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PyTorch nn.LayerNorm now takes bias arg - removed custom class #454

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PyTorch nn.LayerNorm now takes bias arg - removed custom class #454

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17 changes: 3 additions & 14 deletions model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,17 +15,6 @@
import torch.nn as nn
from torch.nn import functional as F

class LayerNorm(nn.Module):
""" LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """

def __init__(self, ndim, bias):
super().__init__()
self.weight = nn.Parameter(torch.ones(ndim))
self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None

def forward(self, input):
return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)

class CausalSelfAttention(nn.Module):

def __init__(self, config):
Expand Down Expand Up @@ -95,9 +84,9 @@ class Block(nn.Module):

def __init__(self, config):
super().__init__()
self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
self.ln_1 = nn.LayerNorm(config.n_embd, bias=config.bias)
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Nice. To add another confirmation, I wrote my own model and used nn.LayerNorm directly with no issues: https://github.com/vhmth/gpt-2/blob/main/model.py#L74

self.attn = CausalSelfAttention(config)
self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
self.ln_2 = nn.LayerNorm(config.n_embd, bias=config.bias)
self.mlp = MLP(config)

def forward(self, x):
Expand Down Expand Up @@ -128,7 +117,7 @@ def __init__(self, config):
wpe = nn.Embedding(config.block_size, config.n_embd),
drop = nn.Dropout(config.dropout),
h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
ln_f = LayerNorm(config.n_embd, bias=config.bias),
ln_f = nn.LayerNorm(config.n_embd, bias=config.bias),
))
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
# with weight tying when using torch.compile() some warnings get generated:
Expand Down