-
-
Notifications
You must be signed in to change notification settings - Fork 229
/
model.py
671 lines (454 loc) · 22.9 KB
/
model.py
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
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
import sys
min_version = (3, 8)
if sys.version_info < min_version:
print("")
print(f" ## Warning: this project requires Python {min_version[0]}.{min_version[1]} or higher.")
print("")
# Set CUDA context to lazy loading since we won't need 95% of the modules in Torch
import os
os.environ['CUDA_MODULE_LOADING']='LAZY'
import torch
import math
from exllamav2.config import ExLlamaV2Config
from exllamav2.cache import ExLlamaV2CacheBase
from exllamav2.linear import ExLlamaV2Linear
from exllamav2.module import ExLlamaV2Module
from exllamav2.rmsnorm import ExLlamaV2RMSNorm
from exllamav2.attn import ExLlamaV2Attention
from exllamav2.lora import ExLlamaV2Lora
from exllamav2.mlp import ExLlamaV2MLP
from exllamav2.moe_mlp import ExLlamaV2MoEMLP
from exllamav2.embedding import ExLlamaV2Embedding
# from exllamav2.util import list_live_tensors, print_vram_usage, set_snapshot, diff_snapshot, print_vram_usage_peak
from exllamav2.compat import safe_move_tensor
import gc
def _torch_device(idx):
if idx == -1: return "cpu"
return f"cuda:{idx}"
class ExLlamaV2DeviceTensors:
model = None
device_idx: int
ready: bool
scratch_bytes: int
scratch_idx: int
sin: torch.tensor
cos: torch.tensor
scratch: torch.tensor = None
def __init__(self, model, device_idx, scratch_bytes):
self.model = model
self.device_idx = device_idx
self.ready = False
self.scratch_bytes = scratch_bytes
self.scratch_idx = 0
def prepare(self, scratch):
self.prepare_sincos()
if scratch:
self.scratch = torch.empty((self.scratch_bytes // 2,), dtype = torch.half, device = _torch_device(self.device_idx))
self.ready = True
def begin_scratch_alloc(self):
self.scratch_idx = 0
def get_scratch_slice(self, size_bytes):
if self.scratch is None: self.prepare(True)
size_bytes = ((size_bytes + 127) // 128) * 128
size_half = size_bytes // 2
scratch_slice = self.scratch.narrow(0, self.scratch_idx, size_half)
self.scratch_idx += size_half
return scratch_slice
def prepare_sincos(self):
base = self.model.config.rotary_embedding_base
alpha = self.model.config.scale_alpha_value or 1.0
scale = self.model.config.scale_pos_emb or 1.0
head_dim = self.model.config.head_dim
device = _torch_device(self.device_idx)
if alpha != 1.0: base *= alpha ** (self.model.config.head_dim / (self.model.config.head_dim - 2))
inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2, device = device).float() / head_dim))
t = torch.arange(self.model.config.max_seq_len, device = device, dtype = torch.float32)
if scale != 1.0: t /= scale
freqs = torch.einsum("i,j->ij", t, inv_freq)
emb = torch.cat((freqs, freqs), dim=-1)
self.sin = emb.sin()[None, None, :, :].half()
self.cos = emb.cos()[None, None, :, :].half()
class ExLlamaV2:
config: ExLlamaV2Config
modules: list = []
modules_dict: dict = {}
device_tensors: list = []
cache_map: dict
last_kv_layer_idx: int
head_layer_idx: int
loaded: bool
def __init__(self, config: ExLlamaV2Config, lazy_load = False):
self.config = config
self.modules = []
self.modules_dict = {}
self.device_tensors = []
self.cache_map = {}
self.loaded = False
# Build model
self.modules.append(ExLlamaV2Embedding(self, "model.embed_tokens"))
self.modules_dict[self.modules[-1].key] = self.modules[-1]
for layer_list in range(self.config.num_hidden_layers):
self.modules.append(ExLlamaV2Attention(self, f"model.layers.{layer_list}", layer_list))
for m in self.modules[-1].submodules: self.modules_dict[m.key] = m
if self.config.architecture == "Mixtral":
self.modules.append(ExLlamaV2MoEMLP(self, f"model.layers.{layer_list}", layer_list))
else:
self.modules.append(ExLlamaV2MLP(self, f"model.layers.{layer_list}", layer_list))
for m in self.modules[-1].submodules: self.modules_dict[m.key] = m
self.modules.append(ExLlamaV2RMSNorm(self, "model.norm"))
self.modules_dict[self.modules[-1].key] = self.modules[-1]
self.head_layer_idx = len(self.modules)
self.modules.append(ExLlamaV2Linear(self, "lm_head", self.config.hidden_size, self.config.vocab_size, False))
self.modules_dict[self.modules[-1].key] = self.modules[-1]
# Find last layer that affects k/v cache
layer_list = len(self.modules)
while True:
layer_list -= 1
if isinstance(self.modules[layer_list], ExLlamaV2Attention):
break
self.last_kv_layer_idx = layer_list
if hasattr(config, 'repeats'):
self.layers_list = []
def listLeftIndex(alist, value):
if value == 0:
return 0
return alist.index(str(value))
def listRightIndex(alist, value):
if value > len(alist):
return -1
return len(alist) - alist[-1::-1].index(str(value)) -1
layer_list = [layer.key.split(".")[-1] for layer in self.modules]
for interval in config.repeats:
start_idx = listLeftIndex(layer_list, interval[0])
end_idx = listRightIndex(layer_list, interval[1])
self.layers_list.extend(list(range(start_idx, end_idx + 1)))
self.layers_list.extend(list(range(listRightIndex(layer_list, config.repeats[-1][1]), len(layer_list))))
# If we have create a Frankenmerge, lets print it to verify!
print("Frankenstein Layers list:")
for i, layer in enumerate(self.layers_list):
print(i, self.modules[layer].key)
def set_device_map(self, allocation, embed_cpu = True):
self.cache_map = {}
# Constant shared between layers
sincos_size = self.config.head_dim * self.config.max_seq_len * 2
constant_size = sincos_size * 2
# Max size of hidden state
# TODO: Option to reserve space for cache while loading model
state_size = self.config.hidden_size * self.config.max_input_len * self.config.max_batch_size * 2
mask_size = self.config.max_input_len ** 2 * self.config.max_batch_size * 2
# Bytes remaining per device
allocation_bytes = [a * 1024**3 - (constant_size + state_size + mask_size) for a in allocation]
# Scratch space required per device
reserve_bytes = [0 for a in allocation]
reserve_bytes_attn = [0 for a in allocation]
fixed_bytes = [0 for a in allocation]
current_idx = 0
for idx, module in enumerate(self.modules):
# Special case for token embeddings on CPU
if idx == 0 and embed_cpu:
module.set_device_idx(-1)
continue
# Special case for attention
attn_bytes_current = 0
if isinstance(module, ExLlamaV2Attention): attn_bytes_current = module.temp_attn_size()
# Advance current_idx until module fits in allocation
footprint = module.weight_footprint() # Footprint, in bytes
scratch = module.scratch_space() # Scratch space required by module
while True:
assert current_idx < len(allocation_bytes), "Insufficient space in device allocation"
dev_scratch = max(scratch, reserve_bytes[current_idx])
dev_scratch_attn = max(attn_bytes_current, reserve_bytes_attn[current_idx])
if footprint + dev_scratch + dev_scratch_attn <= allocation_bytes[current_idx]: break
current_idx += 1
# Size for fixed tensors
scratch_fixed = module.scratch_space_fixed()
fixed_bytes[current_idx] = max(scratch_fixed, fixed_bytes[current_idx])
# Subtract module size from allocation
reserve_bytes[current_idx] = dev_scratch
reserve_bytes_attn[current_idx] = dev_scratch_attn
allocation_bytes[current_idx] -= footprint
module.set_device_idx(current_idx)
# Prepare to prepare device tensors
self.device_tensors = []
for idx, scratch_bytes in enumerate(fixed_bytes):
self.device_tensors.append(ExLlamaV2DeviceTensors(self, idx, scratch_bytes))
# Create map for cache
self.set_cache_map()
# Return unused space, in GB
return [(ab - rb - rba) / 1024**3 for (ab, rb, rba) in zip(allocation_bytes, reserve_bytes, reserve_bytes_attn)]
def load(self, gpu_split = None, lazy = False, stats = False, callback = None, callback_gen = None):
f = self.load_gen(gpu_split, lazy, stats, callback, callback_gen)
for item in f: return item
def load_gen(self, gpu_split = None, lazy = False, stats = False, callback = None, callback_gen = None):
with torch.inference_mode():
stats_ = self.set_device_map(gpu_split or [99999])
# Load module weights
if not lazy:
for idx, module in enumerate(self.modules):
if callback is not None: callback(idx, len(self.modules))
if callback_gen is not None: yield from callback_gen(idx, len(self.modules))
module.load()
if callback is not None: callback(len(self.modules), len(self.modules))
if callback_gen is not None: yield from callback_gen(len(self.modules), len(self.modules))
# Cache map
self.set_cache_map()
self.loaded = True
if stats: yield gpu_split, stats_
else: yield gpu_split
def load_autosplit(self, cache, reserve_vram = None, last_id_only = False, callback = None, callback_gen = None):
f = self.load_autosplit_gen(cache, reserve_vram, last_id_only, callback, callback_gen)
for item in f: x = item
def load_autosplit_gen(self, cache, reserve_vram = None, last_id_only = False, callback = None, callback_gen = None):
# Limit model's max_input_len to max_seq_len if necessary
self.config.max_input_len = min(self.config.max_input_len, self.config.max_seq_len)
minimum_reserve_vram = 256 * 1024**2
last_touched_device = -1
current_device = 0
num_devices = torch.torch.cuda.device_count()
loras = None # TODO:
with torch.inference_mode():
self.device_tensors = []
# Reserved space
if reserve_vram is None:
reserve_vram = [192 * 1024**2] + [64 * 1024**2] * (num_devices - 1)
reserved_vram_tensors = []
minimum_reserve_tensor = None
# Largest hidden state to ever forward through model
hidden_state = torch.zeros((1, self.config.max_input_len), dtype = torch.long)
batch_size, seq_len = hidden_state.shape
past_len = 0
attn_params = ExLlamaV2Attention.Params(batch_size, seq_len, past_len, None, None)
# Size of fixed scratch space
scratch_fixed = 0
for module in self.modules:
scratch_fixed = max(scratch_fixed, module.scratch_space_fixed())
# Load modules and create cache tensors sequentially
self.cache_map = {}
for idx, module in enumerate(self.modules):
if callback is not None: callback(idx, len(self.modules))
if callback_gen is not None: yield from callback_gen(idx, len(self.modules))
# Embedding layer on CPU
if idx == 0:
module.set_device_idx(-1)
module.load()
hidden_state = module.forward(hidden_state)
continue
while True:
# If we've reached a new device, allocate fixed tensors
if current_device > last_touched_device:
self.device_tensors.append(ExLlamaV2DeviceTensors(self, current_device, scratch_fixed))
# if attn_mask is not None:
# reserved_vram_tensors.append(attn_mask)
# attn_mask = safe_move_tensor(attn_mask, _torch_device(current_device))
# else:
# attn_mask = self.build_attn_mask(batch_size, seq_len, past_len, None, _torch_device(current_device))
b = reserve_vram[current_device]
reserved_vram_tensors.append(torch.empty((b,), dtype = torch.int8, device = _torch_device(current_device)))
minimum_reserve_tensor = torch.empty((minimum_reserve_vram,), dtype = torch.int8, device = _torch_device(current_device))
last_touched_device = current_device
# Attempt to load module and forward state
module.set_device_idx(current_device)
hidden_state_backup = safe_move_tensor(hidden_state, "cpu").clone()
try:
if isinstance(module, ExLlamaV2Attention):
self.cache_map[module.layer_idx] = module.device()
cache.update_cache_tensors()
module.load()
if idx == self.head_layer_idx:
if last_id_only:
hidden_state = hidden_state.narrow(-2, -1, 1)
hidden_state = safe_move_tensor(hidden_state, _torch_device(current_device))
hidden_state = module.forward(hidden_state, cache = cache, attn_params = attn_params, past_len = past_len, loras = loras)
fail = False
except Exception as e:
test = 0
if ("CUDA out of memory" in str(e)) or ("HIP out of memory" in str(e)):
fail = True # Exception object will hold references to tensors so we can't free them here
else:
raise
# If we failed, roll back and advance to next device
if fail:
module.unload()
hidden_state = None
if minimum_reserve_tensor is not None: del minimum_reserve_tensor
minimum_reserve_tensor = None
gc.collect()
torch.cuda.empty_cache()
hidden_state = hidden_state_backup.clone()
current_device += 1
if current_device >= num_devices:
raise RuntimeError("Insufficient VRAM for model and cache")
continue
break
if callback is not None: callback(len(self.modules), len(self.modules))
if callback_gen is not None: yield from callback_gen(len(self.modules), len(self.modules))
hidden_state = None
attn_params = None
reserved_vram_tensors = None
gc.collect()
torch.cuda.empty_cache()
self.loaded = True
if 'yield' in locals():
yield
def unload(self):
for module in self.modules:
module.unload()
self.modules = []
self.modules_dict = {}
self.device_tensors = []
def set_cache_map(self):
for module in self.modules:
if isinstance(module, ExLlamaV2Attention): self.cache_map[module.layer_idx] = module.device()
def get_cache_devices(self):
return list(set(self.cache_map.values()))
def create_device_tensors(self, scratch_bytes):
for idx, bytes in enumerate(scratch_bytes):
tensors = ExLlamaV2DeviceTensors(self, idx, bytes)
self.device_tensors.append(tensors)
def get_device_tensors(self, device_idx, scratch = True):
tensors = self.device_tensors[device_idx]
if not tensors.ready: tensors.prepare(scratch)
return tensors
def get_modules(self):
return [module for module in self.modules]
def update_loras(self):
for module in self.modules:
if isinstance(module, ExLlamaV2Attention): module.update_loras()
if isinstance(module, ExLlamaV2MLP): module.update_loras()
if isinstance(module, ExLlamaV2MoEMLP): module.update_loras()
def is_quant(self):
for module in self.modules:
if isinstance(module, ExLlamaV2Attention):
if module.is_quant(): return True
return False
@torch.inference_mode()
def forward(self,
input_ids,
cache = None,
input_mask = None,
preprocess_only = False,
last_id_only = False,
loras = None,
return_last_state = False,
position_offsets = None):
q_len = input_ids.shape[-1]
remaining_q_len = q_len
bsz = input_ids.shape[0]
# Attn and MLP layers have preallocated buffers for temp states, sized by the model config. Effective max input
# length depends on the current batch size
effective_max_input_len = self.config.max_input_len * self.config.max_batch_size // bsz
# Without a cache we can't process the sequence in chunks, so forward the whole thing and assume the input length
# is less than config.max_input_len
if cache is None or not isinstance(cache, ExLlamaV2CacheBase):
assert q_len <= effective_max_input_len, "Maximum input length exceeded in model.forward"
result, last_state = self._forward(input_ids = input_ids,
cache = cache,
input_mask = input_mask,
preprocess_only = preprocess_only,
last_id_only = last_id_only,
loras = loras,
return_last_state = return_last_state,
position_offsets = position_offsets)
if last_state is None:
return result
else:
return result, last_state
# Confirm that the input fits within the allocated cache space
past_len = cache.current_seq_len
assert past_len + q_len <= cache.max_seq_len, "Total sequence length exceeds cache size in model.forward"
# Split sequence
result = None
last_state = None
chunk_begin = 0
while chunk_begin < q_len:
# Limit chunk_size to max_input_len
chunk_size = min(remaining_q_len, effective_max_input_len)
# Limit chunk_size to keep size of attention operation <= max_attention_size
past_len = cache.current_seq_len
attn_size = (past_len + remaining_q_len) * remaining_q_len
max_a = self.config.max_attention_size
if attn_size > max_a:
cs = (math.sqrt(past_len ** 2 + 4 * max_a) - past_len) / 2
chunk_size = min(chunk_size, math.floor(cs))
# Process chunk
chunk_end = min(chunk_begin + chunk_size, q_len)
# print(f"Forward chunk length: {chunk_end - chunk_begin}")
_last_id_only = last_id_only
_preprocess_only = preprocess_only or (chunk_end < q_len and last_id_only)
r, ls = self._forward(input_ids = input_ids[:, chunk_begin : chunk_end],
cache = cache,
input_mask = input_mask,
preprocess_only = _preprocess_only,
last_id_only = _last_id_only,
loras = loras,
return_last_state = return_last_state and remaining_q_len <= chunk_size,
position_offsets = position_offsets)
if not _preprocess_only:
result = r if result is None else torch.cat((result, r), dim = 1)
r = None
chunk_begin = chunk_end
remaining_q_len -= chunk_size
last_state = ls
if last_state is None:
return result
else:
return result, last_state
@torch.inference_mode()
def _forward(self,
input_ids,
cache = None,
input_mask = None,
preprocess_only = False,
last_id_only = False,
loras = None,
return_last_state = False,
position_offsets = None):
def process_module(module, x, last_state):
device = _torch_device(module.device_idx)
if idx == self.head_layer_idx:
if last_id_only and return_last_state:
x = x.narrow(-2, -1, 1)
last_state = x
elif last_id_only:
x = x.narrow(-2, -1, 1)
elif return_last_state:
last_state = x.narrow(-2, -1, 1)
x = safe_move_tensor(x, device)
x = module.forward(x, cache=cache, attn_params=attn_params, past_len=past_len, loras=loras)
return x, last_state
batch_size, seq_len = input_ids.shape
past_len = 0
if cache is not None:
if isinstance(cache, ExLlamaV2CacheBase):
past_len = cache.current_seq_len
else:
past_len = [c.current_seq_len for c in cache]
# assert cache is None or isinstance(cache, list) or batch_size <= cache.batch_size
x = input_ids
attn_params = ExLlamaV2Attention.Params(batch_size, seq_len, past_len, input_mask, position_offsets)
last_state = None
if hasattr(self, 'layers_list'):
for i, idx in enumerate(self.layers_list):
module = self.modules[idx]
x, last_state = process_module(module, x, last_state)
if preprocess_only and idx == self.last_kv_layer_idx:
x = None
break
else:
for idx, module in enumerate(self.modules):
x, last_state = process_module(module, x, last_state)
if preprocess_only and idx == self.last_kv_layer_idx:
x = None
break
# Advance cache
if cache is not None:
if isinstance(cache, list):
for c in cache: c.current_seq_len += seq_len
else:
cache.current_seq_len += seq_len
# Set padding logits to -inf
if x is not None:
head_padding = self.modules[-1].padding
if head_padding > 0:
x[:, :, -head_padding:] = -65504.
return x, last_state