File size: 16,140 Bytes
e284167 |
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 |
import logging
import re
from abc import ABC, abstractmethod
from functools import partial
from types import SimpleNamespace
from typing import Dict, List, Literal, Optional
import numpy as np
import torch
import tqdm as tqdm
from datasets import Dataset
from torch import Tensor
from torch.nn import functional as F
from torch.utils.data import DataLoader
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoTokenizer,
BatchEncoding,
DefaultDataCollator,
T5EncoderModel,
T5Tokenizer,
)
from transformers.modeling_outputs import BaseModelOutput
from .modality import Modality
from .eval_utils import ForwardHook, pool
logger = logging.getLogger(__name__)
class BioSeqTransformer(ABC):
"""
Abstract class to wrap models which map biological sequences (DNA/Prot) to embeddings.
Modelled after SentenceTransformer (https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/SentenceTransformer.py)
Args:
model_name: Name or path to the pretrained model.
layers: List of model layers to probe. Can be integers or "mid" or "last".
devices: List of device ids for inference. If cuda is not available, will use cpu.
num_processes: Number of processes to use for data loading.
max_seq_length: Maximum sequence length of the input sequences.
l2_norm: If true, embeddings are L2-normalized before they are returned.
batch_size: Batch size for encoding.
pool_type: Pooling strategy to use. One of "mean", "max", "cls", "last".
"""
def __init__(
self,
model_name: str,
layers: Optional[List[int] | Literal["mid"] | Literal["last"]] = None,
devices: List[int] = [0],
num_processes: int = 16,
max_seq_length: int = 1024,
l2_norm: bool = False,
batch_size: int = 128,
pool_type: str = "mean",
):
super().__init__()
self.id = self.__class__.__name__
self.hf_name = model_name
self.encoder = self._load_model(model_name)
if not hasattr(self.encoder, "config"):
raise ValueError(
'The model from `self._load_model()` must have a "config" attribute.'
)
self.config = self.encoder.config
self.tokenizer = self._get_tokenizer(model_name)
self.num_param = sum(p.numel() for p in self.encoder.parameters())
self.data_collator = DefaultDataCollator()
self.gpu_count = len(devices)
self.l2_norm = l2_norm
self.device = torch.device(
f"cuda:{devices[0]}" if torch.cuda.is_available() else "cpu"
)
self.num_processes = num_processes
self.max_seq_length = max_seq_length
self.batch_size = batch_size
self.pool_type = pool_type
if self.gpu_count > 1:
self.encoder = torch.nn.DataParallel(self.encoder, device_ids=devices)
self.encoder.to(self.device)
self.encoder.eval()
mid_layer = self.num_layers // 2
last_layer = self.num_layers - 1
mid_layer_label = f"mid ({mid_layer})"
last_layer_label = f"last ({self.num_layers - 1})"
if layers is None:
logger.debug(f"Using default layers: {mid_layer_label}, {last_layer_label}")
self.layers = [mid_layer, last_layer]
self.layer_labels = [mid_layer_label, last_layer_label]
elif layers == "mid":
self.layers = [mid_layer]
self.layer_labels = [mid_layer_label]
elif layers == "last":
self.layers = [last_layer]
self.layer_labels = [last_layer_label]
else:
self.layers = layers
self.layer_labels = [str(layer) for layer in layers]
def _encode_single_batch(self, batch_dict: Dict[str, Tensor]):
"""Returns the output embedding for the given batch with shape [batch, num_layers, D]."""
outputs = self.encoder(**batch_dict, output_hidden_states=True)
embeds = [outputs.hidden_states[layer] for layer in self.layers]
embeds = [
pool(layer_embeds, batch_dict["attention_mask"], self.pool_type)
for layer_embeds in embeds
]
# Stack with shape [B, num_layers, D].
embeds = torch.stack(embeds, dim=1)
return embeds
def _load_model(self, model_name):
return AutoModel.from_pretrained(model_name, trust_remote_code=True)
def _get_tokenizer(self, model_name):
return AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
def _tokenize_func(
self, tokenizer, examples: Dict[str, List], max_seq_length: int
) -> BatchEncoding:
batch_dict = tokenizer(
examples["input_seqs"],
max_length=max_seq_length,
padding=True,
truncation=True,
)
return batch_dict
@property
def metadata(self) -> Dict:
return {
"hf_name": self.hf_name,
"num_layers": self.num_layers,
"num_params": self.num_param,
"embed_dim": self.embed_dim,
}
@property
@abstractmethod
def num_layers(self) -> int:
pass
@property
@abstractmethod
def embed_dim(self) -> int:
pass
@property
@abstractmethod
def modality(self) -> Modality:
pass
@torch.no_grad()
def encode(self, sequences, **kwargs) -> np.ndarray:
"""Returns a list of embeddings for the given sequences.
Args:
sequences (`List[str]`): List of sequences to encode
Returns:
`np.ndarray`: Embeddings for the given sequences of shape [num_sequences, num_layers, embedding_dim].
"""
dataset = Dataset.from_dict({"input_seqs": sequences})
dataset.set_transform(
partial(
self._tokenize_func, self.tokenizer, max_seq_length=self.max_seq_length
)
)
data_loader = DataLoader(
dataset,
batch_size=self.batch_size * self.gpu_count,
shuffle=False,
drop_last=False,
num_workers=self.num_processes,
collate_fn=self.data_collator,
pin_memory=True,
)
if max(self.layers) >= self.num_layers:
raise ValueError(
f"Layer {max(self.layers)} is not available in the model. Choose a layer between 0 and {self.num_layers - 1}"
)
encoded_embeds = []
for batch_dict in tqdm.tqdm(
data_loader, desc="encoding", mininterval=10, disable=len(sequences) < 128
):
batch_dict = {k: v.to(self.device) for k, v in batch_dict.items()}
embeds = self._encode_single_batch(batch_dict)
if self.l2_norm:
embeds = F.normalize(embeds, p=2, dim=-1)
encoded_embeds.append(embeds.cpu().numpy())
return np.concatenate(encoded_embeds, axis=0)
class ESM(BioSeqTransformer):
"""ESM model from https://huggingface.co/docs/transformers/en/model_doc/esm"""
MODEL_NAMES = [
"facebook/esm2_t6_8M_UR50D",
"facebook/esm2_t12_35M_UR50D",
"facebook/esm2_t30_150M_UR50D",
"facebook/esm2_t33_650M_UR50D",
"facebook/esm2_t36_3B_UR50D",
"facebook/esm2_t48_15B_UR50D",
]
@property
def modality(self) -> Modality:
return Modality.PROTEIN
@property
def num_layers(self) -> int:
return self.config.num_hidden_layers
@property
def embed_dim(self) -> int:
return self.config.hidden_size
class ESM3(BioSeqTransformer):
"""ESM3 model from https://github.com/evolutionaryscale/esm"""
MODEL_NAMES = ["esm3_sm_open_v1"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Register forward hooks to store embeddings per layer.
self.hooks = [
ForwardHook(self.encoder.transformer.blocks[layer]) for layer in self.layers
]
@property
def modality(self) -> Modality:
return Modality.PROTEIN
@property
def num_layers(self) -> int:
return self.config.num_hidden_layers
@property
def embed_dim(self) -> int:
return self.config.hidden_size
def _load_model(self, model_name):
try:
from esm.models.esm3 import ESM3 as ModelESM3
except ImportError:
raise ImportError(
"ESM3 is not installed. Please install it with `pip install esm`."
)
model = ModelESM3.from_pretrained("esm3_sm_open_v1")
model.config = SimpleNamespace(
num_hidden_layers=len(model.transformer.blocks),
hidden_size=model.transformer.blocks[0].ffn[-1].out_features,
)
return model
def _get_tokenizer(self, model_name):
try:
from esm.tokenization.sequence_tokenizer import EsmSequenceTokenizer
except ImportError:
raise ImportError(
"ESM3 is not installed. Please install it with `pip install esm`."
)
return EsmSequenceTokenizer()
def _encode_single_batch(self, batch_dict: Dict[str, Tensor]):
_ = self.encoder.forward(sequence_tokens=batch_dict["input_ids"])
embeds = [hook.output for hook in self.hooks]
embeds = [
pool(layer_embeds, batch_dict["attention_mask"], self.pool_type)
for layer_embeds in embeds
]
# Stack with shape [B, num_layers, D].
embeds = torch.stack(embeds, dim=1)
embeds = embeds.to(torch.float32)
return embeds
class ProtT5(BioSeqTransformer):
"""ProtT5 model from https://github.com/agemagician/ProtTrans"""
MODEL_NAMES = [
"Rostlab/prot_t5_xl_uniref50",
"Rostlab/prot_t5_xl_bfd",
"Rostlab/prot_t5_xxl_uniref50",
"Rostlab/prot_t5_xxl_bfd",
]
@property
def modality(self) -> Modality:
return Modality.PROTEIN
@property
def num_layers(self) -> int:
return self.config.num_layers
@property
def embed_dim(self) -> int:
return self.config.d_model
def _load_model(self, model_name):
return T5EncoderModel.from_pretrained(model_name)
def _get_tokenizer(self, model_name):
return T5Tokenizer.from_pretrained(model_name, do_lower_case=False)
def _tokenize_func(
self, tokenizer, examples: Dict[str, List], max_seq_length: int
) -> BatchEncoding:
example_sequences = examples["input_seqs"]
# Add space between amino acids to make sure they are tokenized correctly.
example_sequences = [" ".join(sequence) for sequence in example_sequences]
example_sequences = [
re.sub(r"[UZOB]", "X", sequence) for sequence in example_sequences
]
batch_dict = tokenizer(
example_sequences,
max_length=max_seq_length,
padding=True,
truncation=True,
add_special_tokens=True,
)
return batch_dict
class ProGen(BioSeqTransformer):
"""ProGen models from https://github.com/salesforce/progen."""
MODEL_NAMES = [
"hugohrban/progen2-small",
"hugohrban/progen2-medium",
"hugohrban/progen2-base",
"hugohrban/progen2-large",
"hugohrban/progen2-xlarge",
]
@property
def modality(self) -> Modality:
return Modality.PROTEIN
@property
def num_layers(self) -> int:
return self.config.n_layer
@property
def embed_dim(self) -> int:
return self.config.embed_dim
def _load_model(self, model_name):
return AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
def _get_tokenizer(self, model_name_or_path):
tokenizer = AutoTokenizer.from_pretrained(
model_name_or_path, trust_remote_code=True
)
tokenizer.pad_token = "<|pad|>"
return tokenizer
def _encode_single_batch(self, batch_dict: Dict[str, Tensor]):
"""Returns the output embedding for the given batch with shape [batch, num_layers, D]."""
outputs: BaseModelOutput = self.encoder(
input_ids=batch_dict["input_ids"],
output_hidden_states=True,
use_cache=False,
)
embeds = [outputs.hidden_states[layer] for layer in self.layers]
embeds = [
pool(layer_embeds, batch_dict["attention_mask"], self.pool_type)
for layer_embeds in embeds
]
# Stack with shape [B, num_layers, D].
embeds = torch.stack(embeds, dim=1)
return embeds
class EvoModel(BioSeqTransformer):
"""https://github.com/evo-design/evo."""
MODEL_NAMES = [
"togethercomputer/evo-1-8k-base",
"togethercomputer/evo-1-131k-base",
]
@property
def modality(self) -> Modality:
return Modality.DNA
@property
def num_layers(self) -> int:
return self.config.num_layers
@property
def embed_dim(self) -> int:
return self.config.hidden_size
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Register forward hooks to store embeddings per layer.
self.hooks = []
for layer in self.layers:
# For the last layer, get the output of `backbone.norm`, which directly precedes `backbone.unembed`.
# This is equivalent to the approach in https://github.com/evo-design/evo/issues/32.
if layer == self.num_layers - 1 or layer == -1:
self.hooks.append(ForwardHook(self.encoder.backbone.norm))
else:
self.hooks.append(ForwardHook(self.encoder.backbone.blocks[layer]))
def _load_model(self, model_name):
config = AutoConfig.from_pretrained(
model_name, trust_remote_code=True, revision="1.1_fix"
)
model = AutoModelForCausalLM.from_pretrained(
model_name, config=config, trust_remote_code=True, revision="1.1_fix"
)
return model
def _get_tokenizer(self, model_name):
tokenizer = AutoTokenizer.from_pretrained(
model_name, revision="1.1_fix", trust_remote_code=True
)
# Evo tokenizer is missing pad_token by default.
tokenizer.add_special_tokens({"pad_token": "N"})
return tokenizer
def _encode_single_batch(self, batch_dict: Dict[str, Tensor]):
_ = self.encoder(batch_dict["input_ids"], use_cache=False)
embeds = [hook.output for hook in self.hooks]
# The hook output for Evo middle layers is a tuple (embedding, inference_params=None).
embeds = [x[0] if isinstance(x, tuple) else x for x in embeds]
embeds = [
pool(layer_embeds, batch_dict["attention_mask"], self.pool_type)
for layer_embeds in embeds
]
# Stack with shape [B, num_layers, D].
embeds = torch.stack(embeds, dim=1)
embeds = embeds.to(torch.float32)
return embeds
class NTModel(BioSeqTransformer):
"""Nucleotide Transformer https://github.com/instadeepai/nucleotide-transformer"""
MODEL_NAMES = [
"InstaDeepAI/nucleotide-transformer-v2-50m-multi-species",
"InstaDeepAI/nucleotide-transformer-v2-100m-multi-species",
"InstaDeepAI/nucleotide-transformer-v2-250m-multi-species",
"InstaDeepAI/nucleotide-transformer-v2-500m-multi-species",
"InstaDeepAI/nucleotide-transformer-2.5b-multi-species",
]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.max_seq_length = self.tokenizer.model_max_length
@property
def modality(self) -> Modality:
return Modality.DNA
@property
def num_layers(self) -> int:
return self.config.num_hidden_layers
@property
def embed_dim(self) -> int:
return self.config.hidden_size
def _load_model(self, model_name):
return AutoModelForMaskedLM.from_pretrained(model_name, trust_remote_code=True)
|