399 lines
15 KiB
Python
399 lines
15 KiB
Python
# Copyright 2021 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Dict, NamedTuple, Optional, Union
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from absl import logging
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import fire
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from google.protobuf import json_format
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import os
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from kfp.containers import _gcs_helper
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from kfp.containers import entrypoint_utils
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from kfp.dsl import artifact
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from kfp.pipeline_spec import pipeline_spec_pb2
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FN_SOURCE = 'ml/main.py'
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FN_NAME_ARG = 'function_name'
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PARAM_METADATA_SUFFIX = '_input_param_metadata_file'
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ARTIFACT_METADATA_SUFFIX = '_input_artifact_metadata_file'
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FIELD_NAME_SUFFIX = '_input_field_name'
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ARGO_PARAM_SUFFIX = '_input_argo_param'
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INPUT_URI_SUFFIX = '_input_uri'
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PRODUCER_POD_ID_SUFFIX = '_pod_id'
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OUTPUT_NAME_SUFFIX = '_input_output_name'
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OUTPUT_PARAM_PATH_SUFFIX = '_parameter_output_path'
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OUTPUT_ARTIFACT_PATH_SUFFIX = '_artifact_output_uri'
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METADATA_FILE_ARG = 'executor_metadata_json_file'
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class InputParam(object):
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"""POD that holds an input parameter."""
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def __init__(self,
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value: Optional[Union[str, float, int]] = None,
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metadata_file: Optional[str] = None,
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field_name: Optional[str] = None):
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"""Instantiates an InputParam object.
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Args:
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value: The actual value of the parameter.
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metadata_file: The location of the metadata JSON file output by the
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producer step.
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field_name: The output name of the producer.
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Raises:
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ValueError: when neither of the following is true:
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1) value is provided, and metadata_file and field_name are not; or
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2) both metadata_file and field_name are provided, and value is not.
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"""
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if not (value is not None and not (metadata_file or field_name) or (
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metadata_file and field_name and value is None)):
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raise ValueError('Either value or both metadata_file and field_name '
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'needs to be provided. Got value={value}, field_name='
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'{field_name}, metadata_file={metadata_file}'.format(
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value=value,
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field_name=field_name,
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metadata_file=metadata_file
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))
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if value is not None:
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self._value = value
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else:
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# Parse the value by inspecting the producer's metadata JSON file.
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self._value = entrypoint_utils.get_parameter_from_output(
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metadata_file, field_name)
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self._metadata_file = metadata_file
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self._field_name = field_name
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# Following properties are read-only
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@property
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def value(self) -> Union[float, str, int]:
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return self._value
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@property
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def metadata_file(self) -> str:
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return self._metadata_file
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@property
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def field_name(self) -> str:
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return self._field_name
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class InputArtifact(object):
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"""POD that holds an input artifact."""
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def __init__(self,
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uri: Optional[str] = None,
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metadata_file: Optional[str] = None,
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output_name: Optional[str] = None
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):
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"""Instantiates an InputParam object.
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Args:
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uri: The uri holds the input artifact.
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metadata_file: The location of the metadata JSON file output by the
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producer step.
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output_name: The output name of the artifact in producer step.
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Raises:
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ValueError: when neither of the following is true:
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1) uri is provided, and metadata_file and output_name are not; or
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2) both metadata_file and output_name are provided, and uri is not.
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"""
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if not ((uri and not (metadata_file or output_name) or (
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metadata_file and output_name and not uri))):
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raise ValueError('Either uri or both metadata_file and output_name '
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'needs to be provided. Got uri={uri}, output_name='
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'{output_name}, metadata_file={metadata_file}'.format(
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uri=uri,
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output_name=output_name,
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metadata_file=metadata_file
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))
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self._metadata_file = metadata_file
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self._output_name = output_name
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if uri:
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self._uri = uri
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else:
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self._uri = self.get_artifact().uri
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# Following properties are read-only.
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@property
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def uri(self) -> str:
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return self._uri
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@property
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def metadata_file(self) -> str:
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return self._metadata_file
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@property
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def output_name(self) -> str:
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return self._output_name
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def get_artifact(self) -> artifact.Artifact:
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"""Gets an artifact object by parsing metadata or creating one from uri."""
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if self.metadata_file and self.output_name:
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return entrypoint_utils.get_artifact_from_output(
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self.metadata_file, self.output_name)
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else:
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# Provide an empty schema when returning a raw Artifact.
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result = artifact.Artifact(
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instance_schema=artifact.DEFAULT_ARTIFACT_SCHEMA)
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result.uri = self.uri
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return result
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def _write_output_metadata_file(
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fn_res: Union[int, str, float, NamedTuple],
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output_artifacts: Dict[str, artifact.Artifact],
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output_metadata_path: str
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):
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"""Writes the output metadata file to the designated place."""
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# If output_params is a singleton value, needs to transform it to a mapping.
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output_parameters = {}
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if isinstance(fn_res, (int, str, float)):
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output_parameters['output'] = fn_res
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else:
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# When multiple outputs, we'll need to match each field to the output paths.
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for idx, output_name in enumerate(fn_res._fields):
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output_parameters[output_name] = fn_res[idx]
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executor_output = entrypoint_utils.get_executor_output(
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output_artifacts=output_artifacts,
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output_params=output_parameters)
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with open(output_metadata_path, 'w') as f:
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f.write(json_format.MessageToJson(executor_output))
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return executor_output
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def main(
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executor_input_str: str,
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function_name: str,
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output_metadata_path: Optional[str] = None):
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"""Container entrypoint used by KFP Python function based component
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executor_input_str: A serialized ExecutorInput proto message.
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function_name: The name of the user-defined function.
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output_metadata_path: A local path where the output metadata JSON file should
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be written to.
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"""
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executor_input = pipeline_spec_pb2.ExecutorInput()
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json_format.Parse(text=executor_input_str, message=executor_input)
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output_metadata_path = output_metadata_path or executor_input.outputs.output_file
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parameter_dict = {} # kwargs to be passed to UDF.
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for name, input_param in executor_input.inputs.parameters.items():
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parameter_dict[name] = entrypoint_utils.get_python_value(input_param)
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for name, input_artifacts in executor_input.inputs.artifacts.items():
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parameter_dict[name] = artifact.Artifact.get_from_runtime_artifact(
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input_artifacts.artifacts[0])
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# Also, determine a way to inspect the function signature to decide the type
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# of output artifacts.
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fn = entrypoint_utils.import_func_from_source(FN_SOURCE, function_name)
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# In the ExeuctorInput message passed into the entrypoint, the output artifact
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# URIs are already specified. The output artifact is constructed according to
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# the specified URIs + type information retrieved from function signature.
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output_uris = {}
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for name, output_artifacts in executor_input.outputs.artifacts.items():
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output_uris[name] = output_artifacts.artifacts[0].uri
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output_artifacts = entrypoint_utils.get_output_artifacts(
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fn, output_uris)
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for name, art in output_artifacts.items():
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parameter_dict[name] = art
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# Execute the user function. fn_res is expected to contain output parameters
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# only. It's either an namedtuple or a single primitive value.
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fn_res = fn(**parameter_dict)
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_write_output_metadata_file(
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fn_res=fn_res,
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output_artifacts=output_artifacts,
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output_metadata_path=output_metadata_path)
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def main_2(**kwargs):
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"""Container entrypoint used by KFP Python function based component.
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This function has a dynamic signature, which will be interpreted according to
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the I/O and data-passing contract of KFP Python function components. The
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parameter will be received from command line interface.
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For each declared parameter input of the user function, three command line
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arguments will be recognized:
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1. {name of the parameter}_input_param_metadata_file: The metadata JSON file
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path output by the producer.
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2. {name of the parameter}_input_field_name: The output name of the parameter,
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by which the parameter can be found in the producer metadata JSON file.
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3. {name of the parameter}_input_argo_param: The actual runtime value of the
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input parameter.
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When the producer is a new-styled KFP Python component, 1 and 2 will be
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populated, and when it's a conventional KFP Python component, 3 will be in
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use.
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For each declared artifact input of the user function, three command line args
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will be recognized:
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1. {name of the artifact}_input_path: The actual path, or uri, of the input
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artifact.
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2. {name of the artifact}_input_artifact_metadata_file: The metadata JSON file
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path output by the producer.
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3. {name of the artifact}_input_output_name: The output name of the artifact,
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by which the artifact can be found in the producer metadata JSON file.
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If the producer is a new-styled KFP Python component, 2+3 will be used to give
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user code access to MLMD (custom) properties associated with this artifact;
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if the producer is a conventional KFP Python component, 1 will be used to
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construct an Artifact with only the URI populated.
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For each declared artifact or parameter output of the user function, a command
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line arg, namely, `{name of the artifact|parameter}_(artifact|parameter)_output_path`,
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will be passed to specify the location where the output content is written to.
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In addition, `executor_metadata_json_file` specifies the location where the
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output metadata JSON file will be written.
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"""
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if METADATA_FILE_ARG not in kwargs:
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raise RuntimeError('Must specify executor_metadata_json_file')
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# Group arguments according to suffixes.
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input_params_metadata = {}
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input_params_field_name = {}
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input_params_value = {}
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input_artifacts_metadata = {}
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input_artifacts_uri = {}
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input_artifacts_output_name = {}
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output_artifacts_uri = {}
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output_params_path = {}
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for k, v in kwargs.items():
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if k.endswith(PARAM_METADATA_SUFFIX):
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param_name = k[:-len(PARAM_METADATA_SUFFIX)]
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input_params_metadata[param_name] = v
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elif k.endswith(FIELD_NAME_SUFFIX):
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param_name = k[:-len(FIELD_NAME_SUFFIX)]
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input_params_field_name[param_name] = v
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elif k.endswith(ARGO_PARAM_SUFFIX):
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param_name = k[:-len(ARGO_PARAM_SUFFIX)]
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input_params_value[param_name] = v
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elif k.endswith(ARTIFACT_METADATA_SUFFIX):
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artifact_name = k[:-len(ARTIFACT_METADATA_SUFFIX)]
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input_artifacts_metadata[artifact_name] = v
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elif k.endswith(INPUT_URI_SUFFIX):
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artifact_name = k[:-len(INPUT_URI_SUFFIX)]
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input_artifacts_uri[artifact_name] = v
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elif k.endswith(OUTPUT_NAME_SUFFIX):
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artifact_name = k[:-len(OUTPUT_NAME_SUFFIX)]
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input_artifacts_output_name[artifact_name] = v
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elif k.endswith(OUTPUT_PARAM_PATH_SUFFIX):
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param_name = k[:-len(OUTPUT_PARAM_PATH_SUFFIX)]
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output_params_path[param_name] = v
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elif k.endswith(OUTPUT_ARTIFACT_PATH_SUFFIX):
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artifact_name = k[:-len(OUTPUT_ARTIFACT_PATH_SUFFIX)]
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output_artifacts_uri[artifact_name] = v
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elif k not in (METADATA_FILE_ARG, FN_NAME_ARG):
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logging.warning(
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'Got unexpected command line argument: %s=%s Ignoring', k, v)
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# Instantiate POD objects.
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input_params = {}
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for param_name in (
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input_params_value.keys() |
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input_params_field_name.keys() | input_params_metadata.keys()):
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input_param = InputParam(
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value=input_params_value.get(param_name),
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metadata_file=input_params_metadata.get(param_name),
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field_name=input_params_field_name.get(param_name))
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input_params[param_name] = input_param
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input_artifacts = {}
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for artifact_name in (
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input_artifacts_uri.keys() |
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input_artifacts_metadata.keys() |
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input_artifacts_output_name.keys()
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):
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input_artifact = InputArtifact(
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uri=input_artifacts_uri.get(artifact_name),
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metadata_file=input_artifacts_metadata.get(artifact_name),
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output_name=input_artifacts_output_name.get(artifact_name))
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input_artifacts[artifact_name] = input_artifact
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# Import and invoke the user-provided function.
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# Currently the actual user code is built into container as /ml/main.py
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# which is specified in
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# kfp.containers._component_builder.build_python_component.
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# Also, determine a way to inspect the function signature to decide the type
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# of output artifacts.
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fn_name = kwargs[FN_NAME_ARG]
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fn = entrypoint_utils.import_func_from_source(FN_SOURCE, fn_name)
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# Get the output artifacts and combine them with the provided URIs.
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output_artifacts = entrypoint_utils.get_output_artifacts(
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fn, output_artifacts_uri)
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invoking_kwargs = {}
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for k, v in output_artifacts.items():
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invoking_kwargs[k] = v
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for k, v in input_params.items():
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invoking_kwargs[k] = v.value
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for k, v in input_artifacts.items():
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invoking_kwargs[k] = v.get_artifact()
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# Execute the user function. fn_res is expected to contain output parameters
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# only. It's either an namedtuple or a single primitive value.
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fn_res = fn(**invoking_kwargs)
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if isinstance(fn_res, (int, float, str)) and len(output_params_path) != 1:
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raise RuntimeError('For primitive output a single output param path is '
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'expected. Got %s' % output_params_path)
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if isinstance(fn_res, (int, float, str)):
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output_name = list(output_params_path.keys())[0]
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# Write the output to the provided path.
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_gcs_helper.GCSHelper.write_to_gcs_path(
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path=output_params_path[output_name],
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content=str(fn_res))
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else:
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# When multiple outputs, we'll need to match each field to the output paths.
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for idx, output_name in enumerate(fn_res._fields):
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path = output_params_path[output_name]
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_gcs_helper.GCSHelper.write_to_gcs_path(
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path=path,
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content=str(fn_res[idx]))
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# Write output metadata JSON file.
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output_parameters = {}
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if isinstance(fn_res, (int, float, str)):
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output_parameters['output'] = fn_res
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else:
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for idx, output_name in enumerate(fn_res._fields):
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output_parameters[output_name] = fn_res[idx]
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executor_output = entrypoint_utils.get_executor_output(
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output_artifacts=output_artifacts,
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output_params=output_parameters)
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_gcs_helper.GCSHelper.write_to_gcs_path(
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path=kwargs[METADATA_FILE_ARG],
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content=json_format.MessageToJson(executor_output))
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if __name__ == '__main__':
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fire.Fire(main) |