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feat(components): Use a single inference component for AutoSxS
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...google-cloud/google_cloud_pipeline_components/_implementation/llm/batch_prediction_sxs.py
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| # Copyright 2024 The Kubeflow Authors. All Rights Reserved. | ||
| # | ||
| # Licensed 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. | ||
| """Component for running LLM Batch Prediction jobs side-by-side.""" | ||
|
|
||
| import os | ||
| from typing import Any, Dict, List | ||
|
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||
| from google_cloud_pipeline_components import _placeholders | ||
| from google_cloud_pipeline_components import utils as gcpc_utils | ||
| from google_cloud_pipeline_components._implementation.llm import utils | ||
| from kfp import dsl | ||
|
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| def _resolve_image() -> str: | ||
| """Determines the image URI to create a container from.""" | ||
| return os.environ.get( | ||
| 'AUTOSXS_IMAGE_OVERRIDE' | ||
| ) or utils.get_default_image_uri('autosxs') | ||
|
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| # pylint: disable=unused-argument,dangerous-default-value | ||
| @dsl.container_component | ||
| def batch_prediction_sxs( | ||
| display_name: str, | ||
| evaluation_dataset: str, | ||
| id_columns: List[str], | ||
| task: str, | ||
| autorater_prompt_parameters: Dict[str, Dict[str, str]], | ||
| response_column_a: str, | ||
| response_column_b: str, | ||
| preprocessed_evaluation_dataset: dsl.Output[dsl.Dataset], # pylint: disable=unused-argument # pytype: disable=unsupported-operands | ||
| preprocessed_evaluation_dataset_uri: dsl.OutputPath(str), # pylint: disable=unused-argument # pytype: disable=invalid-annotation | ||
| gcp_resources: dsl.OutputPath(str), # pytype: disable=invalid-annotation | ||
| metadata: dsl.OutputPath(Dict[str, Any]), # pytype: disable=invalid-annotation | ||
| model_a: str = '', | ||
| model_b: str = '', | ||
| model_a_prompt_parameters: Dict[str, Dict[str, str]] = {}, | ||
| model_b_prompt_parameters: Dict[str, Dict[str, str]] = {}, | ||
| model_a_parameters: Dict[str, str] = {}, | ||
| model_b_parameters: Dict[str, str] = {}, | ||
| human_preference_column: str = '', | ||
| ) -> dsl.ContainerSpec: # pylint: disable=g-doc-args | ||
| """Runs up to two LLM Batch Prediction jobs side-by-side. | ||
| Args: | ||
| display_name: Display name for the batch prediction job. | ||
| evaluation_dataset: GCS or BigQuery URIs representing a dataset of prompts | ||
| and responses. | ||
| id_columns: The columns which distinguish unique evaluation examples. | ||
| task: Task to evaluate. | ||
| autorater_prompt_parameters: Map of autorater prompt template parameters to | ||
| columns or templates. | ||
| response_column_a: The column containing responses for model a. | ||
| response_column_b: The column containing responses for model b. | ||
| model_a: A fully-qualified model resource name | ||
| (`projects/{project}/locations/{location}/models/{model}@{version}`) or | ||
| publisher model resource name (`publishers/{publisher}/models/{model}`). | ||
| This parameter is optional if Model A responses are specified. | ||
| model_b: A fully-qualified model resource name | ||
| (`projects/{project}/locations/{location}/models/{model}@{version}`) or | ||
| publisher model resource name (`publishers/{publisher}/models/{model}`). | ||
| This parameter is optional if Model B responses are specified. | ||
| model_a_prompt_parameters: Map of model A prompt template parameters to | ||
| columns or templates. | ||
| model_b_prompt_parameters: Map of model B prompt template parameters to | ||
| columns or templates. | ||
| model_a_parameters: The parameters that govern the predictions from model A, | ||
| such as temperature or maximum output tokens. | ||
| model_b_parameters: The parameters that govern the predictions from model B, | ||
| such as temperature or maximum output tokens. | ||
| human_preference_column: The column containing ground truths. The default | ||
| value is an empty string if not be provided by users. | ||
| Returns: | ||
| preprocessed_evaluation_dataset: Dataset of the table containing the inputs | ||
| expected by the Arbiter. | ||
| preprocessed_evaluation_dataset_uri: URI of the table containing the inputs | ||
| expected by the Arbiter. | ||
| gcp_resources: Tracker for GCP resources created by this component. | ||
| metadata_path: Path to write the object that stores computed metrics | ||
| metadata for the task preprocess component. | ||
| """ | ||
| return gcpc_utils.build_serverless_customjob_container_spec( | ||
| project=_placeholders.PROJECT_ID_PLACEHOLDER, | ||
| location=_placeholders.LOCATION_PLACEHOLDER, | ||
| custom_job_payload=utils.build_payload( | ||
| display_name='batch_prediction_sxs', | ||
| machine_type='n1-standard-4', | ||
| image_uri=_resolve_image(), | ||
| args=[ | ||
| '--', # Used to mark the start of component flags. | ||
| 'batch_prediction_sxs', | ||
| f'--display_name={display_name}', | ||
| f'--evaluation_dataset={evaluation_dataset}', | ||
| ( | ||
| '--id_columns=' | ||
| "{{$.inputs.parameters['id_columns'].json_escape[0]}}" | ||
| ), | ||
| f'--task={task}', | ||
| f'--project={_placeholders.PROJECT_ID_PLACEHOLDER}', | ||
| f'--location={_placeholders.LOCATION_PLACEHOLDER}', | ||
| f'--model_a={model_a}', | ||
| f'--model_b={model_b}', | ||
| ( | ||
| '--model_a_prompt_parameters=' | ||
| "{{$.inputs.parameters['model_a_prompt_parameters']" | ||
| '.json_escape[0]}}' | ||
| ), | ||
| ( | ||
| '--model_b_prompt_parameters=' | ||
| "{{$.inputs.parameters['model_b_prompt_parameters']" | ||
| '.json_escape[0]}}' | ||
| ), | ||
| ( | ||
| '--autorater_prompt_parameters=' | ||
| "{{$.inputs.parameters['autorater_prompt_parameters']" | ||
| '.json_escape[0]}}' | ||
| ), | ||
| f'--response_column_a={response_column_a}', | ||
| f'--response_column_b={response_column_b}', | ||
| ( | ||
| '--model_a_parameters=' | ||
| "{{$.inputs.parameters['model_a_parameters'].json_escape[0]}}" | ||
| ), | ||
| ( | ||
| '--model_b_parameters=' | ||
| "{{$.inputs.parameters['model_b_parameters'].json_escape[0]}}" | ||
| ), | ||
| f'--human_preference_column={human_preference_column}', | ||
| f'--staging_dir={dsl.PIPELINE_ROOT_PLACEHOLDER}', | ||
| f'--preprocessed_evaluation_dataset_uri={preprocessed_evaluation_dataset_uri}', | ||
| f'--metadata_path={metadata}', | ||
| f'--gcp_resources_path={gcp_resources}', | ||
| '--executor_input={{$.json_escape[1]}}', | ||
| ], | ||
| ), | ||
| gcp_resources=gcp_resources, | ||
| ) |
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