[Feature] Add Claude support (#253)

* [Feature] Add Claude support

* [Feature] Add Claude support

* Update opencompass/models/claude_api.py

Co-authored-by: Hubert <42952108+yingfhu@users.noreply.github.com>

* raise import erorr

---------

Co-authored-by: Hubert <42952108+yingfhu@users.noreply.github.com>

configs/eval_claude2.py

@@ -0,0 +1,28 @@
+from mmengine.config import read_base
+from opencompass.models.claude_api import Claude
+from opencompass.partitioners import NaivePartitioner
+from opencompass.runners import LocalRunner
+from opencompass.tasks import OpenICLInferTask
+
+with read_base():
+    # choose a list of datasets
+    from .datasets.collections.chat_medium import datasets
+    # and output the results in a choosen format
+    from .summarizers.medium import summarizer
+
+models = [
+    dict(abbr='Claude2',
+        type=Claude,
+        path='claude-2',
+        key='YOUR_CLAUDE_KEY',
+        query_per_second=1,
+        max_out_len=2048, max_seq_len=2048, batch_size=2),
+]
+
+infer = dict(
+    partitioner=dict(type=NaivePartitioner),
+    runner=dict(
+        type=LocalRunner,
+        max_num_workers=8,
+        task=dict(type=OpenICLInferTask)),
+)

opencompass/models/claude_api.py

@@ -0,0 +1,118 @@
+from concurrent.futures import ThreadPoolExecutor
+from typing import Dict, List, Optional, Union
+
+from opencompass.registry import MODELS
+from opencompass.utils import PromptList
+
+from .base_api import BaseAPIModel
+
+PromptType = Union[PromptList, str]
+
+
+@MODELS.register_module()
+class Claude(BaseAPIModel):
+    """Model wrapper around Claude API.
+
+    Args:
+        key (str): Authorization key.
+        path (str): The model to be used. Defaults to claude-2.
+        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,
+        key: str,
+        path: str = 'claude-2',
+        query_per_second: int = 2,
+        max_seq_len: int = 2048,
+        meta_template: Optional[Dict] = None,
+        retry: int = 2,
+    ):
+        super().__init__(path=path,
+                         max_seq_len=max_seq_len,
+                         query_per_second=query_per_second,
+                         meta_template=meta_template,
+                         retry=retry)
+        try:
+            from anthropic import AI_PROMPT, HUMAN_PROMPT, Anthropic
+        except ImportError:
+            raise ImportError('Import anthropic failed. Please install it '
+                              'with "pip install anthropic" and try again.')
+
+        self.anthropic = Anthropic(api_key=key)
+        self.model = path
+        self.human_prompt = HUMAN_PROMPT
+        self.ai_prompt = AI_PROMPT
+
+    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)))
+        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 = f'{self.human_prompt} {input}{self.ai_prompt}'
+        else:
+            messages = ''
+            for item in input:
+                if item['role'] == 'HUMAN' or item['role'] == 'SYSTEM':
+                    messages += f'{self.human_prompt} {item["prompt"]}'
+                elif item['role'] == 'BOT':
+                    messages += f'{self.ai_prompt} {item["prompt"]}'
+            if not messages.endswith(self.ai_prompt):
+                messages += self.ai_prompt
+
+        num_retries = 0
+        while num_retries < self.retry:
+            self.wait()
+            try:
+                completion = self.anthropic.completions.create(
+                    model=self.model,
+                    max_tokens_to_sample=max_out_len,
+                    prompt=messages)
+                return completion.completion
+            except Exception as e:
+                self.logger.error(e)
+            num_retries += 1
+        raise RuntimeError('Calling Claude API failed after retrying for '
+                           f'{self.retry} times. Check the logs for details.')