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Unverified Commit 57c965a8 authored by amyeroberts's avatar amyeroberts Committed by GitHub
Browse files

Remove deprecated logic and warnings (#30743) 

* Remove deprecated logic and warnings

* Add back some code that seems to be important...

* Let's just add all he nllb stuff back; removing it is a bit more involved

* Remove kwargs

* Remove more kwargs
parent 3d7d3a87
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Showing with 4 additions and 645 deletions
examples/tensorflow/token-classification/run_ner.py
View file @ 57c965a8
......@@ -21,7 +21,6 @@ import json
import logging
import os
import random
import warnings
from dataclasses import dataclass, field
from typing import Optional
......@@ -85,12 +84,6 @@ class ModelArguments:
)
},
)
use_auth_token: bool = field(
default=None,
metadata={
"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
},
)
trust_remote_code: bool = field(
default=False,
metadata={
......@@ -213,15 +206,6 @@ def main():
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if model_args.use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
FutureWarning,
)
if model_args.token is not None:
raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
model_args.token = model_args.use_auth_token
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_ner", model_args, data_args, framework="tensorflow")
......
examples/tensorflow/translation/run_translation.py
View file @ 57c965a8
......@@ -22,7 +22,6 @@ import json
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from typing import Optional
......@@ -103,12 +102,6 @@ class ModelArguments:
)
},
)
use_auth_token: bool = field(
default=None,
metadata={
"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
},
)
trust_remote_code: bool = field(
default=False,
metadata={
......@@ -285,15 +278,6 @@ def main():
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if model_args.use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
FutureWarning,
)
if model_args.token is not None:
raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
model_args.token = model_args.use_auth_token
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_translation", model_args, data_args, framework="tensorflow")
......
src/transformers/image_utils.py
View file @ 57c965a8
......@@ -727,23 +727,11 @@ class ImageFeatureExtractionMixin:
)
def promote_annotation_format(annotation_format: Union[AnnotionFormat, AnnotationFormat]) -> AnnotationFormat:
# can be removed when `AnnotionFormat` is fully deprecated
return AnnotationFormat(annotation_format.value)
def validate_annotations(
annotation_format: AnnotationFormat,
supported_annotation_formats: Tuple[AnnotationFormat, ...],
annotations: List[Dict],
) -> None:
if isinstance(annotation_format, AnnotionFormat):
logger.warning_once(
f"`{annotation_format.__class__.__name__}` is deprecated and will be removed in v4.38. "
f"Please use `{AnnotationFormat.__name__}` instead."
)
annotation_format = promote_annotation_format(annotation_format)
if annotation_format not in supported_annotation_formats:
raise ValueError(f"Unsupported annotation format: {format} must be one of {supported_annotation_formats}")
......
src/transformers/models/auto/image_processing_auto.py
View file @ 57c965a8
......@@ -371,22 +371,10 @@ class AutoImageProcessor:
if image_processor_class is None and image_processor_auto_map is None:
feature_extractor_class = config_dict.pop("feature_extractor_type", None)
if feature_extractor_class is not None:
logger.warning(
"Could not find image processor class in the image processor config or the model config. Loading "
"based on pattern matching with the model's feature extractor configuration. Please open a "
"PR/issue to update `preprocessor_config.json` to use `image_processor_type` instead of "
"`feature_extractor_type`. This warning will be removed in v4.40."
)
image_processor_class = feature_extractor_class.replace("FeatureExtractor", "ImageProcessor")
if "AutoFeatureExtractor" in config_dict.get("auto_map", {}):
feature_extractor_auto_map = config_dict["auto_map"]["AutoFeatureExtractor"]
image_processor_auto_map = feature_extractor_auto_map.replace("FeatureExtractor", "ImageProcessor")
logger.warning(
"Could not find image processor auto map in the image processor config or the model config. "
"Loading based on pattern matching with the model's feature extractor configuration. Please open a "
"PR/issue to update `preprocessor_config.json` to use `AutoImageProcessor` instead of "
"`AutoFeatureExtractor`. This warning will be removed in v4.40."
)
# If we don't find the image processor class in the image processor config, let's try the model config.
if image_processor_class is None and image_processor_auto_map is None:
......
src/transformers/models/cohere/modeling_cohere.py
View file @ 57c965a8
......@@ -23,7 +23,6 @@
"""PyTorch Cohere model."""
import math
import warnings
from typing import List, Optional, Tuple, Union
import torch
......@@ -635,7 +634,6 @@ class CohereDecoderLayer(nn.Module):
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
......@@ -651,11 +649,6 @@ class CohereDecoderLayer(nn.Module):
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
......@@ -669,7 +662,6 @@ class CohereDecoderLayer(nn.Module):
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
**kwargs,
)
# Fully Connected
......
src/transformers/models/conditional_detr/image_processing_conditional_detr.py
View file @ 57c965a8
......@@ -915,31 +915,6 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
raise ValueError(f"Format {format} is not supported.")
return target
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare
def prepare(self, image, target, return_segmentation_masks=None, masks_path=None):
logger.warning_once(
"The `prepare` method is deprecated and will be removed in a v4.33. "
"Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
"does not return the image anymore.",
)
target = self.prepare_annotation(image, target, return_segmentation_masks, masks_path, self.format)
return image, target
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.convert_coco_poly_to_mask
def convert_coco_poly_to_mask(self, *args, **kwargs):
logger.warning_once("The `convert_coco_poly_to_mask` method is deprecated and will be removed in v4.33. ")
return convert_coco_poly_to_mask(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_detection with DETR->ConditionalDetr
def prepare_coco_detection(self, *args, **kwargs):
logger.warning_once("The `prepare_coco_detection` method is deprecated and will be removed in v4.33. ")
return prepare_coco_detection_annotation(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_panoptic
def prepare_coco_panoptic(self, *args, **kwargs):
logger.warning_once("The `prepare_coco_panoptic` method is deprecated and will be removed in v4.33. ")
return prepare_coco_panoptic_annotation(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize
def resize(
self,
......
src/transformers/models/conditional_detr/modeling_conditional_detr.py
View file @ 57c965a8
......@@ -556,23 +556,7 @@ class DetrAttention(nn.Module):
def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor], **kwargs):
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor]):
return tensor if object_queries is None else tensor + object_queries
def forward(
......@@ -583,38 +567,8 @@ class DetrAttention(nn.Module):
key_value_states: Optional[torch.Tensor] = None,
spatial_position_embeddings: Optional[torch.Tensor] = None,
output_attentions: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
position_embeddings = kwargs.pop("position_ebmeddings", None)
key_value_position_embeddings = kwargs.pop("key_value_position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if key_value_position_embeddings is not None and spatial_position_embeddings is not None:
raise ValueError(
"Cannot specify both key_value_position_embeddings and spatial_position_embeddings. Please use just spatial_position_embeddings"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
if key_value_position_embeddings is not None:
logger.warning_once(
"key_value_position_embeddings has been deprecated and will be removed in v4.34. Please use spatial_position_embeddings instead"
)
spatial_position_embeddings = key_value_position_embeddings
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
......@@ -838,7 +792,6 @@ class ConditionalDetrEncoderLayer(nn.Module):
attention_mask: torch.Tensor,
object_queries: torch.Tensor = None,
output_attentions: bool = False,
**kwargs,
):
"""
Args:
......@@ -852,22 +805,6 @@ class ConditionalDetrEncoderLayer(nn.Module):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
residual = hidden_states
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
......@@ -956,7 +893,6 @@ class ConditionalDetrDecoderLayer(nn.Module):
encoder_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
is_first: Optional[bool] = False,
**kwargs,
):
"""
Args:
......@@ -979,22 +915,6 @@ class ConditionalDetrDecoderLayer(nn.Module):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
residual = hidden_states
# ========== Begin of Self-Attention =============
......@@ -1236,7 +1156,6 @@ class ConditionalDetrEncoder(ConditionalDetrPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
r"""
Args:
......@@ -1263,22 +1182,6 @@ class ConditionalDetrEncoder(ConditionalDetrPreTrainedModel):
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
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
......@@ -1377,7 +1280,6 @@ class ConditionalDetrDecoder(ConditionalDetrPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
r"""
Args:
......@@ -1414,22 +1316,6 @@ class ConditionalDetrDecoder(ConditionalDetrPreTrainedModel):
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
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
......
src/transformers/models/deformable_detr/image_processing_deformable_detr.py
View file @ 57c965a8
......@@ -913,31 +913,6 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
raise ValueError(f"Format {format} is not supported.")
return target
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare
def prepare(self, image, target, return_segmentation_masks=None, masks_path=None):
logger.warning_once(
"The `prepare` method is deprecated and will be removed in a v4.33. "
"Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
"does not return the image anymore.",
)
target = self.prepare_annotation(image, target, return_segmentation_masks, masks_path, self.format)
return image, target
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.convert_coco_poly_to_mask
def convert_coco_poly_to_mask(self, *args, **kwargs):
logger.warning_once("The `convert_coco_poly_to_mask` method is deprecated and will be removed in v4.33. ")
return convert_coco_poly_to_mask(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_detection
def prepare_coco_detection(self, *args, **kwargs):
logger.warning_once("The `prepare_coco_detection` method is deprecated and will be removed in v4.33. ")
return prepare_coco_detection_annotation(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_panoptic
def prepare_coco_panoptic(self, *args, **kwargs):
logger.warning_once("The `prepare_coco_panoptic` method is deprecated and will be removed in v4.33. ")
return prepare_coco_panoptic_annotation(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize
def resize(
self,
......
src/transformers/models/deta/image_processing_deta.py
View file @ 57c965a8
......@@ -576,31 +576,6 @@ class DetaImageProcessor(BaseImageProcessor):
raise ValueError(f"Format {format} is not supported.")
return target
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare
def prepare(self, image, target, return_segmentation_masks=None, masks_path=None):
logger.warning_once(
"The `prepare` method is deprecated and will be removed in a v4.33. "
"Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
"does not return the image anymore.",
)
target = self.prepare_annotation(image, target, return_segmentation_masks, masks_path, self.format)
return image, target
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.convert_coco_poly_to_mask
def convert_coco_poly_to_mask(self, *args, **kwargs):
logger.warning_once("The `convert_coco_poly_to_mask` method is deprecated and will be removed in v4.33. ")
return convert_coco_poly_to_mask(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_detection
def prepare_coco_detection(self, *args, **kwargs):
logger.warning_once("The `prepare_coco_detection` method is deprecated and will be removed in v4.33. ")
return prepare_coco_detection_annotation(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_panoptic
def prepare_coco_panoptic(self, *args, **kwargs):
logger.warning_once("The `prepare_coco_panoptic` method is deprecated and will be removed in v4.33. ")
return prepare_coco_panoptic_annotation(*args, **kwargs)
def resize(
self,
image: np.ndarray,
......
src/transformers/models/detr/image_processing_detr.py
View file @ 57c965a8
......@@ -896,27 +896,6 @@ class DetrImageProcessor(BaseImageProcessor):
raise ValueError(f"Format {format} is not supported.")
return target
def prepare(self, image, target, return_segmentation_masks=None, masks_path=None):
logger.warning_once(
"The `prepare` method is deprecated and will be removed in a v4.33. "
"Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
"does not return the image anymore.",
)
target = self.prepare_annotation(image, target, return_segmentation_masks, masks_path, self.format)
return image, target
def convert_coco_poly_to_mask(self, *args, **kwargs):
logger.warning_once("The `convert_coco_poly_to_mask` method is deprecated and will be removed in v4.33. ")
return convert_coco_poly_to_mask(*args, **kwargs)
def prepare_coco_detection(self, *args, **kwargs):
logger.warning_once("The `prepare_coco_detection` method is deprecated and will be removed in v4.33. ")
return prepare_coco_detection_annotation(*args, **kwargs)
def prepare_coco_panoptic(self, *args, **kwargs):
logger.warning_once("The `prepare_coco_panoptic` method is deprecated and will be removed in v4.33. ")
return prepare_coco_panoptic_annotation(*args, **kwargs)
def resize(
self,
image: np.ndarray,
......
src/transformers/models/detr/modeling_detr.py
View file @ 57c965a8
......@@ -524,23 +524,7 @@ class DetrAttention(nn.Module):
def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor], **kwargs):
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor]):
return tensor if object_queries is None else tensor + object_queries
def forward(
......@@ -551,38 +535,8 @@ class DetrAttention(nn.Module):
key_value_states: Optional[torch.Tensor] = None,
spatial_position_embeddings: Optional[torch.Tensor] = None,
output_attentions: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
position_embeddings = kwargs.pop("position_ebmeddings", None)
key_value_position_embeddings = kwargs.pop("key_value_position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if key_value_position_embeddings is not None and spatial_position_embeddings is not None:
raise ValueError(
"Cannot specify both key_value_position_embeddings and spatial_position_embeddings. Please use just spatial_position_embeddings"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
if key_value_position_embeddings is not None:
logger.warning_once(
"key_value_position_embeddings has been deprecated and will be removed in v4.34. Please use spatial_position_embeddings instead"
)
spatial_position_embeddings = key_value_position_embeddings
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
......@@ -688,7 +642,6 @@ class DetrEncoderLayer(nn.Module):
attention_mask: torch.Tensor,
object_queries: torch.Tensor = None,
output_attentions: bool = False,
**kwargs,
):
"""
Args:
......@@ -702,22 +655,6 @@ class DetrEncoderLayer(nn.Module):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
residual = hidden_states
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
......@@ -787,7 +724,6 @@ class DetrDecoderLayer(nn.Module):
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
**kwargs,
):
"""
Args:
......@@ -810,22 +746,6 @@ class DetrDecoderLayer(nn.Module):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
residual = hidden_states
# Self Attention
......@@ -995,7 +915,6 @@ class DetrEncoder(DetrPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
r"""
Args:
......@@ -1022,22 +941,6 @@ class DetrEncoder(DetrPreTrainedModel):
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
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
......@@ -1129,7 +1032,6 @@ class DetrDecoder(DetrPreTrainedModel):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
r"""
Args:
......@@ -1167,22 +1069,6 @@ class DetrDecoder(DetrPreTrainedModel):
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
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
......
src/transformers/models/falcon/modeling_falcon.py
View file @ 57c965a8
......@@ -15,7 +15,6 @@
"""PyTorch Falcon model."""
import math
import warnings
from typing import TYPE_CHECKING, Optional, Tuple, Union
import torch
......@@ -393,13 +392,7 @@ class FalconAttention(nn.Module):
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = False,
output_attentions: bool = False,
**kwargs,
):
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
# 3 x [batch_size, seq_length, num_heads, head_dim]
......@@ -549,16 +542,7 @@ class FalconFlashAttention2(FalconAttention):
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = False,
output_attentions: bool = False,
**kwargs,
):
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
# overwrite attention_mask with padding_mask
attention_mask = kwargs.pop("padding_mask")
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
# 3 x [batch_size, seq_length, num_heads, head_dim]
......@@ -792,13 +776,7 @@ class FalconDecoderLayer(nn.Module):
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = False,
output_attentions: bool = False,
**kwargs,
):
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
residual = hidden_states
if self.config.new_decoder_architecture and self.config.num_ln_in_parallel_attn == 2:
......@@ -817,7 +795,6 @@ class FalconDecoderLayer(nn.Module):
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
**kwargs,
)
attention_output = attn_outputs[0]
......
src/transformers/models/gemma/modeling_gemma.py
View file @ 57c965a8
......@@ -16,7 +16,6 @@
""" PyTorch Gemma model."""
import math
import warnings
from typing import List, Optional, Tuple, Union
import torch
......@@ -616,7 +615,6 @@ class GemmaDecoderLayer(nn.Module):
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
......@@ -632,11 +630,6 @@ class GemmaDecoderLayer(nn.Module):
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
......@@ -650,7 +643,6 @@ class GemmaDecoderLayer(nn.Module):
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
**kwargs,
)
hidden_states = residual + hidden_states
......
src/transformers/models/grounding_dino/image_processing_grounding_dino.py
View file @ 57c965a8
......@@ -920,31 +920,6 @@ class GroundingDinoImageProcessor(BaseImageProcessor):
raise ValueError(f"Format {format} is not supported.")
return target
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare
def prepare(self, image, target, return_segmentation_masks=None, masks_path=None):
logger.warning_once(
"The `prepare` method is deprecated and will be removed in a v4.33. "
"Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
"does not return the image anymore.",
)
target = self.prepare_annotation(image, target, return_segmentation_masks, masks_path, self.format)
return image, target
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.convert_coco_poly_to_mask
def convert_coco_poly_to_mask(self, *args, **kwargs):
logger.warning_once("The `convert_coco_poly_to_mask` method is deprecated and will be removed in v4.33. ")
return convert_coco_poly_to_mask(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_detection
def prepare_coco_detection(self, *args, **kwargs):
logger.warning_once("The `prepare_coco_detection` method is deprecated and will be removed in v4.33. ")
return prepare_coco_detection_annotation(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_panoptic
def prepare_coco_panoptic(self, *args, **kwargs):
logger.warning_once("The `prepare_coco_panoptic` method is deprecated and will be removed in v4.33. ")
return prepare_coco_panoptic_annotation(*args, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize
def resize(
self,
......
src/transformers/models/llama/modeling_llama.py
View file @ 57c965a8
......@@ -20,7 +20,6 @@
"""PyTorch LLaMA model."""
import math
import warnings
from typing import List, Optional, Tuple, Union
import torch
......@@ -104,29 +103,6 @@ class LlamaRotaryEmbedding(nn.Module):
self.register_buffer("inv_freq", inv_freq, persistent=False)
# For BC we register cos and sin cached
self.max_seq_len_cached = max_position_embeddings
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
t = t / self.scaling_factor
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("_cos_cached", emb.cos().to(torch.get_default_dtype()), persistent=False)
self.register_buffer("_sin_cached", emb.sin().to(torch.get_default_dtype()), persistent=False)
@property
def sin_cached(self):
logger.warning_once(
"The sin_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use "
"the forward method of RoPE from now on instead. It is not used in the `LlamaAttention` class"
)
return self._sin_cached
@property
def cos_cached(self):
logger.warning_once(
"The cos_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use "
"the forward method of RoPE from now on instead. It is not used in the `LlamaAttention` class"
)
return self._cos_cached
@torch.no_grad()
def forward(self, x, position_ids):
......@@ -714,7 +690,6 @@ class LlamaDecoderLayer(nn.Module):
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
......@@ -730,11 +705,6 @@ class LlamaDecoderLayer(nn.Module):
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
......@@ -748,7 +718,6 @@ class LlamaDecoderLayer(nn.Module):
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
**kwargs,
)
hidden_states = residual + hidden_states
......
src/transformers/models/maskformer/modeling_maskformer.py
View file @ 57c965a8
......@@ -440,23 +440,7 @@ class DetrAttention(nn.Module):
def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor], **kwargs):
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor]):
return tensor if object_queries is None else tensor + object_queries
def forward(
......@@ -467,38 +451,8 @@ class DetrAttention(nn.Module):
key_value_states: Optional[torch.Tensor] = None,
spatial_position_embeddings: Optional[torch.Tensor] = None,
output_attentions: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
position_embeddings = kwargs.pop("position_ebmeddings", None)
key_value_position_embeddings = kwargs.pop("key_value_position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if key_value_position_embeddings is not None and spatial_position_embeddings is not None:
raise ValueError(
"Cannot specify both key_value_position_embeddings and spatial_position_embeddings. Please use just spatial_position_embeddings"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
if key_value_position_embeddings is not None:
logger.warning_once(
"key_value_position_embeddings has been deprecated and will be removed in v4.34. Please use spatial_position_embeddings instead"
)
spatial_position_embeddings = key_value_position_embeddings
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
......@@ -616,7 +570,6 @@ class DetrDecoderLayer(nn.Module):
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
**kwargs,
):
"""
Args:
......@@ -639,22 +592,6 @@ class DetrDecoderLayer(nn.Module):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
residual = hidden_states
# Self Attention
......@@ -742,7 +679,6 @@ class DetrDecoder(nn.Module):
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
r"""
Args:
......@@ -779,21 +715,6 @@ class DetrDecoder(nn.Module):
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
position_embeddings = kwargs.pop("position_embeddings", None)
if kwargs:
raise ValueError(f"Unexpected arguments {kwargs.keys()}")
if position_embeddings is not None and object_queries is not None:
raise ValueError(
"Cannot specify both position_embeddings and object_queries. Please use just object_queries"
)
if position_embeddings is not None:
logger.warning_once(
"position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
)
object_queries = position_embeddings
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
......
src/transformers/models/mistral/modeling_mistral.py
View file @ 57c965a8
......@@ -20,7 +20,6 @@
""" PyTorch Mistral model."""
import inspect
import math
import warnings
from typing import List, Optional, Tuple, Union
import torch
......@@ -246,12 +245,7 @@ class MistralAttention(nn.Module):
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
......@@ -344,15 +338,7 @@ class MistralFlashAttention2(MistralAttention):
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
):
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
# overwrite attention_mask with padding_mask
attention_mask = kwargs.pop("padding_mask")
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
......@@ -729,12 +715,7 @@ class MistralDecoderLayer(nn.Module):
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
......
src/transformers/models/mixtral/modeling_mixtral.py
View file @ 57c965a8
......@@ -20,7 +20,6 @@
""" PyTorch Mixtral model."""
import inspect
import math
import warnings
from typing import List, Optional, Tuple, Union
import torch
......@@ -323,12 +322,7 @@ class MixtralAttention(nn.Module):
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
......@@ -422,15 +416,7 @@ class MixtralFlashAttention2(MixtralAttention):
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
):
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
# overwrite attention_mask with padding_mask
attention_mask = kwargs.pop("padding_mask")
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
......@@ -805,14 +791,6 @@ class MixtralBlockSparseTop2MLP(nn.Module):
return current_hidden_states
class MixtralBLockSparseTop2MLP(MixtralBlockSparseTop2MLP):
def __init__(self, *args, **kwargs):
logger.warning_once(
"MixtralBLockSparseTop2MLP is deprecated by MixtralBlockSparseTop2MLP and will be removed in v4.40."
)
super().__init__(*args, **kwargs)
class MixtralSparseMoeBlock(nn.Module):
"""
This implementation is
......@@ -901,12 +879,7 @@ class MixtralDecoderLayer(nn.Module):
output_attentions: Optional[bool] = False,
output_router_logits: Optional[bool] = False,
use_cache: Optional[bool] = False,
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
......
src/transformers/models/olmo/modeling_olmo.py
View file @ 57c965a8
......@@ -20,7 +20,6 @@
"""PyTorch OLMo model."""
import math
import warnings
from typing import List, Optional, Tuple, Union
import torch
......@@ -101,29 +100,6 @@ class OlmoRotaryEmbedding(nn.Module):
self.register_buffer("inv_freq", inv_freq, persistent=False)
# For BC we register cos and sin cached
self.max_seq_len_cached = max_position_embeddings
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
t = t / self.scaling_factor
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("_cos_cached", emb.cos().to(torch.get_default_dtype()), persistent=False)
self.register_buffer("_sin_cached", emb.sin().to(torch.get_default_dtype()), persistent=False)
@property
def sin_cached(self):
logger.warning_once(
"The sin_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use "
"the forward method of RoPE from now on instead. It is not used in the `OlmoAttention` class"
)
return self._sin_cached
@property
def cos_cached(self):
logger.warning_once(
"The cos_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use "
"the forward method of RoPE from now on instead. It is not used in the `OlmoAttention` class"
)
return self._cos_cached
@torch.no_grad()
def forward(self, x, position_ids):
......@@ -690,7 +666,6 @@ class OlmoDecoderLayer(nn.Module):
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
......@@ -706,11 +681,6 @@ class OlmoDecoderLayer(nn.Module):
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
......@@ -724,7 +694,6 @@ class OlmoDecoderLayer(nn.Module):
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
**kwargs,
)
hidden_states = residual + hidden_states
......
src/transformers/models/owlv2/modeling_owlv2.py
View file @ 57c965a8
......@@ -14,7 +14,6 @@
# limitations under the License.
""" PyTorch OWLv2 model."""
import warnings
from dataclasses import dataclass
from functools import lru_cache
from typing import Any, Dict, Optional, Tuple, Union
......@@ -1197,16 +1196,7 @@ class Owlv2Model(Owlv2PreTrainedModel):
if return_loss:
loss = owlv2_loss(logits_per_text)
if return_base_image_embeds:
warnings.warn(
"`return_base_image_embeds` is deprecated and will be removed in v4.27 of Transformers, one can"
" obtain the base (unprojected) image embeddings from outputs.vision_model_output.",
FutureWarning,
)
last_hidden_state = vision_outputs[0]
image_embeds = self.vision_model.post_layernorm(last_hidden_state)
else:
text_embeds = text_embeds_norm
text_embeds = text_embeds_norm
if not return_dict:
output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
......
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