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Bug fix to import Llama in OpenLM. #245

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Bug fix to import Llama in OpenLM. #245

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2 changes: 1 addition & 1 deletion open_lm/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -134,7 +134,7 @@ def load_model(args, model, different_seed=False):
# loading a bare (model only) checkpoint for fine-tune or evaluation
start_epoch, global_step = 0, 0
pretrained_seed = None
model.load_state_dict(checkpoint)
model.load_state_dict(checkpoint['state_dict'], strict=False)
logging.info(f"=> loaded checkpoint '{args.resume}' (epoch {start_epoch})")
return start_epoch, global_step, pretrained_seed

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4 changes: 2 additions & 2 deletions open_lm/positional_embedding/rotary.py
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Expand Up @@ -48,7 +48,7 @@ def __init__(self, dim_model: int, seq_len: int, *_, **__):
super().__init__()
# Generate and save the inverse frequency buffer (non trainable)
self.dim_model = dim_model
self.register_buffer("inv_freq", torch.zeros(self.dim_model // 2))
self.inv_freq = torch.zeros(self.dim_model // 2)

self._cos_cached = None
self._sin_cached = None
Expand All @@ -71,7 +71,7 @@ def _update_cos_sin_tables(self, seq_len: int = None, device: torch.device = Non
if seq_len > self._seq_len_cached or self._cos_cached.device != device or self._cos_cached.dtype != dtype:
self._seq_len_cached = seq_len
t = torch.arange(seq_len, device=device, dtype=torch.float32)
freqs = torch.einsum("i,j->ij", t, self.inv_freq.to(dtype))
freqs = torch.einsum("i,j->ij", t, self.inv_freq.to(dtype).to(device))
emb = torch.cat((freqs, freqs), dim=-1).to(device)

self._cos_cached = emb.cos()[None, :, None, :].to(dtype)
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