ViperEkura
445378667f
- Embedding.forward 训练时注入 randn 噪声，缩放系数 neftune_noise_alpha / sqrt(seq_len) - TrainConfig.neftune_alpha 通过 config 传递（默认 0=关闭） - TrainContextBuilder 将 config.neftune_alpha 写入 embed_tokens - --neftune_alpha CLI 参数（典型值 5.0） - label_smoothing 默认值 0.05 -> 0.0
24 lines
716 B
Python
24 lines
716 B
Python
import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import Tensor
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class Embedding(nn.Module):
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def __init__(self, vocab_size: int, embedding_dim: int):
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super().__init__()
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self.weight = nn.Parameter(torch.empty((vocab_size, embedding_dim)))
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self.neftune_noise_alpha = 0.0
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def reset_parameters(self):
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nn.init.normal_(self.weight, mean=0.0, std=0.02)
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def forward(self, x: Tensor) -> Tensor:
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out = F.embedding(x, self.weight)
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if self.training and self.neftune_noise_alpha > 0.0:
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eps = self.neftune_noise_alpha / math.sqrt(out.size(1))
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out = out + eps * torch.randn_like(out)
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return out
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