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#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
"""Configuration for the SageMaker Training Compiler."""
from __future__ import absolute_import
import logging
from typing import Union
from packaging.specifiers import SpecifierSet
from packaging.version import Version
from sagemaker.training_compiler.config import TrainingCompilerConfig as BaseConfig
from sagemaker.workflow.entities import PipelineVariable
logger = logging.getLogger(__name__)
class TrainingCompilerConfig(BaseConfig):
"""The SageMaker Training Compiler configuration class."""
SUPPORTED_INSTANCE_CLASS_PREFIXES = ["p3", "g4dn", "p4d", "g5"]
SUPPORTED_INSTANCE_TYPES_WITH_EFA = [
"ml.g4dn.8xlarge",
"ml.g4dn.12xlarge",
"ml.g5.48xlarge",
"ml.p3dn.24xlarge",
"ml.p4d.24xlarge",
]
def __init__(
self,
enabled: Union[bool, PipelineVariable] = True,
debug: Union[bool, PipelineVariable] = False,
):
"""This class initializes a ``TrainingCompilerConfig`` instance.
`Amazon SageMaker Training Compiler
<https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler.html>`_
is a feature of SageMaker Training
and speeds up training jobs by optimizing model execution graphs.
You can compile Hugging Face models
by passing the object of this configuration class to the ``compiler_config``
parameter of the :class:`~sagemaker.huggingface.HuggingFace`
estimator.
Args:
enabled (bool or PipelineVariable): Optional. Switch to enable SageMaker
Training Compiler. The default is ``True``.
debug (bool or PipelineVariable): Optional. Whether to dump detailed logs
for debugging. This comes with a potential performance slowdown.
The default is ``False``.
**Example**: The following code shows the basic usage of the
:class:`sagemaker.huggingface.TrainingCompilerConfig()` class
to run a HuggingFace training job with the compiler.
.. code-block:: python
from sagemaker.huggingface import HuggingFace, TrainingCompilerConfig
huggingface_estimator=HuggingFace(
...
compiler_config=TrainingCompilerConfig()
)
.. seealso::
For more information about how to enable SageMaker Training Compiler
for various training settings such as using TensorFlow-based models,
PyTorch-based models, and distributed training,
see `Enable SageMaker Training Compiler
<https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler-enable.html>`_
in the `Amazon SageMaker Training Compiler developer guide
<https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler.html>`_.
"""
super(TrainingCompilerConfig, self).__init__(enabled=enabled, debug=debug)
@classmethod
def validate(
cls,
estimator,
):
"""Checks if SageMaker Training Compiler is configured correctly.
Args:
estimator (:class:`sagemaker.huggingface.HuggingFace`): An estimator object.
If SageMaker Training Compiler is enabled, it will validate whether
the estimator is configured to be compatible with Training Compiler.
Raises:
ValueError: Raised if the requested configuration is not compatible
with SageMaker Training Compiler.
"""
super(TrainingCompilerConfig, cls).validate(estimator)
if estimator.image_uri:
error_helper_string = (
"Overriding the image URI is currently not supported "
"for SageMaker Training Compiler."
"Specify the following parameters to run the Hugging Face training job "
"with SageMaker Training Compiler enabled: "
"transformer_version, tensorflow_version or pytorch_version, and compiler_config."
)
raise ValueError(error_helper_string)
if estimator.distribution:
pt_xla_present = "pytorchxla" in estimator.distribution
pt_xla_enabled = estimator.distribution.get("pytorchxla", {}).get("enabled", False)
if pt_xla_enabled:
if estimator.tensorflow_version:
error_helper_string = (
"Distribution mechanism 'pytorchxla' is currently only supported for "
"PyTorch >= 1.11 when SageMaker Training Compiler is enabled. Received "
"tensorflow_version={} which is unsupported."
)
raise ValueError(error_helper_string.format(estimator.tensorflow_version))
if estimator.pytorch_version:
if Version(estimator.pytorch_version) in SpecifierSet("< 1.11"):
error_helper_string = (
"Distribution mechanism 'pytorchxla' is currently only supported for "
"PyTorch >= 1.11 when SageMaker Training Compiler is enabled."
" Received pytorch_version={} which is unsupported."
)
raise ValueError(error_helper_string.format(estimator.pytorch_version))
if estimator.instance_type not in cls.SUPPORTED_INSTANCE_TYPES_WITH_EFA:
logger.warning(
"Consider using instances with EFA support when "
"training with PyTorch >= 1.11 and SageMaker Training Compiler "
"enabled. SageMaker Training Compiler leverages EFA to provide better "
"performance for distributed training."
)
if not pt_xla_present:
if estimator.pytorch_version:
if Version(estimator.pytorch_version) in SpecifierSet(">= 1.11"):
error_helper_string = (
"'pytorchxla' is the only distribution mechanism currently supported "
"for PyTorch >= 1.11 when SageMaker Training Compiler is enabled."
" Received distribution={} which is unsupported."
)
raise ValueError(error_helper_string.format(estimator.distribution))
elif estimator.instance_count and estimator.instance_count > 1:
if estimator.pytorch_version:
if Version(estimator.pytorch_version) in SpecifierSet(">= 1.11"):
logger.warning(
"Consider setting 'distribution' to 'pytorchxla' for distributed "
"training with PyTorch >= 1.11 and SageMaker Training Compiler enabled."
)
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