[Feature] Add support of qwen api (#735)

configs/api_examples/eval_api_qwen.py

@@ -0,0 +1,40 @@
+from mmengine.config import read_base
+from opencompass.models import Qwen
+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='qwen-max',
+        type=Qwen,
+        path='qwen-max',
+        key='xxxxxxxxxxxxxxxx',  # please give you key
+        generation_kwargs={
+            'enable_search': False,
+        },
+        query_per_second=1,
+        max_out_len=2048,
+        max_seq_len=2048,
+        batch_size=8
+    ),
+]
+
+infer = dict(
+    partitioner=dict(type=NaivePartitioner),
+    runner=dict(
+        type=LocalAPIRunner,
+        max_num_workers=1,
+        concurrent_users=1,
+        task=dict(type=OpenICLInferTask)),
+)
+
+work_dir = "outputs/api_qwen/"

opencompass/models/__init__.py

@@ -19,6 +19,7 @@ from .modelscope import ModelScope, ModelScopeCausalLM  # noqa: F401, F403
 from .moonshot_api import MoonShot  # noqa: F401
 from .openai_api import OpenAI  # noqa: F401
 from .pangu_api import PanGu  # noqa: F401
+from .qwen_api import Qwen  # noqa: F401
 from .sensetime_api import SenseTime  # noqa: F401
 from .turbomind import TurboMindModel  # noqa: F401
 from .turbomind_tis import TurboMindTisModel  # noqa: F401

opencompass/models/qwen_api.py

@@ -0,0 +1,153 @@
+import time
+from concurrent.futures import ThreadPoolExecutor
+from typing import Dict, List, Optional, Union
+
+from opencompass.utils.prompt import PromptList
+
+from .base_api import BaseAPIModel
+
+PromptType = Union[PromptList, str]
+
+
+class Qwen(BaseAPIModel):
+    """Model wrapper around Qwen.
+
+    Documentation:
+        https://help.aliyun.com/zh/dashscope/developer-reference/tongyi-thousand-questions/
+
+    Args:
+        path (str): The name of qwen model.
+            e.g. `qwen-max`
+        key (str): Authorization key.
+        query_per_second (int): The maximum queries allowed per second
+            between two consecutive calls of the API. Defaults to 1.
+        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,
+                 query_per_second: int = 1,
+                 max_seq_len: int = 2048,
+                 meta_template: Optional[Dict] = None,
+                 retry: int = 5,
+                 generation_kwargs: Dict = {}):
+        super().__init__(path=path,
+                         max_seq_len=max_seq_len,
+                         query_per_second=query_per_second,
+                         meta_template=meta_template,
+                         retry=retry,
+                         generation_kwargs=generation_kwargs)
+        import dashscope
+        dashscope.api_key = key
+        self.dashscope = dashscope
+
+    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))
+        """
+        {
+          "messages": [
+            {"role":"user","content":"请介绍一下你自己"},
+            {"role":"assistant","content":"我是通义千问"},
+            {"role":"user","content": "我在上海，周末可以去哪里玩？"},
+            {"role":"assistant","content": "上海是一个充满活力和文化氛围的城市"},
+            {"role":"user","content": "周末这里的天气怎么样？"}
+          ]
+        }
+
+        """
+
+        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'
+
+                messages.append(msg)
+        data = {'messages': messages}
+        data.update(self.generation_kwargs)
+
+        max_num_retries = 0
+        while max_num_retries < self.retry:
+            self.acquire()
+            response = self.dashscope.Generation.call(
+                model=self.path,
+                **data,
+            )
+            self.release()
+
+            if 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 response.status_code == 200:
+                try:
+                    msg = response.output.text
+                    return msg
+                except KeyError:
+                    print(response)
+                    self.logger.error(str(response.status_code))
+                    time.sleep(1)
+                    continue
+
+            if ('Range of input length should be ' in response.message
+                    or  # input too long
+                    'Input data may contain inappropriate content.'
+                    in response.message):  # bad input
+                print(response.message)
+                return ''
+            print(response)
+            max_num_retries += 1
+
+        raise RuntimeError(response.message)