Merge aa48a2843d into 408f5caff4

opencompass/models/async_openai_api.py

@@ -0,0 +1,369 @@
+import contextlib
+import multiprocessing
+import os
+import re
+from typing import Dict, List, Optional, Union
+
+import jieba
+import weakref
+from typing import Literal, Tuple, Iterable
+
+from opencompass.utils.prompt import PromptList
+from opencompass.models.base_api import AsyncTokenBucket, BaseAPIModel
+
+import threading
+import asyncio
+from typing import cast
+from contextlib import contextmanager
+
+
+PromptType = Union[PromptList, str]
+OPENAI_API_BASE = os.path.join(
+    os.environ.get('OPENAI_BASE_URL', 'https://api.openai.com/v1/'),
+    'chat/completions')
+
+
+class _APIModelState:
+    _instance: Dict[str, weakref.ReferenceType["_APIModelState"]] = {}
+    _count: int
+    _concurrency: int
+    _locks = [threading.Lock(), multiprocessing.Lock()]
+
+    def __init__(self, *, name: str, concurrency: int, query_per_second=1) -> None:
+        self._name = name
+        self._count = 0
+        self._concurrency = concurrency
+        self._token_bucket = AsyncTokenBucket(rate=query_per_second)
+
+        self._count += 1
+        self._concurrency = max(1, self._concurrency // self._count)
+
+    @property
+    def concurrency(self) -> int:
+        # If update and concurrency are called simultaneously, the values
+        # returned here may be inaccurate, but the impact is likely minimal
+        return self._concurrency
+
+    async def acquire(self):
+        return await self._token_bucket.acquire()
+
+    @property
+    def rpm(self):
+        return self._token_bucket.rpm
+
+    @property
+    def name(self) -> str:
+        return self._name
+
+    @property
+    def count(self):
+        return self._count
+
+    @classmethod
+    def _cleanup(cls, ref: weakref.ReferenceType["_APIModelState"]):
+        with cls._lock():
+            self: _APIModelState = ref()  # type: ignore
+            cls._instance.pop(self._name)
+
+    def __new__(cls, name: str, *args, **kwargs) -> "_APIModelState":
+        with cls._lock():
+            if name not in cls._instance:
+                self = super().__new__(cls)
+                cls._instance[name] = weakref.ref(self, cls._cleanup)
+            return cls._instance[name]()  # type: ignore
+
+    @classmethod
+    @contextmanager
+    def _lock(cls):
+        with contextlib.ExitStack() as stack:
+            [stack.enter_context(lock) for lock in cls._locks]
+            yield
+
+
+
+class AsyncOpenAISDK(BaseAPIModel):
+    states: Dict[str, _APIModelState] = {}
+
+    def __init__(
+        self,
+        path: str = 'gpt-3.5-turbo',
+        max_seq_len: int | None = None,  # type: ignore
+        query_per_second: int = 1,
+        retry: int = 2,
+        key: str = 'ENV',
+        org: str | List[str] | None = None,
+        meta_template: Dict | None = None,
+        openai_api_base: str = OPENAI_API_BASE,
+        openai_proxy_url: Optional[str] = None,
+        mode: Literal['none', 'front', 'mid', 'rear'] = 'none',
+        logprobs: bool | None = False,
+        top_logprobs: int | None = None,
+        temperature: float | None = None,
+        tokenizer_path: str | None = None,
+        extra_body: Dict | None = None,
+        max_completion_tokens: int = 16384,
+        verbose: bool = False,
+        concurrency: int = 64,
+        status_code_mappings: dict = {},
+    ):
+        from openai import AsyncOpenAI
+
+        assert mode in ['none', 'front', 'mid', 'rear']
+        self.mode = mode
+        state_key = self._get_state_key(api_base=openai_api_base, model_name=path)
+        if state_key not in AsyncOpenAISDK.states:
+            AsyncOpenAISDK.states[path] = _APIModelState(
+                name=state_key,
+                concurrency=concurrency,
+                query_per_second=query_per_second,
+            )
+        self.state = AsyncOpenAISDK.states[path]
+        self.openai_client = AsyncOpenAI(base_url=openai_api_base, api_key=key)
+
+        if max_seq_len is None:
+            if '16k' in path:
+                max_seq_len = 16384
+            elif 'gpt-4' in path:
+                max_seq_len = 8192
+            elif 'gpt-3.5' in path:
+                max_seq_len = 4097
+            else:
+                max_seq_len = 32768
+        else:
+            max_seq_len = max_seq_len
+
+        super().__init__(path=path, max_seq_len=max_seq_len, meta_template=meta_template, retry=retry)
+
+        self.logprobs = logprobs
+        self.top_logprobs = top_logprobs
+        self.tokenizer_path = tokenizer_path
+        self.hf_tokenizer = None
+        self.extra_body = extra_body
+        self.max_completion_tokens = max_completion_tokens
+        self.temperature = temperature
+        self.openai_api_base = openai_api_base
+        self.concurrency = concurrency
+
+        self.status_code_mappings = status_code_mappings
+
+        if openai_proxy_url == 'ENV':
+            if 'OPENAI_PROXY_URL' not in os.environ:
+                raise ValueError('OPENAI_PROXY_URL is not set.')
+            self.proxy_url = os.getenv('OPENAI_PROXY_URL')
+        else:
+            self.proxy_url = openai_proxy_url
+
+    async def generate(self,  # type: ignore
+                 inputs: Iterable[PromptType],
+                 max_out_len: int = 512,
+                 temperature: float = 0.7,
+                 **kwargs) -> List[str]:
+        """Generate results given a list of inputs.
+
+        Args:
+            inputs (List[PromptType]): A list of strings or PromptDicts.
+                The PromptDict should be organized in OpenCompass'
+                API format.
+            max_out_len (int): The maximum length of the output.
+            temperature (float): What sampling temperature to use,
+                between 0 and 2. Higher values like 0.8 will make the output
+                more random, while lower values like 0.2 will make it more
+                focused and deterministic. Defaults to 0.7.
+
+        Returns:
+            List[str]: A list of generated strings.
+        """
+        if self.temperature is not None:
+            temperature = self.temperature
+
+        # TODO: This should be an AsyncGenerator if an real `AsyncInference` has been implemented
+        tasks_queue: List[asyncio.Future] = []
+        results_queue: List[Tuple[int, str]] = []
+        inputs_iter = enumerate(inputs)
+
+        data_stop = False
+        while not (data_stop and not tasks_queue):
+            concurrency = self.state.concurrency
+
+            if tasks_queue:
+                done, pending = await asyncio.wait(tasks_queue, return_when=asyncio.FIRST_COMPLETED)
+                tasks_queue = list(pending)
+                for queue in done:
+                    result: Tuple[int, str] = queue.result()
+                    results_queue.append(result)
+
+            while not data_stop and len(tasks_queue) < concurrency:
+                try:
+                    index, _input = next(inputs_iter)
+                except StopIteration:
+                    data_stop = True
+                    break
+                tasks_queue.append(
+                    asyncio.create_task(
+                        self._generate(
+                            input=_input,
+                            max_out_len=self.max_completion_tokens or max_out_len,
+                            temperature=temperature,
+                            index=index,
+                        )
+                    )
+                )
+        results_queue.sort()
+        return [item[1] for item in results_queue]
+
+    async def generate_from_template(self, templates: List[PromptType],  # type: ignore
+                               max_out_len: int, **kwargs):
+        """Generate completion from a list of templates.
+
+        Args:
+            templates (List[PromptType]): A list of templates.
+            max_out_len (int): The maximum length of the output.
+        """
+        inputs = self.parse_template(templates, mode='gen')  # type: ignore
+        return await self.generate(inputs, max_out_len=max_out_len, **kwargs)
+
+    async def _generate(self, input: PromptList | str, max_out_len: int,
+                  temperature: float, index: int) -> Tuple[int, str]:
+        from openai import APIStatusError, BadRequestError
+        assert isinstance(input, (str, PromptList))
+
+        # max num token for gpt-3.5-turbo is 4097
+        # Most models' token limits are above 32k
+
+        # will leave 100 tokens as prompt buffer, triggered if input is str
+        if isinstance(input, str) and self.mode != 'none':
+            context_window = self.max_seq_len
+            input = self.bin_trim(
+                input,
+                context_window - 100 - max_out_len,
+                cast(Literal['front', 'mid', 'rear'], self.mode),
+            )
+
+        if isinstance(input, str):
+            messages = [{'role': 'user', 'content': input}]
+        else:
+            messages = []
+            for item in input:
+                msg = {'content': item['prompt']}
+                if item['role'] == 'HUMAN':
+                    msg['role'] = 'user'
+                elif item['role'] == 'BOT':
+                    msg['role'] = 'assistant'
+                elif item['role'] == 'SYSTEM':
+                    msg['role'] = 'system'
+                messages.append(msg)
+
+
+        # Hold out 100 tokens due to potential errors in tiktoken calculation
+        # try:
+        #     max_out_len = min(
+        #         max_out_len,
+        #         context_window - self.get_token_len(str(input)) - 100)
+        # except KeyError:
+        #     max_out_len = max_out_len
+        # if max_out_len <= 0:
+        #     return ''
+
+        num_retries = 0
+        while num_retries < self.retry:
+            await self.state.acquire()
+
+            query_data = dict(
+                model=self.path,
+                max_tokens=max_out_len,
+                n=1,
+                temperature=self.temperature,
+                messages=messages,
+                extra_body=self.extra_body,
+                timeout=600,
+            )
+
+            try:
+                if self.verbose:
+                    self.logger.info('Start calling OpenAI API')
+                responses = await self.openai_client.chat.completions.create(**query_data)
+
+                if self.verbose:
+                    self.logger.info(
+                        'Successfully get response from OpenAI API')
+                    try:
+                        self.logger.info(responses)
+                    except Exception as e:  # noqa F841
+                        pass
+                if not responses.choices:
+                    self.logger.error(
+                        'Response is empty, it is an internal server error \
+                            from the API provider.')
+                return index, responses.choices[0].message.content
+
+            except (BadRequestError, APIStatusError) as e:
+                # Handle BadRequest status
+                # You can specify self.status_code_mappings to bypass \
+                # API sensitivity blocks
+                # For example: status_code_mappings={400: 'Input data \
+                # may contain inappropriate content.'}
+                status_code = e.status_code
+                if (status_code is not None
+                        and status_code in self.status_code_mappings):
+                    error_message = self.status_code_mappings[status_code]
+                    self.logger.info(f'Status Code: {status_code},\n'
+                                     f'Original Error Message: {e},\n'
+                                     f'Return Message: {error_message} ')
+                    return index, error_message
+                else:
+                    self.logger.warning(f"Failed to get response for {e}, retry {num_retries}/{self.retry}")
+            except Exception as e:
+                self.logger.warning(f"Failed to get response for {e}, retry {num_retries}/{self.retry}")
+            num_retries += 1
+        raise RuntimeError('Calling OpenAI API failed after retrying for '
+                           f'{self.retry} times. Check the logs for details.')
+
+    def _get_state_key(self, api_base: str, model_name: str):
+        return api_base + model_name
+
+    def bin_trim(self, prompt: str, num_token: int, mode: Literal['front', 'mid', 'rear']) -> str:
+        """Get a suffix of prompt which is no longer than num_token tokens.
+
+        Args:
+            prompt (str): Input string.
+            num_token (int): The upper bound of token numbers.
+
+        Returns:
+            str: The trimmed prompt.
+        """
+        token_len = self.get_token_len(prompt)
+        if token_len <= num_token:
+            return prompt
+        pattern = re.compile(r'[\u4e00-\u9fa5]')
+        if pattern.search(prompt):
+            words = list(jieba.cut(prompt, cut_all=False))
+            sep = ''
+        else:
+            words = prompt.split(' ')
+            sep = ' '
+
+        l, r = 1, len(words)
+        while l + 2 < r:
+            # mode: Literal['front', 'mid', 'rear'] = self.mode
+            mid = (l + r) // 2
+            if mode == 'front':
+                cur_prompt = sep.join(words[-mid:])
+            elif mode == 'mid':
+                cur_prompt = sep.join(words[:mid]) + sep.join(words[-mid:])
+            elif mode == 'rear':
+                cur_prompt = sep.join(words[:mid])
+
+            if self.get_token_len(cur_prompt) <= num_token:
+                l = mid  # noqa: E741
+            else:
+                r = mid
+
+        if self.mode == 'front':
+            prompt = sep.join(words[-l:])
+        elif self.mode == 'mid':
+            prompt = sep.join(words[:l]) + sep.join(words[-l:])
+        elif self.mode == 'rear':
+            prompt = sep.join(words[:l])
+        return prompt
+
+

opencompass/models/base_api.py

@@ -4,6 +4,7 @@ import threading
 import time
 import warnings
 from abc import abstractmethod
+from collections import deque
 from copy import deepcopy
 from queue import Queue
 from time import sleep
@@ -12,6 +13,8 @@ from typing import Dict, List, Optional, Tuple, Union
 from opencompass.utils import get_logger
 from opencompass.utils.prompt import PromptList
 
+import asyncio
+
 from .base import BaseModel
 
 PromptType = Union[PromptList, str]
@@ -51,7 +54,7 @@ class BaseAPIModel(BaseModel):
         self.retry = retry
         self.query_per_second = query_per_second
         self.token_bucket = TokenBucket(query_per_second, rpm_verbose)
-        self.template_parser = APITemplateParser(meta_template)
+        self.template_parser = APITemplateParser(meta_template)  # type: ignore
         self.logger = get_logger()
         self.generation_kwargs = generation_kwargs
         self.verbose = verbose
@@ -459,4 +462,69 @@ class TokenBucket:
                 else:
                     break
             self._request_queue.put(cur_time)
-            self.logger.info(f'Current RPM {self._request_queue.qsize()}.')
+            self.logger.info(f"Current RPM {self._request_queue.qsize()}.")
+
+
+
+class AsyncTokenBucket:
+    def __init__(self, rate: int = 1):
+        self._rate = rate
+        self._max_tokens = rate * 60
+        self._tokens: float = float(self._max_tokens)
+        self._last_refill_time: float | None = None
+
+        self._request_timestamps: deque[float] = deque()
+        self._max_window_size = 60
+
+        self._token_available = asyncio.Event()
+
+    async def _release(self) -> None:
+        if self._last_refill_time is None:
+            self._last_refill_time: float = time.monotonic()
+
+        now = time.monotonic()
+        elapsed = now - self._last_refill_time
+        tokens_to_add = elapsed * self._rate
+
+        self._tokens = min(self._max_tokens, self._tokens + tokens_to_add)
+        self._last_refill_time = now
+
+    async def acquire(self) -> bool:
+        while True:
+            await self._release()
+
+            if self._tokens >= 1:
+                self._tokens -= 1
+
+                now = time.monotonic()
+                self._request_timestamps.append(now)
+
+                while (
+                    self._request_timestamps
+                    and now - self._request_timestamps[0] > self._max_window_size
+                ):
+                    self._request_timestamps.popleft()
+
+                self._token_available.set()
+                return True
+
+            self._token_available.clear()
+
+            await self._token_available.wait()
+
+    @property
+    def rpm(self) -> int:
+        now = time.monotonic()
+
+        while (
+            self._request_timestamps
+            and now - self._request_timestamps[0] > self._max_window_size
+        ):
+            self._request_timestamps.popleft()
+
+        return len(self._request_timestamps)
+
+    @property
+    def available_tokens(self) -> float:
+        return self._tokens
+

opencompass/openicl/icl_inferencer/icl_chat_async_inferencer.py

@@ -0,0 +1,397 @@
+"""Chat Inferencer."""
+import os
+import os.path as osp
+from typing import List, Optional, Union
+
+import mmengine
+from mmengine import is_list_of
+from tqdm import tqdm
+
+from opencompass.models import APITemplateParser as _APITemplateParser
+from opencompass.models import BaseModel
+from opencompass.models import LMTemplateParser as _LMTemplateParser
+from opencompass.registry import ICL_INFERENCERS
+from opencompass.utils.prompt import PromptList
+
+from ..icl_prompt_template import PromptTemplate
+from ..icl_retriever import BaseRetriever
+from ..utils.logging import get_logger
+from .icl_base_inferencer import BaseInferencer, dump_results_dict
+
+logger = get_logger(__name__)
+
+
+def promptlist_to_openai(prompt: Union[str, PromptList]):
+    output = []
+    if isinstance(prompt, str):
+        return [dict(role='user', content=prompt)]
+
+    for item in prompt:
+        if 'section' in item:
+            continue
+        if isinstance(item, str) and item:
+            output.append(dict(role='user', content=item))
+        elif item['role'] == 'SYSTEM':
+            output.append(dict(role='system', content=item['prompt']))
+        elif item['role'] == 'HUMAN':
+            output.append(dict(role='user', content=item['prompt']))
+        elif item['role'] == 'BOT':
+            output.append(dict(role='assistant', content=item['prompt']))
+    return output
+
+
+class LMTemplateParser:
+    """LMTemplateParser accepts OpenAI format dialog inputs."""
+
+    def __init__(self, meta_template: Optional[dict] = None):
+        self.meta_template = meta_template
+        self.roles = {}
+        role_mapping = {
+            'SYSTEM': 'system',
+            'HUMAN': 'user',
+            'BOT': 'assistant',
+        }
+        if meta_template:
+            for item in meta_template.get('round', []):
+                role = role_mapping.get(item['role'], item['role'])
+                self.roles[role] = item.copy()
+            for item in meta_template.get('reserved_roles', []):
+                role = role_mapping.get(item['role'], item['role'])
+                self.roles[role] = item.copy()
+
+    def parse_template(self, chat: List[dict], mode='gen') -> str:
+        if is_list_of(chat, list):
+            # Handle batch inputs
+            return [self.parse_template(item) for item in chat]
+
+        assert is_list_of(chat, dict)
+        prompt = ''
+        if self.roles:
+            for dialog in chat:
+                role_cfg = self.roles.get(dialog['role'], {})
+                prompt += (role_cfg.get('begin') or '')
+                prompt += (dialog.get('content') or '')
+                prompt += (role_cfg.get('end') or '')
+            prompt += (self.roles['assistant'].get('begin') or '')
+        else:
+            # in case the model does not have any meta template
+            last_sep = ''
+            for item in chat:
+                prompt += last_sep + (item.get('content') or '')
+                last_sep = '\n'
+        return prompt
+
+
+class APITemplateParser:
+    """APITemplateParser accepts OpenAI format dialog inputs."""
+
+    def __init__(self, meta_template: Optional[dict] = None):
+        self.meta_template = meta_template
+        self.roles = {}
+        role_mapping = {
+            'SYSTEM': 'system',
+            'HUMAN': 'user',
+            'BOT': 'assistant',
+        }
+        if meta_template:
+            for item in meta_template.get('round', []):
+                role = role_mapping.get(item['role'], item['role'])
+                self.roles[role] = item.copy()
+            for item in meta_template.get('reserved_roles', []):
+                role = role_mapping.get(item['role'], item['role'])
+                self.roles[role] = item.copy()
+        else:
+            self.roles = dict(
+                system=dict(api_role='SYSTEM'),
+                user=dict(api_role='HUMAN'),
+                assistant=dict(api_role='BOT', generate=True),
+            )
+
+    def parse_template(self, chat: List[dict], mode='gen') -> str:
+        if is_list_of(chat, list):
+            # Handle batch inputs
+            return [self.parse_template(item) for item in chat]
+
+        assert is_list_of(chat, dict)
+        prompt = []
+        for dialog in chat:
+            if dialog['role'] in self.roles:
+                role = self.roles[dialog['role']]['api_role']
+            else:
+                role = dialog['role']
+            prompt.append(dict(role=role, prompt=dialog.get('content') or ''))
+        return PromptList(prompt)
+
+
+class ChatOutputHandler:
+
+    def __init__(self) -> None:
+        self.results_dict = {}
+
+    def write_to_json(self, save_dir: str, filename: str):
+        """Dump the result to a json file."""
+        dump_results_dict(self.results_dict, osp.join(save_dir, filename))
+
+    def save_results(self,
+                     origin_prompt: list,
+                     prediction: str,
+                     idx: int,
+                     gold: str = None):
+        result_dict = {}
+        if gold:
+            result_dict['gold'] = gold
+        result_dict.update({
+            'prediction': prediction,
+            'origin_prompt': origin_prompt,
+        })
+        self.results_dict[str(idx)] = result_dict
+
+    def save_multiround_results(self,
+                                origin_prompt: list,
+                                prediction: str,
+                                idx: int,
+                                gold: str = None):
+        result_dict = self.results_dict.get(str(idx), {
+            'gold': [],
+            'prediction': [],
+            'origin_prompt': [],
+        })
+        result_dict['gold'].append(gold)
+        result_dict['prediction'].append(prediction)
+        result_dict['origin_prompt'].append(origin_prompt)
+        self.results_dict[str(idx)] = result_dict
+
+
+@ICL_INFERENCERS.register_module()
+class AsyncChatInferencer(BaseInferencer):
+    HandlerType = ChatOutputHandler
+
+    def __init__(
+            self,
+            model,
+            output_json_filepath: Optional[str] = './icl_inference_output',
+            output_json_filename: Optional[str] = 'predictions',
+            save_every: Optional[int] = 1,
+            infer_mode: str = 'last',
+            max_out_len: int = 512,
+            **kwargs) -> None:
+        super().__init__(
+            model=model,
+            output_json_filename=output_json_filename,
+            output_json_filepath=output_json_filepath,
+            **kwargs,
+        )
+        assert infer_mode in ['last', 'every', 'every_with_gt']
+        self.infer_mode = infer_mode
+        self.model: BaseModel
+        self._set_meta_template(self.model)
+
+        if self.model.is_api and save_every is None:
+            save_every = 1
+        self.save_every = save_every
+        self.dialogue_mode = False
+        self.max_out_len = max_out_len
+
+    def _set_meta_template(self, model):
+        origin = model.template_parser
+        if isinstance(origin, _APITemplateParser):
+            model.template_parser = APITemplateParser(origin.meta_template)
+        if isinstance(origin, _LMTemplateParser):
+            model.template_parser = LMTemplateParser(origin.meta_template)
+
+    async def inference(self,  # type: ignore
+                  retriever: BaseRetriever,
+                  ice_template: Optional[PromptTemplate] = None,
+                  prompt_template: Optional[PromptTemplate] = None,
+                  output_json_filepath: Optional[str] = None,
+                  output_json_filename: Optional[str] = None) -> dict:
+        # 1. Preparation for output logs
+        output_handler = self.HandlerType()
+
+        if output_json_filepath is None:
+            output_json_filepath = self.output_json_filepath
+        if output_json_filename is None:
+            output_json_filename = self.output_json_filename
+
+        # 2. Get results of retrieval process
+        ice_idx_list = retriever.retrieve()
+
+        # 3. Generate prompts for testing input
+        chat_list = self.get_chat_list(
+            ice_idx_list,
+            retriever,
+            prompt_template=prompt_template,
+        )
+
+        # Create tmp json file for saving intermediate results and future
+        # resuming
+        index = 0
+        tmp_json_filepath = os.path.join(output_json_filepath,
+                                         'tmp_' + output_json_filename)
+        if osp.exists(tmp_json_filepath):
+            # TODO: move resume to output handler
+            try:
+                tmp_result_dict = mmengine.load(tmp_json_filepath)
+            except Exception:
+                pass
+            else:
+                output_handler.results_dict = tmp_result_dict
+                index = len(tmp_result_dict)
+
+        # 4. Wrap prompts with Dataloader
+        dataloader = self.get_dataloader(chat_list[index:], batch_size=1)
+
+        # 5. Inference for prompts in each batch
+        logger.debug('Starting inference process...')
+        for datum in tqdm(dataloader, disable=not self.is_main_process):
+            chat = datum[0]
+            if self.infer_mode == 'last':
+                await self.infer_last(chat, index, output_handler)
+            elif self.infer_mode == 'every':
+                await self.infer_every(chat, index, output_handler)
+            elif self.infer_mode == 'every_with_gt':
+                await self.infer_every_with_gt(chat, index, output_handler)
+            index += 1
+
+            # Save intermediate results
+            if (self.save_every is not None and index % self.save_every == 0
+                    and self.is_main_process):
+                output_handler.write_to_json(output_json_filepath,
+                                             'tmp_' + output_json_filename)
+
+        # 4. Output
+        if self.is_main_process:
+            os.makedirs(output_json_filepath, exist_ok=True)
+            output_handler.write_to_json(output_json_filepath,
+                                         output_json_filename)
+            if osp.exists(tmp_json_filepath):
+                os.remove(tmp_json_filepath)
+
+        return output_handler.results_dict
+
+    def get_chat_list(self,
+                      ice_idx_list: List[List[int]],
+                      retriever: BaseRetriever,
+                      prompt_template: Optional[PromptTemplate] = None):
+        prompt_list = []
+        input_columns = retriever.dataset_reader.input_columns
+        output_column = retriever.dataset_reader.output_column
+
+        def chat_from_entry(entry):
+            if prompt_template is None and len(input_columns) == 1:
+                # Directly use the input column as the user input
+                user = entry.get(input_columns[0])
+                assistant = entry.get(output_column, '')
+                return [
+                    dict(role='user', content=user),
+                    dict(role='assistant', content=assistant),
+                ]
+            elif prompt_template is not None:
+                # Use prompt template to generate chat history
+                chat = promptlist_to_openai(
+                    prompt_template.generate_item(entry))
+                gold = entry.get(output_column, '')
+                if chat[-1]['role'] != 'assistant':
+                    chat.append(dict(role='assistant', content=gold))
+                return chat
+            else:
+                raise ValueError()
+
+        for idx, ice_idx in enumerate(ice_idx_list):
+            # NOTE: The in-context examples won't be used by now.
+
+            item = {
+                k: v
+                for k, v in retriever.test_ds[idx].items()
+                if k in input_columns or k == output_column
+            }
+            if all(isinstance(value, str) for value in item.values()):
+                # Every column is a single string
+                chat = chat_from_entry(item)
+            elif all(is_list_of(value, str) for value in item.values()):
+                # Every column is a list of string for multi-round chat
+                entries = [dict(zip(item, v)) for v in zip(*item.values())]
+                chat = sum((chat_from_entry(entry) for entry in entries), [])
+            elif len(input_columns) == 1 and is_list_of(
+                    item[input_columns[0]], dict):
+                # Single input column and it's already a chat.
+                chat = item[input_columns[0]]
+            elif 'dialogue' in input_columns:
+                chat = item['dialogue']
+                self.dialogue_mode = True
+            else:
+                raise ValueError('Cannot construct chat from the dataset.')
+
+            prompt_list.append(chat)
+        return prompt_list
+
+    async def infer_last(self, chat: List[dict], index: int, output_handler):
+        assistant_indices = [
+            i for i, item in enumerate(chat) if item['role'] == 'assistant'
+        ]
+
+        history = chat[:assistant_indices[-1]]
+        output = await self.model.generate_from_template(
+            [history], max_out_len=self.max_out_len)[0]
+        output_handler.save_results(
+            origin_prompt=history,
+            prediction=output,
+            idx=index,
+            gold=chat[assistant_indices[-1]]['content'],
+        )
+
+    async def infer_every(self, chat: List[dict], index: int, output_handler):
+        assistant_indices = [
+            i for i, item in enumerate(chat) if item['role'] == 'assistant'
+        ]
+        index_copy = index
+
+        for i in assistant_indices:
+            history = chat[:i]
+            output = await self.model.generate_from_template(
+                [history], max_out_len=self.max_out_len)[0]
+            chat[i]['content'] = output
+            if not self.dialogue_mode:
+                output_handler.save_multiround_results(
+                    origin_prompt=history[-1]['content'],
+                    prediction=output,
+                    idx=index,
+                    gold=chat[i]['content'],
+                )
+                # index += 1
+        if self.dialogue_mode:
+            # dialogue mode for subjective evaluation
+            assert len(chat) % 2 == 0
+            round_num = int(len(chat) / 2)
+            preds_list = []
+            for i in range(round_num):
+                temp_dict = {
+                    'round': i + 1,
+                    'user': chat[i * 2]['content'],
+                    'assistant': chat[i * 2 + 1]['content']
+                }
+                preds_list.append(temp_dict)
+            output_handler.save_results(
+                origin_prompt=None,
+                prediction=preds_list,
+                idx=index_copy,
+                gold=None,
+            )
+
+    async def infer_every_with_gt(self, chat: List[dict], index: int,
+                            output_handler):
+        assistant_indices = [
+            i for i, item in enumerate(chat) if item['role'] == 'assistant'
+        ]
+
+        for i in assistant_indices:
+            history = chat[:i]
+            output = await self.model.generate_from_template(
+                [history], max_out_len=self.max_out_len)[0]
+            output_handler.save_multiround_results(
+                origin_prompt=history[-1]['content'],
+                prediction=output,
+                idx=index,
+                gold=chat[i]['content'],
+            )
+            index += 1

opencompass/openicl/icl_inferencer/icl_gen_async_inferencer.py

@@ -0,0 +1,239 @@
+"""Direct Generation Inferencer."""
+
+import inspect
+import json
+import os
+import os.path as osp
+import time
+from typing import List, Optional
+
+import mmengine
+import torch
+from tqdm import tqdm
+
+from opencompass.models.base import BaseModel
+from opencompass.registry import ICL_INFERENCERS
+from opencompass.utils import batched
+
+from ..icl_prompt_template import PromptTemplate
+from ..icl_retriever import BaseRetriever
+from ..utils.logging import get_logger
+from .icl_base_inferencer import BaseInferencer, GenInferencerOutputHandler
+
+logger = get_logger(__name__)
+
+
+@ICL_INFERENCERS.register_module()
+class AsyncGenInferencer(BaseInferencer):
+    """Generation Inferencer class to directly evaluate by generation.
+
+    Attributes:
+        model (:obj:`BaseModelWrapper`, optional): The module to inference.
+        max_seq_len (:obj:`int`, optional): Maximum number of tokenized words
+            allowed by the LM.
+        min_out_len (:obj:`int`, optional): Minimum number of generated tokens
+            by the LM
+        batch_size (:obj:`int`, optional): Batch size for the
+            :obj:`DataLoader`.
+        output_json_filepath (:obj:`str`, optional): File path for output
+            `JSON` file.
+        output_json_filename (:obj:`str`, optional): File name for output
+            `JSON` file.
+        gen_field_replace_token (:obj:`str`, optional): Used to replace the
+            generation field token when generating prompts.
+        save_every (:obj:`int`, optional): Save intermediate results every
+            `save_every` iters. Defaults to 1.
+        generation_kwargs (:obj:`Dict`, optional): Parameters for the
+            :obj:`model.generate()` method.
+    """
+
+    def __init__(
+            self,
+            model: BaseModel,
+            max_out_len: int,
+            stopping_criteria: List[str] = [],
+            max_seq_len: Optional[int] = None,
+            min_out_len: Optional[int] = None,
+            batch_size: Optional[int] = 1,
+            gen_field_replace_token: Optional[str] = '',
+            output_json_filepath: Optional[str] = './icl_inference_output',
+            output_json_filename: Optional[str] = 'predictions',
+            save_every: Optional[int] = 1,
+            **kwargs) -> None:
+        super().__init__(
+            model=model,
+            max_seq_len=max_seq_len,
+            batch_size=batch_size,
+            output_json_filename=output_json_filename,
+            output_json_filepath=output_json_filepath,
+            **kwargs,
+        )
+
+        self.gen_field_replace_token = gen_field_replace_token
+        self.max_out_len = max_out_len
+        self.min_out_len = min_out_len
+        self.stopping_criteria = stopping_criteria
+        self.dump_timer = kwargs.get('dump_timer', False)
+
+        if self.model.is_api and save_every is None:
+            save_every = 1
+        self.save_every = save_every
+
+    async def inference(self,  # type: ignore
+                  retriever: BaseRetriever,
+                  ice_template: Optional[PromptTemplate] = None,
+                  prompt_template: Optional[PromptTemplate] = None,
+                  output_json_filepath: Optional[str] = None,
+                  output_json_filename: Optional[str] = None) -> List:
+        # 1. Preparation for output logs
+        output_handler = GenInferencerOutputHandler()
+
+        if output_json_filepath is None:
+            output_json_filepath = self.output_json_filepath
+        if output_json_filename is None:
+            output_json_filename = self.output_json_filename
+
+        # 2. Get results of retrieval process
+        ice_idx_list = retriever.retrieve()
+
+        # 3. Generate prompts for testing input
+        prompt_list = self.get_generation_prompt_list_from_retriever_indices(
+            ice_idx_list,
+            retriever,
+            self.gen_field_replace_token,
+            max_seq_len=self.max_seq_len,
+            ice_template=ice_template,
+            prompt_template=prompt_template)
+
+        # 3.1 Fetch and zip prompt & gold answer if output column exists
+        ds_reader = retriever.dataset_reader
+        if ds_reader.output_column:
+            gold_ans = ds_reader.dataset['test'][ds_reader.output_column]
+            prompt_list = list(zip(prompt_list, gold_ans))
+
+        # Create tmp json file for saving intermediate results and future
+        # resuming
+        index = 0
+        tmp_json_filepath = os.path.join(output_json_filepath,
+                                         'tmp_' + output_json_filename)
+        if osp.exists(tmp_json_filepath):
+            # TODO: move resume to output handler
+            try:
+                tmp_result_dict = mmengine.load(tmp_json_filepath)
+            except Exception:
+                pass
+            else:
+                output_handler.results_dict = tmp_result_dict
+                index = len(tmp_result_dict)
+
+        # 4. Wrap prompts with Dataloader
+        logger.debug('Starting build dataloader')
+        dataloader = self.get_dataloader(prompt_list[index:], self.batch_size)
+
+        # 5. Inference for prompts in each batch
+        logger.debug('Starting inference process...')
+
+        start_time_stamp = time.time()
+        num_sample = 0
+        # TODO: batched dataloader shoule be replaced with async fetching
+        for datum in dataloader:
+            if ds_reader.output_column:
+                entry, golds = list(zip(*datum))
+            else:
+                entry = datum
+                golds = [None for _ in range(len(entry))]
+            # 5-1. Inference with local model
+            extra_gen_kwargs = {}
+            sig = inspect.signature(self.model.generate)
+            if 'stopping_criteria' in sig.parameters:
+                extra_gen_kwargs['stopping_criteria'] = self.stopping_criteria
+            if 'min_out_len' in sig.parameters:
+                extra_gen_kwargs['min_out_len'] = self.min_out_len
+            with torch.no_grad():
+                parsed_entries = self.model.parse_template(entry, mode='gen')
+                results = await self.model.generate_from_template(
+                    entry, max_out_len=self.max_out_len, **extra_gen_kwargs)
+                generated = results
+
+            num_return_sequences = getattr(self.model, 'generation_kwargs',
+                                           {}).get('num_return_sequences', 1)
+            # 5-3. Save current output
+            for prompt, prediction, gold in zip(
+                    parsed_entries, batched(generated, num_return_sequences),
+                    golds):
+                if num_return_sequences == 1:
+                    prediction = prediction[0]
+                output_handler.save_results(prompt,
+                                            prediction,
+                                            index,
+                                            gold=gold)
+                index = index + 1
+
+            # 5-4. Save intermediate results
+            if (self.save_every is not None and index % self.save_every == 0
+                    and self.is_main_process):
+                output_handler.write_to_json(output_json_filepath,
+                                             'tmp_' + output_json_filename)
+            num_sample += len(datum)
+
+        end_time_stamp = time.time()
+
+        # 6. Output
+        if self.is_main_process:
+            os.makedirs(output_json_filepath, exist_ok=True)
+            output_handler.write_to_json(output_json_filepath,
+                                         output_json_filename)
+            if osp.exists(tmp_json_filepath):
+                os.remove(tmp_json_filepath)
+
+        if self.dump_timer and self.is_main_process:
+            timer_filepath = os.path.join(output_json_filepath, 'timer',
+                                          'time.jsonl')
+            os.makedirs(os.path.dirname(timer_filepath), exist_ok=True)
+            time_dict = {
+                'dataset_name': output_json_filename.removesuffix('.json'),
+                'time': end_time_stamp - start_time_stamp,
+                'num_sample': num_sample
+            }
+            with open(timer_filepath, 'a') as f:
+                f.write(json.dumps(time_dict) + '\n')
+
+        return [
+            sample['prediction']
+            for sample in output_handler.results_dict.values()
+        ]
+
+    def get_generation_prompt_list_from_retriever_indices(
+            self,
+            ice_idx_list: List[List[int]],
+            retriever: BaseRetriever,
+            gen_field_replace_token: str,
+            max_seq_len: Optional[int] = None,
+            ice_template: Optional[PromptTemplate] = None,
+            prompt_template: Optional[PromptTemplate] = None):
+        prompt_list = []
+        for idx, ice_idx in enumerate(ice_idx_list):
+            ice = retriever.generate_ice(ice_idx, ice_template=ice_template)
+            prompt = retriever.generate_prompt_for_generate_task(
+                idx,
+                ice,
+                gen_field_replace_token=gen_field_replace_token,
+                ice_template=ice_template,
+                prompt_template=prompt_template)
+            if max_seq_len is not None:
+                prompt_token_num = self.model.get_token_len_from_template(
+                    prompt, mode='gen')
+                while len(ice_idx) > 0 and prompt_token_num > max_seq_len:
+                    ice_idx = ice_idx[:-1]
+                    ice = retriever.generate_ice(ice_idx,
+                                                 ice_template=ice_template)
+                    prompt = retriever.generate_prompt_for_generate_task(
+                        idx,
+                        ice,
+                        gen_field_replace_token=gen_field_replace_token,
+                        ice_template=ice_template,
+                        prompt_template=prompt_template)
+                    prompt_token_num = self.model.get_token_len_from_template(
+                        prompt, mode='gen')
+            prompt_list.append(prompt)
+        return prompt_list

opencompass/runners/local_async.py

@@ -0,0 +1,112 @@
+from math import prod
+import os
+import os.path as osp
+import re
+import subprocess
+import sys
+import time
+from concurrent.futures import ThreadPoolExecutor
+from functools import partial
+from threading import Lock
+from typing import Any, Dict, List, Tuple
+
+import mmengine
+import numpy as np
+from mmengine.config import ConfigDict
+from mmengine.device import is_npu_available
+from tqdm import tqdm
+
+from opencompass.registry import RUNNERS, TASKS
+from opencompass.utils import get_logger, model_abbr_from_cfg
+
+from .base import BaseRunner
+from typing import TypedDict, Optional
+from multiprocessing.managers import Namespace
+import threading
+import uuid
+import enum
+import signal
+from enum import IntEnum
+import asyncio
+import traceback
+
+
+class Status(IntEnum):
+    SUCCESS = 0
+    FAILED = -1
+    INTERRUPT = signal.SIGINT
+
+
+@RUNNERS.register_module()
+class AsyncRunner(BaseRunner):
+    """Local runner. Start tasks by local python.
+
+    Args:
+        task (ConfigDict): Task type config.
+        max_num_workers (int): Max number of workers to run in parallel.
+            Defaults to 16.
+        max_workers_per_gpu (int): Max number of workers to run for one GPU.
+            Defaults to 1.
+        debug (bool): Whether to run in debug mode.
+        lark_bot_url (str): Lark bot url.
+    """
+
+    # These is a fake typehint
+
+    def __init__(self,
+                 task: ConfigDict,
+                 debug: bool = False,
+                 *,
+                 max_num_workers: int = 16,
+                 keep_tmp_file: bool = False,
+                 **kwargs):
+        super().__init__(task=task, debug=debug)
+        self.max_num_workers = max_num_workers
+        self.keep_tmp_file = keep_tmp_file
+        logger = get_logger()
+        for k, v in kwargs.items():
+            logger.warning(f'Ignored argument in `AsyncRunner`: {k}={v}')
+
+    def launch(self, tasks: List[Dict[str, Any]]) -> List[Tuple[str, Status]]:  # type: ignore
+        """Launch multiple tasks.
+
+        Args:
+            tasks (list[dict]): A list of task configs, usually generated by
+                Partitioner.
+        Returns:
+
+            list[tuple[str, int]]: A list of (task name, exit code).
+        """
+        from opencompass.tasks.openicl_async_task import OpenICLAsyncInferTask
+
+        if not tasks:
+            return [("", Status.SUCCESS)]
+
+        assert len(tasks) == 1, f"Task num must be 1 for `AsyncRunner`"
+        task_cfg = tasks[0]
+
+        task: OpenICLAsyncInferTask = TASKS.build(dict(cfg=task_cfg, type=self.task_cfg['type']))
+        task_name = task.name
+        # get cmd
+        mmengine.mkdir_or_exist('tmp/')
+
+        try:
+            asyncio.run(task.run())
+        except KeyboardInterrupt:
+            return [(task_name, Status.INTERRUPT)]
+        except:
+            print(traceback.print_exc())
+            return [(task_name, Status.FAILED)]
+        else:
+            return [(task_name, Status.SUCCESS)]
+
+    def __call__(self, tasks: List[Dict[str, Any]]):
+        """Launch multiple tasks and summarize the results.
+
+        Args:
+            tasks (list[dict]): A list of task configs, usually generated by
+                Partitioner.
+        """
+        status = self.launch(tasks)
+        status_list = list(status)  # change into list format
+        self.summarize(status_list)

opencompass/tasks/__init__.py

@@ -1,3 +1,4 @@
 from .openicl_attack import *  # noqa: F401, F403
 from .openicl_eval import *  # noqa: F401, F403
 from .openicl_infer import *  # noqa: F401, F403
+from .openicl_async_task import *  # noqa: F401, F403

opencompass/tasks/openicl_async_task.py

@@ -0,0 +1,168 @@
+import argparse
+import os
+import os.path as osp
+import random
+import sys
+import time
+from typing import Any
+from tqdm.asyncio import tqdm
+
+from mmengine.config import Config, ConfigDict
+import inspect
+from mmengine.utils import mkdir_or_exist
+
+from opencompass.registry import (ICL_INFERENCERS, ICL_PROMPT_TEMPLATES,
+                                  ICL_RETRIEVERS, TASKS)
+from opencompass.tasks.base import BaseTask
+from opencompass.utils import (build_dataset_from_cfg, build_model_from_cfg,
+                               get_infer_output_path, get_logger,
+                               task_abbr_from_cfg)
+from opencompass.openicl.icl_inferencer.icl_gen_async_inferencer import AsyncGenInferencer
+from opencompass.openicl.icl_inferencer.icl_chat_async_inferencer import AsyncChatInferencer
+from opencompass.openicl.icl_inferencer import GenInferencer, ChatInferencer
+from concurrent.futures import ThreadPoolExecutor
+import asyncio
+import resource
+from more_itertools import consume
+
+
+soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
+resource.setrlimit(resource.RLIMIT_NOFILE, (8192, hard))
+
+
+@TASKS.register_module()
+class OpenICLAsyncInferTask(BaseTask):
+    """OpenICL Inference Task.
+
+    This task is used to run the inference process.
+    """
+
+    name_prefix = 'OpenICLInfer'
+    log_subdir = 'logs/infer'
+    output_subdir = 'predictions'
+
+    def __init__(self, cfg: ConfigDict):
+        super().__init__(cfg)
+        run_cfg = self.model_cfgs[0].get('run_cfg', {})
+        self.nproc = run_cfg.get('nproc_per_worker', 16)
+
+    def get_command(self, cfg_path, template) -> str:
+        # TODO:
+        raise NotImplementedError()
+        return ""
+
+    async def run(self):  # type: ignore
+        _dataset_cfgs = []
+        infer_cfgs = []
+        sub_cfgs = []
+        datasets = []
+        model_cfgs = []
+        for model_cfg, dataset_cfgs in zip(self.model_cfgs, self.dataset_cfgs):
+            self.max_out_len = model_cfg.get('max_out_len', None)
+            self.batch_size = model_cfg.get('batch_size', None)
+            self.min_out_len = model_cfg.get('min_out_len', None)
+
+            for dataset_cfg in dataset_cfgs:
+                self.dataset_cfg = dataset_cfg
+                out_path = get_infer_output_path(
+                    model_cfg, dataset_cfg,
+                    osp.join(self.work_dir, 'predictions'))
+
+                if osp.exists(out_path):
+                    continue
+                _dataset_cfgs.append(dataset_cfg)
+                datasets.append(build_dataset_from_cfg(dataset_cfg))
+                infer_cfgs.append(dataset_cfg['infer_cfg'])
+                model_cfgs.append(model_cfg)
+                sub_cfg = {
+                    'models': [model_cfg],
+                    'datasets': [[dataset_cfg]],
+                }
+                sub_cfgs.append(sub_cfg)
+
+        tasks = []
+        args = list(zip(_dataset_cfgs, infer_cfgs, datasets, model_cfgs, sub_cfgs))
+        for arg in tqdm(
+            args,
+            total=len(args),
+            desc=f"Starting building tasks..."
+        ):
+            tasks.append(asyncio.create_task(self._inference(*arg)))
+
+        bar = tqdm(desc="Inferencing...", total=len(tasks))
+        bar.refresh()
+
+        while tasks:
+            done, tasks = await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED)
+            for _ in done:
+                bar.update()
+                bar.refresh()
+
+        # TODO: Needs a debug mode
+        # for arg in zip(_dataset_cfgs, infer_cfgs, datasets, model_cfgs, sub_cfgs):
+        #     await self._inference(*arg)
+
+    async def _inference(self, dataset_cfg, infer_cfg, dataset, model_cfg, sub_cfg):
+        model = build_model_from_cfg(model_cfg)
+        assert hasattr(infer_cfg, 'ice_template') or hasattr(infer_cfg, 'prompt_template'), \
+            'Both ice_template and prompt_template cannot be None simultaneously.'  # noqa: E501
+
+        infer_kwargs: dict = {}
+        if hasattr(infer_cfg, 'ice_template'):
+            ice_template = ICL_PROMPT_TEMPLATES.build(
+                infer_cfg['ice_template'])
+            infer_kwargs['ice_template'] = ice_template
+
+        if hasattr(infer_cfg, 'prompt_template'):
+            prompt_template = ICL_PROMPT_TEMPLATES.build(
+                infer_cfg['prompt_template'])
+            infer_kwargs['prompt_template'] = prompt_template
+
+        retriever_cfg = infer_cfg['retriever'].copy()
+        retriever_cfg['dataset'] = dataset
+        retriever = ICL_RETRIEVERS.build(retriever_cfg)
+
+        # set inferencer's default value according to model's config'
+        inferencer_cfg: dict = infer_cfg['inferencer']
+        inferencer_cfg['model'] = model
+        inferencer_cfg['max_seq_len'] = model_cfg.get('max_seq_len')
+
+        infer_type = inferencer_cfg["type"]
+        if inspect.isclass(infer_type):
+            infer_name = infer_type.__name__
+        else:
+            infer_name = infer_type
+
+        if infer_name.split(".")[-1] == "ChatInferencer":
+            inferencer_cfg["type"] = AsyncChatInferencer
+
+        elif infer_name.split(".")[-1] == "GenInferencer":
+            inferencer_cfg["type"] = AsyncGenInferencer
+
+        inferencer_cfg.setdefault('max_out_len', self.max_out_len)
+        inferencer_cfg.setdefault('min_out_len', self.min_out_len)
+        inferencer_cfg.setdefault('batch_size', self.batch_size)
+        inferencer = ICL_INFERENCERS.build(inferencer_cfg)
+
+        out_path = get_infer_output_path(
+            model_cfg, dataset_cfg,
+            osp.join(self.work_dir, 'predictions'))
+        out_dir, out_file = osp.split(out_path)
+        mkdir_or_exist(out_dir)
+
+        infer_kwargs['output_json_filepath'] = out_dir
+        infer_kwargs['output_json_filename'] = out_file
+
+        await inferencer.inference(retriever, **infer_kwargs)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Model Inferencer')
+    parser.add_argument('config', help='Config file path')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    # TODO:
+    raise NotImplementedError()