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Fix Unnecessary move of tensors from CPU to GPU in LlamaRotaryEmbedding #22234

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Mar 17, 2023
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Fix Unnecessary move of tensors from CPU to GPU in LlamaRotaryEmbedding #22234

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12 changes: 6 additions & 6 deletions src/transformers/models/llama/modeling_llama.py
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Original file line number Diff line number Diff line change
Expand Up @@ -99,8 +99,8 @@ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
freqs = torch.einsum("i,j->ij", t, self.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.cos_cached = emb.cos()[None, None, :, :]
self.sin_cached = emb.sin()[None, None, :, :]
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
Comment on lines +102 to +103
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Suggested change
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
self.register_buffer("cos_cached", emb.cos()[None, None, :, :])
self.register_buffer("sin_cached", emb.sin()[None, None, :, :])

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Hi, I set persistent=False in this PR because cos_cached and sin_cached are not included in the model's state_dict of the original checkpoint. Setting persistent=True will induce missing key warnings when loading the llama model with from_pretrained().
But if this breaks the process of model loading on the meta device, please feel free to correct them.

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if persistent=True is aboslutely needed to make the meta device loading work, you could add r".*.cos_cached" in _keys_to_ignore_on_missing and _keys_to_ignore_on_unexpected (same for sin_cached)

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I've done some testing and this change isn't necessary: those buffers are not affected by with init_empty_weights(): and the problem was somewhere else.
Consider this suggestion invalid.
Thank you!


def forward(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
Expand All @@ -111,11 +111,11 @@ def forward(self, x, seq_len=None):
freqs = torch.einsum("i,j->ij", t, self.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).to(x.device)
self.cos_cached = emb.cos()[None, None, :, :].to(dtype=x.dtype)
self.sin_cached = emb.sin()[None, None, :, :].to(dtype=x.dtype)
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
Comment on lines +114 to +115
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Suggested change
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
self.register_buffer("cos_cached", emb.cos()[None, None, :, :])
self.register_buffer("sin_cached", emb.sin()[None, None, :, :])

return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype, device=x.device),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype, device=x.device),
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
)


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