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Use nn.GELU
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11
model.py
11
model.py
@ -15,14 +15,6 @@ import torch
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import torch.nn as nn
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import torch.nn as nn
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from torch.nn import functional as F
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from torch.nn import functional as F
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# @torch.jit.script # good to enable when not using torch.compile, disable when using (our default)
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def new_gelu(x):
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"""
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Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT).
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Reference: Gaussian Error Linear Units (GELU) paper: https://arxiv.org/abs/1606.08415
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"""
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return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
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class LayerNorm(nn.Module):
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class LayerNorm(nn.Module):
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""" LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
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""" LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
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@ -88,12 +80,13 @@ class MLP(nn.Module):
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def __init__(self, config):
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def __init__(self, config):
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super().__init__()
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super().__init__()
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self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
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self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
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self.gelu = nn.GELU()
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self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
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self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
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self.dropout = nn.Dropout(config.dropout)
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self.dropout = nn.Dropout(config.dropout)
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def forward(self, x):
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def forward(self, x):
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x = self.c_fc(x)
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x = self.c_fc(x)
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x = new_gelu(x)
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x = self.gelu(x)
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x = self.c_proj(x)
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x = self.c_proj(x)
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x = self.dropout(x)
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x = self.dropout(x)
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return x
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return x
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