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import torch
import transformers
class LlamaComboScaledRope(torch.nn.Module):
"""
stolen from: https://huggingface.co/kaiokendev/superhot-13b-8k-no-rlhf-test
https://github.com/jquesnelle/scaled-rope
"""
def __init__(
self,
dim,
max_position_embeddings=2048,
base=10000,
scale=1,
alpha=1,
device=None,
):
super().__init__()
if alpha != 1:
base = base * alpha ** (dim / (dim - 2))
self.scale = 1 / scale
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
self.register_buffer("inv_freq", inv_freq)
# Build here to make `torch.jit.trace` work.
self.max_seq_len_cached = max_position_embeddings
t = torch.arange(
self.max_seq_len_cached,
device=self.inv_freq.device,
dtype=self.inv_freq.dtype,
)
t *= self.scale
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)
dtype = torch.get_default_dtype()
self.register_buffer(
"cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False
)
self.register_buffer(
"sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
)
def forward(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
if seq_len > self.max_seq_len_cached:
self.max_seq_len_cached = seq_len
t = torch.arange(
self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype
)
t *= self.scale
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.register_buffer(
"cos_cached", emb.cos()[None, None, :, :].to(x.dtype), persistent=False
)
self.register_buffer(
"sin_cached", emb.sin()[None, None, :, :].to(x.dtype), persistent=False
)
return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
)
def llama_scale_rope(model: transformers.LlamaForCausalLM, **kwargs):
kwargs.update({"device": model.device})
for layer in model.model.layers:
layer.self_attn.rotary_emb = LlamaComboScaledRope(
layer.self_attn.head_dim, **kwargs
)
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