[Fix] Fix MultiRound Subjective Evaluation(#1043)

* fix multiround

* fix

configs/datasets/subjective/multiround/functionalmt_zh_judgeby_gpt4.py

@@ -30,6 +30,7 @@ for _name in subjective_all_sets:
     subjective_eval_cfg = dict(
         evaluator=dict(
             type=LMEvaluator,
+            wrap_all_predictions=True,
             prompt_template=dict(
                 type=PromptTemplate,
                 template=dict(round=[

configs/eval_subjective_functional_multiround.py

@@ -0,0 +1,111 @@
+from opencompass.models import HuggingFaceCausalLM
+from copy import deepcopy
+from opencompass.models import TurboMindModel
+from mmengine.config import read_base
+
+from opencompass.models import HuggingFaceCausalLM, HuggingFace, HuggingFaceChatGLM3, OpenAI
+from opencompass.partitioners import NaivePartitioner, SizePartitioner
+from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
+from opencompass.partitioners.sub_size import SubjectiveSizePartitioner
+from opencompass.runners import LocalRunner
+from opencompass.runners import SlurmSequentialRunner
+from opencompass.tasks import OpenICLInferTask
+from opencompass.tasks.subjective_eval import SubjectiveEvalTask
+from opencompass.summarizers import MultiroundSummarizer
+
+with read_base():
+    from .datasets.subjective.multiround.functionalmt_zh_judgeby_gpt4 import subjective_datasets
+
+api_meta_template = dict(
+    round=[
+        dict(role='HUMAN', api_role='HUMAN'),
+        dict(role='BOT', api_role='BOT', generate=True),
+    ]
+)
+
+_meta_template = dict(
+    round=[
+        dict(role="HUMAN", begin='<|im_start|>user\n', end='<|im_end|>\n'),
+        dict(role="BOT", begin="<|im_start|>assistant\n", end='<|im_end|>\n', generate=True),
+    ],
+    eos_token_id=151645,
+)
+
+models = [
+    dict(
+        type=HuggingFaceCausalLM,
+        abbr='qwen1.5-7b-chat-hf',
+        path="Qwen/Qwen1.5-7B-Chat",
+        model_kwargs=dict(
+            device_map='auto',
+            trust_remote_code=True
+        ),
+        tokenizer_kwargs=dict(
+            padding_side='left',
+            truncation_side='left',
+            trust_remote_code=True,
+            use_fast=False,
+        ),
+        generation_kwargs=dict(
+            do_sample=True,
+        ),
+        meta_template=_meta_template,
+        pad_token_id=151645,
+        max_out_len=100,
+        max_seq_len=2048,
+        batch_size=8,
+        run_cfg=dict(num_gpus=1, num_procs=1),
+        end_str='<|im_end|>',
+    )
+]
+
+datasets = [*subjective_datasets]
+
+work_dir = 'outputs/multiround/'
+# -------------Inferen Stage ----------------------------------------
+
+
+infer = dict(
+    partitioner=dict(type=SizePartitioner, max_task_size=1000),
+    runner=dict(
+        type=SlurmSequentialRunner,
+        partition='your part',
+        quotatype='auto',
+        max_num_workers=256,
+        task=dict(type=OpenICLInferTask)),
+)
+
+judge_models = [dict(
+    abbr='GPT4-Turbo',
+    type=OpenAI,
+    path='gpt-4-1106-preview',
+    key='',  
+        meta_template=api_meta_template,
+        query_per_second=1,
+        max_out_len=1024,
+        max_seq_len=4096,
+        batch_size=10,
+        retry=10,
+        temperature = 0,
+)]
+
+## ------------- Evaluation Configuration
+eval = dict(
+    partitioner=dict(
+        type=SubjectiveSizePartitioner,
+        max_task_size=1000,
+        mode='singlescore',
+        models = models,
+        judge_models=judge_models
+    ),
+    runner=dict(
+        type=SlurmSequentialRunner,
+        partition='your part',
+        quotatype='auto',
+        max_num_workers=256,
+        task=dict(type=SubjectiveEvalTask)),
+)
+
+summarizer = dict(
+    type=MultiroundSummarizer
+)

opencompass/openicl/icl_evaluator/lm_evaluator.py

@@ -75,9 +75,11 @@ class LMEvaluator:
             keywords, ``{prediction}`` and ``{reference}``, referring to
             the prediction and optionally the reference answer.
         judge_cfg (ConfigDict): The config of language model as a judge.
+        meta_review_prompt_template (ConfigDict, optional): Prompt template for meta judge model.
         output_path (str): The path to prediction output.
         dataset_cfg (ConfigDict, optional): The config of the dataset to be
             evaluated.
+        pack_all_predictions (bool, optional): For multiround evaluation, judge all round or judge every single round.
         postprocessor (ConfigDict): The model prediction's postprocessor
             config.
     """
@@ -88,6 +90,7 @@ class LMEvaluator:
         judge_cfg: ConfigDict,
         output_path: str,
         meta_review_prompt_template: Optional[ConfigDict] = None,
+        pack_all_predictions: Optional[bool] = False,
         dataset_cfg: Optional[ConfigDict] = None,
         postprocessor: ConfigDict = dict(type=first_number_postprocess)
     ) -> None:
@@ -112,6 +115,7 @@ class LMEvaluator:
         self.postprocessor = get_type_from_cfg(postprocessor)
         self.logger = get_logger()
         self.dataset_cfg = dataset_cfg
+        self.pack_all_predictions = pack_all_predictions
 
     def score(self,
               predictions,
@@ -171,12 +175,17 @@ class LMEvaluator:
         elif isinstance(
                 predictions[0][0], list
         ):  #multi round for format like [[[{'round':1, 'user':'', 'assistant':''}, {'round':2, 'user':'', 'assistant':''}], [{'round':1, 'user':'', 'assistant':''}, {'round':2, 'user':'', 'assistant':''}]]]
-            for i in range(len(predictions)):
-                multiround_predictions = extract_dicts(predictions[i])
-                for j in range(len(multiround_predictions)):
-                    key = 'prediction' if i == 0 else f'prediction{i}'
-                    key += '_r' + str(j + 1)
-                    pred_dict[key] = multiround_predictions[j]
+            if self.pack_all_predictions:
+                for i in range(len(predictions)):
+                    key = 'prediction' if i == 0 else f'prediction{i + 1}'
+                    pred_dict[key] = predictions[i]
+            else:
+                for i in range(len(predictions)):
+                    multiround_predictions = extract_dicts(predictions[i])
+                    for j in range(len(multiround_predictions)):
+                        key = 'prediction' if i == 0 else f'prediction{i}'
+                        key += '_r' + str(j + 1)
+                        pred_dict[key] = multiround_predictions[j]
             if judgements:
                 raise NotImplementedError(
                     'Not applied meta-reivew judge on multi-round dataset')

opencompass/openicl/icl_inferencer/icl_chat_inferencer.py

@@ -172,8 +172,6 @@ class ChatInferencer(BaseInferencer):
             output_json_filepath: Optional[str] = './icl_inference_output',
             output_json_filename: Optional[str] = 'predictions',
             save_every: Optional[int] = 1,
-            temperature: Optional[float] = 0.0,
-            do_sample: Optional[bool] = False,
             infer_mode: str = 'last',
             max_out_len: int = 512,
             **kwargs) -> None:
@@ -185,8 +183,6 @@ class ChatInferencer(BaseInferencer):
         )
         assert infer_mode in ['last', 'every', 'every_with_gt']
         self.infer_mode = infer_mode
-        self.temperature = temperature
-        self.do_sample = do_sample
         self.model: BaseModel
         self._set_meta_template(self.model)
 
@@ -353,16 +349,8 @@ class ChatInferencer(BaseInferencer):
 
         for i in assistant_indices:
             history = chat[:i]
-            if self.do_sample:
-                output = self.model.generate_from_template(
-                    [history],
-                    do_sample=self.do_sample,
-                    temperature=self.temperature,
-                    max_out_len=self.max_out_len)[0]
-            else:
-                output = self.model.generate_from_template(
-                    [history], do_sample=False,
-                    max_out_len=self.max_out_len)[0]
+            output = 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(

opencompass/summarizers/subjective/multiround.py

@@ -128,7 +128,8 @@ class MultiroundSummarizer:
         self.eval_model_abbrs = [
             model_abbr_from_cfg(model) for model in self.eval_model_cfgs
         ]
-        self.judge_abbr = model_abbr_from_cfg(self.cfg['judge_model'])
+        self.judge_abbr = model_abbr_from_cfg(
+            self.cfg['eval']['partitioner']['judge_models'][0])
 
     def summarize(self,
                   time_str: str = datetime.now().strftime('%Y%m%d_%H%M%S')):