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[Tune, Ray SGD] Update PTL integrations (#11271)

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This commit is contained in:
Amog Kamsetty
2020-10-08 13:43:07 -07:00
committed by GitHub
parent a6f91664c1
commit 1027bfd4b8
4 changed files with 25 additions and 24 deletions
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python/ray/util/sgd/torch/ptl_operator.py
+9 -8
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@@ -5,6 +5,7 @@ import torch
from pytorch_lightning.core.step_result import Result
from pytorch_lightning.overrides.data_parallel import \
LightningDistributedDataParallel
from pytorch_lightning.utilities.model_utils import is_overridden
from pytorch_lightning.trainer.model_hooks import TrainerModelHooksMixin
from pytorch_lightning.trainer.optimizers import TrainerOptimizersMixin
import pytorch_lightning as ptl
@@ -39,8 +40,8 @@ class LightningOperator(TrainingOperator, TrainerModelHooksMixin,
assert len(models) == 1
model = models[0]
assert isinstance(model, ptl.LightningModule)
# This will default to LightningDistributedDataParallel.
model = model.configure_ddp(model=model, device_ids=device_ids)
model = LightningDistributedDataParallel(
model, device_ids=device_ids, find_unused_parameters=True)
return [model]
@property
@@ -110,7 +111,7 @@ class LightningOperator(TrainingOperator, TrainerModelHooksMixin,
# Call model.setup.
ptl_module.setup("fit")
if not self.is_overridden("configure_optimizers", ptl_module):
if not is_overridden("configure_optimizers", ptl_module):
raise MisconfigurationException(
"No `configure_optimizers()` method defined.")
@@ -232,7 +233,7 @@ class LightningOperator(TrainingOperator, TrainerModelHooksMixin,
break
processed_outputs = None
if self.is_overridden("training_epoch_end", model):
if is_overridden("training_epoch_end", model):
raw_outputs = [eo["raw_output"] for eo in epoch_outputs]
processed_outputs = model.training_epoch_end(raw_outputs)
@@ -316,7 +317,7 @@ class LightningOperator(TrainingOperator, TrainerModelHooksMixin,
# allow any mode to define training_step_end
# do something will all the dp outputs (like softmax)
if self.is_overridden("training_step_end", model):
if is_overridden("training_step_end", model):
output = model.training_step_end(output)
# Extract loss from output if dictionary.
@@ -397,7 +398,7 @@ class LightningOperator(TrainingOperator, TrainerModelHooksMixin,
val_outputs.append(batch_output)
processed_outputs = None
if self.is_overridden("validation_epoch_end", model):
if is_overridden("validation_epoch_end", model):
raw_outputs = [vo["raw_output"] for vo in val_outputs]
processed_outputs = model.training_epoch_end(raw_outputs)
@@ -440,7 +441,7 @@ class LightningOperator(TrainingOperator, TrainerModelHooksMixin,
def validate_batch(self, batch, batch_info):
model = self.get_model()
batch_idx = batch_info["batch_idx"]
if self.is_overridden("on_validation_batch_start", model):
if is_overridden("on_validation_batch_start", model):
model.on_validation_batch_start(
batch=batch, batch_idx=batch_idx, dataloader_idx=0)
args = [batch, batch_idx]
@@ -462,7 +463,7 @@ class LightningOperator(TrainingOperator, TrainerModelHooksMixin,
raise ValueError("EvalResult objects are not supported. Please "
"return a dictionary instead.")
if self.is_overridden("on_validation_step_end", model):
if is_overridden("on_validation_step_end", model):
output = model.validation_step_end(output)
if self.is_function_implemented("on_validation_batch_end", model):