[Feature] Support Omni-Math

opencompass/configs/datasets/omni_math/README.md

@@ -0,0 +1,43 @@
+# Omni-Math
+
+[Omni-Math](https://huggingface.co/datasets/KbsdJames/Omni-MATH) contains 4428 competition-level problems. These problems are meticulously categorized into 33 (and potentially more) sub-domains and span across 10 distinct difficulty levels, enabling a nuanced analysis of model performance across various mathematical disciplines and levels of complexity.
+
+* Project Page: https://omni-math.github.io/
+* Github Repo: https://github.com/KbsdJames/Omni-MATH
+* Omni-Judge (opensource evaluator of this dataset): https://huggingface.co/KbsdJames/Omni-Judge
+
+## Omni-Judge
+
+> Omni-Judge is an open-source mathematical evaluation model designed to assess whether a solution generated by a model is correct given a problem and a standard answer. 
+
+You should deploy the omni-judge server like:
+```bash
+set -x
+
+lmdeploy serve api_server KbsdJames/Omni-Judge --server-port 8000 \
+    --tp 1 \
+    --cache-max-entry-count 0.9 \
+    --log-level INFO
+```
+
+and set the server url in opencompass config file:
+
+```python
+from mmengine.config import read_base
+
+with read_base():
+    from opencompass.configs.datasets.omni_math.omni_math_gen import omni_math_datasets
+
+
+omni_math_dataset = omni_math_datasets[0]
+omni_math_dataset['eval_cfg']['evaluator'].update(
+    url=['http://172.30.8.45:8000',
+         'http://172.30.16.113:8000'],
+)
+```
+
+## Performance
+
+| llama-3_1-8b-instruct | qwen-2_5-7b-instruct | InternLM3-8b-Instruct |
+| -- | -- | -- |
+| 15.18 | 29.97 | 32.75 |

opencompass/configs/datasets/omni_math/omni_math_gen.py

@@ -0,0 +1,4 @@
+from mmengine.config import read_base
+
+with read_base():
+    from .omni_math_gen_18cc08 import omni_math_datasets  # noqa: F401, F403

opencompass/configs/datasets/omni_math/omni_math_gen_18cc08.py

@@ -0,0 +1,45 @@
+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.openicl.icl_inferencer import GenInferencer
+
+from opencompass.datasets.omni_math import OmniMathDataset, OmniMathEvaluator
+
+
+reader_cfg = dict(
+    input_columns=['problem'], 
+    output_column='answer'
+)
+
+infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(
+            round=[
+                dict(role='HUMAN', prompt='please answer the following mathematical question, put your final answer in \\boxed{}.\n\n{problem}'),
+            ]
+        )
+    ),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(
+        type=GenInferencer, 
+        max_out_len=2048,
+        temperature=0.0
+    )
+)
+
+eval_cfg = dict(
+    evaluator=dict(
+        type=OmniMathEvaluator,
+        url=[]
+    )
+)
+
+omni_math_datasets = [
+    dict(
+        type=OmniMathDataset,
+        abbr='OmniMath',
+        reader_cfg=reader_cfg,
+        infer_cfg=infer_cfg,
+        eval_cfg=eval_cfg
+    )
+]

opencompass/datasets/omni_math.py

@@ -0,0 +1,118 @@
+import concurrent.futures
+from typing import List
+
+import numpy as np
+from datasets import load_dataset
+from transformers import AutoTokenizer
+
+from opencompass.models.turbomind_api import TurboMindAPIModel
+from opencompass.openicl.icl_evaluator import BaseEvaluator
+from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET, MODELS
+
+from .base import BaseDataset
+
+
+@LOAD_DATASET.register_module()
+class OmniMathDataset(BaseDataset):
+
+    @staticmethod
+    def load():
+        dataset = load_dataset('KbsdJames/Omni-MATH')['test']
+        return dataset
+
+
+@ICL_EVALUATORS.register_module()
+class OmniMathEvaluator(BaseEvaluator):
+    api_meta_template = dict(round=[
+        dict(role='HUMAN', api_role='HUMAN'),
+        dict(role='BOT', api_role='BOT', generate=True),
+    ])
+
+    def __init__(self, url):
+        if isinstance(url, str):
+            url = [url]
+
+        self.model = [
+            MODELS.build(
+                dict(
+                    type=TurboMindAPIModel,
+                    model_name='KbsdJames/Omni-Judge',
+                    api_addr=url,
+                    meta_template=self.api_meta_template,
+                    temperature=0.0,
+                    max_seq_len=8192,
+                )) for url in url
+        ]
+        self.tokenizer = AutoTokenizer.from_pretrained('KbsdJames/Omni-Judge',
+                                                       trust_remote_code=True)
+
+    def batch_infer(self, models: List[TurboMindAPIModel],
+                    inputs: List[str]) -> List[str]:
+        batch_num = len(models)
+        batch_size = (len(inputs) + batch_num - 1) // batch_num
+        result_responses = []
+
+        with concurrent.futures.ThreadPoolExecutor(
+                max_workers=batch_num) as executor:
+            futures = [
+                executor.submit(models[i].generate,
+                                inputs[i * batch_size:(i + 1) * batch_size])
+                for i in range(batch_num)
+            ]
+            for response in executor.map(lambda f: f.result(), futures):
+                result_responses.extend(response)
+
+        return result_responses
+
+    def parse_response(self, response):
+        response = '## Student Final Answer\n' + response.strip()
+
+        parts = response.split('## ')
+        info = {}
+
+        for part in parts[1:]:
+            lines = part.strip().split('\n')
+            title = lines[0].strip()
+            content = '\n'.join(lines[1:]).strip()
+
+            if title == 'Justification':
+                info[title] = content
+            else:
+                info[title] = lines[1].strip() if len(lines) > 1 else ''
+
+        if info == {}:
+            return False
+        try:
+            correctness = info['Equivalence Judgement']
+            if correctness == 'TRUE':
+                return True
+            else:
+                return False
+        except Exception as e:
+            print(e)
+            return False
+
+    def score(self, predictions, references, origin_prompt, test_set):
+        questions = [d['problem'] for d in test_set]
+
+        contexts = []
+        for question, reference, candidate in zip(questions, references,
+                                                  predictions):
+            context = self.tokenizer.get_context(question, reference,
+                                                 candidate)
+            contexts.append(context)
+
+        responses = self.batch_infer(self.model, contexts)
+        labels = list(map(self.parse_response, responses))
+
+        details = []
+        for question, reference, candidate, response, label in zip(
+                questions, references, predictions, responses, labels):
+            details.append({
+                'question': question,
+                'reference': reference,
+                'candidate': candidate,
+                'response': response,
+                'label': label
+            })
+        return {'details': details, 'accuracy': np.mean(labels) * 100}

opencompass/models/turbomind_api.py

@@ -39,25 +39,28 @@ class TurboMindAPIModel(BaseModel):
     is_api: bool = True
 
     def __init__(self,
+                 model_name: str = None,
                  api_addr: str = 'http://0.0.0.0:23333',
                  api_key: str | None = None,
                  max_seq_len: int = 2048,
                  meta_template: Optional[Dict] = None,
                  end_str: Optional[str] = None,
+                 temperature: float = None,
                  **kwargs):
         super().__init__(path='',
                          max_seq_len=max_seq_len,
                          meta_template=meta_template)
         from lmdeploy.serve.openai.api_client import APIClient
         self.chatbot = APIClient(api_addr, api_key)
-        self.model_name = self.chatbot.available_models[0]
+        self.model_name = model_name
         self.logger = get_logger()
         self.template_parser = LMTemplateParser(meta_template)
         self.eos_token_id = None
         if meta_template and 'eos_token_id' in meta_template:
             self.eos_token_id = meta_template['eos_token_id']
         self.api_addr = api_addr
         self.end_str = end_str
+        self.temperature = temperature
 
     def generate(
         self,
@@ -84,6 +87,9 @@ def generate(
             List[str]: A list of generated strings.
         """
 
+        if self.temperature is not None:
+            temperature = self.temperature
+
         with ThreadPoolExecutor() as executor:
             results = list(
                 executor.map(self._generate, inputs,
@@ -125,13 +131,14 @@ def _generate(self, prompt: PromptType, max_out_len: int,
 
         response = ''
         for output in self.chatbot.completions_v1(
-                session_id=threading.currentThread().ident,
                 prompt=prompt,
                 model=self.model_name,
                 max_tokens=max_out_len,
                 temperature=temperature,
                 top_p=0.8,
-                top_k=1):
+                top_k=50,
+                session_id=threading.currentThread().ident,
+        ):
             response += output['choices'][0]['text']
         response = valid_str(response)
         if end_str: