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GeezerVison is showing github.com — BEND/bend/utils/embedders.py at main · MachineLearningLifeScience/BEND Expand file tree/ Copy pathembedders.pyMore file actions 1422 lines (1142 loc) · 62.3 KB / Copy pathembedders.pyFile metadata and controls1422 lines (1142 loc) · 62.3 KB 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 ''' embedders.py ------------ Wrapper classes for embedding sequences with pretrained DNA language models using a common interface. The wrapper classes handle loading the models and tokenizers, and embedding the sequences. As far as possible, models are downloaded automatically. They also handle removal of special tokens, and optionally upsample the embeddings to the original sequence length. Embedders can be used as follows. Please check the individual classes for more details on the arguments. ``embedder = EmbedderClass(model_name, some_additional_config_argument=6)`` ``embedding = embedder(sequence, remove_special_tokens=True, upsample_embeddings=True)`` ''' import torch import numpy as np from typing import List, Iterable from functools import partial import os from bend.models.awd_lstm import AWDLSTMModelForInference from bend.models.dilated_cnn import ConvNetModel from bend.models.gena_lm import BertModel as GenaLMBertModel from bend.models.hyena_dna import HyenaDNAPreTrainedModel, CharacterTokenizer from bend.models.dnabert2 import BertModel as DNABert2BertModel from bend.models.dnabert2 import BertForMaskedLM as DNABert2BertForMaskedLM from bend.utils.download import download_model, download_model_zenodo from tqdm.auto import tqdm from transformers import logging, BertModel, BertConfig, BertTokenizer, AutoModel, AutoTokenizer, BigBirdModel, AutoModelForMaskedLM from sklearn.preprocessing import LabelEncoder logging.set_verbosity_error() # TODO graceful auto downloading solution for everything that is hosted in a nice way # https://github.com/huggingface/transformers/blob/main/src/transformers/utils/hub.py device = torch.device("cuda" if torch.cuda.is_available() else "cpu") ## ## GPN https://www.biorxiv.org/content/10.1101/2022.08.22.504706v1 ## class BaseEmbedder(): """Base class for embedders. All embedders should inherit from this class. """ def __init__(self, *args, **kwargs): """Initialize the embedder. Calls `load_model` with the given arguments. Parameters ---------- *args Positional arguments. Passed to `load_model`. **kwargs Keyword arguments. Passed to `load_model`. """ self.load_model(*args, **kwargs) def load_model(self, *args, **kwargs): """Load the model. Should be implemented by the inheriting class.""" raise NotImplementedError def embed(self, sequences:str, *args, **kwargs): """Embed a sequence. Should be implemented by the inheriting class. Parameters ---------- sequences : str The sequences to embed. """ raise NotImplementedError def __call__(self, sequence: str, *args, **kwargs): """Embed a single sequence. Calls `embed` with the given arguments. Parameters ---------- sequence : str The sequence to embed. *args Positional arguments. Passed to `embed`. **kwargs Keyword arguments. Passed to `embed`. Returns ------- np.ndarray The embedding of the sequence. """ return self.embed([sequence], *args, disable_tqdm=True, **kwargs)[0] class GPNEmbedder(BaseEmbedder): '''Embed using the GPN model https://www.biorxiv.org/content/10.1101/2022.08.22.504706v1''' def load_model(self, model_name: str = "songlab/gpn-brassicales" , **kwargs): """Load the GPN model. Parameters ---------- model_name : str The name of the model to load. Defaults to "songlab/gpn-brassicales". When providing a name, the model will be loaded from the HuggingFace model hub. Alternatively, you can provide a path to a local model directory. Raises ------ ModuleNotFoundError If the gpn module is not installed. Notes ----- The gpn module can be installed with `pip install git+https://github.com/songlab-cal/gpn.git` """ try: import gpn.model except ModuleNotFoundError as e: raise ModuleNotFoundError('GPN requires gpn. Install with: pip install git+https://github.com/songlab-cal/gpn.git') self.model = AutoModel.from_pretrained(model_name) self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model.to(device) self.model.eval() def embed(self, sequences: List[str], disable_tqdm: bool = False, upsample_embeddings: bool = False) -> List[np.ndarray]: """ Embed a list of sequences. Parameters ---------- sequences : List[str] The sequences to embed. disable_tqdm : bool, optional Whether to disable the tqdm progress bar. Defaults to False. upsample_embeddings : bool, optional Whether to upsample the embeddings to the length of the input sequence. Defaults to False. Only provided for compatibility with other embedders. GPN embeddings are already the same length as the input sequence. Returns ------- List[np.ndarray] The embeddings of the sequences. """ # '''Run the GPN model https://www.biorxiv.org/content/10.1101/2022.08.22.504706v1''' embeddings = [] with torch.no_grad(): for seq in tqdm(sequences, disable=disable_tqdm): input_ids = self.tokenizer(seq, return_tensors="pt", return_attention_mask=False, return_token_type_ids=False)["input_ids"] input_ids = input_ids.to(device) embedding = self.model(input_ids=input_ids).last_hidden_state embeddings.append(embedding.detach().cpu().numpy()) return embeddings ## ## DNABert https://doi.org/10.1093/bioinformatics/btab083 ## Download from https://github.com/jerryji1993/DNABERT class DNABertEmbedder(BaseEmbedder): '''Embed using the DNABert model https://doi.org/10.1093/bioinformatics/btab083''' def load_model(self, model_path: str = '../../external-models/DNABERT/', kmer: int = 6, **kwargs): """Load the DNABert model. Parameters ---------- model_path : str The path to the model directory. Defaults to "../../external-models/DNABERT/". The DNABERT models need to be downloaded manually as indicated in the DNABERT repository at https://github.com/jerryji1993/DNABERT. kmer : int The kmer size of the model. Defaults to 6. """ dnabert_path = model_path #dnabert_path = f'{dnabert_path}/DNABERT{kmer}/' # check if path exists if not os.path.exists(dnabert_path): print(f'Path {dnabert_path} does not exists, check if the wrong path was given. If not download from https://github.com/jerryji1993/DNABERT') config = BertConfig.from_pretrained(dnabert_path) self.tokenizer = BertTokenizer.from_pretrained(dnabert_path) self.bert_model = BertModel.from_pretrained(dnabert_path, config=config) self.bert_model.to(device) self.bert_model.eval() self.kmer = kmer def embed(self, sequences: List[str], disable_tqdm: bool = False, remove_special_tokens: bool = True, upsample_embeddings: bool = False): """ Embed a list of sequences. Parameters ---------- sequences : List[str] The sequences to embed. disable_tqdm : bool, optional Whether to disable the tqdm progress bar. Defaults to False. remove_special_tokens : bool, optional Whether to remove the special tokens from the embeddings. Defaults to True. upsample_embeddings : bool, optional Whether to upsample the embeddings to the length of the input sequence. Defaults to False. Returns ------- List[np.ndarray] The embeddings of the sequences. """ embeddings = [] with torch.no_grad(): for sequence in tqdm(sequences, disable=disable_tqdm): sequence = [sequence] kmers = self._seq2kmer_batch(sequence, self.kmer) model_input = self.tokenizer.batch_encode_plus(kmers, add_special_tokens=True, return_tensors='pt', )["input_ids"] if model_input.shape[1] > 512: model_input = torch.split(model_input, 512, dim=1) output = [] for chunk in model_input: output.append(self.bert_model(chunk.to(device))[0].detach().cpu()) output = torch.cat(output, dim=1).numpy() else: output = self.bert_model(model_input.to(device))[0].detach().cpu().numpy() embedding = output if upsample_embeddings: embedding = self._repeat_embedding_vectors(embedding) embeddings.append(embedding[:,1:-1] if remove_special_tokens else embedding) return embeddings @staticmethod def _seq2kmer(seq, k): """ Convert original sequence to kmers Arguments: seq -- str, original sequence. k -- int, kmer of length k specified. Returns: kmers -- str, kmers separated by space """ kmer = [seq[x:x+k] for x in range(len(seq)+1-k)] kmers = " ".join(kmer) return kmers def _seq2kmer_batch(self, batch, k=3, step_size=1, kmerise=True): return list(map(partial(self._seq2kmer, k = k), batch)) # repeating. # GATTTATTAGGGGAGATTTTATATATCCCGA # kmer =3, input = 31 --> embedding = 29 --> repeat first and last once. # kmer =3, input = 32 --> embedding = 30 --> repeat first and last once. # kmer=4 input = 31 --> embedding = 28 --> repeat first once and last twice. # kmer=4 input = 32 --> embedding = 29 # kmer=4 input = 33 --> embedding = 30 # kmer=5 input = 31 --> embedding = 27 --> repeat first twice and last twice. # kmer=5 input = 32 --> embedding = 28 --> repeat first twice and last twice. # kmer=6 input = 31 --> embedding = 26 --> repeat first twice and last three times. def _repeat_embedding_vectors(self, embeddings: np.ndarray, has_special_tokens: bool = True): '''Repeat embeddings at sequence edges to match input length''' if has_special_tokens: cls_vector = embeddings[:, [0]] sep_vector = embeddings[:, [-1]] embeddings = embeddings[:,1:-1] # repeat first and last embedding if self.kmer == 3: embeddings = np.concatenate([embeddings[:, [0]], embeddings, embeddings[:, [-1]]], axis=1) elif self.kmer == 4: embeddings = np.concatenate([embeddings[:, [0]], embeddings, embeddings[:, [-1]], embeddings[:, [-1]]], axis=1) elif self.kmer == 5: embeddings = np.concatenate([embeddings[:, [0]], embeddings, embeddings[:, [0]], embeddings[:, [-1]], embeddings[:, [-1]]], axis=1) elif self.kmer == 6: embeddings = np.concatenate([embeddings[:, [0]], embeddings, embeddings[:, [0]], embeddings[:, [-1]], embeddings[:, [-1]], embeddings[:, [-1]]], axis=1) if has_special_tokens: embeddings = np.concatenate([cls_vector, embeddings, sep_vector], axis=1) return embeddings # https://www.biorxiv.org/content/10.1101/2023.01.11.523679v2.full class NucleotideTransformerEmbedder(BaseEmbedder): """ Embed using the Nuclieotide Transformer (NT) model https://www.biorxiv.org/content/10.1101/2023.01.11.523679v2.full """ def load_model(self, model_name, return_logits: bool = False, return_loss: bool = False, **kwargs): """ Load the Nuclieotide Transformer (NT) model. Parameters ---------- model_name : str The name of the model to load. When providing a name, the model will be loaded from the HuggingFace model hub. Alternatively, you can provide a path to a local model directory. We check whether the model_name contains 'v2' to determine whether we need to follow the V2 model API or not. return_logits : bool, optional Whether to return the logits. Note that we do not apply any masking. Defaults to False. return_loss : bool, optional Whether to return the loss. Note that we do not apply any masking. ``remove_special_tokens`` also ignores these dimensions when computing the loss. Defaults to False. """ if return_logits and return_loss: raise ValueError('Only one of return_logits and return_loss can be True.') # Get pretrained model if 'v2' in model_name: self.model = AutoModelForMaskedLM.from_pretrained(model_name, trust_remote_code=True) self.tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) self.max_seq_len = 12282 # "model_max_length": 2048, --> 12,288 self.max_tokens = 2048 self.is_v2 = True else: self.model = AutoModelForMaskedLM.from_pretrained(model_name) self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.max_seq_len = 5994 # "model_max_length": 1000, 6-mer --> 6000 self.max_tokens = 1000 self.is_v2 = False self.model.to(device) self.model.eval() self.return_logits = return_logits self.return_loss = return_loss def embed(self, sequences: List[str], disable_tqdm: bool = False, remove_special_tokens: bool = True, upsample_embeddings: bool = False): """ Embed sequences using the Nuclieotide Transformer (NT) model. Parameters ---------- sequences : List[str] List of sequences to embed. disable_tqdm : bool, optional Whether to disable the tqdm progress bar. Defaults to False. remove_special_tokens : bool, optional Whether to remove the special tokens from the embeddings. Defaults to True. upsample_embeddings : bool, optional Whether to upsample the embeddings to the length of the input sequence. Defaults to False. Returns ------- List[np.ndarray] List of embeddings. """ cls_tokens = [] embeddings = [] with torch.no_grad(): for n, s in enumerate(tqdm(sequences, disable=disable_tqdm)): #print('sequence', n) s_chunks = [s[chunk : chunk + self.max_seq_len] for chunk in range(0, len(s), self.max_seq_len)] # split into chunks embedded_seq = [] for n_chunk, chunk in enumerate(s_chunks): # embed each chunk tokens_ids = self.tokenizer(chunk, return_tensors = 'pt')['input_ids'].int().to(device) if len(tokens_ids[0]) > self.max_tokens: # too long to fit into the model split = torch.split(tokens_ids, self.max_tokens, dim=-1) if self.return_logits: outs = [self.model(item)['logits'].detach().cpu().numpy() for item in split] elif self.return_loss: outs = [] for item in split: out = self.model(item)['logits'].detach() out = out[:,1:,4:-2 ] if remove_special_tokens else out # unk, pad, mask,cls , ... actual tokens ... eos, bos item_subset = item[:,1:] - 4 if remove_special_tokens else item # remove special tokens out = torch.nn.functional.cross_entropy(out.view(-1, out.shape[-1]), item_subset.view(-1).to(torch.long), reduction='none') out = out.unsqueeze(0).detach().cpu().numpy() outs.append(out) else: outs = [self.model(item, output_hidden_states=True)['hidden_states'][-1].detach().cpu().numpy() for item in split] outs = np.concatenate(outs, axis=1) else: if self.return_logits: outs = self.model(tokens_ids)['logits'].detach().cpu().numpy() elif self.return_loss: outs = self.model(tokens_ids)['logits'].detach() # NOTE in V1 only is shape 4105, even though vocab_size is 4107. Correct in V2. # NOTE order in V1: unk, pad, mask,cls , ... actual tokens ... eos, bos --> last 2 tokens are not used in the model. # in V2: unk, pad, mask,cls , eos, bos, ... actual tokens if self.is_v2: outs = outs[:,1:,6:] if remove_special_tokens else outs tokens_ids_subset = tokens_ids[:,1:] - 6 if remove_special_tokens else tokens_ids else: outs = outs[:,1:,4:] if remove_special_tokens else outs # unk, pad, mask,cls , ... actual tokens ... ( eos, bos) tokens_ids_subset = tokens_ids[:,1:] - 4 if remove_special_tokens else tokens_ids # token 4104 needs to be preseverd outs = torch.nn.functional.cross_entropy(outs.view(-1, outs.shape[-1]), tokens_ids_subset.view(-1).to(torch.long), reduction='none') outs = outs.unsqueeze(0).detach().cpu().numpy() else: outs = self.model(tokens_ids, output_hidden_states=True)['hidden_states'][-1].detach().cpu().numpy() if upsample_embeddings and not (self.return_loss and remove_special_tokens): outs = self._repeat_embedding_vectors(self.tokenizer.convert_ids_to_tokens(tokens_ids[0]), outs) elif upsample_embeddings and (self.return_loss and remove_special_tokens): # special case - we already had to remove special tokens before when computing outs. outs = self._repeat_embedding_vectors(self.tokenizer.convert_ids_to_tokens(tokens_ids[0,1:]), outs, has_special_tokens=False) if self.return_loss and remove_special_tokens: # again, cls is already removed. embedded_seq.append(outs) else: embedded_seq.append(outs[:,1:] if remove_special_tokens else outs) embeddings.append(np.concatenate(embedded_seq, axis=1)) return embeddings @staticmethod def _repeat_embedding_vectors(tokens: Iterable[str], embeddings: np.ndarray, has_special_tokens: bool = True): ''' Nucleotide transformer uses 6-mer embedding, but single-embedding for remaining nucleotides. ''' assert len(tokens) == embeddings.shape[1], 'Number of tokens and embeddings must match.' new_embeddings = [] for idx, token in enumerate(tokens): if has_special_tokens and idx == 0: new_embeddings.append(embeddings[:, [idx]]) # (1, hidden_dim) continue token_embedding = embeddings[:, [idx]] # (1, hidden_dim) new_embeddings.extend([token_embedding] * len(token)) # list of (1,1, 768) arrays new_embeddings = np.concatenate(new_embeddings, axis=1) return new_embeddings class AWDLSTMEmbedder(BaseEmbedder): """ Embed using the AWD-LSTM (https://arxiv.org/abs/1708.02182) baseline LM trained in BEND. """ def load_model(self, model_path, **kwargs): """ Load the AWD-LSTM baseline LM trained in BEND. Parameters ---------- model_path : str The path to the model directory. If the model path does not exist, it will be downloaded from https://sid.erda.dk/cgi-sid/ls.py?share_id=dbQM0pgSlM¤t_dir=pretrained_models&flags=f """ # download model if not exists if not os.path.exists(model_path): print(f'Path {model_path} does not exists, model is downloaded from https://sid.erda.dk/cgi-sid/ls.py?share_id=dbQM0pgSlM¤t_dir=pretrained_models&flags=f') download_model(model = 'awd_lstm', destination_dir = model_path) # Get pretrained model self.model = AWDLSTMModelForInference.from_pretrained(model_path) self.model.to(device) self.model.eval() self.tokenizer = AutoTokenizer.from_pretrained(model_path) def embed(self, sequences: List[str], disable_tqdm: bool = False, upsample_embeddings: bool = False): """ Embed sequences using the AWD-LSTM baseline LM trained in BEND. Parameters ---------- sequences : List[str] List of sequences to embed. disable_tqdm : bool, optional Whether to disable the tqdm progress bar. Defaults to False. upsample_embeddings : bool, optional Whether to upsample the embeddings to the length of the input sequence. Defaults to False. Only provided for compatibility with other embedders. GPN embeddings are already the same length as the input sequence. Returns ------- List[np.ndarray] List of embeddings. """ embeddings = [] with torch.no_grad(): for s in tqdm(sequences, disable=disable_tqdm): input_ids = self.tokenizer(s, return_tensors="pt", return_attention_mask=False, return_token_type_ids=False)["input_ids"] input_ids = input_ids.to(device) embedding = self.model(input_ids=input_ids).last_hidden_state embeddings.append(embedding.detach().cpu().numpy()) # embeddings.append(embedding.detach().cpu().numpy()[:,1:]) return embeddings class ConvNetEmbedder(BaseEmbedder): """ Embed using the GPN-inspired ConvNet baseline LM trained in BEND. """ def load_model(self, model_path, **kwargs): """ Load the GPN-inspired ConvNet baseline LM trained in BEND. Parameters ---------- model_path : str The path to the model directory. If the model path does not exist, it will be downloaded from https://sid.erda.dk/cgi-sid/ls.py?share_id=dbQM0pgSlM¤t_dir=pretrained_models&flags=f """ logging.set_verbosity_error() if not os.path.exists(model_path): print(f'Path {model_path} does not exists, model is downloaded from https://sid.erda.dk/cgi-sid/ls.py?share_id=dbQM0pgSlM¤t_dir=pretrained_models&flags=f') download_model(model = 'convnet', destination_dir = model_path) # load tokenizer self.tokenizer = AutoTokenizer.from_pretrained(model_path) # load model self.model = ConvNetModel.from_pretrained(model_path).to(device).eval() def embed(self, sequences: List[str], disable_tqdm: bool = False, upsample_embeddings: bool = False): """ Embed sequences using the GPN-inspired ConvNet baseline LM trained in BEND. Parameters ---------- sequences : List[str] List of sequences to embed. disable_tqdm : bool, optional Whether to disable the tqdm progress bar. Defaults to False. upsample_embeddings : bool, optional Whether to upsample the embeddings to the length of the input sequence. Defaults to False. Only provided for compatibility with other embedders. GPN embeddings are already the same length as the input sequence. Returns ------- List[np.ndarray] List of embeddings. """ embeddings = [] with torch.no_grad(): for s in tqdm(sequences, disable=disable_tqdm): input_ids = self.tokenizer(s, return_tensors="pt", return_attention_mask=False, return_token_type_ids=False)["input_ids"] input_ids = input_ids.to(device) embedding = self.model(input_ids=input_ids).last_hidden_state embeddings.append(embedding.detach().cpu().numpy()) return embeddings class GENALMEmbedder(BaseEmbedder): """ Embed using the GENA-LM model https://www.biorxiv.org/content/10.1101/2023.06.12.544594v1.full""" def load_model(self, model_name, **kwargs): """ Load the GENA-LM model. Parameters ---------- model_name : str The name of the model to load. When providing a name, the model will be loaded from the HuggingFace model hub. Alternatively, you can provide a path to a local model directory. """ if not any(['bigbird' in model_name, 'bert' in model_name]): raise ValueError('Model path must contain either bigbird or bert in order to be loaded correctly.') if 'bigbird' in model_name: self.model = BigBirdModel.from_pretrained(model_name) else: self.model = GenaLMBertModel.from_pretrained(model_name) self.model.to(device) self.model.eval() self.max_length = 4096-2 if 'bigbird' in model_name else 512-2 # 4096 BPE tokens (bigbird) # or 512 BPE tokens (bert) self.tokenizer = AutoTokenizer.from_pretrained(model_name) def embed(self, sequences: List[str], disable_tqdm: bool = False, remove_special_tokens: bool = True, upsample_embeddings: bool = False): """ Embed sequences using the GENA-LM model. Parameters ---------- sequences : List[str] List of sequences to embed. disable_tqdm : bool, optional Whether to disable the tqdm progress bar. Defaults to False. remove_special_tokens : bool, optional Whether to remove the [CLS] and [SEP] tokens from the output. Defaults to True. upsample_embeddings : bool, optional Whether to upsample the embeddings to the length of the input sequence. Defaults to False. Returns ------- List[np.ndarray] List of embeddings. """ # Note that this model uses byte pair encoding. # upsample_embedding repeats BPE token embeddings so that each nucleotide has its own embedding. # The [CLS] and [SEP] tokens are removed from the output if remove_special_tokens is True. # TODO The handling of gaps in upsample_embeddings is not tested extensively. # The second tokenizer, trained on T2T+1000G SNPs+Multispieces, includes a preprocessing step for long gaps: more than 10 consecutive N are replaced by a single - token. embeddings = [] with torch.no_grad(): for s in tqdm(sequences, disable=disable_tqdm): input_ids = self.tokenizer(s, return_tensors="pt", return_attention_mask=False, return_token_type_ids=False)["input_ids"] input_ids_nospecial = input_ids[:,1:-1] # remove the special tokens. we add them to each chunk ourselves id_chunks = [input_ids_nospecial[:, chunk : chunk + self.max_length] for chunk in range(0, input_ids_nospecial.shape[1], self.max_length)] # split into chunks embedded_seq = [] for n_chunk, chunk in enumerate(id_chunks): # embed each chunk # add the special tokens chunk = torch.cat([torch.ones((chunk.shape[0], 1), dtype=torch.long) * self.tokenizer.cls_token_id, chunk, torch.ones((chunk.shape[0], 1), dtype=torch.long) * self.tokenizer.sep_token_id], dim=1) chunk = chunk.to(device) outs = self.model(chunk)['last_hidden_state'].detach().cpu().numpy() # print(outs.shape) # for intermediate chunks the special tokens need to go. # if we only have 1 chunk, keep them for now. if len(id_chunks) != 1: if n_chunk == 0: outs = outs[:,:-1] # no SEP elif n_chunk == len(id_chunks) - 1: outs = outs[:,1:] # no CLS else: outs = outs[:,1:-1] # no CLS and no SEP embedded_seq.append(outs) embedding = np.concatenate(embedded_seq, axis=1) if upsample_embeddings: embedding = self._repeat_embedding_vectors(self.tokenizer.convert_ids_to_tokens(input_ids[0]), embedding) if remove_special_tokens: embedding = embedding[:,1:-1] embeddings.append(embedding) #extended token_ids # ext_token_ids = [[x] * len(self.tokenizer.convert_ids_to_tokens([x])[0]) for x in input_ids[0,1:-1]] # ext_token_ids = [item for sublist in ext_token_ids for item in sublist] return embeddings # GATTTATTAGGGGAGATTTTATATATCCCGA # ['[CLS]', 'G', 'ATTTATT', 'AGGGG', 'AGATT', 'TTATAT', 'ATCCCG', 'A', '[SEP]'] @staticmethod def _repeat_embedding_vectors(tokens: Iterable[str], embeddings: np.ndarray, has_special_tokens: bool = True): ''' Byte-pair encoding merges a variable number of letters into one token. We need to repeat each token's embedding vector for each letter in the token. ''' assert len(tokens) == embeddings.shape[1], 'Number of tokens and embeddings must match.' new_embeddings = [] for idx, token in enumerate(tokens): if has_special_tokens and (idx == 0 or idx == len(tokens) - 1): new_embeddings.append(embeddings[:, [idx]]) # (1, 768) continue token_embedding = embeddings[:, [idx]] # (1, 768) new_embeddings.extend([token_embedding] * len(token)) # list of (1,1, 768) arrays new_embeddings = np.concatenate(new_embeddings, axis=1) return new_embeddings class HyenaDNAEmbedder(BaseEmbedder): '''Embed using the HyenaDNA model https://arxiv.org/abs/2306.15794''' def load_model(self, model_path = 'pretrained_models/hyenadna/hyenadna-tiny-1k-seqlen', return_logits: bool=False, return_loss: bool=False, **kwargs): # '''Load the model from the checkpoint path # 'hyenadna-tiny-1k-seqlen' # 'hyenadna-small-32k-seqlen' # 'hyenadna-medium-160k-seqlen' # 'hyenadna-medium-450k-seqlen' # 'hyenadna-large-1m-seqlen' # ''' # you only need to select which model to use here, we'll do the rest! """ Load the HyenaDNA model. Parameters ---------- model_path : str, optional Path to the model checkpoint. Defaults to 'pretrained_models/hyenadna/hyenadna-tiny-1k-seqlen'. If the path does not exist, the model will be downloaded from HuggingFace. Rather than just downloading the model, HyenaDNA's `from_pretrained` method relies on cloning the HuggingFace-hosted repository, and using git lfs to download the model. This requires git lfs to be installed on your system, and will fail if it is not. return_logits : bool, optional If True, returns logits instead of embeddings. Defaults to False. return_loss : bool, optional If True, returns the unreduced next token prediction loss. Incompatible with return_logits. We trim special tokens from the output so that the loss is only computed on the ACTGN vocabulary. Defaults to False. """ checkpoint_path, model_name = os.path.split(model_path) max_lengths = { 'hyenadna-tiny-1k-seqlen': 1024, 'hyenadna-small-32k-seqlen': 32768, 'hyenadna-medium-160k-seqlen': 160000, 'hyenadna-medium-450k-seqlen': 450000, 'hyenadna-large-1m-seqlen': 1_000_000, } self.max_length = max_lengths[model_name] # auto selects if return_logits and return_loss: raise ValueError('Only one of return_logits and return_loss can be True') self.return_logits = return_logits self.return_loss = return_loss # all these settings are copied directly from huggingface.py # data settings: use_padding = True rc_aug = False # reverse complement augmentation add_eos = False # add end of sentence token # we need these for the decoder head, if using use_head = False n_classes = 2 # not used for embeddings only use_lm_head = return_logits or return_loss # the head we added back in. # you can override with your own backbone config here if you want, # otherwise we'll load the HF one in None backbone_cfg = None is_git_lfs_repo = os.path.exists('.git/hooks/pre-push') # use the pretrained Huggingface wrapper instead model = HyenaDNAPreTrainedModel.from_pretrained( checkpoint_path, model_name, download=not os.path.exists(model_path), config=backbone_cfg, device=device, use_head=use_head, use_lm_head=use_lm_head, n_classes=n_classes, ) model.eval() model.to(device) self.model = model # NOTE the git lfs download command will add this, # but we actually dont use LFS for BEND itself. if not is_git_lfs_repo: try: os.remove('.git/hooks/pre-push') except FileNotFoundError: pass # create tokenizer - NOTE this adds CLS and SEP tokens when add_special_tokens=False self.tokenizer = CharacterTokenizer( characters=['A', 'C', 'G', 'T', 'N'], # add DNA characters, N is uncertain model_max_length=self.max_length + 2, # to account for special tokens, like EOS add_special_tokens=False, # we handle special tokens elsewhere padding_side='left', # since HyenaDNA is causal, we pad on the left ) def embed(self, sequences: List[str], disable_tqdm: bool = False, remove_special_tokens: bool = True, upsample_embeddings: bool = False): '''Embeds a list of sequences using the HyenaDNA model. Parameters ---------- sequences : List[str] List of sequences to embed. disable_tqdm : bool, optional Whether to disable the tqdm progress bar. Defaults to False. remove_special_tokens : bool, optional Whether to remove the CLS and SEP tokens from the embeddings. Defaults to True. Cannot be set to False if the return_loss option of the embedder is True (autoregression forces us to discard the BOS token position either way). upsample_embeddings : bool, optional Whether to upsample the embeddings to match the length of the input sequences. Defaults to False. Only provided for compatibility with other embedders. HyenaDNA embeddings are already the same length as the input sequence. Returns ------- embeddings : List[np.ndarray] List of embeddings. ''' embeddings = [] with torch.inference_mode(): for s in tqdm(sequences, disable=disable_tqdm): chunks = [s[chunk : chunk + self.max_length] for chunk in range(0, len(s), self.max_length)] # split into chunks embedded_chunks = [] for n_chunk, chunk in enumerate(chunks): # reference: https://colab.research.google.com/drive/1wyVEQd4R3HYLTUOXEEQmp_I8aNC_aLhL?usp=sharing#scrollTo=-1wq2uwUctPV #### Single embedding example #### # create a sample 450k long, prepare # sequence = 'ACTG' * int(self.max_length/4) tok_seq = self.tokenizer(chunk) # adds CLS and SEP tokens (0=CLS, 1=EOS) tok_seq = tok_seq["input_ids"] # grab ids # place on device, convert to tensor tok_seq = torch.LongTensor(tok_seq).unsqueeze(0) # unsqueeze for batch dim tok_seq = tok_seq.to(device) output = self.model(tok_seq) if self.return_loss and remove_special_tokens: # vocab: # {0: '[CLS]', 1: '[SEP]', 2: '[BOS]', 3: '[MASK]', 4: '[PAD]', 5: '[RESERVED]', 6: '[UNK]', 7: 'A', 8: 'C', 9: 'G', 10: 'T', 11: 'N'} output = output[:, :,7: 12] shift_logits = output[..., :-2, :].contiguous() # remove EOS and last AA shift_labels = tok_seq[..., 1:-1] # remove BOS and EOS shift_labels = shift_labels - 7 # shift to 0-indexed loss = torch.nn.functional.cross_entropy(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1), reduction='none') output = loss.unsqueeze(0) # dim 0 gets lost because of view elif self.return_loss and not remove_special_tokens: raise ValueError('return_loss is incompatible with remove_special_tokens=False. We always remove EOS and BOS tokens to calculate the loss.') elif remove_special_tokens: output = output[:,1:-1] embedded_chunks.append(output.detach().cpu().numpy()) embedding = np.concatenate(embedded_chunks, axis=1) embeddings.append(embedding) return embeddings class DNABert2Embedder(BaseEmbedder): """ Embed using the DNABERT2 model https://arxiv.org/pdf/2306.15006.pdf """ def load_model(self, model_name = "zhihan1996/DNABERT-2-117M", return_logits: bool = False, return_loss: bool = False, **kwargs): """ Load the DNABERT2 model. Parameters ---------- model_name : str, optional The name of the model to load. Defaults to "zhihan1996/DNABERT-2-117M". When providing a name, the model will be loaded from the HuggingFace model hub. Alternatively, you can provide a path to a local model directory. return_logits : bool, optional If True, returns logits instead of embeddings. Defaults to False. return_loss : bool, optional If True, returns the unreduced next token prediction loss. Incompatible with return_logits. If ``remove_special_tokens`` is True, the loss is only computed on the BPE vocabulary without the special tokens. Defaults to False. """ # keep the source in this repo to avoid using flash attn. self.model = DNABert2BertForMaskedLM.from_pretrained(model_name) self.tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) self.model.eval() self.model.to(device) # https://github.com/Zhihan1996/DNABERT_2/issues/2 self.max_length = 10000 #nucleotides. self.return_logits = return_logits self.return_loss = return_loss def embed(self, sequences: List[str], disable_tqdm: bool = False, remove_special_tokens: bool = True, upsample_embeddings: bool = False): '''Embeds a list sequences using the DNABERT2 model. Parameters ---------- sequences : List[str] List of sequences to embed. disable_tqdm : bool, optional Whether to disable the tqdm progress bar. Defaults to False. remove_special_tokens : bool, optional Whether to remove the CLS and SEP tokens from the embeddings. Defaults to True. upsample_embeddings : bool, optional Whether to upsample the embeddings to match the length of the input sequences. Defaults to False. Returns ------- embeddings : List[np.ndarray] List of embeddings. ''' # ''' # Note that this model uses byte pair encoding. # upsample_embedding repeats BPE token embeddings so that each nucleotide has its own embedding. # The [CLS] and [SEP] tokens are removed from the output if remove_special_tokens is True. # ''' embeddings = [] with torch.no_grad(): for sequence in tqdm(sequences, disable=disable_tqdm): chunks = [sequence[chunk : chunk + self.max_length] for chunk in range(0, len(sequence), self.max_length)] # split into chunks embedded_chunks = [] for n_chunk, chunk in enumerate(chunks): input_ids = self.tokenizer(chunk, return_tensors="pt", return_attention_mask=False, return_token_type_ids=False)["input_ids"] if self.return_logits: output = self.model(input_ids.to(device))['logits'].detach().cpu().numpy() elif self.return_loss: output = self.model(input_ids.to(device))['logits'].detach() # (1, len, 4096) dim_to_remove = [1, 2, 3, 4] # indices for '[CLS]', '[SEP]', '[PAD]', '[MASK]'. We preserve UNK at 0. mask = torch.ones(output.shape[2], dtype=bool) # create a mask of True values mask[dim_to_remove] = False # set the dimensions you want to remove to False output = output[:,1:-1,mask] if remove_special_tokens else output # remove CLS and SEP, cut dimensions ['[CLS]', '[SEP]', '[PAD]', '[MASK]', ... # shift and offset input_ids greater_than_4 = input_ids > 4 input_ids_shifted = input_ids - 4 * greater_than_4 # Subtract 4 from the tokens that are greater than 4 input_ids_shifted = input_ids_shifted[:,1:-1] if remove_special_tokens else input_ids # remove CLS and SEP, shift to 0-indexed output = torch.nn.functional.cross_entropy(output.view(-1, output.shape[-1]), input_ids_shifted.view(-1).to(torch.long).to(device), reduction='none').cpu().unsqueeze(0).numpy() else: output = self.model(input_ids.to(device), output_hidden_states=True)['hidden_states'][-1].detach().cpu().numpy() if upsample_embeddings and not (self.return_loss and remove_special_tokens): output = self._repeat_embedding_vectors(self.tokenizer.convert_ids_to_tokens(input_ids[0]), output) elif upsample_embeddings and (self.return_loss and remove_special_tokens): output = self._repeat_embedding_vectors(self.tokenizer.convert_ids_to_tokens(input_ids[0,1:-1]), output, has_special_tokens=False) # for intermediate chunks the special tokens need to go. # if we only have 1 chunk, keep them for now. if len(chunks) != 1: if n_chunk == 0: output = output[:,:-1] # no SEP elif n_chunk == len(chunks) - 1: output = output[:,1:] # no CLS else: output = output[:,1:-1] # no CLS and no SEP embedded_chunks.append(output) embedding = np.concatenate(embedded_chunks, axis=1) if remove_special_tokens and not self.return_loss: embedding = embedding[:,1:-1] embeddings.append(embedding) return embeddings # GATTTATTAGGGGAGATTTTATATATCCCGA # ['[CLS]', 'G', 'ATTTATT', 'AGGGG', 'AGATT', 'TTATAT', 'ATCCCG', 'A', '[SEP]'] @staticmethod def _repeat_embedding_vectors(tokens: Iterable[str], embeddings: np.ndarray, has_special_tokens: bool = True): ''' Byte-pair encoding merges a variable number of letters into one token. We need to repeat each token's embedding vector for each letter in the token. ''' assert len(tokens) == embeddings.shape[1], 'Number of tokens and embeddings must match.' new_embeddings = [] for idx, token in enumerate(tokens): |
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