diff options
Diffstat (limited to 'training/functional.py')
-rw-r--r-- | training/functional.py | 100 |
1 files changed, 23 insertions, 77 deletions
diff --git a/training/functional.py b/training/functional.py index 4ca7470..c01595a 100644 --- a/training/functional.py +++ b/training/functional.py | |||
@@ -33,6 +33,7 @@ def const(result=None): | |||
33 | @dataclass | 33 | @dataclass |
34 | class TrainingCallbacks(): | 34 | class TrainingCallbacks(): |
35 | on_prepare: Callable[[float], None] = const() | 35 | on_prepare: Callable[[float], None] = const() |
36 | on_model: Callable[[], torch.nn.Module] = const(None) | ||
36 | on_log: Callable[[], dict[str, Any]] = const({}) | 37 | on_log: Callable[[], dict[str, Any]] = const({}) |
37 | on_train: Callable[[int], _GeneratorContextManager] = const(nullcontext()) | 38 | on_train: Callable[[int], _GeneratorContextManager] = const(nullcontext()) |
38 | on_before_optimize: Callable[[int], None] = const() | 39 | on_before_optimize: Callable[[int], None] = const() |
@@ -267,6 +268,7 @@ def loss_step( | |||
267 | noise_scheduler: DDPMScheduler, | 268 | noise_scheduler: DDPMScheduler, |
268 | unet: UNet2DConditionModel, | 269 | unet: UNet2DConditionModel, |
269 | text_encoder: CLIPTextModel, | 270 | text_encoder: CLIPTextModel, |
271 | with_prior_preservation: bool, | ||
270 | prior_loss_weight: float, | 272 | prior_loss_weight: float, |
271 | seed: int, | 273 | seed: int, |
272 | step: int, | 274 | step: int, |
@@ -322,7 +324,7 @@ def loss_step( | |||
322 | else: | 324 | else: |
323 | raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") | 325 | raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") |
324 | 326 | ||
325 | if batch["with_prior"].all(): | 327 | if with_prior_preservation: |
326 | # Chunk the noise and model_pred into two parts and compute the loss on each part separately. | 328 | # Chunk the noise and model_pred into two parts and compute the loss on each part separately. |
327 | model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) | 329 | model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) |
328 | target, target_prior = torch.chunk(target, 2, dim=0) | 330 | target, target_prior = torch.chunk(target, 2, dim=0) |
@@ -347,7 +349,6 @@ def train_loop( | |||
347 | accelerator: Accelerator, | 349 | accelerator: Accelerator, |
348 | optimizer: torch.optim.Optimizer, | 350 | optimizer: torch.optim.Optimizer, |
349 | lr_scheduler: torch.optim.lr_scheduler._LRScheduler, | 351 | lr_scheduler: torch.optim.lr_scheduler._LRScheduler, |
350 | model: torch.nn.Module, | ||
351 | train_dataloader: DataLoader, | 352 | train_dataloader: DataLoader, |
352 | val_dataloader: DataLoader, | 353 | val_dataloader: DataLoader, |
353 | loss_step: Union[Callable[[int, Any], Tuple[Any, Any, int]], Callable[[int, Any, bool], Tuple[Any, Any, int]]], | 354 | loss_step: Union[Callable[[int, Any], Tuple[Any, Any, int]], Callable[[int, Any, bool], Tuple[Any, Any, int]]], |
@@ -387,28 +388,37 @@ def train_loop( | |||
387 | ) | 388 | ) |
388 | global_progress_bar.set_description("Total progress") | 389 | global_progress_bar.set_description("Total progress") |
389 | 390 | ||
391 | model = callbacks.on_model() | ||
392 | on_log = callbacks.on_log | ||
393 | on_train = callbacks.on_train | ||
394 | on_before_optimize = callbacks.on_before_optimize | ||
395 | on_after_optimize = callbacks.on_after_optimize | ||
396 | on_eval = callbacks.on_eval | ||
397 | on_sample = callbacks.on_sample | ||
398 | on_checkpoint = callbacks.on_checkpoint | ||
399 | |||
390 | try: | 400 | try: |
391 | for epoch in range(num_epochs): | 401 | for epoch in range(num_epochs): |
392 | if accelerator.is_main_process: | 402 | if accelerator.is_main_process: |
393 | if epoch % sample_frequency == 0: | 403 | if epoch % sample_frequency == 0: |
394 | callbacks.on_sample(global_step + global_step_offset) | 404 | on_sample(global_step + global_step_offset) |
395 | 405 | ||
396 | if epoch % checkpoint_frequency == 0 and epoch != 0: | 406 | if epoch % checkpoint_frequency == 0 and epoch != 0: |
397 | callbacks.on_checkpoint(global_step + global_step_offset, "training") | 407 | on_checkpoint(global_step + global_step_offset, "training") |
398 | 408 | ||
399 | local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}") | 409 | local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}") |
400 | local_progress_bar.reset() | 410 | local_progress_bar.reset() |
401 | 411 | ||
402 | model.train() | 412 | model.train() |
403 | 413 | ||
404 | with callbacks.on_train(epoch): | 414 | with on_train(epoch): |
405 | for step, batch in enumerate(train_dataloader): | 415 | for step, batch in enumerate(train_dataloader): |
406 | with accelerator.accumulate(model): | 416 | with accelerator.accumulate(model): |
407 | loss, acc, bsz = loss_step(step, batch) | 417 | loss, acc, bsz = loss_step(step, batch) |
408 | 418 | ||
409 | accelerator.backward(loss) | 419 | accelerator.backward(loss) |
410 | 420 | ||
411 | callbacks.on_before_optimize(epoch) | 421 | on_before_optimize(epoch) |
412 | 422 | ||
413 | optimizer.step() | 423 | optimizer.step() |
414 | lr_scheduler.step() | 424 | lr_scheduler.step() |
@@ -419,7 +429,7 @@ def train_loop( | |||
419 | 429 | ||
420 | # Checks if the accelerator has performed an optimization step behind the scenes | 430 | # Checks if the accelerator has performed an optimization step behind the scenes |
421 | if accelerator.sync_gradients: | 431 | if accelerator.sync_gradients: |
422 | callbacks.on_after_optimize(lr_scheduler.get_last_lr()[0]) | 432 | on_after_optimize(lr_scheduler.get_last_lr()[0]) |
423 | 433 | ||
424 | local_progress_bar.update(1) | 434 | local_progress_bar.update(1) |
425 | global_progress_bar.update(1) | 435 | global_progress_bar.update(1) |
@@ -433,7 +443,7 @@ def train_loop( | |||
433 | "train/cur_acc": acc.item(), | 443 | "train/cur_acc": acc.item(), |
434 | "lr": lr_scheduler.get_last_lr()[0], | 444 | "lr": lr_scheduler.get_last_lr()[0], |
435 | } | 445 | } |
436 | logs.update(callbacks.on_log()) | 446 | logs.update(on_log()) |
437 | 447 | ||
438 | accelerator.log(logs, step=global_step) | 448 | accelerator.log(logs, step=global_step) |
439 | 449 | ||
@@ -449,7 +459,7 @@ def train_loop( | |||
449 | cur_loss_val = AverageMeter() | 459 | cur_loss_val = AverageMeter() |
450 | cur_acc_val = AverageMeter() | 460 | cur_acc_val = AverageMeter() |
451 | 461 | ||
452 | with torch.inference_mode(), callbacks.on_eval(): | 462 | with torch.inference_mode(), on_eval(): |
453 | for step, batch in enumerate(val_dataloader): | 463 | for step, batch in enumerate(val_dataloader): |
454 | loss, acc, bsz = loss_step(step, batch, True) | 464 | loss, acc, bsz = loss_step(step, batch, True) |
455 | 465 | ||
@@ -485,80 +495,16 @@ def train_loop( | |||
485 | if avg_acc_val.avg.item() > max_acc_val: | 495 | if avg_acc_val.avg.item() > max_acc_val: |
486 | accelerator.print( | 496 | accelerator.print( |
487 | f"Global step {global_step}: Validation accuracy reached new maximum: {max_acc_val:.2e} -> {avg_acc_val.avg.item():.2e}") | 497 | f"Global step {global_step}: Validation accuracy reached new maximum: {max_acc_val:.2e} -> {avg_acc_val.avg.item():.2e}") |
488 | callbacks.on_checkpoint(global_step + global_step_offset, "milestone") | 498 | on_checkpoint(global_step + global_step_offset, "milestone") |
489 | max_acc_val = avg_acc_val.avg.item() | 499 | max_acc_val = avg_acc_val.avg.item() |
490 | 500 | ||
491 | # Create the pipeline using using the trained modules and save it. | 501 | # Create the pipeline using using the trained modules and save it. |
492 | if accelerator.is_main_process: | 502 | if accelerator.is_main_process: |
493 | print("Finished!") | 503 | print("Finished!") |
494 | callbacks.on_checkpoint(global_step + global_step_offset, "end") | 504 | on_checkpoint(global_step + global_step_offset, "end") |
495 | callbacks.on_sample(global_step + global_step_offset) | 505 | on_sample(global_step + global_step_offset) |
496 | accelerator.end_training() | ||
497 | 506 | ||
498 | except KeyboardInterrupt: | 507 | except KeyboardInterrupt: |
499 | if accelerator.is_main_process: | 508 | if accelerator.is_main_process: |
500 | print("Interrupted") | 509 | print("Interrupted") |
501 | callbacks.on_checkpoint(global_step + global_step_offset, "end") | 510 | on_checkpoint(global_step + global_step_offset, "end") |
502 | accelerator.end_training() | ||
503 | |||
504 | |||
505 | def train( | ||
506 | accelerator: Accelerator, | ||
507 | unet: UNet2DConditionModel, | ||
508 | text_encoder: CLIPTextModel, | ||
509 | vae: AutoencoderKL, | ||
510 | noise_scheduler: DDPMScheduler, | ||
511 | train_dataloader: DataLoader, | ||
512 | val_dataloader: DataLoader, | ||
513 | dtype: torch.dtype, | ||
514 | seed: int, | ||
515 | optimizer: torch.optim.Optimizer, | ||
516 | lr_scheduler: torch.optim.lr_scheduler._LRScheduler, | ||
517 | num_train_epochs: int = 100, | ||
518 | sample_frequency: int = 20, | ||
519 | checkpoint_frequency: int = 50, | ||
520 | global_step_offset: int = 0, | ||
521 | prior_loss_weight: float = 0, | ||
522 | callbacks: TrainingCallbacks = TrainingCallbacks(), | ||
523 | ): | ||
524 | unet, text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare( | ||
525 | unet, text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler | ||
526 | ) | ||
527 | |||
528 | vae.to(accelerator.device, dtype=dtype) | ||
529 | |||
530 | for model in (unet, text_encoder, vae): | ||
531 | model.requires_grad_(False) | ||
532 | model.eval() | ||
533 | |||
534 | callbacks.on_prepare() | ||
535 | |||
536 | loss_step_ = partial( | ||
537 | loss_step, | ||
538 | vae, | ||
539 | noise_scheduler, | ||
540 | unet, | ||
541 | text_encoder, | ||
542 | prior_loss_weight, | ||
543 | seed, | ||
544 | ) | ||
545 | |||
546 | if accelerator.is_main_process: | ||
547 | accelerator.init_trackers("textual_inversion") | ||
548 | |||
549 | train_loop( | ||
550 | accelerator=accelerator, | ||
551 | optimizer=optimizer, | ||
552 | lr_scheduler=lr_scheduler, | ||
553 | model=text_encoder, | ||
554 | train_dataloader=train_dataloader, | ||
555 | val_dataloader=val_dataloader, | ||
556 | loss_step=loss_step_, | ||
557 | sample_frequency=sample_frequency, | ||
558 | checkpoint_frequency=checkpoint_frequency, | ||
559 | global_step_offset=global_step_offset, | ||
560 | num_epochs=num_train_epochs, | ||
561 | callbacks=callbacks, | ||
562 | ) | ||
563 | |||
564 | accelerator.free_memory() | ||