcase_modelname}}Decoder({{cookiecutter.camelcase_model if getattr(self.config, "gradient_checkpointing", False) and self.training: if use_cache: - logger.warn("`use_cache = True` is incompatible with `config.gradient_checkpointing = True`. Setting `use_cache = False`...") + logger.warning("`use_cache = True` is incompatible with `config.gradient_checkpointing = True`. Setting `use_cache = False`...") use_cache = False def create_custom_forward(module): diff --git a/tests/sagemaker/scripts/pytorch/run_glue_model_parallelism.py b/tests/sagemaker/scripts/pytorch/run_glue_model_parallelism.py index 1bc9ed4ce8..1476a687a9 100644 --- a/tests/sagemaker/scripts/pytorch/run_glue_model_parallelism.py +++ b/tests/sagemaker/scripts/pytorch/run_glue_model_parallelism.py @@ -353,7 +353,7 @@ def main(): if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)): label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)} else: - logger.warn( + logger.warning( "Your model seems to have been trained with labels, but they don't match the dataset: ", f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}." "\nIgnoring the model labels as a result.", @@ -362,7 +362,7 @@ def main(): label_to_id = {v: i for i, v in enumerate(label_list)} if data_args.max_seq_length > tokenizer.model_max_length: - logger.warn( + logger.warning( f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) diff --git a/tests/test_logging.py b/tests/test_logging.py index f85fe260ca..d0633bfbe4 100644 --- a/tests/test_logging.py +++ b/tests/test_logging.py @@ -51,7 +51,7 @@ class HfArgumentParserTest(unittest.TestCase): # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`) if level_origin <= logging.WARNING: with CaptureLogger(logger) as cl: - logger.warn(msg) + logger.warning(msg) self.assertEqual(cl.out, msg + "\n") # this is setting the level for all of `transformers.*` loggers @@ -59,7 +59,7 @@ class HfArgumentParserTest(unittest.TestCase): # should not be able to log warnings with CaptureLogger(logger) as cl: - logger.warn(msg) + logger.warning(msg) self.assertEqual(cl.out, "") # should be able to log warnings again diff --git a/tests/test_trainer_callback.py b/tests/test_trainer_callback.py index 7f97766d31..6ce90b8554 100644 --- a/tests/test_trainer_callback.py +++ b/tests/test_trainer_callback.py @@ -234,7 +234,7 @@ class TrainerCallbackTest(unittest.TestCase): self.assertEqual(events, self.get_expected_events(trainer)) # warning should be emitted for duplicated callbacks - with unittest.mock.patch("transformers.trainer_callback.logger.warn") as warn_mock: + with unittest.mock.patch("transformers.trainer_callback.logger.warning") as warn_mock: trainer = self.get_trainer( callbacks=[MyTestTrainerCallback, MyTestTrainerCallback], )