File size: 6,670 Bytes
33b38c9 | 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 | import argparse
import json
import os
import time
from pathlib import Path
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
from helpfulness_scheme_interface import (
_compute_allowed_ids_scheme_a,
_compute_allowed_ids_scheme_b_temp,
)
PROMPT_BEGIN = "BEGINNING OF CONVERSATION: "
PROMPT_USER = "USER: {input} "
PROMPT_ASSISTANT = "ASSISTANT:"
PROMPT_INPUT_ALPACA = PROMPT_BEGIN + PROMPT_USER + PROMPT_ASSISTANT
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--prompt_file", type=str, required=True)
parser.add_argument("--output_dir", type=str, required=True)
parser.add_argument("--model_base_name_or_path", type=str, required=True)
parser.add_argument("--model_arm_helpfulness_name_or_path", type=str, required=True)
parser.add_argument("--model_arm_harmlessness_name_or_path", type=str, required=True)
parser.add_argument("--alpha_helpfulness", type=float, required=True)
parser.add_argument("--alpha_harmlessness", type=float, required=True)
parser.add_argument("--model_name_for_logging", type=str, required=True)
parser.add_argument("--scheme", choices=["a", "b_temp_support"], required=True)
parser.add_argument("--base_prob_threshold", type=float, default=0.0008)
parser.add_argument("--support_temperature", type=float, default=10.0)
parser.add_argument("--max_new_tokens", type=int, default=512)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--resume", action="store_true")
return parser.parse_args()
def load_models(args: argparse.Namespace):
tokenizer = AutoTokenizer.from_pretrained(args.model_base_name_or_path)
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
base_model = AutoModelForCausalLM.from_pretrained(
args.model_base_name_or_path,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
model = PeftModel.from_pretrained(
base_model,
args.model_arm_helpfulness_name_or_path,
adapter_name="helpfulness",
)
model.load_adapter(args.model_arm_harmlessness_name_or_path, adapter_name="harmlessness")
model.eval()
return tokenizer, model
def forward_last_logprobs(model, input_ids: list[int], mode: str) -> torch.Tensor:
device = next(model.parameters()).device
tensor = torch.tensor([input_ids], device=device)
with torch.no_grad():
if mode == "base":
with model.disable_adapter():
logits = model(input_ids=tensor, use_cache=False).logits[0, -1].float().cpu()
else:
model.set_adapter(mode)
logits = model(input_ids=tensor, use_cache=False).logits[0, -1].float().cpu()
return torch.log_softmax(logits, dim=-1)
def sample_response(
prompt: str,
tokenizer,
model,
args: argparse.Namespace,
) -> tuple[str, list[dict]]:
prompt_ids = tokenizer(prompt, add_special_tokens=False)["input_ids"]
response_ids: list[int] = []
trace: list[dict] = []
for step_idx in range(args.max_new_tokens):
full_prefix = prompt_ids + response_ids
logp_base = forward_last_logprobs(model, full_prefix, "base")
logp_help = forward_last_logprobs(model, full_prefix, "helpfulness")
logp_harm = forward_last_logprobs(model, full_prefix, "harmlessness")
prob_base = torch.exp(logp_base)
if args.scheme == "a":
allowed_ids = _compute_allowed_ids_scheme_a(prob_base, args.base_prob_threshold)
support_temperature = None
else:
allowed_ids, _ = _compute_allowed_ids_scheme_b_temp(logp_base, args.support_temperature)
support_temperature = args.support_temperature
score_s = (
logp_base
+ args.alpha_helpfulness * logp_help
+ args.alpha_harmlessness * logp_harm
)
filtered_score = score_s[allowed_ids]
filtered_probs = torch.softmax(filtered_score, dim=-1)
sampled_index = int(torch.multinomial(filtered_probs, 1).item())
token_id = int(allowed_ids[sampled_index].item())
trace.append(
{
"step_index": step_idx + 1,
"token_id": token_id,
"token_text": tokenizer.decode([token_id]),
"allowed_token_count": int(allowed_ids.numel()),
"scheme": args.scheme,
"base_prob_threshold": args.base_prob_threshold if args.scheme == "a" else None,
"support_temperature": support_temperature,
}
)
if token_id == tokenizer.eos_token_id:
break
response_ids.append(token_id)
return tokenizer.decode(response_ids, skip_special_tokens=True), trace
def main() -> None:
args = parse_args()
torch.manual_seed(args.seed)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
out_path = output_dir / "generation.json"
if out_path.exists() and not args.resume:
raise SystemExit(f"{out_path} exists; pass --resume to continue.")
with open(args.prompt_file, "r", encoding="utf-8") as handle:
prompts = json.load(handle)
tokenizer, model = load_models(args)
if args.resume and out_path.exists():
with open(out_path, "r", encoding="utf-8") as handle:
outputs = json.load(handle)
start = len(outputs)
else:
outputs = []
start = 0
for idx in range(start, len(prompts)):
row = prompts[idx]
formatted_prompt = PROMPT_INPUT_ALPACA.format(input=row["prompt"])
tic = time.time()
response, trace = sample_response(formatted_prompt, tokenizer, model, args)
elapsed = time.time() - tic
outputs.append(
{
"uid": row["uid"],
"prompt": row["prompt"],
"response": response,
"model": args.model_name_for_logging,
"elapsed": elapsed,
"scheme": args.scheme,
"alpha_helpfulness": args.alpha_helpfulness,
"alpha_harmlessness": args.alpha_harmlessness,
"trace_first_10_steps": trace[:10],
}
)
if idx % 3 == 1:
with open(out_path, "w", encoding="utf-8") as handle:
json.dump(outputs, handle, ensure_ascii=False, indent=2)
with open(out_path, "w", encoding="utf-8") as handle:
json.dump(outputs, handle, ensure_ascii=False, indent=2)
if __name__ == "__main__":
main()
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