File size: 12,033 Bytes
4eae728 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 |
"""
Data preparation utilities for converting SFT data to DPO/GRPO formats.
This script helps generate multiple outputs and create preference/ranking datasets.
"""
import json
import argparse
from pathlib import Path
from typing import List, Dict
from f1_score_utils import (
compute_file_level_f1,
rank_outputs_by_f1,
create_dpo_pairs_from_generations
)
def load_model_for_generation(model_path: str):
"""
Load a model for generation. This is a placeholder - implement based on your setup.
"""
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
print(f"Loading model from {model_path}...")
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto"
)
return model, tokenizer
def generate_multiple_outputs(
model,
tokenizer,
prompt: str,
num_samples: int = 4,
temperatures: List[float] = None,
max_new_tokens: int = 512
) -> List[str]:
"""
Generate multiple outputs for a single prompt using different temperatures.
"""
if temperatures is None:
temperatures = [0.6, 0.8, 1.0, 1.2][:num_samples]
outputs = []
for temp in temperatures:
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
generated = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=temp,
do_sample=True,
pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
)
# Extract only the new tokens (not the prompt)
output_text = tokenizer.decode(
generated[0][inputs.input_ids.shape[1]:],
skip_special_tokens=True
)
outputs.append(output_text)
return outputs
def convert_sft_to_dpo(
sft_jsonl: str,
output_jsonl: str,
model_path: str = None,
num_samples: int = 4,
min_f1_difference: float = 0.1,
max_examples: int = None
):
"""
Convert SFT dataset to DPO format by generating multiple outputs and creating pairs.
Args:
sft_jsonl: Path to SFT JSONL file
output_jsonl: Path to output DPO JSONL file
model_path: Path to model for generation (if None, you need pre-generated outputs)
num_samples: Number of outputs to generate per prompt
min_f1_difference: Minimum F1 difference to create a pair
max_examples: Maximum number of examples to process (None = all)
"""
if model_path:
model, tokenizer = load_model_for_generation(model_path)
else:
print("Warning: No model path provided. Expecting pre-generated outputs in data.")
model, tokenizer = None, None
pairs_created = 0
examples_processed = 0
with open(sft_jsonl, 'r') as f_in, open(output_jsonl, 'w') as f_out:
for line in f_in:
if max_examples and examples_processed >= max_examples:
break
data = json.loads(line)
prompt = data.get("input", "")
ground_truth = data.get("output", "")
if not prompt or not ground_truth:
continue
# Generate multiple outputs
if model and tokenizer:
try:
generations = generate_multiple_outputs(
model, tokenizer, prompt, num_samples
)
except Exception as e:
print(f"Error generating outputs: {e}")
continue
else:
# Expect outputs in the data
generations = data.get("outputs", [])
if len(generations) < 2:
print(f"Skipping example: need at least 2 outputs")
continue
# Create DPO pairs
pairs = create_dpo_pairs_from_generations(
prompt, generations, ground_truth, min_f1_difference
)
# Write pairs to output
for pair in pairs:
f_out.write(json.dumps(pair) + '\n')
pairs_created += 1
examples_processed += 1
if examples_processed % 10 == 0:
print(f"Processed {examples_processed} examples, created {pairs_created} pairs")
print(f"\nDone! Processed {examples_processed} examples, created {pairs_created} DPO pairs")
print(f"Output saved to: {output_jsonl}")
def convert_sft_to_grpo(
sft_jsonl: str,
output_jsonl: str,
model_path: str = None,
num_samples: int = 4,
max_examples: int = None
):
"""
Convert SFT dataset to GRPO format by generating multiple outputs and computing scores.
Args:
sft_jsonl: Path to SFT JSONL file
output_jsonl: Path to output GRPO JSONL file
model_path: Path to model for generation
num_samples: Number of outputs to generate per prompt
max_examples: Maximum number of examples to process (None = all)
"""
if model_path:
model, tokenizer = load_model_for_generation(model_path)
else:
print("Warning: No model path provided. Expecting pre-generated outputs in data.")
model, tokenizer = None, None
examples_created = 0
examples_processed = 0
with open(sft_jsonl, 'r') as f_in, open(output_jsonl, 'w') as f_out:
for line in f_in:
if max_examples and examples_processed >= max_examples:
break
data = json.loads(line)
prompt = data.get("input", "")
ground_truth = data.get("output", "")
if not prompt or not ground_truth:
continue
# Generate multiple outputs
if model and tokenizer:
try:
generations = generate_multiple_outputs(
model, tokenizer, prompt, num_samples
)
except Exception as e:
print(f"Error generating outputs: {e}")
continue
else:
# Expect outputs in the data
generations = data.get("outputs", [])
if len(generations) < 2:
print(f"Skipping example: need at least 2 outputs")
continue
# Compute F1 scores for all generations
scores = []
for generation in generations:
metrics = compute_file_level_f1(generation, ground_truth)
scores.append(metrics["f1"])
# Create GRPO example
grpo_example = {
"prompt": prompt,
"completions": generations,
"scores": scores
}
f_out.write(json.dumps(grpo_example) + '\n')
examples_created += 1
examples_processed += 1
if examples_processed % 10 == 0:
print(f"Processed {examples_processed} examples")
print(f" Last example F1 scores: {[f'{s:.3f}' for s in scores]}")
print(f"\nDone! Created {examples_created} GRPO examples from {examples_processed} SFT examples")
print(f"Output saved to: {output_jsonl}")
def analyze_dataset(jsonl_path: str, dataset_type: str = "auto"):
"""
Analyze a dataset and print statistics.
Args:
jsonl_path: Path to JSONL file
dataset_type: "sft", "dpo", "grpo", or "auto" (auto-detect)
"""
with open(jsonl_path, 'r') as f:
lines = f.readlines()
if not lines:
print("Empty dataset")
return
first = json.loads(lines[0])
# Auto-detect type
if dataset_type == "auto":
if "chosen" in first and "rejected" in first:
dataset_type = "dpo"
elif "completions" in first and "scores" in first:
dataset_type = "grpo"
else:
dataset_type = "sft"
print(f"\nDataset Analysis: {jsonl_path}")
print(f"Type: {dataset_type.upper()}")
print(f"Total examples: {len(lines)}")
if dataset_type == "dpo":
f1_diffs = []
for line in lines:
data = json.loads(line)
chosen_f1 = data.get("chosen_f1", 1.0)
rejected_f1 = data.get("rejected_f1", 0.0)
f1_diffs.append(chosen_f1 - rejected_f1)
print(f"Average F1 difference: {sum(f1_diffs) / len(f1_diffs):.3f}")
print(f"Min F1 difference: {min(f1_diffs):.3f}")
print(f"Max F1 difference: {max(f1_diffs):.3f}")
elif dataset_type == "grpo":
all_scores = []
completion_counts = []
for line in lines:
data = json.loads(line)
scores = data.get("scores", [])
all_scores.extend(scores)
completion_counts.append(len(scores))
print(f"Average completions per prompt: {sum(completion_counts) / len(completion_counts):.1f}")
print(f"Min completions: {min(completion_counts)}")
print(f"Max completions: {max(completion_counts)}")
print(f"Average F1 score: {sum(all_scores) / len(all_scores):.3f}")
print(f"F1 score range: [{min(all_scores):.3f}, {max(all_scores):.3f}]")
def main():
parser = argparse.ArgumentParser(description="Convert SFT data to DPO/GRPO formats")
parser.add_argument("--input", required=True, help="Input SFT JSONL file")
parser.add_argument("--output", required=True, help="Output JSONL file")
parser.add_argument("--format", choices=["dpo", "grpo"], required=True,
help="Output format")
parser.add_argument("--model", default=None,
help="Path to model for generation (optional)")
parser.add_argument("--num-samples", type=int, default=4,
help="Number of outputs to generate per prompt")
parser.add_argument("--max-examples", type=int, default=None,
help="Maximum number of examples to process")
parser.add_argument("--min-f1-diff", type=float, default=0.1,
help="Minimum F1 difference for DPO pairs")
parser.add_argument("--analyze", action="store_true",
help="Analyze the output dataset after creation")
args = parser.parse_args()
print(f"Converting {args.input} to {args.format.upper()} format...")
print(f"Output: {args.output}")
if args.format == "dpo":
convert_sft_to_dpo(
args.input,
args.output,
args.model,
args.num_samples,
args.min_f1_diff,
args.max_examples
)
elif args.format == "grpo":
convert_sft_to_grpo(
args.input,
args.output,
args.model,
args.num_samples,
args.max_examples
)
if args.analyze:
analyze_dataset(args.output, args.format)
if __name__ == "__main__":
# Example usage without CLI
import sys
if len(sys.argv) == 1:
print("Data Preparation Utilities")
print("=" * 50)
print("\nUsage:")
print(" python prepare_data.py --input instruct_data.jsonl --output dpo_data.jsonl --format dpo")
print(" python prepare_data.py --input instruct_data.jsonl --output grpo_data.jsonl --format grpo")
print("\nWith model generation:")
print(" python prepare_data.py --input instruct_data.jsonl --output dpo_data.jsonl --format dpo \\")
print(" --model ./runs/instruct_run_14b_v1/merged_14b_instruct_lora --num-samples 4")
print("\nAnalyze dataset:")
print(" python prepare_data.py --input dpo_data.jsonl --output /dev/null --format dpo --analyze")
sys.exit(0)
main()
|