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Instead go with evaluation_mode #1540

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Jun 7, 2023
Merged

Instead go with evaluation_mode #1540

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21 changes: 11 additions & 10 deletions src/accelerate/accelerator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1220,7 +1220,7 @@ def prepare(self, *args, device_placement=None):

return result if len(result) > 1 else result[0]

def prepare_model(self, model: torch.nn.Module, device_placement: bool = None, mixed_precision_only: bool = False):
def prepare_model(self, model: torch.nn.Module, device_placement: bool = None, evaluation_mode: bool = False):
"""
Prepares a PyTorch model for training in any distributed setup. It is recommended to use
[`Accelerator.prepare`] instead.
Expand All @@ -1231,8 +1231,9 @@ def prepare_model(self, model: torch.nn.Module, device_placement: bool = None, m
any kind of mixed precision
device_placement (`bool`, *optional*):
Whether or not to place the model on the proper device. Will default to `self.device_placement`.
mixed_precision_only (`bool`, *optional*, defaults to `False`):
Whether or not to *only* apply mixed precision to the model, and not do any other model wrapping.
evaluation_mode (`bool`, *optional*, defaults to `False`):
Whether or not to set the model for evaluation only, by just applying mixed precision and
`torch.compile` (if configured in the `Accelerator` object).

Example:

Expand Down Expand Up @@ -1284,7 +1285,7 @@ def prepare_model(self, model: torch.nn.Module, device_placement: bool = None, m
elif device_placement and not has_hf_device_map:
model = model.to(self.device)

if not mixed_precision_only:
if not evaluation_mode:
if self.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_XPU):
if any(p.requires_grad for p in model.parameters()):
kwargs = self.ddp_handler.to_kwargs() if self.ddp_handler is not None else {}
Expand Down Expand Up @@ -1351,14 +1352,14 @@ def prepare_model(self, model: torch.nn.Module, device_placement: bool = None, m
"or higher, compute capability of 8.9 or higher). Will use FP16 instead."
)
model.forward = fp8_autocast(enabled=fp8_enabled, fp8_recipe=fp8_recipe)(model.forward)
if not mixed_precision_only:
if not evaluation_mode:
if self.distributed_type == DistributedType.TPU and self.state.fork_launched:
model = xmp.MpModelWrapper(model).to(self.device)
# torch.compile should be called last.
if self.state.dynamo_plugin.backend != DynamoBackend.NO:
if not is_torch_version(">=", "2.0"):
raise ValueError("Using `torch.compile` requires PyTorch 2.0 or higher.")
model = torch.compile(model, **self.state.dynamo_plugin.to_kwargs())
# torch.compile should be called last.
if self.state.dynamo_plugin.backend != DynamoBackend.NO:
if not is_torch_version(">=", "2.0"):
raise ValueError("Using `torch.compile` requires PyTorch 2.0 or higher.")
model = torch.compile(model, **self.state.dynamo_plugin.to_kwargs())
return model

def _prepare_deepspeed(self, *args):
Expand Down