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Update llama.py

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Daniel Han-Chen 2024-02-25 02:02:02 +11:00
commit ecd21b375b
1 changed files with 7 additions and 3 deletions
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unsloth/models/llama.py
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@ -812,11 +812,15 @@ class LlamaRotaryEmbedding(torch.nn.Module):
inv_freq = 1.0 / (
self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device="cpu").float() / self.dim)
)
t = torch.arange(self.max_seq_len_cached, device="cpu", dtype=torch.int64).float()
freqs = torch.outer(t, inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
position_ids = torch.arange(self.max_seq_len_cached, device="cpu", dtype=torch.int64)#.float()
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
freqs = (inv_freq_expanded @ position_ids_expanded).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
# freqs = torch.outer(t, inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
# emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype=dtype, device=device, non_blocking=True), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype=dtype, device=device, non_blocking=True), persistent=False)
pass