File size: 8,439 Bytes
00db46c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 |
#!/usr/bin/env python3
"""
Script to generate a CSV file containing arithmetic problems for SFT training data.
This script uses the arithmetic utilities to generate problems and creates a CSV
with columns: id, problem_description, correct_answer, and reasoning.
The reasoning column contains the step-by-step thought process for solving the problem.
"""
import argparse
import csv
import logging
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path
from typing import Any
from dotenv import load_dotenv
from src.dataset.grpo import get_reasoning_for_answer
from src.utils.arithmetics import (
ArithmeticProblemDescriptionGenerator,
ArithmeticProblemGenerator,
Mode,
)
# Load environment variables
load_dotenv()
# Configure logging
logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)
def generate_single_problem_with_reasoning(
problem_generator: ArithmeticProblemGenerator,
description_generator: ArithmeticProblemDescriptionGenerator,
problem_id: int,
) -> dict[str, Any] | None:
"""
Generate a single problem with reasoning.
Args:
problem_generator: The arithmetic problem generator
description_generator: The problem description generator
problem_id: The ID for this problem
Returns:
Optional[Dict[str, Any]]: Training data entry with reasoning, or None if generation failed
"""
# Generate a problem
problem = problem_generator.generate_problem()
if problem is None:
return None
# Generate description
problem_description, _ = description_generator.generate_description(problem)
correct_answer = problem.expression
# Generate reasoning using OpenAI
logger.info(f"Generating reasoning for problem {problem_id}...")
reasoning = get_reasoning_for_answer(
problem_description,
problem.expression
)
# Create training data entry
training_entry = {
"id": problem_id,
"problem_description": problem_description,
"correct_answer": correct_answer,
"reasoning": reasoning,
}
return training_entry
def generate_training_data(
num_problems: int, max_workers: int = 8
) -> list[dict[str, Any]]:
"""
Generate training data with arithmetic problems and reasoning using threading.
Args:
num_problems: Number of problems to generate
max_workers: Maximum number of worker threads for parallel processing
Returns:
List[Dict[str, Any]]: List of dictionaries containing training data with reasoning
"""
problem_generator = ArithmeticProblemGenerator(mode=Mode.MUL_DIV)
description_generator = ArithmeticProblemDescriptionGenerator()
training_data = []
max_total_attempts = num_problems * 10 # Allow more attempts than problems
logger.info(
f"Starting generation of {num_problems} problems with reasoning using {max_workers} workers..."
)
# Generate problems in batches to avoid over-submission
attempts = 0
while len(training_data) < num_problems and attempts < max_total_attempts:
# Calculate how many more problems we need
remaining_problems = num_problems - len(training_data)
# Submit a batch of tasks (no more than we need + small buffer for failures)
batch_size = min(
max_workers, remaining_problems + 2, max_total_attempts - attempts
)
logger.info(
f"Submitting batch of {batch_size} tasks. Need {remaining_problems} more problems."
)
with ThreadPoolExecutor(max_workers=max_workers) as executor:
# Submit batch of tasks
futures = []
for i in range(batch_size):
future = executor.submit(
generate_single_problem_with_reasoning,
problem_generator,
description_generator,
attempts + i + 1,
)
futures.append(future)
# Collect results from this batch
batch_results = []
for future in as_completed(futures):
try:
result = future.result()
if result is not None:
batch_results.append(result)
# Stop collecting if we have enough problems
if len(training_data) + len(batch_results) >= num_problems:
break
except Exception as e:
logger.error(f"Error generating problem: {e}")
# Add successful results to training data
for _, result in enumerate(batch_results):
if len(training_data) >= num_problems:
break
result["id"] = len(training_data) + 1
training_data.append(result)
if len(training_data) % 10 == 0:
logger.info(
f"Generated {len(training_data)} problems with reasoning..."
)
attempts += batch_size
# Log progress
logger.info(
f"Completed batch. Have {len(training_data)} problems, need {num_problems}"
)
# Stop if we have enough problems
if len(training_data) >= num_problems:
break
if len(training_data) < num_problems:
logger.warning(
f"Only generated {len(training_data)} out of {num_problems} requested problems after {attempts} attempts"
)
else:
logger.info(
f"Successfully generated {len(training_data)} problems with reasoning in {attempts} attempts"
)
# Sort training data by ID to maintain order
training_data.sort(key=lambda x: x["id"])
return training_data
def save_to_csv(training_data: list[dict[str, Any]], output_file: Path) -> None:
"""
Save training data to a CSV file.
Args:
training_data: List of training data dictionaries
output_file: Path to the output CSV file
"""
if not training_data:
logger.error("No training data to save")
return
# Create output directory if it doesn't exist
output_file.parent.mkdir(parents=True, exist_ok=True)
logger.info(f"Saving {len(training_data)} problems to {output_file}")
with open(output_file, "w", newline="", encoding="utf-8") as csvfile:
fieldnames = ["id", "problem_description", "correct_answer", "reasoning"]
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
# Write header
writer.writeheader()
# Write data
for entry in training_data:
writer.writerow(entry)
logger.info(f"Successfully saved training data to {output_file}")
def main() -> None:
"""
Main function to handle command line arguments and orchestrate the generation process.
"""
parser = argparse.ArgumentParser(
description="Generate arithmetic problems for SFT training data in CSV format with reasoning"
)
parser.add_argument(
"--num_problems", type=int, required=True, help="Number of problems to generate"
)
parser.add_argument(
"--output_file", type=str, required=True, help="Path to the output CSV file"
)
parser.add_argument(
"--max_workers",
type=int,
default=8,
help="Maximum number of worker threads for parallel processing (default: 8)",
)
args = parser.parse_args()
# Validate arguments
if args.num_problems <= 0:
logger.error("Number of problems must be positive")
return
if args.max_workers <= 0:
logger.error("Number of workers must be positive")
return
output_path = Path(args.output_file)
# Generate training data
training_data = generate_training_data(args.num_problems, args.max_workers)
if not training_data:
logger.error("Failed to generate any training data")
return
# Save to CSV
save_to_csv(training_data, output_path)
if __name__ == "__main__":
main()
|