eland/bin/eland_import_hub_model

#!/usr/bin/env python

#  Licensed to Elasticsearch B.V. under one or more contributor
#  license agreements. See the NOTICE file distributed with
#  this work for additional information regarding copyright
#  ownership. Elasticsearch B.V. 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.

"""
Copies a model from the Hugging Face model hub into an Elasticsearch cluster.
This will create local cached copies that will be traced (necessary) before
uploading to Elasticsearch. This will also check that the task type is supported
as well as the model and tokenizer types. All necessary configuration is
uploaded along with the model.
"""
import argparse
import logging
import os
import sys
import tempfile
import textwrap

from elastic_transport.client_utils import DEFAULT
from elasticsearch import AuthenticationException, Elasticsearch

MODEL_HUB_URL = "https://huggingface.co"


def get_arg_parser():
    parser = argparse.ArgumentParser()
    location_args = parser.add_mutually_exclusive_group(required=True)
    location_args.add_argument(
        "--url",
        default=os.environ.get("ES_URL"),
        help="An Elasticsearch connection URL, e.g. http://localhost:9200",
    )
    location_args.add_argument(
        "--cloud-id",
        default=os.environ.get("CLOUD_ID"),
        help="Cloud ID as found in the 'Manage Deployment' page of an Elastic Cloud deployment",
    )
    parser.add_argument(
        "--hub-model-id",
        required=True,
        help="The model ID in the Hugging Face model hub, "
             "e.g. dbmdz/bert-large-cased-finetuned-conll03-english",
    )
    parser.add_argument(
        "--es-model-id",
        required=False,
        default=None,
        help="The model ID to use in Elasticsearch, "
             "e.g. bert-large-cased-finetuned-conll03-english."
             "When left unspecified, this will be auto-created from the `hub-id`",
    )
    parser.add_argument(
        "-u", "--es-username",
        required=False,
        default=os.environ.get("ES_USERNAME"),
        help="Username for Elasticsearch"
    )
    parser.add_argument(
        "-p", "--es-password",
        required=False,
        default=os.environ.get("ES_PASSWORD"),
        help="Password for the Elasticsearch user specified with -u/--username"
    )
    parser.add_argument(
        "--es-api-key",
        required=False,
        default=os.environ.get("ES_API_KEY"),
        help="Password for the Elasticsearch user specified with -u/--username"
    )
    parser.add_argument(
        "--task-type",
        required=True,
        choices=SUPPORTED_TASK_TYPES,
        help="The task type for the model usage.",
    )
    parser.add_argument(
        "--quantize",
        action="store_true",
        default=False,
        help="Quantize the model before uploading. Default: False",
    )
    parser.add_argument(
        "--start",
        action="store_true",
        default=False,
        help="Start the model deployment after uploading. Default: False",
    )
    parser.add_argument(
        "--clear-previous",
        action="store_true",
        default=False,
        help="Should the model previously stored with `es-model-id` be deleted"
    )
    parser.add_argument(
        "--insecure",
        action="store_false",
        default=True,
        help="Do not verify SSL certificates"
    )
    parser.add_argument(
        "--ca-certs",
        required=False,
        default=DEFAULT,
        help="Path to CA bundle"
    )

    return parser


def get_es_client(cli_args):
    try:
        es_args = {
            'request_timeout': 300,
            'verify_certs': cli_args.insecure,
            'ca_certs': cli_args.ca_certs
        }

        # Deployment location
        if cli_args.url:
            es_args['hosts'] = cli_args.url

        if cli_args.cloud_id:
            es_args['cloud_id'] = cli_args.cloud_id

        # Authentication
        if cli_args.es_api_key:
            es_args['api_key'] = cli_args.es_api_key
        elif cli_args.es_username:
            if not cli_args.es_password:
                logging.error(f"Password for user {cli_args.es_username} was not specified.")
                exit(1)

            es_args['basic_auth'] = (cli_args.es_username, cli_args.es_password)

        es_client = Elasticsearch(**es_args)
        es_info = es_client.info()
        logger.info(f"Connected to cluster named '{es_info['cluster_name']}' (version: {es_info['version']['number']})")

        return es_client
    except AuthenticationException as e:
        logger.error(e)
        exit(1)


if __name__ == "__main__":
    # Configure logging
    logging.basicConfig(format='%(asctime)s %(levelname)s : %(message)s')
    logger = logging.getLogger(__name__)
    logger.setLevel(logging.INFO)

    try:
        from eland.ml.pytorch import PyTorchModel
        from eland.ml.pytorch.transformers import SUPPORTED_TASK_TYPES, TransformerModel
    except ModuleNotFoundError as e:
        logger.error(textwrap.dedent(f"""\
            \033[31mFailed to run because module '{e.name}' is not available.\033[0m
            
            This script requires PyTorch extras to run. You can install these by running:
            
                \033[1m{sys.executable} -m pip install 'eland[pytorch]'
            \033[0m"""))
        exit(1)

    # Parse arguments
    args = get_arg_parser().parse_args()

    # Connect to ES
    logger.info("Establishing connection to Elasticsearch")
    es = get_es_client(args)

    # Trace and save model, then upload it from temp file
    with tempfile.TemporaryDirectory() as tmp_dir:
        logger.info(f"Loading HuggingFace transformer tokenizer and model '{args.hub_model_id}'")

        tm = TransformerModel(args.hub_model_id, args.task_type, args.quantize)
        model_path, config_path, vocab_path = tm.save(tmp_dir)

        ptm = PyTorchModel(es, args.es_model_id if args.es_model_id else tm.elasticsearch_model_id())
        model_exists = es.options(ignore_status=404).ml.get_trained_models(model_id=ptm.model_id).meta.status == 200

        if model_exists:
            if args.clear_previous:
                logger.info(f"Stopping deployment for model with id '{ptm.model_id}'")
                ptm.stop()

                logger.info(f"Deleting model with id '{ptm.model_id}'")
                ptm.delete()
            else:
                logger.error(f"Trained model with id '{ptm.model_id}' already exists")
                logger.info("Run the script with the '--clear-previous' flag if you want to overwrite the existing model.")
                exit(1)

        logger.info(f"Creating model with id '{ptm.model_id}'")
        ptm.put_config(config_path)

        logger.info(f"Uploading model definition")
        ptm.put_model(model_path)

        logger.info(f"Uploading model vocabulary")
        ptm.put_vocab(vocab_path)

    # Start the deployed model
    if args.start:
        logger.info(f"Starting model deployment")
        ptm.start()

    logger.info(f"Model successfully imported with id '{ptm.model_id}'")