File size: 56,351 Bytes
9c6594c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 |
# Copyright 2021 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.
from __future__ import annotations
import logging
import os
import threading
import warnings
import weakref
from contextlib import contextmanager
from functools import partial
from typing import Any, Callable
import torch
from .utils import (
DistributedType,
DynamoBackend,
GradientAccumulationPlugin,
check_cuda_fp8_capability,
check_cuda_p2p_ib_support,
deepspeed_required,
get_cpu_distributed_information,
get_int_from_env,
is_ccl_available,
is_datasets_available,
is_deepspeed_available,
is_fp8_available,
is_habana_gaudi1,
is_hpu_available,
is_ipex_available,
is_mlu_available,
is_mps_available,
is_musa_available,
is_npu_available,
is_sdaa_available,
is_torch_xla_available,
is_xccl_available,
is_xpu_available,
parse_choice_from_env,
parse_flag_from_env,
set_numa_affinity,
)
from .utils.dataclasses import SageMakerDistributedType
if is_torch_xla_available():
import torch_xla.core.xla_model as xm
import torch_xla.runtime as xr
if is_mlu_available(check_device=False):
import torch_mlu # noqa: F401
if is_sdaa_available(check_device=False):
import torch_sdaa # noqa: F401
if is_musa_available(check_device=False):
import torch_musa # noqa: F401
if is_npu_available(check_device=False):
import torch_npu # noqa: F401
logger = logging.getLogger(__name__)
def is_initialized() -> bool:
"""
Checks if the `AcceleratorState` has been initialized from `Accelerator`. Same as `AcceleratorState.initialized`,
but works as a module method.
"""
return AcceleratorState._shared_state != {}
# Lambda function that does nothing
def do_nothing(*args, **kwargs):
return None
class ThreadLocalSharedDict(threading.local):
"""
Descriptor that holds a dict shared between instances of a class in the same thread.
Note: Descriptors have slightly different semantics than just a dict field on its own.
`PartialState(...)._shared_state` and `PartialState._shared_state` (instance vs class) give the same value: the
underlying _storage dict. Likewise, `PartialState(...)._shared_state = {...}` overrides the _storage dict inside
the descriptor as you would expect. However, `PartialState._shared_state = {}` actually replaces the descriptor
object with a dict instead Thus, you should modify the _storage dict in-place (e.g. `_shared_state.clear()`).
See Python documentation for an explanation of descriptors: https://docs.python.org/3/howto/descriptor.html
This is required for using PyTorch/XLA with PJRT in multithreaded mode (required for TPU v2 and v3).
See https://github.com/pytorch/xla/blob/r2.0/docs/pjrt.md#multithreading-on-tpu-v2v3
"""
def __init__(self, thread_local: bool = False):
self._storage = {}
def __get__(self, obj, objtype=None):
return self._storage
def __set__(self, obj, value):
self._storage = value
# Prefer global shared dictionary, except when using TPU.
SharedDict = dict if not is_torch_xla_available() else ThreadLocalSharedDict
# Inspired by Alex Martelli's 'Borg'.
class PartialState:
"""
Singleton class that has information about the current training environment and functions to help with process
control. Designed to be used when only process control and device execution states are needed. Does *not* need to
be initialized from `Accelerator`.
Args:
cpu (`bool`, *optional*):
Whether or not to force the script to execute on CPU. Will ignore any accelerators available if set to
`True` and force the execution on the CPU.
kwargs (additional keyword arguments, *optional*):
Additional keyword arguments to pass to the relevant `init_process_group` function. Valid `kwargs` can be
found in [`utils.InitProcessGroupKwargs`]. See the example section for detailed usage.
**Available attributes:**
- **device** (`torch.device`) -- The device to use.
- **distributed_type** ([`~accelerate.state.DistributedType`]) -- The type of distributed environment currently
in use.
- **local_process_index** (`int`) -- The index of the current process on the current server.
- **mixed_precision** (`str`) -- Whether or not the current script will use mixed precision, and if so the type
of mixed precision being performed. (Choose from 'no','fp16','bf16 or 'fp8').
- **num_processes** (`int`) -- The number of processes currently launched in parallel.
- **process_index** (`int`) -- The index of the current process.
- **is_last_process** (`bool`) -- Whether or not the current process is the last one.
- **is_main_process** (`bool`) -- Whether or not the current process is the main one.
- **is_local_main_process** (`bool`) -- Whether or not the current process is the main one on the local node.
- **debug** (`bool`) -- Whether or not the current script is being run in debug mode.
Example:
```python
from accelerate.utils import InitProcessGroupKwargs
# To include `InitProcessGroupKwargs`, init then call `.to_kwargs()`
kwargs = InitProcessGroupKwargs(...).to_kwargs()
state = PartialState(**kwargs)
```
"""
_shared_state = SharedDict()
_known_attrs = [
"_cpu",
"_mixed_precision",
"_shared_state",
"backend",
"debug",
"device",
"distributed_type",
"fork_launched",
"local_process_index",
"num_processes",
"process_index",
]
def __init__(self, cpu: bool = False, **kwargs):
self.__dict__ = self._shared_state
if not self.initialized:
self._cpu = cpu
self.backend = None
env_device = os.environ.get("ACCELERATE_TORCH_DEVICE", None)
self.device = torch.device(env_device) if env_device is not None else None
self.debug = parse_flag_from_env("ACCELERATE_DEBUG_MODE")
use_sagemaker_dp = kwargs.pop("_use_sagemaker_dp", None)
dist_information = None
if use_sagemaker_dp is None:
use_sagemaker_dp = (
os.environ.get("ACCELERATE_USE_SAGEMAKER", "false") == "true"
and os.environ.get("ACCELERATE_SAGEMAKER_DISTRIBUTED_TYPE") != SageMakerDistributedType.NO
)
# Sets up self.backend + imports
original_backend = kwargs.pop("backend", None)
backend, distributed_type = self._prepare_backend(cpu, use_sagemaker_dp, original_backend)
if original_backend is not None and backend != original_backend:
raise ValueError(f"Your assigned backend {original_backend} is not avaliable, please use {backend}")
self.backend = backend
self.distributed_type = distributed_type
use_deepspeed = False
if not cpu and self.backend != "xla":
if int(os.environ.get("LOCAL_RANK", -1)) != -1:
# Deal with spawning deepspeed
if os.environ.get("ACCELERATE_USE_DEEPSPEED", "false") == "true":
if not is_deepspeed_available():
raise ImportError(
"DeepSpeed is not available => install it using `pip3 install deepspeed` or build it from source"
)
from deepspeed import comm as dist
if not dist.is_initialized():
if self.backend == "tccl":
local_rank = os.environ.get("LOCAL_RANK", -1)
torch.sdaa.set_device(f"sdaa:{local_rank}")
if (
self.backend == "nccl"
and os.environ.get("ACCELERATE_USE_FSDP", "false") == "true"
and os.environ.get("FSDP_OFFLOAD_PARAMS", "false") == "true"
):
self.backend = "cuda:nccl,cpu:gloo"
dist.init_distributed(dist_backend=self.backend, auto_mpi_discovery=False, **kwargs)
# We need to flag to `use_deepspeed` to be True to override `distributed_type` later
use_deepspeed = True
# Deal with all other backends but XPU and CPU, that gets handled special later
elif (
self.distributed_type not in (DistributedType.MULTI_XPU, DistributedType.MULTI_CPU)
and not torch.distributed.is_initialized()
):
if self.backend == "tccl":
local_rank = os.environ.get("LOCAL_RANK", -1)
torch.sdaa.set_device(f"sdaa:{local_rank}")
torch.distributed.init_process_group(backend=self.backend, **kwargs)
# XPU and CPU require special env configs to be set
if self.distributed_type in (DistributedType.MULTI_XPU, DistributedType.MULTI_CPU):
dist_information = get_cpu_distributed_information()
os.environ["RANK"] = str(dist_information.rank)
os.environ["WORLD_SIZE"] = str(dist_information.world_size)
os.environ["LOCAL_RANK"] = str(dist_information.local_rank)
os.environ["LOCAL_WORLD_SIZE"] = str(dist_information.local_world_size)
if not os.environ.get("MASTER_PORT", None):
os.environ["MASTER_PORT"] = "29500"
if (
not os.environ.get("MASTER_ADDR", None)
and dist_information.local_world_size != dist_information.world_size
and self.backend != "mpi"
):
raise ValueError(
"Tried to launch on distributed with multinode, but `MASTER_ADDR` env was not set, "
"please try exporting rank 0's hostname as `MASTER_ADDR`"
)
kwargs["rank"] = dist_information.rank
kwargs["world_size"] = dist_information.world_size
if (
self.distributed_type == DistributedType.MULTI_CPU
and get_int_from_env(["OMP_NUM_THREADS"], 0) == 0
):
import psutil
num_cpu_threads_per_process = int(
psutil.cpu_count(logical=False) / dist_information.local_world_size
)
if num_cpu_threads_per_process == 0:
num_cpu_threads_per_process = 1
torch.set_num_threads(num_cpu_threads_per_process)
warnings.warn(
f"OMP_NUM_THREADS/MKL_NUM_THREADS unset, we set it at {num_cpu_threads_per_process} to improve oob"
" performance."
)
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend=self.backend, **kwargs)
# No backend == no distributed training
if self.backend is None:
self.distributed_type = DistributedType.NO
self.num_processes = 1
self.process_index = 0
self.local_process_index = 0
elif self.backend == "xla":
# XLA needs device setting first for `set_replication`
self.set_device()
xm.set_replication(self.device, xm.get_xla_supported_devices())
self.num_processes = xr.world_size()
self.process_index = xr.global_ordinal()
if is_torch_xla_available(check_is_tpu=True):
self.local_process_index = xm.get_local_ordinal()
else:
self.local_process_index = int(os.environ.get("LOCAL_RANK", -1))
else:
self.num_processes = torch.distributed.get_world_size()
self.process_index = torch.distributed.get_rank()
self.local_process_index = (
int(os.environ.get("LOCAL_RANK", -1)) if dist_information is None else dist_information.local_rank
)
self.set_device()
# Now we can change to deepseed
if use_deepspeed:
self.distributed_type = DistributedType.DEEPSPEED
# Set CPU affinity if enabled
if parse_flag_from_env("ACCELERATE_CPU_AFFINITY", False):
set_numa_affinity(self.local_process_index)
# Check for old RTX 4000's that can't use P2P or IB and are on old drivers
if self.device.type == "cuda" and not check_cuda_p2p_ib_support():
if "NCCL_P2P_DISABLE" not in os.environ or "NCCL_IB_DISABLE" not in os.environ:
raise NotImplementedError(
"Using RTX 4000 series doesn't support faster communication broadband via P2P or IB. "
'Please set `NCCL_P2P_DISABLE="1"` and `NCCL_IB_DISABLE="1" or use `accelerate launch` which '
"will do this automatically."
)
# Important: This should be the *only* code outside of `self.initialized!`
self.fork_launched = parse_flag_from_env("FORK_LAUNCHED", 0)
def __repr__(self) -> str:
return (
f"Distributed environment: {self.distributed_type}{(' Backend: ' + self.backend) if self.backend else ''}\n"
f"Num processes: {self.num_processes}\n"
f"Process index: {self.process_index}\n"
f"Local process index: {self.local_process_index}\n"
f"Device: {self.device}\n"
)
@staticmethod
def _reset_state():
"Resets `_shared_state`, is used internally and should not be called"
PartialState._shared_state.clear()
@property
def initialized(self) -> bool:
"Returns whether the `PartialState` has been initialized"
return self._shared_state != {}
@property
def use_distributed(self):
"""
Whether the Accelerator is configured for distributed training
"""
return self.distributed_type != DistributedType.NO and self.num_processes > 1
@property
def is_last_process(self) -> bool:
"Returns whether the current process is the last one"
return self.process_index == self.num_processes - 1
@property
def is_main_process(self) -> bool:
"Returns whether the current process is the main process"
return (
self.process_index == 0 if self.distributed_type != DistributedType.MEGATRON_LM else self.is_last_process
)
@property
def is_local_main_process(self) -> bool:
"Returns whether the current process is the main process on the local node"
return (
self.local_process_index == 0
if self.distributed_type != DistributedType.MEGATRON_LM
else self.is_last_process
)
def wait_for_everyone(self):
"""
Will stop the execution of the current process until every other process has reached that point (so this does
nothing when the script is only run in one process). Useful to do before saving a model.
Example:
```python
>>> # Assuming two GPU processes
>>> import time
>>> from accelerate.state import PartialState
>>> state = PartialState()
>>> if state.is_main_process:
... time.sleep(2)
>>> else:
... print("I'm waiting for the main process to finish its sleep...")
>>> state.wait_for_everyone()
>>> # Should print on every process at the same time
>>> print("Everyone is here")
```
"""
if self.distributed_type in (
DistributedType.MULTI_GPU,
DistributedType.MULTI_MLU,
DistributedType.MULTI_SDAA,
DistributedType.MULTI_MUSA,
DistributedType.MULTI_NPU,
DistributedType.MULTI_XPU,
DistributedType.MULTI_CPU,
DistributedType.MULTI_HPU,
DistributedType.DEEPSPEED,
DistributedType.FSDP,
):
torch.distributed.barrier()
elif self.distributed_type == DistributedType.XLA:
xm.rendezvous("accelerate.utils.wait_for_everyone")
def _goes_first(self, is_main: bool):
if not is_main:
self.wait_for_everyone()
yield
if is_main:
self.wait_for_everyone()
@contextmanager
def split_between_processes(self, inputs: list | tuple | dict | torch.Tensor, apply_padding: bool = False):
"""
Splits `input` between `self.num_processes` quickly and can be then used on that process. Useful when doing
distributed inference, such as with different prompts.
Note that when using a `dict`, all keys need to have the same number of elements.
Args:
inputs (`list`, `tuple`, `torch.Tensor`, `dict` of `list`/`tuple`/`torch.Tensor`, or `datasets.Dataset`):
The input to split between processes.
apply_padding (`bool`, `optional`, defaults to `False`):
Whether to apply padding by repeating the last element of the input so that all processes have the same
number of elements. Useful when trying to perform actions such as `gather()` on the outputs or passing
in less inputs than there are processes. If so, just remember to drop the padded elements afterwards.
Example:
```python
# Assume there are two processes
from accelerate import PartialState
state = PartialState()
with state.split_between_processes(["A", "B", "C"]) as inputs:
print(inputs)
# Process 0
["A", "B"]
# Process 1
["C"]
with state.split_between_processes(["A", "B", "C"], apply_padding=True) as inputs:
print(inputs)
# Process 0
["A", "B"]
# Process 1
["C", "C"]
```
"""
if self.num_processes == 1:
yield inputs
return
length = len(inputs)
# Nested dictionary of any types
if isinstance(inputs, dict):
length = len(inputs[list(inputs.keys())[0]])
if not all(len(v) == length for v in inputs.values()):
raise ValueError("All values in the dictionary must have the same length")
num_samples_per_process, num_extras = divmod(length, self.num_processes)
start_index = self.process_index * num_samples_per_process + min(self.process_index, num_extras)
end_index = start_index + num_samples_per_process + (1 if self.process_index < num_extras else 0)
def _split_values(inputs, start_index, end_index):
if isinstance(inputs, (list, tuple, torch.Tensor)):
if start_index >= len(inputs):
result = inputs[-1:]
else:
result = inputs[start_index:end_index]
if apply_padding:
if isinstance(result, torch.Tensor):
from accelerate.utils import pad_across_processes, send_to_device
# The tensor needs to be on the device before we can pad it
tensorized_result = send_to_device(result, self.device)
result = pad_across_processes(tensorized_result, pad_index=inputs[-1])
else:
result += [result[-1]] * (num_samples_per_process + (1 if num_extras > 0 else 0) - len(result))
return result
elif isinstance(inputs, dict):
for key in inputs.keys():
inputs[key] = _split_values(inputs[key], start_index, end_index)
return inputs
else:
if is_datasets_available():
from datasets import Dataset
if isinstance(inputs, Dataset):
if start_index >= len(inputs):
start_index = len(inputs) - 1
if end_index > len(inputs):
end_index = len(inputs)
result_idcs = list(range(start_index, end_index))
if apply_padding:
result_idcs += [end_index - 1] * (
num_samples_per_process + (1 if num_extras > 0 else 0) - len(result_idcs)
)
return inputs.select(result_idcs)
return inputs
yield _split_values(inputs, start_index, end_index)
@contextmanager
def main_process_first(self):
"""
Lets the main process go first inside a with block.
The other processes will enter the with block after the main process exits.
Example:
```python
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
>>> with accelerator.main_process_first():
... # This will be printed first by process 0 then in a seemingly
... # random order by the other processes.
... print(f"This will be printed by process {accelerator.process_index}")
```
"""
yield from self._goes_first(self.is_main_process)
@contextmanager
def local_main_process_first(self):
"""
Lets the local main process go inside a with block.
The other processes will enter the with block after the main process exits.
Example:
```python
>>> from accelerate.state import PartialState
>>> state = PartialState()
>>> with state.local_main_process_first():
... # This will be printed first by local process 0 then in a seemingly
... # random order by the other processes.
... print(f"This will be printed by process {state.local_process_index}")
```
"""
yield from self._goes_first(self.is_local_main_process)
def on_main_process(self, function: Callable[..., Any] = None):
"""
Decorator that only runs the decorated function on the main process.
Args:
function (`Callable`): The function to decorate.
Example:
```python
>>> from accelerate.state import PartialState
>>> state = PartialState()
>>> @state.on_main_process
... def print_something():
... print("This will be printed by process 0 only.")
>>> print_something()
"This will be printed by process 0 only"
```
"""
if not self.initialized:
raise ValueError("The `PartialState` or `Accelerator` must be initialized before calling this function.")
if self.is_main_process or not self.use_distributed:
return function
return do_nothing
def on_local_main_process(self, function: Callable[..., Any] = None):
"""
Decorator that only runs the decorated function on the local main process.
Args:
function (`Callable`): The function to decorate.
Example:
```python
# Assume we have 2 servers with 4 processes each.
from accelerate.state import PartialState
state = PartialState()
@state.on_local_main_process
def print_something():
print("This will be printed by process 0 only on each server.")
print_something()
# On server 1:
"This will be printed by process 0 only"
# On server 2:
"This will be printed by process 0 only"
```
"""
if self.is_local_main_process or not self.use_distributed:
return function
return do_nothing
def on_last_process(self, function: Callable[..., Any]):
"""
Decorator that only runs the decorated function on the last process.
Args:
function (`Callable`): The function to decorate.
Example:
```python
# Assume we have 4 processes.
from accelerate.state import PartialState
state = PartialState()
@state.on_last_process
def print_something():
print(f"Printed on process {state.process_index}")
print_something()
"Printed on process 3"
```
"""
if self.is_last_process or not self.use_distributed:
return function
return do_nothing
def on_process(self, function: Callable[..., Any] = None, process_index: int = None):
"""
Decorator that only runs the decorated function on the process with the given index.
Args:
function (`Callable`, `optional`):
The function to decorate.
process_index (`int`, `optional`):
The index of the process on which to run the function.
Example:
```python
# Assume we have 4 processes.
from accelerate.state import PartialState
state = PartialState()
@state.on_process(process_index=2)
def print_something():
print(f"Printed on process {state.process_index}")
print_something()
"Printed on process 2"
```
"""
if function is None:
return partial(self.on_process, process_index=process_index)
if (self.process_index == process_index) or (not self.use_distributed):
return function
return do_nothing
def on_local_process(self, function: Callable[..., Any] = None, local_process_index: int = None):
"""
Decorator that only runs the decorated function on the process with the given index on the current node.
Args:
function (`Callable`, *optional*):
The function to decorate.
local_process_index (`int`, *optional*):
The index of the local process on which to run the function.
Example:
```python
# Assume we have 2 servers with 4 processes each.
from accelerate import Accelerator
accelerator = Accelerator()
@accelerator.on_local_process(local_process_index=2)
def print_something():
print(f"Printed on process {accelerator.local_process_index}")
print_something()
# On server 1:
"Printed on process 2"
# On server 2:
"Printed on process 2"
```
"""
if function is None:
return partial(self.on_local_process, local_process_index=local_process_index)
if (self.local_process_index == local_process_index) or (not self.use_distributed):
return function
return do_nothing
def print(self, *args, **kwargs):
if self.is_local_main_process:
print(*args, **kwargs)
@property
def default_device(self) -> torch.device:
"""
Returns the default device which is:
- MPS if `torch.backends.mps.is_available()` and `torch.backends.mps.is_built()` both return True.
- CUDA if `torch.cuda.is_available()`
- MLU if `is_mlu_available()`
- SDAA if `is_sdaa_available()`
- MUSA if `is_musa_available()`
- NPU if `is_npu_available()`
- HPU if `is_hpu_available()`
- CPU otherwise
"""
if is_mps_available():
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
return torch.device("mps")
elif is_mlu_available():
return torch.device("mlu")
elif is_sdaa_available():
return torch.device("sdaa")
elif is_musa_available():
return torch.device("musa")
# NPU should be checked before CUDA when using `transfer_to_npu`
# See issue #3020: https://github.com/huggingface/accelerate/issues/3020
elif is_npu_available():
return torch.device("npu")
elif is_hpu_available():
return torch.device("hpu")
elif torch.cuda.is_available():
return torch.device("cuda")
elif is_xpu_available():
return torch.device("xpu")
else:
return torch.device("cpu")
def _prepare_backend(
self, cpu: bool = False, sagemaker_dp=False, backend: str = None
) -> tuple[str, DistributedType]:
"Prepares any imports needed before initializing the distributed backend and sets `self.backend` properly"
distributed_type = None
if sagemaker_dp:
import smdistributed.dataparallel.torch.torch_smddp # noqa
backend = "smddp"
distributed_type = DistributedType.MULTI_GPU
elif is_torch_xla_available():
backend = "xla"
distributed_type = DistributedType.XLA
elif int(os.environ.get("LOCAL_RANK", -1)) != -1 and not cpu:
if is_mlu_available():
backend = "cncl"
distributed_type = DistributedType.MULTI_MLU
if is_sdaa_available():
backend = "tccl"
distributed_type = DistributedType.MULTI_SDAA
elif is_musa_available():
backend = "mccl"
distributed_type = DistributedType.MULTI_MUSA
# NPU should be checked before CUDA when using `transfer_to_npu`
# See issue #3020: https://github.com/huggingface/accelerate/issues/3020
elif is_npu_available():
backend = "hccl"
distributed_type = DistributedType.MULTI_NPU
elif is_hpu_available(init_hccl=True):
if backend is None:
backend = "hccl"
distributed_type = DistributedType.MULTI_HPU
elif torch.cuda.is_available():
if backend is None:
backend = "nccl"
distributed_type = DistributedType.MULTI_GPU
elif is_xpu_available() and is_xccl_available():
if backend is None:
backend = "xccl"
distributed_type = DistributedType.MULTI_XPU
if distributed_type is None and (
int(os.environ.get("LOCAL_RANK", -1)) != -1
or get_int_from_env(["PMI_SIZE", "OMPI_COMM_WORLD_SIZE", "MV2_COMM_WORLD_SIZE", "WORLD_SIZE"], 1) > 1
):
if not cpu and is_xpu_available():
distributed_type = DistributedType.MULTI_XPU
else:
distributed_type = DistributedType.MULTI_CPU
if (
backend in (None, "ccl")
and is_ccl_available()
and (get_int_from_env(["CCL_WORKER_COUNT"], 0) > 0 or distributed_type == DistributedType.MULTI_XPU)
):
import oneccl_bindings_for_pytorch # noqa: F401
backend = "ccl"
elif backend in (None, "mpi") and torch.distributed.is_mpi_available():
backend = "mpi"
else:
backend = "gloo"
if distributed_type is None:
distributed_type = DistributedType.NO
return backend, distributed_type
def set_device(self):
"""
Sets the device in `self.device` to the current distributed environment.
"""
if self.device is not None:
return
if self.distributed_type == DistributedType.NO:
self.device = torch.device("cpu") if self._cpu else self.default_device
return
device = str(self.distributed_type).split(".")[-1].replace("MULTI_", "").lower()
if device not in ("cpu", "gpu", "mlu", "musa", "npu", "xpu", "xla", "hpu", "sdaa"):
raise ValueError(
f"Can't set device for {self.distributed_type} ({device}), verify we should be calling `_set_device()` for it!"
)
if device == "xla":
self.device = xm.xla_device()
elif device == "hpu":
self.device = torch.device("hpu", torch.hpu.current_device())
else:
if device == "gpu":
device = "cuda"
device_module = getattr(torch, device)
device_index = self.local_process_index % device_module.device_count()
self.device = torch.device(device, device_index)
device_module.set_device(self.device)
def destroy_process_group(self, group=None):
"""
Destroys the process group. If one is not specified, the default process group is destroyed.
"""
if self.fork_launched and group is None:
return
# needed when using torch.distributed.init_process_group
if torch.distributed.is_initialized():
torch.distributed.destroy_process_group(group)
def __getattr__(self, name: str):
# By this point we know that no attributes of `self` contain `name`,
# so we just modify the error message
if name in self._known_attrs:
raise AttributeError(
f"`PartialState` object has no attribute `{name}`. "
"This happens if `PartialState._reset_state()` was called and "
"an `Accelerator` or `PartialState` was not reinitialized."
)
# Raise a typical AttributeError
raise AttributeError(f"'PartialState' object has no attribute '{name}'")
class AcceleratorState:
"""
Singleton class that has information about the current training environment.
**Available attributes:**
- **device** (`torch.device`) -- The device to use.
- **distributed_type** ([`~accelerate.state.DistributedType`]) -- The type of distributed environment currently
in use.
- **initialized** (`bool`) -- Whether or not the `AcceleratorState` has been initialized from `Accelerator`.
- **local_process_index** (`int`) -- The index of the current process on the current server.
- **mixed_precision** (`str`) -- Whether or not the current script will use mixed precision, and if so the type
of mixed precision being performed. (Choose from 'no','fp16','bf16 or 'fp8').
- **num_processes** (`int`) -- The number of processes currently launched in parallel.
- **process_index** (`int`) -- The index of the current process.
- **is_last_process** (`bool`) -- Whether or not the current process is the last one.
- **is_main_process** (`bool`) -- Whether or not the current process is the main one.
- **is_local_main_process** (`bool`) -- Whether or not the current process is the main one on the local node.
- **debug** (`bool`) -- Whether or not the current script is being run in debug mode.
"""
_shared_state = SharedDict()
_known_attrs = PartialState._known_attrs + [
"deepspeed_plugin",
"use_ipex",
"fsdp_plugin",
"megatron_lm_plugin",
"dynamo_plugin",
]
def __init__(
self,
mixed_precision: str = None,
cpu: bool = False,
dynamo_plugin=None,
deepspeed_plugin=None,
fsdp_plugin=None,
torch_tp_plugin=None,
megatron_lm_plugin=None,
_from_accelerator: bool = False,
**kwargs,
):
self.__dict__ = self._shared_state
if parse_flag_from_env("ACCELERATE_USE_CPU"):
cpu = True
if PartialState._shared_state == {}:
PartialState(cpu, **kwargs)
self.__dict__.update(PartialState._shared_state)
self._check_initialized(mixed_precision, cpu)
if not self.initialized:
self.deepspeed_plugins = None
self.use_ipex = None
self.torch_tp_plugin = torch_tp_plugin
mixed_precision = (
parse_choice_from_env("ACCELERATE_MIXED_PRECISION", "no")
if mixed_precision is None
else mixed_precision.lower()
)
if mixed_precision == "fp8":
# this is confusing, why is is_fp8_available only checks for library availability ?
if not is_fp8_available():
raise ValueError(
"Using `fp8` precision requires `transformer_engine` or `MS-AMP` to be installed."
)
elif torch.cuda.is_available() and not check_cuda_fp8_capability():
logger.warning(
f"The current device has compute capability of {torch.cuda.get_device_capability()} which is "
"insufficient for FP8 mixed precision training (requires a GPU Hopper/Ada Lovelace "
"or higher, compute capability of 8.9 or higher). Will use FP16 instead."
)
mixed_precision = "fp16"
elif is_habana_gaudi1():
logger.warning(
"The current HPU device is Gaudi1 which does not support FP8 mixed precision training (requires "
"Gaudi2 or higher). Will use BF16 instead."
)
mixed_precision = "bf16"
self.dynamo_plugin = dynamo_plugin
if not _from_accelerator:
raise ValueError(
"Please make sure to properly initialize your accelerator via `accelerator = Accelerator()` "
"before using any functionality from the `accelerate` library."
)
# deepspeed handles mixed_precision using deepspeed_config
self._mixed_precision = "no" if self.distributed_type == DistributedType.DEEPSPEED else mixed_precision
if self.distributed_type == DistributedType.XLA and is_torch_xla_available(check_is_tpu=True):
if mixed_precision == "bf16":
if os.environ.get("ACCELERATE_DOWNCAST_BF16"):
os.environ["XLA_USE_BF16"] = str(0)
os.environ["XLA_DOWNCAST_BF16"] = str(1)
self.downcast_bfloat = True
else:
os.environ["XLA_USE_BF16"] = str(1)
os.environ["XLA_DOWNCAST_BF16"] = str(0)
self.downcast_bfloat = False
elif os.environ.get("ACCELERATE_USE_DEEPSPEED", "false") == "true" and not cpu:
self.distributed_type = DistributedType.DEEPSPEED
if not isinstance(deepspeed_plugin, dict):
deepspeed_plugin.set_mixed_precision(mixed_precision)
deepspeed_plugin.select(_from_accelerator_state=True)
else:
for plugin in deepspeed_plugin.values():
plugin.set_mixed_precision(mixed_precision)
# The first plugin passed in is always the active one
first_plugin = next(iter(deepspeed_plugin.values()))
first_plugin.select(_from_accelerator_state=True)
self.deepspeed_plugins = deepspeed_plugin
elif self.distributed_type in [
DistributedType.MULTI_GPU,
DistributedType.MULTI_MLU,
DistributedType.MULTI_SDAA,
DistributedType.MULTI_MUSA,
DistributedType.MULTI_NPU,
DistributedType.MULTI_XPU,
DistributedType.MULTI_HPU,
]:
if os.environ.get("ACCELERATE_USE_FSDP", "false") == "true" or fsdp_plugin is not None:
self.distributed_type = DistributedType.FSDP
if self._mixed_precision != "no":
fsdp_plugin.set_mixed_precision(self._mixed_precision)
self.fsdp_plugin = fsdp_plugin
if os.environ.get("ACCELERATE_USE_MEGATRON_LM", "false") == "true" and self.distributed_type not in [
DistributedType.MULTI_XPU,
]:
self.distributed_type = DistributedType.MEGATRON_LM
megatron_lm_plugin.set_mixed_precision(self._mixed_precision)
self.megatron_lm_plugin = megatron_lm_plugin
if self.torch_tp_plugin is not None:
self.distributed_type = DistributedType.TP
elif self.distributed_type in [DistributedType.MULTI_CPU, DistributedType.MULTI_XPU, DistributedType.NO]:
if is_ipex_available():
# check if user disables it explicitly
self.use_ipex = parse_flag_from_env("ACCELERATE_USE_IPEX", default=True)
else:
self.use_ipex = False
if (
self.dynamo_plugin.backend != DynamoBackend.NO
and self._mixed_precision == "no"
and self.device.type == "cuda"
):
torch.backends.cuda.matmul.allow_tf32 = True
if (
self.dynamo_plugin.backend != DynamoBackend.NO
and self._mixed_precision == "no"
and self.device.type == "musa"
):
torch.backends.musa.matmul.allow_tf32 = True
PartialState._shared_state["distributed_type"] = self.distributed_type
@property
def initialized(self) -> bool:
return self._shared_state != PartialState._shared_state
def __repr__(self):
repr = PartialState().__repr__() + f"\nMixed precision type: {self.mixed_precision}\n"
if self.distributed_type == DistributedType.DEEPSPEED:
repr += f"ds_config: {self.deepspeed_plugin.deepspeed_config}\n"
return repr
def _check_initialized(self, mixed_precision=None, cpu=None):
"Checks if a modification is trying to be made and the `AcceleratorState` has already been initialized"
if self.initialized:
err = "AcceleratorState has already been initialized and cannot be changed, restart your runtime completely and pass `{flag}` to `Accelerator()`."
if cpu and self.device.type != "cpu":
raise ValueError(err.format(flag="cpu=True"))
if (
mixed_precision is not None
and mixed_precision != self._mixed_precision
and self.distributed_type != DistributedType.DEEPSPEED
):
raise ValueError(err.format(flag=f"mixed_precision='{mixed_precision}'"))
@property
def mixed_precision(self):
if self.distributed_type == DistributedType.DEEPSPEED:
config = self.deepspeed_plugin.deepspeed_config
if config.get("fp16", {}).get("enabled", False):
mixed_precision = "fp16"
elif config.get("bf16", {}).get("enabled", False):
mixed_precision = "bf16"
else:
mixed_precision = "no"
else:
mixed_precision = self._mixed_precision
return mixed_precision
@staticmethod
def _reset_state(reset_partial_state: bool = False):
"Resets `_shared_state`, is used internally and should not be called"
AcceleratorState._shared_state.clear()
if reset_partial_state:
PartialState._reset_state()
def destroy_process_group(self, group=None):
"""
Destroys the process group. If one is not specified, the default process group is destroyed.
If `self.fork_lauched` is `True` and `group` is `None`, nothing happens.
"""
PartialState().destroy_process_group(group)
@property
def fork_launched(self):
return PartialState().fork_launched
@property
def use_distributed(self):
"""
Whether the Accelerator is configured for distributed training
"""
return PartialState().use_distributed
@property
def is_fsdp2(self) -> bool:
return self.distributed_type == DistributedType.FSDP and self.fsdp_plugin.fsdp_version == 2
@property
def is_last_process(self) -> bool:
"Returns whether the current process is the last one"
return PartialState().is_last_process
@property
def is_main_process(self) -> bool:
"Returns whether the current process is the main process"
return PartialState().is_main_process
@property
def is_local_main_process(self) -> bool:
"Returns whether the current process is the main process on the local node"
return PartialState().is_local_main_process
def wait_for_everyone(self):
PartialState().wait_for_everyone()
@contextmanager
def split_between_processes(self, inputs: list | tuple | dict | torch.Tensor, apply_padding: bool = False):
"""
Splits `input` between `self.num_processes` quickly and can be then used on that process. Useful when doing
distributed inference, such as with different prompts.
Note that when using a `dict`, all keys need to have the same number of elements.
Args:
inputs (`list`, `tuple`, `torch.Tensor`, or `dict` of `list`/`tuple`/`torch.Tensor`):
The input to split between processes.
apply_padding (`bool`, `optional`, defaults to `False`):
Whether to apply padding by repeating the last element of the input so that all processes have the same
number of elements. Useful when trying to perform actions such as `gather()` on the outputs or passing
in less inputs than there are processes. If so, just remember to drop the padded elements afterwards.
Example:
```python
# Assume there are two processes
from accelerate.state import AcceleratorState
state = AcceleratorState()
with state.split_between_processes(["A", "B", "C"]) as inputs:
print(inputs)
# Process 0
["A", "B"]
# Process 1
["C"]
with state.split_between_processes(["A", "B", "C"], apply_padding=True) as inputs:
print(inputs)
# Process 0
["A", "B"]
# Process 1
["C", "C"]
```
"""
with PartialState().split_between_processes(inputs, apply_padding=apply_padding) as inputs:
yield inputs
@contextmanager
def main_process_first(self):
"""
Lets the main process go first inside a with block.
The other processes will enter the with block after the main process exits.
"""
with PartialState().main_process_first():
yield
@contextmanager
def local_main_process_first(self):
"""
Lets the local main process go inside a with block.
The other processes will enter the with block after the main process exits.
"""
with PartialState().local_main_process_first():
yield
@property
def deepspeed_plugin(self):
"""
Returns the currently active DeepSpeedPlugin.
If not using deepspeed, returns `None`.
"""
# To maintain original behavior, return None if not using deepspeed.
if self.distributed_type != DistributedType.DEEPSPEED:
return None
from accelerate.utils.deepspeed import get_active_deepspeed_plugin
return get_active_deepspeed_plugin(self)
@deepspeed_required
def get_deepspeed_plugin(self, name: str):
"""
Returns the DeepSpeedPlugin with the given plugin_key.
"""
return self.deepspeed_plugins[name]
@deepspeed_required
def select_deepspeed_plugin(self, name: str = None):
"""
Activates the DeepSpeedPlugin with the given `name`, and will disable all other plugins.
"""
for key, plugin in self.deepspeed_plugins.items():
if key != name:
plugin._unselect()
self.deepspeed_plugins[name].select(_from_accelerator_state=True)
def print(self, *args, **kwargs):
PartialState().print(*args, **kwargs)
def __getattr__(self, name: str):
# By this point we know that no attributes of `self` contain `name`,
# so we just modify the error message
if name in self._known_attrs:
raise AttributeError(
f"`AcceleratorState` object has no attribute `{name}`. "
"This happens if `AcceleratorState._reset_state()` was called and "
"an `Accelerator` or `PartialState` was not reinitialized."
)
# Raise a typical AttributeError
raise AttributeError(f"'AcceleratorState' object has no attribute '{name}'")
class GradientState:
"""
Singleton class that has information related to gradient synchronization for gradient accumulation
**Available attributes:**
- **end_of_dataloader** (`bool`) -- Whether we have reached the end the current dataloader
- **remainder** (`int`) -- The number of extra samples that were added from padding the dataloader
- **sync_gradients** (`bool`) -- Whether the gradients should be synced across all devices
- **active_dataloader** (`Optional[DataLoader]`) -- The dataloader that is currently being iterated over
- **dataloader_references** (`List[Optional[DataLoader]]`) -- A list of references to the dataloaders that are
being iterated over
- **num_steps** (`int`) -- The number of steps to accumulate over
- **adjust_scheduler** (`bool`) -- Whether the scheduler should be adjusted to account for the gradient
accumulation
- **sync_with_dataloader** (`bool`) -- Whether the gradients should be synced at the end of the dataloader
iteration and the number of total steps reset
- **is_xla_gradients_synced** (`bool`) -- Whether the XLA gradients have been synchronized. It is initialized
as false. Once gradients have been reduced before the optimizer step, this flag is set to true. Subsequently,
after each step, the flag is reset to false. FSDP will always synchronize the gradients, hence
is_xla_gradients_synced is always true.
"""
_shared_state = SharedDict()
def __init__(self, gradient_accumulation_plugin: GradientAccumulationPlugin | None = None):
self.__dict__ = self._shared_state
if not self.initialized:
self.sync_gradients = True
self._dataloader_references_ref = [None]
self.plugin_kwargs = (
gradient_accumulation_plugin.to_kwargs() if gradient_accumulation_plugin is not None else {}
)
self._is_xla_gradients_synced = False
# Plugin args are different and can be updated
if gradient_accumulation_plugin is not None and self.plugin_kwargs != gradient_accumulation_plugin.to_kwargs():
self.plugin_kwargs = gradient_accumulation_plugin.to_kwargs()
@property
def num_steps(self) -> int:
"Returns the number of steps to accumulate over"
return self.plugin_kwargs.get("num_steps", 1)
@property
def adjust_scheduler(self) -> bool:
"Returns whether the scheduler should be adjusted"
return self.plugin_kwargs.get("adjust_scheduler", False)
@property
def sync_with_dataloader(self) -> bool:
"Returns whether the gradients should be synced at the end of the dataloader iteration and the number of total steps reset"
return self.plugin_kwargs.get("sync_with_dataloader", True)
@property
def initialized(self) -> bool:
"Returns whether the `GradientState` has been initialized"
return GradientState._shared_state != {}
@property
def end_of_dataloader(self) -> bool:
"Returns whether we have reached the end of the current dataloader"
if not self.in_dataloader:
return False
return self.active_dataloader.end_of_dataloader
@property
def remainder(self) -> int:
"Returns the number of extra samples that were added from padding the dataloader"
if not self.in_dataloader:
return -1
return self.active_dataloader.remainder
def __repr__(self):
return (
f"Sync Gradients: {self.sync_gradients}\n"
f"At end of current dataloader: {self.end_of_dataloader}\n"
f"Extra samples added: {self.remainder}\n"
f"Gradient accumulation plugin: {self.plugin_kwargs}\n"
)
@property
def is_xla_gradients_synced(self):
"Returns the value of is_xla_gradients_synced. FSDP will always synchronize the gradients, hence is_xla_gradients_synced is always true."
if parse_flag_from_env("ACCELERATE_USE_FSDP", default=False):
return True
return self._is_xla_gradients_synced
@is_xla_gradients_synced.setter
def is_xla_gradients_synced(self, is_synced):
"Set the _is_xla_gradients_synced attribute."
self._is_xla_gradients_synced = is_synced
def _set_sync_gradients(self, sync_gradients):
"Private function that sets whether gradients should be synchronized. Users should not have to call this."
self.sync_gradients = sync_gradients
# Allow grad-sync to automatically work on TPUs
if (
self.sync_gradients
and is_torch_xla_available(check_is_tpu=True)
and PartialState().distributed_type == DistributedType.XLA
):
xm.mark_step()
def _add_dataloader(self, dataloader):
"Private function that adds a dataloader to `self.dataloader_references` and sets `in_dataloader` to `True`. Users should not have to call this."
# We explicitly use assignment to ensure that the property setter is triggered, which is required for garbage collection.
# Avoid using self.dataloader_references.append as it will not trigger the setter.
self.dataloader_references += [dataloader]
def _remove_dataloader(self, dataloader):
"Private function that removes a dataloader from `self.dataloader_references` and sets `in_dataloader` to `False` if there are no more dataloaders. Users should not have to call this."
# We explicitly use assignment to ensure that the property setter is triggered.
self.dataloader_references = [
dataloader_ref for dataloader_ref in self.dataloader_references if dataloader_ref != dataloader
]
@property
def active_dataloader(self):
return self.dataloader_references[-1]
@property
def dataloader_references(self):
# We use a property getter and setter with weakrefs to avoid circular references that prevent garbage collection
return [reference() if reference is not None else reference for reference in self._dataloader_references_ref]
@dataloader_references.setter
def dataloader_references(self, references):
self._dataloader_references_ref = [
weakref.ref(dataloader) if dataloader is not None else dataloader for dataloader in references
]
@property
def in_dataloader(self) -> bool:
"Returns whether the current process is in a dataloader"
return self.active_dataloader is not None
@staticmethod
def _reset_state():
"Resets `_shared_state`, is used internally and should not be called"
GradientState._shared_state.clear()
|