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Fix weight initialization in LoRA and Adapter finetuning #117

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Apr 11, 2023
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Fix weight initialization in LoRA and Adapter finetuning #117

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6 changes: 4 additions & 2 deletions finetune_adapter.py
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Expand Up @@ -55,8 +55,10 @@ def main():

checkpoint = torch.load("checkpoints/lit-llama/7B/state_dict.pth")

with EmptyInitOnDevice(device=fabric.device, dtype=torch.bfloat16):
model = LLaMA(config)
with fabric.device:
torch.set_default_tensor_type(torch.HalfTensor)
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model = LLaMA(config).bfloat16()
torch.set_default_tensor_type(torch.FloatTensor)
# strict=False because missing keys due to adapter weights not containted in state dict
model.load_state_dict(checkpoint, strict=False)

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13 changes: 7 additions & 6 deletions finetune_lora.py
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Expand Up @@ -52,14 +52,15 @@ def main():
config = LLaMAConfig.from_name("7B")
config.block_size = block_size

with EmptyInitOnDevice(device=fabric.device, dtype=torch.bfloat16):
with lora(r=lora_r, alpha=lora_alpha, dropout=lora_dropout, enabled=True):
model = LLaMA(config)

checkpoint = torch.load("checkpoints/lit-llama/7B/state_dict.pth")

with fabric.device, lora(r=lora_r, alpha=lora_alpha, dropout=lora_dropout, enabled=True):
torch.set_default_tensor_type(torch.HalfTensor)
model = LLaMA(config).bfloat16()
torch.set_default_tensor_type(torch.FloatTensor)
# strict=False because missing keys due to LoRA weights not contained in checkpoint state
model.load_state_dict(checkpoint, strict=False)

# strict=False because missing keys due to LoRA weights not contained in checkpoint state
model.load_state_dict(checkpoint, strict=False)
mark_only_lora_as_trainable(model)

optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
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2 changes: 1 addition & 1 deletion lit_llama/adapter.py
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Expand Up @@ -93,7 +93,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
ak = ak.view(1, aT, self.n_head, head_size).repeat(B, 1, 1, 1).transpose(1, 2)
av = av.view(1, aT, self.n_head, head_size).repeat(B, 1, 1, 1).transpose(1, 2)

amask = torch.ones(q.shape[-2], ak.shape[-2], dtype=torch.bool)
amask = torch.ones(q.shape[-2], ak.shape[-2], dtype=torch.bool, device=x.device)
ay = F.scaled_dot_product_attention(q, ak, av, attn_mask=amask, dropout_p=0.0, is_causal=False)
y = y + self.gating_factor * ay

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