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# Code adapted from https://huggingface.co/kaiokendev/superhot-13b-8k-no-rlhf-test/blob/main/llama_rope_scaled_monkey_patch.py
from functools import partial
import torch
import transformers
import transformers.models.llama.modeling_llama
class CondenseRotaryEmbedding(torch.nn.Module):
def __init__(
self, dim, ratio, max_position_embeddings=2048, base=10000, device=None
):
super().__init__()
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.ratio = ratio
max_position_embeddings *= ratio
self.max_seq_len_cached = max_position_embeddings
# print(f"Monkey Patching condense ratio {ratio}")
t = (
torch.arange(
self.max_seq_len_cached,
device=self.inv_freq.device,
dtype=self.inv_freq.dtype,
)
/ ratio
)
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
)
/ self.ratio
)
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 replace_llama_with_condense(ratio):
transformers.models.llama.modeling_llama.LlamaRotaryEmbedding = partial(
CondenseRotaryEmbedding, ratio=ratio
)
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