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Revert DeepSpeed stuff from accelerate integration (huggingface#22899)
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@muellerzr @novice03
muellerzr authored and novice03 committed Jun 23, 2023
1 parent 0d0f3e6 commit 7624ad2
Showing 1 changed file with 62 additions and 47 deletions.
109 changes: 62 additions & 47 deletions src/transformers/training_args.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1544,69 +1544,84 @@ def _setup_devices(self) -> "torch.device":
self._n_gpu = 1
torch.cuda.set_device(device)
elif self.deepspeed:
self.distributed_state = PartialState(timeout=timedelta(seconds=self.ddp_timeout))
# 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)
self._n_gpu = 1
else:
self.distributed_state = PartialState(backend=self.xpu_backend)
self._n_gpu = 1
if not is_sagemaker_mp_enabled():
if not is_sagemaker_mp_enabled() and not self.deepspeed:
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 is_torch_tpu_available():
device = self.distributed_state.device
self._n_gpu = 0
elif is_sagemaker_dp_enabled():
self._n_gpu = 1
elif self.distributed_state.distributed_type == DistributedType.NO:
if self.use_mps_device:
if not torch.backends.mps.is_available():
if not torch.backends.mps.is_built():
raise AssertionError(
"MPS not available because the current PyTorch install was not "
"built with MPS enabled. Please install torch version >=1.12.0 on "
"your Apple silicon Mac running macOS 12.3 or later with a native "
"version (arm64) of Python"
)
if not self.deepspeed:
if is_torch_tpu_available():
device = self.distributed_state.device
self._n_gpu = 0
elif is_sagemaker_dp_enabled():
self._n_gpu = 1
elif self.distributed_state.distributed_type == DistributedType.NO:
if self.use_mps_device:
if not torch.backends.mps.is_available():
if not torch.backends.mps.is_built():
raise AssertionError(
"MPS not available because the current PyTorch install was not "
"built with MPS enabled. Please install torch version >=1.12.0 on "
"your Apple silicon Mac running macOS 12.3 or later with a native "
"version (arm64) of Python"
)
else:
raise AssertionError(
"MPS not available because the current MacOS version is not 12.3+ "
"and/or you do not have an MPS-enabled device on this machine."
)
else:
raise AssertionError(
"MPS not available because the current MacOS version is not 12.3+ "
"and/or you do not have an MPS-enabled device on this machine."
)
else:
if not version.parse(version.parse(torch.__version__).base_version) > version.parse("1.12.0"):
warnings.warn(
"We strongly recommend to install PyTorch >= 1.13 (nightly version at the time of writing)"
" on your MacOS machine. It has major fixes related to model correctness and performance"
" improvements for transformer based models. Please refer to"
" https://github.com/pytorch/pytorch/issues/82707 for more details."
)
device = torch.device("mps")
self._n_gpu = 1
if not version.parse(version.parse(torch.__version__).base_version) > version.parse("1.12.0"):
warnings.warn(
"We strongly recommend to install PyTorch >= 1.13 (nightly version at the time of writing)"
" on your MacOS machine. It has major fixes related to model correctness and performance"
" improvements for transformer based models. Please refer to"
" https://github.com/pytorch/pytorch/issues/82707 for more details."
)
device = torch.device("mps")
self._n_gpu = 1

else:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# 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.
self._n_gpu = torch.cuda.device_count()
if device.type == "cuda":
torch.cuda.set_device(device)
else:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# 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.
self._n_gpu = torch.cuda.device_count()
if device.type == "cuda":
torch.cuda.set_device(device)
return device

@property
Expand Down Expand Up @@ -1649,7 +1664,7 @@ def parallel_mode(self):
return ParallelMode.SAGEMAKER_MODEL_PARALLEL
elif is_sagemaker_dp_enabled():
return ParallelMode.SAGEMAKER_DATA_PARALLEL
elif hasattr(self, "distributed_state") and (self.distributed_state.distributed_type != DistributedType.NO):
elif self.deepspeed or self.distributed_state.distributed_type != DistributedType.NO:
return ParallelMode.DISTRIBUTED
elif self.n_gpu > 1:
return ParallelMode.NOT_DISTRIBUTED
Expand Down

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