{{ message }}
Repository navigation
Expand file tree
/
Copy pathattention_flax.py
More file actions
524 lines (436 loc) · 21 KB
/
Copy pathattention_flax.py
File metadata and controls
524 lines (436 loc) · 21 KB
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
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import math
import flax.linen as nn
import jax
import jax.numpy as jnp
from ..utils import logging
logger = logging.get_logger(__name__)
def _query_chunk_attention(query, key, value, precision, key_chunk_size: int = 4096):
"""Multi-head dot product attention with a limited number of queries."""
num_kv, num_heads, k_features = key.shape[-3:]
v_features = value.shape[-1]
key_chunk_size = min(key_chunk_size, num_kv)
query = query / jnp.sqrt(k_features)
@functools.partial(jax.checkpoint, prevent_cse=False)
def summarize_chunk(query, key, value):
attn_weights = jnp.einsum("...qhd,...khd->...qhk", query, key, precision=precision)
max_score = jnp.max(attn_weights, axis=-1, keepdims=True)
max_score = jax.lax.stop_gradient(max_score)
exp_weights = jnp.exp(attn_weights - max_score)
exp_values = jnp.einsum("...vhf,...qhv->...qhf", value, exp_weights, precision=precision)
max_score = jnp.einsum("...qhk->...qh", max_score)
return (exp_values, exp_weights.sum(axis=-1), max_score)
def chunk_scanner(chunk_idx):
# julienne key array
key_chunk = jax.lax.dynamic_slice(
operand=key,
start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0], # [...,k,h,d]
slice_sizes=list(key.shape[:-3]) + [key_chunk_size, num_heads, k_features], # [...,k,h,d]
)
# julienne value array
value_chunk = jax.lax.dynamic_slice(
operand=value,
start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0], # [...,v,h,d]
slice_sizes=list(value.shape[:-3]) + [key_chunk_size, num_heads, v_features], # [...,v,h,d]
)
return summarize_chunk(query, key_chunk, value_chunk)
chunk_values, chunk_weights, chunk_max = jax.lax.map(f=chunk_scanner, xs=jnp.arange(0, num_kv, key_chunk_size))
global_max = jnp.max(chunk_max, axis=0, keepdims=True)
max_diffs = jnp.exp(chunk_max - global_max)
chunk_values *= jnp.expand_dims(max_diffs, axis=-1)
chunk_weights *= max_diffs
all_values = chunk_values.sum(axis=0)
all_weights = jnp.expand_dims(chunk_weights, -1).sum(axis=0)
return all_values / all_weights
def jax_memory_efficient_attention(
query, key, value, precision=jax.lax.Precision.HIGHEST, query_chunk_size: int = 1024, key_chunk_size: int = 4096
):
r"""
Flax Memory-efficient multi-head dot product attention. https://huggingface.co/papers/2112.05682v2
https://github.com/AminRezaei0x443/memory-efficient-attention
Args:
query (`jnp.ndarray`): (batch..., query_length, head, query_key_depth_per_head)
key (`jnp.ndarray`): (batch..., key_value_length, head, query_key_depth_per_head)
value (`jnp.ndarray`): (batch..., key_value_length, head, value_depth_per_head)
precision (`jax.lax.Precision`, *optional*, defaults to `jax.lax.Precision.HIGHEST`):
numerical precision for computation
query_chunk_size (`int`, *optional*, defaults to 1024):
chunk size to divide query array value must divide query_length equally without remainder
key_chunk_size (`int`, *optional*, defaults to 4096):
chunk size to divide key and value array value must divide key_value_length equally without remainder
Returns:
(`jnp.ndarray`) with shape of (batch..., query_length, head, value_depth_per_head)
"""
num_q, num_heads, q_features = query.shape[-3:]
def chunk_scanner(chunk_idx, _):
# julienne query array
query_chunk = jax.lax.dynamic_slice(
operand=query,
start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0], # [...,q,h,d]
slice_sizes=list(query.shape[:-3]) + [min(query_chunk_size, num_q), num_heads, q_features], # [...,q,h,d]
)
return (
chunk_idx + query_chunk_size, # unused ignore it
_query_chunk_attention(
query=query_chunk, key=key, value=value, precision=precision, key_chunk_size=key_chunk_size
),
)
_, res = jax.lax.scan(
f=chunk_scanner,
init=0,
xs=None,
length=math.ceil(num_q / query_chunk_size), # start counter # stop counter
)
return jnp.concatenate(res, axis=-3) # fuse the chunked result back
class FlaxAttention(nn.Module):
r"""
A Flax multi-head attention module as described in: https://huggingface.co/papers/1706.03762
Parameters:
query_dim (:obj:`int`):
Input hidden states dimension
heads (:obj:`int`, *optional*, defaults to 8):
Number of heads
dim_head (:obj:`int`, *optional*, defaults to 64):
Hidden states dimension inside each head
dropout (:obj:`float`, *optional*, defaults to 0.0):
Dropout rate
use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
enable memory efficient attention https://huggingface.co/papers/2112.05682
split_head_dim (`bool`, *optional*, defaults to `False`):
Whether to split the head dimension into a new axis for the self-attention computation. In most cases,
enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
Parameters `dtype`
"""
query_dim: int
heads: int = 8
dim_head: int = 64
dropout: float = 0.0
use_memory_efficient_attention: bool = False
split_head_dim: bool = False
dtype: jnp.dtype = jnp.float32
def setup(self):
logger.warning(
"Flax classes are deprecated and will be removed in Diffusers v1.0.0. We "
"recommend migrating to PyTorch classes or pinning your version of Diffusers."
)
inner_dim = self.dim_head * self.heads
self.scale = self.dim_head**-0.5
# Weights were exported with old names {to_q, to_k, to_v, to_out}
self.query = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_q")
self.key = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_k")
self.value = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_v")
self.proj_attn = nn.Dense(self.query_dim, dtype=self.dtype, name="to_out_0")
self.dropout_layer = nn.Dropout(rate=self.dropout)
def reshape_heads_to_batch_dim(self, tensor):
batch_size, seq_len, dim = tensor.shape
head_size = self.heads
tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
tensor = jnp.transpose(tensor, (0, 2, 1, 3))
tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)
return tensor
def reshape_batch_dim_to_heads(self, tensor):
batch_size, seq_len, dim = tensor.shape
head_size = self.heads
tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
tensor = jnp.transpose(tensor, (0, 2, 1, 3))
tensor = tensor.reshape(batch_size // head_size, seq_len, dim * head_size)
return tensor
def __call__(self, hidden_states, context=None, deterministic=True):
context = hidden_states if context is None else context
query_proj = self.query(hidden_states)
key_proj = self.key(context)
value_proj = self.value(context)
if self.split_head_dim:
b = hidden_states.shape[0]
query_states = jnp.reshape(query_proj, (b, -1, self.heads, self.dim_head))
key_states = jnp.reshape(key_proj, (b, -1, self.heads, self.dim_head))
value_states = jnp.reshape(value_proj, (b, -1, self.heads, self.dim_head))
else:
query_states = self.reshape_heads_to_batch_dim(query_proj)
key_states = self.reshape_heads_to_batch_dim(key_proj)
value_states = self.reshape_heads_to_batch_dim(value_proj)
if self.use_memory_efficient_attention:
query_states = query_states.transpose(1, 0, 2)
key_states = key_states.transpose(1, 0, 2)
value_states = value_states.transpose(1, 0, 2)
# this if statement create a chunk size for each layer of the unet
# the chunk size is equal to the query_length dimension of the deepest layer of the unet
flatten_latent_dim = query_states.shape[-3]
if flatten_latent_dim % 64 == 0:
query_chunk_size = int(flatten_latent_dim / 64)
elif flatten_latent_dim % 16 == 0:
query_chunk_size = int(flatten_latent_dim / 16)
elif flatten_latent_dim % 4 == 0:
query_chunk_size = int(flatten_latent_dim / 4)
else:
query_chunk_size = int(flatten_latent_dim)
hidden_states = jax_memory_efficient_attention(
query_states, key_states, value_states, query_chunk_size=query_chunk_size, key_chunk_size=4096 * 4
)
hidden_states = hidden_states.transpose(1, 0, 2)
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
else:
# compute attentions
if self.split_head_dim:
attention_scores = jnp.einsum("b t n h, b f n h -> b n f t", key_states, query_states)
else:
attention_scores = jnp.einsum("b i d, b j d->b i j", query_states, key_states)
attention_scores = attention_scores * self.scale
attention_probs = nn.softmax(attention_scores, axis=-1 if self.split_head_dim else 2)
# attend to values
if self.split_head_dim:
hidden_states = jnp.einsum("b n f t, b t n h -> b f n h", attention_probs, value_states)
b = hidden_states.shape[0]
hidden_states = jnp.reshape(hidden_states, (b, -1, self.heads * self.dim_head))
else:
hidden_states = jnp.einsum("b i j, b j d -> b i d", attention_probs, value_states)
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
hidden_states = self.proj_attn(hidden_states)
return self.dropout_layer(hidden_states, deterministic=deterministic)
class FlaxBasicTransformerBlock(nn.Module):
r"""
A Flax transformer block layer with `GLU` (Gated Linear Unit) activation function as described in:
https://huggingface.co/papers/1706.03762
Parameters:
dim (:obj:`int`):
Inner hidden states dimension
n_heads (:obj:`int`):
Number of heads
d_head (:obj:`int`):
Hidden states dimension inside each head
dropout (:obj:`float`, *optional*, defaults to 0.0):
Dropout rate
only_cross_attention (`bool`, defaults to `False`):
Whether to only apply cross attention.
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
Parameters `dtype`
use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
enable memory efficient attention https://huggingface.co/papers/2112.05682
split_head_dim (`bool`, *optional*, defaults to `False`):
Whether to split the head dimension into a new axis for the self-attention computation. In most cases,
enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
"""
dim: int
n_heads: int
d_head: int
dropout: float = 0.0
only_cross_attention: bool = False
dtype: jnp.dtype = jnp.float32
use_memory_efficient_attention: bool = False
split_head_dim: bool = False
def setup(self):
logger.warning(
"Flax classes are deprecated and will be removed in Diffusers v1.0.0. We "
"recommend migrating to PyTorch classes or pinning your version of Diffusers."
)
# self attention (or cross_attention if only_cross_attention is True)
self.attn1 = FlaxAttention(
self.dim,
self.n_heads,
self.d_head,
self.dropout,
self.use_memory_efficient_attention,
self.split_head_dim,
dtype=self.dtype,
)
# cross attention
self.attn2 = FlaxAttention(
self.dim,
self.n_heads,
self.d_head,
self.dropout,
self.use_memory_efficient_attention,
self.split_head_dim,
dtype=self.dtype,
)
self.ff = FlaxFeedForward(dim=self.dim, dropout=self.dropout, dtype=self.dtype)
self.norm1 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)
self.norm2 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)
self.norm3 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)
self.dropout_layer = nn.Dropout(rate=self.dropout)
def __call__(self, hidden_states, context, deterministic=True):
# self attention
residual = hidden_states
if self.only_cross_attention:
hidden_states = self.attn1(self.norm1(hidden_states), context, deterministic=deterministic)
else:
hidden_states = self.attn1(self.norm1(hidden_states), deterministic=deterministic)
hidden_states = hidden_states + residual
# cross attention
residual = hidden_states
hidden_states = self.attn2(self.norm2(hidden_states), context, deterministic=deterministic)
hidden_states = hidden_states + residual
# feed forward
residual = hidden_states
hidden_states = self.ff(self.norm3(hidden_states), deterministic=deterministic)
hidden_states = hidden_states + residual
return self.dropout_layer(hidden_states, deterministic=deterministic)
class FlaxTransformer2DModel(nn.Module):
r"""
A Spatial Transformer layer with Gated Linear Unit (GLU) activation function as described in:
https://huggingface.co/papers/1506.02025
Parameters:
in_channels (:obj:`int`):
Input number of channels
n_heads (:obj:`int`):
Number of heads
d_head (:obj:`int`):
Hidden states dimension inside each head
depth (:obj:`int`, *optional*, defaults to 1):
Number of transformers block
dropout (:obj:`float`, *optional*, defaults to 0.0):
Dropout rate
use_linear_projection (`bool`, defaults to `False`): tbd
only_cross_attention (`bool`, defaults to `False`): tbd
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
Parameters `dtype`
use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
enable memory efficient attention https://huggingface.co/papers/2112.05682
split_head_dim (`bool`, *optional*, defaults to `False`):
Whether to split the head dimension into a new axis for the self-attention computation. In most cases,
enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
"""
in_channels: int
n_heads: int
d_head: int
depth: int = 1
dropout: float = 0.0
use_linear_projection: bool = False
only_cross_attention: bool = False
dtype: jnp.dtype = jnp.float32
use_memory_efficient_attention: bool = False
split_head_dim: bool = False
def setup(self):
logger.warning(
"Flax classes are deprecated and will be removed in Diffusers v1.0.0. We "
"recommend migrating to PyTorch classes or pinning your version of Diffusers."
)
self.norm = nn.GroupNorm(num_groups=32, epsilon=1e-5)
inner_dim = self.n_heads * self.d_head
if self.use_linear_projection:
self.proj_in = nn.Dense(inner_dim, dtype=self.dtype)
else:
self.proj_in = nn.Conv(
inner_dim,
kernel_size=(1, 1),
strides=(1, 1),
padding="VALID",
dtype=self.dtype,
)
self.transformer_blocks = [
FlaxBasicTransformerBlock(
inner_dim,
self.n_heads,
self.d_head,
dropout=self.dropout,
only_cross_attention=self.only_cross_attention,
dtype=self.dtype,
use_memory_efficient_attention=self.use_memory_efficient_attention,
split_head_dim=self.split_head_dim,
)
for _ in range(self.depth)
]
if self.use_linear_projection:
self.proj_out = nn.Dense(inner_dim, dtype=self.dtype)
else:
self.proj_out = nn.Conv(
inner_dim,
kernel_size=(1, 1),
strides=(1, 1),
padding="VALID",
dtype=self.dtype,
)
self.dropout_layer = nn.Dropout(rate=self.dropout)
def __call__(self, hidden_states, context, deterministic=True):
batch, height, width, channels = hidden_states.shape
residual = hidden_states
hidden_states = self.norm(hidden_states)
if self.use_linear_projection:
hidden_states = hidden_states.reshape(batch, height * width, channels)
hidden_states = self.proj_in(hidden_states)
else:
hidden_states = self.proj_in(hidden_states)
hidden_states = hidden_states.reshape(batch, height * width, channels)
for transformer_block in self.transformer_blocks:
hidden_states = transformer_block(hidden_states, context, deterministic=deterministic)
if self.use_linear_projection:
hidden_states = self.proj_out(hidden_states)
hidden_states = hidden_states.reshape(batch, height, width, channels)
else:
hidden_states = hidden_states.reshape(batch, height, width, channels)
hidden_states = self.proj_out(hidden_states)
hidden_states = hidden_states + residual
return self.dropout_layer(hidden_states, deterministic=deterministic)
class FlaxFeedForward(nn.Module):
r"""
Flax module that encapsulates two Linear layers separated by a non-linearity. It is the counterpart of PyTorch's
[`FeedForward`] class, with the following simplifications:
- The activation function is currently hardcoded to a gated linear unit from:
https://huggingface.co/papers/2002.05202
- `dim_out` is equal to `dim`.
- The number of hidden dimensions is hardcoded to `dim * 4` in [`FlaxGELU`].
Parameters:
dim (:obj:`int`):
Inner hidden states dimension
dropout (:obj:`float`, *optional*, defaults to 0.0):
Dropout rate
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
Parameters `dtype`
"""
dim: int
dropout: float = 0.0
dtype: jnp.dtype = jnp.float32
def setup(self):
logger.warning(
"Flax classes are deprecated and will be removed in Diffusers v1.0.0. We "
"recommend migrating to PyTorch classes or pinning your version of Diffusers."
)
# The second linear layer needs to be called
# net_2 for now to match the index of the Sequential layer
self.net_0 = FlaxGEGLU(self.dim, self.dropout, self.dtype)
self.net_2 = nn.Dense(self.dim, dtype=self.dtype)
def __call__(self, hidden_states, deterministic=True):
hidden_states = self.net_0(hidden_states, deterministic=deterministic)
hidden_states = self.net_2(hidden_states)
return hidden_states
class FlaxGEGLU(nn.Module):
r"""
Flax implementation of a Linear layer followed by the variant of the gated linear unit activation function from
https://huggingface.co/papers/2002.05202.
Parameters:
dim (:obj:`int`):
Input hidden states dimension
dropout (:obj:`float`, *optional*, defaults to 0.0):
Dropout rate
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
Parameters `dtype`
"""
dim: int
dropout: float = 0.0
dtype: jnp.dtype = jnp.float32
def setup(self):
logger.warning(
"Flax classes are deprecated and will be removed in Diffusers v1.0.0. We "
"recommend migrating to PyTorch classes or pinning your version of Diffusers."
)
inner_dim = self.dim * 4
self.proj = nn.Dense(inner_dim * 2, dtype=self.dtype)
self.dropout_layer = nn.Dropout(rate=self.dropout)
def __call__(self, hidden_states, deterministic=True):
hidden_states = self.proj(hidden_states)
hidden_linear, hidden_gelu = jnp.split(hidden_states, 2, axis=2)
return self.dropout_layer(hidden_linear * nn.gelu(hidden_gelu), deterministic=deterministic)
You can’t perform that action at this time.