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Merged
regisss merged 1 commit into from
Oct 9, 2023
Merged

Remove sharded_ddp #456

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20 changes: 2 additions & 18 deletions optimum/habana/transformers/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -634,7 +634,7 @@ def hpu_deepspeed_checkpointing(function, *checkpoint_args):

# as the model is wrapped, don't use `accelerator.prepare`
# this is for unhandled cases such as
# Fairscale Sharded DDP, FSDP-XLA, SageMaker MP/DP, DataParallel, IPEX
# FSDP-XLA, SageMaker MP/DP, DataParallel, IPEX
use_accelerator_prepare = True if model is self.model else False

# prepare using `accelerator` prepare
Expand Down Expand Up @@ -1179,22 +1179,16 @@ def _save_checkpoint(self, model, trial, metrics=None):
# This block is exectuted by the main process only
optim_dict = self.optimizer.state_dict()
scheduler_dict = self.lr_scheduler.state_dict()
if self.do_grad_scaling:
scaler_dict = self.scaler.state_dict()
if self.args.use_habana:
# Move the state dict from HPU to CPU before saving
optim_dict = to_device_dtype(optim_dict, target_device=torch.device("cpu"))
scheduler_dict = to_device_dtype(scheduler_dict, target_device=torch.device("cpu"))
if self.do_grad_scaling:
scaler_dict = to_device_dtype(scaler_dict, target_device=torch.device("cpu"))
torch.save(optim_dict, os.path.join(output_dir, OPTIMIZER_NAME))

# Save SCHEDULER & SCALER
with warnings.catch_warnings(record=True) as caught_warnings:
torch.save(scheduler_dict, os.path.join(output_dir, SCHEDULER_NAME))
reissue_pt_warnings(caught_warnings)
if self.do_grad_scaling:
torch.save(scaler_dict, os.path.join(output_dir, SCALER_NAME))

# Determine the new best metric / best model checkpoint
if metrics is not None and self.args.metric_for_best_model is not None:
Expand Down Expand Up @@ -1279,16 +1273,9 @@ def _load_optimizer_and_scheduler(self, checkpoint):
)
reissue_pt_warnings(caught_warnings)

if self.do_grad_scaling and os.path.isfile(os.path.join(checkpoint, SCALER_NAME)):
self.scaler.load_state_dict(
torch.load(os.path.join(checkpoint, SCALER_NAME), map_location=map_location)
)

# Move optimizer state to HPU
if self.args.use_habana:
to_device_dtype(self.optimizer.state.values(), target_device=torch.device("hpu"))
if self.do_grad_scaling:
to_device_dtype(self.scaler.state.values(), target_device=torch.device("hpu"))

def log(self, logs: Dict[str, float]) -> None:
"""
Expand Down Expand Up @@ -1374,10 +1361,7 @@ def training_step(self, model: torch.nn.Module, inputs: Dict[str, Union[torch.Te
if self.args.pipelining_fwd_bwd:
self.htcore.mark_step()

if self.do_grad_scaling:
self.scaler.scale(loss).backward()
else:
self.accelerator.backward(loss)
self.accelerator.backward(loss)

return loss.detach() / self.args.gradient_accumulation_steps

Expand Down
1 change: 0 additions & 1 deletion optimum/habana/transformers/trainer_seq2seq.py
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Expand Up @@ -256,7 +256,6 @@ def prediction_step(
has_labels = "labels" in inputs
inputs = self._prepare_inputs(inputs)

# XXX: adapt synced_gpus for fairscale as well
# Priority (handled in generate):
# non-`None` gen_kwargs > model.generation_config > default GenerationConfig()
if len(gen_kwargs) == 0 and hasattr(self, "_gen_kwargs"):
Expand Down
3 changes: 0 additions & 3 deletions optimum/habana/transformers/training_args.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,7 +60,6 @@
"fp16_opt_level",
"fsdp",
"mp_parameters",
"sharded_ddp",
"tf32",
"tpu_metrics_debug",
"tpu_num_cores",
Expand Down Expand Up @@ -304,8 +303,6 @@ def __post_init__(self):
raise ValueError("TPUs are not supported by optimum-habana.")
if self.mp_parameters:
raise ValueError("--mp_parameters is not supported by optimum-habana.")
if self.sharded_ddp:
raise ValueError("--sharded_ddp is not supported by optimum-habana.")
if self.tf32:
raise ValueError("--tf32 is not supported by optimum-habana.")

Expand Down