saymrwulf/transformers
1
0
Fork 0
mirror of https://github.com/saymrwulf/transformers.git synced 2026-05-14 20:58:08 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Clean-up deprecated code (#33446)

Browse source 
* update

* update modeling
This commit is contained in:
Raushan Turganbay 2024-09-12 14:19:02 +02:00 committed by GitHub
parent 8ed635258c
commit c8ea675324
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
4 changed files with 5 additions and 55 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

19
src/transformers/models/fuyu/configuration_fuyu.py
View file

@ -14,8 +14,6 @@
# limitations under the License.
"""Fuyu model configuration"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
@ -207,20 +205,3 @@ class FuyuConfig(PretrainedConfig):
)
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
@property
def vocab_size(self):
warnings.warn(
"The `vocab_size` attribute is deprecated and will be removed in v4.44, Please use `text_config.vocab_size` instead.",
FutureWarning,
)
return self._vocab_size
@vocab_size.setter
def vocab_size(self, value):
self._vocab_size = value
def to_dict(self):
output = super().to_dict()
output.pop("_vocab_size", None)
return output

13
src/transformers/models/fuyu/modeling_fuyu.py
View file

@ -183,15 +183,6 @@ class FuyuForCausalLM(FuyuPreTrainedModel):
def tie_weights(self):
return self.language_model.tie_weights()
def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
# TODO: config.vocab_size is deprecated and will be removed in v4.43.
# `resize_token_embeddings` should work from `modeling_utils.py``
model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
self.config.text_config.vocab_size = model_embeds.num_embeddings
self.config.vocab_size = model_embeds.num_embeddings
self.vocab_size = model_embeds.num_embeddings
return model_embeds
def gather_continuous_embeddings(
self,
word_embeddings: torch.Tensor,
@ -254,8 +245,8 @@ class FuyuForCausalLM(FuyuPreTrainedModel):
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (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]`.
config.text_config.vocab_size]` or -100 (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.text_config.vocab_size]`.
Returns:

13
src/transformers/models/paligemma/configuration_paligemma.py
View file

@ -141,20 +141,7 @@ class PaliGemmaConfig(PretrainedConfig):
def ignore_index(self, value):
self._ignore_index = value
@property
def vocab_size(self):
warnings.warn(
"The `vocab_size` attribute is deprecated and will be removed in v4.44, Please use `text_config.vocab_size` instead.",
FutureWarning,
)
return self._vocab_size
@vocab_size.setter
def vocab_size(self, value):
self._vocab_size = value
def to_dict(self):
output = super().to_dict()
output.pop("_vocab_size", None)
output.pop("_ignore_index", None)
return output

15
src/transformers/models/paligemma/modeling_paligemma.py
View file

@ -53,7 +53,7 @@ class PaliGemmaCausalLMOutputWithPast(ModelOutput):
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.text_config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
@ -283,15 +283,6 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
def tie_weights(self):
return self.language_model.tie_weights()
def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
# TODO: config.vocab_size is deprecated and will be removed in v4.43.
# `resize_token_embeddings` should work from `modeling_utils.py``
model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
self.config.text_config.vocab_size = model_embeds.num_embeddings
self.config.vocab_size = model_embeds.num_embeddings
self.vocab_size = model_embeds.num_embeddings
return model_embeds
def _update_causal_mask(
self, attention_mask, token_type_ids, inputs_embeds, past_key_values, cache_position, is_training: bool = False
):
@ -362,8 +353,8 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (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]`.
config.text_config.vocab_size]` or -100 (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.text_config.vocab_size]`.
num_logits_to_keep (`int`, *optional*):
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all