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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.
"""Placeholder docstring"""
from __future__ import absolute_import
import logging
import re
from typing import Optional, Union, Dict
from sagemaker.deprecations import renamed_kwargs
from sagemaker.estimator import Framework, EstimatorBase
from sagemaker.fw_utils import (
framework_name_from_image,
warn_if_parameter_server_with_multi_gpu,
validate_smdistributed,
)
from sagemaker.huggingface.model import HuggingFaceModel
from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
from sagemaker.huggingface.training_compiler.config import TrainingCompilerConfig
from sagemaker.workflow.entities import PipelineVariable
logger = logging.getLogger("sagemaker")
class HuggingFace(Framework):
"""Handle training of custom HuggingFace code."""
_framework_name = "huggingface"
def __init__(
self,
py_version: str,
entry_point: Union[str, PipelineVariable],
transformers_version: Optional[str] = None,
tensorflow_version: Optional[str] = None,
pytorch_version: Optional[str] = None,
source_dir: Optional[Union[str, PipelineVariable]] = None,
hyperparameters: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
image_uri: Optional[Union[str, PipelineVariable]] = None,
distribution: Optional[Dict] = None,
compiler_config: Optional[TrainingCompilerConfig] = None,
**kwargs,
):
"""This estimator runs a Hugging Face training script in a SageMaker training environment.
The estimator initiates the SageMaker-managed Hugging Face environment
by using the pre-built Hugging Face Docker container and runs
the Hugging Face training script that user provides through
the ``entry_point`` argument.
After configuring the estimator class, use the class method
:meth:`~sagemaker.amazon.estimator.Framework.fit()` to start a training job.
Args:
py_version (str): Python version you want to use for executing your model training
code. Defaults to ``None``. Required unless ``image_uri`` is provided. If
using PyTorch, the current supported version is ``py36``. If using TensorFlow,
the current supported version is ``py37``.
entry_point (str or PipelineVariable): Path (absolute or relative) to the Python source
file which should be executed as the entry point to training.
If ``source_dir`` is specified, then ``entry_point``
must point to a file located at the root of ``source_dir``.
transformers_version (str): Transformers version you want to use for
executing your model training code. Defaults to ``None``. Required unless
``image_uri`` is provided. The current supported version is ``4.6.1``.
tensorflow_version (str): TensorFlow version you want to use for
executing your model training code. Defaults to ``None``. Required unless
``pytorch_version`` is provided. The current supported version is ``2.4.1``.
pytorch_version (str): PyTorch version you want to use for
executing your model training code. Defaults to ``None``. Required unless
``tensorflow_version`` is provided. The current supported versions are ``1.7.1`` and ``1.6.0``.
source_dir (str or PipelineVariable): Path (absolute, relative or an S3 URI) to a
directory with any other training source code dependencies aside from the entry
point file (default: None). If ``source_dir`` is an S3 URI, it must
point to a tar.gz file. Structure within this directory are preserved
when training on Amazon SageMaker.
hyperparameters (dict[str, str] or dict[str, PipelineVariable]): Hyperparameters
that will be used for training (default: None). The hyperparameters are made
accessible as a dict[str, str] to the training code on
SageMaker. For convenience, this accepts other types for keys
and values, but ``str()`` will be called to convert them before
training.
image_uri (str or PipelineVariable): If specified, the estimator will use this image
for training and hosting, instead of selecting the appropriate
SageMaker official image based on framework_version and
py_version. It can be an ECR url or dockerhub image and tag.
Examples:
* ``123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0``
* ``custom-image:latest``
If ``framework_version`` or ``py_version`` are ``None``, then
``image_uri`` is required. If also ``None``, then a ``ValueError``
will be raised.
distribution (dict): A dictionary with information on how to run distributed training
(default: None). Currently, the following are supported:
distributed training with parameter servers, SageMaker Distributed (SMD) Data
and Model Parallelism, and MPI. SMD Model Parallelism can only be used with MPI.
To enable parameter server use the following setup:
.. code:: python
{
"parameter_server": {
"enabled": True
}
}
To enable MPI:
.. code:: python
{
"mpi": {
"enabled": True
}
}
To enable SMDistributed Data Parallel or Model Parallel:
.. code:: python
{
"smdistributed": {
"dataparallel": {
"enabled": True
},
"modelparallel": {
"enabled": True,
"parameters": {}
}
}
}
To enable distributed training with
`SageMaker Training Compiler <https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler.html>`_
for Hugging Face Transformers with PyTorch:
.. code:: python
{
"pytorchxla": {
"enabled": True
}
}
To learn more, see `SageMaker Training Compiler
<https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler.html>`_
in the *Amazon SageMaker Developer Guide*.
.. note::
When you use this PyTorch XLA option for distributed training strategy,
you must add the ``compiler_config`` parameter and activate SageMaker
Training Compiler.
compiler_config (:class:`~sagemaker.huggingface.TrainingCompilerConfig`):
Configures SageMaker Training Compiler to accelerate training.
**kwargs: Additional kwargs passed to the :class:`~sagemaker.estimator.Framework`
constructor.
.. tip::
You can find additional parameters for initializing this class at
:class:`~sagemaker.estimator.Framework` and
:class:`~sagemaker.estimator.EstimatorBase`.
"""
self.framework_version = transformers_version
self.py_version = py_version
self.tensorflow_version = tensorflow_version
self.pytorch_version = pytorch_version
self._validate_args(image_uri=image_uri)
instance_type = renamed_kwargs(
"train_instance_type", "instance_type", kwargs.get("instance_type"), kwargs
)
base_framework_name = "tensorflow" if tensorflow_version is not None else "pytorch"
base_framework_version = (
tensorflow_version if tensorflow_version is not None else pytorch_version
)
if distribution is not None:
validate_smdistributed(
instance_type=instance_type,
framework_name=base_framework_name,
framework_version=base_framework_version,
py_version=self.py_version,
distribution=distribution,
image_uri=image_uri,
)
warn_if_parameter_server_with_multi_gpu(
training_instance_type=instance_type, distribution=distribution
)
if "enable_sagemaker_metrics" not in kwargs:
kwargs["enable_sagemaker_metrics"] = True
kwargs["py_version"] = self.py_version
super(HuggingFace, self).__init__(
entry_point, source_dir, hyperparameters, image_uri=image_uri, **kwargs
)
self.distribution = distribution or {}
if compiler_config is not None:
if not isinstance(compiler_config, TrainingCompilerConfig):
error_string = (
f"Expected instance of type {TrainingCompilerConfig}"
f"for argument compiler_config. "
f"Instead got {type(compiler_config)}"
)
raise ValueError(error_string)
if compiler_config:
compiler_config.validate(self)
elif distribution is not None and "pytorchxla" in distribution:
raise ValueError(
"Distributed training through PyTorch XLA is currently only supported "
"when SageMaker Training Compiler is enabled. To learn more, "
"see Enable SageMaker Training Compiler at "
"https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler-enable.html."
)
self.compiler_config = compiler_config
def _validate_args(self, image_uri):
"""Placeholder docstring"""
if image_uri is not None:
return
if self.framework_version is None and image_uri is None:
raise ValueError(
"transformers_version, and image_uri are both None. "
"Specify either transformers_version or image_uri"
)
if self.tensorflow_version is not None and self.pytorch_version is not None:
raise ValueError(
"tensorflow_version and pytorch_version are both not None. "
"Specify only tensorflow_version or pytorch_version."
)
if self.tensorflow_version is None and self.pytorch_version is None:
raise ValueError(
"tensorflow_version and pytorch_version are both None. "
"Specify either tensorflow_version or pytorch_version."
)
base_framework_version_len = (
len(self.tensorflow_version.split("."))
if self.tensorflow_version is not None
else len(self.pytorch_version.split("."))
)
transformers_version_len = len(self.framework_version.split("."))
if transformers_version_len != base_framework_version_len:
raise ValueError(
"Please use either full version or shortened version for both "
"transformers_version, tensorflow_version and pytorch_version."
)
def hyperparameters(self):
"""Return hyperparameters used by your custom PyTorch code during model training."""
hyperparameters = super(HuggingFace, self).hyperparameters()
distributed_training_hyperparameters = self._distribution_configuration(
distribution=self.distribution
)
hyperparameters.update(
EstimatorBase._json_encode_hyperparameters(distributed_training_hyperparameters)
)
if self.compiler_config:
training_compiler_hyperparameters = self.compiler_config._to_hyperparameter_dict()
hyperparameters.update(
EstimatorBase._json_encode_hyperparameters(training_compiler_hyperparameters)
)
return hyperparameters
def create_model(
self,
model_server_workers=None,
role=None,
vpc_config_override=VPC_CONFIG_DEFAULT,
entry_point=None,
source_dir=None,
dependencies=None,
**kwargs,
):
"""Create a SageMaker ``HuggingFaceModel`` object that can be deployed to an ``Endpoint``.
Args:
model_server_workers (int): Optional. The number of worker processes
used by the inference server. If None, server will use one
worker per vCPU.
role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``,
which is also used during transform jobs. If not specified, the
role from the Estimator will be used.
vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
the model. Default: use subnets and security groups from this Estimator.
* 'Subnets' (list[str]): List of subnet ids.
* 'SecurityGroupIds' (list[str]): List of security group ids.
entry_point (str): Path (absolute or relative) to the local Python source file which
should be executed as the entry point to training. If ``source_dir`` is specified,
then ``entry_point`` must point to a file located at the root of ``source_dir``.
Defaults to `None`.
source_dir (str): Path (absolute or relative) to a directory with any other serving
source code dependencies aside from the entry point file.
If not specified, the model source directory from training is used.
dependencies (list[str]): A list of paths to directories (absolute or relative) with
any additional libraries that will be exported to the container.
If not specified, the dependencies from training are used.
This is not supported with "local code" in Local Mode.
**kwargs: Additional kwargs passed to the :class:`~sagemaker.huggingface.model.HuggingFaceModel`
constructor.
Returns:
sagemaker.huggingface.model.HuggingFaceModel: A SageMaker ``HuggingFaceModel``
object. See :func:`~sagemaker.huggingface.model.HuggingFaceModel` for full details.
"""
if "image_uri" not in kwargs:
kwargs["image_uri"] = self.image_uri
kwargs["name"] = self._get_or_create_name(kwargs.get("name"))
return HuggingFaceModel(
role or self.role,
model_data=self.model_data,
entry_point=entry_point,
transformers_version=self.framework_version,
tensorflow_version=self.tensorflow_version,
pytorch_version=self.pytorch_version,
py_version=self.py_version,
source_dir=(source_dir or self._model_source_dir()),
container_log_level=self.container_log_level,
code_location=self.code_location,
model_server_workers=model_server_workers,
sagemaker_session=self.sagemaker_session,
vpc_config=self.get_vpc_config(vpc_config_override),
dependencies=(dependencies or self.dependencies),
**kwargs,
)
@classmethod
def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
"""Convert the job description to init params that can be handled by the class constructor.
Args:
job_details: The returned job details from a describe_training_job
API call.
model_channel_name (str): Name of the channel where pre-trained
model data will be downloaded.
Returns:
dictionary: The transformed init_params
"""
init_params = super(HuggingFace, cls)._prepare_init_params_from_job_description(
job_details, model_channel_name
)
image_uri = init_params.pop("image_uri")
framework, py_version, tag, _ = framework_name_from_image(image_uri)
if tag is None:
framework_version = None
else:
framework, pt_or_tf = framework.split("-")[:2]
tag_pattern = re.compile(r"^(.*)-transformers(.*)-(cpu|gpu)-(py2|py3\d*)$")
tag_match = tag_pattern.match(tag)
pt_or_tf_version = tag_match.group(1)
framework_version = tag_match.group(2)
if pt_or_tf == "pytorch":
init_params["pytorch_version"] = pt_or_tf_version
else:
init_params["tensorflow_version"] = pt_or_tf_version
init_params["transformers_version"] = framework_version
init_params["py_version"] = py_version
if not framework:
# If we were unable to parse the framework name from the image it is not one of our
# officially supported images, in this case just add the image to the init params.
init_params["image_uri"] = image_uri
return init_params
if framework != cls._framework_name:
raise ValueError(
"Training job: {} didn't use image for requested framework".format(
job_details["TrainingJobName"]
)
)
return init_params
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