From 5f1cc5c316fa367016d23a19e45bfbb8cb360845 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Mon, 24 Mar 2025 09:28:02 -0400 Subject: [PATCH 1/5] Refactor EMA integration in IJEPA task --- mmlearn/modules/ema.py | 26 ++++++++++++++++++-------- mmlearn/tasks/ijepa.py | 39 ++++++++++++++++----------------------- 2 files changed, 34 insertions(+), 31 deletions(-) diff --git a/mmlearn/modules/ema.py b/mmlearn/modules/ema.py index 5248669..0c0741a 100644 --- a/mmlearn/modules/ema.py +++ b/mmlearn/modules/ema.py @@ -23,8 +23,6 @@ class ExponentialMovingAverage: The final decay value for EMA. ema_anneal_end_step : int The number of steps to anneal the decay from ``ema_decay`` to ``ema_end_decay``. - device_id : Optional[Union[int, torch.device]], optional, default=None - The device to move the model to. skip_keys : Optional[Union[list[str], Set[str]]], optional, default=None The keys to skip in the EMA update. These parameters will be copied directly from the model to the EMA model. @@ -41,14 +39,9 @@ def __init__( ema_decay: float, ema_end_decay: float, ema_anneal_end_step: int, - device_id: Optional[Union[int, torch.device]] = None, skip_keys: Optional[Union[list[str], Set[str]]] = None, - ): + ) -> None: self.model = self.deepcopy_model(model) - self.model.requires_grad_(False) - - if device_id is not None: - self.model.to(device_id) self.skip_keys: Union[list[str], set[str]] = skip_keys or set() self.num_updates = 0 @@ -57,6 +50,8 @@ def __init__( self.ema_end_decay = ema_end_decay self.ema_anneal_end_step = ema_anneal_end_step + self._model_configured = False + @staticmethod def deepcopy_model(model: torch.nn.Module) -> torch.nn.Module: """Deep copy the model. @@ -93,8 +88,23 @@ def get_annealed_rate( pct_remaining = 1 - curr_step / total_steps return end - r * pct_remaining + def configure_model(self, device_id: Union[int, torch.device]) -> None: + """Configure the model for EMA.""" + if self._model_configured: + return + + self.model.requires_grad_(False) + self.model.to(device_id) + + self._model_configured = True + def step(self, new_model: torch.nn.Module) -> None: """Perform single EMA update step.""" + if not self._model_configured: + raise RuntimeError( + "Model is not configured for EMA. Call `configure_model` first." + ) + self._update_weights(new_model) self._update_ema_decay() diff --git a/mmlearn/tasks/ijepa.py b/mmlearn/tasks/ijepa.py index 8dca337..02cec25 100644 --- a/mmlearn/tasks/ijepa.py +++ b/mmlearn/tasks/ijepa.py @@ -90,9 +90,6 @@ def __init__( self.modality = Modalities.get_modality(modality) self.mask_generator = IJEPAMaskGenerator() - self.current_step = 0 - self.total_steps = None - self.encoder = encoder self.predictor = predictor @@ -100,26 +97,22 @@ def __init__( self.predictor.embed_dim = encoder.embed_dim self.predictor.num_heads = encoder.num_heads - self.ema = ExponentialMovingAverage( - self.encoder, - ema_decay, - ema_decay_end, - ema_anneal_end_step, - device_id=self.device, + self.target_encoder = ExponentialMovingAverage( + self.encoder, ema_decay, ema_decay_end, ema_anneal_end_step ) def configure_model(self) -> None: """Configure the model.""" - self.ema.model.to(device=self.device, dtype=self.dtype) + self.target_encoder.configure_model(self.device) def on_before_zero_grad(self, optimizer: torch.optim.Optimizer) -> None: """Perform exponential moving average update of target encoder. - This is done right after the optimizer step, which comes just before `zero_grad` - to account for gradient accumulation. + This is done right after the ``optimizer.step()`, which comes just before + ``optimizer.zero_grad()`` to account for gradient accumulation. """ - if self.ema is not None: - self.ema.step(self.encoder) + if self.target_encoder is not None: + self.target_encoder.step(self.encoder) def training_step(self, batch: dict[str, Any], batch_idx: int) -> torch.Tensor: """Perform a single training step. @@ -200,10 +193,10 @@ def on_save_checkpoint(self, checkpoint: dict[str, Any]) -> None: checkpoint : dict[str, Any] The state dictionary to save the EMA state to. """ - if self.ema is not None: + if self.target_encoder is not None: checkpoint["ema_params"] = { - "decay": self.ema.decay, - "num_updates": self.ema.num_updates, + "decay": self.target_encoder.decay, + "num_updates": self.target_encoder.num_updates, } def on_load_checkpoint(self, checkpoint: dict[str, Any]) -> None: @@ -214,12 +207,12 @@ def on_load_checkpoint(self, checkpoint: dict[str, Any]) -> None: checkpoint : dict[str, Any] The state dictionary to restore the EMA state from. """ - if "ema_params" in checkpoint and self.ema is not None: + if "ema_params" in checkpoint and self.target_encoder is not None: ema_params = checkpoint.pop("ema_params") - self.ema.decay = ema_params["decay"] - self.ema.num_updates = ema_params["num_updates"] + self.target_encoder.decay = ema_params["decay"] + self.target_encoder.num_updates = ema_params["num_updates"] - self.ema.restore(self.encoder) + self.target_encoder.restore(self.encoder) def _shared_step( self, batch: dict[str, Any], batch_idx: int, step_type: str @@ -237,7 +230,7 @@ def _shared_step( # Forward pass through target encoder to get h with torch.no_grad(): - h = self.ema.model(batch)[0] + h = self.target_encoder.model(batch)[0] h = F.layer_norm(h, h.size()[-1:]) h_masked = apply_masks(h, predictor_masks) h_masked = repeat_interleave_batch( @@ -252,7 +245,7 @@ def _shared_step( z_pred = self.predictor(z, encoder_masks, predictor_masks) if step_type == "train": - self.log("train/ema_decay", self.ema.decay, prog_bar=True) + self.log("train/ema_decay", self.target_encoder.decay, prog_bar=True) if self.loss_fn is not None and ( step_type == "train" From 1aca5d4ae89ee7817fbdcff85e9e212baeb2fa0c Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Mon, 24 Mar 2025 09:28:24 -0400 Subject: [PATCH 2/5] Update aspect ratio and rename color jitter parameter for clarity --- mmlearn/datasets/processors/masking.py | 2 +- projects/ijepa/configs/__init__.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/mmlearn/datasets/processors/masking.py b/mmlearn/datasets/processors/masking.py index f9330bd..e4a17e2 100644 --- a/mmlearn/datasets/processors/masking.py +++ b/mmlearn/datasets/processors/masking.py @@ -321,7 +321,7 @@ class IJEPAMaskGenerator: allow_overlap: bool = False enc_mask_scale: tuple[float, float] = (0.85, 1.0) pred_mask_scale: tuple[float, float] = (0.15, 0.2) - aspect_ratio: tuple[float, float] = (0.75, 1.0) + aspect_ratio: tuple[float, float] = (0.75, 1.5) nenc: int = 1 npred: int = 4 diff --git a/projects/ijepa/configs/__init__.py b/projects/ijepa/configs/__init__.py index aefa1e4..5808fff 100644 --- a/projects/ijepa/configs/__init__.py +++ b/projects/ijepa/configs/__init__.py @@ -15,7 +15,7 @@ def ijepa_transforms( crop_size: int = 224, crop_scale: tuple = (0.3, 1.0), - color_jitter: float = 0.0, + color_jitter_strength: float = 0.0, horizontal_flip: bool = False, color_distortion: bool = False, gaussian_blur: bool = False, @@ -31,7 +31,7 @@ def ijepa_transforms( Size of the image crop. crop_scale : tuple, default=(0.3, 1.0) Range for the random resized crop scaling. - color_jitter : float, default=0.0 + color_jitter_strength : float, default=0.0 Strength of color jitter. horizontal_flip : bool, default=False Whether to apply random horizontal flip. @@ -89,7 +89,7 @@ def __call__(self, img): if horizontal_flip: transforms_list.append(transforms.RandomHorizontalFlip()) if color_distortion: - transforms_list.append(get_color_distortion(s=color_jitter)) + transforms_list.append(get_color_distortion(s=color_jitter_strength)) if gaussian_blur: transforms_list.append(GaussianBlur(p=0.5)) else: From 151bd1e7286e46e2686c92a5f5fe96f6a38c2224 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Mon, 24 Mar 2025 09:28:45 -0400 Subject: [PATCH 3/5] Rename ImageNet config and update parameters for IJEPA task --- ...duce_imagenet.yaml => in1k_vit_small.yaml} | 60 ++++++++++++------- 1 file changed, 37 insertions(+), 23 deletions(-) rename projects/ijepa/configs/experiment/{reproduce_imagenet.yaml => in1k_vit_small.yaml} (57%) diff --git a/projects/ijepa/configs/experiment/reproduce_imagenet.yaml b/projects/ijepa/configs/experiment/in1k_vit_small.yaml similarity index 57% rename from projects/ijepa/configs/experiment/reproduce_imagenet.yaml rename to projects/ijepa/configs/experiment/in1k_vit_small.yaml index 58b01fe..4f76c8c 100644 --- a/projects/ijepa/configs/experiment/reproduce_imagenet.yaml +++ b/projects/ijepa/configs/experiment/in1k_vit_small.yaml @@ -5,13 +5,12 @@ defaults: - /datasets/transforms@datasets.train.transform: ijepa_transforms - /datasets@datasets.val: ImageNet - /datasets/transforms@datasets.val.transform: ijepa_transforms - - /modules/encoders@task.encoder: vit_base + - /modules/encoders@task.encoder: vit_small - /modules/encoders@task.predictor: vit_predictor - /modules/optimizers@task.optimizer: AdamW - - /modules/lr_schedulers@task.lr_scheduler.scheduler: CosineAnnealingLR + - /modules/lr_schedulers@task.lr_scheduler.scheduler: linear_warmup_cosine_annealing_lr - /trainer/callbacks@trainer.callbacks.lr_monitor: LearningRateMonitor - /trainer/callbacks@trainer.callbacks.model_checkpoint: ModelCheckpoint - - /trainer/callbacks@trainer.callbacks.early_stopping: EarlyStopping - /trainer/callbacks@trainer.callbacks.model_summary: ModelSummary - /trainer/logger@trainer.logger.wandb: WandbLogger - override /task: IJEPA @@ -20,6 +19,16 @@ defaults: seed: 0 datasets: + train: + transform: + color_jitter_strength: 0.4 + horizontal_flip: true + color_distortion: true + gaussian_blur: false + crop_scale: + - 0.3 + - 1.0 + crop_size: 224 val: split: val transform: @@ -28,45 +37,50 @@ datasets: dataloader: train: batch_size: 256 - num_workers: 10 + num_workers: 8 + pin_memory: true + drop_last: true val: batch_size: 256 - num_workers: 10 + num_workers: 8 + pin_memory: false task: + ema_decay: 0.996 + ema_decay_end: 1.0 + ema_anneal_end_step: ${task.lr_scheduler.scheduler.max_steps} + predictor: + kwargs: + embed_dim: 384 + predictor_embed_dim: 384 + depth: 6 + num_heads: 6 optimizer: - betas: - - 0.9 - - 0.999 lr: 1.0e-3 weight_decay: 0.05 - eps: 1.0e-8 lr_scheduler: scheduler: - T_max: ${trainer.max_epochs} + warmup_steps: 12_510 + max_steps: 125_100 + start_factor: 0.2 + eta_min: 1.0e-6 extras: - interval: epoch + interval: step trainer: - max_epochs: 300 - precision: 16-mixed + max_epochs: 100 + precision: bf16-mixed deterministic: False benchmark: True sync_batchnorm: False # Set to True if using DDP with batchnorm - log_every_n_steps: 100 - accumulate_grad_batches: 4 + log_every_n_steps: 10 + accumulate_grad_batches: 1 check_val_every_n_epoch: 1 callbacks: model_checkpoint: - monitor: val/loss - save_top_k: 1 save_last: True - every_n_epochs: 1 - dirpath: /checkpoint/${oc.env:USER}/${oc.env:SLURM_JOB_ID} # only works on Vector SLURM environment - early_stopping: - monitor: val/loss - patience: 5 - mode: min + every_n_epochs: 10 + dirpath: /checkpoint/${oc.env:USER}/${oc.env:SLURM_JOB_ID} # only works on VI's SLURM environment model_summary: max_depth: 2 From 33d1a56ee00abe6a30b5e376bc727dab6a352ac1 Mon Sep 17 00:00:00 2001 From: fcogidi <41602287+fcogidi@users.noreply.github.com> Date: Mon, 24 Mar 2025 09:39:38 -0400 Subject: [PATCH 4/5] Update uv.lock file --- uv.lock | 803 ++++++++++++++++++++++++++++---------------------------- 1 file changed, 403 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torch.rand(1, 3, 224, 224)