File size: 23,223 Bytes
3a464db | 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 | import math
import copy
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
def add_gumbel_noise(logits, temperature):
'''
The Gumbel max is a method for sampling categorical distributions.
According to arXiv:2409.02908, for MDM, low-precision Gumbel Max improves perplexity score but reduces generation quality.
Thus, we use float64.
'''
if math.isclose(temperature, 0.0):
return logits
logits = logits.to(torch.float64)
noise = torch.rand_like(logits, dtype=torch.float64)
gumbel_noise = (- torch.log(noise)) ** temperature
return logits.exp() / gumbel_noise
def get_num_transfer_tokens(mask_index, steps):
'''
In the reverse process, the interval [0, 1] is uniformly discretized into steps intervals.
Furthermore, because LLaDA employs a linear noise schedule (as defined in Eq. (8)),
the expected number of tokens transitioned at each step should be consistent.
This function is designed to precompute the number of tokens that need to be transitioned at each step.
'''
mask_num = mask_index.sum(dim=1, keepdim=True)
base = mask_num // steps
remainder = mask_num % steps
num_transfer_tokens = torch.zeros(mask_num.size(0), steps, device=mask_index.device, dtype=torch.int64) + base
for i in range(mask_num.size(0)):
num_transfer_tokens[i, :remainder[i]] += 1
return num_transfer_tokens
def calculate_op_num(x, hidden_size=4096, mlp_hidden_size = 12288, vocab_size = 126464, num_hidden_layers=32, cache_length=0):
cfg_factor = 1
qkv_ops = 4*x.shape[0]*hidden_size*hidden_size*x.shape[1]*2
attn_ops = x.shape[0]*(cache_length)*x.shape[1]*hidden_size*2
ffn_ops = 3*x.shape[0]*hidden_size*mlp_hidden_size*x.shape[1]*2
layer_ops = qkv_ops + attn_ops + ffn_ops
op_num = cfg_factor * (num_hidden_layers*layer_ops + x.shape[0]*hidden_size*vocab_size*x.shape[1]*2)
return op_num/1e12
class TokenArray:
""" A token array to support read, update and expansion.
We need to access the tokens that have been generated and write new tokens to the array.
Some algorithms require to expand the token array.
Parameters
----------
prompt : Torch.Tensor
The array that contains the input prompt.
gen_length : int
The number of tokens to be generated.
mask_id : int
the mask id of the masked tokens
device : Torch.Device
The device where the token array is placed on.
"""
def __init__(self, prompt, gen_length, mask_id, eos_id, device):
self.prompt = prompt.to(device)
self.data = torch.full((prompt.shape[0], prompt.shape[1] + gen_length), mask_id, dtype=torch.long).to(device)
self.data[:, :prompt.shape[1]] = prompt.clone()
self.gen_length = gen_length
self.eos_id = eos_id
self.mask_id = mask_id
@property
def total_length(self):
return self.prompt.shape[1] + self.gen_length
@property
def batch_size(self):
return self.prompt.shape[0]
@property
def device(self):
return self.data.device
def expand(self, new_len):
pass
def get_generated_tokens(self):
if self.batch_size == 1:
return self.data[self.data != self.eos_id].unsqueeze(0)
else:
self.data[self.data == self.mask_id] = self.eos_id
return self.data
def select_seqs(self, idx):
arr = copy.copy(self)
arr.prompt = self.prompt[idx]
arr.data = self.data[idx]
return arr
def __getitem__(self, idx):
return self.data[idx]
def __setitem__(self, idx, vals):
self.data[idx] = vals
class DistAlignedTokenArray:
""" A token array to support read, update and expansion in the distributed setting.
In this setting, each process still contains the full copy of the token array.
The main difference from TokenArray is that this class makes sure that the length of the token array
is rounded to the world size.
Parameters
----------
prompt : Torch.Tensor
The array that contains the input prompt.
gen_length : int
The number of tokens to be generated.
mask_id : int
the mask id of the masked tokens
device : Torch.Device
The device where the token array is placed on.
rank : int
The rank of the process
world_size : int
The number of processes.
"""
def __init__(self, prompt, gen_length, mask_id, eos_id, device, rank, world_size):
total_length = prompt.shape[1] + gen_length
if total_length % world_size != 0:
total_length = (total_length // world_size + 1) * world_size
self.data = torch.full((prompt.shape[0], total_length), mask_id, dtype=torch.long).to(device)
self.data[:, :prompt.shape[1]] = prompt.clone()
self.orig_gen_length = gen_length
self.gen_length = total_length - prompt.shape[1]
self.prompt = prompt
self.eos_id = eos_id
self.mask_id = mask_id
@property
def total_length(self):
return self.prompt.shape[1] + self.gen_length
@property
def device(self):
return self.data.device
def get_generated_tokens(self):
return self.data[self.data != self.eos_id].unsqueeze(0)
def expand(self, new_len):
pass
def __getitem__(self, idx):
return self.data[idx]
def __setitem__(self, idx, vals):
self.data[idx] = vals
class BlockLoc:
""" The location of the block in the token array.
"""
def __init__(self, start, end):
self.start = start
self.end = end
class BlockIterator:
""" Block iterator
This performs block-wise iteration on the input token array for diffusion decoding.
Parameters
----------
x : TokenArray
The token array that contains decoded tokens and stores the new generated tokens
block_length : int
The length of the block
start_block_align : bool
Align the first decoding block to the block size. The first block may overlap with the prompt.
"""
def __init__(self, x, block_length, start_block_align=False):
self.x = x
self.iter = 0
self.block_length = block_length
self.start_block_align = start_block_align
if start_block_align:
self.first_block_start = self._get_first_block_start()
else:
self.first_block_start = self.x.prompt.shape[1]
def _get_first_block_start(self):
prompt = self.x.prompt
non_mask_number = (prompt != self.x.mask_id).sum(dim=-1).min().item()
start = ((non_mask_number) // self.block_length) * self.block_length
return start
def __iter__(self):
self.iter = 0
return self
def __next__(self):
current_block_start = self.first_block_start + self.iter * self.block_length
if current_block_start >= self.x.total_length:
raise StopIteration
current_block_end = min(current_block_start + self.block_length, self.x.total_length)
assert current_block_end <= self.x.total_length
self.iter += 1
return BlockLoc(current_block_start, current_block_end), self.x[:, current_block_start:current_block_end]
class BlockDiffusionIterator():
""" Block iterator
This performs block-wise iteration on the input token array for diffusion decoding.
Parameters
----------
x : TokenArray
The token array that contains decoded tokens and stores the new generated tokens
block_length : int
The length of the block
"""
def __init__(self, x, block_length):
self.x = x
self.iter = 0
self.block_length = block_length
self.first_block_start = self._get_first_block_start()
def _get_first_block_start(self):
prompt = self.x.prompt
non_mask_number = (prompt != self.x.mask_id).sum(dim=-1).min().item()
start = ((non_mask_number) // self.block_length) * self.block_length
return start
def __iter__(self):
self.iter = 0
return self
def __next__(self):
current_block_start = self.first_block_start + self.iter * self.block_length
if current_block_start >= self.x.total_length:
raise StopIteration
current_block_end = min(current_block_start + self.block_length, self.x.total_length)
assert current_block_end <= self.x.total_length
self.iter += 1
return BlockLoc(current_block_start, current_block_end), self.x[current_block_start:current_block_end]
class BlockIteratorFactory:
""" Iterator factory
This generates iterators for DiffusionLLM to iterate over a sequence.
Parameters
----------
start_block_align : bool
Align the first decoding block to the block size. The first block may overlap with the prompt.
use_block_diffusion: bool
If this flag set to True, the block diffusion iteration will be used and start_block_algin will be ignored.
Returns
-------
BlockIterator : the block iterator.
"""
def __init__(self, start_block_align=False, use_block_diffusion=False):
self._start_block_align = start_block_align
self._use_bd = use_block_diffusion
def create(self, x, block_length):
if self._use_bd:
return BlockDiffusionIterator(x, block_length)
else:
return BlockIterator(x, block_length, start_block_align=self._start_block_align)
class KVCache:
""" The KV-cache
Parameters
----------
past_key_values : List[torch.Tensor]
The keys and values of each transformer layer.
"""
def __init__(self, past_key_values, backend='vllm', length=2048, cache_align_size=128):
if backend == 'vllm':
assert len(past_key_values) % 2 == 0
self._data = past_key_values
else:
self.cache_align_size = cache_align_size
assert len(past_key_values) % 2 == 0
self._raw_data = past_key_values
self._consolidate_raw()
n = -(-(self._raw_data.shape[4] + 64) // self.cache_align_size)
next_pow2 = 1 << (n - 1).bit_length() if n > 1 else 1
self.length = next_pow2 * self.cache_align_size
device = self._raw_data.device
num_layer, _, batch_size, num_heads, seq_len, hidden_dim = self._raw_data.shape
self._data = torch.zeros(num_layer, 2, batch_size, num_heads, self.length, hidden_dim, device=device, dtype=torch.bfloat16)
self._data[:, :, :, :, :seq_len] = self._raw_data
def consolidate(self):
if isinstance(self._data, torch.Tensor):
return
num_layers = len(self._data) // 2
inner_shape = self._data[0].shape
# The shape is [num_layers, 2, batch_size, num_heads, seq_len, hidden_dim]
self._data = torch.stack(self._data, dim=0).reshape(num_layers, 2, *inner_shape)
def _consolidate_raw(self):
if isinstance(self._raw_data, torch.Tensor):
return
num_layers = len(self._raw_data) // 2
inner_shape = self._raw_data[0].shape
# The shape is [num_layers, 2, batch_size, num_heads, seq_len, hidden_dim]
self._raw_data = torch.stack(self._raw_data, dim=0).reshape(num_layers, 2, *inner_shape)
@property
def num_layers(self):
assert isinstance(self._data, torch.Tensor)
return self._data.shape[0]
@property
def seq_len(self):
assert isinstance(self._data, torch.Tensor)
return self._data.shape[4]
def get_keys(self, layer_idx):
""" Get the keys of a transformer layer.
"""
assert isinstance(self._data, torch.Tensor)
return self._data[layer_idx][0]
def get_values(self, layer_idx):
""" Get the values of a transformer layer.
"""
assert isinstance(self._data, torch.Tensor)
return self._data[layer_idx][1]
def update(self, key_states, val_states, layer_idx, replace_position=None, backend='vllm'):
""" Update the keys and values of a transformer layer.
Parameters
----------
key_states : torch.Tensor
The keys in a block of tokens. The shape is [batch_size, num_heads, seq_len, hidden_dim]
val_states : torch.Tensor
The values in a block of tokens. The shape is [batch_size, num_heads, seq_len, hidden_dim]
layer_idx : int
The index of the transformer layer
replace_position : Tuple[int]
The start and the end position where keys and values should be updated.
Returns
-------
torch.Tensor: the new keys for the entire sequence of the transformer layer.
torch.Tensor: the new values for the entire sequence of the transformer layer.
"""
if backend == 'vllm':
# This is dual cache.
if replace_position is not None:
keys = self.get_keys(layer_idx).slice_scatter(key_states, dim=2, start=replace_position[0], end=replace_position[1])
values = self.get_values(layer_idx).slice_scatter(val_states, dim=2, start=replace_position[0], end=replace_position[1])
else:
# This is prefix cache.
keys = torch.cat([self.get_keys(layer_idx), key_states], dim=2)
values = torch.cat([self.get_values(layer_idx), val_states], dim=2)
else:
cache_length = self.get_keys(layer_idx).shape[2]
block_length = key_states.shape[2]
keys = self.get_keys(layer_idx).slice_scatter(key_states, dim=2, start=cache_length - block_length, end=cache_length)
values = self.get_values(layer_idx).slice_scatter(val_states, dim=2, start=cache_length - block_length, end=cache_length)
return keys, values
class DiffusionKVCacheManager:
""" KV-cache for diffusion LLM.
The KV-cache caches the KV of the tokens before and after the block that is being decoded.
Because diffusion LLM uses bidirectional attention, the KV-cache has to be updated frequently in the diffusion iterations.
This class basically defines the KV-cache update policy in the diffusion iterations. This includes the locations where
keys and values can be updated and the frequency of the keys and values can be updated.
"""
def __init__(self, cache_update_freq=None, cache_type='prefix', backend='vllm', max_length=2048):
self.past_key_values = None
self.block_start = None
self.block_end = None
self.cache_update_freq = cache_update_freq
assert cache_type in ['prefix', 'dual']
self.cache_type = cache_type
self.backend=backend
self.max_length = max_length
def require_update(self, iter_no, block_start, block_end):
""" require to update the kv-cache.
Parameters
----------
iter_no : int
The diffusion iteration number
block_start : int
The start of the block that is being decoded.
block_end : int
The end of the block that is being decoded.
"""
if self.past_key_values is None:
_require_update = True
# If self.cache_update_freq is not specified, the KV-cache is updated when we enter a new block.
if self.cache_update_freq is None:
_require_update = self.block_start != block_start or self.block_end != block_end
else:
# Otherwise, we update the KV-cache when we enter a new block or the specified number of
# diffusion iterations is reached.
_require_update = iter_no % self.cache_update_freq == 0 \
or (self.block_start != block_start or self.block_end != block_end)
# TODO(zhengda) change update logic to block idx
self.block_start = block_start
self.block_end = block_end
return _require_update
def update(self, past_key_values, range_start=None, range_end=None):
""" update the KV-cache
Parameters
----------
past_key_values : List[torch.Tensor]
The key values in all transformer layers.
range_start : int
The start of the range that is being updated.
range_end : int
The end of the range that is being updated.
"""
if isinstance(past_key_values, KVCache):
self.past_key_values = past_key_values
else:
self.past_key_values = KVCache(past_key_values, self.backend, self.max_length)
# We should make sure the kv-cache in all layers are converted into a tensor.
self.past_key_values.consolidate()
def range_update(self, past_key_values, range_start=0, range_end=0, block_length=32):
""" update the KV-cache
Parameters
----------
past_key_values : List[torch.Tensor]
The key values in all transformer layers.
range_start : int
The start of the range that is being updated.
range_end : int
The end of the range that is being updated.
"""
if isinstance(past_key_values, KVCache):
# raise ValueError("past_key_values should be a list of tensors")
self.past_key_values = past_key_values
else:
num_layers = len(past_key_values) // 2
inner_shape = past_key_values[0].shape
compact_kvcache = torch.stack(past_key_values, dim=0).reshape(num_layers, 2, *inner_shape)
if block_length>0:
self.past_key_values = KVCache(torch.cat((compact_kvcache[:, :, :, :, range_start:range_end-block_length], compact_kvcache[:, :, :, :, -block_length:]), dim=4), self.backend, self.max_length)
else:
self.past_key_values = KVCache(compact_kvcache[:, :, :, :, range_start:range_end], self.backend, self.max_length)
# We should make sure the kv-cache in all layers are converted into a tensor.
self.past_key_values.consolidate()
# print(self.past_key_values._data.shape)
def get_key_values(self, block_start, block_end):
""" Get the key-values given the block that is being decoded.
Parameters
----------
block_start : int
The start of the block that is being decoded.
block_end : int
The end of the block that is being decoded.
Returns
-------
List[List[torch.Tensor]] : the key-values required to decode the specified block.
torch.Tensor : the tensor indicates the valid locations in the returned key-values.
"""
# The key-value cache cannot be empty.
assert self.past_key_values is not None
if self.cache_type == 'prefix':
replace_position = (int(block_start), int(self.past_key_values.seq_len))
else:
replace_position = (int(block_start), int(block_end))
return self.past_key_values, replace_position
class BlockDiffusionPrefixCacheManager(DiffusionKVCacheManager):
"""
KVcache manager of block diffusion.
"""
def get_key_values(self, block_start, block_end):
# use prefix cache for block diffusion.
return self.past_key_values, (int(block_start), int(block_end))
def extend_cache(self, end):
"""
When move to new block, extend the kvcache length from previous block end to new block end location.
Parameters
----------
length : int
The extended length (equvelent to block length)
"""
if self.backend == 'vllm':
cur_kv_length = self.past_key_values._data.shape[-2]
extended_cache = F.pad(self.past_key_values._data, pad=(0, 0, 0, end-cur_kv_length), mode='constant', value=0)
self.past_key_values._data = extended_cache
else:
cur_kv_length = self.past_key_values._data.shape[-2]
n = -(-end // self.past_key_values.cache_align_size) # 等价于 ceil(target / cache_align_size)，纯整数
next_pow2 = 1 << (n - 1).bit_length() if n > 1 else 1
aligned_end = next_pow2 * self.past_key_values.cache_align_size
if aligned_end <= cur_kv_length:
return
extended_cache = F.pad(self.past_key_values._data, pad=(0, 0, 0, aligned_end-cur_kv_length), mode='constant', value=0)
self.past_key_values._data = extended_cache
class KVCacheFactory:
""" KV-cache factory.
This class generates KV-cache for the diffusion LLM when it runs diffusion iterations.
"""
def __init__(self, cache_type, cache_update_freq=None, is_bd_model=False, backend='vllm', max_length=2048):
self.cache_type = cache_type
self.cache_update_freq = cache_update_freq
self.is_bd_model = is_bd_model
self.backend = backend
self.max_length = max_length
def create(self):
if self.is_bd_model:
return BlockDiffusionPrefixCacheManager(cache_update_freq=self.cache_update_freq, cache_type=self.cache_type, backend=self.backend, max_length=self.max_length)
else:
return DiffusionKVCacheManager(cache_update_freq=self.cache_update_freq, cache_type=self.cache_type)
def gather_sequence_block(partial_data, partial_start, partial_end, block_start, block_end, rank, world_size):
""" Gather the wanted block data from the partitioned data.
Each process contains a partition specified by `partial_start` and `partial_end`.
The wanted block is located between `block_start` and `block_end`.
We want to gather the data within the block range from the partitioned data.
"""
if partial_start >= block_end or partial_end <= block_start:
# there is no overlap, nothing is needed from partial_data
arr = partial_data[:, 0:0]
elif block_start >= partial_start and block_end <= partial_end:
# the needed block is within partial_data.
arr = partial_data[:, (block_start - partial_start):(block_end - partial_start)]
elif block_start <= partial_start and block_end >= partial_end:
# the needed partition is within the block.
arr = partial_data
elif partial_start >= block_start and partial_end >= block_end:
# the needed block is overlapped in the front of partial_data
arr = partial_data[:, 0:(block_end - partial_start)]
else:
# the needed block is overlapped at the end of partial_data
arr = partial_data[:, (block_start - partial_start):(partial_end - partial_start)]
arr = arr.contiguous()
shape_list = [
torch.zeros(len(arr.shape), dtype=torch.int64, device=partial_data.device) for _ in range(world_size)
]
dist.all_gather(shape_list, torch.tensor(arr.shape, dtype=torch.int64, device=partial_data.device))
part_list = [
torch.zeros(*tuple(shape.tolist()), dtype=partial_data.dtype, device=partial_data.device) for shape in shape_list
]
dist.all_gather(part_list, arr)
return torch.cat(part_list, dim=1)
|