File size: 5,574 Bytes
c7a6fe6 | 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 | import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
from unsloth import FastLanguageModel
import torch
from health_classifier import classifier
max_seq_length = 8192
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "/home/mshahidul/readctrl_model/RL_model/readability_sft_lora_model",
max_seq_length = max_seq_length,
load_in_4bit = False, # Set to False if you have enough VRAM
fast_inference = False,
)
# Simply enable gradient checkpointing and prepare for training
model = FastLanguageModel.for_training(model)
# /home/mshahidul/readctrl/data/extracting_subclaim/extracted_subclaims_multiclinsum_test_en_full.json
with open("/home/mshahidul/readctrl/data/extracting_subclaim/extracted_subclaims_multiclinsum_test_en_full.json", "r") as f:
import json
data = json.load(f)
from datasets import Dataset
dataset = Dataset.from_list(data)
with open('/home/mshahidul/readctrl/code/RL_model/prompt', 'r') as f:
prompt_template = f.read()
dataset = dataset.map(lambda x: {
"prompt" : [
{"role": "system", "content": prompt_template},
{"role": "user", "content": f'''
- Input Language: English
- Gold Summary (the anchor reference summary): {x['summary']}
- Source Text (detailed content): {x['fulltext']}
'''},
],
"answer": {
"fulltext_subclaims": x['fulltext_subclaims'],
"summary_subclaims": x['summary_subclaims'],
},
})
import requests
import json
import re
from claim_verifier import MedicalClaimVerifier
verifier = MedicalClaimVerifier()
def claim_reward_func(prompts, completions, answer, **kwargs):
# import ipdb; ipdb.set_trace()
"""
GRPO reward function.
Expects 'summary_subclaims' and 'fulltext_subclaims' to be in the dataset.
"""
rewards = []
# We loop through the group of completions
for i in range(len(completions)):
reward = verifier.get_reward_score(
completions[i],
answer[i]["summary_subclaims"],
answer[i]["fulltext_subclaims"]
)
rewards.append(reward)
return rewards
# def format_reward_func(completions, **kwargs):
# required_keys = ["low_health_literacy", "intermediate_health_literacy", "proficient_health_literacy"]
# scores = []
# for completion in completions:
# try:
# match = re.search(r"<SOLUTION>(.*?)</SOLUTION>", completion, re.DOTALL)
# content = match.group(1) if match else completion
# data = json.loads(content)
# if all(k in data for k in required_keys):
# scores.append(2.0)
# else:
# scores.append(-1.0)
# except:
# scores.append(-2.0)
# return scores
import json
def literacy_classifier_reward_func(completions, **kwargs):
scores = []
for completion in completions:
try:
# 1. Clean up potential Markdown formatting
cleaned_content = completion[0]['content'].strip()
if cleaned_content.startswith("```"):
# Removes leading ```json or ``` and trailing ```
cleaned_content = cleaned_content.split("```")[1]
if cleaned_content.startswith("json"):
cleaned_content = cleaned_content[4:]
# 2. Parse the JSON
data = json.loads(cleaned_content.strip())
alignment_score = 0.0
target_labels = ["low", "intermediate", "proficient"]
for label in target_labels:
key = f"{label}_health_literacy"
text_to_test = data.get(key, "")
if text_to_test:
# Run the DSPy classifier
result = classifier(summary_text=text_to_test)
predicted = result.label # Expected format: "low_health_literacy"
# import ipdb; ipdb.set_trace()
if predicted == key:
alignment_score += 1.0
else:
# Soft penalty for misclassification
alignment_score -= 0.5
else:
# Penalty if a specific literacy level is missing from the JSON
alignment_score -= 0.3
scores.append(alignment_score)
except (json.JSONDecodeError, Exception):
# Significant penalty for malformed JSON or failed processing
scores.append(-1.0)
return scores
from trl import GRPOConfig, GRPOTrainer
training_args = GRPOConfig(
learning_rate = 5e-6,
lr_scheduler_type = "cosine",
weight_decay = 0.1,
max_prompt_length = 8192,
max_completion_length = 4096,
# num_of_epochs = 10,
num_generations = 4, # GRPO group size
per_device_train_batch_size = 4,
gradient_accumulation_steps = 4,
max_steps = 500,
bf16 = True,
output_dir = "medical_grpo_outputs",
)
trainer = GRPOTrainer(
model = model,
reward_funcs = [
claim_reward_func,
# format_reward_func,
literacy_classifier_reward_func
],
args = training_args,
train_dataset = dataset, # Use the same dataset from your SFT prep
tokenizer = tokenizer,
)
trainer.train()
model.save_pretrained("/home/mshahidul/readctrl_model/readability_GRPO_model_v1")
tokenizer.save_pretrained("/home/mshahidul/readctrl_model/readability_GRPO_model_v1") |