swift/infer_engine/infer_engine.py

# Copyright (c) ModelScope Contributors. All rights reserved.
import asyncio
import concurrent.futures
import os
from queue import Queue
from threading import Thread
from tqdm import tqdm
from typing import Any, Dict, Iterator, List, Optional, Union

from swift.metrics import Metric
from swift.model import get_ckpt_dir
from swift.template import Template, get_template
from swift.utils import Processor, ProcessorMixin, get_logger
from .base import BaseInferEngine
from .protocol import (ChatCompletionMessageToolCall, ChatCompletionResponse, ChatCompletionStreamResponse,
                       InferRequest, RequestConfig, UsageInfo)

logger = get_logger()


class InferEngine(BaseInferEngine, ProcessorMixin):

    def __init__(self, template: Template):
        processor = template.processor
        self.template = template
        self.template_type = template.template_meta.template_type
        self.processor = processor
        self.model_info = processor.model_info
        self.model_meta = processor.model_meta
        self.model_dir = self.model_info.model_dir
        self.model_name = self.model_info.model_name
        self.max_model_len = self.model_info.max_model_len
        self.task_type = self.model_info.task_type
        self.config = self.model_info.config
        self.max_tokens_offset = 0

    def _get_template(self, processor: Processor, template_type: Optional[str] = None):
        ckpt_dir = get_ckpt_dir(processor.model_info.model_dir, getattr(self, 'adapters', None))
        logger.info('Create the template for the infer_engine')
        if ckpt_dir:
            from swift.arguments import BaseArguments
            args = BaseArguments.from_pretrained(ckpt_dir)
            template = args.get_template(processor)
        else:
            template = get_template(processor, template_type=template_type)
        return template

    def _get_stop_words(self, stop_words: List[Union[str, List[int], None]]) -> List[str]:
        stop: List[str] = []
        for stop_word in stop_words:
            if stop_word is None:
                continue
            elif isinstance(stop_word, list):
                stop_word = self.tokenizer.decode(stop_word)
            assert isinstance(stop_word, str)
            if stop_word not in stop:
                stop.append(stop_word)
        return stop

    def _get_stop_token_ids(self, stop_words: List[Union[str, List[int], None]]) -> List[int]:
        stop_token_ids: List[int] = []
        for stop_word in stop_words:
            if stop_word is None:
                continue
            if isinstance(stop_word, str):
                stop_word = self.tokenizer.encode(stop_word, add_special_tokens=False)
            if isinstance(stop_word, list):
                if len(stop_word) != 1:
                    continue
                else:
                    stop_token = stop_word[0]
            elif isinstance(stop_word, int):
                stop_token = stop_word
            assert isinstance(stop_token, int)
            if stop_token not in stop_token_ids:
                stop_token_ids.append(stop_token)
        return stop_token_ids

    def async_iter_to_iter(self, async_iter, prog_bar, metrics) -> Iterator:
        queue = Queue()

        async def _run_async_iter():
            try:
                async for item in await async_iter:
                    queue.put(item)
            except Exception as e:
                if getattr(self, 'strict', True):
                    raise
                queue.put(e)
            else:
                queue.put(None)

        try:
            loop = asyncio.get_event_loop()
        except RuntimeError:
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)
        thread = Thread(target=lambda: loop.run_until_complete(_run_async_iter()))
        thread.start()
        pre_output = None
        while True:
            output = queue.get()
            if output is None or isinstance(output, Exception):
                prog_bar.update()
                self._update_metrics(pre_output, metrics)
                return
            pre_output = output
            yield output

    @staticmethod
    async def batch_run(tasks):
        return await asyncio.gather(*tasks)

    def _batch_infer_stream(
        self,
        tasks,
        stream: bool = True,
        use_tqdm: bool = True,
        metrics: Optional[List[Metric]] = None
    ) -> List[Union[ChatCompletionResponse, Iterator[ChatCompletionStreamResponse]]]:

        prog_bar = tqdm(total=len(tasks), dynamic_ncols=True, disable=not use_tqdm)
        if stream:
            return [self.async_iter_to_iter(task, prog_bar, metrics) for task in tasks]
        else:

            async def _new_run(task):
                try:
                    res = await task
                except Exception as e:
                    if getattr(self, 'strict', True):
                        raise
                    res = e
                prog_bar.update()
                self._update_metrics(res, metrics)
                return res

            new_tasks = [_new_run(task) for task in tasks]
            try:
                loop = asyncio.get_event_loop()
            except RuntimeError:
                loop = asyncio.new_event_loop()
                asyncio.set_event_loop(loop)
            return loop.run_until_complete(self.batch_run(new_tasks))

    @staticmethod
    def _get_usage_info(num_prompt_tokens: int, num_generated_tokens: int) -> UsageInfo:
        return UsageInfo(
            prompt_tokens=num_prompt_tokens,
            completion_tokens=num_generated_tokens,
            total_tokens=num_prompt_tokens + num_generated_tokens,
        )

    @staticmethod
    def _update_usage_info(origin_use_info: UsageInfo, num_generated_tokens: int) -> UsageInfo:
        return UsageInfo(
            prompt_tokens=origin_use_info.prompt_tokens,
            completion_tokens=origin_use_info.completion_tokens + num_generated_tokens,
            total_tokens=origin_use_info.total_tokens + num_generated_tokens,
        )

    @staticmethod
    def _update_metrics(result, metrics: Optional[List[Metric]] = None):
        if metrics is None:
            return result
        result_origin = result
        if not isinstance(result, (list, tuple)):
            result = [result]
        for response in result:
            if response is None or isinstance(response, Exception):
                continue
            for metric in metrics:
                metric.update(response)
        return result_origin

    def infer(self,
              infer_requests: List[InferRequest],
              request_config: Optional[RequestConfig] = None,
              metrics: Optional[List[Metric]] = None,
              *,
              use_tqdm: Optional[bool] = None,
              **kwargs) -> List[Union[ChatCompletionResponse, Iterator[ChatCompletionStreamResponse]]]:
        if request_config is None:
            request_config = RequestConfig()
        tasks = [self.infer_async(infer_request, request_config, **kwargs) for infer_request in infer_requests]
        if use_tqdm is None:
            use_tqdm = not request_config.stream and len(infer_requests) > 1
        return self._batch_infer_stream(tasks, request_config.stream, use_tqdm, metrics)

    def _get_toolcall(self, response: str) -> Optional[List[ChatCompletionMessageToolCall]]:
        try:
            functions = self.template.agent_template.get_toolcall(response)
        except Exception:
            functions = None
        if functions:
            return [ChatCompletionMessageToolCall(function=function) for function in functions]

    @staticmethod
    def _get_num_tokens(inputs: Dict[str, Any]) -> int:
        if 'input_ids' in inputs:  # 1d or 2d
            input_ids = inputs['input_ids']
            if isinstance(input_ids, list):
                return len(input_ids)
            else:
                return input_ids.shape[-1]
        elif 'inputs_embeds' in inputs:  # 2d or 3d
            return inputs['inputs_embeds'].shape[-2]
        raise ValueError(f'Unable to retrieve input_ids and inputs_embeds. inputs: {inputs}')

    def set_default_max_tokens(self, request_config: RequestConfig, inputs: Dict[str, Any]) -> None:
        max_model_len = self.max_model_len
        assert isinstance(inputs, dict)
        # The num_tokens takes the maximum value from inputs_list.
        num_tokens = self._get_num_tokens(inputs)
        max_tokens = request_config.max_tokens
        if max_model_len is None:
            max_model_len = 8192
            logger.warning(
                'The current model is unable to retrieve `max_model_len`. It is set to the default value of 8192.')
        max_max_tokens = max_model_len - num_tokens + self.max_tokens_offset
        if max_tokens is None:
            request_config.max_tokens = max_max_tokens
        elif max_max_tokens < request_config.max_tokens:
            logger.warning(f'max_model_len({max_model_len}) - num_tokens({num_tokens}) < max_tokens({max_tokens}). '
                           f'Setting max_tokens: {max_model_len - num_tokens}')
            request_config.max_tokens = max_max_tokens

    def _get_logprobs(self,
                      logprobs_list: Optional[List[Dict[int, float]]],
                      token_ids: List[int],
                      top_logprobs: Optional[int] = None) -> Optional[Dict[str, Any]]:
        if logprobs_list is None or len(token_ids) == 0:
            return None
        if len(token_ids) > 0:
            logprobs_list = logprobs_list[-len(token_ids):]
        res = []
        for logprobs, token_id in zip(logprobs_list, token_ids):
            token = self.tokenizer.decode(token_id)
            _res = {'token': token, 'logprob': logprobs[token_id], 'bytes': list(token.encode('utf8'))}
            if top_logprobs is not None:
                logprobs = {k: logprobs[k] for k in sorted(logprobs, key=lambda k: -logprobs[k])[:top_logprobs]}
                res_top_logprobs = []
                for k, logprob in logprobs.items():
                    if logprob == float('-inf'):
                        continue
                    token = self.tokenizer.decode(k)
                    res_top_logprobs.append({'token': token, 'logprob': logprob, 'bytes': list(token.encode('utf8'))})
                _res['top_logprobs'] = res_top_logprobs
            res.append(_res)
        return {'content': res}

    @staticmethod
    def _get_finish_reason(max_tokens: int, completion_tokens: int, is_finished: bool):
        if is_finished:
            if completion_tokens >= max_tokens:
                finish_reason = 'length'
            else:
                finish_reason = 'stop'
        else:
            finish_reason = None
        return finish_reason

    @staticmethod
    def thread_run(target, args=(), kwargs=None):
        kwargs = kwargs or {}

        def func(target, queue, args, kwargs):
            try:
                queue.put(target(*args, **kwargs))
            except Exception as e:
                queue.put(e)

        queue = Queue()
        thread = Thread(target=func, args=(target, queue, args, kwargs))
        thread.start()
        thread.join()
        result = queue.get()
        if isinstance(result, Exception):
            raise result
        return result

    @staticmethod
    def safe_asyncio_run(coro):

        def asyncio_run(core):
            return asyncio.run(core)

        return InferEngine.thread_run(asyncio_run, args=(coro, ))

    def _batch_encode(self, infer_requests: List[InferRequest], strict: bool):
        max_workers = max(min(32, os.cpu_count(), len(infer_requests)), 1)
        error_list = []
        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
            futures = [
                executor.submit(self.template.encode, infer_request, return_template_inputs=True)
                for infer_request in infer_requests
            ]
            concurrent.futures.wait(futures)
            batched_inputs = []
            for i, future in enumerate(futures):
                try:
                    batched_inputs.append(future.result())
                except Exception as e:
                    if strict:
                        raise
                    error_list.append((i, e))
                    continue
        return batched_inputs, error_list

    @staticmethod
    def _add_error_list(outputs, error_list):
        for i, error in error_list:
            outputs.insert(i, error)
        return outputs