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long_short_transformer.py
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long_short_transformer.py
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from math import gcd
import functools
import torch
from torch import nn, einsum
import torch.nn.functional as F
from rotary_embedding_torch import RotaryEmbedding, apply_rotary_emb
from einops import rearrange, repeat
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def lcm(*numbers):
return int(functools.reduce(lambda x, y: int((x * y) / gcd(x, y)), numbers, 1))
def pad_to_multiple(tensor, multiple, dim = -1, value = 0):
seqlen = tensor.shape[dim]
m = seqlen / multiple
if m.is_integer():
return tensor
remainder = math.ceil(m) * multiple - seqlen
pad_offset = (0,) * (-1 - dim) * 2
return F.pad(tensor, (*pad_offset, 0, remainder), value=value)
def look_around(x, backward = 1, forward = 0, pad_value = -1, dim = 2):
t = x.shape[1]
dims = (len(x.shape) - dim) * (0, 0)
padded_x = F.pad(x, (*dims, backward, forward), value= pad_value)
tensors = [padded_x[:, ind:(ind + t), ...] for ind in range(forward + backward + 1)]
return torch.cat(tensors, dim=dim)
# classes
class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.fn = fn
self.norm = nn.LayerNorm(dim)
def forward(self, x, **kwargs):
x = self.norm(x)
return self.fn(x, **kwargs)
class FeedForward(nn.Module):
def __init__(self, dim, mult = 4, dropout = 0.):
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, dim * mult),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(dim * mult, dim)
)
def forward(self, x):
return self.net(x)
class LongShortAttention(nn.Module):
def __init__(
self,
*,
dim,
heads = 8,
dim_head = 64,
causal = True,
window_size = 128,
pos_emb = None,
segment_size = 16,
r = 1,
dropout = 0.
):
super().__init__()
assert not (causal and r >= segment_size), 'r should be less than segment size, if autoregressive'
inner_dim = heads * dim_head
self.scale = dim_head ** -0.5
self.heads = heads
self.causal = causal
self.window_size = window_size
self.segment_size = segment_size
self.pad_to_multiple = window_size if not causal else lcm(window_size, segment_size)
self.to_dynamic_proj = nn.Linear(dim_head, r, bias = False)
self.local_norm = nn.LayerNorm(dim_head)
self.global_norm = nn.LayerNorm(dim_head)
self.pos_emb = default(pos_emb, RotaryEmbedding(dim_head))
self.attn_dropout = nn.Dropout(dropout)
self.to_q = nn.Linear(dim, inner_dim, bias = False)
self.to_kv = nn.Linear(dim, inner_dim, bias = False)
self.to_out = nn.Linear(inner_dim, dim)
def forward(self, x, mask = None):
b, n, *_, h, device, causal, w, s = *x.shape, self.heads, x.device, self.causal, self.window_size, self.segment_size
# pad input sequence to multiples of window size (or window size and segment length if causal)
x = pad_to_multiple(x, self.pad_to_multiple, dim = -2, value = 0.)
# derive from variables
padded_len = x.shape[-2]
windows = padded_len // w
is_padded = padded_len != n
mask_value = -torch.finfo(x.dtype).max
# handle mask if padding was needed and mask was not given
if is_padded:
mask = default(mask, torch.ones((b, n), device = device).bool())
mask = pad_to_multiple(mask, w, dim = -1, value = False)
# get queries, keys, values
qkv = (self.to_q(x), self.to_kv(x))
# get sequence range, for calculating mask
seq_range = torch.arange(padded_len, device = device)
# split heads
q, kv = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h = h), qkv)
# rotary embedding
if exists(self.pos_emb):
rotary_emb = self.pos_emb(seq_range, cache_key = padded_len)
rotary_emb = rearrange(rotary_emb, 'n d -> () n d')
q, kv = map(lambda t: apply_rotary_emb(rotary_emb, t), (q, kv))
# scale queries
q = q * self.scale
# get local queries and keys similarity scores
window_fn = lambda t: rearrange(t, 'b (w n) d -> b w n d', n = w)
lq, lkv = map(window_fn, (q, kv))
lookaround_kwargs = {'backward': 1, 'forward': (0 if causal else 1)}
lkv = look_around(lkv, **lookaround_kwargs)
lkv = self.local_norm(lkv)
lsim = einsum('b w i d, b w j d -> b w i j', lq, lkv)
# prepare global key / values
if self.causal:
# autoregressive global attention is handled in segments
# later on, these segments are carefully masked to prevent leakage
gkv = rearrange(kv, 'b (n s) d -> b n s d', s = s)
pkv = self.to_dynamic_proj(gkv)
if exists(mask):
pmask = rearrange(mask, 'b (n s) -> b n s', s = s)
pkv.masked_fill_(~pmask[..., None], mask_value)
pkv = pkv.softmax(dim = -2)
gkv = einsum('b n s d, b n s r -> b n r d', gkv, pkv)
gkv = rearrange(gkv, 'b n r d -> b (n r) d')
else:
# equation (3) in the paper
pkv = self.to_dynamic_proj(kv)
if exists(mask):
pkv.masked_fill_(~mask[..., None], mask_value)
pkv = pkv.softmax(dim = -2)
gkv = einsum('b n d, b n r -> b r d', kv, pkv)
# calculate global queries and keys similarity scores
gkv = self.global_norm(gkv)
gsim = einsum('b n d, b r d -> b n r', q, gkv)
# concat values together (same as keys)
gkv = repeat(gkv, 'b r d -> b w r d', w = windows)
v = torch.cat((gkv, lkv), dim = -2)
# masking
buckets, i, j = lsim.shape[-3:]
if exists(mask):
mask = repeat(mask, 'b (w n) -> (b h) w n', n = w, h = h)
mask = look_around(mask, pad_value = False, **lookaround_kwargs)
mask = rearrange(mask, 'b w n -> b w () n')
lsim.masked_fill_(~mask, mask_value)
# mask out padding
seq_range_windowed = rearrange(seq_range, '(w n) -> () w n', w = windows)
pad_mask = look_around(seq_range_windowed, pad_value = -1, **lookaround_kwargs) == -1
lsim.masked_fill_(pad_mask[:, :, None], mask_value)
# calculate causal masking for both global and local
if self.causal:
g_range = rearrange(seq_range, '(n s) -> n s', s = s)
g_range_max = g_range.amax(dim = -1)
g_mask = seq_range[:, None] >= g_range_max[None, :]
g_mask = rearrange(g_mask, 'i j -> () i j')
gsim.masked_fill_(~g_mask, mask_value)
causal_mask = torch.ones(i, j, device = device).triu_(j - i + 1).bool()
causal_mask = repeat(causal_mask, 'i j -> () u i j', u = buckets)
lsim.masked_fill_(causal_mask, mask_value)
# concat local and global similarities together to ready for attention
gsim = rearrange(gsim, 'b (w n) r -> b w n r', w = windows)
sim = torch.cat((gsim, lsim), dim = -1)
# attention
attn = sim.softmax(dim = -1)
attn = self.attn_dropout(attn)
# aggregate values (same as keys, since tied) and project out
out = einsum('b w i j, b w j d -> b w i d', attn, v)
out = rearrange(out, '(b h) w n d -> b (w n) (h d)', h = h)
out = out[:, :n]
return self.to_out(out)
# main class
class LongShortTransformer(nn.Module):
def __init__(
self,
*,
num_tokens,
dim,
depth,
max_seq_len,
window_size = 128,
causal = True,
dim_head = 64,
heads = 8,
ff_mult = 4,
segment_size = None,
r = None,
ff_dropout = 0.,
attn_dropout = 0.
):
super().__init__()
self.max_seq_len = max_seq_len
self.token_emb = nn.Embedding(num_tokens, dim)
pos_emb = RotaryEmbedding(dim_head)
# handle autoregressive default variables differently
# specifically, segments are only used for autoregressive case
# r is the projected r << n in the non-autoregressive case, and the projected r per segment for the autoregressive case
# yea, it is confusing, i know
segment_size = default(segment_size, 16 if causal else None)
r = default(r, 1 if causal else 128)
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
PreNorm(dim, LongShortAttention(dim = dim, heads = heads, dim_head = dim_head, window_size = window_size, causal = causal, pos_emb = pos_emb, segment_size = segment_size, r = r, dropout = attn_dropout)),
PreNorm(dim, FeedForward(dim = dim, mult = ff_mult, dropout = ff_dropout))
]))
self.to_logits = nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, num_tokens)
)
def forward(self, x, mask = None):
x = self.token_emb(x)
for attn, ff in self.layers:
x = attn(x, mask = mask) + x
x = ff(x) + x
return self.to_logits(x)