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https://github.com/facebookresearch/pytorch3d.git
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option to avoid accelerate
Summary: For debugging, introduce PYTORCH3D_NO_ACCELERATE env var. Reviewed By: shapovalov Differential Revision: D37885393 fbshipit-source-id: de080080c0aa4b6d874028937083a0113bb97c23
This commit is contained in:
Jeremy Reizenstein
Facebook GitHub Bot
parent
0f966217e5
commit
9b2e570536
@ -97,13 +97,16 @@ try:
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except ModuleNotFoundError:
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pass
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no_accelerate = os.environ.get("PYTORCH3D_NO_ACCELERATE") is not None
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def init_model(
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*,
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cfg: DictConfig,
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accelerator: Optional[Accelerator] = None,
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force_load: bool = False,
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clear_stats: bool = False,
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load_model_only: bool = False,
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accelerator: Accelerator = None,
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) -> Tuple[GenericModel, Stats, Optional[Dict[str, Any]]]:
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"""
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Returns an instance of `GenericModel`.
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@ -166,7 +169,7 @@ def init_model(
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logger.info(" -> resuming")
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map_location = None
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if not accelerator.is_local_main_process:
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if accelerator is not None and not accelerator.is_local_main_process:
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map_location = {
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"cuda:%d" % 0: "cuda:%d" % accelerator.local_process_index
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}
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@ -228,13 +231,14 @@ def trainvalidate(
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loader,
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optimizer,
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validation: bool,
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*,
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accelerator: Optional[Accelerator],
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device: torch.device,
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bp_var: str = "objective",
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metric_print_interval: int = 5,
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visualize_interval: int = 100,
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visdom_env_root: str = "trainvalidate",
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clip_grad: float = 0.0,
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device: str = "cuda:0",
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accelerator: Accelerator = None,
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**kwargs,
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) -> None:
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"""
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@ -286,7 +290,7 @@ def trainvalidate(
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last_iter = it == n_batches - 1
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# move to gpu where possible (in place)
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net_input = net_input.to(accelerator.device)
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net_input = net_input.to(device)
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# run the forward pass
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if not validation:
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@ -313,7 +317,7 @@ def trainvalidate(
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# visualize results
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if (
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accelerator.is_local_main_process
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(accelerator is None or accelerator.is_local_main_process)
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and visualize_interval > 0
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and it % visualize_interval == 0
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):
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@ -331,7 +335,10 @@ def trainvalidate(
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loss = preds[bp_var]
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assert torch.isfinite(loss).all(), "Non-finite loss!"
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# backprop
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accelerator.backward(loss)
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if accelerator is None:
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loss.backward()
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else:
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accelerator.backward(loss)
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if clip_grad > 0.0:
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# Optionally clip the gradient norms.
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total_norm = torch.nn.utils.clip_grad_norm(
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@ -353,15 +360,16 @@ def run_training(cfg: DictConfig) -> None:
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"""
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# Initialize the accelerator
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accelerator = Accelerator(device_placement=False)
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logger.info(accelerator.state)
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if no_accelerate:
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accelerator = None
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device = torch.device("cuda:0")
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else:
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accelerator = Accelerator(device_placement=False)
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logger.info(accelerator.state)
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device = accelerator.device
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device = accelerator.device
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logger.info(f"Running experiment on device: {device}")
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if accelerator.is_local_main_process:
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logger.info(OmegaConf.to_yaml(cfg))
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# set the debug mode
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if cfg.detect_anomaly:
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logger.info("Anomaly detection!")
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@ -386,11 +394,11 @@ def run_training(cfg: DictConfig) -> None:
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all_train_cameras = datasource.get_all_train_cameras()
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# init the model
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model, stats, optimizer_state = init_model(cfg, accelerator=accelerator)
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model, stats, optimizer_state = init_model(cfg=cfg, accelerator=accelerator)
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start_epoch = stats.epoch + 1
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# move model to gpu
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model.to(accelerator.device)
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model.to(device)
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# only run evaluation on the test dataloader
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if cfg.eval_only:
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@ -403,7 +411,6 @@ def run_training(cfg: DictConfig) -> None:
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model,
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stats,
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device=device,
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accelerator=accelerator,
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)
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return
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@ -422,12 +429,15 @@ def run_training(cfg: DictConfig) -> None:
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# Wrap all modules in the distributed library
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# Note: we don't pass the scheduler to prepare as it
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# doesn't need to be stepped at each optimizer step
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(
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model,
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optimizer,
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train_loader,
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val_loader,
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) = accelerator.prepare(model, optimizer, dataloaders.train, dataloaders.val)
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train_loader = dataloaders.train
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val_loader = dataloaders.val
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if accelerator is not None:
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(
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model,
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optimizer,
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train_loader,
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val_loader,
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) = accelerator.prepare(model, optimizer, train_loader, val_loader)
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past_scheduler_lrs = []
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# loop through epochs
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@ -485,19 +495,22 @@ def run_training(cfg: DictConfig) -> None:
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task,
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camera_difficulty_bin_breaks=cfg.camera_difficulty_bin_breaks,
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device=device,
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accelerator=accelerator,
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)
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assert stats.epoch == epoch, "inconsistent stats!"
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# delete previous models if required
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# save model only on the main process
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if cfg.store_checkpoints and accelerator.is_local_main_process:
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if cfg.store_checkpoints and (
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accelerator is None or accelerator.is_local_main_process
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):
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if cfg.store_checkpoints_purge > 0:
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for prev_epoch in range(epoch - cfg.store_checkpoints_purge):
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model_io.purge_epoch(cfg.exp_dir, prev_epoch)
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outfile = model_io.get_checkpoint(cfg.exp_dir, epoch)
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unwrapped_model = accelerator.unwrap_model(model)
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unwrapped_model = (
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model if accelerator is None else accelerator.unwrap_model(model)
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)
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model_io.safe_save_model(
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unwrapped_model, stats, outfile, optimizer=optimizer
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)
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@ -530,7 +543,6 @@ def _eval_and_dump(
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model,
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stats,
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device,
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accelerator: Accelerator = None,
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) -> None:
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"""
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Run the evaluation loop with the test data loader and
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@ -549,7 +561,6 @@ def _eval_and_dump(
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task,
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camera_difficulty_bin_breaks=cfg.camera_difficulty_bin_breaks,
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device=device,
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accelerator=accelerator,
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)
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# add the evaluation epoch to the results
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@ -584,20 +595,19 @@ def _run_eval(
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task: Task,
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camera_difficulty_bin_breaks: Tuple[float, float],
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device,
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accelerator: Accelerator = None,
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):
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"""
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Run the evaluation loop on the test dataloader
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"""
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lpips_model = lpips.LPIPS(net="vgg")
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lpips_model = lpips_model.to(accelerator.device)
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lpips_model = lpips_model.to(device)
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model.eval()
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per_batch_eval_results = []
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logger.info("Evaluating model ...")
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for frame_data in tqdm.tqdm(loader):
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frame_data = frame_data.to(accelerator.device)
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frame_data = frame_data.to(device)
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# mask out the unknown images so that the model does not see them
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frame_data_for_eval = _get_eval_frame_data(frame_data)
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@ -344,7 +344,7 @@ def export_scenes(
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os.environ["CUDA_VISIBLE_DEVICES"] = str(config.gpu_idx)
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# Load the previously trained model
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model, _, _ = init_model(config, force_load=True, load_model_only=True)
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model, _, _ = init_model(cfg=config, force_load=True, load_model_only=True)
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model.cuda()
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model.eval()
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