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[Fix] Fix version mismatch of CIBench (#1380)
* update crb * update crbbench * update crbbench * update crbbench * minor update wildbench * [Fix] Update doc of wildbench, and merge wildbench into subjective * [Fix] Update doc of wildbench, and merge wildbench into subjective, fix crbbench * Update crb.md * Update crb_pair_judge.py * Update crb_single_judge.py * Update subjective_evaluation.md * Update openai_api.py * [Update] update wildbench readme * [Update] update wildbench readme * [Update] update wildbench readme, remove crb * Delete configs/eval_subjective_wildbench_pair.py * Delete configs/eval_subjective_wildbench_single.py * Update __init__.py * [Fix] fix version mismatch for CIBench * [Fix] fix version mismatch for CIBench, local runer * [Fix] fix version mismatch for CIBench, local runer, remove oracle mode --------- Co-authored-by: bittersweet1999 <148421775+bittersweet1999@users.noreply.github.com>
This commit is contained in:
klein
GitHub
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commit
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@ -1,96 +0,0 @@
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from mmengine.config import read_base
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from opencompass.lagent.actions.ipython_interpreter import IPythonInterpreter
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from opencompass.lagent.agents.react import CIReAct, ReActProtocol
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from opencompass.models.lagent import CodeAgent
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from opencompass.models.openai_api import OpenAI
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from opencompass.partitioners import SizePartitioner
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from opencompass.runners import LocalRunner
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from opencompass.tasks import OpenICLInferTask
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with read_base():
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from .datasets.CIBench.CIBench_template_gen_e6b12a import \
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cibench_datasets as datasets
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FORCE_STOP_PROMPT_EN = """You should directly give results based on history information."""
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FEWSHOT_INSTRUCTION = """\
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You are an assistant who can utilize external tools.
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{tool_description}
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To use a tool, please response with the following format:
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```
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{thought} Think what you need to solve, do you need to use tools?
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{action} The tool name, should be one of [{action_names}].
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{action_input} The input to the tool that you want to use.
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```
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The tool will give you response after your response using the following format:
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```
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{response} the results after call the tool.
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```
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Therefore DO NOT generate tool response by yourself.
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Also please follow the guidelines:
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1. Always use code interpreter to solve the problem.
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2. The generated codes should always in a markdown code block format.
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3. The generated codes will be executed in an ipython manner and the results will be cached.
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4. Your responded code should always be simple and only solves the problem in current step.
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For example:
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File url: `xxxx`
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### Step 1. Load the dataset from the url into a pandas DataFrame named `df`.
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{thought} We should use `pandas` to solve this step.
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{action} IPythonInterpreter
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{action_input} ```python
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import pandas as pd
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url = "xxxx"
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data = pd.read_csv(url)
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```
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{response} The code is succeed without any outputs.
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Let us begin from here!
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"""
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IPYTHON_INTERPRETER_DESCRIPTION = '''\
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It can run Python code in a manner as jupyter notebook. The code must be a valid code that contains only python method.'''
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models = [
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dict(
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abbr='gpt-3.5-code',
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type=CodeAgent,
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agent_type=CIReAct,
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max_turn=3,
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llm=dict(
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type=OpenAI,
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path='gpt-3.5-turbo',
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key='ENV',
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query_per_second=1,
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max_seq_len=4096,
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),
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actions=[
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dict(type=IPythonInterpreter,
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description=IPYTHON_INTERPRETER_DESCRIPTION,
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user_data_dir='./data/cibench_dataset/datasources')
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],
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protocol=dict(
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type=ReActProtocol,
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call_protocol=FEWSHOT_INSTRUCTION,
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force_stop=FORCE_STOP_PROMPT_EN,
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finish=dict(role='FINISH', begin='Final Answer:', end='\n'),
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),
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batch_size=1,
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use_system_role=False, # use `user` role instead of system role
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first_system_role=False, # use `user` role of the first instruction prompt
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merge_adjacent_role=True, # merge adjacent same user content
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),
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]
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infer = dict(
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partitioner=dict(type=SizePartitioner, max_task_size=1000),
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runner=dict(
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type=LocalRunner,
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max_num_workers=16,
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task=dict(type=OpenICLInferTask)),
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)
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@ -1,15 +1,37 @@
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from copy import deepcopy
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from mmengine.config import read_base
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from opencompass.partitioners import SizePartitioner
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from opencompass.runners import LocalRunner, SlurmRunner
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from opencompass.tasks import OpenICLInferTask
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from opencompass.models import OpenAI
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from opencompass.models.lagent import LagentAgent
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from lagent import ReAct
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from lagent.agents.react import ReActProtocol
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from opencompass.models.lagent import CodeAgent
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from opencompass.lagent.actions.python_interpreter import PythonInterpreter
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from opencompass.lagent.actions.ipython_interpreter import IPythonInterpreter
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from opencompass.lagent.agents.react import CIReAct
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from opencompass.models.lagent import CodeAgent
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from lagent.agents.react import ReActProtocol
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from opencompass.models import HuggingFaceCausalLM
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from opencompass.partitioners import SizePartitioner
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from opencompass.runners import LocalRunner
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from opencompass.runners import SlurmSequentialRunner
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from opencompass.tasks import OpenICLInferTask
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from opencompass.partitioners import NaivePartitioner
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with read_base():
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from .datasets.CIBench.CIBench_gen_eb42f9 import cibench_datasets as datasets
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# Note that it might occur cuda OOM error for hf model
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from .models.hf_llama.lmdeploy_llama3_8b_instruct import models as lmdeploy_llama3_8b_instruct_model
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from .summarizers.cibench import summarizer
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from .datasets.CIBench.CIBench_template_gen_e6b12a import cibench_datasets as cibench_datasets_template
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from .datasets.CIBench.CIBench_generation_gen_8ab0dc import cibench_datasets as cibench_datasets_generation
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# Oracle mode for analysis
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# from .datasets.CIBench.CIBench_template_oracle_gen_fecda1 import cibench_datasets as cibench_datasets_template_oracle
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# from .datasets.CIBench.CIBench_generation_oracle_gen_c4a7c1 import cibench_datasets as cibench_datasets_generation_oracle
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datasets = []
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datasets += cibench_datasets_template
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datasets += cibench_datasets_generation
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# datasets += cibench_datasets_template_oracle
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# datasets += cibench_datasets_generation_oracle
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_origin_models = sum([v for k, v in locals().items() if k.endswith('_model')], [])
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FORCE_STOP_PROMPT_EN = """You should directly give results based on history information."""
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@ -34,47 +56,87 @@ Also please follow the guidelines:
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3. The generated codes will be executed in an ipython manner and the results will be cached.
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4. Your responded code should always be simple and only solves the problem in current step.
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Begin!
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For example:
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File url: `xxxx`
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### Step 1. Load the dataset from the url into a pandas DataFrame named `df`.
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{thought} We should use `pandas` to solve this step.
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{action} IPythonInterpreter
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{action_input} ```python
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import pandas as pd
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url = "xxxx"
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data = pd.read_csv(url)
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```
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{response} The code is succeed without any outputs.
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Let us begin from here!
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"""
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models = [
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dict(
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abbr='gpt-3.5-turbo',
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type=CodeAgent,
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agent_type=CIReAct,
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mutli_rounds=True,
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max_turn=3,
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llm=dict(
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type=OpenAI,
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path='gpt-3.5-turbo',
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key='ENV',
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query_per_second=1,
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max_seq_len=4096,
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),
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actions=[
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dict(
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type=IPythonInterpreter,
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description=
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'''It can run Python code in a manner as jupyter notebook. The code must be a valid code that contains only python method.
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'''),
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],
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IPYTHON_INTERPRETER_DESCRIPTION = '''\
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It can run Python code in a manner as jupyter notebook. The code must be a valid code that contains only python method.'''
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actions=[dict(type=IPythonInterpreter, user_data_dir='./data/cibench_dataset/datasources',
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description=IPYTHON_INTERPRETER_DESCRIPTION)]
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protocol=dict(
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type=ReActProtocol,
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call_protocol=FEWSHOT_INSTRUCTION,
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force_stop=FORCE_STOP_PROMPT_EN,
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action=dict(role='ACTION', begin='Tool:', end='\n'),
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action_input=dict(role='ARGS', begin='Tool Input:', end='\n'),
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response=dict(role='RESPONSE', begin='Tool Response:', end='\n'),
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finish=dict(role='FINISH', begin='Final Answer:', end='\n'),
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),
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batch_size=8,
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),
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]
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)
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work_dir = './outputs/cibench/'
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_agent_models = []
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for m in _origin_models:
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m = deepcopy(m)
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if 'meta_template' in m and 'round' in m['meta_template']:
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round = m['meta_template']['round']
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if all(r['role'].upper() != 'SYSTEM' for r in round): # no system round
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if not any('api_role' in r for r in round):
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m['meta_template']['round'].append(dict(role='system', begin='System response:', end='\n'))
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else:
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m['meta_template']['round'].append(dict(role='system', api_role='SYSTEM'))
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print(f'WARNING: adding SYSTEM round in meta_template for {m.get("abbr", None)}')
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_agent_models.append(m)
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protocol=dict(
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type=ReActProtocol,
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call_protocol=FEWSHOT_INSTRUCTION,
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force_stop=FORCE_STOP_PROMPT_EN,
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finish=dict(role='FINISH', begin='Final Answer:', end='\n'),
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)
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models = []
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for m in _agent_models:
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m = deepcopy(m)
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origin_abbr = m.pop('abbr')
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abbr = origin_abbr
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m.pop('batch_size', None)
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m.pop('max_out_len', None)
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m.pop('max_seq_len', None)
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run_cfg = m.pop('run_cfg', {})
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agent_model = dict(
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abbr=abbr,
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summarizer_abbr=origin_abbr,
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type=CodeAgent,
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agent_type=CIReAct,
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max_turn=3,
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llm=m,
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actions=[dict(type=IPythonInterpreter, user_data_dir='./data/cibench_dataset/datasources', description=IPYTHON_INTERPRETER_DESCRIPTION)],
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protocol=protocol,
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batch_size=1,
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run_cfg=run_cfg,
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)
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models.append(agent_model)
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infer = dict(
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partitioner=dict(type=SizePartitioner, max_task_size=50, gen_task_coef=1),
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partitioner=dict(type=NaivePartitioner),
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runner=dict(
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type=SlurmRunner, max_num_workers=8, retry=2,
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type=LocalRunner,
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max_num_workers=4,
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task=dict(type=OpenICLInferTask)),
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)
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@ -1,17 +1,28 @@
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from lagent.agents.react import ReActProtocol
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from mmengine.config import read_base
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from opencompass.partitioners import NaivePartitioner
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from opencompass.lagent.actions.ipython_interpreter import IPythonInterpreter
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from opencompass.lagent.agents.react import CIReAct
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from opencompass.models.lagent import CodeAgent
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from opencompass.models.openai_api import OpenAI
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from opencompass.partitioners import SizePartitioner
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from opencompass.models import OpenAI
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from opencompass.runners import LocalRunner
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from opencompass.tasks import OpenICLInferTask
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from opencompass.lagent.actions.ipython_interpreter import IPythonInterpreter
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from opencompass.lagent.agents.react import CIReAct
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with read_base():
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from .datasets.CIBench.CIBench_gen_8ab0dc import \
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cibench_datasets as datasets
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from .datasets.CIBench.CIBench_template_gen_e6b12a import cibench_datasets as cibench_datasets_template
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from .datasets.CIBench.CIBench_generation_gen_8ab0dc import cibench_datasets as cibench_datasets_generation
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# Oracle mode for analysis
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# from .datasets.CIBench.CIBench_template_oracle_gen_fecda1 import cibench_datasets as cibench_datasets_template_oracle
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# from .datasets.CIBench.CIBench_generation_oracle_gen_c4a7c1 import cibench_datasets as cibench_datasets_generation_oracle
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from .summarizers.cibench import summarizer
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datasets = []
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datasets += cibench_datasets_template
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datasets += cibench_datasets_generation
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# datasets += cibench_datasets_template_oracle
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# datasets += cibench_datasets_generation_oracle
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FORCE_STOP_PROMPT_EN = """You should directly give results based on history information."""
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@ -56,38 +67,52 @@ Let us begin from here!
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IPYTHON_INTERPRETER_DESCRIPTION = '''\
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It can run Python code in a manner as jupyter notebook. The code must be a valid code that contains only python method.'''
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models = [
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dict(
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abbr='gpt-3.5-code',
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type=CodeAgent,
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agent_type=CIReAct,
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max_turn=3,
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llm=dict(
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type=OpenAI,
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path='gpt-3.5-turbo',
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key='ENV',
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query_per_second=1,
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max_seq_len=4096,
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),
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actions=[
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dict(type=IPythonInterpreter,
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description=IPYTHON_INTERPRETER_DESCRIPTION)
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api_meta_template = dict(
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round=[
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dict(role='HUMAN', api_role='HUMAN'),
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dict(role='BOT', api_role='BOT', generate=True),
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dict(role='SYSTEM', api_role='SYSTEM'),
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],
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)
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actions=[dict(type=IPythonInterpreter, user_data_dir='./data/cibench_dataset/datasources',
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description=IPYTHON_INTERPRETER_DESCRIPTION)]
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protocol=dict(
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type=ReActProtocol,
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call_protocol=FEWSHOT_INSTRUCTION,
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force_stop=FORCE_STOP_PROMPT_EN,
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finish=dict(role='FINISH', begin='Final Answer:', end='\n'),
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)
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work_dir = 'outputs/cibench/'
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models = [
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dict(
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abbr='gpt-4o',
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type=CodeAgent,
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agent_type=CIReAct,
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max_turn=3,
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llm=dict(
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type=OpenAI,
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path='gpt-4o',
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rpm_verbose=True,
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retry=99,
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meta_template=api_meta_template,
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query_per_second=1,
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max_seq_len=2048,
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temperature=0,
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),
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actions=actions,
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protocol=protocol,
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batch_size=1,
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),
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]
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infer = dict(
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partitioner=dict(type=SizePartitioner, max_task_size=1000),
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partitioner=dict(type=NaivePartitioner),
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runner=dict(
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type=LocalRunner,
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max_num_workers=16,
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max_num_workers=4,
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task=dict(type=OpenICLInferTask)),
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)
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Reference in New Issue
Block a user