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haloscope
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weixin_43297441 2025-02-27 11:10:39 +08:00
parent 5481f9cf1f
commit c37a5314a6
3 changed files with 59 additions and 13 deletions
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2
a.sh
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@ -1,2 +1,2 @@
export HF_ENDPOINT=https://hf-mirror.com
CUDA_VISIBLE_DEVICES=0 python hal_det_llama.py --dataset_name triviaqa --model_name llama2_chat_7B --most_likely 0 --use_rouge 0 --generate_gt 1
CUDA_VISIBLE_DEVICES=9 python hal_generate.py

53
hal_det_llama.py
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@ -168,6 +168,8 @@ def main():
device_map="auto").cuda()
# firstly get the embeddings of the generated question and answers.
embed_generated = []
embed_generated_h =[]
embed_generated_t=[]
if args.dataset_name == 'tydiqa':
length = len(used_indices)
@ -180,13 +182,16 @@ def main():
question = dataset[i]['question']
answers = np.load(
f'save_for_eval/{args.dataset_name}_hal_det/answers/most_likely_hal_det_{args.model_name}_{args.dataset_name}_answers_index_{i}.npy')
truths= np.load(
f'save_for_eval/{args.dataset_name}_hal_det/answers/most_likely_hal_det_{args.model_name}_{args.dataset_name}_answers_index_{i}.npy')
hallucinations= np.load(
f'save_for_eval/{args.dataset_name}_hal_det/hallucinations/most_likely_hal_det_{args.model_name}_{args.dataset_name}_hallucinations_index_{i}.npy')
for anw in answers:
if args.dataset_name == 'tydiqa':
prompt = tokenizer(
"Concisely answer the following question based on the information in the given passage: \n" + \
" Passage: " + dataset[int(used_indices[i])]['context'] + " \n Q: " + question + " \n A:",
" Passage: " + dataset[int(used_indices[i])]['context'] + " \n Q: " + question + " \n A:" + anw,
return_tensors='pt').input_ids.cuda()
elif args.dataset_name == 'coqa':
prompt = tokenizer(dataset[i]['prompt'] + anw, return_tensors='pt').input_ids.cuda()
@ -202,6 +207,48 @@ def main():
embed_generated = np.asarray(np.stack(embed_generated), dtype=np.float32)
np.save(f'save_for_eval/{args.dataset_name}_hal_det/most_likely_{args.model_name}_gene_embeddings_layer_wise.npy', embed_generated)
for tru in truths:
if args.dataset_name == 'tydiqa':
prompt = tokenizer(
"Concisely answer the following question based on the information in the given passage: \n" + \
" Passage: " + dataset[int(used_indices[i])]['context'] + " \n Q: " + question + " \n A:" + tru,
return_tensors='pt').input_ids.cuda()
elif args.dataset_name == 'coqa':
prompt = tokenizer(dataset[i]['prompt'] + tru, return_tensors='pt').input_ids.cuda()
else:
prompt = tokenizer(
f"Answer the question concisely. Q: {question}" + " A:" + tru,
return_tensors='pt').input_ids.cuda()
with torch.no_grad():
hidden_states = model(prompt, output_hidden_states=True).hidden_states
hidden_states = torch.stack(hidden_states, dim=0).squeeze()
hidden_states = hidden_states.detach().cpu().numpy()[:, -1, :]
embed_generated_t.append(hidden_states)
embed_generated_t = np.asarray(np.stack(embed_generated_t), dtype=np.float32)
np.save(f'save_for_eval/{args.dataset_name}_hal_det/most_likely_{args.model_name}_gene_embeddings_t_layer_wise.npy', embed_generated_t)
for hal in hallucinations:
if args.dataset_name == 'tydiqa':
prompt = tokenizer(
"Concisely answer the following question based on the information in the given passage: \n" + \
" Passage: " + dataset[int(used_indices[i])]['context'] + " \n Q: " + question + " \n A:" + hal,
return_tensors='pt').input_ids.cuda()
elif args.dataset_name == 'coqa':
prompt = tokenizer(dataset[i]['prompt'] + hal, return_tensors='pt').input_ids.cuda()
else:
prompt = tokenizer(
f"Answer the question concisely. Q: {question}" + " A:" + hal,
return_tensors='pt').input_ids.cuda()
with torch.no_grad():
hidden_states = model(prompt, output_hidden_states=True).hidden_states
hidden_states = torch.stack(hidden_states, dim=0).squeeze()
hidden_states = hidden_states.detach().cpu().numpy()[:, -1, :]
embed_generated_h.append(hidden_states)
embed_generated_h = np.asarray(np.stack(embed_generated_h), dtype=np.float32)
np.save(f'save_for_eval/{args.dataset_name}_hal_det/most_likely_{args.model_name}_gene_embeddings_h_layer_wise.npy', embed_generated_h)
HEADS = [f"model.layers.{i}.self_attn.head_out" for i in range(model.config.num_hidden_layers)]
MLPS = [f"model.layers.{i}.mlp" for i in range(model.config.num_hidden_layers)]
embed_generated_loc2 = []
@ -421,7 +468,7 @@ def main():
# returned_results = svd_embed_score(embed_generated_wild, gt_label_wild,
# 1, 11, mean=0, svd=0, weight=args.weighted_svd)
# get the best hyper-parameters on validation set
returned_results = svd_embed_score(embed_generated_eval, gt_label_val,
returned_results = svd_embed_score(embed_generated_eval, gt_label_val,
1, 11, mean=1, svd=1, wei1ght=args.weighted_svd)
pca_model = PCA(n_components=returned_results['k'], whiten=False).fit(embed_generated_wild[:,returned_results['best_layer'],:])

17
hal_generate.py
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@ -1,4 +1,5 @@
import os
import time
import torch
import torch.nn.functional as F
import evaluate
@ -26,6 +27,10 @@ API={
'gpt-3.5-turbo':{'base_url':"https://api.agicto.cn/v1",'key':''},
'deepseek-chat':{'base_url':"https://api.deepseek.com/v1",'key':'sk-5f06261529bb44df86d9b2fdbae1a6b5'},
'qwen-plus':{'base_url':"https://dashscope.aliyuncs.com/compatible-mode/v1",'key':'sk-5be20597fa574155a9e56d7df1acfc7f'},
'step-1-8k':{'base_url':"https://api.stepfun.com/v1",'key':'2hqEtnMCWe5cugi1mAVWRZat5hydLFG8tEJWPRW5XnxglpWxRBp5W0M0dvPAFXhC3'},
'moonshot-v1-8k':{'base_url':"https://api.moonshot.cn/v1",'key':'sk-8zjQm3CMAI7qQUWYLgFxSCCQxCOkVfuSkRcs6kNxUZY2L4aV'},
'ERNIE-3.5-8K':{'base_url':"https://api.agicto.cn/v1",'key':'sk-BmLsx7BClpqtmIwxLNB5pH5lJ36WJ7GxiV3nV5PiwF7Iwauf'},
}
def seed_everything(seed: int):
@ -56,11 +61,11 @@ def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model_name', type=str, default='qwen-plus')
parser.add_argument('--model_name', type=str, default='step-1-8k')
parser.add_argument('--dataset_name', type=str, default='triviaqa')
parser.add_argument('--num_gene', type=int, default=1)
parser.add_argument('--use_api', type=bool, default=True)
parser.add_argument('--most_likely', type=bool, default=False)
parser.add_argument('--most_likely', type=bool, default=True)
parser.add_argument("--model_dir", type=str, default=None, help='local directory with model data')
parser.add_argument("--instruction", type=str, default='/home/liwenyun/code/haloscope/generation/qa/qa_one-turn_instruction.txt', help='local directory of instruction file.')
args = parser.parse_args()
@ -216,13 +221,6 @@ def main():
top_p=1,
temperature = 1,
)
# openai.ChatCompletion.create(
# model=args.model_name,
# messages=prompt,
# temperature=1,
# max_tokens=256,
# top_p=1
# )
hallucination_response = client.chat.completions.create(
model = args.model_name,
messages = hallucination_prompt,
@ -254,6 +252,7 @@ def main():
)
decoded=response.choices[0].message.content
hallucination_decoded=hallucination_response.choices[0].message.content
time.sleep(20)
# decoded = tokenizer.decode(generated[0, prompt.shape[-1]:],