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import json
import torch
from dataclasses import dataclass
####################################
# SCRIPT ARGUMENTS
####################################
@dataclass
class ScriptArguments:
"""
Arguments for the Bradley-Terry evaluation script.
"""
sft_generations_file: str = '/raid/lingo/jen_ben/HF-RLHF/eval/test/gen_examples_idan_mini.json'
kto_generations_file: str = '/raid/lingo/jen_ben/HF-RLHF/eval/test/gen_examples_idan_mini.json'
output_file: str = 'bt_results_test_mini.json'
####################################
# FUNCTIONS
####################################
def load_rewards(file_path):
"""
Load the rewards from a JSON file.
Args:
file_path (str): Path to the JSON file containing model generations and rewards.
Returns:
list: List of dictionaries with prompts, outputs, and rewards.
"""
with open(file_path, 'r') as f:
return json.load(f)
def bradley_terry_comparison(sft_rewards, kto_rewards):
"""
Perform Bradley-Terry comparison between two sets of model generations.
Args:
sft_rewards (list): List of dictionaries for the SFT model's generations and rewards.
kto_rewards (list): List of dictionaries for the KTO model's generations and rewards.
Returns:
list: Comparison results including preferred outputs and probabilities.
dict: Metrics summary including percentage preferred and average probabilities.
"""
results = []
kto_preferred_count = 0
sft_preferred_count = 0
probabilities = []
for ix in range(len(sft_rewards)):
sft = sft_rewards[ix]
kto = kto_rewards[ix]
# Ensure prompts match
assert sft['prompt'] == kto['prompt'], f"ERROR: Prompts at index {ix} do not match."
# Compute Bradley-Terry probability
kto_reward = torch.tensor(kto['reward'], dtype=torch.float32)
sft_reward = torch.tensor(sft['reward'], dtype=torch.float32)
prob_kto_preferred = torch.sigmoid(kto_reward - sft_reward).item()
probabilities.append(prob_kto_preferred)
preferred_model = 'kto' if prob_kto_preferred > 0.5 else 'sft'
# Count preferences
if preferred_model == 'kto':
kto_preferred_count += 1
else:
sft_preferred_count += 1
# Log results
bt_result = {
'prompt': sft['prompt'],
'sft_output': sft['output'],
'kto_output': kto['output'],
'sft_reward': sft['reward'],
'kto_reward': kto['reward'],
'preferred': preferred_model,
'prob_kto_preferred': prob_kto_preferred
}
results.append(bt_result)
# Calculate metrics
total_examples = len(sft_rewards)
metrics = {
'total_examples': total_examples,
'kto_preferred_percentage': 100 * kto_preferred_count / total_examples,
'sft_preferred_percentage': 100 * sft_preferred_count / total_examples,
'avg_probability_kto_preferred': sum(probabilities) / total_examples
}
return results, metrics
def save_results(results, output_path):
"""
Save the comparison results to a JSON file.
Args:
results (list): List of comparison results.
output_path (str): Path to the output JSON file.
"""
with open(output_path, "w") as f:
json.dump(results, f, indent=4)
print(f"Results saved to {output_path}")
def print_metrics(metrics):
"""
Print evaluation metrics.
Args:
metrics (dict): Dictionary containing evaluation metrics.
"""
print("\nEVALUATION METRICS:")
print(f"Total examples: {metrics['total_examples']}")
print(f"Percentage preferred - KTO model: {metrics['kto_preferred_percentage']:.2f}%")
print(f"Percentage preferred - SFT model: {metrics['sft_preferred_percentage']:.2f}%")
print(f"Average probability of KTO model being preferred: {metrics['avg_probability_kto_preferred']:.4f}")
####################################
# MAIN SCRIPT
####################################
def main():
# Initialize script arguments
args = ScriptArguments()
# Load data
print("Loading data...")
sft_rewards = load_rewards(args.sft_generations_file)
kto_rewards = load_rewards(args.kto_generations_file)
# Perform Bradley-Terry comparison
print("Performing Bradley-Terry comparison...")
results, metrics = bradley_terry_comparison(sft_rewards, kto_rewards)
# Save results
save_results(results, args.output_file)
# Print metrics
print_metrics(metrics)
if __name__ == "__main__":
main()
# import json
# import torch
# output_file_path = 'bt_results.json'
# ref_generations_rewards_file_path = 'ref_models_generations_reward_trl-libqwen1.5-1.8b-sft.json'
# finetuned_generations_rewards_file_path = 'finetuned_models_generations_reward_trl-libqwen1.5-1.8b-sft.json'
# # Open and read JSON files
# with open(ref_generations_rewards_file_path, 'r') as f:
# ref_rewards = json.load(f)
# with open(finetuned_generations_rewards_file_path, 'r') as g:
# finetuned_rewards = json.load(g)
# # assert len(ref_rewards) != len(finetuned_rewards), 'ERROR: files are not with the same length.'
# results = []
# finetuned_preffered = 0
# for ix in range(len(ref_rewards)):
# ref = ref_rewards[ix]
# finetuned = finetuned_rewards[ix]
# assert ref['prompt'] == finetuned['prompt'], 'ERROR: ref and finetuned prompt are not the same.'
# # Bradely Terry
# finetuned_reward = torch.tensor(finetuned['reward'], dtype=torch.float32)
# ref_reward = torch.tensor(ref['reward'], dtype=torch.float32)
# prob_finetuned_preferred = torch.sigmoid(finetuned_reward - ref_reward)
# if prob_finetuned_preferred > 0.5:
# finetuned_preffered +=1
# print(f'example {ix}: finetuned preffered')
# else:
# print(f'example {ix}: ref preffered')
# # log results
# bt_result = {}
# bt_result['prompt'] = ref['prompt']
# bt_result['ref_output'] = ref['output']
# bt_result['finetuned_output'] = finetuned['output']
# bt_result['ref_reward'] = ref['output']
# bt_result['finetuned_reward'] = finetuned['output']
# bt_result['preffered'] = 'finetuned' if prob_finetuned_preferred > 0.5 else 'ref'
# results.append(bt_result)
# # save results in json files
# with open(output_file_path, "w") as f:
# json.dump(results, f, indent=4)
# print('BT EVALUATION COMPLETED.')
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