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Remove more unused attributes in config classes (#21543)

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* Remove unused decoder_layerdrop

* Update SPECIAL_CASES_TO_ALLOW for MT5Config

* Remove unused position_embedding_init_scale

* Remove unused decoder_max_relative_position

* Use unused decoder_max_relative_position

* Remove unused init_std

* Remove unused forgotten attributes

* Remove unused patch_norm

* Remove unused max_seq_len

* Update SPECIAL_CASES_TO_ALLOW for OneFormerConfig

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2023-02-10 22:57:28 +01:00
committed by GitHub
parent 862e8e4f4a
commit b47a16743b
15 changed files with 7 additions and 54 deletions
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5
src/transformers/models/deformable_detr/configuration_deformable_detr.py
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@@ -80,9 +80,6 @@ class DeformableDetrConfig(PretrainedConfig):
encoder_layerdrop: (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop: (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
auxiliary_loss (`bool`, *optional*, defaults to `False`):
Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
position_embedding_type (`str`, *optional*, defaults to `"sine"`):
@@ -163,7 +160,6 @@ class DeformableDetrConfig(PretrainedConfig):
decoder_ffn_dim=1024,
decoder_attention_heads=8,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
is_encoder_decoder=True,
activation_function="relu",
d_model=256,
@@ -225,7 +221,6 @@ class DeformableDetrConfig(PretrainedConfig):
self.init_std = init_std
self.init_xavier_std = init_xavier_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.auxiliary_loss = auxiliary_loss
self.position_embedding_type = position_embedding_type
self.backbone = backbone

5
src/transformers/models/deta/configuration_deta.py
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@@ -74,9 +74,6 @@ class DetaConfig(PretrainedConfig):
encoder_layerdrop: (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop: (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
auxiliary_loss (`bool`, *optional*, defaults to `False`):
Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
position_embedding_type (`str`, *optional*, defaults to `"sine"`):
@@ -146,7 +143,6 @@ class DetaConfig(PretrainedConfig):
decoder_ffn_dim=1024,
decoder_attention_heads=8,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
is_encoder_decoder=True,
activation_function="relu",
d_model=256,
@@ -202,7 +198,6 @@ class DetaConfig(PretrainedConfig):
self.init_std = init_std
self.init_xavier_std = init_xavier_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.auxiliary_loss = auxiliary_loss
self.position_embedding_type = position_embedding_type
# deformable attributes

4
src/transformers/models/dinat/configuration_dinat.py
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@@ -64,8 +64,6 @@ class DinatConfig(PretrainedConfig):
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
`"selu"` and `"gelu_new"` are supported.
patch_norm (`bool`, *optional*, defaults to `True`):
Whether or not to add layer normalization after patch embedding.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
@@ -112,7 +110,6 @@ class DinatConfig(PretrainedConfig):
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
layer_scale_init_value=0.0,
@@ -135,7 +132,6 @@ class DinatConfig(PretrainedConfig):
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.path_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel

4
src/transformers/models/donut/configuration_donut_swin.py
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@@ -66,8 +66,6 @@ class DonutSwinConfig(PretrainedConfig):
`"selu"` and `"gelu_new"` are supported.
use_absolute_embeddings (`bool`, *optional*, defaults to False):
Whether or not to add absolute position embeddings to the patch embeddings.
patch_norm (`bool`, *optional*, defaults to True):
Whether or not to add layer normalization after patch embedding.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
@@ -110,7 +108,6 @@ class DonutSwinConfig(PretrainedConfig):
drop_path_rate=0.1,
hidden_act="gelu",
use_absolute_embeddings=False,
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
**kwargs,
@@ -132,7 +129,6 @@ class DonutSwinConfig(PretrainedConfig):
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.use_absolute_embeddings = use_absolute_embeddings
self.path_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel

4
src/transformers/models/jukebox/configuration_jukebox.py
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@@ -539,8 +539,6 @@ class JukeboxConfig(PretrainedConfig):
metadata_conditioning (`bool`, *optional*, defaults to `True`):
Whether or not to use metadata conditioning, corresponding to the artist, the genre and the min/maximum
duration.
init_std (`float`, *optional*, defaults to 0.2):
Standard deviation used to initial the model.
Example:
@@ -572,7 +570,6 @@ class JukeboxConfig(PretrainedConfig):
max_duration=600.0,
max_nb_genres=5,
metadata_conditioning=True,
init_std=0.2,
**kwargs,
):
if vqvae_config is None:
@@ -596,7 +593,6 @@ class JukeboxConfig(PretrainedConfig):
self.hop_fraction = self.vqvae_config.hop_fraction
self.init_std = init_std
self.nb_priors = nb_priors
# Metadata conditioning

4
src/transformers/models/maskformer/configuration_maskformer_swin.py
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@@ -62,8 +62,6 @@ class MaskFormerSwinConfig(PretrainedConfig):
`"selu"` and `"gelu_new"` are supported.
use_absolute_embeddings (`bool`, *optional*, defaults to False):
Whether or not to add absolute position embeddings to the patch embeddings.
patch_norm (`bool`, *optional*, defaults to True):
Whether or not to add layer normalization after patch embedding.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
@@ -109,7 +107,6 @@ class MaskFormerSwinConfig(PretrainedConfig):
drop_path_rate=0.1,
hidden_act="gelu",
use_absolute_embeddings=False,
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
out_features=None,
@@ -132,7 +129,6 @@ class MaskFormerSwinConfig(PretrainedConfig):
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.use_absolute_embeddings = use_absolute_embeddings
self.path_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel

4
src/transformers/models/nat/configuration_nat.py
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@@ -62,8 +62,6 @@ class NatConfig(PretrainedConfig):
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
`"selu"` and `"gelu_new"` are supported.
patch_norm (`bool`, *optional*, defaults to `True`):
Whether or not to add layer normalization after patch embedding.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
@@ -109,7 +107,6 @@ class NatConfig(PretrainedConfig):
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
layer_scale_init_value=0.0,
@@ -131,7 +128,6 @@ class NatConfig(PretrainedConfig):
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.path_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel

4
src/transformers/models/oneformer/configuration_oneformer.py
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@@ -83,8 +83,6 @@ class OneFormerConfig(PretrainedConfig):
List containing the strides for feature maps in the encoder.
task_seq_len (`int`, *optional*, defaults to 77)
Sequence length for tokenizing text list input.
max_seq_len (`int`, *optional*, defaults to 77)
Sequence length for tokenizing task input.
text_encoder_width (`int`, *optional*, defaults to 256)
Hidden size for text encoder.
text_encoder_context_length (`int`, *optional*, defaults to 77):
@@ -165,7 +163,6 @@ class OneFormerConfig(PretrainedConfig):
output_auxiliary_logits: bool = True,
strides: Optional[list] = [4, 8, 16, 32],
task_seq_len: int = 77,
max_seq_len: int = 77,
text_encoder_width: int = 256,
text_encoder_context_length: int = 77,
text_encoder_num_layers: int = 6,
@@ -229,7 +226,6 @@ class OneFormerConfig(PretrainedConfig):
self.output_auxiliary_logits = output_auxiliary_logits
self.strides = strides
self.task_seq_len = task_seq_len
self.max_seq_len = max_seq_len
self.text_encoder_width = text_encoder_width
self.text_encoder_context_length = text_encoder_context_length
self.text_encoder_num_layers = text_encoder_num_layers

1
src/transformers/models/perceiver/configuration_perceiver.py
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@@ -133,7 +133,6 @@ class PerceiverConfig(PretrainedConfig):
cross_attention_widening_factor=1,
hidden_act="gelu",
attention_probs_dropout_prob=0.1,
position_embedding_init_scale=0.02,
initializer_range=0.02,
layer_norm_eps=1e-12,
use_query_residual=True,

4
src/transformers/models/speecht5/configuration_speecht5.py
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@@ -168,8 +168,6 @@ class SpeechT5Config(PretrainedConfig):
The maximum sequence length of text features that this model might ever be used with.
encoder_max_relative_position (`int`, *optional*, defaults to 160):
Maximum distance for relative position embedding in the encoder.
decoder_max_relative_position (`int`, *optional*, defaults to 160):
Maximum distance for relative position embedding in the dencoder.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
@@ -243,7 +241,6 @@ class SpeechT5Config(PretrainedConfig):
max_speech_positions=4000,
max_text_positions=450,
encoder_max_relative_position=160,
decoder_max_relative_position=160,
use_cache=True,
is_encoder_decoder=True,
**kwargs,
@@ -314,7 +311,6 @@ class SpeechT5Config(PretrainedConfig):
self.max_speech_positions = max_speech_positions
self.max_text_positions = max_text_positions
self.encoder_max_relative_position = encoder_max_relative_position
self.decoder_max_relative_position = decoder_max_relative_position
self.use_cache = use_cache
self.is_encoder_decoder = is_encoder_decoder

4
src/transformers/models/swin/configuration_swin.py
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@@ -75,8 +75,6 @@ class SwinConfig(PretrainedConfig):
`"selu"` and `"gelu_new"` are supported.
use_absolute_embeddings (`bool`, *optional*, defaults to False):
Whether or not to add absolute position embeddings to the patch embeddings.
patch_norm (`bool`, *optional*, defaults to True):
Whether or not to add layer normalization after patch embedding.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
@@ -124,7 +122,6 @@ class SwinConfig(PretrainedConfig):
drop_path_rate=0.1,
hidden_act="gelu",
use_absolute_embeddings=False,
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
encoder_stride=32,
@@ -148,7 +145,6 @@ class SwinConfig(PretrainedConfig):
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.use_absolute_embeddings = use_absolute_embeddings
self.path_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.encoder_stride = encoder_stride

4
src/transformers/models/swin2sr/configuration_swin2sr.py
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@@ -67,8 +67,6 @@ class Swin2SRConfig(PretrainedConfig):
`"selu"` and `"gelu_new"` are supported.
use_absolute_embeddings (`bool`, *optional*, defaults to `False`):
Whether or not to add absolute position embeddings to the patch embeddings.
patch_norm (`bool`, *optional*, defaults to `True`):
Whether or not to add layer normalization after patch embedding.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
@@ -121,7 +119,6 @@ class Swin2SRConfig(PretrainedConfig):
drop_path_rate=0.1,
hidden_act="gelu",
use_absolute_embeddings=False,
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
upscale=2,
@@ -147,7 +144,6 @@ class Swin2SRConfig(PretrainedConfig):
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.use_absolute_embeddings = use_absolute_embeddings
self.path_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.upscale = upscale

4
src/transformers/models/swinv2/configuration_swinv2.py
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@@ -68,8 +68,6 @@ class Swinv2Config(PretrainedConfig):
`"selu"` and `"gelu_new"` are supported.
use_absolute_embeddings (`bool`, *optional*, defaults to `False`):
Whether or not to add absolute position embeddings to the patch embeddings.
patch_norm (`bool`, *optional*, defaults to `True`):
Whether or not to add layer normalization after patch embedding.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
@@ -114,7 +112,6 @@ class Swinv2Config(PretrainedConfig):
drop_path_rate=0.1,
hidden_act="gelu",
use_absolute_embeddings=False,
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
encoder_stride=32,
@@ -137,7 +134,6 @@ class Swinv2Config(PretrainedConfig):
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.use_absolute_embeddings = use_absolute_embeddings
self.path_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.encoder_stride = encoder_stride

2
src/transformers/models/van/modeling_van.py
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@@ -374,7 +374,7 @@ class VanPreTrainedModel(PreTrainedModel):
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
nn.init.trunc_normal_(module.weight, std=0.02)
nn.init.trunc_normal_(module.weight, std=self.config.initializer_range)
if isinstance(module, nn.Linear) and module.bias is not None:
nn.init.constant_(module.bias, 0)
elif isinstance(module, nn.LayerNorm):

8
utils/check_config_attributes.py
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@@ -45,13 +45,17 @@ SPECIAL_CASES_TO_ALLOW = {
"EsmConfig": ["is_folding_model"],
# used during training (despite we don't have training script for these models yet)
"Mask2FormerConfig": ["ignore_value"],
# used during training (despite we don't have training script for these models yet)
"OneFormerConfig": ["ignore_value"],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
"OneFormerConfig": ["ignore_value", "norm"],
# used during preprocessing and collation, see `collating_graphormer.py`
"GraphormerConfig": ["spatial_pos_max"],
# used internally in the configuration class file
"T5Config": ["feed_forward_proj"],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
"MT5Config": ["feed_forward_proj", "tokenizer_class"],
# used internally in the configuration class file
"LongT5Config": ["feed_forward_proj"],
# used internally in the configuration class file
"SwitchTransformersConfig": ["feed_forward_proj"],