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[Wav2Vec2-Conf / LLaMA] Style fix (#26188)

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* torch.nn -> nn

* fix llama

* copies
This commit is contained in:
Sanchit Gandhi 2023-09-18 17:24:35 +01:00 committed by GitHub
parent 8b5da9fc6e
commit e4e55af79c
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3 changed files with 9 additions and 9 deletions
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2
src/transformers/models/deprecated/open_llama/modeling_open_llama.py
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@ -99,7 +99,7 @@ class OpenLlamaRMSNorm(nn.Module):
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->OpenLlama
class OpenLlamaRotaryEmbedding(torch.nn.Module):
class OpenLlamaRotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()

2
src/transformers/models/llama/modeling_llama.py
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@ -89,7 +89,7 @@ class LlamaRMSNorm(nn.Module):
return self.weight * hidden_states.to(input_dtype)
class LlamaRotaryEmbedding(torch.nn.Module):
class LlamaRotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()

14
src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py
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@ -584,7 +584,7 @@ class Wav2Vec2ConformerConvolutionModule(nn.Module):
if (config.conv_depthwise_kernel_size - 1) % 2 == 1:
raise ValueError("`config.conv_depthwise_kernel_size` should be a odd number for 'SAME' padding")
self.layer_norm = nn.LayerNorm(config.hidden_size)
self.pointwise_conv1 = torch.nn.Conv1d(
self.pointwise_conv1 = nn.Conv1d(
config.hidden_size,
2 * config.hidden_size,
kernel_size=1,
@ -592,8 +592,8 @@ class Wav2Vec2ConformerConvolutionModule(nn.Module):
padding=0,
bias=False,
)
self.glu = torch.nn.GLU(dim=1)
self.depthwise_conv = torch.nn.Conv1d(
self.glu = nn.GLU(dim=1)
self.depthwise_conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
config.conv_depthwise_kernel_size,
@ -602,9 +602,9 @@ class Wav2Vec2ConformerConvolutionModule(nn.Module):
groups=config.hidden_size,
bias=False,
)
self.batch_norm = torch.nn.BatchNorm1d(config.hidden_size)
self.batch_norm = nn.BatchNorm1d(config.hidden_size)
self.activation = ACT2FN[config.hidden_act]
self.pointwise_conv2 = torch.nn.Conv1d(
self.pointwise_conv2 = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=1,
@ -612,7 +612,7 @@ class Wav2Vec2ConformerConvolutionModule(nn.Module):
padding=0,
bias=False,
)
self.dropout = torch.nn.Dropout(config.conformer_conv_dropout)
self.dropout = nn.Dropout(config.conformer_conv_dropout)
def forward(self, hidden_states):
hidden_states = self.layer_norm(hidden_states)
@ -798,7 +798,7 @@ class Wav2Vec2ConformerEncoderLayer(nn.Module):
# Self-Attention
self.self_attn_layer_norm = nn.LayerNorm(embed_dim)
self.self_attn_dropout = torch.nn.Dropout(dropout)
self.self_attn_dropout = nn.Dropout(dropout)
self.self_attn = Wav2Vec2ConformerSelfAttention(config)
# Conformer Convolution