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Update AFlow

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didi 2024-10-16 11:44:01 +08:00
parent 040a7324eb
commit eae351466f
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optimize.py
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@ -3,15 +3,23 @@
# @Author : didi
# @Desc : Experiment of graph optimization
from examples.ags.scripts.optimizer import Optimizer
from examples.aflow.scripts.optimizer import Optimizer
from metagpt.configs.models_config import ModelsConfig
from typing import Literal
# DatasetType, QuestionType, and OptimizerType definitions
DatasetType = Literal["HumanEval", "MBPP", "GSM8K", "MATH", "HotpotQa", "DROP"]
QuestionType = Literal["math", "code", "quiz"]
OptimizerType = Literal["Graph", "Test"]
# Crucial Parameters
dataset = "HumanEval" # DatasetType
sample = 4 # Sample Count, which means how many workflows will be resampled from generated workflows
question_type = "code" # Question Type
optimized_path = "examples/ags/scripts/optimized" # Optimized Result Save Path
dataset: DatasetType = "GSM8K" # Ensure the type is consistent with DatasetType
sample: int = 4 # Sample Count, which means how many workflows will be resampled from generated workflows
question_type: QuestionType = "math" # Ensure the type is consistent with QuestionType
optimized_path: str = "examples/aflow/scripts/optimized" # Optimized Result Save Path
initial_round: int = 1 # Corrected the case from Initial_round to initial_round
max_rounds: int = 20
check_convergence: bool = True
# Initialize LLM Model
four_o_llm_config = ModelsConfig.default().get("gpt-4o")
@ -21,25 +29,24 @@ claude_llm_config = ModelsConfig.default().get("claude-3-5-sonnet-20240620")
# Initialize Operators List
operators = [
"Custom",
"CustomCodeGenerate",
"ScEnsemble",
"Test",
"Custom"
]
# Create an optimizer instance
optimizer = Optimizer(
dataset=dataset,
question_type=question_type,
opt_llm_config=claude_llm_config,
exec_llm_config=four_o_llm_config,
exec_llm_config=mini_llm_config,
check_convergence=check_convergence,
operators=operators,
optimized_path=optimized_path,
sample=sample,
question_type=question_type,
initial_round=initial_round,
max_rounds=max_rounds
)
# Run the optimizer
# optimizer.optimize("Graph", 30)
optimizer.optimize("Test")
# optimizer.optimize("Operator")
if __name__ == "__main__":
# Run the optimizer
optimizer.optimize("Graph")
# optimizer.optimize("Test")