Spaces:
Build error
Build error
File size: 22,496 Bytes
28c256d |
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 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Union
import torch
import torch.distributed as dist
import torch.nn as nn
from torch.distributed import ProcessGroup
# yapf: disable
from torch.distributed.fsdp.api import (FullStateDictConfig,
LocalOptimStateDictConfig,
LocalStateDictConfig,
OptimStateDictConfig,
ShardedOptimStateDictConfig,
ShardedStateDictConfig,
ShardingStrategy, StateDictConfig,
StateDictSettings, StateDictType)
from torch.distributed.fsdp.fully_sharded_data_parallel import (
BackwardPrefetch, CPUOffload, FullOptimStateDictConfig,
FullyShardedDataParallel, MixedPrecision)
# yapf: enable
from mmengine.optim import OptimWrapper
from mmengine.registry import FUNCTIONS, MODEL_WRAPPERS
from mmengine.structures import BaseDataElement
from mmengine.utils import digit_version, is_seq_of
@MODEL_WRAPPERS.register_module()
class MMFullyShardedDataParallel(FullyShardedDataParallel):
"""A wrapper for sharding Module parameters across data parallel workers.
Different from FullyShardedDataParallel, MMFullyShardedDataParallel
implements three methods :meth:`train_step`, :meth:`val_step` and
:meth:`test_step`, which will be called by ``train_loop``, ``val_loop``
and ``test_loop``.
- ``train_step``: Called by ``runner.train_loop``, and implement
default model forward, gradient back propagation, parameter updating
logic.
- ``val_step``: Called by ``runner.val_loop`` and get the inference
results. Specially, since MMFullyShardedDataParallel will wrap model
recursively, it may cause some problem if one just use
``BaseModel.val_step`` to implement ``val_step`` here. To avoid that,
``val_step`` will call methods of :obj:`BaseModel` to pre-process
data first, and use ``FullyShardedDataParallel.forward`` to get result.
- ``test_step``: Called by ``runner.test_loop`` and get the inference
results. Its logic is equivalent to ``val_loop``.
Args:
module (nn.Module): module to be wrapped with FSDP.
process_group (ProcessGroup, optional): process group for sharding.
cpu_offload (bool, CPUOffload, optional):
CPU offloading config.
Different from FullyShardedDataParallel,Since it can be set by
users' pre-defined config in MMEngine,its type is expected to be
`None`, `bool` or `CPUOffload`.
Currently, only parameter and gradient CPU offload is supported.
It can be enabled via passing in
``cpu_offload=CPUOffload(offload_params=True)``. Note that this
currently implicitly enables gradient offloading to CPU in order
for params and grads to be on same device to work with optimizer.
This API is subject to change. Default is ``None`` in which case
there will be no offloading.
auto_wrap_policy (str or Callable, optional):
Specifying a policy to recursively wrap layers with FSDP.
Different from FullyShardedDataParallel, Since it can be set by
users' pre-defined config in MMEngine, its type is expected to be
`None`, `str` or `Callable`. If it's `str`, then
MMFullyShardedDataParallel will try to get specified method in
``FSDP_WRAP_POLICIES`` registry,and this method will be passed to
FullyShardedDataParallel to finally initialize model.
Note that this policy currently will only apply to child modules of
the passed in module. The remainder modules are always wrapped in
the returned FSDP root instance.
``default_auto_wrap_policy`` written in
``torch.distributed.fsdp.wrap`` is an example of
``auto_wrap_policy`` callable, this policy wraps layers with
parameter sizes larger than 100M. Users can supply the customized
``auto_wrap_policy`` callable that should accept following
arguments: ``module: nn.Module``, ``recurse: bool``,
``unwrapped_params: int``, extra customized arguments could be
added to the customized ``auto_wrap_policy`` callable as well.
Example::
>>> def custom_auto_wrap_policy(
>>> module: nn.Module,
>>> recurse: bool,
>>> unwrapped_params: int,
>>> # These are customizable for this policy function.
>>> min_num_params: int = int(1e8),
>>> ) -> bool:
>>> return unwrapped_params >= min_num_params
backward_prefetch (str or BackwardPrefetch, optional):
Different from FullyShardedDataParallel, this argument could be a
string or a BackwardPrefetch instance. If it's a string, then
it should be ``BACKWARD_PRE`` or ``BACKWARD_POST``
mixed_precision (dict or MixedPrecision, optional):
This configures native mixed precision for FSDP. If this is set to
``None``. Different from the native FSDP, this argument can a dict
like this:
Examples:
>>> mixed_precision=dict(param_dtype='float16',
>>> buffer_dtype='float32',
>>> reduce_dtype='float32')
Defaults to None.
use_orig_params (bool): Different from native
``FullyShardedDataParallel``, it defaults to True.
**kwargs: Keyword arguments passed to
:class:`FullyShardedDataParallel`.
"""
def __init__(
self,
module: nn.Module,
process_group: Union[dict, ProcessGroup, None] = None,
sharding_strategy: Union[str, ShardingStrategy] = None,
cpu_offload: Union[bool, CPUOffload, None] = None,
auto_wrap_policy: Union[str, Callable, None] = None,
backward_prefetch: Union[str, BackwardPrefetch, None] = None,
mixed_precision: Union[dict, MixedPrecision, None] = None,
param_init_fn: Union[str, Callable[[nn.Module], None]] = None,
use_orig_params: bool = True,
**kwargs,
):
if isinstance(sharding_strategy, str):
sharding_strategy = ShardingStrategy[sharding_strategy]
if not (isinstance(sharding_strategy, ShardingStrategy)
or sharding_strategy is None):
raise TypeError(
'sharding_strategy must be str or enum of `ShardingStrategy` '
f', but got {sharding_strategy}')
if isinstance(cpu_offload, bool):
cpu_offload = CPUOffload(offload_params=cpu_offload)
if not (isinstance(cpu_offload, CPUOffload) or cpu_offload is None):
raise TypeError(
'`cpu_offload` should be `None`, `bool`'
f'or `CPUOffload`, but has type {type(cpu_offload)}')
if isinstance(auto_wrap_policy, str):
auto_wrap_policy = FUNCTIONS.get( # type: ignore
auto_wrap_policy)
if auto_wrap_policy is None:
raise ValueError('`auto_wrap_policy` is not registered!')
elif isinstance(auto_wrap_policy, dict):
policy = auto_wrap_policy.pop('type')
if isinstance(policy, str):
# NOTE(julieta) special handling for transformer_auto_wrap_policy
if policy == 'torch.distributed.fsdp.wrap.transformer_auto_wrap_policy':
transformer_layer_cls = auto_wrap_policy.pop('transformer_layer_cls')
# TODO(julieta) support multiple classes
auto_wrap_policy['transformer_layer_cls'] = (FUNCTIONS.get(transformer_layer_cls),)
policy = FUNCTIONS.get(policy) # type: ignore
if policy is None:
raise ValueError('`auto_wrap_policy` is not registered!')
auto_wrap_policy = partial(policy, **auto_wrap_policy)
if not (auto_wrap_policy is None
or callable(auto_wrap_policy)): # type: ignore
raise TypeError('`auto_wrap_policy` should be a str, a '
'callable, a dict or None, but has type '
f'{type(auto_wrap_policy)}')
if isinstance(backward_prefetch, str):
backward_prefetch = BackwardPrefetch[backward_prefetch]
if not (isinstance(backward_prefetch, BackwardPrefetch)
or backward_prefetch is None):
raise TypeError(
'`backward_prefetch` should be `None`, string of '
'"BACKWARD_PRE" and "BACKWARD_POST", or '
f'`BackwardPrefetch`, but has type {type(backward_prefetch)}')
if isinstance(param_init_fn, str):
param_init_fn = FUNCTIONS.get( # type: ignore
param_init_fn)
if param_init_fn is None:
raise ValueError('`param_init_fn` is not registered!')
elif isinstance(param_init_fn, dict):
init_fn = param_init_fn.pop('type')
if isinstance(param_init_fn, str):
init_fn = FUNCTIONS.get(init_fn) # type: ignore
if init_fn is None:
raise ValueError('`param_init_fn` is not registered!')
param_init_fn = partial(init_fn, **param_init_fn)
if not (callable(param_init_fn) or param_init_fn is None):
raise TypeError('`param_init_fn` should be a str, a '
'callable, a dict or None, but has type '
f'{type(param_init_fn)}')
def parse_dtype(dtype):
if dtype is None:
return None
elif isinstance(dtype, str):
return getattr(torch, dtype)
elif isinstance(dtype, torch.dtype):
return dtype
else:
raise TypeError(
'`dtype` should be `None`, `str` or `torch.dtype`, '
f'but has type {type(dtype)}')
if isinstance(mixed_precision, dict):
mixed_precision['param_dtype'] = parse_dtype(
mixed_precision.get('param_dtype', None))
mixed_precision['reduce_dtype'] = parse_dtype(
mixed_precision.get('reduce_dtype', None))
mixed_precision['buffer_dtype'] = parse_dtype(
mixed_precision.get('buffer_dtype', None))
mixed_precision = MixedPrecision(**mixed_precision)
elif isinstance(mixed_precision, MixedPrecision):
mixed_precision = mixed_precision
elif mixed_precision is not None:
raise TypeError(
'`mixed_precision` should be `None`, `dict` or '
f'`MixedPrecision`, but has type {type(mixed_precision)}')
# ignored_parameters and ignored_modules will be deprecated by PyTorch.
# Therefore we hide them in **kwargs.
# TODO: Update when PyTorch 2.1.0 released
if 'ignored_parameters' in kwargs:
kwargs['ignored_parameters'] = self._get_ignored_params(
module, kwargs['ignored_parameters'])
if 'ignored_modules' in kwargs:
kwargs['ignored_modules'] = self._get_ignored_modules(
module, kwargs['ignored_modules'])
super().__init__(
module=module,
process_group=process_group,
sharding_strategy=sharding_strategy,
auto_wrap_policy=auto_wrap_policy,
cpu_offload=cpu_offload,
backward_prefetch=backward_prefetch,
mixed_precision=mixed_precision,
param_init_fn=param_init_fn,
use_orig_params=use_orig_params,
**kwargs)
def train_step(self, data: dict,
optim_wrapper: OptimWrapper) -> Dict[str, torch.Tensor]:
"""Interface for model forward, backward and parameters updating during
training process.
:meth:`train_step` will perform the following steps in order:
- If :attr:`module` defines the preprocess method,
call ``module.preprocess`` to pre-processing data.
- Call ``module.forward(**data)`` and get losses.
- Parse losses.
- Call ``optim_wrapper.optimizer_step`` to update parameters.
- Return log messages of losses.
Args:
data (dict): Data sampled by dataloader.
optim_wrapper (OptimWrapper): A wrapper of optimizer to
update parameters.
Returns:
Dict[str, torch.Tensor]: A ``dict`` of tensor for logging.
"""
# enable automatic mixed precision training context.
with optim_wrapper.optim_context(self):
data = self.module.data_preprocessor(data, training=True)
if isinstance(data, dict):
losses = self(**data, mode='loss')
elif isinstance(data, (list, tuple)):
losses = self(*data, mode='loss')
else:
raise TypeError('Output of `data_preprocessor` should be '
f'list tuple or dict, but got {type(data)}')
preds = None
masks = None
## for mmpretrain
if isinstance(losses, tuple) and len(losses) == 3:
losses, preds, masks = losses
## mmpose and mmseg
elif isinstance(losses, tuple) and len(losses) == 2:
losses, preds = losses
parsed_loss, log_vars = self.module.parse_losses(losses)
optim_wrapper.update_params(parsed_loss)
## mmpretrain
if preds is not None and masks is not None:
log_vars['vis_preds'] = preds
log_vars['vis_masks'] = masks
## mmpose and mmseg
elif preds is not None:
log_vars['vis_preds'] = preds
return log_vars
def val_step(self, data: dict) -> List[BaseDataElement]:
"""Gets the prediction of module during validation process.
Args:
data (dict): Data sampled by dataloader.
Returns:
List[BaseDataElement] or dict: The predictions of given data.
"""
data = self.module.data_preprocessor(data, False)
return self._run_forward(data, mode='predict') # type: ignore
def test_step(self, data: dict) -> List[BaseDataElement]:
"""Gets the predictions of module during testing process.
Args:
data (dict): Data sampled by dataloader.
Returns:
List[BaseDataElement]: The predictions of given data.
"""
data = self.module.data_preprocessor(data, False)
return self._run_forward(data, mode='predict') # type: ignore
def _run_forward(self, data: Union[dict, tuple, list],
mode: str) -> Union[Dict[str, torch.Tensor], list]:
"""Unpacks data for :meth:`forward`
Args:
data (dict or tuple or list): Data sampled from dataset.
mode (str): Mode of forward.
Returns:
dict or list: Results of training or testing mode.
"""
if isinstance(data, dict):
results = self(**data, mode=mode)
elif isinstance(data, (list, tuple)):
results = self(*data, mode=mode)
else:
raise TypeError('Output of `data_preprocessor` should be '
f'list, tuple or dict, but got {type(data)}')
return results
def _get_ignored_params(self, module: nn.Module,
ignored_parameters: Union[Iterable[str],
Iterable[nn.Module]]):
"""Get params from string."""
params_dict = dict(module.named_parameters())
if is_seq_of(ignored_parameters, str):
ignored_parameters = [
params_dict[name] for name in ignored_parameters
]
if not is_seq_of(ignored_parameters,
nn.Parameter) and ignored_parameters is not None:
raise TypeError(
'`ignored_modules` should be `None`, `Iterable[str]` or '
'`Iterable[nn.Parameters]`, but has type '
f'{type(ignored_parameters)}')
return ignored_parameters
def _get_ignored_modules(self, module: nn.Module,
ignored_modules: Union[Iterable[str],
Iterable[nn.Module]]):
"""Get modules from string."""
modules_dict = dict(module.named_modules())
if is_seq_of(ignored_modules, str):
ignored_modules = [modules_dict[name] for name in ignored_modules]
if not is_seq_of(ignored_modules,
nn.Module) and ignored_modules is not None:
raise TypeError(
'`ignored_modules` should be `None`, `Iterable[str]` or '
'`Iterable[nn.Module]`, but has type '
f'{type(ignored_modules)}')
return ignored_modules
if digit_version(torch.__version__) < digit_version('2.0.1'):
@staticmethod
def optim_state_dict(
model: torch.nn.Module,
optim: torch.optim.Optimizer,
group: Optional[dist.ProcessGroup] = None,
) -> Dict[str, Any]:
"""copied from pytorch 2.0.1 which has fixed some bugs."""
state_dict_settings = FullyShardedDataParallel.get_state_dict_type(
model)
return FullyShardedDataParallel._optim_state_dict_impl(
model=model,
optim=optim,
optim_state_dict=optim.state_dict(),
optim_input=None,
rank0_only=getattr(state_dict_settings.optim_state_dict_config,
'rank0_only', False),
full_state_dict=state_dict_settings.state_dict_type ==
StateDictType.FULL_STATE_DICT,
group=group,
)
@staticmethod
def set_state_dict_type(
module: nn.Module,
state_dict_type: StateDictType,
state_dict_config: Optional[StateDictConfig] = None,
optim_state_dict_config: Optional[OptimStateDictConfig] = None,
) -> StateDictSettings:
"""copied from pytorch 2.0.1 which has fixed some bugs."""
import torch.distributed.fsdp._traversal_utils as traversal_utils
_state_dict_type_to_config = {
StateDictType.FULL_STATE_DICT: FullStateDictConfig,
StateDictType.LOCAL_STATE_DICT: LocalStateDictConfig,
StateDictType.SHARDED_STATE_DICT: ShardedStateDictConfig,
}
_optim_state_dict_type_to_config = {
StateDictType.FULL_STATE_DICT: FullOptimStateDictConfig,
StateDictType.LOCAL_STATE_DICT: LocalOptimStateDictConfig,
StateDictType.SHARDED_STATE_DICT: ShardedOptimStateDictConfig,
}
# Use the default config if a state_dict config is not set.
state_dict_config_type = _state_dict_type_to_config[
state_dict_type]
optim_state_dict_config_type = _optim_state_dict_type_to_config[
state_dict_type]
if state_dict_config is None:
state_dict_config = state_dict_config_type()
if optim_state_dict_config is None:
optim_state_dict_config = optim_state_dict_config_type()
if state_dict_config_type != type(state_dict_config):
raise RuntimeError('Expected state_dict_config of type '
f'{state_dict_config_type} '
f'but got {type(state_dict_config)}')
if optim_state_dict_config_type != type(optim_state_dict_config):
raise RuntimeError('Expected optim_state_dict_config of type '
f'{optim_state_dict_config_type} '
f'but got {type(optim_state_dict_config)}')
# Set the state_dict type and configurations.
prev_state_dict_type = None
prev_state_dict_config = None
prev_optim_state_dict_config = None
for submodule in traversal_utils._get_fsdp_states(module):
if prev_state_dict_type is None:
prev_state_dict_type = submodule._state_dict_type
else:
assert (
prev_state_dict_type == submodule._state_dict_type
), 'All FSDP modules should have the same state_dict_type.'
if prev_state_dict_config is None:
prev_state_dict_config = submodule._state_dict_config
else:
assert isinstance(
submodule._state_dict_config,
type(prev_state_dict_config)), (
'All FSDP modules must have the same type of '
'state_dict_config.')
if prev_optim_state_dict_config is None:
prev_optim_state_dict_config = \
submodule._optim_state_dict_config
else:
assert isinstance(
submodule._optim_state_dict_config,
type(prev_optim_state_dict_config),
), ('All FSDP modules must have the same type of '
'optim_state_dict_config.')
submodule._state_dict_type = state_dict_type
submodule._state_dict_config = state_dict_config
submodule._optim_state_dict_config = optim_state_dict_config
return StateDictSettings(prev_state_dict_type,
prev_state_dict_config,
prev_optim_state_dict_config)
|