[Feature] Add support for Nanbeige API (#786)

Co-authored-by: notoschord <wangzekai@kanzhun.com>

configs/api_examples/eval_api_nanbeige.py

@@ -0,0 +1,36 @@
+from mmengine.config import read_base
+from opencompass.models import Nanbeige
+from opencompass.partitioners import NaivePartitioner
+from opencompass.runners.local_api import LocalAPIRunner
+from opencompass.tasks import OpenICLInferTask
+
+
+with read_base():
+    from ..summarizers.medium import summarizer
+    from ..datasets.ceval.ceval_gen import ceval_datasets
+
+datasets = [
+    *ceval_datasets,
+]
+
+models = [
+    dict(
+        abbr='nanbeige-plus',
+        type=Nanbeige,
+        path='nanbeige-plus',
+        key="xxxxxx",
+        query_per_second=1,
+        max_out_len=2048,
+        batch_size=8),
+]
+
+infer = dict(
+    partitioner=dict(type=NaivePartitioner),
+    runner=dict(
+        type=LocalAPIRunner,
+        max_num_workers=2,
+        concurrent_users=2,
+        task=dict(type=OpenICLInferTask)),
+)
+
+work_dir ="./output/nanbeige-plus"

opencompass/models/__init__.py

@@ -18,6 +18,7 @@ from .minimax_api import MiniMax  # noqa: F401
 from .mixtral import Mixtral  # noqa: F401
 from .modelscope import ModelScope, ModelScopeCausalLM  # noqa: F401, F403
 from .moonshot_api import MoonShot  # noqa: F401
+from .nanbeige_api import Nanbeige  # noqa: F401
 from .openai_api import OpenAI  # noqa: F401
 from .pangu_api import PanGu  # noqa: F401
 from .qwen_api import Qwen  # noqa: F401

opencompass/models/nanbeige_api.py

@@ -0,0 +1,148 @@
+import time
+from concurrent.futures import ThreadPoolExecutor
+from typing import Dict, List, Optional, Union
+
+import requests
+
+from opencompass.utils.prompt import PromptList
+
+from .base_api import BaseAPIModel
+
+PromptType = Union[PromptList, str]
+
+
+class Nanbeige(BaseAPIModel):
+    """Model wrapper around Nanbeige.
+
+    Documentations:
+
+    Args:
+        path (str): Model name, e.g. `nanbeige-plus`
+        key (str): Provide API Key
+        url (str): Provided URL
+        query_per_second (int): The maximum queries allowed per second
+            between two consecutive calls of the API. Defaults to 2.
+        max_seq_len (int): Unused here.
+        meta_template (Dict, optional): The model's meta prompt
+            template if needed, in case the requirement of injecting or
+            wrapping of any meta instructions.
+        retry (int): Number of retires if the API call fails. Defaults to 2.
+    """
+
+    def __init__(self,
+                 path: str,
+                 key: str,
+                 url: str = None,
+                 query_per_second: int = 2,
+                 max_seq_len: int = 2048,
+                 meta_template: Optional[Dict] = None,
+                 retry: int = 3):
+        super().__init__(path=path,
+                         max_seq_len=max_seq_len,
+                         query_per_second=query_per_second,
+                         meta_template=meta_template,
+                         retry=retry)
+        self.headers = {
+            'Authorization': 'Bearer ' + key,
+            'Content-Type': 'application/json',
+        }
+        self.model = path
+        self.url = url if url is not None \
+            else 'http://stardustlm.zhipin.com/api/gpt/open/chat/send/sync'
+
+    def generate(
+        self,
+        inputs: List[str or PromptList],
+        max_out_len: int = 512,
+    ) -> List[str]:
+        """Generate results given a list of inputs.
+
+        Args:
+            inputs (List[str or PromptList]): 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.
+
+        Returns:
+            List[str]: A list of generated strings.
+        """
+        with ThreadPoolExecutor() as executor:
+            results = list(
+                executor.map(self._generate, inputs,
+                             [max_out_len] * len(inputs)))
+        self.flush()
+        return results
+
+    def _generate(
+        self,
+        input: str or PromptList,
+        max_out_len: int = 512,
+    ) -> str:
+        """Generate results given an input.
+
+        Args:
+            inputs (str or PromptList): A string or PromptDict.
+                The PromptDict should be organized in OpenCompass'
+                API format.
+            max_out_len (int): The maximum length of the output.
+
+        Returns:
+            str: The generated string.
+        """
+        assert isinstance(input, (str, PromptList))
+
+        if isinstance(input, str):
+            messages = [{'sender_type': 'USER', 'text': input}]
+        else:
+            messages = []
+            for item in input:
+                msg = {'text': item['prompt']}
+                if item['role'] == 'HUMAN':
+                    msg['sender_type'] = 'USER'
+                elif item['role'] == 'BOT':
+                    msg['sender_type'] = 'BOT'
+
+                messages.append(msg)
+
+        data = {
+            'model': self.model,
+            'messages': messages,
+        }
+
+        max_num_retries = 0
+        while max_num_retries < self.retry:
+            self.acquire()
+            raw_response = requests.request('POST',
+                                            url=self.url,
+                                            headers=self.headers,
+                                            json=data)
+            self.release()
+
+            if raw_response is None:
+                print('Connection error, reconnect.')
+                # if connect error, frequent requests will casuse
+                # continuous unstable network, therefore wait here
+                # to slow down the request
+                self.wait()
+                continue
+
+            if raw_response.status_code != 200:
+                print('请求失败：', raw_response)
+                print('失败信息：', raw_response.text)
+                max_num_retries += 1
+                continue
+
+            response = raw_response.json()
+            if response['stardustCode'] == 0:
+                return response['reply']
+
+            # exceed concurrency limit
+            if response['stardustCode'] == 20035:
+                print(response)
+                time.sleep(2)
+                continue
+
+            print(response)
+            max_num_retries += 1
+
+        raise RuntimeError(raw_response.text)