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[BLOOM] Deprecate `position_ids` (#18342)

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Thomas Wang 2022-07-28 20:21:43 +02:00 committed by GitHub
parent 9c336657a9
commit 7b0908769b
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1 changed files with 43 additions and 21 deletions
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64
src/transformers/models/bloom/modeling_bloom.py
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@ -15,6 +15,7 @@
"""PyTorch BLOOM model."""
import math
import warnings
from typing import Tuple, Union
import torch
@ -522,11 +523,6 @@ BLOOM_INPUTS_DOCSTRING = r"""
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
@ -617,14 +613,24 @@ class BloomModel(BloomPreTrainedModel):
input_ids=None,
past_key_values=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**deprecated_arguments
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
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
@ -772,16 +778,7 @@ class BloomForCausalLM(BloomPreTrainedModel):
input_ids = input_ids[:, -1].unsqueeze(-1)
attention_mask = kwargs.get("attention_mask", None)
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past:
position_ids = position_ids[:, -1].unsqueeze(-1)
else:
position_ids = None
return {
"input_ids": input_ids,
"past_key_values": past,
@ -801,7 +798,6 @@ class BloomForCausalLM(BloomPreTrainedModel):
input_ids=None,
past_key_values=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
@ -809,6 +805,7 @@ class BloomForCausalLM(BloomPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**deprecated_arguments
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
@ -816,13 +813,22 @@ class BloomForCausalLM(BloomPreTrainedModel):
`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 deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
@ -907,7 +913,6 @@ class BloomForSequenceClassification(BloomPreTrainedModel):
input_ids=None,
past_key_values=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
@ -915,6 +920,7 @@ class BloomForSequenceClassification(BloomPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**deprecated_arguments
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
@ -922,6 +928,15 @@ class BloomForSequenceClassification(BloomPreTrainedModel):
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
@ -929,7 +944,6 @@ class BloomForSequenceClassification(BloomPreTrainedModel):
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
@ -1036,7 +1050,6 @@ class BloomForTokenClassification(BloomPreTrainedModel):
input_ids=None,
past_key_values=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
@ -1044,6 +1057,7 @@ class BloomForTokenClassification(BloomPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**deprecated_arguments
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
@ -1051,6 +1065,15 @@ class BloomForTokenClassification(BloomPreTrainedModel):
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
@ -1058,7 +1081,6 @@ class BloomForTokenClassification(BloomPreTrainedModel):
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,