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import functools
import torch.nn as nn
import torch.nn.functional as F
import lightning as L
from typing import Any, Dict, Tuple, Optional
from src.models.components.spectrogram import Spectrogram
from src.models.components.masking import MaskingGenerator
from src.models.components.patch_embed import PatchEmbed
from src.models.components.vit import ViT
from src.utils.lr_schedulers import LinearWarmupCosineDecay
class AudioJEPAModule(L.LightningModule):
"""
Audio-JEPA Lightning Module.
Args:
optimizer (torch.optim.Optimizer): Optimizer configuration (partial).
net (Dict[str, Any]): Configuration for sub-modules (spectrogram, patch_embed, masking, encoder, predictor).
warmup_pct (float): Percentage of total steps for warmup.
final_lr_ratio (float): Ratio of final learning rate to initial learning rate.
ema_decay (float): Initial EMA decay rate.
ema_end_decay (float): Final EMA decay rate.
ema_anneal_end_step (int): Step at which EMA decay reaches ema_end_decay.
spectrogram_adjustment_mode (str): 'pad' or 'truncate' for spectrogram time dimension.
criterion (torch.nn.Module): Loss function (defaults to MSELoss).
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
net: Dict[str, Any],
warmup_pct: float = 0.1,
final_lr_ratio: float = 0.001,
ema_decay: float = 0.996,
ema_end_decay: float = 1.0,
ema_anneal_end_step: Optional[int] = None,
spectrogram_adjustment_mode: str = "pad",
criterion: Optional[torch.nn.Module] = None,
):
super().__init__()
self.save_hyperparameters(
logger=False, ignore=["criterion", "net", "optimizer"]
)
self.warmup_pct = warmup_pct
self.final_lr_ratio = final_lr_ratio
self.spectrogram_adjustment_mode = spectrogram_adjustment_mode
# Handle Criterion (support partials/factories to avoid checkpointing warnings)
if criterion is not None:
self.criterion = (
criterion()
if isinstance(criterion, (type, functools.partial))
or callable(criterion)
and not isinstance(criterion, nn.Module)
else criterion
)
else:
self.criterion = nn.MSELoss()
# Store optimizer partial to avoid saving it in hparams
self.optimizer_config = optimizer
# Components
self.spectrogram = Spectrogram(**net.get("spectrogram", {}))
self.patch_embed = PatchEmbed(**net.get("patch_embed", {}))
self.mask_generator = MaskingGenerator(**net.get("masking", {}))
# Student (Encoder)
self.student = ViT(**net.get("encoder", {}))
# Teacher (Encoder) - same arch as student
self.teacher = ViT(**net.get("encoder", {}))
# Initialize teacher with student weights
self.teacher.load_state_dict(self.student.state_dict())
# stop gradient (teacher will be updated by EMA)
for p in self.teacher.parameters():
p.requires_grad = False
# Predictor
predictor_config = net.get("predictor", {})
self.predictor = ViT(**predictor_config)
# Projections for Predictor
encoder_dim = net.get("encoder", {}).get("embed_dim", 768)
predictor_embed_dim = predictor_config.get("embed_dim", 768)
self.predictor_input_proj = nn.Linear(encoder_dim, predictor_embed_dim)
self.predictor_output_proj = nn.Linear(predictor_embed_dim, encoder_dim)
# Mask Token
self.mask_token = nn.Parameter(torch.zeros(1, 1, predictor_embed_dim))
nn.init.trunc_normal_(self.mask_token, std=0.02)
# EMA parameters
self.ema_decay = ema_decay
self.ema_end_decay = ema_end_decay
self.ema_anneal_end_step = ema_anneal_end_step
self.current_ema_decay = ema_decay
def setup(self, stage: Optional[str] = None) -> None:
# Calculate ema_anneal_end_step if not provided
if self.ema_anneal_end_step is None:
self.ema_anneal_end_step = getattr(self.trainer, "max_steps", 0)
if self.ema_anneal_end_step <= 0:
self.ema_anneal_end_step = getattr(
self.trainer, "estimated_stepping_batches", 100000
)
if self.ema_anneal_end_step <= 0:
print(
"Warning: Could not determine total steps for EMA annealing. Using 100000 as default."
)
self.ema_anneal_end_step = 100000
def on_train_batch_start(self, batch: Any, batch_idx: int) -> None:
# Update EMA decay
step = self.global_step
progress = (self.ema_anneal_end_step - step) / self.ema_anneal_end_step
decay = self.ema_end_decay - (self.ema_end_decay - self.ema_decay) * progress
decay = min(self.ema_end_decay, max(self.ema_decay, decay))
self.current_ema_decay = decay
def _update_teacher(self) -> None:
with torch.no_grad():
m = self.current_ema_decay
for param_q, param_k in zip(
self.student.parameters(), self.teacher.parameters()
):
param_k.data.mul_(m).add_((1 - m) * param_q.data)
def _adjust_spectrogram(self, spec: torch.Tensor) -> torch.Tensor:
"""
Adjusts the spectrogram time dimension to be divisible by the patch size.
Args:
spec (torch.Tensor): Spectrogram [B, C, F, T].
Returns:
torch.Tensor: Adjusted spectrogram.
"""
# PatchEmbed stores patch_size as (H, W) corresponding to (F, T)
patch_size = self.patch_embed.patch_embed.patch_size
patch_time_dim = patch_size[1]
T = spec.shape[-1]
remainder = T % patch_time_dim
if remainder != 0:
if self.spectrogram_adjustment_mode == "pad":
pad_amount = patch_time_dim - remainder
spec = F.pad(spec, (0, pad_amount))
elif self.spectrogram_adjustment_mode == "truncate":
spec = spec[..., : T - remainder]
else:
raise ValueError(
f"Unknown spectrogram_adjustment_mode: {self.spectrogram_adjustment_mode}"
)
return spec
def _process_audio(
self, waveform: torch.Tensor
) -> Tuple[torch.Tensor, Tuple[int, int]]:
"""
Processes raw waveform into patches and returns patches and grid size.
Returns:
patches: [B, N, D]
grid_size: (H, W)
"""
# 1. Spectrogram
spec = self.spectrogram(waveform) # [B, 1, F, T]
spec = self._adjust_spectrogram(spec)
# 2. Patchify
patches = self.patch_embed(spec) # [B, N, D]
# Calculate grid size
patch_size = self.patch_embed.patch_embed.patch_size
F_pix = spec.shape[2]
T_pix = spec.shape[3]
H_grid = F_pix // patch_size[0]
W_grid = T_pix // patch_size[1]
grid_size = (H_grid, W_grid)
return patches, grid_size
def compute_student(
self, patches: torch.Tensor, mask: torch.Tensor, grid_size: Tuple[int, int]
) -> torch.Tensor:
"""
Computes the student output for unmasked patches.
Args:
patches: [B, N, D]
mask: [B, N]
grid_size: (H, W)
Returns:
student_out: [B, N_keep, D]
"""
B, N, _ = patches.shape
m = mask[0] # [N]
keep_indices = torch.nonzero(~m).flatten() # [N_keep]
# Student input (Context)
context_patches = patches[:, keep_indices, :] # [B, N_keep, D]
# Context Pos Ids
context_pos_ids = keep_indices.unsqueeze(0).expand(B, -1) # [B, N_keep]
# Student forward
student_out = self.student(
context_patches, pos_ids=context_pos_ids, grid_size=grid_size
) # [B, N_keep, D]
return student_out
def compute_predictor(
self, student_out: torch.Tensor, mask: torch.Tensor, grid_size: Tuple[int, int]
) -> torch.Tensor:
"""
Computes the predictor output at masked locations.
Args:
student_out: [B, N_keep, D]
mask: [B, N]
grid_size: (H, W)
Returns:
predictions_raw: [B, N_mask, pred_dim]
"""
B, N_keep, _ = student_out.shape
# Note: B derived from student_out might be different if batch size changes, but it shouldn't here.
# N is implicit in mask.
m = mask[0] # [N]
keep_indices = torch.nonzero(~m).flatten() # [N_keep]
mask_indices = torch.nonzero(m).flatten() # [N_mask]
num_mask = len(mask_indices)
# Predictor Input Construction
student_out_proj = self.predictor_input_proj(
student_out
) # [B, N_keep, pred_dim]
# Mask tokens: [1, 1, pred_dim] -> [B, N_mask, pred_dim]
mask_tokens = self.mask_token.expand(B, num_mask, -1)
if self.predictor.pos_embed_type != "rope":
# Absolute pos embed added to mask tokens
mask_pos_embed = self.predictor.pos_embed[:, mask_indices, :].expand(
B, -1, -1
)
mask_tokens = mask_tokens + mask_pos_embed
pred_input = torch.cat(
[student_out_proj, mask_tokens], dim=1
) # [B, N, pred_dim]
# Reorder to original sequence order
all_indices = torch.cat([keep_indices, mask_indices]) # [N]
sort_indices = torch.argsort(all_indices) # [N]
pred_input = pred_input[:, sort_indices, :] # [B, N, pred_dim]
if self.predictor.pos_embed_type == "rope":
# Rope handles positions internally if full sequence is provided
pred_out = self.predictor(pred_input, pos_ids=None, grid_size=grid_size)
else:
pred_out = self.predictor(pred_input, add_pos_embed=False)
# Predictions at mask locations (returns raw embeddings in pred_dim)
predictions_raw = pred_out[:, mask_indices, :] # [B, N_mask, pred_dim]
return predictions_raw
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass for inference/eval. Returns student representation.
"""
patches, grid_size = self._process_audio(x)
x = self.student(patches, grid_size=grid_size)
return x
def training_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
waveform = batch["waveform"] # [B, 1, T]
patches, current_grid_size = self._process_audio(waveform)
B, N, D = patches.shape
# Generate shared mask for the batch: [1, N] -> [B, N]
mask = self.mask_generator(1, device=self.device, grid_size=current_grid_size)
mask = mask.expand(B, -1)
# Update teacher EMA
self._update_teacher()
# Compute Student
student_out = self.compute_student(patches, mask, current_grid_size)
# Compute Predictor
predictions_raw = self.compute_predictor(student_out, mask, current_grid_size)
# Teacher forward (full)
with torch.no_grad():
teacher_full = self.teacher(
patches, grid_size=current_grid_size
) # [B, N, D]
# Calculate Loss
loss = self._calculate_jepa_loss(
student_out, predictions_raw, teacher_full, mask, current_grid_size
)
self.log(
"train/loss", loss, on_step=True, on_epoch=True, prog_bar=True, batch_size=B
)
return loss
def validation_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
waveform = batch["waveform"]
patches, current_grid_size = self._process_audio(waveform)
B, N, D = patches.shape
# Shared mask for validation as well to enable vectorization
mask = self.mask_generator(1, device=self.device, grid_size=current_grid_size)
mask = mask.expand(B, -1)
# Compute Student
student_out = self.compute_student(patches, mask, current_grid_size)
# Compute Predictor
predictions_raw = self.compute_predictor(student_out, mask, current_grid_size)
# Teacher forward (full)
with torch.no_grad():
teacher_full = self.teacher(patches, grid_size=current_grid_size)
# Calculate Loss
loss = self._calculate_jepa_loss(
student_out, predictions_raw, teacher_full, mask, current_grid_size
)
self.log(
"val/loss", loss, on_step=False, on_epoch=True, prog_bar=True, batch_size=B
)
return loss
def test_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
return self.validation_step(batch, batch_idx)
def _calculate_jepa_loss(
self,
student_out: torch.Tensor,
predictions_raw: torch.Tensor,
teacher_full: torch.Tensor,
mask: torch.Tensor,
grid_size: Tuple[int, int],
) -> torch.Tensor:
"""
Shared JEPA loss calculation logic.
"""
m = mask[0]
mask_indices = torch.nonzero(m).flatten()
# Project back to encoder dimension
predictions = self.predictor_output_proj(
predictions_raw
) # [B, N_mask, encoder_dim]
# Targets
teacher_targets = teacher_full[:, mask_indices, :] # [B, N_mask, encoder_dim]
return self.criterion(predictions, teacher_targets)
def configure_optimizers(self) -> Dict[str, Any]:
optimizer = self.optimizer_config(params=self.parameters())
# Determine total steps
if self.trainer.max_steps and self.trainer.max_steps > 0:
total_steps = self.trainer.max_steps
else:
total_steps = self.trainer.estimated_stepping_batches
warmup_steps = int(total_steps * self.warmup_pct)
lr_lambda = LinearWarmupCosineDecay(
warmup_steps=warmup_steps,
total_steps=total_steps,
final_lr_ratio=self.final_lr_ratio,
)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
return {
"optimizer": optimizer,
"lr_scheduler": {
"scheduler": scheduler,
"monitor": "val_loss",
"interval": "step",
"frequency": 1,
},
}
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