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[LongFormer] code nits, removed unused parameters (#23749)

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* remove unused parameters

* remove unused parameters in config
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Arthur 2023-05-25 16:06:14 +02:00 committed by GitHub
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commit 3416bba7c7
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8
src/transformers/models/longformer/configuration_longformer.py
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@ -86,12 +86,6 @@ class LongformerConfig(PretrainedConfig):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
attention_window (`int` or `List[int]`, *optional*, defaults to 512):
Size of an attention window around each token. If an `int`, use the same size for all layers. To specify a
different window size for each layer, use a `List[int]` where `len(attention_window) == num_hidden_layers`.
@ -131,7 +125,6 @@ class LongformerConfig(PretrainedConfig):
type_vocab_size: int = 2,
initializer_range: float = 0.02,
layer_norm_eps: float = 1e-12,
position_embedding_type: str = "absolute",
onnx_export: bool = False,
**kwargs,
):
@ -154,7 +147,6 @@ class LongformerConfig(PretrainedConfig):
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.onnx_export = onnx_export

2
src/transformers/models/longformer/modeling_longformer.py
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@ -445,8 +445,6 @@ class LongformerEmbeddings(nn.Module):
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
self.padding_idx = config.pad_token_id
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx