from logging import debug import random import pytorch_lightning as pl import torch import pickle import torch.nn as nn import torch.nn.functional as F import numpy as np import json # from transformers.utils.dummy_pt_objects import PrefixConstrainedLogitsProcessor from .base import BaseLitModel from transformers.optimization import get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup from functools import partial from .utils import rank_score, acc, LabelSmoothSoftmaxCEV1 from typing import Callable, Iterable, List def lmap(f: Callable, x: Iterable) -> List: """list(map(f, x))""" return list(map(f, x)) def multilabel_categorical_crossentropy(y_pred, y_true): y_pred = (1 - 2 * y_true) * y_pred y_pred_neg = y_pred - y_true * 1e12 y_pred_pos = y_pred - (1 - y_true) * 1e12 zeros = torch.zeros_like(y_pred[..., :1]) y_pred_neg = torch.cat([y_pred_neg, zeros], dim=-1) y_pred_pos = torch.cat([y_pred_pos, zeros], dim=-1) neg_loss = torch.logsumexp(y_pred_neg, dim=-1) pos_loss = torch.logsumexp(y_pred_pos, dim=-1) return (neg_loss + pos_loss).mean() def decode(output_ids, tokenizer): return lmap(str.strip, tokenizer.batch_decode(output_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True)) class TransformerLitModel(BaseLitModel): def __init__(self, model, args, tokenizer=None, data_config={}): super().__init__(model, args) self.save_hyperparameters(args) if args.bce: self.loss_fn = torch.nn.BCEWithLogitsLoss() elif args.label_smoothing != 0.0: self.loss_fn = LabelSmoothSoftmaxCEV1(lb_smooth=args.label_smoothing) else: self.loss_fn = nn.CrossEntropyLoss() self.best_acc = 0 self.first = True self.tokenizer = tokenizer self.__dict__.update(data_config) # resize the word embedding layer self.model.resize_token_embeddings(len(self.tokenizer)) self.decode = partial(decode, tokenizer=self.tokenizer) if args.pretrain: self._freaze_attention() elif "ind" in args.data_dir: # for inductive setting, use feeaze the word embedding self._freaze_word_embedding() def forward(self, x): return self.model(x) def training_step(self, batch, batch_idx): # pylint: disable=unused-argument # embed();exit() # print(self.optimizers().param_groups[1]['lr']) labels = batch.pop("labels") label = batch.pop("label") pos = batch.pop("pos") try: en = batch.pop("en") rel = batch.pop("rel") except KeyError: pass input_ids = batch['input_ids'] logits = self.model(**batch, return_dict=True).logits _, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True) bs = input_ids.shape[0] mask_logits = logits[torch.arange(bs), mask_idx][:, self.entity_id_st:self.entity_id_ed] assert mask_idx.shape[0] == bs, "only one mask in sequence!" if self.args.bce: loss = self.loss_fn(mask_logits, labels) else: loss = self.loss_fn(mask_logits, label) if batch_idx == 0: print('\n'.join(self.decode(batch['input_ids'][:4]))) return loss def _eval(self, batch, batch_idx, ): labels = batch.pop("labels") input_ids = batch['input_ids'] # single label label = batch.pop('label') pos = batch.pop('pos') my_keys = list(batch.keys()) for k in my_keys: if k not in ["input_ids", "attention_mask", "token_type_ids"]: batch.pop(k) logits = self.model(**batch, return_dict=True).logits[:, :, self.entity_id_st:self.entity_id_ed] _, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True) bsz = input_ids.shape[0] logits = logits[torch.arange(bsz), mask_idx] # get the entity ranks # filter the entity assert labels[0][label[0]], "correct ids must in filiter!" labels[torch.arange(bsz), label] = 0 assert logits.shape == labels.shape logits += labels * -100 # mask entityj # for i in range(bsz): # logits[i][labels] _, outputs = torch.sort(logits, dim=1, descending=True) _, outputs = torch.sort(outputs, dim=1) ranks = outputs[torch.arange(bsz), label].detach().cpu() + 1 return dict(ranks = np.array(ranks)) def validation_step(self, batch, batch_idx): result = self._eval(batch, batch_idx) return result def validation_epoch_end(self, outputs) -> None: ranks = np.concatenate([_['ranks'] for _ in outputs]) total_ranks = ranks.shape[0] if not self.args.pretrain: l_ranks = ranks[np.array(list(np.arange(0, total_ranks, 2)))] r_ranks = ranks[np.array(list(np.arange(0, total_ranks, 2))) + 1] self.log("Eval/lhits10", (l_ranks<=10).mean()) self.log("Eval/rhits10", (r_ranks<=10).mean()) hits20 = (ranks<=20).mean() hits10 = (ranks<=10).mean() hits3 = (ranks<=3).mean() hits1 = (ranks<=1).mean() self.log("Eval/hits10", hits10) self.log("Eval/hits20", hits20) self.log("Eval/hits3", hits3) self.log("Eval/hits1", hits1) self.log("Eval/mean_rank", ranks.mean()) self.log("Eval/mrr", (1. / ranks).mean()) self.log("hits10", hits10, prog_bar=True) self.log("hits1", hits1, prog_bar=True) def test_step(self, batch, batch_idx): # pylint: disable=unused-argument # ranks = self._eval(batch, batch_idx) result = self._eval(batch, batch_idx) # self.log("Test/ranks", np.mean(ranks)) return result def test_epoch_end(self, outputs) -> None: ranks = np.concatenate([_['ranks'] for _ in outputs]) hits20 = (ranks<=20).mean() hits10 = (ranks<=10).mean() hits3 = (ranks<=3).mean() hits1 = (ranks<=1).mean() self.log("Test/hits10", hits10) self.log("Test/hits20", hits20) self.log("Test/hits3", hits3) self.log("Test/hits1", hits1) self.log("Test/mean_rank", ranks.mean()) self.log("Test/mrr", (1. / ranks).mean()) def configure_optimizers(self): no_decay_param = ["bias", "LayerNorm.weight"] optimizer_group_parameters = [ {"params": [p for n, p in self.model.named_parameters() if p.requires_grad and not any(nd in n for nd in no_decay_param)], "weight_decay": self.args.weight_decay}, {"params": [p for n, p in self.model.named_parameters() if p.requires_grad and any(nd in n for nd in no_decay_param)], "weight_decay": 0} ] optimizer = self.optimizer_class(optimizer_group_parameters, lr=self.lr, eps=1e-8) scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=self.num_training_steps * self.args.warm_up_radio, num_training_steps=self.num_training_steps) return { "optimizer": optimizer, "lr_scheduler":{ 'scheduler': scheduler, 'interval': 'step', # or 'epoch' 'frequency': 1, } } def _freaze_attention(self): for k, v in self.model.named_parameters(): if "word" not in k: v.requires_grad = False else: print(k) def _freaze_word_embedding(self): for k, v in self.model.named_parameters(): if "word" in k: print(k) v.requires_grad = False @staticmethod def add_to_argparse(parser): parser = BaseLitModel.add_to_argparse(parser) parser.add_argument("--label_smoothing", type=float, default=0.1, help="") parser.add_argument("--bce", type=int, default=0, help="") return parser import faiss import os class GetEntityEmbeddingLitModel(TransformerLitModel): def __init__(self, model, args, tokenizer, data_config={}): super().__init__(model, args, tokenizer, data_config) self.faissid2entityid = {} # self.index = faiss.IndexFlatL2(d) # build the index d, measure = self.model.config.hidden_size, faiss.METRIC_L2 # param = 'HNSW64' # self.index = faiss.index_factory(d, param, measure) self.index = faiss.IndexFlatL2(d) # build the index # print(self.index.is_trained) # 此时输出为True # index.add(xb) self.cnt_batch = 0 self.total_embedding = [] def test_step(self, batch, batch_idx): labels = batch.pop("labels") mask_idx = batch.pop("pos") input_ids = batch['input_ids'] # single label label = batch.pop('label') # last layer hidden_states = self.model(**batch, return_dict=True, output_hidden_states=True).hidden_states[-1] # _, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True) bsz = input_ids.shape[0] entity_embedding = hidden_states[torch.arange(bsz), mask_idx].cpu() # use normalize or not ? # entity_embedding = F.normalize(entity_embedding, dim=-1, p = 2) self.total_embedding.append(entity_embedding) # self.index.add(np.array(entity_embedding, dtype=np.float32)) for i, l in zip(range(bsz), label): self.faissid2entityid[i+self.cnt_batch] = l.cpu() self.cnt_batch += bsz def test_epoch_end(self, outputs) -> None: self.total_embedding = np.concatenate(self.total_embedding, axis=0) # self.index.train(self.total_embedding) print(faiss.MatrixStats(self.total_embedding).comments) self.index.add(self.total_embedding) faiss.write_index(self.index, os.path.join(self.args.data_dir, "faiss_dump.index")) with open(os.path.join(self.args.data_dir, "faissid2entityid.pkl") ,'wb') as file: pickle.dump(self.faissid2entityid, file) with open(os.path.join(self.args.data_dir, "total_embedding.pkl") ,'wb') as file: pickle.dump(self.total_embedding, file) # print(f"number of entity embedding : {len(self.faissid2entityid)}") @staticmethod def add_to_argparse(parser): parser = TransformerLitModel.add_to_argparse(parser) parser.add_argument("--faiss_init", type=int, default=1, help="get the embedding and save it the file.") return parser class UseEntityEmbeddingLitModel(TransformerLitModel): def __init__(self, model, args, tokenizer, data_config={}): super().__init__(model, args, tokenizer, data_config) self.faissid2entityid = pickle.load(open(os.path.join(self.args.data_dir, "faissid2entityid.pkl") ,'rb')) self.index = faiss.read_index(os.path.join(self.args.data_dir, "faiss_dump.index")) self.dis2logits = distance2logits_2 def _eval(self, batch, batch_idx, ): labels = batch.pop("labels") pos = batch.pop("pos") input_ids = batch['input_ids'] # single label label = batch.pop('label') hidden_states = self.model(**batch, return_dict=True, output_hidden_states=True).hidden_states[-1] _, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True) bsz = input_ids.shape[0] mask_embedding = np.array(hidden_states[torch.arange(bsz), mask_idx].cpu(), dtype=np.float32) topk = 200 D, I = self.index.search(mask_embedding, topk) labels[torch.arange(bsz), label] = 0 # logits = torch.zeros_like(labels) # D = torch.softmax(torch.exp(-1. * torch.tensor(D)), dim=-1) # for i in range(bsz): # for j in range(topk): # logits[i][self.faissid2entityid[I[i][j]]] += D[i][j] # # get the entity ranks # # filter the entity # assert labels[0][label[0]], "correct ids must in filiter!" # labels[torch.arange(bsz), label] = 0 # assert logits.shape == labels.shape # logits += labels * -100 # mask entityj # _, outputs = torch.sort(logits, dim=1, descending=True) # _, outputs = torch.sort(outputs, dim=1) # ranks = outputs[torch.arange(bsz), label].detach().cpu() + 1 entity_logits = torch.full(labels.shape, -100.).to(self.device) D = self.dis2logits(D) for i in range(bsz): for j in range(topk): # filter entity in labels if I[i][j] not in self.faissid2entityid: print(I[i][j]) break # assert I[i][j] in self.faissid2entityid, print(I[i][j]) if labels[i][self.faissid2entityid[I[i][j]]]: continue if entity_logits[i][self.faissid2entityid[I[i][j]]] == -100.: entity_logits[i][self.faissid2entityid[I[i][j]]] = D[i][j] # no added together # else: # entity_logits[i][self.faissid2entityid[I[i][j]]] += D[i][j] # get the entity ranks # filter the entity assert entity_logits.shape == labels.shape _, outputs = torch.sort(entity_logits, dim=1, descending=True) _, outputs = torch.sort(outputs, dim=1) ranks = outputs[torch.arange(bsz), label].detach().cpu() + 1 return dict(ranks = np.array(ranks)) @staticmethod def add_to_argparse(parser): parser = TransformerLitModel.add_to_argparse(parser) parser.add_argument("--faiss_init", type=int, default=0, help="get the embedding and save it the file.") parser.add_argument("--faiss_use", type=int, default=1, help="get the embedding and save it the file.") return parser class CombineEntityEmbeddingLitModel(UseEntityEmbeddingLitModel): def __init__(self, model, args, tokenizer, data_config={}): super().__init__(model, args, tokenizer, data_config=data_config) self.dis2logits = distance2logits_2 self.id2entity = {} with open("./dataset/FB15k-237/entity2textlong.txt", 'r') as file: cnt = 0 lines = file.readlines() lines.append('[NEG]\t') for line in lines: e, d = line.strip().split("\t") self.id2entity[cnt] = e cnt += 1 self.id2entity_t = {} with open("./dataset/FB15k-237/entity2text.txt", 'r') as file: lines = file.readlines() lines.append('[NEG]\t') for line in lines: e, d = line.strip().split("\t") self.id2entity_t[e] = d for k, v in self.id2entity.items(): self.id2entity[k] = self.id2entity_t[v] def _eval(self, batch, batch_idx, ): labels = batch.pop("labels") input_ids = batch['input_ids'] # single label label = batch.pop('label') pos = batch.pop("pos") result = self.model(**batch, return_dict=True, output_hidden_states=True) hidden_states = result.hidden_states[-1] _, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True) bsz = input_ids.shape[0] mask_embedding = np.array(hidden_states[torch.arange(bsz), mask_idx].cpu(), dtype=np.float32) # mask_embedding = np.array(hidden_states[torch.arange(bsz), mask_idx].cpu(), dtype=np.float32) topk = self.args.knn_topk D, I = self.index.search(mask_embedding, topk) D = torch.from_numpy(D).to(self.device) assert labels[0][label[0]], "correct ids must in filiter!" labels[torch.arange(bsz), label] = 0 mask_logits = result.logits[:, :, self.entity_id_st:self.entity_id_ed] mask_logits = mask_logits[torch.arange(bsz), mask_idx] entity_logits = torch.full(labels.shape, 1000.).to(self.device) # D = self.dis2logits(D) for i in range(bsz): for j in range(topk): # filter entity in labels if labels[i][self.faissid2entityid[I[i][j]]]: continue if entity_logits[i][self.faissid2entityid[I[i][j]]] == 1000.: entity_logits[i][self.faissid2entityid[I[i][j]]] = D[i][j] # else: # entity_logits[i][self.faissid2entityid[I[i][j]]] += D[i][j] entity_logits = self.dis2logits(entity_logits) # get the entity ranks # filter the entity assert entity_logits.shape == labels.shape assert mask_logits.shape == labels.shape # entity_logits = torch.softmax(entity_logits + labels * -100, dim=-1) # mask entityj entity_logits = entity_logits + labels* -100. mask_logits = torch.softmax(mask_logits + labels* -100, dim=-1) # logits = mask_logits logits = combine_knn_and_vocab_probs(entity_logits, mask_logits, self.args.knn_lambda) # logits = entity_logits + mask_logits knn_topk_logits, knn_topk_id = entity_logits.topk(20) mask_topk_logits, mask_topk_id = mask_logits.topk(20) union_topk = [] for i in range(bsz): num_same = len(list(set(knn_topk_id[i].cpu().tolist()) & set(mask_topk_id[i].cpu().tolist()))) union_topk.append(num_same/ 20.) knn_topk_id = knn_topk_id.to("cpu") mask_topk_id = mask_topk_id.to("cpu") mask_topk_logits = mask_topk_logits.to("cpu") knn_topk_logits = knn_topk_logits.to("cpu") label = label.to("cpu") for t in range(bsz): if knn_topk_id[t][0] == label[t] and knn_topk_logits[t][0] > mask_topk_logits[t][0] and mask_topk_logits[t][0] <= 0.4: print(knn_topk_logits[t], knn_topk_id[t]) print(lmap(lambda x: self.id2entity[x.item()], knn_topk_id[t])) print(mask_topk_logits[t], mask_topk_id[t]) print(lmap(lambda x: self.id2entity[x.item()], mask_topk_id[t])) print(label[t]) print() _, outputs = torch.sort(logits, dim=1, descending=True) _, outputs = torch.sort(outputs, dim=1) ranks = outputs[torch.arange(bsz), label].detach().cpu() + 1 return dict(ranks = np.array(ranks), knn_topk_id=knn_topk_id, knn_topk_logits=knn_topk_logits, mask_topk_id=mask_topk_id, mask_topk_logits=mask_topk_logits, num_same = np.array(union_topk)) def test_epoch_end(self, outputs) -> None: ranks = np.concatenate([_['ranks'] for _ in outputs]) num_same = np.concatenate([_['num_same'] for _ in outputs]) results_keys = list(outputs[0].keys()) results = {} # for k in results_keys: # results. self.log("Test/num_same", num_same.mean()) hits20 = (ranks<=20).mean() hits10 = (ranks<=10).mean() hits3 = (ranks<=3).mean() hits1 = (ranks<=1).mean() self.log("Test/hits10", hits10) self.log("Test/hits20", hits20) self.log("Test/hits3", hits3) self.log("Test/hits1", hits1) self.log("Test/mean_rank", ranks.mean()) self.log("Test/mrr", (1. / ranks).mean()) def add_to_argparse(parser): parser = TransformerLitModel.add_to_argparse(parser) parser.add_argument("--knn_lambda", type=float, default=0.5, help="lambda * knn + (1-lambda) * mask logits , lambda of knn logits and mask logits.") parser.add_argument("--knn_topk", type=int, default=100, help="") return parser def combine_knn_and_vocab_probs(knn_p, vocab_p, coeff=0.5): combine_probs = torch.stack([vocab_p, knn_p], dim=0) coeffs = torch.ones_like(combine_probs) coeffs[0] = np.log(1 - coeff) coeffs[1] = np.log(coeff) curr_prob = torch.logsumexp(combine_probs + coeffs, dim=0) return curr_prob def distance2logits(D): return torch.softmax( -1. * torch.tensor(D) / 30., dim=-1) def distance2logits_2(D, n=10): if not isinstance(D, torch.Tensor): D = torch.tensor(D) if torch.sum(D) != 0.0: distances = torch.exp(-D/n) / torch.sum(torch.exp(-D/n), dim=-1, keepdim=True) return distances