Spaces:
Sleeping
Sleeping
File size: 6,153 Bytes
e8943e7 a264cff e8943e7 a264cff e8943e7 |
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 |
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer
import json
import torch
import os
import argparse
def load_jsonl(file_path):
with open(file_path, "r") as f:
return [json.loads(line) for line in f]
def get_hidden_p_and_r(model, tokenizer, prompts, responses, layer_list=None):
max_layer = model.config.num_hidden_layers
if layer_list is None:
layer_list = list(range(max_layer + 1))
prompt_avg = [[] for _ in range(max_layer + 1)]
response_avg = [[] for _ in range(max_layer + 1)]
prompt_last = [[] for _ in range(max_layer + 1)]
texts = [p + a for p, a in zip(prompts, responses)]
for text, prompt in tqdm(zip(texts, prompts), total=len(texts)):
inputs = tokenizer(text, return_tensors="pt", add_special_tokens=False).to(
model.device
)
prompt_len = len(tokenizer.encode(prompt, add_special_tokens=False))
with torch.no_grad():
outputs = model(**inputs, output_hidden_states=True)
for layer in layer_list:
prompt_avg[layer].append(
outputs.hidden_states[layer][:, :prompt_len, :]
.mean(dim=1)
.detach()
.cpu()
)
response_avg[layer].append(
outputs.hidden_states[layer][:, prompt_len:, :]
.mean(dim=1)
.detach()
.cpu()
)
prompt_last[layer].append(
outputs.hidden_states[layer][:, prompt_len - 1, :].detach().cpu()
)
del outputs
for layer in layer_list:
prompt_avg[layer] = torch.cat(prompt_avg[layer], dim=0)
prompt_last[layer] = torch.cat(prompt_last[layer], dim=0)
response_avg[layer] = torch.cat(response_avg[layer], dim=0)
return prompt_avg, prompt_last, response_avg
import pandas as pd
import os
def get_persona_effective(pos_path, neg_path, trait, threshold=50):
persona_pos = pd.read_csv(pos_path)
persona_neg = pd.read_csv(neg_path)
mask = (
(persona_pos[trait] >= threshold)
& (persona_neg[trait] < 100 - threshold)
& (persona_pos["coherence"] >= 50)
& (persona_neg["coherence"] >= 50)
)
persona_pos_effective = persona_pos[mask]
persona_neg_effective = persona_neg[mask]
persona_pos_effective_prompts = persona_pos_effective["prompt"].tolist()
persona_neg_effective_prompts = persona_neg_effective["prompt"].tolist()
persona_pos_effective_responses = persona_pos_effective["answer"].tolist()
persona_neg_effective_responses = persona_neg_effective["answer"].tolist()
return (
persona_pos_effective,
persona_neg_effective,
persona_pos_effective_prompts,
persona_neg_effective_prompts,
persona_pos_effective_responses,
persona_neg_effective_responses,
)
def save_persona_vector(model_name, pos_path, neg_path, trait, save_dir, threshold=75):
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
(
persona_pos_effective,
persona_neg_effective,
persona_pos_effective_prompts,
persona_neg_effective_prompts,
persona_pos_effective_responses,
persona_neg_effective_responses,
) = get_persona_effective(pos_path, neg_path, trait, threshold)
(
persona_effective_prompt_avg,
persona_effective_prompt_last,
persona_effective_response_avg,
) = ({}, {}, {})
(
persona_effective_prompt_avg["pos"],
persona_effective_prompt_last["pos"],
persona_effective_response_avg["pos"],
) = get_hidden_p_and_r(
model, tokenizer, persona_pos_effective_prompts, persona_pos_effective_responses
)
(
persona_effective_prompt_avg["neg"],
persona_effective_prompt_last["neg"],
persona_effective_response_avg["neg"],
) = get_hidden_p_and_r(
model, tokenizer, persona_neg_effective_prompts, persona_neg_effective_responses
)
persona_effective_prompt_avg_diff = torch.stack(
[
persona_effective_prompt_avg["pos"][l].mean(0).float()
- persona_effective_prompt_avg["neg"][l].mean(0).float()
for l in range(len(persona_effective_prompt_avg["pos"]))
],
dim=0,
)
persona_effective_response_avg_diff = torch.stack(
[
persona_effective_response_avg["pos"][l].mean(0).float()
- persona_effective_response_avg["neg"][l].mean(0).float()
for l in range(len(persona_effective_response_avg["pos"]))
],
dim=0,
)
persona_effective_prompt_last_diff = torch.stack(
[
persona_effective_prompt_last["pos"][l].mean(0).float()
- persona_effective_prompt_last["neg"][l].mean(0).float()
for l in range(len(persona_effective_prompt_last["pos"]))
],
dim=0,
)
os.makedirs(save_dir, exist_ok=True)
torch.save(
persona_effective_prompt_avg_diff, f"{save_dir}/{trait}_prompt_avg_diff.pt"
)
torch.save(
persona_effective_response_avg_diff, f"{save_dir}/{trait}_response_avg_diff.pt"
)
torch.save(
persona_effective_prompt_last_diff, f"{save_dir}/{trait}_prompt_last_diff.pt"
)
print(f"Persona vectors saved to {save_dir}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", type=str, required=True)
parser.add_argument("--pos_path", type=str, required=True)
parser.add_argument("--neg_path", type=str, required=True)
parser.add_argument("--trait", type=str, required=True)
parser.add_argument("--save_dir", type=str, required=True)
parser.add_argument("--threshold", type=int, default=85)
args = parser.parse_args()
save_persona_vector(
args.model_name,
args.pos_path,
args.neg_path,
args.trait,
args.save_dir,
args.threshold,
)
|