[Fix] fix corev2 (#838)

* fix corev2

* fix corev2

configs/datasets/subjective/subjective_cmp/subjective_corev2.py

@@ -12,7 +12,7 @@ subjective_reader_cfg = dict(
     )
 
 subjective_all_sets = [
-    "COREV2_6A_",
+    "COREV2_6A_all",
 ]
 
 

configs/eval_subjective_corev2.py

@@ -4,21 +4,22 @@ with read_base():
     from .models.qwen.hf_qwen_14b_chat import models as hf_qwen_14b_chat
     from .models.chatglm.hf_chatglm3_6b import models as hf_chatglm3_6b
     from .models.baichuan.hf_baichuan2_7b_chat import models as hf_baichuan2_7b
-    from .models.hf_internlm.hf_internlm_chat_7b import models as hf_internlm_chat_7b
+    from .models.hf_internlm.hf_internlm2_chat_7b import models as internlm2_7b
-    from .models.hf_internlm.hf_internlm_chat_20b import models as hf_internlm_chat_20b
+    from .models.hf_internlm.hf_internlm2_chat_20b import models as internlm2_20b
-    from .datasets.subjective_cmp.subjective_corev2 import subjective_datasets
+    from .datasets.subjective.subjective_cmp.subjective_corev2 import subjective_datasets
 
 datasets = [*subjective_datasets]
 
 from opencompass.models import HuggingFaceCausalLM, HuggingFace, OpenAI
-from opencompass.partitioners import NaivePartitioner
+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 Corev2Summarizer
-models = [*hf_baichuan2_7b, *hf_chatglm3_6b, *hf_internlm_chat_20b, *hf_qwen_7b_chat, *hf_internlm_chat_7b, *hf_qwen_14b_chat]
+models = [*internlm2_7b, *internlm2_20b]
 
 api_meta_template = dict(
     round=[
@@ -31,10 +32,10 @@ api_meta_template = dict(
 )
 
 infer = dict(
-    partitioner=dict(type=NaivePartitioner),
+    partitioner=dict(type=SizePartitioner, max_task_size=500),
     runner=dict(
         type=SlurmSequentialRunner,
-        partition='llmeval',
+        partition='llm_dev2',
         quotatype='auto',
         max_num_workers=256,
         task=dict(type=OpenICLInferTask)),
@@ -71,17 +72,17 @@ judge_model =    dict(
         run_cfg=dict(num_gpus=1, num_procs=1),
     )
 
-
 eval = dict(
     partitioner=dict(
-        type=SubjectiveNaivePartitioner,
+        type=SubjectiveSizePartitioner,
         mode='m2n',
-        base_models = [*hf_baichuan2_7b, *hf_chatglm3_6b],
+        max_task_size=500,
-        compare_models = [*hf_baichuan2_7b, *hf_qwen_7b_chat, *hf_chatglm3_6b, *hf_qwen_14b_chat]
+        base_models = [*internlm2_7b],
+        compare_models = [*internlm2_20b]
     ),
     runner=dict(
         type=SlurmSequentialRunner,
-        partition='llmeval',
+        partition='llm_dev2',
         quotatype='auto',
         max_num_workers=256,
         task=dict(
@@ -89,7 +90,7 @@ eval = dict(
         judge_cfg=judge_model
         )),
 )
-work_dir = './corev2/'
+work_dir = 'outputs/corev2/'
 
 summarizer = dict(
     type=Corev2Summarizer,

configs/eval_subjective_score.py

@@ -1,96 +0,0 @@
-from mmengine.config import read_base
-with read_base():
-    from .models.qwen.hf_qwen_7b_chat import models as hf_qwen_7b_chat
-    from .models.qwen.hf_qwen_14b_chat import models as hf_qwen_14b_chat
-    from .models.chatglm.hf_chatglm3_6b import models as hf_chatglm3_6b
-    from .models.baichuan.hf_baichuan2_7b_chat import models as hf_baichuan2_7b
-    from .models.hf_internlm.hf_internlm_chat_7b import models as hf_internlm_chat_7b
-    from .models.hf_internlm.hf_internlm_chat_20b import models as hf_internlm_chat_20b
-    from .datasets.subjective_cmp.subjective_creation import subjective_datasets
-
-datasets = [*subjective_datasets]
-
-from opencompass.models import HuggingFaceCausalLM, HuggingFace, OpenAI
-from opencompass.partitioners import NaivePartitioner
-from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
-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 Creationv01Summarizer
-models = [*hf_baichuan2_7b, *hf_chatglm3_6b, *hf_internlm_chat_20b, *hf_qwen_7b_chat, *hf_internlm_chat_7b, *hf_qwen_14b_chat]
-
-api_meta_template = dict(
-    round=[
-        dict(role='HUMAN', api_role='HUMAN'),
-        dict(role='BOT', api_role='BOT', generate=True)
-    ],
-    reserved_roles=[
-        dict(role='SYSTEM', api_role='SYSTEM'),
-    ],
-)
-
-infer = dict(
-    partitioner=dict(type=NaivePartitioner),
-    runner=dict(
-        type=SlurmSequentialRunner,
-        partition='llmeval',
-        quotatype='auto',
-        max_num_workers=256,
-        task=dict(type=OpenICLInferTask)),
-)
-
-
-_meta_template = dict(
-    round=[
-        dict(role="HUMAN", begin='\n<|im_start|>user\n', end='<|im_end|>'),
-        dict(role="BOT", begin="\n<|im_start|>assistant\n", end='<|im_end|>', generate=True),
-    ],
-)
-
-
-judge_model = dict(
-        type=HuggingFaceCausalLM,
-        abbr='qwen-7b-chat-hf',
-        path="Qwen/Qwen-7B-Chat",
-        tokenizer_path='Qwen/Qwen-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,),
-        pad_token_id=151643,
-        max_out_len=2048,
-        max_seq_len=2048,
-        batch_size=8,
-        meta_template=_meta_template,
-        run_cfg=dict(num_gpus=1, num_procs=1),
-    )
-
-
-eval = dict(
-    partitioner=dict(
-        type=SubjectiveNaivePartitioner,
-        mode='singlescore',
-        models = [*hf_baichuan2_7b]
-    ),
-    runner=dict(
-        type=SlurmSequentialRunner,
-        partition='llmeval',
-        quotatype='auto',
-        max_num_workers=256,
-        task=dict(
-            type=SubjectiveEvalTask,
-            judge_cfg=judge_model
-        )),
-)
-work_dir = './creation/'
-
-summarizer = dict(
-    type=Creationv01Summarizer,
-    match_method='smart',
-)