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Update init #92

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merged 2 commits into from
Nov 9, 2023
Merged

Update init #92

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66d2535
Update tinyllama.py
jzhang38 Nov 9, 2023
89c75f4
Update init
jzhang38 Nov 9, 2023
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12 changes: 5 additions & 7 deletions lit_gpt/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,20 +40,18 @@ def __init__(self, config: Config) -> None:
def _init_weights(self, module: nn.Module, n_layer) -> None:
"""Meant to be used with `gpt.apply(gpt._init_weights)`."""
# GPT-NeoX https://arxiv.org/pdf/2204.06745.pdf
# print module name
if isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=math.sqrt(2.0 / 5 / self.config.n_embd))
# RWKV: set it to 1e-4
torch.nn.init.normal_(module.weight, mean=0.0, std=math.sqrt(2.0 / 5 / module.weight.size(1)))
# torch.nn.init.normal_(module.weight, -1e-4, 1e-4)
# torch.nn.init.uniform_(module.weight, -1e-4, 1e-4)
elif isinstance(module, nn.Linear):
# fan-in variance scaling intializer
torch.nn.init.normal_(module.weight, mean=0.0, std=math.sqrt(2.0 / 5 / module.weight.size(1)))
torch.nn.init.normal_(module.weight, mean=0.0, std=math.sqrt(2.0 / 5 / self.config.n_embd))
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
# GPT-NeoX
for name, p in module.named_parameters():
if (name == "proj.weight" and isinstance(module, LLaMAMLP)) or (name == "w3.weight" and isinstance(module, SwiGLU)): #if use xformer swiglu, fc2 layer will be renamed to w3
nn.init.normal_(p, mean=0.0, std=1 / math.sqrt(p.shape[-1]) / n_layer)
if (name == "proj.weight" and isinstance(module, LLaMAMLP)) or (name == "w3.weight" and isinstance(module, SwiGLU) or (name=="proj.weight" and isinstance(module, CausalSelfAttention))): #if use xformer swiglu, fc2 layer will be renamed to w3
nn.init.normal_(p, mean=0.0, std=1 / math.sqrt(self.config.n_embd) / n_layer)


def reset_cache(self) -> None:
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2 changes: 1 addition & 1 deletion pretrain/tinyllama.py
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Expand Up @@ -132,7 +132,7 @@ def main(fabric, train_data_dir, val_data_dir, resume):

fabric.print(f"Loading model with {config.__dict__}")
t0 = time.perf_counter()
with fabric.init_module(empty_init=(fabric.world_size > 1)):
with fabric.init_module(empty_init=False):
model = GPT(config)
model.apply(partial(model._init_weights ,n_layer=config.n_layer))

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