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Code Issues Releases Wiki Activity

add the bias option to config, default it to True for now

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This commit is contained in:
Andrej Karpathy 2023-01-27 20:29:45 +00:00
parent 2bf07a3fbf
commit cc5444e194
1 changed files with 9 additions and 8 deletions
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17
model.py
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@ -25,7 +25,7 @@ def new_gelu(x):
class LayerNorm(nn.Module): class LayerNorm(nn.Module):
""" LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """ """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
def __init__(self, ndim, bias=True): def __init__(self, ndim, bias):
super().__init__() super().__init__()
self.weight = nn.Parameter(torch.ones(ndim)) self.weight = nn.Parameter(torch.ones(ndim))
self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
@ -39,9 +39,9 @@ class CausalSelfAttention(nn.Module):
super().__init__() super().__init__()
assert config.n_embd % config.n_head == 0 assert config.n_embd % config.n_head == 0
# key, query, value projections for all heads, but in a batch # key, query, value projections for all heads, but in a batch
self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=False) self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
# output projection # output projection
self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=False) self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
# regularization # regularization
self.attn_dropout = nn.Dropout(config.dropout) self.attn_dropout = nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.dropout) self.resid_dropout = nn.Dropout(config.dropout)
@ -76,8 +76,8 @@ class MLP(nn.Module):
def __init__(self, config): def __init__(self, config):
super().__init__() super().__init__()
self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=False) self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=False) self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
self.dropout = nn.Dropout(config.dropout) self.dropout = nn.Dropout(config.dropout)
def forward(self, x): def forward(self, x):
@ -91,9 +91,9 @@ class Block(nn.Module):
def __init__(self, config): def __init__(self, config):
super().__init__() super().__init__()
self.ln_1 = LayerNorm(config.n_embd, bias=False) self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
self.attn = CausalSelfAttention(config) self.attn = CausalSelfAttention(config)
self.ln_2 = LayerNorm(config.n_embd, bias=False) self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
self.mlp = MLP(config) self.mlp = MLP(config)
def forward(self, x): def forward(self, x):
@ -109,6 +109,7 @@ class GPTConfig:
n_head: int = 12 n_head: int = 12
n_embd: int = 768 n_embd: int = 768
dropout: float = 0.1 dropout: float = 0.1
bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
class GPT(nn.Module): class GPT(nn.Module):
@ -123,7 +124,7 @@ class GPT(nn.Module):
wpe = nn.Embedding(config.block_size, config.n_embd), wpe = nn.Embedding(config.block_size, config.n_embd),
drop = nn.Dropout(config.dropout), drop = nn.Dropout(config.dropout),
h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]), h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
ln_f = LayerNorm(config.n_embd, bias=False), ln_f = LayerNorm(config.n_embd, bias=config.bias),
)) ))
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
# with weight tying when using torch.compile() some warnings get generated: # with weight tying when using torch.compile() some warnings get generated: