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weixin_43297441
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222
hal_det_llama.py
222
hal_det_llama.py
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@ -18,6 +18,9 @@ from baukit import Trace, TraceDict
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from metric_utils import get_measures, print_measures
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import re
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from torch.autograd import Variable
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from scipy.spatial import distance
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from sklearn.linear_model import Perceptron
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from sklearn.ensemble import GradientBoostingRegressor
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@ -52,14 +55,14 @@ def main():
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parser.add_argument('--model_name', type=str, default='llama2_chat_7B')
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parser.add_argument('--dataset_name', type=str, default='triviaqa')
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parser.add_argument('--num_gene', type=int, default=1)
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parser.add_argument('--gene', type=int, default=0)
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parser.add_argument('--generate_gt', type=int, default=0)
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parser.add_argument('--use_rouge', type=int, default=0)
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# parser.add_argument('--gene', type=int, default=0)
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# parser.add_argument('--generate_gt', type=int, default=0)
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parser.add_argument('--use_rouge', type=bool, default= False)
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parser.add_argument('--weighted_svd', type=int, default=0)
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parser.add_argument('--feat_loc_svd', type=int, default=0)
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parser.add_argument('--wild_ratio', type=float, default=0.75)
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parser.add_argument('--thres_gt', type=float, default=0.5)
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parser.add_argument('--most_likely', type=int, default=0)
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parser.add_argument('--most_likely', type=bool, default=True)
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parser.add_argument("--model_dir", type=str, default=None, help='local directory with model data')
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args = parser.parse_args()
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@ -158,165 +161,7 @@ def main():
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else:
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raise ValueError("Invalid dataset name")
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if args.gene:
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tokenizer = llama_iti.LlamaTokenizer.from_pretrained(MODEL, trust_remote_code=True)
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model = llama_iti.LlamaForCausalLM.from_pretrained(MODEL, low_cpu_mem_usage=True, torch_dtype=torch.float16,
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device_map="auto").cuda()
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begin_index = 0
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if args.dataset_name == 'tydiqa':
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end_index = len(used_indices)
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else:
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end_index = len(dataset)
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if not os.path.exists(f'./save_for_eval/{args.dataset_name}_hal_det/'):
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os.mkdir(f'./save_for_eval/{args.dataset_name}_hal_det/')
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if not os.path.exists(f'./save_for_eval/{args.dataset_name}_hal_det/answers'):
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os.mkdir(f'./save_for_eval/{args.dataset_name}_hal_det/answers')
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period_token_id = [tokenizer(_)['input_ids'][-1] for _ in ['\n']]
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period_token_id += [tokenizer.eos_token_id]
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for i in range(begin_index, end_index):
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answers = [None] * args.num_gene
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if args.dataset_name == 'tydiqa':
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question = dataset[int(used_indices[i])]['question']
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prompt = tokenizer(
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"Concisely answer the following question based on the information in the given passage: \n" + \
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" Passage: " + dataset[int(used_indices[i])]['context'] + " \n Q: " + question + " \n A:",
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return_tensors='pt').input_ids.cuda()
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elif args.dataset_name == 'coqa':
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prompt = tokenizer(
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dataset[i]['prompt'], return_tensors='pt').input_ids.cuda()
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else:
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question = dataset[i]['question']
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prompt = tokenizer(f"Answer the question concisely. Q: {question}" + " A:", return_tensors='pt').input_ids.cuda()
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for gen_iter in range(args.num_gene):
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if args.most_likely:
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generated = model.generate(prompt,
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num_beams=5,
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num_return_sequences=1,
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do_sample=False,
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max_new_tokens=64,
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)
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else:
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generated = model.generate(prompt,
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do_sample=True,
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num_return_sequences=1,
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num_beams=1,
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max_new_tokens=64,
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temperature=0.5,
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top_p=1.0)
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decoded = tokenizer.decode(generated[0, prompt.shape[-1]:],
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skip_special_tokens=True)
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if args.dataset_name == 'tqa' or args.dataset_name == 'triviaqa':
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# corner case.
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if 'Answer the question concisely' in decoded:
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print('#####error')
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print(decoded.split('Answer the question concisely')[1])
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print('#####error')
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decoded = decoded.split('Answer the question concisely')[0]
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if args.dataset_name == 'coqa':
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if 'Q:' in decoded:
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print('#####error')
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print(decoded.split('Q:')[1])
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print('#####error')
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decoded = decoded.split('Q:')[0]
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print(decoded)
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answers[gen_iter] = decoded
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print('sample: ', i)
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if args.most_likely:
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info = 'most_likely_'
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else:
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info = 'batch_generations_'
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print("Saving answers")
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np.save(f'./save_for_eval/{args.dataset_name}_hal_det/answers/' + info + f'hal_det_{args.model_name}_{args.dataset_name}_answers_index_{i}.npy',
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answers)
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elif args.generate_gt:
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from bleurt_pytorch import BleurtConfig, BleurtForSequenceClassification, BleurtTokenizer
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model = BleurtForSequenceClassification.from_pretrained('lucadiliello/BLEURT-20').cuda()
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tokenizer = BleurtTokenizer.from_pretrained('lucadiliello/BLEURT-20')
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model.eval()
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rouge = evaluate.load('rouge')
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gts = np.zeros(0)
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if args.dataset_name == 'tydiqa':
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length = len(used_indices)
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else:
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length = len(dataset)
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for i in range(length):
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if args.dataset_name == 'tqa':
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best_answer = dataset[i]['best_answer']
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correct_answer = dataset[i]['correct_answers']
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all_answers = [best_answer] + correct_answer
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elif args.dataset_name == 'triviaqa':
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all_answers = dataset[i]['answer']['aliases']
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elif args.dataset_name == 'coqa':
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all_answers = dataset[i]['answer']
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elif args.dataset_name == 'tydiqa':
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all_answers = dataset[int(used_indices[i])]['answers']['text']
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if args.most_likely:
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answers = np.load(
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f'./save_for_eval/{args.dataset_name}_hal_det/answers/most_likely_hal_det_{args.model_name}_{args.dataset_name}_answers_index_{i}.npy')
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else:
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answers = np.load(
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f'./save_for_eval/{args.dataset_name}_hal_det/answers/batch_generations_hal_det_{args.model_name}_{args.dataset_name}_answers_index_{i}.npy')
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# get the gt.
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if args.use_rouge:
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predictions = answers
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all_results = np.zeros((len(all_answers), len(predictions)))
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all_results1 = np.zeros((len(all_answers), len(predictions)))
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all_results2 = np.zeros((len(all_answers), len(predictions)))
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for anw in range(len(all_answers)):
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results = rouge.compute(predictions=predictions,
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references=[all_answers[anw]] * len(predictions),
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use_aggregator=False)
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all_results[anw] = results['rougeL']
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all_results1[anw] = results['rouge1']
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all_results2[anw] = results['rouge2']
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# breakpoint()
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gts = np.concatenate([gts, np.max(all_results, axis=0)], 0)
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if i % 50 == 0:
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print("samples passed: ", i)
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else:
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predictions = answers
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all_results = np.zeros((len(all_answers), len(predictions)))
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with torch.no_grad():
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for anw in range(len(all_answers)):
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inputs = tokenizer(predictions.tolist(), [all_answers[anw]] * len(predictions),
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padding='longest', return_tensors='pt')
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for key in list(inputs.keys()):
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inputs[key] = inputs[key].cuda()
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res = np.asarray(model(**inputs).logits.flatten().tolist())
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all_results[anw] = res
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gts = np.concatenate([gts, np.max(all_results, axis=0)], 0)
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if i % 10 == 0:
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print("samples passed: ", i)
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# breakpoint()
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if args.most_likely:
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if args.use_rouge:
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np.save(f'./ml_{args.dataset_name}_rouge_score.npy', gts)
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else:
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np.save(f'./ml_{args.dataset_name}_bleurt_score.npy', gts)
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else:
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if args.use_rouge:
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np.save(f'./bg_{args.dataset_name}_rouge_score.npy', gts)
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else:
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np.save(f'./bg_{args.dataset_name}_bleurt_score.npy', gts)
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else:
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tokenizer = llama_iti.LlamaTokenizer.from_pretrained(MODEL, trust_remote_code=True)
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model = llama_iti.LlamaForCausalLM.from_pretrained(MODEL, low_cpu_mem_usage=True,
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torch_dtype=torch.float16,
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@ -441,8 +286,9 @@ def main():
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def svd_embed_score(embed_generated_wild, gt_label, begin_k, k_span, mean=1, svd=1, weight=0):
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def svd_embed_score(embed_generated_wild, gt_label,embed_generated_h,embed_generated_t, begin_k, k_span, mean=1, svd=1, weight=0):
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embed_generated = embed_generated_wild
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# embed_hallucination= embed_generated_h
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best_auroc_over_k = 0
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best_layer_over_k = 0
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best_scores_over_k = None
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@ -450,9 +296,9 @@ def main():
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for k in tqdm(range(begin_k, k_span)):
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best_auroc = 0
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best_layer = 0
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best_scores = None
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# best_scores = None
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mean_recorded = None
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best_projection = None
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# best_projection = None
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for layer in range(len(embed_generated_wild[0])):
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if mean:
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mean_recorded = embed_generated[:, layer, :].mean(0)
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@ -460,22 +306,27 @@ def main():
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else:
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centered = embed_generated[:, layer, :]
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if not svd:
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pca_model = PCA(n_components=k, whiten=False).fit(centered)
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projection = pca_model.components_.T
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mean_recorded = pca_model.mean_
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if weight:
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projection = pca_model.singular_values_ * projection
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else:
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mean_h=embed_generated_h[:, layer, :].mean(0)
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centered_h=embed_generated_h[:, layer, :]-mean_h
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mean_t=embed_generated_t[:, layer, :].mean(0)
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centered_t=embed_generated_t[:, layer, :].mean(0)-mean_t
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# if not svd:
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# assert "Not implemented!"
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# else:
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_, sin_value, V_p = torch.linalg.svd(torch.from_numpy(centered).cuda())
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projection = V_p[:k, :].T.cpu().data.numpy()
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if weight:
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projection = sin_value[:k] * projection
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C=(1 / centered.shape[0])* V_p.T.cpu().data.numpy() @ np.diag(sin_value.cpu().data.numpy() ** 2)
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_, sin_value_h, V_p_h = torch.linalg.svd(torch.from_numpy(centered_h).cuda())
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C_h=(1 / centered_h.shape[0])* V_p_h.T.cpu().data.numpy() @ np.diag(sin_value_h.cpu().data.numpy() ** 2)
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_, sin_value_t, V_p_t = torch.linalg.svd(torch.from_numpy(centered_t).cuda())
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C_t=(1 / centered_t.shape[0])* V_p_t.T.cpu().data.numpy() @ np.diag(sin_value_t.cpu().data.numpy() ** 2)
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scores= (centered*np.invert(C)*centered.T) ** 0.5 - ((embed_generated[:, layer, :]-mean_t)*np.invert(C_t)*(embed_generated[:, layer, :]-mean_t).T) ** 0.5
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+ ((embed_generated[:, layer, :]-mean_h)*np.invert(C_h)*(embed_generated[:, layer, :]-mean_h).T) ** 0.5
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# clf = Perceptron(tol=1e-3, random_state=0)
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scores = np.mean(np.matmul(centered, projection), -1, keepdims=True)
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assert scores.shape[1] == 1
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scores = np.sqrt(np.sum(np.square(scores), axis=1))
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# not sure about whether true and false data the direction will point to,
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# so we test both. similar practices are in the representation engineering paper
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@ -496,12 +347,12 @@ def main():
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best_auroc = measures[0]
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best_result = [100 * measures[2], 100 * measures[0]]
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best_layer = layer
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best_scores = sign_layer * scores
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best_projection = projection
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# best_scores = sign_layer * scores
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# best_projection = projection
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best_mean = mean_recorded
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best_sign = sign_layer
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print('k: ', k, 'best result: ', best_result, 'layer: ', best_layer,
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'mean: ', mean, 'svd: ', svd)
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'best_auroc: ', best_auroc)
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if best_auroc > best_auroc_over_k:
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best_auroc_over_k = best_auroc
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@ -509,8 +360,8 @@ def main():
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best_layer_over_k = best_layer
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best_k = k
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best_sign_over_k = best_sign
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best_scores_over_k = best_scores
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best_projection_over_k = best_projection
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# best_scores_over_k = best_scores
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# best_projection_over_k = best_projection
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best_mean_over_k = best_mean
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@ -521,7 +372,8 @@ def main():
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'best_scores':best_scores_over_k,
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'best_mean': best_mean_over_k,
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'best_sign':best_sign_over_k,
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'best_projection':best_projection_over_k}
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# 'best_projection':best_projection_over_k
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}
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from sklearn.decomposition import PCA
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@ -570,7 +422,7 @@ def main():
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# 1, 11, mean=0, svd=0, weight=args.weighted_svd)
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# get the best hyper-parameters on validation set
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returned_results = svd_embed_score(embed_generated_eval, gt_label_val,
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1, 11, mean=0, svd=0, weight=args.weighted_svd)
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1, 11, mean=1, svd=1, wei1ght=args.weighted_svd)
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pca_model = PCA(n_components=returned_results['k'], whiten=False).fit(embed_generated_wild[:,returned_results['best_layer'],:])
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projection = pca_model.components_.T
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@ -24,8 +24,8 @@ import openai
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API={
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'gpt-3.5-turbo':{'base_url':"https://api.agicto.cn/v1",'key':''},
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'deepseek-chat':{'base_url':"https://api.agicto.cn/v1",'key':''},
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'qwen-turbo':{'base_url':"https://api.agicto.cn/v1",'key':''},
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'deepseek-chat':{'base_url':"https://api.deepseek.com/v1",'key':'sk-5f06261529bb44df86d9b2fdbae1a6b5'},
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'qwen-plus':{'base_url':"https://dashscope.aliyuncs.com/compatible-mode/v1",'key':'sk-5be20597fa574155a9e56d7df1acfc7f'},
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}
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def seed_everything(seed: int):
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@ -56,15 +56,25 @@ def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--model_name', type=str, default='llama2_chat_7B')
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parser.add_argument('--model_name', type=str, default='qwen-plus')
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parser.add_argument('--dataset_name', type=str, default='triviaqa')
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parser.add_argument('--num_gene', type=int, default=1)
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parser.add_argument('--use_api', type=bool, default=False)
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parser.add_argument('--use_api', type=bool, default=True)
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parser.add_argument('--most_likely', type=bool, default=False)
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parser.add_argument("--model_dir", type=str, default=None, help='local directory with model data')
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parser.add_argument("--instruction", type=str, default=None, help='local directory of instruction file.')
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parser.add_argument("--instruction", type=str, default='/home/liwenyun/code/haloscope/generation/qa/qa_one-turn_instruction.txt', help='local directory of instruction file.')
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args = parser.parse_args()
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if args.use_api:
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# openai.api_base=API[args.model_name]['base_url']
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# openai.api_key=API[args.model_name]['key']
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client = OpenAI(
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api_key = API[args.model_name]['key'],
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base_url = API[args.model_name]['base_url'],
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)
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else:
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MODEL = HF_NAMES[args.model_name] if not args.model_dir else args.model_dir
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@ -168,6 +178,9 @@ def main():
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else:
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end_index = len(dataset)
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if not os.path.exists(f'./save_for_eval/{args.dataset_name}/'):
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os.mkdir(f'./save_for_eval/{args.dataset_name}/')
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if not os.path.exists(f'./save_for_eval/{args.dataset_name}/{args.model_name}_hal_det/'):
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os.mkdir(f'./save_for_eval/{args.dataset_name}/{args.model_name}_hal_det/')
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@ -196,41 +209,51 @@ def main():
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for gen_iter in range(args.num_gene):
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if args.most_likely:
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response = openai.ChatCompletion.create(
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model=args.model_name,
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messages=prompt,
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temperature=1,
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response = client.chat.completions.create(
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model = args.model_name,
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messages = prompt,
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max_tokens=256,
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top_p=1
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top_p=1,
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temperature = 1,
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)
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hallucination_response = openai.ChatCompletion.create(
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model=args.model_name,
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messages=hallucination_prompt,
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temperature=1,
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# openai.ChatCompletion.create(
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# model=args.model_name,
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# messages=prompt,
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# temperature=1,
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# max_tokens=256,
|
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# top_p=1
|
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# )
|
||||
hallucination_response = client.chat.completions.create(
|
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model = args.model_name,
|
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messages = hallucination_prompt,
|
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max_tokens=256,
|
||||
top_p=1
|
||||
top_p=1,
|
||||
temperature = 1,
|
||||
)
|
||||
decoded=response.choices[0].text
|
||||
hallucination_decoded=hallucination_response.choices[0].text
|
||||
decoded=response.choices[0].message.content
|
||||
hallucination_decoded=hallucination_response.choices[0].message.content
|
||||
else:
|
||||
response = openai.Completion.create(model=args.model_name,
|
||||
prompt=prompt,
|
||||
response = client.chat.completions.create(
|
||||
model = args.model_name,
|
||||
messages = prompt,
|
||||
max_tokens=256,
|
||||
n=5,
|
||||
best_of=1,
|
||||
stop=None,
|
||||
n=1,
|
||||
# best_of=1,
|
||||
top_p=0.5,
|
||||
temperature=0.5,)
|
||||
hallucination_response = openai.Completion.create(model=args.model_name,
|
||||
prompt=hallucination_prompt,
|
||||
temperature = 0.5,
|
||||
)
|
||||
|
||||
hallucination_response = client.chat.completions.create(
|
||||
model = args.model_name,
|
||||
messages = hallucination_prompt,
|
||||
max_tokens=256,
|
||||
n=5,
|
||||
best_of=1,
|
||||
stop=None,
|
||||
n=1,
|
||||
# best_of=1,
|
||||
top_p=0.5,
|
||||
temperature=0.5,)
|
||||
decoded=response.choices[0].text
|
||||
hallucination_decoded=hallucination_response.choices[0].text
|
||||
temperature = 0.5,
|
||||
)
|
||||
decoded=response.choices[0].message.content
|
||||
hallucination_decoded=hallucination_response.choices[0].message.content
|
||||
|
||||
|
||||
# decoded = tokenizer.decode(generated[0, prompt.shape[-1]:],
|
||||
|
|
@ -256,10 +279,10 @@ def main():
|
|||
else:
|
||||
info = 'batch_generations_'
|
||||
print("Saving answers")
|
||||
np.save(f'./save_for_eval/{args.dataset_name}_hal_det/answers/' + info + f'hal_det_{args.model_name}_{args.dataset_name}_answers_index_{i}.npy',
|
||||
np.save(f'./save_for_eval/{args.dataset_name}/{args.model_name}_hal_det/answers/' + info + f'hal_det_{args.model_name}_{args.dataset_name}_answers_index_{i}.npy',
|
||||
answers)
|
||||
print("Saving hallucinations")
|
||||
np.save(f'./save_for_eval/{args.dataset_name}_hal_det/hallucinations/' + info + f'hal_det_{args.model_name}_{args.dataset_name}_hallucinations_index_{i}.npy',
|
||||
np.save(f'./save_for_eval/{args.dataset_name}/{args.model_name}_hal_det/hallucinations/' + info + f'hal_det_{args.model_name}_{args.dataset_name}_hallucinations_index_{i}.npy',
|
||||
hallucinations)
|
||||
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -8,7 +8,6 @@ import datasets
|
|||
from tqdm import tqdm
|
||||
import numpy as np
|
||||
import pickle
|
||||
# from utils import get_llama_activations_bau, tokenized_tqa, tokenized_tqa_gen, tokenized_tqa_gen_end_q
|
||||
import llama_iti
|
||||
import pickle
|
||||
import argparse
|
||||
|
|
@ -57,6 +56,8 @@ def main():
|
|||
parser.add_argument('--most_likely', type=bool, default=False)
|
||||
parser.add_argument("--model_dir", type=str, default=None, help='local directory with model data')
|
||||
parser.add_argument("--instruction", type=str, default=None, help='local directory of instruction file.')
|
||||
parser.add_argument('--use_rouge', type=bool, default=True)
|
||||
parser.add_argument('--thres_gt', type=float, default=0.5)
|
||||
|
||||
# parser.add_argument('--model_name', type=str, default='llama2_chat_7B')
|
||||
# parser.add_argument('--dataset_name', type=str, default='triviaqa')
|
||||
|
|
@ -70,7 +71,7 @@ def main():
|
|||
# parser.add_argument('--thres_gt', type=float, default=0.5)
|
||||
# parser.add_argument('--most_likely', type=int, default=0)
|
||||
|
||||
parser.add_argument("--model_dir", type=str, default=None, help='local directory with model data')
|
||||
# parser.add_argument("--model_dir", type=str, default=None, help='local directory with model data')
|
||||
args = parser.parse_args()
|
||||
|
||||
MODEL = HF_NAMES[args.model_name] if not args.model_dir else args.model_dir
|
||||
|
|
|
|||
Loading…
Reference in New Issue