Spaces:
Running
on
Zero
Running
on
Zero
asigalov61
commited on
Commit
•
d8af9f8
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Parent(s):
a856cc3
Upload 2 files
Browse files- TMIDIX.py +0 -0
- x_transformer.py +2001 -0
TMIDIX.py
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x_transformer.py
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@@ -0,0 +1,2001 @@
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|
1 |
+
#===================================================================================================================
|
2 |
+
|
3 |
+
# X Trasformer Module
|
4 |
+
# Partial x-transformers code With useful modifications
|
5 |
+
#
|
6 |
+
# Version 1.0
|
7 |
+
#
|
8 |
+
# Original source code courtesy of lucidrains
|
9 |
+
# https://github.com/lucidrains/x-transformers
|
10 |
+
#
|
11 |
+
# Original source code retrieved on 05/10/2023
|
12 |
+
#
|
13 |
+
# Project Los Angeles
|
14 |
+
# Tegridy Code 2023
|
15 |
+
|
16 |
+
#===================================================================================================================
|
17 |
+
|
18 |
+
# Critical dependencies
|
19 |
+
#
|
20 |
+
# !pip install torch
|
21 |
+
# !pip install einops
|
22 |
+
|
23 |
+
#===================================================================================================================
|
24 |
+
|
25 |
+
from functools import partial
|
26 |
+
|
27 |
+
import torch
|
28 |
+
from torch import nn, einsum, Tensor
|
29 |
+
import torch.nn.functional as F
|
30 |
+
|
31 |
+
from collections import namedtuple
|
32 |
+
from functools import wraps
|
33 |
+
from packaging import version
|
34 |
+
from dataclasses import dataclass
|
35 |
+
|
36 |
+
from einops import rearrange
|
37 |
+
|
38 |
+
import math
|
39 |
+
from random import random
|
40 |
+
|
41 |
+
from functools import partial
|
42 |
+
from inspect import isfunction
|
43 |
+
|
44 |
+
from dataclasses import dataclass
|
45 |
+
from typing import List
|
46 |
+
|
47 |
+
from einops import rearrange, repeat, reduce
|
48 |
+
from einops.layers.torch import Rearrange
|
49 |
+
|
50 |
+
from math import ceil
|
51 |
+
|
52 |
+
from einops import rearrange, pack, unpack
|
53 |
+
|
54 |
+
#===================================================================================================================
|
55 |
+
|
56 |
+
# constants
|
57 |
+
|
58 |
+
EfficientAttentionConfig = namedtuple('EfficientAttentionConfig', ['enable_flash', 'enable_math', 'enable_mem_efficient'])
|
59 |
+
|
60 |
+
@dataclass
|
61 |
+
class Intermediates:
|
62 |
+
qk_similarities: Tensor = None
|
63 |
+
pre_softmax_attn: Tensor = None
|
64 |
+
post_softmax_attn: Tensor = None
|
65 |
+
|
66 |
+
# helpers
|
67 |
+
|
68 |
+
def exists(val):
|
69 |
+
return val is not None
|
70 |
+
|
71 |
+
def default(val, d):
|
72 |
+
return val if exists(val) else d
|
73 |
+
|
74 |
+
def once(fn):
|
75 |
+
called = False
|
76 |
+
@wraps(fn)
|
77 |
+
def inner(x):
|
78 |
+
nonlocal called
|
79 |
+
if called:
|
80 |
+
return
|
81 |
+
called = True
|
82 |
+
return fn(x)
|
83 |
+
return inner
|
84 |
+
|
85 |
+
print_once = once(print)
|
86 |
+
|
87 |
+
# main class
|
88 |
+
|
89 |
+
class Attend(nn.Module):
|
90 |
+
def __init__(
|
91 |
+
self,
|
92 |
+
*,
|
93 |
+
dropout = 0.,
|
94 |
+
causal = False,
|
95 |
+
heads = None,
|
96 |
+
talking_heads = False,
|
97 |
+
scale = None,
|
98 |
+
qk_norm = False,
|
99 |
+
flash = False,
|
100 |
+
):
|
101 |
+
super().__init__()
|
102 |
+
self.scale = scale
|
103 |
+
self.qk_norm = qk_norm
|
104 |
+
self.causal = causal
|
105 |
+
self.attn_fn = partial(F.softmax, dtype = torch.float32) if not qk_norm else F.softmax
|
106 |
+
|
107 |
+
self.dropout = dropout
|
108 |
+
self.attn_dropout = nn.Dropout(dropout)
|
109 |
+
|
110 |
+
# talking heads
|
111 |
+
|
112 |
+
assert not (flash and talking_heads), 'talking heads not compatible with flash attention'
|
113 |
+
|
114 |
+
self.talking_heads = talking_heads
|
115 |
+
if talking_heads:
|
116 |
+
self.pre_softmax_talking_heads = nn.Conv2d(heads, heads, 1, bias = False)
|
117 |
+
self.post_softmax_talking_heads = nn.Conv2d(heads, heads, 1, bias = False)
|
118 |
+
|
119 |
+
# flash attention
|
120 |
+
|
121 |
+
self.flash = flash
|
122 |
+
assert not (flash and version.parse(torch.__version__) < version.parse('2.0.0')), 'in order to use flash attention, you must be using pytorch 2.0 or above'
|
123 |
+
|
124 |
+
# determine efficient attention configs for cuda and cpu
|
125 |
+
|
126 |
+
self.cpu_config = EfficientAttentionConfig(True, True, True)
|
127 |
+
self.cuda_config = None
|
128 |
+
|
129 |
+
if not torch.cuda.is_available() or not flash:
|
130 |
+
return
|
131 |
+
|
132 |
+
device_properties = torch.cuda.get_device_properties(torch.device('cuda'))
|
133 |
+
|
134 |
+
if device_properties.major == 8 and device_properties.minor == 0:
|
135 |
+
print_once('A100 GPU detected, using flash attention if input tensor is on cuda')
|
136 |
+
self.cuda_config = EfficientAttentionConfig(True, False, False)
|
137 |
+
else:
|
138 |
+
print_once('Non-A100 GPU detected, using math or mem efficient attention if input tensor is on cuda')
|
139 |
+
self.cuda_config = EfficientAttentionConfig(False, True, True)
|
140 |
+
|
141 |
+
def flash_attn(
|
142 |
+
self,
|
143 |
+
q, k, v,
|
144 |
+
mask = None,
|
145 |
+
attn_bias = None
|
146 |
+
):
|
147 |
+
batch, heads, q_len, _, k_len, is_cuda, device = *q.shape, k.shape[-2], q.is_cuda, q.device
|
148 |
+
|
149 |
+
# Recommended for multi-query single-key-value attention by Tri Dao
|
150 |
+
# kv shape torch.Size([1, 512, 64]) -> torch.Size([1, 8, 512, 64])
|
151 |
+
|
152 |
+
if k.ndim == 3:
|
153 |
+
k = rearrange(k, 'b ... -> b 1 ...').expand_as(q)
|
154 |
+
|
155 |
+
if v.ndim == 3:
|
156 |
+
v = rearrange(v, 'b ... -> b 1 ...').expand_as(q)
|
157 |
+
|
158 |
+
# handle scale - by default they scale by dim_head ** -0.5, but need to take care if using cosine sim attention
|
159 |
+
|
160 |
+
if self.qk_norm:
|
161 |
+
default_scale = q.shape[-1] ** -0.5
|
162 |
+
q = q * (default_scale / self.scale)
|
163 |
+
|
164 |
+
# Check if mask exists and expand to compatible shape
|
165 |
+
# The mask is B L, so it would have to be expanded to B H N L
|
166 |
+
|
167 |
+
causal = self.causal
|
168 |
+
|
169 |
+
if exists(mask):
|
170 |
+
assert mask.ndim == 4
|
171 |
+
mask = mask.expand(batch, heads, q_len, k_len)
|
172 |
+
|
173 |
+
# manually handle causal mask, if another mask was given
|
174 |
+
|
175 |
+
if causal:
|
176 |
+
causal_mask = torch.ones((q_len, k_len), dtype = torch.bool, device = device).triu(k_len - q_len + 1)
|
177 |
+
mask = mask | causal_mask
|
178 |
+
causal = False
|
179 |
+
|
180 |
+
# handle alibi positional bias
|
181 |
+
# convert from bool to float
|
182 |
+
|
183 |
+
if exists(attn_bias):
|
184 |
+
attn_bias = rearrange(attn_bias, 'h i j -> 1 h i j').expand(batch, -1, -1, -1)
|
185 |
+
|
186 |
+
# if mask given, the mask would already contain the causal mask from above logic
|
187 |
+
# otherwise, if no mask given but still causal, mask out alibi positional bias to a large negative number
|
188 |
+
|
189 |
+
mask_value = -torch.finfo(q.dtype).max
|
190 |
+
|
191 |
+
if exists(mask):
|
192 |
+
attn_bias = attn_bias.masked_fill(mask, mask_value // 2)
|
193 |
+
elif causal:
|
194 |
+
causal_mask = torch.ones((q_len, k_len), dtype = torch.bool, device = device).triu(k_len - q_len + 1)
|
195 |
+
attn_bias = attn_bias.masked_fill(causal_mask, mask_value // 2)
|
196 |
+
causal = False
|
197 |
+
|
198 |
+
# scaled_dot_product_attention handles attn_mask either as bool or additive bias
|
199 |
+
# make it an additive bias here
|
200 |
+
|
201 |
+
mask = attn_bias
|
202 |
+
|
203 |
+
# Check if there is a compatible device for flash attention
|
204 |
+
|
205 |
+
config = self.cuda_config if is_cuda else self.cpu_config
|
206 |
+
|
207 |
+
# pytorch 2.0 flash attn: q, k, v, mask, dropout, causal, softmax_scale
|
208 |
+
|
209 |
+
with torch.backends.cuda.sdp_kernel(**config._asdict()):
|
210 |
+
out = F.scaled_dot_product_attention(
|
211 |
+
q, k, v,
|
212 |
+
attn_mask = mask,
|
213 |
+
dropout_p = self.dropout if self.training else 0.,
|
214 |
+
is_causal = causal
|
215 |
+
)
|
216 |
+
|
217 |
+
return out, Intermediates()
|
218 |
+
|
219 |
+
def forward(
|
220 |
+
self,
|
221 |
+
q, k, v,
|
222 |
+
mask = None,
|
223 |
+
attn_bias = None,
|
224 |
+
prev_attn = None
|
225 |
+
):
|
226 |
+
"""
|
227 |
+
einstein notation
|
228 |
+
b - batch
|
229 |
+
h - heads
|
230 |
+
n, i, j - sequence length (base sequence length, source, target)
|
231 |
+
d - feature dimension
|
232 |
+
"""
|
233 |
+
|
234 |
+
n, device = q.shape[-2], q.device
|
235 |
+
|
236 |
+
scale = default(self.scale, q.shape[-1] ** -0.5)
|
237 |
+
|
238 |
+
if self.flash:
|
239 |
+
assert not exists(prev_attn), 'residual attention not compatible with flash attention'
|
240 |
+
return self.flash_attn(q, k, v, mask = mask, attn_bias = attn_bias)
|
241 |
+
|
242 |
+
kv_einsum_eq = 'b j d' if k.ndim == 3 else 'b h j d'
|
243 |
+
|
244 |
+
dots = einsum(f'b h i d, {kv_einsum_eq} -> b h i j', q, k) * scale
|
245 |
+
|
246 |
+
if exists(prev_attn):
|
247 |
+
dots = dots + prev_attn
|
248 |
+
|
249 |
+
qk_similarities = dots.clone()
|
250 |
+
|
251 |
+
if self.talking_heads:
|
252 |
+
dots = self.pre_softmax_talking_heads(dots)
|
253 |
+
|
254 |
+
if exists(attn_bias):
|
255 |
+
dots = dots + attn_bias
|
256 |
+
|
257 |
+
dtype = dots.dtype
|
258 |
+
pre_softmax_attn = dots.clone()
|
259 |
+
|
260 |
+
mask_value = -torch.finfo(dots.dtype).max
|
261 |
+
|
262 |
+
if exists(mask):
|
263 |
+
dots = dots.masked_fill(mask, mask_value)
|
264 |
+
|
265 |
+
if self.causal:
|
266 |
+
i, j = dots.shape[-2:]
|
267 |
+
causal_mask = torch.ones((i, j), dtype = torch.bool, device = device).triu(j - i + 1)
|
268 |
+
dots = dots.masked_fill(causal_mask, mask_value)
|
269 |
+
|
270 |
+
attn = self.attn_fn(dots, dim = -1)
|
271 |
+
attn = attn.type(dtype)
|
272 |
+
|
273 |
+
post_softmax_attn = attn.clone()
|
274 |
+
|
275 |
+
attn = self.attn_dropout(attn)
|
276 |
+
|
277 |
+
if self.talking_heads:
|
278 |
+
attn = self.post_softmax_talking_heads(attn)
|
279 |
+
|
280 |
+
out = einsum(f'b h i j, {kv_einsum_eq} -> b h i d', attn, v)
|
281 |
+
|
282 |
+
intermediates = Intermediates(
|
283 |
+
qk_similarities = qk_similarities,
|
284 |
+
pre_softmax_attn = pre_softmax_attn,
|
285 |
+
post_softmax_attn = post_softmax_attn
|
286 |
+
)
|
287 |
+
|
288 |
+
return out, intermediates
|
289 |
+
|
290 |
+
#===================================================================================================================
|
291 |
+
|
292 |
+
# constants
|
293 |
+
|
294 |
+
DEFAULT_DIM_HEAD = 64
|
295 |
+
|
296 |
+
@dataclass
|
297 |
+
class LayerIntermediates:
|
298 |
+
hiddens: List[Tensor] = None,
|
299 |
+
attn_intermediates: List[Intermediates] = None
|
300 |
+
|
301 |
+
# helpers
|
302 |
+
|
303 |
+
def exists(val):
|
304 |
+
return val is not None
|
305 |
+
|
306 |
+
def default(val, d):
|
307 |
+
if exists(val):
|
308 |
+
return val
|
309 |
+
return d() if isfunction(d) else d
|
310 |
+
|
311 |
+
def cast_tuple(val, depth):
|
312 |
+
return val if isinstance(val, tuple) else (val,) * depth
|
313 |
+
|
314 |
+
def maybe(fn):
|
315 |
+
@wraps(fn)
|
316 |
+
def inner(x, *args, **kwargs):
|
317 |
+
if not exists(x):
|
318 |
+
return x
|
319 |
+
return fn(x, *args, **kwargs)
|
320 |
+
return inner
|
321 |
+
|
322 |
+
class always():
|
323 |
+
def __init__(self, val):
|
324 |
+
self.val = val
|
325 |
+
def __call__(self, *args, **kwargs):
|
326 |
+
return self.val
|
327 |
+
|
328 |
+
class not_equals():
|
329 |
+
def __init__(self, val):
|
330 |
+
self.val = val
|
331 |
+
def __call__(self, x, *args, **kwargs):
|
332 |
+
return x != self.val
|
333 |
+
|
334 |
+
class equals():
|
335 |
+
def __init__(self, val):
|
336 |
+
self.val = val
|
337 |
+
def __call__(self, x, *args, **kwargs):
|
338 |
+
return x == self.val
|
339 |
+
|
340 |
+
# tensor helpers
|
341 |
+
|
342 |
+
def max_neg_value(tensor):
|
343 |
+
return -torch.finfo(tensor.dtype).max
|
344 |
+
|
345 |
+
def l2norm(t, groups = 1):
|
346 |
+
t = rearrange(t, '... (g d) -> ... g d', g = groups)
|
347 |
+
t = F.normalize(t, p = 2, dim = -1)
|
348 |
+
return rearrange(t, '... g d -> ... (g d)')
|
349 |
+
|
350 |
+
def pad_at_dim(t, pad, dim = -1, value = 0.):
|
351 |
+
dims_from_right = (- dim - 1) if dim < 0 else (t.ndim - dim - 1)
|
352 |
+
zeros = ((0, 0) * dims_from_right)
|
353 |
+
return F.pad(t, (*zeros, *pad), value = value)
|
354 |
+
|
355 |
+
def or_reduce(masks):
|
356 |
+
head, *body = masks
|
357 |
+
for rest in body:
|
358 |
+
head = head | rest
|
359 |
+
return head
|
360 |
+
|
361 |
+
# init helpers
|
362 |
+
|
363 |
+
def init_zero_(layer):
|
364 |
+
nn.init.constant_(layer.weight, 0.)
|
365 |
+
if exists(layer.bias):
|
366 |
+
nn.init.constant_(layer.bias, 0.)
|
367 |
+
|
368 |
+
# keyword argument helpers
|
369 |
+
|
370 |
+
def pick_and_pop(keys, d):
|
371 |
+
values = list(map(lambda key: d.pop(key), keys))
|
372 |
+
return dict(zip(keys, values))
|
373 |
+
|
374 |
+
def group_dict_by_key(cond, d):
|
375 |
+
return_val = [dict(),dict()]
|
376 |
+
for key in d.keys():
|
377 |
+
match = bool(cond(key))
|
378 |
+
ind = int(not match)
|
379 |
+
return_val[ind][key] = d[key]
|
380 |
+
return (*return_val,)
|
381 |
+
|
382 |
+
def string_begins_with(prefix, str):
|
383 |
+
return str.startswith(prefix)
|
384 |
+
|
385 |
+
def group_by_key_prefix(prefix, d):
|
386 |
+
return group_dict_by_key(partial(string_begins_with, prefix), d)
|
387 |
+
|
388 |
+
def groupby_prefix_and_trim(prefix, d):
|
389 |
+
kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
|
390 |
+
kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
|
391 |
+
return kwargs_without_prefix, kwargs
|
392 |
+
|
393 |
+
# initializations
|
394 |
+
|
395 |
+
def deepnorm_init(
|
396 |
+
transformer,
|
397 |
+
beta,
|
398 |
+
module_name_match_list = ['.ff.', '.to_v', '.to_out']
|
399 |
+
):
|
400 |
+
for name, module in transformer.named_modules():
|
401 |
+
if type(module) != nn.Linear:
|
402 |
+
continue
|
403 |
+
|
404 |
+
needs_beta_gain = any(map(lambda substr: substr in name, module_name_match_list))
|
405 |
+
gain = beta if needs_beta_gain else 1
|
406 |
+
nn.init.xavier_normal_(module.weight.data, gain = gain)
|
407 |
+
|
408 |
+
if exists(module.bias):
|
409 |
+
nn.init.constant_(module.bias.data, 0)
|
410 |
+
|
411 |
+
# structured dropout, more effective than traditional attention dropouts
|
412 |
+
|
413 |
+
def dropout_seq(seq, mask, dropout):
|
414 |
+
b, n, *_, device = *seq.shape, seq.device
|
415 |
+
logits = torch.randn(b, n, device = device)
|
416 |
+
|
417 |
+
if exists(mask):
|
418 |
+
mask_value = max_neg_value(logits)
|
419 |
+
logits = logits.masked_fill(~mask, mask_value)
|
420 |
+
|
421 |
+
keep_prob = 1. - dropout
|
422 |
+
num_keep = max(1, int(keep_prob * n))
|
423 |
+
keep_indices = logits.topk(num_keep, dim = 1).indices
|
424 |
+
|
425 |
+
batch_indices = torch.arange(b, device = device)
|
426 |
+
batch_indices = rearrange(batch_indices, 'b -> b 1')
|
427 |
+
|
428 |
+
seq = seq[batch_indices, keep_indices]
|
429 |
+
|
430 |
+
if exists(mask):
|
431 |
+
seq_counts = mask.sum(dim = -1)
|
432 |
+
seq_keep_counts = torch.ceil(seq_counts * keep_prob).int()
|
433 |
+
keep_mask = torch.arange(num_keep, device = device) < rearrange(seq_keep_counts, 'b -> b 1')
|
434 |
+
|
435 |
+
mask = mask[batch_indices, keep_indices] & keep_mask
|
436 |
+
|
437 |
+
return seq, mask
|
438 |
+
|
439 |
+
# activations
|
440 |
+
|
441 |
+
class ReluSquared(nn.Module):
|
442 |
+
def forward(self, x):
|
443 |
+
return F.relu(x) ** 2
|
444 |
+
|
445 |
+
# embedding
|
446 |
+
|
447 |
+
class TokenEmbedding(nn.Module):
|
448 |
+
def __init__(self, dim, num_tokens, l2norm_embed = False):
|
449 |
+
super().__init__()
|
450 |
+
self.l2norm_embed = l2norm_embed
|
451 |
+
self.emb = nn.Embedding(num_tokens, dim)
|
452 |
+
|
453 |
+
def forward(self, x):
|
454 |
+
token_emb = self.emb(x)
|
455 |
+
return l2norm(token_emb) if self.l2norm_embed else token_emb
|
456 |
+
|
457 |
+
# positional embeddings
|
458 |
+
|
459 |
+
class AbsolutePositionalEmbedding(nn.Module):
|
460 |
+
def __init__(self, dim, max_seq_len, l2norm_embed = False):
|
461 |
+
super().__init__()
|
462 |
+
self.scale = dim ** -0.5 if not l2norm_embed else 1.
|
463 |
+
self.max_seq_len = max_seq_len
|
464 |
+
self.l2norm_embed = l2norm_embed
|
465 |
+
self.emb = nn.Embedding(max_seq_len, dim)
|
466 |
+
|
467 |
+
def forward(self, x, pos = None):
|
468 |
+
seq_len, device = x.shape[1], x.device
|
469 |
+
assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}'
|
470 |
+
|
471 |
+
if not exists(pos):
|
472 |
+
pos = torch.arange(seq_len, device = device)
|
473 |
+
|
474 |
+
pos_emb = self.emb(pos)
|
475 |
+
pos_emb = pos_emb * self.scale
|
476 |
+
return l2norm(pos_emb) if self.l2norm_embed else pos_emb
|
477 |
+
|
478 |
+
class ScaledSinusoidalEmbedding(nn.Module):
|
479 |
+
def __init__(self, dim, theta = 10000):
|
480 |
+
super().__init__()
|
481 |
+
assert (dim % 2) == 0
|
482 |
+
self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5)
|
483 |
+
|
484 |
+
half_dim = dim // 2
|
485 |
+
freq_seq = torch.arange(half_dim).float() / half_dim
|
486 |
+
inv_freq = theta ** -freq_seq
|
487 |
+
self.register_buffer('inv_freq', inv_freq, persistent = False)
|
488 |
+
|
489 |
+
def forward(self, x, pos = None):
|
490 |
+
seq_len, device = x.shape[1], x.device
|
491 |
+
|
492 |
+
if not exists(pos):
|
493 |
+
pos = torch.arange(seq_len, device = device)
|
494 |
+
|
495 |
+
emb = einsum('i, j -> i j', pos, self.inv_freq)
|
496 |
+
emb = torch.cat((emb.sin(), emb.cos()), dim = -1)
|
497 |
+
return emb * self.scale
|
498 |
+
|
499 |
+
class RelativePositionBias(nn.Module):
|
500 |
+
def __init__(self, scale, causal = False, num_buckets = 32, max_distance = 128, heads = 8):
|
501 |
+
super().__init__()
|
502 |
+
self.scale = scale
|
503 |
+
self.causal = causal
|
504 |
+
self.num_buckets = num_buckets
|
505 |
+
self.max_distance = max_distance
|
506 |
+
self.relative_attention_bias = nn.Embedding(num_buckets, heads)
|
507 |
+
|
508 |
+
@staticmethod
|
509 |
+
def _relative_position_bucket(relative_position, causal = True, num_buckets = 32, max_distance = 128):
|
510 |
+
ret = 0
|
511 |
+
n = -relative_position
|
512 |
+
if not causal:
|
513 |
+
num_buckets //= 2
|
514 |
+
ret += (n < 0).long() * num_buckets
|
515 |
+
n = torch.abs(n)
|
516 |
+
else:
|
517 |
+
n = torch.max(n, torch.zeros_like(n))
|
518 |
+
|
519 |
+
max_exact = num_buckets // 2
|
520 |
+
is_small = n < max_exact
|
521 |
+
|
522 |
+
val_if_large = max_exact + (
|
523 |
+
torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
|
524 |
+
).long()
|
525 |
+
val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
|
526 |
+
|
527 |
+
ret += torch.where(is_small, n, val_if_large)
|
528 |
+
return ret
|
529 |
+
|
530 |
+
@property
|
531 |
+
def device(self):
|
532 |
+
return next(self.parameters()).device
|
533 |
+
|
534 |
+
def forward(self, i, j):
|
535 |
+
device = self.device
|
536 |
+
q_pos = torch.arange(j - i, j, dtype = torch.long, device = device)
|
537 |
+
k_pos = torch.arange(j, dtype = torch.long, device = device)
|
538 |
+
rel_pos = k_pos[None, :] - q_pos[:, None]
|
539 |
+
rp_bucket = self._relative_position_bucket(rel_pos, causal = self.causal, num_buckets = self.num_buckets, max_distance = self.max_distance)
|
540 |
+
values = self.relative_attention_bias(rp_bucket)
|
541 |
+
bias = rearrange(values, 'i j h -> h i j')
|
542 |
+
return bias * self.scale
|
543 |
+
|
544 |
+
class DynamicPositionBias(nn.Module):
|
545 |
+
def __init__(self, dim, *, heads, depth, log_distance = False, norm = False):
|
546 |
+
super().__init__()
|
547 |
+
assert depth >= 1, 'depth for dynamic position bias MLP must be greater or equal to 1'
|
548 |
+
self.log_distance = log_distance
|
549 |
+
|
550 |
+
self.mlp = nn.ModuleList([])
|
551 |
+
|
552 |
+
self.mlp.append(nn.Sequential(
|
553 |
+
nn.Linear(1, dim),
|
554 |
+
nn.LayerNorm(dim) if norm else nn.Identity(),
|
555 |
+
nn.SiLU()
|
556 |
+
))
|
557 |
+
|
558 |
+
for _ in range(depth - 1):
|
559 |
+
self.mlp.append(nn.Sequential(
|
560 |
+
nn.Linear(dim, dim),
|
561 |
+
nn.LayerNorm(dim) if norm else nn.Identity(),
|
562 |
+
nn.SiLU()
|
563 |
+
))
|
564 |
+
|
565 |
+
self.mlp.append(nn.Linear(dim, heads))
|
566 |
+
|
567 |
+
@property
|
568 |
+
def device(self):
|
569 |
+
return next(self.parameters()).device
|
570 |
+
|
571 |
+
def forward(self, i, j):
|
572 |
+
assert i == j
|
573 |
+
n, device = j, self.device
|
574 |
+
|
575 |
+
# get the (n x n) matrix of distances
|
576 |
+
seq_arange = torch.arange(n, device = device)
|
577 |
+
context_arange = torch.arange(n, device = device)
|
578 |
+
indices = rearrange(seq_arange, 'i -> i 1') - rearrange(context_arange, 'j -> 1 j')
|
579 |
+
indices += (n - 1)
|
580 |
+
|
581 |
+
# input to continuous positions MLP
|
582 |
+
pos = torch.arange(-n + 1, n, device = device).float()
|
583 |
+
pos = rearrange(pos, '... -> ... 1')
|
584 |
+
|
585 |
+
if self.log_distance:
|
586 |
+
pos = torch.sign(pos) * torch.log(pos.abs() + 1) # log of distance is sign(rel_pos) * log(abs(rel_pos) + 1)
|
587 |
+
|
588 |
+
for layer in self.mlp:
|
589 |
+
pos = layer(pos)
|
590 |
+
|
591 |
+
# get position biases
|
592 |
+
bias = pos[indices]
|
593 |
+
bias = rearrange(bias, 'i j h -> h i j')
|
594 |
+
return bias
|
595 |
+
|
596 |
+
class AlibiPositionalBias(nn.Module):
|
597 |
+
def __init__(self, heads, total_heads, **kwargs):
|
598 |
+
super().__init__()
|
599 |
+
self.heads = heads
|
600 |
+
self.total_heads = total_heads
|
601 |
+
|
602 |
+
slopes = Tensor(self._get_slopes(heads))
|
603 |
+
slopes = rearrange(slopes, 'h -> h 1 1')
|
604 |
+
self.register_buffer('slopes', slopes, persistent = False)
|
605 |
+
self.register_buffer('bias', None, persistent = False)
|
606 |
+
|
607 |
+
def get_bias(self, i, j, device):
|
608 |
+
i_arange = torch.arange(j - i, j, device = device)
|
609 |
+
j_arange = torch.arange(j, device = device)
|
610 |
+
bias = -torch.abs(rearrange(j_arange, 'j -> 1 1 j') - rearrange(i_arange, 'i -> 1 i 1'))
|
611 |
+
return bias
|
612 |
+
|
613 |
+
@staticmethod
|
614 |
+
def _get_slopes(heads):
|
615 |
+
def get_slopes_power_of_2(n):
|
616 |
+
start = (2**(-2**-(math.log2(n)-3)))
|
617 |
+
ratio = start
|
618 |
+
return [start*ratio**i for i in range(n)]
|
619 |
+
|
620 |
+
if math.log2(heads).is_integer():
|
621 |
+
return get_slopes_power_of_2(heads)
|
622 |
+
|
623 |
+
closest_power_of_2 = 2 ** math.floor(math.log2(heads))
|
624 |
+
return get_slopes_power_of_2(closest_power_of_2) + get_slopes_power_of_2(2 * closest_power_of_2)[0::2][:heads-closest_power_of_2]
|
625 |
+
|
626 |
+
@property
|
627 |
+
def device(self):
|
628 |
+
return next(self.buffers()).device
|
629 |
+
|
630 |
+
def forward(self, i, j):
|
631 |
+
h, device = self.total_heads, self.device
|
632 |
+
|
633 |
+
if exists(self.bias) and self.bias.shape[-1] >= j:
|
634 |
+
return self.bias[..., :i, :j]
|
635 |
+
|
636 |
+
bias = self.get_bias(i, j, device)
|
637 |
+
bias = bias * self.slopes
|
638 |
+
|
639 |
+
num_heads_unalibied = h - bias.shape[0]
|
640 |
+
bias = pad_at_dim(bias, (0, num_heads_unalibied), dim = 0)
|
641 |
+
self.register_buffer('bias', bias, persistent = False)
|
642 |
+
|
643 |
+
return self.bias
|
644 |
+
|
645 |
+
class LearnedAlibiPositionalBias(AlibiPositionalBias):
|
646 |
+
def __init__(self, heads, total_heads):
|
647 |
+
super().__init__(heads, total_heads)
|
648 |
+
log_slopes = torch.log(self.slopes)
|
649 |
+
self.learned_logslopes = nn.Parameter(log_slopes)
|
650 |
+
|
651 |
+
def forward(self, i, j):
|
652 |
+
h, i, j, device = self.heads, self.device
|
653 |
+
|
654 |
+
def get_slopes(param):
|
655 |
+
return pad_at_dim(param.exp(), (0, h - param.shape[0]), dim = -2)
|
656 |
+
|
657 |
+
if exists(self.bias) and self.bias.shape[-1] >= j:
|
658 |
+
bias = self.bias[..., :i, :j]
|
659 |
+
else:
|
660 |
+
bias = self.get_bias(i, j, device)
|
661 |
+
self.register_buffer('bias', bias, persistent = False)
|
662 |
+
|
663 |
+
slopes = get_slopes(self.learned_logslopes)
|
664 |
+
bias = bias * slopes
|
665 |
+
|
666 |
+
return bias
|
667 |
+
|
668 |
+
class RotaryEmbedding(nn.Module):
|
669 |
+
def __init__(
|
670 |
+
self,
|
671 |
+
dim,
|
672 |
+
use_xpos = False,
|
673 |
+
scale_base = 512
|
674 |
+
):
|
675 |
+
super().__init__()
|
676 |
+
inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
|
677 |
+
self.register_buffer('inv_freq', inv_freq)
|
678 |
+
|
679 |
+
if not use_xpos:
|
680 |
+
self.register_buffer('scale', None)
|
681 |
+
return
|
682 |
+
|
683 |
+
scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
|
684 |
+
|
685 |
+
self.scale_base = scale_base
|
686 |
+
self.register_buffer('scale', scale)
|
687 |
+
|
688 |
+
def forward(self, seq_len, device):
|
689 |
+
t = torch.arange(seq_len, device = device).type_as(self.inv_freq)
|
690 |
+
freqs = torch.einsum('i , j -> i j', t, self.inv_freq)
|
691 |
+
freqs = torch.cat((freqs, freqs), dim = -1)
|
692 |
+
|
693 |
+
if not exists(self.scale):
|
694 |
+
return freqs, 1.
|
695 |
+
|
696 |
+
power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base
|
697 |
+
scale = self.scale ** rearrange(power, 'n -> n 1')
|
698 |
+
scale = torch.cat((scale, scale), dim = -1)
|
699 |
+
|
700 |
+
return freqs, scale
|
701 |
+
|
702 |
+
|
703 |
+
def rotate_half(x):
|
704 |
+
x = rearrange(x, '... (j d) -> ... j d', j = 2)
|
705 |
+
x1, x2 = x.unbind(dim = -2)
|
706 |
+
return torch.cat((-x2, x1), dim = -1)
|
707 |
+
|
708 |
+
def apply_rotary_pos_emb(t, freqs, scale = 1):
|
709 |
+
seq_len = t.shape[-2]
|
710 |
+
freqs = freqs[-seq_len:, :]
|
711 |
+
return (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
|
712 |
+
|
713 |
+
# norms
|
714 |
+
|
715 |
+
class Scale(nn.Module):
|
716 |
+
def __init__(self, value, fn):
|
717 |
+
super().__init__()
|
718 |
+
self.value = value
|
719 |
+
self.fn = fn
|
720 |
+
|
721 |
+
def forward(self, x, **kwargs):
|
722 |
+
out = self.fn(x, **kwargs)
|
723 |
+
scale_fn = lambda t: t * self.value
|
724 |
+
|
725 |
+
if not isinstance(out, tuple):
|
726 |
+
return scale_fn(out)
|
727 |
+
|
728 |
+
return (scale_fn(out[0]), *out[1:])
|
729 |
+
|
730 |
+
class ScaleNorm(nn.Module):
|
731 |
+
def __init__(self, dim, eps = 1e-5):
|
732 |
+
super().__init__()
|
733 |
+
self.eps = eps
|
734 |
+
self.g = nn.Parameter(torch.ones(1) * (dim ** -0.5))
|
735 |
+
|
736 |
+
def forward(self, x):
|
737 |
+
norm = torch.norm(x, dim = -1, keepdim = True)
|
738 |
+
return x / norm.clamp(min = self.eps) * self.g
|
739 |
+
|
740 |
+
class RMSNorm(nn.Module):
|
741 |
+
def __init__(self, dim, eps = 1e-8):
|
742 |
+
super().__init__()
|
743 |
+
self.scale = dim ** -0.5
|
744 |
+
self.eps = eps
|
745 |
+
self.g = nn.Parameter(torch.ones(dim))
|
746 |
+
|
747 |
+
def forward(self, x):
|
748 |
+
norm = torch.norm(x, dim = -1, keepdim = True) * self.scale
|
749 |
+
return x / norm.clamp(min = self.eps) * self.g
|
750 |
+
|
751 |
+
# residual and residual gates
|
752 |
+
|
753 |
+
class Residual(nn.Module):
|
754 |
+
def __init__(self, dim, scale_residual = False, scale_residual_constant = 1.):
|
755 |
+
super().__init__()
|
756 |
+
self.residual_scale = nn.Parameter(torch.ones(dim)) if scale_residual else None
|
757 |
+
self.scale_residual_constant = scale_residual_constant
|
758 |
+
|
759 |
+
def forward(self, x, residual):
|
760 |
+
if exists(self.residual_scale):
|
761 |
+
residual = residual * self.residual_scale
|
762 |
+
|
763 |
+
if self.scale_residual_constant != 1:
|
764 |
+
residual = residual * self.scale_residual_constant
|
765 |
+
|
766 |
+
return x + residual
|
767 |
+
|
768 |
+
class GRUGating(nn.Module):
|
769 |
+
def __init__(self, dim, scale_residual = False, **kwargs):
|
770 |
+
super().__init__()
|
771 |
+
self.gru = nn.GRUCell(dim, dim)
|
772 |
+
self.residual_scale = nn.Parameter(torch.ones(dim)) if scale_residual else None
|
773 |
+
|
774 |
+
def forward(self, x, residual):
|
775 |
+
if exists(self.residual_scale):
|
776 |
+
residual = residual * self.residual_scale
|
777 |
+
|
778 |
+
gated_output = self.gru(
|
779 |
+
rearrange(x, 'b n d -> (b n) d'),
|
780 |
+
rearrange(residual, 'b n d -> (b n) d')
|
781 |
+
)
|
782 |
+
|
783 |
+
return gated_output.reshape_as(x)
|
784 |
+
|
785 |
+
# token shifting
|
786 |
+
|
787 |
+
def shift(t, amount, mask = None):
|
788 |
+
if amount == 0:
|
789 |
+
return t
|
790 |
+
else:
|
791 |
+
amount = min(amount, t.shape[1])
|
792 |
+
|
793 |
+
if exists(mask):
|
794 |
+
t = t.masked_fill(~mask[..., None], 0.)
|
795 |
+
|
796 |
+
return pad_at_dim(t, (amount, -amount), dim = - 2, value = 0.)
|
797 |
+
|
798 |
+
class ShiftTokens(nn.Module):
|
799 |
+
def __init__(self, shifts, fn):
|
800 |
+
super().__init__()
|
801 |
+
self.fn = fn
|
802 |
+
self.shifts = tuple(shifts)
|
803 |
+
|
804 |
+
def forward(self, x, **kwargs):
|
805 |
+
mask = kwargs.get('mask', None)
|
806 |
+
shifts = self.shifts
|
807 |
+
segments = len(shifts)
|
808 |
+
feats_per_shift = x.shape[-1] // segments
|
809 |
+
splitted = x.split(feats_per_shift, dim = -1)
|
810 |
+
segments_to_shift, rest = splitted[:segments], splitted[segments:]
|
811 |
+
segments_to_shift = list(map(lambda args: shift(*args, mask = mask), zip(segments_to_shift, shifts)))
|
812 |
+
x = torch.cat((*segments_to_shift, *rest), dim = -1)
|
813 |
+
return self.fn(x, **kwargs)
|
814 |
+
|
815 |
+
# feedforward
|
816 |
+
|
817 |
+
class GLU(nn.Module):
|
818 |
+
def __init__(self, dim_in, dim_out, activation):
|
819 |
+
super().__init__()
|
820 |
+
self.act = activation
|
821 |
+
self.proj = nn.Linear(dim_in, dim_out * 2)
|
822 |
+
|
823 |
+
def forward(self, x):
|
824 |
+
x, gate = self.proj(x).chunk(2, dim = -1)
|
825 |
+
return x * self.act(gate)
|
826 |
+
|
827 |
+
class FeedForward(nn.Module):
|
828 |
+
def __init__(
|
829 |
+
self,
|
830 |
+
dim,
|
831 |
+
dim_out = None,
|
832 |
+
mult = 4,
|
833 |
+
glu = False,
|
834 |
+
swish = False,
|
835 |
+
relu_squared = False,
|
836 |
+
post_act_ln = False,
|
837 |
+
dropout = 0.,
|
838 |
+
no_bias = False,
|
839 |
+
zero_init_output = False
|
840 |
+
):
|
841 |
+
super().__init__()
|
842 |
+
inner_dim = int(dim * mult)
|
843 |
+
dim_out = default(dim_out, dim)
|
844 |
+
|
845 |
+
if relu_squared:
|
846 |
+
activation = ReluSquared()
|
847 |
+
elif swish:
|
848 |
+
activation = nn.SiLU()
|
849 |
+
else:
|
850 |
+
activation = nn.GELU()
|
851 |
+
|
852 |
+
project_in = nn.Sequential(
|
853 |
+
nn.Linear(dim, inner_dim, bias = not no_bias),
|
854 |
+
activation
|
855 |
+
) if not glu else GLU(dim, inner_dim, activation)
|
856 |
+
|
857 |
+
self.ff = nn.Sequential(
|
858 |
+
project_in,
|
859 |
+
nn.LayerNorm(inner_dim) if post_act_ln else nn.Identity(),
|
860 |
+
nn.Dropout(dropout),
|
861 |
+
nn.Linear(inner_dim, dim_out, bias = not no_bias)
|
862 |
+
)
|
863 |
+
|
864 |
+
# init last linear layer to 0
|
865 |
+
if zero_init_output:
|
866 |
+
init_zero_(self.ff[-1])
|
867 |
+
|
868 |
+
def forward(self, x):
|
869 |
+
return self.ff(x)
|
870 |
+
|
871 |
+
# attention. it is all we need
|
872 |
+
|
873 |
+
class Attention(nn.Module):
|
874 |
+
def __init__(
|
875 |
+
self,
|
876 |
+
dim,
|
877 |
+
dim_head = DEFAULT_DIM_HEAD,
|
878 |
+
heads = 8,
|
879 |
+
causal = False,
|
880 |
+
flash = False,
|
881 |
+
talking_heads = False,
|
882 |
+
head_scale = False,
|
883 |
+
sparse_topk = None,
|
884 |
+
num_mem_kv = 0,
|
885 |
+
dropout = 0.,
|
886 |
+
on_attn = False,
|
887 |
+
gate_values = False,
|
888 |
+
zero_init_output = False,
|
889 |
+
max_attend_past = None,
|
890 |
+
qk_norm = False,
|
891 |
+
qk_norm_groups = 1,
|
892 |
+
qk_norm_scale = 10,
|
893 |
+
qk_norm_dim_scale = False,
|
894 |
+
one_kv_head = False,
|
895 |
+
shared_kv = False,
|
896 |
+
value_dim_head = None,
|
897 |
+
tensor_product = False # https://arxiv.org/abs/2208.06061
|
898 |
+
):
|
899 |
+
super().__init__()
|
900 |
+
self.scale = dim_head ** -0.5
|
901 |
+
|
902 |
+
self.heads = heads
|
903 |
+
self.causal = causal
|
904 |
+
self.max_attend_past = max_attend_past
|
905 |
+
|
906 |
+
value_dim_head = default(value_dim_head, dim_head)
|
907 |
+
q_dim = k_dim = dim_head * heads
|
908 |
+
v_dim = out_dim = value_dim_head * heads
|
909 |
+
|
910 |
+
self.one_kv_head = one_kv_head
|
911 |
+
if one_kv_head:
|
912 |
+
k_dim = dim_head
|
913 |
+
v_dim = value_dim_head
|
914 |
+
out_dim = v_dim * heads
|
915 |
+
|
916 |
+
self.to_q = nn.Linear(dim, q_dim, bias = False)
|
917 |
+
self.to_k = nn.Linear(dim, k_dim, bias = False)
|
918 |
+
|
919 |
+
# shared key / values, for further memory savings during inference
|
920 |
+
assert not (shared_kv and value_dim_head != dim_head), 'key and value head dimensions must be equal for shared key / values'
|
921 |
+
self.to_v = nn.Linear(dim, v_dim, bias = False) if not shared_kv else None
|
922 |
+
|
923 |
+
# relations projection from tp-attention
|
924 |
+
self.to_r = nn.Linear(dim, v_dim, bias = False) if tensor_product else None
|
925 |
+
|
926 |
+
# add GLU gating for aggregated values, from alphafold2
|
927 |
+
self.to_v_gate = None
|
928 |
+
if gate_values:
|
929 |
+
self.to_v_gate = nn.Linear(dim, out_dim)
|
930 |
+
nn.init.constant_(self.to_v_gate.weight, 0)
|
931 |
+
nn.init.constant_(self.to_v_gate.bias, 1)
|
932 |
+
|
933 |
+
# cosine sim attention
|
934 |
+
self.qk_norm = qk_norm
|
935 |
+
self.qk_norm_groups = qk_norm_groups
|
936 |
+
self.qk_norm_scale = qk_norm_scale
|
937 |
+
|
938 |
+
# whether to use the rmsnorm (equivalent to cosine sim attention when scale is equal to 1) - https://arxiv.org/abs/2302.05442
|
939 |
+
self.qk_norm_dim_scale = qk_norm_dim_scale
|
940 |
+
|
941 |
+
self.qk_norm_q_scale = self.qk_norm_k_scale = 1
|
942 |
+
if qk_norm and qk_norm_dim_scale:
|
943 |
+
self.qk_norm_q_scale = nn.Parameter(torch.ones(dim_head))
|
944 |
+
self.qk_norm_k_scale = nn.Parameter(torch.ones(dim_head))
|
945 |
+
|
946 |
+
assert (not qk_norm) or (dim_head % qk_norm_groups) == 0, 'dimension per attention head must be divisible by the qk norm groups'
|
947 |
+
assert not (qk_norm and (dim_head // qk_norm_groups) <= 2), 'the group dimension may be too small (2 was too small in my tests, but 4 still works, surprisingly)'
|
948 |
+
|
949 |
+
# attend class - includes core attention algorithm + talking heads
|
950 |
+
|
951 |
+
self.attend = Attend(
|
952 |
+
heads = heads,
|
953 |
+
causal = causal,
|
954 |
+
talking_heads = talking_heads,
|
955 |
+
dropout = dropout,
|
956 |
+
qk_norm = qk_norm,
|
957 |
+
scale = qk_norm_scale if qk_norm else self.scale,
|
958 |
+
flash = flash
|
959 |
+
)
|
960 |
+
|
961 |
+
# head scaling
|
962 |
+
self.head_scale = head_scale
|
963 |
+
if head_scale:
|
964 |
+
self.head_scale_params = nn.Parameter(torch.ones(1, heads, 1, 1))
|
965 |
+
|
966 |
+
# explicit topk sparse attention
|
967 |
+
self.sparse_topk = sparse_topk
|
968 |
+
|
969 |
+
# add memory key / values
|
970 |
+
self.num_mem_kv = num_mem_kv
|
971 |
+
if num_mem_kv > 0:
|
972 |
+
self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
|
973 |
+
self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
|
974 |
+
|
975 |
+
# attention on attention
|
976 |
+
self.attn_on_attn = on_attn
|
977 |
+
self.to_out = nn.Sequential(nn.Linear(out_dim, dim * 2, bias = False), nn.GLU()) if on_attn else nn.Linear(out_dim, dim, bias = False)
|
978 |
+
|
979 |
+
# init output projection 0
|
980 |
+
if zero_init_output:
|
981 |
+
init_zero_(self.to_out)
|
982 |
+
|
983 |
+
def forward(
|
984 |
+
self,
|
985 |
+
x,
|
986 |
+
context = None,
|
987 |
+
mask = None,
|
988 |
+
context_mask = None,
|
989 |
+
attn_mask = None,
|
990 |
+
rel_pos = None,
|
991 |
+
rotary_pos_emb = None,
|
992 |
+
prev_attn = None,
|
993 |
+
mem = None
|
994 |
+
):
|
995 |
+
b, n, _, h, head_scale, device, has_context = *x.shape, self.heads, self.head_scale, x.device, exists(context)
|
996 |
+
kv_input = default(context, x)
|
997 |
+
|
998 |
+
q_input = x
|
999 |
+
k_input = kv_input
|
1000 |
+
v_input = kv_input
|
1001 |
+
r_input = x
|
1002 |
+
|
1003 |
+
if exists(mem):
|
1004 |
+
k_input = torch.cat((mem, k_input), dim = -2)
|
1005 |
+
v_input = torch.cat((mem, v_input), dim = -2)
|
1006 |
+
|
1007 |
+
q = self.to_q(q_input)
|
1008 |
+
k = self.to_k(k_input)
|
1009 |
+
v = self.to_v(v_input) if exists(self.to_v) else k
|
1010 |
+
r = self.to_r(r_input) if exists(self.to_r) else None
|
1011 |
+
|
1012 |
+
q = rearrange(q, 'b n (h d) -> b h n d', h = h)
|
1013 |
+
|
1014 |
+
if not self.one_kv_head:
|
1015 |
+
k, v, r = map(lambda t: maybe(rearrange)(t, 'b n (h d) -> b h n d', h = h), (k, v, r))
|
1016 |
+
|
1017 |
+
if self.qk_norm:
|
1018 |
+
qk_l2norm = partial(l2norm, groups = self.qk_norm_groups)
|
1019 |
+
q, k = map(qk_l2norm, (q, k))
|
1020 |
+
scale = self.qk_norm_scale
|
1021 |
+
|
1022 |
+
q = q * self.qk_norm_q_scale
|
1023 |
+
k = k * self.qk_norm_k_scale
|
1024 |
+
|
1025 |
+
if exists(rotary_pos_emb) and not has_context:
|
1026 |
+
freqs, xpos_scale = rotary_pos_emb
|
1027 |
+
l = freqs.shape[-1]
|
1028 |
+
|
1029 |
+
q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale ** -1.) if exists(xpos_scale) else (1., 1.)
|
1030 |
+
(ql, qr), (kl, kr), (vl, vr) = map(lambda t: (t[..., :l], t[..., l:]), (q, k, v))
|
1031 |
+
|
1032 |
+
ql, kl, vl = map(lambda arg: apply_rotary_pos_emb(arg[0], freqs, arg[1]), ((ql, q_xpos_scale), (kl, k_xpos_scale), (vl, k_xpos_scale)))
|
1033 |
+
q, k, v = map(lambda t: torch.cat(t, dim = -1), ((ql, qr), (kl, kr), (vl, vr)))
|
1034 |
+
|
1035 |
+
input_mask = default(context_mask, mask)
|
1036 |
+
|
1037 |
+
if self.num_mem_kv > 0:
|
1038 |
+
mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b = b), (self.mem_k, self.mem_v))
|
1039 |
+
|
1040 |
+
if self.qk_norm:
|
1041 |
+
mem_k = l2norm(mem_k)
|
1042 |
+
mem_k = mem_k * self.qk_norm_k_scale
|
1043 |
+
|
1044 |
+
k = torch.cat((mem_k, k), dim = -2)
|
1045 |
+
v = torch.cat((mem_v, v), dim = -2)
|
1046 |
+
|
1047 |
+
if exists(input_mask):
|
1048 |
+
input_mask = pad_at_dim(input_mask, (self.num_mem_kv, 0), dim = -1, value = True)
|
1049 |
+
|
1050 |
+
|
1051 |
+
i, j = map(lambda t: t.shape[-2], (q, k))
|
1052 |
+
|
1053 |
+
# determine masking
|
1054 |
+
|
1055 |
+
mask_value = max_neg_value(q)
|
1056 |
+
masks = []
|
1057 |
+
final_attn_mask = None
|
1058 |
+
|
1059 |
+
if exists(input_mask):
|
1060 |
+
input_mask = rearrange(input_mask, 'b j -> b 1 1 j')
|
1061 |
+
masks.append(~input_mask)
|
1062 |
+
|
1063 |
+
if exists(attn_mask):
|
1064 |
+
assert 2 <= attn_mask.ndim <= 4, 'attention mask must have greater than 2 dimensions but less than or equal to 4'
|
1065 |
+
if attn_mask.ndim == 2:
|
1066 |
+
attn_mask = rearrange(attn_mask, 'i j -> 1 1 i j')
|
1067 |
+
elif attn_mask.ndim == 3:
|
1068 |
+
attn_mask = rearrange(attn_mask, 'h i j -> 1 h i j')
|
1069 |
+
masks.append(~attn_mask)
|
1070 |
+
|
1071 |
+
if exists(self.max_attend_past):
|
1072 |
+
range_q = torch.arange(j - i, j, device = device)
|
1073 |
+
range_k = torch.arange(j, device = device)
|
1074 |
+
dist = rearrange(range_q, 'i -> 1 1 i 1') - rearrange(range_k, 'j -> 1 1 1 j')
|
1075 |
+
max_attend_past_mask = dist > self.max_attend_past
|
1076 |
+
masks.append(max_attend_past_mask)
|
1077 |
+
|
1078 |
+
if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
|
1079 |
+
top, _ = dots.topk(self.sparse_topk, dim = -1)
|
1080 |
+
vk = rearrange(top[..., -1], '... -> ... 1')
|
1081 |
+
sparse_topk_mask = dots < vk
|
1082 |
+
masks.append(sparse_topk_mask)
|
1083 |
+
|
1084 |
+
if len(masks) > 0:
|
1085 |
+
final_attn_mask = or_reduce(masks)
|
1086 |
+
|
1087 |
+
# prepare relative positional bias, if needed
|
1088 |
+
|
1089 |
+
attn_bias = None
|
1090 |
+
if exists(rel_pos):
|
1091 |
+
attn_bias = rel_pos(i, j)
|
1092 |
+
|
1093 |
+
# attention is all we need
|
1094 |
+
|
1095 |
+
out, intermediates = self.attend(
|
1096 |
+
q, k, v,
|
1097 |
+
mask = final_attn_mask,
|
1098 |
+
attn_bias = attn_bias,
|
1099 |
+
prev_attn = prev_attn
|
1100 |
+
)
|
1101 |
+
|
1102 |
+
# https://arxiv.org/abs/2208.06061 proposes to add a residual for better gradients
|
1103 |
+
|
1104 |
+
if exists(r):
|
1105 |
+
out = out * r + out
|
1106 |
+
|
1107 |
+
# normformer scaling of heads
|
1108 |
+
|
1109 |
+
if head_scale:
|
1110 |
+
out = out * self.head_scale_params
|
1111 |
+
|
1112 |
+
# merge heads
|
1113 |
+
|
1114 |
+
out = rearrange(out, 'b h n d -> b n (h d)')
|
1115 |
+
|
1116 |
+
# alphafold2 styled gating of the values
|
1117 |
+
|
1118 |
+
if exists(self.to_v_gate):
|
1119 |
+
gates = self.to_v_gate(x)
|
1120 |
+
out = out * gates.sigmoid()
|
1121 |
+
|
1122 |
+
# combine the heads
|
1123 |
+
|
1124 |
+
out = self.to_out(out)
|
1125 |
+
|
1126 |
+
if exists(mask):
|
1127 |
+
mask = rearrange(mask, 'b n -> b n 1')
|
1128 |
+
out = out.masked_fill(~mask, 0.)
|
1129 |
+
|
1130 |
+
return out, intermediates
|
1131 |
+
|
1132 |
+
class AttentionLayers(nn.Module):
|
1133 |
+
def __init__(
|
1134 |
+
self,
|
1135 |
+
dim,
|
1136 |
+
depth,
|
1137 |
+
heads = 8,
|
1138 |
+
causal = False,
|
1139 |
+
cross_attend = False,
|
1140 |
+
only_cross = False,
|
1141 |
+
use_scalenorm = False,
|
1142 |
+
use_rmsnorm = False,
|
1143 |
+
alibi_pos_bias = False,
|
1144 |
+
alibi_num_heads = None,
|
1145 |
+
alibi_learned = False,
|
1146 |
+
rel_pos_bias = False,
|
1147 |
+
rel_pos_num_buckets = 32,
|
1148 |
+
rel_pos_max_distance = 128,
|
1149 |
+
dynamic_pos_bias = False,
|
1150 |
+
dynamic_pos_bias_log_distance = False,
|
1151 |
+
dynamic_pos_bias_mlp_depth = 2,
|
1152 |
+
dynamic_pos_bias_norm = False,
|
1153 |
+
rotary_pos_emb = False,
|
1154 |
+
rotary_emb_dim = None,
|
1155 |
+
rotary_xpos = False,
|
1156 |
+
rotary_xpos_scale_base = 512,
|
1157 |
+
custom_layers = None,
|
1158 |
+
sandwich_coef = None,
|
1159 |
+
par_ratio = None,
|
1160 |
+
residual_attn = False,
|
1161 |
+
cross_residual_attn = False,
|
1162 |
+
macaron = False,
|
1163 |
+
pre_norm = True,
|
1164 |
+
gate_residual = False,
|
1165 |
+
scale_residual = False,
|
1166 |
+
scale_residual_constant = 1.,
|
1167 |
+
deepnorm = False,
|
1168 |
+
shift_tokens = 0,
|
1169 |
+
sandwich_norm = False,
|
1170 |
+
resi_dual = False,
|
1171 |
+
zero_init_branch_output = False,
|
1172 |
+
layer_dropout = 0.,
|
1173 |
+
cross_attn_tokens_dropout = 0.,
|
1174 |
+
**kwargs
|
1175 |
+
):
|
1176 |
+
super().__init__()
|
1177 |
+
rotary_pos_emb = rotary_pos_emb or rotary_xpos
|
1178 |
+
|
1179 |
+
ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
|
1180 |
+
attn_kwargs, kwargs = groupby_prefix_and_trim('attn_', kwargs)
|
1181 |
+
|
1182 |
+
dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
|
1183 |
+
|
1184 |
+
self.dim = dim
|
1185 |
+
self.depth = depth
|
1186 |
+
self.layers = nn.ModuleList([])
|
1187 |
+
|
1188 |
+
self.has_pos_emb = rel_pos_bias or rotary_pos_emb
|
1189 |
+
|
1190 |
+
rotary_emb_dim = max(default(rotary_emb_dim, dim_head // 2), 32)
|
1191 |
+
|
1192 |
+
assert not (rotary_xpos and not causal), 'rotary xpos is not compatible with bidirectional attention'
|
1193 |
+
self.rotary_pos_emb = RotaryEmbedding(rotary_emb_dim, use_xpos = rotary_xpos, scale_base = rotary_xpos_scale_base) if rotary_pos_emb else None
|
1194 |
+
|
1195 |
+
assert not (alibi_pos_bias and rel_pos_bias), 'you can only choose Alibi positional bias or T5 relative positional bias, not both'
|
1196 |
+
assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
|
1197 |
+
|
1198 |
+
# relative positional bias
|
1199 |
+
|
1200 |
+
flash_attn = attn_kwargs.get('flash', False)
|
1201 |
+
assert (int(rel_pos_bias) + int(dynamic_pos_bias) + int(alibi_pos_bias)) <= 1, 'you can only choose up to one of t5, alibi, or dynamic positional bias'
|
1202 |
+
|
1203 |
+
self.rel_pos = None
|
1204 |
+
if rel_pos_bias:
|
1205 |
+
assert not flash_attn, 'flash attention not compatible with t5 relative positional bias'
|
1206 |
+
self.rel_pos = RelativePositionBias(scale = dim_head ** 0.5, causal = causal, heads = heads, num_buckets = rel_pos_num_buckets, max_distance = rel_pos_max_distance)
|
1207 |
+
elif dynamic_pos_bias:
|
1208 |
+
assert not flash_attn, 'flash attention not compatible with dynamic positional bias'
|
1209 |
+
self.rel_pos = DynamicPositionBias(dim = dim // 4, heads = heads, log_distance = dynamic_pos_bias_log_distance, depth = dynamic_pos_bias_mlp_depth, norm = dynamic_pos_bias_norm)
|
1210 |
+
elif alibi_pos_bias:
|
1211 |
+
alibi_num_heads = default(alibi_num_heads, heads)
|
1212 |
+
assert alibi_num_heads <= heads, 'number of ALiBi heads must be less than the total number of heads'
|
1213 |
+
alibi_pos_klass = LearnedAlibiPositionalBias if alibi_learned else AlibiPositionalBias
|
1214 |
+
self.rel_pos = alibi_pos_klass(heads = alibi_num_heads, total_heads = heads)
|
1215 |
+
|
1216 |
+
# determine deepnorm and residual scale
|
1217 |
+
|
1218 |
+
if deepnorm:
|
1219 |
+
assert scale_residual_constant == 1, 'scale residual constant is being overridden by deep norm settings'
|
1220 |
+
pre_norm = sandwich_norm = resi_dual = False
|
1221 |
+
scale_residual = True
|
1222 |
+
scale_residual_constant = (2 * depth) ** 0.25
|
1223 |
+
|
1224 |
+
assert (int(sandwich_norm) + int(resi_dual)) <= 1, 'either sandwich norm or resiDual is selected, but not both'
|
1225 |
+
assert not (not pre_norm and sandwich_norm), 'sandwich norm cannot be used when not using prenorm'
|
1226 |
+
assert not (not pre_norm and resi_dual), 'resiDualcannot be used when not using prenorm'
|
1227 |
+
self.pre_norm = pre_norm
|
1228 |
+
self.sandwich_norm = sandwich_norm
|
1229 |
+
self.resi_dual = resi_dual
|
1230 |
+
|
1231 |
+
self.residual_attn = residual_attn
|
1232 |
+
self.cross_residual_attn = cross_residual_attn
|
1233 |
+
self.cross_attend = cross_attend
|
1234 |
+
|
1235 |
+
norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
|
1236 |
+
norm_class = RMSNorm if use_rmsnorm else norm_class
|
1237 |
+
norm_fn = partial(norm_class, dim)
|
1238 |
+
|
1239 |
+
if cross_attend and not only_cross:
|
1240 |
+
default_block = ('a', 'c', 'f')
|
1241 |
+
elif cross_attend and only_cross:
|
1242 |
+
default_block = ('c', 'f')
|
1243 |
+
else:
|
1244 |
+
default_block = ('a', 'f')
|
1245 |
+
|
1246 |
+
if macaron:
|
1247 |
+
default_block = ('f',) + default_block
|
1248 |
+
|
1249 |
+
# zero init
|
1250 |
+
|
1251 |
+
if zero_init_branch_output:
|
1252 |
+
attn_kwargs = {**attn_kwargs, 'zero_init_output': True}
|
1253 |
+
ff_kwargs = {**ff_kwargs, 'zero_init_output': True}
|
1254 |
+
|
1255 |
+
# calculate layer block order
|
1256 |
+
|
1257 |
+
if exists(custom_layers):
|
1258 |
+
layer_types = custom_layers
|
1259 |
+
elif exists(par_ratio):
|
1260 |
+
par_depth = depth * len(default_block)
|
1261 |
+
assert 1 < par_ratio <= par_depth, 'par ratio out of range'
|
1262 |
+
default_block = tuple(filter(not_equals('f'), default_block))
|
1263 |
+
par_attn = par_depth // par_ratio
|
1264 |
+
depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper
|
1265 |
+
par_width = (depth_cut + depth_cut // par_attn) // par_attn
|
1266 |
+
assert len(default_block) <= par_width, 'default block is too large for par_ratio'
|
1267 |
+
par_block = default_block + ('f',) * (par_width - len(default_block))
|
1268 |
+
par_head = par_block * par_attn
|
1269 |
+
layer_types = par_head + ('f',) * (par_depth - len(par_head))
|
1270 |
+
elif exists(sandwich_coef):
|
1271 |
+
assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
|
1272 |
+
layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
|
1273 |
+
else:
|
1274 |
+
layer_types = default_block * depth
|
1275 |
+
|
1276 |
+
self.layer_types = layer_types
|
1277 |
+
self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
|
1278 |
+
|
1279 |
+
# stochastic depth
|
1280 |
+
|
1281 |
+
self.layer_dropouts = cast_tuple(layer_dropout, len(layer_types))
|
1282 |
+
|
1283 |
+
# structured dropout for cross attending
|
1284 |
+
|
1285 |
+
self.cross_attn_tokens_dropout = cross_attn_tokens_dropout
|
1286 |
+
|
1287 |
+
# calculate token shifting
|
1288 |
+
|
1289 |
+
shift_tokens = cast_tuple(shift_tokens, len(layer_types))
|
1290 |
+
|
1291 |
+
# iterate and construct layers
|
1292 |
+
|
1293 |
+
for ind, (layer_type, layer_shift_tokens) in enumerate(zip(self.layer_types, shift_tokens)):
|
1294 |
+
is_last_layer = ind == (len(self.layer_types) - 1)
|
1295 |
+
|
1296 |
+
if layer_type == 'a':
|
1297 |
+
layer = Attention(dim, heads = heads, causal = causal, **attn_kwargs)
|
1298 |
+
elif layer_type == 'c':
|
1299 |
+
layer = Attention(dim, heads = heads, **attn_kwargs)
|
1300 |
+
elif layer_type == 'f':
|
1301 |
+
layer = FeedForward(dim, **ff_kwargs)
|
1302 |
+
layer = layer if not macaron else Scale(0.5, layer)
|
1303 |
+
else:
|
1304 |
+
raise Exception(f'invalid layer type {layer_type}')
|
1305 |
+
|
1306 |
+
if layer_shift_tokens > 0:
|
1307 |
+
shift_range_upper = layer_shift_tokens + 1
|
1308 |
+
shift_range_lower = -layer_shift_tokens if not causal else 0
|
1309 |
+
layer = ShiftTokens(range(shift_range_lower, shift_range_upper), layer)
|
1310 |
+
|
1311 |
+
residual_fn = GRUGating if gate_residual else Residual
|
1312 |
+
residual = residual_fn(dim, scale_residual = scale_residual, scale_residual_constant = scale_residual_constant)
|
1313 |
+
|
1314 |
+
pre_branch_norm = norm_fn() if pre_norm else None
|
1315 |
+
post_branch_norm = norm_fn() if sandwich_norm else None
|
1316 |
+
post_main_norm = norm_fn() if (resi_dual or not pre_norm) and not is_last_layer else None
|
1317 |
+
|
1318 |
+
norms = nn.ModuleList([
|
1319 |
+
pre_branch_norm,
|
1320 |
+
post_branch_norm,
|
1321 |
+
post_main_norm
|
1322 |
+
])
|
1323 |
+
|
1324 |
+
self.layers.append(nn.ModuleList([
|
1325 |
+
norms,
|
1326 |
+
layer,
|
1327 |
+
residual
|
1328 |
+
]))
|
1329 |
+
|
1330 |
+
if deepnorm:
|
1331 |
+
init_gain = (8 * depth) ** -0.25
|
1332 |
+
deepnorm_init(self, init_gain)
|
1333 |
+
|
1334 |
+
def forward(
|
1335 |
+
self,
|
1336 |
+
x,
|
1337 |
+
context = None,
|
1338 |
+
mask = None,
|
1339 |
+
context_mask = None,
|
1340 |
+
attn_mask = None,
|
1341 |
+
self_attn_context_mask = None,
|
1342 |
+
mems = None,
|
1343 |
+
return_hiddens = False
|
1344 |
+
):
|
1345 |
+
assert not (self.cross_attend ^ exists(context)), 'context must be passed in if cross_attend is set to True'
|
1346 |
+
|
1347 |
+
hiddens = []
|
1348 |
+
intermediates = []
|
1349 |
+
prev_attn = None
|
1350 |
+
prev_cross_attn = None
|
1351 |
+
|
1352 |
+
mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
|
1353 |
+
|
1354 |
+
rotary_pos_emb = None
|
1355 |
+
if exists(self.rotary_pos_emb):
|
1356 |
+
max_rotary_emb_length = max(list(map(lambda m: (m.shape[1] if exists(m) else 0) + x.shape[1], mems)))
|
1357 |
+
rotary_pos_emb = self.rotary_pos_emb(max_rotary_emb_length, x.device)
|
1358 |
+
|
1359 |
+
outer_residual = x
|
1360 |
+
|
1361 |
+
for ind, (layer_type, (norm, block, residual_fn), layer_dropout) in enumerate(zip(self.layer_types, self.layers, self.layer_dropouts)):
|
1362 |
+
is_last = ind == (len(self.layers) - 1)
|
1363 |
+
|
1364 |
+
if self.training and layer_dropout > 0. and random() < layer_dropout:
|
1365 |
+
continue
|
1366 |
+
|
1367 |
+
if layer_type == 'a':
|
1368 |
+
if return_hiddens:
|
1369 |
+
hiddens.append(x)
|
1370 |
+
layer_mem = mems.pop(0) if mems else None
|
1371 |
+
|
1372 |
+
if layer_type == 'c':
|
1373 |
+
if self.training and self.cross_attn_tokens_dropout > 0.:
|
1374 |
+
context, context_mask = dropout_seq(context, context_mask, self.cross_attn_tokens_dropout)
|
1375 |
+
|
1376 |
+
inner_residual = x
|
1377 |
+
|
1378 |
+
pre_norm, post_branch_norm, post_main_norm = norm
|
1379 |
+
|
1380 |
+
if exists(pre_norm) and not self.resi_dual:
|
1381 |
+
x = pre_norm(x)
|
1382 |
+
|
1383 |
+
if layer_type == 'a':
|
1384 |
+
out, inter = block(x, mask = mask, context_mask = self_attn_context_mask, attn_mask = attn_mask, rel_pos = self.rel_pos, rotary_pos_emb = rotary_pos_emb, prev_attn = prev_attn, mem = layer_mem)
|
1385 |
+
elif layer_type == 'c':
|
1386 |
+
out, inter = block(x, context = context, mask = mask, context_mask = context_mask, prev_attn = prev_cross_attn)
|
1387 |
+
elif layer_type == 'f':
|
1388 |
+
out = block(x)
|
1389 |
+
|
1390 |
+
if self.resi_dual:
|
1391 |
+
outer_residual = residual_fn(out, outer_residual)
|
1392 |
+
|
1393 |
+
if exists(post_branch_norm):
|
1394 |
+
out = post_branch_norm(out)
|
1395 |
+
|
1396 |
+
x = residual_fn(out, inner_residual)
|
1397 |
+
|
1398 |
+
if layer_type in ('a', 'c') and return_hiddens:
|
1399 |
+
intermediates.append(inter)
|
1400 |
+
|
1401 |
+
if layer_type == 'a' and self.residual_attn:
|
1402 |
+
prev_attn = inter.pre_softmax_attn
|
1403 |
+
elif layer_type == 'c' and self.cross_residual_attn:
|
1404 |
+
prev_cross_attn = inter.pre_softmax_attn
|
1405 |
+
|
1406 |
+
if exists(post_main_norm):
|
1407 |
+
x = post_main_norm(x)
|
1408 |
+
|
1409 |
+
if self.resi_dual:
|
1410 |
+
x = x + pre_norm(outer_residual)
|
1411 |
+
|
1412 |
+
if return_hiddens:
|
1413 |
+
intermediates = LayerIntermediates(
|
1414 |
+
hiddens = hiddens,
|
1415 |
+
attn_intermediates = intermediates
|
1416 |
+
)
|
1417 |
+
|
1418 |
+
return x, intermediates
|
1419 |
+
|
1420 |
+
return x
|
1421 |
+
|
1422 |
+
class Encoder(AttentionLayers):
|
1423 |
+
def __init__(self, **kwargs):
|
1424 |
+
assert 'causal' not in kwargs, 'cannot set causality on encoder'
|
1425 |
+
super().__init__(causal = False, **kwargs)
|
1426 |
+
|
1427 |
+
class Decoder(AttentionLayers):
|
1428 |
+
def __init__(self, **kwargs):
|
1429 |
+
assert 'causal' not in kwargs, 'cannot set causality on decoder'
|
1430 |
+
super().__init__(causal = True, **kwargs)
|
1431 |
+
|
1432 |
+
class CrossAttender(AttentionLayers):
|
1433 |
+
def __init__(self, **kwargs):
|
1434 |
+
super().__init__(cross_attend = True, only_cross = True, **kwargs)
|
1435 |
+
|
1436 |
+
class ViTransformerWrapper(nn.Module):
|
1437 |
+
def __init__(
|
1438 |
+
self,
|
1439 |
+
*,
|
1440 |
+
image_size,
|
1441 |
+
patch_size,
|
1442 |
+
attn_layers,
|
1443 |
+
channels = 3,
|
1444 |
+
num_classes = None,
|
1445 |
+
dropout = 0.,
|
1446 |
+
post_emb_norm = False,
|
1447 |
+
emb_dropout = 0.
|
1448 |
+
):
|
1449 |
+
super().__init__()
|
1450 |
+
assert isinstance(attn_layers, Encoder), 'attention layers must be an Encoder'
|
1451 |
+
assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size'
|
1452 |
+
dim = attn_layers.dim
|
1453 |
+
num_patches = (image_size // patch_size) ** 2
|
1454 |
+
patch_dim = channels * patch_size ** 2
|
1455 |
+
|
1456 |
+
self.patch_size = patch_size
|
1457 |
+
|
1458 |
+
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
|
1459 |
+
|
1460 |
+
self.patch_to_embedding = nn.Sequential(
|
1461 |
+
nn.LayerNorm(patch_dim),
|
1462 |
+
nn.Linear(patch_dim, dim),
|
1463 |
+
nn.LayerNorm(dim)
|
1464 |
+
)
|
1465 |
+
|
1466 |
+
self.post_emb_norm = nn.LayerNorm(dim) if post_emb_norm else nn.Identity()
|
1467 |
+
self.dropout = nn.Dropout(emb_dropout)
|
1468 |
+
|
1469 |
+
self.attn_layers = attn_layers
|
1470 |
+
self.norm = nn.LayerNorm(dim)
|
1471 |
+
self.mlp_head = nn.Linear(dim, num_classes) if exists(num_classes) else nn.Identity()
|
1472 |
+
|
1473 |
+
def forward(
|
1474 |
+
self,
|
1475 |
+
img,
|
1476 |
+
return_embeddings = False
|
1477 |
+
):
|
1478 |
+
p = self.patch_size
|
1479 |
+
|
1480 |
+
x = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = p, p2 = p)
|
1481 |
+
x = self.patch_to_embedding(x)
|
1482 |
+
n = x.shape[1]
|
1483 |
+
|
1484 |
+
x = x + self.pos_embedding[:, :n]
|
1485 |
+
|
1486 |
+
x = self.post_emb_norm(x)
|
1487 |
+
x = self.dropout(x)
|
1488 |
+
|
1489 |
+
x = self.attn_layers(x)
|
1490 |
+
x = self.norm(x)
|
1491 |
+
|
1492 |
+
if not exists(self.mlp_head) or return_embeddings:
|
1493 |
+
return x
|
1494 |
+
|
1495 |
+
x = x.mean(dim = -2)
|
1496 |
+
return self.mlp_head(x)
|
1497 |
+
|
1498 |
+
class TransformerWrapper(nn.Module):
|
1499 |
+
def __init__(
|
1500 |
+
self,
|
1501 |
+
*,
|
1502 |
+
num_tokens,
|
1503 |
+
max_seq_len,
|
1504 |
+
attn_layers,
|
1505 |
+
emb_dim = None,
|
1506 |
+
max_mem_len = 0.,
|
1507 |
+
shift_mem_down = 0,
|
1508 |
+
emb_dropout = 0.,
|
1509 |
+
post_emb_norm = False,
|
1510 |
+
num_memory_tokens = None,
|
1511 |
+
tie_embedding = False,
|
1512 |
+
logits_dim = None,
|
1513 |
+
use_abs_pos_emb = True,
|
1514 |
+
scaled_sinu_pos_emb = False,
|
1515 |
+
l2norm_embed = False,
|
1516 |
+
emb_frac_gradient = 1. # GLM-130B and Cogview successfully used this, set at 0.1
|
1517 |
+
):
|
1518 |
+
super().__init__()
|
1519 |
+
assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
|
1520 |
+
|
1521 |
+
dim = attn_layers.dim
|
1522 |
+
emb_dim = default(emb_dim, dim)
|
1523 |
+
self.emb_dim = emb_dim
|
1524 |
+
self.num_tokens = num_tokens
|
1525 |
+
self.token_pad = num_tokens
|
1526 |
+
|
1527 |
+
self.max_seq_len = max_seq_len
|
1528 |
+
self.max_mem_len = max_mem_len
|
1529 |
+
self.shift_mem_down = shift_mem_down
|
1530 |
+
|
1531 |
+
self.l2norm_embed = l2norm_embed
|
1532 |
+
self.token_emb = TokenEmbedding(emb_dim, num_tokens, l2norm_embed = l2norm_embed)
|
1533 |
+
|
1534 |
+
if not (use_abs_pos_emb and not attn_layers.has_pos_emb):
|
1535 |
+
self.pos_emb = always(0)
|
1536 |
+
elif scaled_sinu_pos_emb:
|
1537 |
+
self.pos_emb = ScaledSinusoidalEmbedding(emb_dim)
|
1538 |
+
else:
|
1539 |
+
self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len, l2norm_embed = l2norm_embed)
|
1540 |
+
|
1541 |
+
self.emb_frac_gradient = emb_frac_gradient # fraction of the gradient that should go to the embedding, https://arxiv.org/abs/2105.13290
|
1542 |
+
|
1543 |
+
self.post_emb_norm = nn.LayerNorm(emb_dim) if post_emb_norm else nn.Identity()
|
1544 |
+
self.emb_dropout = nn.Dropout(emb_dropout)
|
1545 |
+
|
1546 |
+
self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
|
1547 |
+
self.attn_layers = attn_layers
|
1548 |
+
self.norm = nn.LayerNorm(dim)
|
1549 |
+
|
1550 |
+
self.init_()
|
1551 |
+
|
1552 |
+
logits_dim = default(logits_dim, num_tokens)
|
1553 |
+
self.to_logits = nn.Linear(dim, logits_dim) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
|
1554 |
+
|
1555 |
+
# memory tokens (like [cls]) from Memory Transformers paper
|
1556 |
+
num_memory_tokens = default(num_memory_tokens, 0)
|
1557 |
+
self.num_memory_tokens = num_memory_tokens
|
1558 |
+
if num_memory_tokens > 0:
|
1559 |
+
self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
|
1560 |
+
|
1561 |
+
def init_(self):
|
1562 |
+
if self.l2norm_embed:
|
1563 |
+
nn.init.normal_(self.token_emb.emb.weight, std = 1e-5)
|
1564 |
+
if not isinstance(self.pos_emb, always):
|
1565 |
+
nn.init.normal_(self.pos_emb.emb.weight, std = 1e-5)
|
1566 |
+
return
|
1567 |
+
|
1568 |
+
nn.init.kaiming_normal_(self.token_emb.emb.weight)
|
1569 |
+
|
1570 |
+
def forward(
|
1571 |
+
self,
|
1572 |
+
x,
|
1573 |
+
return_embeddings = False,
|
1574 |
+
return_logits_and_embeddings = False,
|
1575 |
+
return_intermediates = False,
|
1576 |
+
mask = None,
|
1577 |
+
return_mems = False,
|
1578 |
+
return_attn = False,
|
1579 |
+
mems = None,
|
1580 |
+
pos = None,
|
1581 |
+
prepend_embeds = None,
|
1582 |
+
sum_embeds = None,
|
1583 |
+
**kwargs
|
1584 |
+
):
|
1585 |
+
b, n, device, num_mem, emb_frac_gradient = *x.shape, x.device, self.num_memory_tokens, self.emb_frac_gradient
|
1586 |
+
return_hiddens = return_mems | return_attn
|
1587 |
+
|
1588 |
+
# absolute positional embedding
|
1589 |
+
|
1590 |
+
external_pos_emb = exists(pos) and pos.dtype != torch.long
|
1591 |
+
pos_emb = self.pos_emb(x, pos = pos) if not external_pos_emb else pos
|
1592 |
+
x = self.token_emb(x) + pos_emb
|
1593 |
+
|
1594 |
+
# for summing embeddings passed externally - needs this for self-conditioning in non-autoregressive training
|
1595 |
+
|
1596 |
+
if exists(sum_embeds):
|
1597 |
+
x = x + sum_embeds
|
1598 |
+
|
1599 |
+
# post embedding norm, purportedly leads to greater stabilization
|
1600 |
+
|
1601 |
+
x = self.post_emb_norm(x)
|
1602 |
+
|
1603 |
+
# whether to append embeds, as in PaLI, for image embeddings
|
1604 |
+
|
1605 |
+
if exists(prepend_embeds):
|
1606 |
+
prepend_seq, prepend_dim = prepend_embeds.shape[1:]
|
1607 |
+
assert prepend_dim == x.shape[-1], 'prepended embeddings need to have same dimensions as text model dimensions'
|
1608 |
+
|
1609 |
+
x = torch.cat((prepend_embeds, x), dim = -2)
|
1610 |
+
|
1611 |
+
# whether to reduce the gradient going to the embedding, from cogview paper, corroborated by GLM-130B model
|
1612 |
+
|
1613 |
+
if emb_frac_gradient < 1:
|
1614 |
+
assert emb_frac_gradient > 0
|
1615 |
+
x = x * emb_frac_gradient + x.detach() * (1 - emb_frac_gradient)
|
1616 |
+
|
1617 |
+
# embedding dropout
|
1618 |
+
|
1619 |
+
x = self.emb_dropout(x)
|
1620 |
+
|
1621 |
+
x = self.project_emb(x)
|
1622 |
+
|
1623 |
+
if num_mem > 0:
|
1624 |
+
mem = repeat(self.memory_tokens, 'n d -> b n d', b = b)
|
1625 |
+
x = torch.cat((mem, x), dim = 1)
|
1626 |
+
|
1627 |
+
# auto-handle masking after appending memory tokens
|
1628 |
+
if exists(mask):
|
1629 |
+
mask = pad_at_dim(mask, (num_mem, 0), dim = -1, value = True)
|
1630 |
+
|
1631 |
+
if self.shift_mem_down and exists(mems):
|
1632 |
+
mems_l, mems_r = mems[:self.shift_mem_down], mems[self.shift_mem_down:]
|
1633 |
+
mems = [*mems_r, *mems_l]
|
1634 |
+
|
1635 |
+
if return_hiddens:
|
1636 |
+
x, intermediates = self.attn_layers(x, mask = mask, mems = mems, return_hiddens = True, **kwargs)
|
1637 |
+
else:
|
1638 |
+
x = self.attn_layers(x, mask = mask, mems = mems, **kwargs)
|
1639 |
+
|
1640 |
+
x = self.norm(x)
|
1641 |
+
|
1642 |
+
mem, x = x[:, :num_mem], x[:, num_mem:]
|
1643 |
+
|
1644 |
+
if return_logits_and_embeddings:
|
1645 |
+
out = (self.to_logits(x), x)
|
1646 |
+
elif return_embeddings:
|
1647 |
+
out = x
|
1648 |
+
else:
|
1649 |
+
out = self.to_logits(x)
|
1650 |
+
|
1651 |
+
if return_intermediates:
|
1652 |
+
return out, intermediates
|
1653 |
+
|
1654 |
+
if return_mems:
|
1655 |
+
hiddens = intermediates.hiddens
|
1656 |
+
new_mems = list(map(lambda pair: torch.cat(pair, dim = -2), zip(mems, hiddens))) if exists(mems) else hiddens
|
1657 |
+
new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
|
1658 |
+
return out, new_mems
|
1659 |
+
|
1660 |
+
if return_attn:
|
1661 |
+
attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
|
1662 |
+
return out, attn_maps
|
1663 |
+
|
1664 |
+
return out
|
1665 |
+
|
1666 |
+
class ContinuousTransformerWrapper(nn.Module):
|
1667 |
+
def __init__(
|
1668 |
+
self,
|
1669 |
+
*,
|
1670 |
+
max_seq_len,
|
1671 |
+
attn_layers,
|
1672 |
+
dim_in = None,
|
1673 |
+
dim_out = None,
|
1674 |
+
emb_dim = None,
|
1675 |
+
post_emb_norm = False,
|
1676 |
+
emb_dropout = 0.,
|
1677 |
+
use_abs_pos_emb = True,
|
1678 |
+
scaled_sinu_pos_emb = False
|
1679 |
+
):
|
1680 |
+
super().__init__()
|
1681 |
+
assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
|
1682 |
+
|
1683 |
+
dim = attn_layers.dim
|
1684 |
+
|
1685 |
+
self.max_seq_len = max_seq_len
|
1686 |
+
|
1687 |
+
if not (use_abs_pos_emb and not attn_layers.has_pos_emb):
|
1688 |
+
self.pos_emb = always(0)
|
1689 |
+
elif scaled_sinu_pos_emb:
|
1690 |
+
self.pos_emb = ScaledSinusoidalEmbedding(dim)
|
1691 |
+
else:
|
1692 |
+
self.pos_emb = AbsolutePositionalEmbedding(dim, max_seq_len)
|
1693 |
+
|
1694 |
+
self.post_emb_norm = nn.LayerNorm(dim) if post_emb_norm else nn.Identity()
|
1695 |
+
self.emb_dropout = nn.Dropout(emb_dropout)
|
1696 |
+
|
1697 |
+
self.project_in = nn.Linear(dim_in, dim) if exists(dim_in) else nn.Identity()
|
1698 |
+
|
1699 |
+
self.attn_layers = attn_layers
|
1700 |
+
self.norm = nn.LayerNorm(dim)
|
1701 |
+
|
1702 |
+
self.project_out = nn.Linear(dim, dim_out) if exists(dim_out) else nn.Identity()
|
1703 |
+
|
1704 |
+
def forward(
|
1705 |
+
self,
|
1706 |
+
x,
|
1707 |
+
return_embeddings = False,
|
1708 |
+
return_intermediates = False,
|
1709 |
+
mask = None,
|
1710 |
+
return_attn = False,
|
1711 |
+
mems = None,
|
1712 |
+
pos = None,
|
1713 |
+
prepend_embeds = None,
|
1714 |
+
**kwargs
|
1715 |
+
):
|
1716 |
+
x = self.project_in(x)
|
1717 |
+
x = x + self.pos_emb(x, pos = pos)
|
1718 |
+
|
1719 |
+
x = self.post_emb_norm(x)
|
1720 |
+
|
1721 |
+
# whether to append embeds, as in PaLI, for image embeddings
|
1722 |
+
|
1723 |
+
if exists(prepend_embeds):
|
1724 |
+
_, prepend_dim = prepend_embeds.shape[1:]
|
1725 |
+
assert prepend_dim == x.shape[-1], 'prepended embeddings need to have same dimensions as model dimensions'
|
1726 |
+
|
1727 |
+
x = torch.cat((prepend_embeds, x), dim = -2)
|
1728 |
+
|
1729 |
+
x = self.emb_dropout(x)
|
1730 |
+
|
1731 |
+
x, intermediates = self.attn_layers(x, mask = mask, mems = mems, return_hiddens = True, **kwargs)
|
1732 |
+
x = self.norm(x)
|
1733 |
+
|
1734 |
+
out = self.project_out(x) if not return_embeddings else x
|
1735 |
+
|
1736 |
+
if return_intermediates:
|
1737 |
+
return out, intermediates
|
1738 |
+
|
1739 |
+
if return_attn:
|
1740 |
+
attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
|
1741 |
+
return out, attn_maps
|
1742 |
+
|
1743 |
+
return out
|
1744 |
+
|
1745 |
+
class XTransformer(nn.Module):
|
1746 |
+
def __init__(
|
1747 |
+
self,
|
1748 |
+
*,
|
1749 |
+
dim,
|
1750 |
+
tie_token_emb = False,
|
1751 |
+
ignore_index = -100,
|
1752 |
+
pad_value = 0,
|
1753 |
+
deepnorm = False,
|
1754 |
+
cross_attn_tokens_dropout = 0.,
|
1755 |
+
**kwargs
|
1756 |
+
):
|
1757 |
+
super().__init__()
|
1758 |
+
enc_kwargs, kwargs = groupby_prefix_and_trim('enc_', kwargs)
|
1759 |
+
dec_kwargs, kwargs = groupby_prefix_and_trim('dec_', kwargs)
|
1760 |
+
|
1761 |
+
assert 'dim' not in enc_kwargs and 'dim' not in dec_kwargs, 'dimension of either encoder or decoder must be set with `dim` keyword'
|
1762 |
+
enc_transformer_kwargs = pick_and_pop(['num_tokens', 'max_seq_len'], enc_kwargs)
|
1763 |
+
enc_transformer_kwargs['emb_dropout'] = enc_kwargs.pop('emb_dropout', 0)
|
1764 |
+
enc_transformer_kwargs['num_memory_tokens'] = enc_kwargs.pop('num_memory_tokens', None)
|
1765 |
+
enc_transformer_kwargs['scaled_sinu_pos_emb'] = enc_kwargs.pop('scaled_sinu_pos_emb', False)
|
1766 |
+
enc_transformer_kwargs['use_abs_pos_emb'] = enc_kwargs.pop('use_abs_pos_emb', True)
|
1767 |
+
|
1768 |
+
dec_transformer_kwargs = pick_and_pop(['num_tokens', 'max_seq_len'], dec_kwargs)
|
1769 |
+
dec_transformer_kwargs['emb_dropout'] = dec_kwargs.pop('emb_dropout', 0)
|
1770 |
+
dec_transformer_kwargs['scaled_sinu_pos_emb'] = dec_kwargs.pop('scaled_sinu_pos_emb', False)
|
1771 |
+
dec_transformer_kwargs['use_abs_pos_emb'] = dec_kwargs.pop('use_abs_pos_emb', True)
|
1772 |
+
|
1773 |
+
self.cross_attn_tokens_dropout = cross_attn_tokens_dropout # how many tokens from the encoder to dropout when cross attending from decoder - seen in a couple papers, including Perceiver AR - this will also be very effective regularization when cross attending to very long memories
|
1774 |
+
|
1775 |
+
if deepnorm:
|
1776 |
+
enc_kwargs['scale_residual'] = True
|
1777 |
+
dec_kwargs['scale_residual'] = True
|
1778 |
+
|
1779 |
+
enc_depth = enc_kwargs['depth']
|
1780 |
+
dec_depth = dec_kwargs['depth']
|
1781 |
+
|
1782 |
+
enc_kwargs['scale_residual_constant'] = 0.81 * ((enc_depth ** 4) * dec_depth) ** .0625
|
1783 |
+
dec_kwargs['scale_residual_constant'] = (3 * dec_depth) ** 0.25
|
1784 |
+
|
1785 |
+
self.encoder = TransformerWrapper(
|
1786 |
+
**enc_transformer_kwargs,
|
1787 |
+
attn_layers = Encoder(dim = dim, **enc_kwargs)
|
1788 |
+
)
|
1789 |
+
|
1790 |
+
self.decoder = TransformerWrapper(
|
1791 |
+
**dec_transformer_kwargs,
|
1792 |
+
attn_layers = Decoder(dim = dim, cross_attend = True, **dec_kwargs)
|
1793 |
+
)
|
1794 |
+
|
1795 |
+
if deepnorm:
|
1796 |
+
deepnorm_init(self.encoder, 0.87 * ((enc_depth ** 4) * dec_depth) ** -0.0625)
|
1797 |
+
deepnorm_init(self.decoder, (12 * dec_depth) ** -0.25)
|
1798 |
+
|
1799 |
+
if tie_token_emb:
|
1800 |
+
self.decoder.token_emb = self.encoder.token_emb
|
1801 |
+
|
1802 |
+
self.decoder = AutoregressiveWrapper(self.decoder, ignore_index=ignore_index, pad_value=pad_value)
|
1803 |
+
|
1804 |
+
@torch.no_grad()
|
1805 |
+
def generate(self, seq_in, seq_out_start, seq_len, mask = None, attn_mask = None, **kwargs):
|
1806 |
+
encodings = self.encoder(seq_in, mask = mask, attn_mask = attn_mask, return_embeddings = True)
|
1807 |
+
return self.decoder.generate(seq_out_start, seq_len, context = encodings, context_mask = mask, **kwargs)
|
1808 |
+
|
1809 |
+
def forward(self, src, tgt, mask = None, attn_mask = None, src_prepend_embeds = None):
|
1810 |
+
|
1811 |
+
if exists(src_prepend_embeds) and exists(mask):
|
1812 |
+
mask = pad_at_dim(mask, (src_prepend_embeds.shape[-2], 0), dim = -1, value = True)
|
1813 |
+
|
1814 |
+
enc = self.encoder(src, mask = mask, attn_mask = attn_mask, prepend_embeds = src_prepend_embeds, return_embeddings = True)
|
1815 |
+
|
1816 |
+
if self.training and self.cross_attn_tokens_dropout > 0:
|
1817 |
+
enc, mask = dropout_seq(enc, mask, self.cross_attn_tokens_dropout)
|
1818 |
+
|
1819 |
+
out = self.decoder(tgt, context = enc, context_mask = mask)
|
1820 |
+
return out
|
1821 |
+
|
1822 |
+
#===================================================================================================================
|
1823 |
+
|
1824 |
+
def exists(val):
|
1825 |
+
return val is not None
|
1826 |
+
|
1827 |
+
def eval_decorator(fn):
|
1828 |
+
def inner(self, *args, **kwargs):
|
1829 |
+
was_training = self.training
|
1830 |
+
self.eval()
|
1831 |
+
out = fn(self, *args, **kwargs)
|
1832 |
+
self.train(was_training)
|
1833 |
+
return out
|
1834 |
+
return inner
|
1835 |
+
|
1836 |
+
# nucleus
|
1837 |
+
|
1838 |
+
def top_p(logits, thres = 0.9):
|
1839 |
+
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
|
1840 |
+
cum_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
|
1841 |
+
|
1842 |
+
sorted_indices_to_remove = cum_probs > (1 - thres)
|
1843 |
+
sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
|
1844 |
+
sorted_indices_to_remove[:, 0] = 0
|
1845 |
+
|
1846 |
+
sorted_logits[sorted_indices_to_remove] = float('-inf')
|
1847 |
+
return sorted_logits.scatter(1, sorted_indices, sorted_logits)
|
1848 |
+
|
1849 |
+
# topk
|
1850 |
+
|
1851 |
+
def top_k(logits, thres = 0.9):
|
1852 |
+
k = ceil((1 - thres) * logits.shape[-1])
|
1853 |
+
val, ind = torch.topk(logits, k)
|
1854 |
+
probs = torch.full_like(logits, float('-inf'))
|
1855 |
+
probs.scatter_(1, ind, val)
|
1856 |
+
return probs
|
1857 |
+
|
1858 |
+
# top_a
|
1859 |
+
|
1860 |
+
def top_a(logits, min_p_pow=2.0, min_p_ratio=0.02):
|
1861 |
+
probs = F.softmax(logits, dim=-1)
|
1862 |
+
limit = torch.pow(torch.max(probs), min_p_pow) * min_p_ratio
|
1863 |
+
logits[probs < limit] = float('-inf')
|
1864 |
+
logits[probs >= limit] = 1
|
1865 |
+
return logits
|
1866 |
+
|
1867 |
+
# autoregressive wrapper class
|
1868 |
+
|
1869 |
+
class AutoregressiveWrapper(nn.Module):
|
1870 |
+
def __init__(
|
1871 |
+
self,
|
1872 |
+
net,
|
1873 |
+
ignore_index = -100,
|
1874 |
+
pad_value = 0,
|
1875 |
+
mask_prob = 0.
|
1876 |
+
):
|
1877 |
+
super().__init__()
|
1878 |
+
self.pad_value = pad_value
|
1879 |
+
self.ignore_index = ignore_index
|
1880 |
+
|
1881 |
+
self.net = net
|
1882 |
+
self.max_seq_len = net.max_seq_len
|
1883 |
+
|
1884 |
+
# paper shows masking (MLM) in conjunction with autoregressive decoder-only training leads to big improvements https://arxiv.org/abs/2210.13432
|
1885 |
+
assert mask_prob < 1.
|
1886 |
+
self.mask_prob = mask_prob
|
1887 |
+
|
1888 |
+
@torch.no_grad()
|
1889 |
+
@eval_decorator
|
1890 |
+
def generate(
|
1891 |
+
self,
|
1892 |
+
start_tokens,
|
1893 |
+
seq_len,
|
1894 |
+
eos_token = None,
|
1895 |
+
temperature = 1.,
|
1896 |
+
filter_logits_fn = top_k,
|
1897 |
+
filter_thres = 0.9,
|
1898 |
+
min_p_pow = 2.0,
|
1899 |
+
min_p_ratio = 0.02,
|
1900 |
+
verbose=True,
|
1901 |
+
return_prime=False,
|
1902 |
+
**kwargs
|
1903 |
+
):
|
1904 |
+
device = start_tokens.device
|
1905 |
+
num_dims = start_tokens.ndim
|
1906 |
+
|
1907 |
+
start_tokens, ps = pack([start_tokens], '* n')
|
1908 |
+
|
1909 |
+
b, t = start_tokens.shape
|
1910 |
+
|
1911 |
+
out = start_tokens
|
1912 |
+
|
1913 |
+
if verbose:
|
1914 |
+
print("Generating sequence of max length:", seq_len)
|
1915 |
+
|
1916 |
+
for s in range(seq_len):
|
1917 |
+
x = out[:, -self.max_seq_len:]
|
1918 |
+
|
1919 |
+
logits = self.net(x, **kwargs)[:, -1]
|
1920 |
+
|
1921 |
+
if filter_logits_fn in {top_k, top_p}:
|
1922 |
+
filtered_logits = filter_logits_fn(logits, thres = filter_thres)
|
1923 |
+
probs = F.softmax(filtered_logits / temperature, dim=-1)
|
1924 |
+
|
1925 |
+
elif filter_logits_fn is top_a:
|
1926 |
+
filtered_logits = filter_logits_fn(logits, min_p_pow = min_p_pow, min_p_ratio= min_p_ratio)
|
1927 |
+
probs = F.softmax(filtered_logits / temperature, dim=-1)
|
1928 |
+
|
1929 |
+
sample = torch.multinomial(probs, 1)
|
1930 |
+
|
1931 |
+
out = torch.cat((out, sample), dim=-1)
|
1932 |
+
|
1933 |
+
if verbose:
|
1934 |
+
if s % 32 == 0:
|
1935 |
+
print(s, '/', seq_len)
|
1936 |
+
|
1937 |
+
if exists(eos_token):
|
1938 |
+
is_eos_tokens = (out == eos_token)
|
1939 |
+
|
1940 |
+
if is_eos_tokens.any(dim = -1).all():
|
1941 |
+
# mask out everything after the eos tokens
|
1942 |
+
shifted_is_eos_tokens = F.pad(is_eos_tokens, (1, -1))
|
1943 |
+
mask = shifted_is_eos_tokens.float().cumsum(dim = -1) >= 1
|
1944 |
+
out = out.masked_fill(mask, self.pad_value)
|
1945 |
+
|
1946 |
+
if verbose:
|
1947 |
+
print('Model called the end of sequence at:', s, '/', seq_len)
|
1948 |
+
|
1949 |
+
break
|
1950 |
+
|
1951 |
+
if return_prime:
|
1952 |
+
return out[:, :]
|
1953 |
+
|
1954 |
+
else:
|
1955 |
+
return out[:, t:]
|
1956 |
+
|
1957 |
+
out, = unpack(out, ps, '* n')
|
1958 |
+
|
1959 |
+
return out
|
1960 |
+
|
1961 |
+
def compute_accuracy(self, logits, labels):
|
1962 |
+
out = torch.argmax(logits, dim=-1)
|
1963 |
+
out = out.flatten()
|
1964 |
+
labels = labels.flatten()
|
1965 |
+
|
1966 |
+
mask = (labels != 999999) # dummy pad value / supposed to be self.token_pad / will fix later
|
1967 |
+
out = out[mask]
|
1968 |
+
labels = labels[mask]
|
1969 |
+
|
1970 |
+
num_right = (out == labels)
|
1971 |
+
num_right = torch.sum(num_right).type(torch.float32)
|
1972 |
+
|
1973 |
+
acc = num_right / len(labels)
|
1974 |
+
return acc
|
1975 |
+
|
1976 |
+
def forward(self, x, labels = None, **kwargs):
|
1977 |
+
seq, ignore_index = x.shape[1], self.ignore_index
|
1978 |
+
|
1979 |
+
inp, target = x[:, :-1], x[:, 1:]
|
1980 |
+
|
1981 |
+
if self.mask_prob > 0.:
|
1982 |
+
rand = torch.randn(inp.shape, device = x.device)
|
1983 |
+
rand[:, 0] = -torch.finfo(rand.dtype).max # first token should not be masked out
|
1984 |
+
num_mask = min(int(seq * self.mask_prob), seq - 1)
|
1985 |
+
indices = rand.topk(num_mask, dim = -1).indices
|
1986 |
+
mask = ~torch.zeros_like(inp).scatter(1, indices, 1.).bool()
|
1987 |
+
kwargs.update(self_attn_context_mask = mask)
|
1988 |
+
|
1989 |
+
logits = self.net(inp, **kwargs)
|
1990 |
+
|
1991 |
+
acc = self.compute_accuracy(logits, target)
|
1992 |
+
|
1993 |
+
loss = F.cross_entropy(
|
1994 |
+
rearrange(logits, 'b n c -> b c n'),
|
1995 |
+
target,
|
1996 |
+
ignore_index = ignore_index
|
1997 |
+
)
|
1998 |
+
|
1999 |
+
return loss, acc
|
2000 |
+
|
2001 |
+
#===================================================================================================================
|