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Remove deprecated (#8604)

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* Remove old deprecated arguments

Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>

* Remove needless imports

* Fix tests

Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>
This commit is contained in:
Sylvain Gugger
2020-11-17 15:11:29 -05:00
committed by GitHub
parent 3095ee9dab
commit dd52804f5f
37 changed files with 22 additions and 610 deletions
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4
examples/seq2seq/test_finetune_trainer.py
View File

@@ -138,7 +138,7 @@ class TestFinetuneTrainer(TestCasePlus):
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
predict_with_generate=True,
evaluate_during_training=True,
evaluation_strategy="steps",
do_train=True,
do_eval=True,
warmup_steps=0,
@@ -179,7 +179,7 @@ class TestFinetuneTrainer(TestCasePlus):
--per_device_eval_batch_size 4
--learning_rate 3e-3
--warmup_steps 8
--evaluate_during_training
--evaluation_strategy steps
--predict_with_generate
--logging_steps 0
--save_steps {str(eval_steps)}

4
examples/token-classification/run_ner_old.py
View File

@@ -254,7 +254,7 @@ def main():
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir)
# Evaluation
@@ -265,7 +265,7 @@ def main():
result = trainer.evaluate()
output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
if trainer.is_world_master():
if trainer.is_world_process_zero():
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key, value in result.items():

6
src/transformers/data/processors/squad.py
View File

@@ -145,11 +145,11 @@ def squad_convert_example_to_features(
# in the way they compute mask of added tokens.
tokenizer_type = type(tokenizer).__name__.replace("Tokenizer", "").lower()
sequence_added_tokens = (
tokenizer.max_len - tokenizer.max_len_single_sentence + 1
tokenizer.model_max_length - tokenizer.max_len_single_sentence + 1
if tokenizer_type in MULTI_SEP_TOKENS_TOKENIZERS_SET
else tokenizer.max_len - tokenizer.max_len_single_sentence
else tokenizer.model_max_length - tokenizer.max_len_single_sentence
)
sequence_pair_added_tokens = tokenizer.max_len - tokenizer.max_len_sentences_pair
sequence_pair_added_tokens = tokenizer.model_max_length - tokenizer.max_len_sentences_pair
span_doc_tokens = all_doc_tokens
while len(spans) * doc_stride < len(all_doc_tokens):

22
src/transformers/models/albert/modeling_albert.py
View File

@@ -16,7 +16,6 @@
import math
import os
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple
@@ -742,7 +741,6 @@ class AlbertForPreTraining(AlbertPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
r"""
labels (``torch.LongTensor`` of shape ``(batch_size, sequence_length)``, `optional`):
@@ -753,8 +751,6 @@ class AlbertForPreTraining(AlbertPreTrainedModel):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see :obj:`input_ids` docstring) Indices should be in ``[0, 1]``. ``0`` indicates original order (sequence
A, then sequence B), ``1`` indicates switched order (sequence B, then sequence A).
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
Returns:
@@ -773,14 +769,6 @@ class AlbertForPreTraining(AlbertPreTrainedModel):
>>> sop_logits = outputs.sop_logits
"""
if "masked_lm_labels" in kwargs:
warnings.warn(
"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("masked_lm_labels")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.albert(
@@ -898,23 +886,13 @@ class AlbertForMaskedLM(AlbertPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
"""
if "masked_lm_labels" in kwargs:
warnings.warn(
"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("masked_lm_labels")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.albert(

40
src/transformers/models/bart/modeling_bart.py
View File

@@ -15,7 +15,6 @@
"""PyTorch BART model, ported from the fairseq repo."""
import math
import random
import warnings
from typing import Dict, List, Optional, Tuple
import numpy as np
@@ -529,7 +528,6 @@ class BartDecoder(nn.Module):
output_attentions=False,
output_hidden_states=False,
return_dict=True,
**unused,
):
"""
Includes several features from "Jointly Learning to Align and Translate with Transformer Models" (Garg et al.,
@@ -551,18 +549,6 @@ class BartDecoder(nn.Module):
- hidden states
- attentions
"""
if "decoder_cached_states" in unused:
warnings.warn(
"The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = unused.pop("decoder_cached_states")
if "decoder_past_key_values" in unused:
warnings.warn(
"The `decoder_past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = unused.pop("decoder_past_key_values")
# check attention mask and invert
if encoder_padding_mask is not None:
@@ -873,14 +859,7 @@ class BartModel(PretrainedBartModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
if "decoder_past_key_values" in kwargs:
warnings.warn(
"The `decoder_past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("decoder_past_key_values")
if decoder_input_ids is None:
use_cache = False
@@ -1006,7 +985,6 @@ class BartForConditionalGeneration(PretrainedBartModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**unused,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
@@ -1034,24 +1012,6 @@ class BartForConditionalGeneration(PretrainedBartModel):
>>> tokenizer.decode(predictions).split()
>>> # ['good', 'great', 'all', 'really', 'very']
"""
if "lm_labels" in unused:
warnings.warn(
"The `lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
FutureWarning,
)
labels = unused.pop("lm_labels")
if "decoder_cached_states" in unused:
warnings.warn(
"The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = unused.pop("decoder_cached_states")
if "decoder_past_key_values" in unused:
warnings.warn(
"The `decoder_past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = unused.pop("decoder_past_key_values")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:

19
src/transformers/models/bert/modeling_bert.py
View File

@@ -896,7 +896,6 @@ class BertForPreTraining(BertPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs
):
r"""
labels (:obj:`torch.LongTensor` of shape ``(batch_size, sequence_length)``, `optional`):
@@ -928,13 +927,6 @@ class BertForPreTraining(BertPreTrainedModel):
>>> prediction_logits = outputs.prediction_logits
>>> seq_relationship_logits = outputs.seq_relationship_logits
"""
if "masked_lm_labels" in kwargs:
warnings.warn(
"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("masked_lm_labels")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
@@ -1136,24 +1128,13 @@ class BertForMaskedLM(BertPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
"""
if "masked_lm_labels" in kwargs:
warnings.warn(
"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("masked_lm_labels")
assert "lm_labels" not in kwargs, "Use `BertWithLMHead` for autoregressive language modeling task."
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
return_dict = return_dict if return_dict is not None else self.config.use_return_dict

19
src/transformers/models/ctrl/modeling_ctrl.py
View File

@@ -15,9 +15,6 @@
# limitations under the License.
""" PyTorch CTRL model."""
import warnings
import numpy as np
import torch
import torch.nn as nn
@@ -369,15 +366,7 @@ class CTRLModel(CTRLPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
if "past" in kwargs:
warnings.warn(
"The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("past")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
use_cache = use_cache if use_cache is not None else self.config.use_cache
@@ -542,7 +531,6 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
@@ -550,13 +538,6 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to
``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]``
"""
if "past" in kwargs:
warnings.warn(
"The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("past")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(

11
src/transformers/models/distilbert/modeling_distilbert.py
View File

@@ -20,7 +20,6 @@
import copy
import math
import warnings
import numpy as np
import torch
@@ -526,23 +525,13 @@ class DistilBertForMaskedLM(DistilBertPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``.
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
"""
if "masked_lm_labels" in kwargs:
warnings.warn(
"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("masked_lm_labels")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
dlbrt_output = self.distilbert(

11
src/transformers/models/electra/modeling_electra.py
View File

@@ -16,7 +16,6 @@
import math
import os
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple
@@ -1000,23 +999,13 @@ class ElectraForMaskedLM(ElectraPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
"""
if "masked_lm_labels" in kwargs:
warnings.warn(
"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("masked_lm_labels")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
generator_hidden_states = self.electra(

24
src/transformers/models/fsmt/modeling_fsmt.py
View File

@@ -29,7 +29,6 @@
import math
import random
import warnings
from typing import Any, Dict, List, Optional, Tuple
import torch
@@ -618,7 +617,6 @@ class FSMTDecoder(nn.Module):
output_attentions=False,
output_hidden_states=False,
return_dict=True,
**unused,
):
"""
Includes several features from "Jointly Learning to Align and Translate with Transformer Models" (Garg et al.,
@@ -640,19 +638,6 @@ class FSMTDecoder(nn.Module):
- hidden states
- attentions
"""
if "decoder_cached_states" in unused:
warnings.warn(
"The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = unused.pop("decoder_cached_states")
if "decoder_past_key_values" in unused:
warnings.warn(
"The `decoder_past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = unused.pop("decoder_past_key_values")
# check attention mask and invert
if encoder_padding_mask is not None:
encoder_padding_mask = invert_mask(encoder_padding_mask)
@@ -933,15 +918,7 @@ class FSMTModel(PretrainedFSMTModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
if "decoder_past_key_values" in kwargs:
warnings.warn(
"The `decoder_past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("decoder_past_key_values")
if decoder_input_ids is None:
use_cache = False
@@ -1071,7 +1048,6 @@ class FSMTForConditionalGeneration(PretrainedFSMTModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**unused,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):

33
src/transformers/models/gpt2/modeling_gpt2.py
View File

@@ -16,7 +16,6 @@
"""PyTorch OpenAI GPT-2 model."""
import os
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple
@@ -528,16 +527,7 @@ class GPT2Model(GPT2PreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
if "past" in kwargs:
warnings.warn(
"The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("past")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -758,7 +748,6 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
@@ -766,13 +755,6 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to
``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]``
"""
if "past" in kwargs:
warnings.warn(
"The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("past")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
@@ -900,8 +882,6 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
num_choices]`` where `num_choices` is the size of the second dimension of the input tensors. (see
`input_ids` above)
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
Return:
@@ -930,19 +910,6 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
>>> mc_logits = outputs.mc_logits
"""
if "lm_labels" in kwargs:
warnings.warn(
"The `lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("lm_labels")
if "past" in kwargs:
warnings.warn(
"The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("past")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(

8
src/transformers/models/gpt2/tokenization_gpt2.py
View File

@@ -17,7 +17,6 @@
import json
import os
import warnings
from functools import lru_cache
from typing import Optional, Tuple
@@ -293,13 +292,6 @@ class GPT2Tokenizer(PreTrainedTokenizer):
return vocab_file, merge_file
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
if "is_pretokenized" in kwargs:
warnings.warn(
"`is_pretokenized` is deprecated and will be removed in a future version, use `is_split_into_words` instead.",
FutureWarning,
)
is_split_into_words = kwargs.pop("is_pretokenized")
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
if is_split_into_words or add_prefix_space:
text = " " + text

17
src/transformers/models/gpt2/tokenization_gpt2_fast.py
View File

@@ -16,7 +16,6 @@
import json
import warnings
from typing import Optional, Tuple
from tokenizers import pre_tokenizers
@@ -151,13 +150,6 @@ class GPT2TokenizerFast(PreTrainedTokenizerFast):
self.add_prefix_space = add_prefix_space
def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
if "is_pretokenized" in kwargs:
warnings.warn(
"`is_pretokenized` is deprecated and will be removed in a future version, use `is_split_into_words` instead.",
FutureWarning,
)
is_split_into_words = kwargs.pop("is_pretokenized")
is_split_into_words = kwargs.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
@@ -167,14 +159,7 @@ class GPT2TokenizerFast(PreTrainedTokenizerFast):
return super()._batch_encode_plus(*args, **kwargs)
def _encode_plus(self, *args, **kwargs) -> BatchEncoding:
if "is_pretokenized" in kwargs:
warnings.warn(
"`is_pretokenized` is deprecated and will be removed in a future version, use `is_split_into_words` instead.",
FutureWarning,
)
is_split_into_words = kwargs.pop("is_pretokenized")
else:
is_split_into_words = kwargs.get("is_split_into_words", False)
is_split_into_words = kwargs.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "

10
src/transformers/models/longformer/modeling_longformer.py
View File

@@ -15,7 +15,6 @@
"""PyTorch Longformer model. """
import math
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple
@@ -1509,7 +1508,6 @@ class LongformerForMaskedLM(LongformerPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
@@ -1538,14 +1536,6 @@ class LongformerForMaskedLM(LongformerPreTrainedModel):
>>> loss = outputs.loss
>>> prediction_logits = output.logits
"""
if "masked_lm_labels" in kwargs:
warnings.warn(
"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("masked_lm_labels")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.longformer(

7
src/transformers/models/mobilebert/modeling_mobilebert.py
View File

@@ -1109,7 +1109,6 @@ class MobileBertForMaskedLM(MobileBertPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
@@ -1119,12 +1118,6 @@ class MobileBertForMaskedLM(MobileBertPreTrainedModel):
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
"""
if "masked_lm_labels" in kwargs:
warnings.warn(
"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("masked_lm_labels")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mobilebert(

11
src/transformers/models/openai/modeling_openai.py
View File

@@ -19,7 +19,6 @@
import json
import math
import os
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple
@@ -645,7 +644,6 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs
):
r"""
mc_token_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input):
@@ -659,8 +657,6 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
num_choices]`` where `num_choices` is the size of the second dimension of the input tensors. (see
`input_ids` above)
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
Return:
@@ -683,13 +679,6 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
>>> mc_logits = outputs.mc_logits
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if "lm_labels" in kwargs:
warnings.warn(
"The `lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("lm_labels")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
transformer_outputs = self.transformer(
input_ids,

2
src/transformers/models/prophetnet/tokenization_prophetnet.py
View File

@@ -302,7 +302,7 @@ class ProphetNetTokenizer(PreTrainedTokenizer):
**kwargs,
) -> BatchEncoding:
if max_length is None:
max_length = self.max_len
max_length = self.model_max_length
model_inputs = self(
src_texts,
add_special_tokens=True,

9
src/transformers/models/roberta/modeling_roberta.py
View File

@@ -16,7 +16,6 @@
"""PyTorch RoBERTa model. """
import math
import warnings
import torch
import torch.nn as nn
@@ -872,7 +871,6 @@ class RobertaForMaskedLM(RobertaPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
@@ -882,13 +880,6 @@ class RobertaForMaskedLM(RobertaPreTrainedModel):
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
"""
if "masked_lm_labels" in kwargs:
warnings.warn(
"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("masked_lm_labels")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roberta(

8
src/transformers/models/roberta/tokenization_roberta.py
View File

@@ -14,7 +14,6 @@
# limitations under the License.
"""Tokenization classes for RoBERTa."""
import warnings
from typing import List, Optional
from ...tokenization_utils import AddedToken
@@ -251,13 +250,6 @@ class RobertaTokenizer(GPT2Tokenizer):
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
if "is_pretokenized" in kwargs:
warnings.warn(
"`is_pretokenized` is deprecated and will be removed in a future version, use `is_split_into_words` instead.",
FutureWarning,
)
is_split_into_words = kwargs.pop("is_pretokenized")
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
text = " " + text

40
src/transformers/models/t5/modeling_t5.py
View File

@@ -18,7 +18,6 @@
import copy
import math
import os
import warnings
import torch
import torch.nn.functional as F
@@ -1048,7 +1047,6 @@ class T5Model(T5PreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
r"""
Returns:
@@ -1066,20 +1064,6 @@ class T5Model(T5PreTrainedModel):
>>> last_hidden_states = outputs.last_hidden_state
"""
if "decoder_past_key_value_states" in kwargs:
warnings.warn(
"The `decoder_past_key_value_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("decoder_past_key_value_states")
if "decoder_past_key_values" in kwargs:
warnings.warn(
"The `decoder_past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("decoder_past_key_values")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
@@ -1198,15 +1182,12 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[-100, 0, ...,
config.vocab_size - 1]`. All labels set to ``-100`` are ignored (masked), the loss is only computed for
labels in ``[0, ..., config.vocab_size]``
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
Returns:
@@ -1226,27 +1207,6 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
>>> input_ids = tokenizer("summarize: studies have shown that owning a dog is good for you ", return_tensors="pt").input_ids # Batch size 1
>>> outputs = model.generate(input_ids)
"""
if "lm_labels" in kwargs:
warnings.warn(
"The `lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("lm_labels")
if "decoder_past_key_value_states" in kwargs:
warnings.warn(
"The `decoder_past_key_value_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("decoder_past_key_value_states")
if "decoder_past_key_values" in kwargs:
warnings.warn(
"The `decoder_past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("decoder_past_key_values")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict

42
src/transformers/models/t5/modeling_tf_t5.py
View File

@@ -595,7 +595,6 @@ class TFT5MainLayer(tf.keras.layers.Layer):
output_attentions=None,
output_hidden_states=None,
training=False,
**kwargs,
) -> Tuple:
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
@@ -621,21 +620,8 @@ class TFT5MainLayer(tf.keras.layers.Layer):
output_attentions = inputs.get("output_attentions", output_attentions)
output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
assert len(inputs) <= 10, "Too many inputs."
if "past_key_values" in inputs:
warnings.warn(
"The `past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = inputs.pop("past_key_values")
else:
input_ids = inputs
if "past_key_values" in kwargs:
warnings.warn(
"The `past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("past_key_values")
output_attentions = output_attentions if output_attentions is not None else self.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
@@ -1078,23 +1064,9 @@ class TFT5Model(TFT5PreTrainedModel):
output_attentions = inputs.get("output_attentions", output_attentions)
output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
assert len(inputs) <= 13, "Too many inputs."
if "past_key_value_states" in inputs:
warnings.warn(
"The `past_key_value_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = inputs.pop("past_key_value_states")
else:
input_ids = inputs
if "past_key_value_states" in kwargs:
warnings.warn(
"The `past_key_value_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("past_key_value_states")
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_attentions = output_attentions if output_attentions else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states else self.config.output_hidden_states
@@ -1294,23 +1266,9 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModeling
output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
return_dict = inputs.get("return_dict", return_dict)
assert len(inputs) <= 14, "Too many inputs."
if "past_key_value_states" in inputs:
warnings.warn(
"The `past_key_value_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = inputs.pop("past_key_value_states")
else:
input_ids = inputs
if "past_key_value_states" in kwargs:
warnings.warn(
"The `past_key_value_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("past_key_value_states")
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_attentions = output_attentions if output_attentions else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states else self.config.output_hidden_states

10
src/transformers/models/transfo_xl/configuration_transfo_xl.py
View File

@@ -15,9 +15,6 @@
# limitations under the License.
""" Transformer XL configuration """
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
@@ -139,13 +136,6 @@ class TransfoXLConfig(PretrainedConfig):
eos_token_id=0,
**kwargs
):
if "tie_weight" in kwargs:
warnings.warn(
"The config parameter `tie_weight` is deprecated. Please use `tie_word_embeddings` instead.",
FutureWarning,
)
kwargs["tie_word_embeddings"] = kwargs["tie_weight"]
super().__init__(eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.cutoffs = []

8
src/transformers/models/transfo_xl/modeling_tf_transfo_xl.py
View File

@@ -16,7 +16,6 @@
"""
TF 2.0 Transformer XL model.
"""
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple
@@ -865,13 +864,6 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
return self.crit.out_layers[-1]
return None
def reset_length(self, tgt_len, ext_len, mem_len):
warnings.warn(
"The method `reset_length` is deprecated and will be removed in a future version, use `reset_memory_length` instead.",
FutureWarning,
)
self.transformer.reset_memory_length(mem_len)
def reset_memory_length(self, mem_len):
self.transformer.reset_memory_length(mem_len)

8
src/transformers/models/transfo_xl/modeling_transfo_xl.py
View File

@@ -17,7 +17,6 @@
PyTorch Transformer XL model. Adapted from https://github.com/kimiyoung/transformer-xl. In particular
https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/mem_transformer.py
"""
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple
@@ -1010,13 +1009,6 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
else:
self.crit.out_projs[i] = self.transformer.word_emb.emb_projs[i]
def reset_length(self, tgt_len, ext_len, mem_len):
warnings.warn(
"The method `reset_length` is deprecated and will be removed in a future version, use `reset_memory_length` instead.",
FutureWarning,
)
self.transformer.reset_memory_length(mem_len)
def reset_memory_length(self, mem_len):
self.transformer.reset_memory_length(mem_len)

5
src/transformers/models/xlm/modeling_tf_xlm.py
View File

@@ -16,9 +16,7 @@
TF 2.0 XLM model.
"""
import itertools
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple
@@ -997,10 +995,9 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss):
)
if lengths is not None:
warnings.warn(
logger.warn(
"The `lengths` parameter cannot be used with the XLM multiple choice models. Please use the "
"attention mask instead.",
FutureWarning,
)
lengths = None

7
src/transformers/models/xlm/modeling_xlm.py
View File

@@ -16,10 +16,8 @@
PyTorch XLM model.
"""
import itertools
import math
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple
@@ -1228,10 +1226,9 @@ class XLMForMultipleChoice(XLMPreTrainedModel):
)
if lengths is not None:
warnings.warn(
logger.warn(
"The `lengths` parameter cannot be used with the XLM multiple choice models. Please use the "
"attention mask instead.",
FutureWarning,
"attention mask instead."
)
lengths = None

11
src/transformers/pipelines.py
View File

@@ -1182,7 +1182,6 @@ class FillMaskPipeline(Pipeline):
device: int = -1,
top_k=5,
task: str = "",
**kwargs
):
super().__init__(
model=model,
@@ -1196,15 +1195,7 @@ class FillMaskPipeline(Pipeline):
)
self.check_model_type(TF_MODEL_WITH_LM_HEAD_MAPPING if self.framework == "tf" else MODEL_FOR_MASKED_LM_MAPPING)
if "topk" in kwargs:
warnings.warn(
"The `topk` argument is deprecated and will be removed in a future version, use `top_k` instead.",
FutureWarning,
)
self.top_k = kwargs.pop("topk")
else:
self.top_k = top_k
self.top_k = top_k
def ensure_exactly_one_mask_token(self, masked_index: np.ndarray):
numel = np.prod(masked_index.shape)

21
src/transformers/tokenization_utils.py
View File

@@ -19,7 +19,6 @@
import itertools
import re
import unicodedata
import warnings
from typing import Any, Dict, List, Optional, Tuple, Union, overload
from .file_utils import add_end_docstrings
@@ -246,12 +245,6 @@ class PreTrainedTokenizer(PreTrainedTokenizerBase):
Returns:
:obj:`List[str]`: The list of tokens.
"""
if "is_pretokenized" in kwargs:
warnings.warn(
"`is_pretokenized` is deprecated and will be removed in a future version, use `is_split_into_words` instead.",
FutureWarning,
)
kwargs["is_split_into_words"] = kwargs.pop("is_pretokenized")
# Simple mapping string => AddedToken for special tokens with specific tokenization behaviors
all_special_tokens_extended = dict(
(str(t), t) for t in self.all_special_tokens_extended if isinstance(t, AddedToken)
@@ -448,13 +441,6 @@ class PreTrainedTokenizer(PreTrainedTokenizerBase):
"https://github.com/huggingface/transformers/pull/2674"
)
if "is_pretokenized" in kwargs:
warnings.warn(
"`is_pretokenized` is deprecated and will be removed in a future version, use `is_split_into_words` instead.",
FutureWarning,
)
is_split_into_words = kwargs.pop("is_pretokenized")
first_ids = get_input_ids(text)
second_ids = get_input_ids(text_pair) if text_pair is not None else None
@@ -530,13 +516,6 @@ class PreTrainedTokenizer(PreTrainedTokenizerBase):
"transformers.PreTrainedTokenizerFast."
)
if "is_pretokenized" in kwargs:
warnings.warn(
"`is_pretokenized` is deprecated and will be removed in a future version, use `is_split_into_words` instead.",
FutureWarning,
)
is_split_into_words = kwargs.pop("is_pretokenized")
input_ids = []
for ids_or_pair_ids in batch_text_or_text_pairs:
if not isinstance(ids_or_pair_ids, (list, tuple)):

21
src/transformers/tokenization_utils_base.py
View File

@@ -1532,18 +1532,6 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
super().__init__(**kwargs)
@property
def max_len(self) -> int:
"""
:obj:`int`: **Deprecated** Kept here for backward compatibility. Now renamed to :obj:`model_max_length` to
avoid ambiguity.
"""
warnings.warn(
"The `max_len` attribute has been deprecated and will be removed in a future version, use `model_max_length` instead.",
FutureWarning,
)
return self.model_max_length
@property
def max_len_single_sentence(self) -> int:
"""
@@ -2785,15 +2773,6 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
and ``convert_tokens_to_ids`` methods.
"""
if "return_lengths" in kwargs:
if verbose:
warnings.warn(
"The PreTrainedTokenizerBase.prepare_for_model `return_lengths` parameter is deprecated. "
"Please use `return_length` instead.",
FutureWarning,
)
return_length = kwargs["return_lengths"]
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,

18
src/transformers/tokenization_utils_fast.py
View File

@@ -19,7 +19,6 @@
import json
import os
import warnings
from collections import defaultdict
from typing import Any, Dict, List, Optional, Tuple, Union
@@ -357,7 +356,6 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
if not isinstance(batch_text_or_text_pairs, list):
@@ -365,16 +363,6 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
"batch_text_or_text_pairs has to be a list (got {})".format(type(batch_text_or_text_pairs))
)
if "is_pretokenized" in kwargs:
warnings.warn(
"`is_pretokenized` is deprecated and will be removed in a future version, use `is_split_into_words` instead.",
FutureWarning,
)
is_split_into_words = kwargs.pop("is_pretokenized")
if kwargs:
raise ValueError(f"Keyword arguments {kwargs} not recognized.")
# Set the truncation and padding strategy and restore the initial configuration
self.set_truncation_and_padding(
padding_strategy=padding_strategy,
@@ -453,12 +441,6 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
verbose: bool = True,
**kwargs
) -> BatchEncoding:
if "is_pretokenized" in kwargs:
warnings.warn(
"`is_pretokenized` is deprecated and will be removed in a future version, use `is_split_into_words` instead.",
FutureWarning,
)
is_split_into_words = kwargs.pop("is_pretokenized")
batched_input = [(text, text_pair)] if text_pair else [text]
batched_output = self._batch_encode_plus(

76
src/transformers/trainer.py
View File

@@ -213,8 +213,6 @@ class Trainer:
containing the optimizer and the scheduler to use. Will default to an instance of
:class:`~transformers.AdamW` on your model and a scheduler given by
:func:`~transformers.get_linear_schedule_with_warmup` controlled by :obj:`args`.
kwargs:
Deprecated keyword arguments.
"""
def __init__(
@@ -229,7 +227,6 @@ class Trainer:
compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
callbacks: Optional[List[TrainerCallback]] = None,
optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
**kwargs,
):
if args is None:
logger.info("No `TrainingArguments` passed, using the current path as `output_dir`.")
@@ -262,27 +259,6 @@ class Trainer:
self.callback_handler = CallbackHandler(callbacks, self.model, self.optimizer, self.lr_scheduler)
self.add_callback(PrinterCallback if self.args.disable_tqdm else DEFAULT_PROGRESS_CALLBACK)
# Deprecated arguments
if "tb_writer" in kwargs:
warnings.warn(
"Passing `tb_writer` as a keyword argument is deprecated and won't be possible in a "
+ "future version. Use `TensorBoardCallback(tb_writer=...)` instead and pass it to the `callbacks`"
+ "argument",
FutureWarning,
)
tb_writer = kwargs.pop("tb_writer")
self.remove_callback(TensorBoardCallback)
self.add_callback(TensorBoardCallback(tb_writer=tb_writer))
if "prediction_loss_only" in kwargs:
warnings.warn(
"Passing `prediction_loss_only` as a keyword argument is deprecated and won't be possible in a "
+ "future version. Use `args.prediction_loss_only` instead. Setting "
+ f"`args.prediction_loss_only={kwargs['prediction_loss_only']}",
FutureWarning,
)
self.args.prediction_loss_only = kwargs.pop("prediction_loss_only")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
# Will be set to True by `self._setup_loggers()` on first call to `self.log()`.
self._loggers_initialized = False
@@ -294,14 +270,7 @@ class Trainer:
# We'll find a more elegant and not need to do this in the future.
self.model.config.xla_device = True
if not callable(self.data_collator) and callable(getattr(self.data_collator, "collate_batch", None)):
self.data_collator = self.data_collator.collate_batch
warnings.warn(
(
"The `data_collator` should now be a simple callable (function, class with `__call__`), classes "
+ "with a `collate_batch` are deprecated and won't be supported in a future version."
),
FutureWarning,
)
raise ValueError("The `data_collator` should be a simple callable (function, class with `__call__`).")
if args.max_steps > 0:
logger.info("max_steps is given, it will override any value given in num_train_epochs")
@@ -1050,12 +1019,6 @@ class Trainer:
logs (:obj:`Dict[str, float]`):
The values to log.
"""
if hasattr(self, "_log"):
warnings.warn(
"The `_log` method is deprecated and won't be called in a future version, define `log` in your subclass.",
FutureWarning,
)
return self._log(logs)
if self.state.epoch is not None:
logs["epoch"] = self.state.epoch
@@ -1095,12 +1058,6 @@ class Trainer:
Return:
:obj:`torch.Tensor`: The tensor with training loss on this batch.
"""
if hasattr(self, "_training_step"):
warnings.warn(
"The `_training_step` method is deprecated and won't be called in a future version, define `training_step` in your subclass.",
FutureWarning,
)
return self._training_step(model, inputs, self.optimizer)
model.train()
inputs = self._prepare_inputs(inputs)
@@ -1140,18 +1097,6 @@ class Trainer:
# We don't use .loss here since the model may return tuples instead of ModelOutput.
return outputs[0]
def is_local_master(self) -> bool:
"""
Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
machines) main process.
.. warning::
This method is deprecated, use :meth:`~transformers.Trainer.is_local_process_zero` instead.
"""
warnings.warn("This method is deprecated, use `Trainer.is_local_process_zero()` instead.", FutureWarning)
return self.is_local_process_zero()
def is_local_process_zero(self) -> bool:
"""
Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
@@ -1162,18 +1107,6 @@ class Trainer:
else:
return self.args.local_rank in [-1, 0]
def is_world_master(self) -> bool:
"""
Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be :obj:`True` for one process).
.. warning::
This method is deprecated, use :meth:`~transformers.Trainer.is_world_process_zero` instead.
"""
warnings.warn("This method is deprecated, use `Trainer.is_world_process_zero()` instead.", FutureWarning)
return self.is_world_process_zero()
def is_world_process_zero(self) -> bool:
"""
Whether or not this process is the global main process (when training in a distributed fashion on several
@@ -1362,13 +1295,6 @@ class Trainer:
Works both with or without labels.
"""
if hasattr(self, "_prediction_loop"):
warnings.warn(
"The `_prediction_loop` method is deprecated and won't be called in a future version, define `prediction_loop` in your subclass.",
FutureWarning,
)
return self._prediction_loop(dataloader, description, prediction_loss_only=prediction_loss_only)
if not isinstance(dataloader.dataset, collections.abc.Sized):
raise ValueError("dataset must implement __len__")
prediction_loss_only = (

37
src/transformers/trainer_tf.py
View File

@@ -3,7 +3,6 @@
import datetime
import math
import os
import warnings
from typing import Callable, Dict, Optional, Tuple
@@ -66,8 +65,6 @@ class TFTrainer:
:class:`~transformers.AdamWeightDecay`. The scheduler will default to an instance of
:class:`tf.keras.optimizers.schedules.PolynomialDecay` if :obj:`args.num_warmup_steps` is 0 else an
instance of :class:`~transformers.WarmUp`.
kwargs:
Deprecated keyword arguments.
"""
def __init__(
@@ -82,7 +79,6 @@ class TFTrainer:
None,
None,
),
**kwargs,
):
assert parse(tf.__version__).release >= (2, 2, 0), (
"You need to run the TensorFlow trainer with at least the version 2.2.0, your version is %r "
@@ -98,13 +94,6 @@ class TFTrainer:
self.gradient_accumulator = GradientAccumulator()
self.global_step = 0
self.epoch_logging = 0
if "prediction_loss_only" in kwargs:
warnings.warn(
"Passing `prediction_loss_only` as a keyword argument is deprecated and won't be possible in a future version. Use `args.prediction_loss_only` instead.",
FutureWarning,
)
self.args.prediction_loss_only = kwargs.pop("prediction_loss_only")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
if tb_writer is not None:
self.tb_writer = tb_writer
@@ -249,12 +238,6 @@ class TFTrainer:
WANDB_DISABLED:
(Optional): boolean - defaults to false, set to "true" to disable wandb entirely.
"""
if hasattr(self, "_setup_wandb"):
warnings.warn(
"The `_setup_wandb` method is deprecated and won't be called in a future version, define `setup_wandb` in your subclass.",
FutureWarning,
)
return self._setup_wandb()
logger.info('Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"')
combined_dict = {**self.model.config.to_dict(), **self.args.to_sanitized_dict()}
@@ -304,14 +287,6 @@ class TFTrainer:
Works both with or without labels.
"""
if hasattr(self, "_prediction_loop"):
warnings.warn(
"The `_prediction_loop` method is deprecated and won't be called in a future version, define `prediction_loop` in your subclass.",
FutureWarning,
)
return self._prediction_loop(
dataset, steps, num_examples, description, prediction_loss_only=prediction_loss_only
)
prediction_loss_only = (
prediction_loss_only if prediction_loss_only is not None else self.args.prediction_loss_only
@@ -393,12 +368,6 @@ class TFTrainer:
logs (:obj:`Dict[str, float]`):
The values to log.
"""
if hasattr(self, "_log"):
warnings.warn(
"The `_log` method is deprecated and won't be called in a future version, define `log` in your subclass.",
FutureWarning,
)
return self._log(logs)
logs["epoch"] = self.epoch_logging
if self.tb_writer:
@@ -733,12 +702,6 @@ class TFTrainer:
Returns:
A tuple of two :obj:`tf.Tensor`: The loss and logits.
"""
if hasattr(self, "_run_model"):
warnings.warn(
"The `_run_model` method is deprecated and won't be called in a future version, define `run_model` in your subclass.",
FutureWarning,
)
return self._run_model(features, labels, training)
if self.args.past_index >= 0 and getattr(self, "_past", None) is not None:
features["mems"] = self._past

11
src/transformers/training_args.py
View File

@@ -1,7 +1,6 @@
import dataclasses
import json
import os
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple
@@ -198,10 +197,6 @@ class TrainingArguments:
do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
do_eval: bool = field(default=None, metadata={"help": "Whether to run eval on the dev set."})
do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."})
evaluate_during_training: bool = field(
default=False,
metadata={"help": "Run evaluation during training at each logging step."},
)
evaluation_strategy: EvaluationStrategy = field(
default="no",
metadata={"help": "Run evaluation during training at each logging step."},
@@ -340,12 +335,6 @@ class TrainingArguments:
def __post_init__(self):
if self.disable_tqdm is None:
self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN
if self.evaluate_during_training is True:
self.evaluation_strategy = EvaluationStrategy.STEPS
warnings.warn(
"The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)",
FutureWarning,
)
self.evaluation_strategy = EvaluationStrategy(self.evaluation_strategy)
if self.do_eval is False and self.evaluation_strategy != EvaluationStrategy.NO:
self.do_eval = True

31
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_{{cookiecutter.lowercase_modelname}}.py
View File

@@ -73,7 +73,6 @@ class {{cookiecutter.camelcase_modelname}}TokenizerFast(BertTokenizerFast):
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
{%- elif cookiecutter.tokenizer_type == "Standalone" %}
import warnings
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
@@ -234,13 +233,6 @@ class {{cookiecutter.camelcase_modelname}}Tokenizer(PreTrainedTokenizer):
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
if "is_pretokenized" in kwargs:
warnings.warn(
"`is_pretokenized` is deprecated and will be removed in a future version, use `is_split_into_words` instead.",
FutureWarning,
)
is_split_into_words = kwargs.pop("is_pretokenized")
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
text = " " + text
@@ -285,29 +277,6 @@ class {{cookiecutter.camelcase_modelname}}TokenizerFast(PreTrainedTokenizerFast)
)
self.add_prefix_space = add_prefix_space
def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = None
if "is_pretokenized" in kwargs:
warnings.warn(
"`is_pretokenized` is deprecated and will be removed in a future version, use `is_split_into_words` instead.",
FutureWarning,
)
is_split_into_words = kwargs.pop("is_pretokenized")
is_split_into_words = kwargs.get("is_split_into_words", False) if is_split_into_words is None else is_split_into_words
return super()._batch_encode_plus(*args, **kwargs)
def _encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = None
if "is_pretokenized" in kwargs:
warnings.warn(
"`is_pretokenized` is deprecated and will be removed in a future version, use `is_split_into_words` instead.",
FutureWarning,
)
is_split_into_words = kwargs.get("is_split_into_words", False) if is_split_into_words is None else is_split_into_words
return super()._encode_plus(*args, **kwargs)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
if token_ids_1 is None:

10
tests/test_modeling_gpt2.py
View File

@@ -213,7 +213,9 @@ class GPT2ModelTester:
next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
output_from_past = model(next_tokens, token_type_ids=next_token_types, past=past)["last_hidden_state"]
output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
@@ -255,7 +257,7 @@ class GPT2ModelTester:
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, past=past, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
@@ -286,7 +288,9 @@ class GPT2ModelTester:
next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
output_from_past = model(next_tokens, token_type_ids=next_token_types, past=past)["last_hidden_state"]
output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past)[
"last_hidden_state"
]
self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
# select random slice

9
tests/test_pipelines_fill_mask.py
View File

@@ -1,7 +1,5 @@
import unittest
import pytest
from transformers import pipeline
from transformers.testing_utils import require_tf, require_torch, slow
@@ -53,13 +51,6 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
]
expected_check_keys = ["sequence"]
@require_torch
def test_torch_topk_deprecation(self):
# At pipeline initialization only it was not enabled at pipeline
# call site before
with pytest.warns(FutureWarning, match=r".*use `top_k`.*"):
pipeline(task="fill-mask", model=self.small_models[0], topk=1)
@require_torch
def test_torch_fill_mask(self):
valid_inputs = "My name is <mask>"

2
tests/test_tokenization_auto.py
View File

@@ -83,7 +83,7 @@ class AutoTokenizerTest(unittest.TestCase):
else:
self.assertEqual(tokenizer.do_lower_case, False)
self.assertEqual(tokenizer.max_len, 512)
self.assertEqual(tokenizer.model_max_length, 512)
@require_tokenizers
def test_tokenizer_identifier_non_existent(self):