#
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# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License 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.
import abc
import collections
import logging
import os
import tempfile
import uuid
from typing import Any
from typing import Dict
from typing import Generic
from typing import List
from typing import Mapping
from typing import Optional
from typing import Sequence
from typing import Tuple
from typing import TypeVar
from typing import Union
import jsonpickle
import numpy as np
import apache_beam as beam
from apache_beam.io.filesystems import FileSystems
from apache_beam.metrics.metric import Metrics
from apache_beam.ml.inference.base import ModelHandler
from apache_beam.ml.inference.base import ModelT
from apache_beam.ml.inference.base import RunInferenceDLQ
from apache_beam.options.pipeline_options import PipelineOptions
_LOGGER = logging.getLogger(__name__)
_ATTRIBUTE_FILE_NAME = 'attributes.json'
__all__ = [
'MLTransform',
'ProcessHandler',
'MLTransformProvider',
'BaseOperation',
'EmbeddingsManager'
]
TransformedDatasetT = TypeVar('TransformedDatasetT')
TransformedMetadataT = TypeVar('TransformedMetadataT')
# Input/Output types to the MLTransform.
MLTransformOutputT = TypeVar('MLTransformOutputT')
ExampleT = TypeVar('ExampleT')
# Input to the apply() method of BaseOperation.
OperationInputT = TypeVar('OperationInputT')
# Output of the apply() method of BaseOperation.
OperationOutputT = TypeVar('OperationOutputT')
def _convert_list_of_dicts_to_dict_of_lists(
list_of_dicts: Sequence[Dict[str, Any]]) -> Dict[str, List[Any]]:
keys_to_element_list = collections.defaultdict(list)
input_keys = list_of_dicts[0].keys()
for d in list_of_dicts:
if set(d.keys()) != set(input_keys):
extra_keys = set(d.keys()) - set(input_keys) if len(
d.keys()) > len(input_keys) else set(input_keys) - set(d.keys())
raise RuntimeError(
f'All the dicts in the input data should have the same keys. '
f'Got: {extra_keys} instead.')
for key, value in d.items():
keys_to_element_list[key].append(value)
return keys_to_element_list
def _convert_dict_of_lists_to_lists_of_dict(
dict_of_lists: Dict[str, List[Any]]) -> List[Dict[str, Any]]:
batch_length = len(next(iter(dict_of_lists.values())))
result: List[Dict[str, Any]] = [{} for _ in range(batch_length)]
# all the values in the dict_of_lists should have same length
for key, values in dict_of_lists.items():
assert len(values) == batch_length, (
"This function expects all the values "
"in the dict_of_lists to have same length."
)
for i in range(len(values)):
result[i][key] = values[i]
return result
def _map_errors_to_beam_row(element, cls_name=None):
row_elements = {
'element': element[0],
'msg': str(element[1][1]),
'stack': str(element[1][2]),
}
if cls_name is not None:
row_elements['transform_name'] = cls_name
return beam.Row(**row_elements)
class ArtifactMode(object):
PRODUCE = 'produce'
CONSUME = 'consume'
[docs]class BaseOperation(Generic[OperationInputT, OperationOutputT],
MLTransformProvider,
abc.ABC):
def __init__(self, columns: List[str]) -> None:
"""
Base Opertation class data processing transformations.
Args:
columns: List of column names to apply the transformation.
"""
self.columns = columns
def __call__(self, data: OperationInputT,
output_column_name: str) -> Dict[str, OperationOutputT]:
"""
This method is called when the instance of the class is called.
This method will invoke the apply() method of the class.
"""
transformed_data = self.apply_transform(data, output_column_name)
return transformed_data
[docs]class ProcessHandler(
beam.PTransform[beam.PCollection[ExampleT],
Union[beam.PCollection[MLTransformOutputT],
Tuple[beam.PCollection[MLTransformOutputT],
beam.PCollection[beam.Row]]]],
abc.ABC):
"""
Only for internal use. No backwards compatibility guarantees.
"""
# TODO:https://github.com/apache/beam/issues/29356
# Add support for inference_fn
[docs]class EmbeddingsManager(MLTransformProvider):
def __init__(
self,
columns: List[str],
*,
# common args for all ModelHandlers.
load_model_args: Optional[Dict[str, Any]] = None,
min_batch_size: Optional[int] = None,
max_batch_size: Optional[int] = None,
large_model: bool = False,
**kwargs):
self.load_model_args = load_model_args or {}
self.min_batch_size = min_batch_size
self.max_batch_size = max_batch_size
self.large_model = large_model
self.columns = columns
self.inference_args = kwargs.pop('inference_args', {})
if kwargs:
_LOGGER.warning("Ignoring the following arguments: %s", kwargs.keys())
# TODO:https://github.com/apache/beam/pull/29564 add set_model_handler method
[docs] @abc.abstractmethod
def get_model_handler(self) -> ModelHandler:
"""
Return framework specific model handler.
"""
[docs] def get_columns_to_apply(self):
return self.columns
class MLTransformMetricsUsage(beam.PTransform):
def __init__(self, ml_transform: MLTransform):
self._ml_transform = ml_transform
self._ml_transform._counter.inc()
def expand(self, pipeline):
def _increment_counters():
# increment for MLTransform.
self._ml_transform._counter.inc()
# increment if data processing transforms are passed.
transforms = self._ml_transform.transforms
if transforms:
for transform in transforms:
transform.get_counter().inc()
_ = (
pipeline
| beam.Create([None])
| beam.Map(lambda _: _increment_counters()))
class _TransformAttributeManager:
"""
Base class used for saving and loading the attributes.
"""
@staticmethod
def save_attributes(artifact_location):
"""
Save the attributes to json file using stdlib json.
"""
raise NotImplementedError
@staticmethod
def load_attributes(artifact_location):
"""
Load the attributes from json file.
"""
raise NotImplementedError
class _JsonPickleTransformAttributeManager(_TransformAttributeManager):
"""
Use Jsonpickle to save and load the attributes. Here the attributes refer
to the list of PTransforms that are used to process the data.
jsonpickle is used to serialize the PTransforms and save it to a json file and
is compatible across python versions.
"""
@staticmethod
def _is_remote_path(path):
is_gcs = path.find('gs://') != -1
# TODO:https://github.com/apache/beam/issues/29356
# Add support for other remote paths.
if not is_gcs and path.find('://') != -1:
raise RuntimeError(
"Artifact locations are currently supported for only available for "
"local paths and GCS paths. Got: %s" % path)
return is_gcs
@staticmethod
def save_attributes(
ptransform_list,
artifact_location,
**kwargs,
):
# if an artifact location is present, instead of overwriting the
# existing file, raise an error since the same artifact location
# can be used by multiple beam jobs and this could result in undesired
# behavior.
if FileSystems.exists(FileSystems.join(artifact_location,
_ATTRIBUTE_FILE_NAME)):
raise FileExistsError(
"The artifact location %s already exists and contains %s. Please "
"specify a different location." %
(artifact_location, _ATTRIBUTE_FILE_NAME))
if _JsonPickleTransformAttributeManager._is_remote_path(artifact_location):
temp_dir = tempfile.mkdtemp()
temp_json_file = os.path.join(temp_dir, _ATTRIBUTE_FILE_NAME)
with open(temp_json_file, 'w+') as f:
f.write(jsonpickle.encode(ptransform_list))
with open(temp_json_file, 'rb') as f:
from apache_beam.runners.dataflow.internal import apiclient
_LOGGER.info('Creating artifact location: %s', artifact_location)
# pipeline options required to for the client to configure project.
options = kwargs.get('options')
try:
apiclient.DataflowApplicationClient(options=options).stage_file(
gcs_or_local_path=artifact_location,
file_name=_ATTRIBUTE_FILE_NAME,
stream=f,
mime_type='application/json')
except Exception as exc:
if not options:
raise RuntimeError(
"Failed to create Dataflow client. "
"Pipeline options are required to save the attributes."
"in the artifact location %s" % artifact_location) from exc
raise
else:
if not FileSystems.exists(artifact_location):
FileSystems.mkdirs(artifact_location)
# FileSystems.open() fails if the file does not exist.
with open(os.path.join(artifact_location, _ATTRIBUTE_FILE_NAME),
'w+') as f:
f.write(jsonpickle.encode(ptransform_list))
@staticmethod
def load_attributes(artifact_location):
with FileSystems.open(os.path.join(artifact_location, _ATTRIBUTE_FILE_NAME),
'rb') as f:
return jsonpickle.decode(f.read())
_transform_attribute_manager = _JsonPickleTransformAttributeManager
class _MLTransformToPTransformMapper:
"""
This class takes in a list of data processing transforms compatible to be
wrapped around MLTransform and returns a list of PTransforms that are used to
run the data processing transforms.
The _MLTransformToPTransformMapper is responsible for loading and saving the
PTransforms or attributes of PTransforms to the artifact location to seal
the gap between the training and inference pipelines.
"""
def __init__(
self,
transforms: List[MLTransformProvider],
artifact_location: str,
artifact_mode: str = ArtifactMode.PRODUCE,
pipeline_options: Optional[PipelineOptions] = None,
):
self.transforms = transforms
self._parent_artifact_location = artifact_location
self.artifact_mode = artifact_mode
self.pipeline_options = pipeline_options
def create_and_save_ptransform_list(self):
ptransform_list = self.create_ptransform_list()
self.save_transforms_in_artifact_location(ptransform_list)
return ptransform_list
def create_ptransform_list(self):
previous_ptransform_type = None
current_ptransform = None
ptransform_list = []
for transform in self.transforms:
if not isinstance(transform, MLTransformProvider):
raise RuntimeError(
'Transforms must be instances of MLTransformProvider and '
'implement get_ptransform_for_processing() method.')
# for each instance of PTransform, create a new artifact location
current_ptransform = transform.get_ptransform_for_processing(
artifact_location=os.path.join(
self._parent_artifact_location, uuid.uuid4().hex[:6]),
artifact_mode=self.artifact_mode)
append_transform = hasattr(current_ptransform, 'append_transform')
if (type(current_ptransform) !=
previous_ptransform_type) or not append_transform:
ptransform_list.append(current_ptransform)
previous_ptransform_type = type(current_ptransform)
# If different PTransform is appended to the list and the PTransform
# supports append_transform, append the transform to the PTransform.
if append_transform:
ptransform_list[-1].append_transform(transform)
return ptransform_list
def save_transforms_in_artifact_location(self, ptransform_list):
"""
Save the ptransform references to json file.
"""
_transform_attribute_manager.save_attributes(
ptransform_list=ptransform_list,
artifact_location=self._parent_artifact_location,
options=self.pipeline_options)
@staticmethod
def load_transforms_from_artifact_location(artifact_location):
return _transform_attribute_manager.load_attributes(artifact_location)
class _EmbeddingHandler(ModelHandler):
"""
A ModelHandler intended to be work on list[dict[str, Any]] inputs.
The inputs to the model handler are expected to be a list of dicts.
For example, if the original mode is used with RunInference to take a
PCollection[E] to a PCollection[P], this ModelHandler would take a
PCollection[Dict[str, E]] to a PCollection[Dict[str, P]].
_EmbeddingHandler will accept an EmbeddingsManager instance, which
contains the details of the model to be loaded and the inference_fn to be
used. The purpose of _EmbeddingHandler is to generate embeddings for
general inputs using the EmbeddingsManager instance.
This is an internal class and offers no backwards compatibility guarantees.
Args:
embeddings_manager: An EmbeddingsManager instance.
"""
def __init__(self, embeddings_manager: EmbeddingsManager):
self.embedding_config = embeddings_manager
self._underlying = self.embedding_config.get_model_handler()
self.columns = self.embedding_config.get_columns_to_apply()
def load_model(self):
model = self._underlying.load_model()
return model
def _validate_column_data(self, batch):
pass
def _validate_batch(self, batch: Sequence[Dict[str, Any]]):
if not batch or not isinstance(batch[0], dict):
raise TypeError(
'Expected data to be dicts, got '
f'{type(batch[0])} instead.')
def _process_batch(
self,
dict_batch: Dict[str, List[Any]],
model: ModelT,
inference_args: Optional[Dict[str, Any]]) -> Dict[str, List[Any]]:
result: Dict[str, List[Any]] = collections.defaultdict(list)
input_keys = dict_batch.keys()
missing_columns_in_data = set(self.columns) - set(input_keys)
if missing_columns_in_data:
raise RuntimeError(
f'Data does not contain the following columns '
f': {missing_columns_in_data}.')
for key, batch in dict_batch.items():
if key in self.columns:
self._validate_column_data(batch)
prediction = self._underlying.run_inference(
batch, model, inference_args)
if isinstance(prediction, np.ndarray):
prediction = prediction.tolist()
result[key] = prediction # type: ignore[assignment]
else:
result[key] = prediction # type: ignore[assignment]
else:
result[key] = batch
return result
def run_inference(
self,
batch: Sequence[Dict[str, List[str]]],
model: ModelT,
inference_args: Optional[Dict[str, Any]] = None,
) -> List[Dict[str, Union[List[float], List[str]]]]:
"""
Runs inference on a batch of text inputs. The inputs are expected to be
a list of dicts. Each dict should have the same keys, and the shape
should be of the same size for a single key across the batch.
"""
self._validate_batch(batch)
dict_batch = _convert_list_of_dicts_to_dict_of_lists(list_of_dicts=batch)
transformed_batch = self._process_batch(dict_batch, model, inference_args)
return _convert_dict_of_lists_to_lists_of_dict(
dict_of_lists=transformed_batch,
)
def get_metrics_namespace(self) -> str:
return (
self._underlying.get_metrics_namespace() or 'BeamML_EmbeddingHandler')
def batch_elements_kwargs(self) -> Mapping[str, Any]:
batch_sizes_map = {}
if self.embedding_config.max_batch_size:
batch_sizes_map['max_batch_size'] = self.embedding_config.max_batch_size
if self.embedding_config.min_batch_size:
batch_sizes_map['min_batch_size'] = self.embedding_config.min_batch_size
return (self._underlying.batch_elements_kwargs() or batch_sizes_map)
def __repr__(self):
return self._underlying.__repr__()
def validate_inference_args(self, _):
pass
class _TextEmbeddingHandler(_EmbeddingHandler):
"""
A ModelHandler intended to be work on list[dict[str, str]] inputs.
The inputs to the model handler are expected to be a list of dicts.
For example, if the original mode is used with RunInference to take a
PCollection[E] to a PCollection[P], this ModelHandler would take a
PCollection[Dict[str, E]] to a PCollection[Dict[str, P]].
_TextEmbeddingHandler will accept an EmbeddingsManager instance, which
contains the details of the model to be loaded and the inference_fn to be
used. The purpose of _TextEmbeddingHandler is to generate embeddings for
text inputs using the EmbeddingsManager instance.
If the input is not a text column, a RuntimeError will be raised.
This is an internal class and offers no backwards compatibility guarantees.
Args:
embeddings_manager: An EmbeddingsManager instance.
"""
def _validate_column_data(self, batch):
if not isinstance(batch[0], (str, bytes)):
raise TypeError(
'Embeddings can only be generated on Dict[str, str].'
f'Got Dict[str, {type(batch[0])}] instead.')
def get_metrics_namespace(self) -> str:
return (
self._underlying.get_metrics_namespace() or
'BeamML_TextEmbeddingHandler')
class _ImageEmbeddingHandler(_EmbeddingHandler):
"""
A ModelHandler intended to be work on list[dict[str, Image]] inputs.
The inputs to the model handler are expected to be a list of dicts.
For example, if the original mode is used with RunInference to take a
PCollection[E] to a PCollection[P], this ModelHandler would take a
PCollection[Dict[str, E]] to a PCollection[Dict[str, P]].
_ImageEmbeddingHandler will accept an EmbeddingsManager instance, which
contains the details of the model to be loaded and the inference_fn to be
used. The purpose of _ImageEmbeddingHandler is to generate embeddings for
image inputs using the EmbeddingsManager instance.
If the input is not an Image representation column, a RuntimeError will be
raised.
This is an internal class and offers no backwards compatibility guarantees.
Args:
embeddings_manager: An EmbeddingsManager instance.
"""
def _validate_column_data(self, batch):
# Don't want to require framework-specific imports
# here, so just catch columns of primatives for now.
if isinstance(batch[0], (int, str, float, bool)):
raise TypeError(
'Embeddings can only be generated on Dict[str, Image].'
f'Got Dict[str, {type(batch[0])}] instead.')
def get_metrics_namespace(self) -> str:
return (
self._underlying.get_metrics_namespace() or
'BeamML_ImageEmbeddingHandler')