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Bring back PartialState DeepSpeed (#22921)

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* Bring back deepspeed integration

* Branchname

* Self-scheduled

* newline

* Use deepspeed env var

* Remove comment

* Del env var after partialstate
This commit is contained in:
Zachary Mueller
2023-04-26 15:35:59 -04:00
committed by GitHub
parent 4331923b97
commit 8b129030cb
1 changed files with 48 additions and 62 deletions
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26
src/transformers/training_args.py
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@@ -1544,39 +1544,26 @@ class TrainingArguments:
self._n_gpu = 1
torch.cuda.set_device(device)
elif self.deepspeed:
# deepspeed inits torch.distributed internally
from .deepspeed import is_deepspeed_available
if not is_deepspeed_available():
raise ImportError("--deepspeed requires deepspeed: `pip install deepspeed`.")
import deepspeed
deepspeed.init_distributed(timeout=timedelta(seconds=self.ddp_timeout))
# workaround for setups like notebooks where the launcher can't be used,
# but deepspeed requires a dist env.
# env LOCAL_RANK could be set manually by the user, or via init_distributed if mpi4py is installed
self.local_rank = int(os.environ.get("LOCAL_RANK", "-1"))
device = torch.device("cuda", self.local_rank)
# Need to do similar for Accelerator init
os.environ["ACCELERATE_USE_DEEPSPEED"] = "true"
self.distributed_state = PartialState(timeout=timedelta(seconds=self.ddp_timeout))
del os.environ["ACCELERATE_USE_DEEPSPEED"]
self._n_gpu = 1
else:
self.distributed_state = PartialState(backend=self.xpu_backend)
self._n_gpu = 1
if not is_sagemaker_mp_enabled() and not self.deepspeed:
if not is_sagemaker_mp_enabled():
device = self.distributed_state.device
self.local_rank = self.distributed_state.local_process_index
if (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and hasattr(self, "distributed_state")
and self.distributed_state.distributed_type == DistributedType.NO
):
logger.warning(
"torch.distributed process group is initialized, but parallel_mode != ParallelMode.DISTRIBUTED. "
"In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"
)
if not self.deepspeed:
if is_torch_tpu_available():
device = self.distributed_state.device
self._n_gpu = 0
@@ -1615,7 +1602,6 @@ class TrainingArguments:
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
# device = self.distributed_state.device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
@@ -1664,7 +1650,7 @@ class TrainingArguments:
return ParallelMode.SAGEMAKER_MODEL_PARALLEL
elif is_sagemaker_dp_enabled():
return ParallelMode.SAGEMAKER_DATA_PARALLEL
elif self.deepspeed or self.distributed_state.distributed_type != DistributedType.NO:
elif hasattr(self, "distributed_state") and self.distributed_state.distributed_type != DistributedType.NO:
return ParallelMode.DISTRIBUTED
elif self.n_gpu > 1:
return ParallelMode.NOT_DISTRIBUTED