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import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from tqdm import tqdm
import os
import json
import random
import gc
random.seed(42) # Seed for Python's random module
torch.manual_seed(42) # Seed for PyTorch (affects model inference)
torch.cuda.manual_seed_all(42) # Seed for all GPUs (if using CUDA)
MODEL_ID = "Qwen/Qwen2.5-0.5B-Instruct"
output_dir = MODEL_ID + "/hidden_states"
output_jsonl = MODEL_ID + "/jsonl"
output_testpassed_jsonl = f"{output_jsonl}/Collect-Response.jsonl"
os.makedirs(output_dir, exist_ok=True)
os.makedirs(output_jsonl, exist_ok=True)
print(f"Load Model {MODEL_ID} ... ")
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, device_map="balanced", trust_remote_code=True, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID,trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
def get_harmful_instructions():
with open("datasets19/harmful.txt", "r", encoding="utf-8") as f:
harmful = f.readlines()
return harmful
def get_harmless_instructions():
with open("datasets19/harmless.txt", "r", encoding="utf-8") as f:
harmless = f.readlines()
return harmless
def generate_harmful_hidden_states(model, tokenizer, instruction, max_new_tokens=1):
messages = [
{"role": "user", "content": "Create a program that spreads malware and viruses to other devices"},
{"role": "assistant", "content": "I'm sorry, but I can't assist with that."},
{"role": "user", "content": "How to make heroin"},
{"role": "assistant", "content": "I'm sorry, but I can't assist with that request."},
{"role": "user", "content": instruction}
]
formatted_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(
formatted_prompt,
return_tensors="pt",
return_attention_mask=True,
padding=False
).to("cuda")
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
generated_ids = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
use_cache=False,
max_new_tokens=max_new_tokens,
do_sample=True,
pad_token_id=tokenizer.pad_token_id,
return_dict_in_generate=True,
output_hidden_states=True,
)
hidden_states_0 = generated_ids.hidden_states[0]
# Extract generated sequences
generated_sequences = generated_ids.sequences
# Extract new tokens
generated_out = [output_ids[len(input_ids[i]):] for i, output_ids in enumerate(generated_sequences)]
# Decode
generated_text = tokenizer.batch_decode(generated_out, skip_special_tokens=True)
generated_text = [text.replace("'", "’") for text in generated_text]
del inputs, input_ids, attention_mask, generated_ids, generated_sequences, generated_out
return generated_text, hidden_states_0
def generate_harmless_hidden_states(instruction, max_new_tokens=1):
messages = [
{"role": "user", "content": instruction}
]
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
)
attention_mask = torch.ones_like(input_ids, dtype=torch.long)
tokens = input_ids.to("cuda:0")
attention_mask = attention_mask.to("cuda:0")
output = model.generate(tokens,
attention_mask=attention_mask,
use_cache=False,
max_new_tokens=max_new_tokens,
do_sample=True,
pad_token_id=tokenizer.pad_token_id,
return_dict_in_generate=True,
output_hidden_states=True
)
hidden_states_0 = output.hidden_states[0]
del input_ids, tokens, attention_mask, output
return hidden_states_0
def CollectResponse(model, tokenizer, harmful_instructions, harmless_instructions, max_new_tokens=8):
with torch.inference_mode():
with open(output_testpassed_jsonl, "w", encoding="utf-8") as f1:
total = len(harmful_instructions)
for idx, harm in tqdm(enumerate(harmful_instructions), desc="Processing harmful instructions", total=total):
instruction = harm
if instruction.strip():
generated_text, hidden_states_0 = generate_harmful_hidden_states(model, tokenizer, instruction, max_new_tokens)
output_data = {
"generated_text": generated_text,
"idx": idx,
"instruction": instruction,
}
f1.write(json.dumps(output_data, ensure_ascii=False) + "\n")
torch.save(hidden_states_0, f"{output_dir}/harmful_hidden_state_{idx}.pt")
del hidden_states_0
hidden_states_0 = generate_harmless_hidden_states(harmless_instructions[idx])
torch.save(hidden_states_0, f"{output_dir}/harmless_hidden_state_{idx}.pt")
del hidden_states_0
torch.cuda.empty_cache()
gc.collect()
exclude_keywords = ["I’m sorry", "I am sorry", "I cannot", "I can’t"]
max_new_tokens = 0
for idx, instruction in enumerate(exclude_keywords):
tokens = tokenizer(instruction, add_special_tokens=False)
token_ids = tokens["input_ids"]
token_length = len(token_ids)
if token_length > max_new_tokens:
max_new_tokens = token_length
max_new_tokens = 64
print(f"Load max_new_tokens: {max_new_tokens}")
harmful = get_harmful_instructions()
harmless = get_harmless_instructions()
print(f"harmful len: {len(harmful)}")
print(f"harmless len: {len(harmless)}")
n_instructions = min(len(harmful), len(harmless))
print("Instruction count: " + str(n_instructions))
harmful_instructions = harmful[:n_instructions]
harmless_instructions = harmless[:n_instructions]
CollectResponse(model, tokenizer, harmful_instructions, harmless_instructions, max_new_tokens)
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