File size: 2,492 Bytes
cf149e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | import torch
import transformers
import transformers.models.llama.modeling_llama
from einops import rearrange
import random
class ScaledRotaryEmbedding(torch.nn.Module):
def __init__(self, dim, 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)
max_position_embeddings = 8192
# 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,
)
self.scale = 1 / 4
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)
self.register_buffer(
"cos_cached", emb.cos()[None, None, :, :], persistent=False
)
self.register_buffer(
"sin_cached", emb.sin()[None, None, :, :], 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, :, :], persistent=False
)
self.register_buffer(
"sin_cached", emb.sin()[None, None, :, :], persistent=False
)
return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
)
def replace_llama_rope_with_scaled_rope():
transformers.models.llama.modeling_llama.LlamaRotaryEmbedding = (
ScaledRotaryEmbedding
) |