HuggingFace_transformer/src/transformers/commands/serving.py

import logging
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional

from starlette.responses import JSONResponse
from transformers import Pipeline
from transformers.commands import BaseTransformersCLICommand
from transformers.pipelines import SUPPORTED_TASKS, pipeline


try:
    from uvicorn import run
    from fastapi import FastAPI, HTTPException, Body
    from fastapi.routing import APIRoute
    from pydantic import BaseModel

    _serve_dependancies_installed = True
except (ImportError, AttributeError):
    BaseModel = object

    def Body(*x, **y):
        pass

    _serve_dependancies_installed = False


logger = logging.getLogger("transformers-cli/serving")


def serve_command_factory(args: Namespace):
    """
    Factory function used to instantiate serving server from provided command line arguments.
    :return: ServeCommand
    """
    nlp = pipeline(
        task=args.task,
        model=args.model if args.model else None,
        config=args.config,
        tokenizer=args.tokenizer,
        device=args.device,
    )
    return ServeCommand(nlp, args.host, args.port, args.workers)


class ServeModelInfoResult(BaseModel):
    """
    Expose model information
    """

    infos: dict


class ServeTokenizeResult(BaseModel):
    """
    Tokenize result model
    """

    tokens: List[str]
    tokens_ids: Optional[List[int]]


class ServeDeTokenizeResult(BaseModel):
    """
    DeTokenize result model
    """

    text: str


class ServeForwardResult(BaseModel):
    """
    Forward result model
    """

    output: Any


class ServeCommand(BaseTransformersCLICommand):
    @staticmethod
    def register_subcommand(parser: ArgumentParser):
        """
        Register this command to argparse so it's available for the transformer-cli
        :param parser: Root parser to register command-specific arguments
        :return:
        """
        serve_parser = parser.add_parser(
            "serve", help="CLI tool to run inference requests through REST and GraphQL endpoints."
        )
        serve_parser.add_argument(
            "--task", type=str, choices=SUPPORTED_TASKS.keys(), help="The task to run the pipeline on"
        )
        serve_parser.add_argument("--host", type=str, default="localhost", help="Interface the server will listen on.")
        serve_parser.add_argument("--port", type=int, default=8888, help="Port the serving will listen to.")
        serve_parser.add_argument("--workers", type=int, default=1, help="Number of http workers")
        serve_parser.add_argument("--model", type=str, help="Model's name or path to stored model.")
        serve_parser.add_argument("--config", type=str, help="Model's config name or path to stored model.")
        serve_parser.add_argument("--tokenizer", type=str, help="Tokenizer name to use.")
        serve_parser.add_argument(
            "--device",
            type=int,
            default=-1,
            help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)",
        )
        serve_parser.set_defaults(func=serve_command_factory)

    def __init__(self, pipeline: Pipeline, host: str, port: int, workers: int):

        self._pipeline = pipeline

        self.host = host
        self.port = port
        self.workers = workers

        if not _serve_dependancies_installed:
            raise RuntimeError(
                "Using serve command requires FastAPI and unicorn. "
                'Please install transformers with [serving]: pip install "transformers[serving]".'
                "Or install FastAPI and unicorn separately."
            )
        else:
            logger.info("Serving model over {}:{}".format(host, port))
            self._app = FastAPI(
                routes=[
                    APIRoute(
                        "/",
                        self.model_info,
                        response_model=ServeModelInfoResult,
                        response_class=JSONResponse,
                        methods=["GET"],
                    ),
                    APIRoute(
                        "/tokenize",
                        self.tokenize,
                        response_model=ServeTokenizeResult,
                        response_class=JSONResponse,
                        methods=["POST"],
                    ),
                    APIRoute(
                        "/detokenize",
                        self.detokenize,
                        response_model=ServeDeTokenizeResult,
                        response_class=JSONResponse,
                        methods=["POST"],
                    ),
                    APIRoute(
                        "/forward",
                        self.forward,
                        response_model=ServeForwardResult,
                        response_class=JSONResponse,
                        methods=["POST"],
                    ),
                ],
                timeout=600,
            )

    def run(self):
        run(self._app, host=self.host, port=self.port, workers=self.workers)

    def model_info(self):
        return ServeModelInfoResult(infos=vars(self._pipeline.model.config))

    def tokenize(self, text_input: str = Body(None, embed=True), return_ids: bool = Body(False, embed=True)):
        """
        Tokenize the provided input and eventually returns corresponding tokens id:
        - **text_input**: String to tokenize
        - **return_ids**: Boolean flags indicating if the tokens have to be converted to their integer mapping.
        """
        try:
            tokens_txt = self._pipeline.tokenizer.tokenize(text_input)

            if return_ids:
                tokens_ids = self._pipeline.tokenizer.convert_tokens_to_ids(tokens_txt)
                return ServeTokenizeResult(tokens=tokens_txt, tokens_ids=tokens_ids)
            else:
                return ServeTokenizeResult(tokens=tokens_txt)

        except Exception as e:
            raise HTTPException(status_code=500, detail={"model": "", "error": str(e)})

    def detokenize(
        self,
        tokens_ids: List[int] = Body(None, embed=True),
        skip_special_tokens: bool = Body(False, embed=True),
        cleanup_tokenization_spaces: bool = Body(True, embed=True),
    ):
        """
        Detokenize the provided tokens ids to readable text:
        - **tokens_ids**: List of tokens ids
        - **skip_special_tokens**: Flag indicating to not try to decode special tokens
        - **cleanup_tokenization_spaces**: Flag indicating to remove all leading/trailing spaces and intermediate ones.
        """
        try:
            decoded_str = self._pipeline.tokenizer.decode(tokens_ids, skip_special_tokens, cleanup_tokenization_spaces)
            return ServeDeTokenizeResult(model="", text=decoded_str)
        except Exception as e:
            raise HTTPException(status_code=500, detail={"model": "", "error": str(e)})

    async def forward(self, inputs=Body(None, embed=True)):
        """
        **inputs**:
        **attention_mask**:
        **tokens_type_ids**:
        """

        # Check we don't have empty string
        if len(inputs) == 0:
            return ServeForwardResult(output=[], attention=[])

        try:
            # Forward through the model
            output = self._pipeline(inputs)
            return ServeForwardResult(output=output)
        except Exception as e:
            raise HTTPException(500, {"error": str(e)})