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fix: clone distribution in validate_distribution #4205

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Merged
merged 1 commit into from
Oct 17, 2023
Merged

fix: clone distribution in validate_distribution #4205

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47 changes: 25 additions & 22 deletions src/sagemaker/fw_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,10 +21,11 @@
import shutil
import tempfile
from collections import namedtuple
from typing import Optional, Union, Dict
from typing import List, Optional, Union, Dict
from packaging import version

import sagemaker.image_uris
from sagemaker.instance_group import InstanceGroup
from sagemaker.s3_utils import s3_path_join
from sagemaker.session_settings import SessionSettings
import sagemaker.utils
Expand Down Expand Up @@ -828,14 +829,14 @@ def _validate_smdataparallel_args(


def validate_distribution(
distribution,
instance_groups,
framework_name,
framework_version,
py_version,
image_uri,
kwargs,
):
distribution: Dict,
instance_groups: List[InstanceGroup],
framework_name: str,
framework_version: str,
py_version: str,
image_uri: str,
kwargs: Dict,
) -> Dict:
"""Check if distribution strategy is correctly invoked by the user.

Currently, check for `dataparallel`, `modelparallel` and heterogeneous cluster set up.
Expand Down Expand Up @@ -872,7 +873,9 @@ def validate_distribution(
strategy-specific inputs are incorrect/unsupported or
heterogeneous cluster set up is incorrect
"""
train_instance_groups = distribution.get("instance_groups", [])
validated_distribution = dict(distribution)

train_instance_groups = validated_distribution.get("instance_groups", [])
if instance_groups is None:
if len(train_instance_groups) >= 1:
# if estimator's instance_groups is not defined but
Expand Down Expand Up @@ -902,77 +905,77 @@ def validate_distribution(
instance_type = train_instance_group.instance_type
validate_distribution_for_instance_type(
instance_type=instance_type,
distribution=distribution,
distribution=validated_distribution,
)
validate_smdistributed(
instance_type=instance_type,
framework_name=framework_name,
framework_version=framework_version,
py_version=py_version,
distribution=distribution,
distribution=validated_distribution,
image_uri=image_uri,
)
if framework_name and framework_name == "pytorch":
# We need to validate only for PyTorch framework
validate_pytorch_distribution(
distribution=distribution,
distribution=validated_distribution,
framework_name=framework_name,
framework_version=framework_version,
py_version=py_version,
image_uri=image_uri,
)
validate_torch_distributed_distribution(
instance_type=instance_type,
distribution=distribution,
distribution=validated_distribution,
framework_version=framework_version,
py_version=py_version,
image_uri=image_uri,
entry_point=kwargs["entry_point"],
)
warn_if_parameter_server_with_multi_gpu(
training_instance_type=instance_type, distribution=distribution
training_instance_type=instance_type, distribution=validated_distribution
)
# get instance group names
instance_group_names.append(train_instance_group.instance_group_name)
distribution["instance_groups"] = instance_group_names
validated_distribution["instance_groups"] = instance_group_names
else:
# in this case, we are handling a normal training job (without heterogeneous cluster)
instance_type = renamed_kwargs(
"train_instance_type", "instance_type", kwargs.get("instance_type"), kwargs
)
validate_distribution_for_instance_type(
instance_type=instance_type,
distribution=distribution,
distribution=validated_distribution,
)
validate_smdistributed(
instance_type=instance_type,
framework_name=framework_name,
framework_version=framework_version,
py_version=py_version,
distribution=distribution,
distribution=validated_distribution,
image_uri=image_uri,
)
if framework_name and framework_name == "pytorch":
# We need to validate only for PyTorch framework
validate_pytorch_distribution(
distribution=distribution,
distribution=validated_distribution,
framework_name=framework_name,
framework_version=framework_version,
py_version=py_version,
image_uri=image_uri,
)
validate_torch_distributed_distribution(
instance_type=instance_type,
distribution=distribution,
distribution=validated_distribution,
framework_version=framework_version,
py_version=py_version,
image_uri=image_uri,
entry_point=kwargs["entry_point"],
)
warn_if_parameter_server_with_multi_gpu(
training_instance_type=instance_type, distribution=distribution
training_instance_type=instance_type, distribution=validated_distribution
)
return distribution
return validated_distribution


def validate_distribution_for_instance_type(instance_type, distribution):
Expand Down
22 changes: 22 additions & 0 deletions tests/unit/test_fw_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -784,6 +784,28 @@ def test_validate_distribution_raises():
)


def test_validate_distribution_copy():
train_group = InstanceGroup("train_group", "ml.p3.16xlarge", 1)
instance_groups = [train_group]
framework = "tensorflow"
distribution = {"smdistributed": {"dataparallel": {"enabled": True}}}
validated = fw_utils.validate_distribution(
distribution,
instance_groups,
framework,
None,
None,
"custom-container",
{"entry_point": "train.py"},
)

assert validated == {
"instance_groups": ["train_group"],
"smdistributed": {"dataparallel": {"enabled": True}},
}
assert validated is not distribution


def test_validate_smdistributed_not_raises():
smdataparallel_enabled = {"smdistributed": {"dataparallel": {"enabled": True}}}
smdataparallel_enabled_custom_mpi = {
Expand Down