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deprecate no_cuda (#24863)

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* deprecate no_cuda

* style

* remove doc

* remove doc 2

* fix style
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Marc Sun 2023-07-17 14:52:28 -04:00 committed by GitHub
parent 0f4502d335
commit 9dc965bb40
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1 changed files with 23 additions and 9 deletions
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src/transformers/training_args.py
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@ -297,8 +297,8 @@ class TrainingArguments:
This should not be activated when the different nodes use the same storage as the files will be saved with
the same names for each node.
no_cuda (`bool`, *optional*, defaults to `False`):
Whether to not use CUDA even when it is available or not.
use_cpu (`bool`, *optional*, defaults to `False`):
Whether or not to use cpu. If set to False, we will use cuda or mps device if available.
seed (`int`, *optional*, defaults to 42):
Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the
[`~Trainer.model_init`] function to instantiate the model if it has some randomly initialized parameters.
@ -313,7 +313,7 @@ class TrainingArguments:
installation](https://github.com/intel/intel-extension-for-pytorch).
bf16 (`bool`, *optional*, defaults to `False`):
Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher
NVIDIA architecture or using CPU (no_cuda). This is an experimental API and it may change.
NVIDIA architecture or using CPU (use_cpu). This is an experimental API and it may change.
fp16 (`bool`, *optional*, defaults to `False`):
Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training.
fp16_opt_level (`str`, *optional*, defaults to 'O1'):
@ -793,7 +793,14 @@ class TrainingArguments:
)
},
)
no_cuda: bool = field(default=False, metadata={"help": "Do not use CUDA even when it is available"})
no_cuda: bool = field(
default=False,
metadata={"help": "This argument is deprecated. It will be removed in version 5.0 of 🤗 Transformers."},
)
use_cpu: bool = field(
default=False,
metadata={"help": " Whether or not to use cpu. If set to False, we will use cuda or mps device if available."},
)
use_mps_device: bool = field(
default=False,
metadata={
@ -820,7 +827,7 @@ class TrainingArguments:
metadata={
"help": (
"Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA"
" architecture or using CPU (no_cuda). This is an experimental API and it may change."
" architecture or using CPU (use_cpu). This is an experimental API and it may change."
)
},
)
@ -1211,6 +1218,13 @@ class TrainingArguments:
)
# Go back to the underlying string or we won't be able to instantiate `IntervalStrategy` on it.
self.evaluation_strategy = self.evaluation_strategy.value
if self.no_cuda:
warnings.warn(
"using `no_cuda` is deprecated and will be removed in version 5.0 of 🤗 Transformers. "
"Use `use_cpu` instead",
FutureWarning,
)
self.use_cpu = self.no_cuda
self.evaluation_strategy = IntervalStrategy(self.evaluation_strategy)
self.logging_strategy = IntervalStrategy(self.logging_strategy)
@ -1305,10 +1319,10 @@ class TrainingArguments:
self.half_precision_backend = self.fp16_backend
if self.bf16 or self.bf16_full_eval:
if self.no_cuda and not is_torch_bf16_cpu_available() and not is_torch_tpu_available():
if self.use_cpu and not is_torch_bf16_cpu_available() and not is_torch_tpu_available():
# cpu
raise ValueError("Your setup doesn't support bf16/(cpu, tpu, neuroncore). You need torch>=1.10")
elif not self.no_cuda and torch.cuda.is_available() and not is_torch_bf16_gpu_available():
elif not self.use_cpu and torch.cuda.is_available() and not is_torch_bf16_gpu_available():
# gpu
raise ValueError(
"Your setup doesn't support bf16/gpu. You need torch>=1.10, using Ampere GPU with cuda>=11.0"
@ -1702,7 +1716,7 @@ class TrainingArguments:
)
AcceleratorState._reset_state(reset_partial_state=True)
self.distributed_state = None
if self.no_cuda:
if self.use_cpu:
self.distributed_state = PartialState(cpu=True, backend=self.ddp_backend)
self._n_gpu = 0
elif is_sagemaker_mp_enabled():
@ -1752,7 +1766,7 @@ class TrainingArguments:
)
if device.type == "mps":
self._n_gpu = 1
elif self.no_cuda:
elif self.use_cpu:
device = torch.device("cpu")
self._n_gpu = 0
else: