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import torch.nn as nn
import torch.nn.functional as F
import lightning as L
from typing import Any, Dict, Tuple
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.models.components.random_projection_quantizer import RandomProjectionQuantizer
from src.utils.lr_schedulers import LinearWarmupCosineDecay
class BestRQModule(L.LightningModule):
"""
Best-RQ Lightning Module.
Implements a single-stage Masked Audio Modeling approach using Random Projection Quantization targets.
Args:
optimizer (torch.optim.Optimizer): Optimizer configuration.
net (Dict[str, Any]): Configuration for sub-modules.
warmup_pct (float): Percentage of total steps for warmup.
final_lr_ratio (float): Ratio of final learning rate to initial learning rate.
spectrogram_adjustment_mode (str): 'pad' or 'truncate' for spectrogram time dimension.
codebook_dim (int): Codebook dimension for RandomProjectionQuantizer.
vocab_size (int): Vocabulary size for RandomProjectionQuantizer.
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
net: Dict[str, Any],
warmup_pct: float = 0.1,
final_lr_ratio: float = 0.001,
spectrogram_adjustment_mode: str = "pad",
codebook_dim: int = 16,
vocab_size: int = 8192,
):
super().__init__()
self.save_hyperparameters(logger=False, ignore=["net", "optimizer"])
self.warmup_pct = warmup_pct
self.final_lr_ratio = final_lr_ratio
self.spectrogram_adjustment_mode = spectrogram_adjustment_mode
self.vocab_size = vocab_size
# Store optimizer partial
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", {}))
# Encoder (ViT)
self.encoder = ViT(**net.get("encoder", {}))
# Mask Token
encoder_dim = net.get("encoder", {}).get("embed_dim", 768)
self.mask_token = nn.Parameter(torch.zeros(1, 1, encoder_dim))
nn.init.trunc_normal_(self.mask_token, std=0.02)
# Random Projection Quantizer
# Input to quantizer is raw patches
patch_size = self.patch_embed.patch_size
in_chans = self.patch_embed.in_chans
quantizer_input_dim = patch_size[0] * patch_size[1] * in_chans
self.quantizer = RandomProjectionQuantizer(
input_dim=quantizer_input_dim, cb_dim=codebook_dim, cb_vocab=vocab_size
)
# Freeze quantizer
for p in self.quantizer.parameters():
p.requires_grad = False
# Projection head
self.output_proj = nn.Linear(encoder_dim, vocab_size)
# Loss
self.criterion = nn.CrossEntropyLoss()
def _adjust_spectrogram(self, spec: torch.Tensor) -> torch.Tensor:
"""
Adjusts the spectrogram time dimension to be divisible by the patch size.
"""
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.
"""
# 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 _get_raw_patches(self, spec: torch.Tensor) -> torch.Tensor:
"""
Extract raw key-value patches from spectrogram for quantization.
"""
patch_size = self.patch_embed.patch_size # (H, W)
# F.unfold returns [B, C*pH*pW, L]
# Spectrogram is [B, C, F, T]
# patch_size is (H, W) -> (freq_patch, time_patch)
patches = F.unfold(spec, kernel_size=patch_size, stride=patch_size) # [B, D, N]
patches = patches.transpose(1, 2) # [B, N, D]
return patches
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass for inference/eval. Returns encoder representation.
"""
patches, grid_size = self._process_audio(x)
x = self.encoder(patches, grid_size=grid_size)
return x
def training_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
waveform = batch["waveform"]
# 1. Process Audio
patches, current_grid_size = self._process_audio(waveform)
B, N, D = patches.shape
# 2. Generate Mask
mask = self.mask_generator(1, device=self.device, grid_size=current_grid_size)
mask = mask.expand(B, -1) # [B, N]
# 3. Prepare Inputs (Encoder sees full sequence with mask tokens)
encoder_input = patches.clone()
mask_tokens_expanded = self.mask_token.expand(B, N, -1)
# Replace masked patches with mask tokens
mask_bool = mask.bool() # [B, N]
encoder_input[mask_bool] = mask_tokens_expanded[mask_bool]
# 4. Encoder Forward
# We pass the full sequence.
# For RoPE, pos_ids are auto-generated as 0..N-1 if None, which matches the grid layout.
encoder_out = self.encoder(
encoder_input, grid_size=current_grid_size
) # [B, N, D]
# 5. Get Targets (Quantized Raw Patches)
with torch.no_grad():
# Re-compute spec for raw patches
spec = self.spectrogram(waveform)
spec = self._adjust_spectrogram(spec)
raw_patches = self._get_raw_patches(spec) # [B, N, raw_dim]
# Select masked patches for targets
m = mask[0]
mask_indices = torch.nonzero(m).flatten()
target_input = raw_patches[:, mask_indices, :] # [B, N_mask, raw_dim]
targets = self.quantizer(target_input) # [B, N_mask]
# 6. Get Predictions
# Select masked outputs
predictions = encoder_out[:, mask_indices, :] # [B, N_mask, D]
logits = self.output_proj(predictions) # [B, N_mask, vocab_size]
# 7. Loss
loss = self.criterion(logits.reshape(-1, self.vocab_size), targets.reshape(-1))
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
mask = self.mask_generator(1, device=self.device, grid_size=current_grid_size)
mask = mask.expand(B, -1)
encoder_input = patches.clone()
mask_tokens_expanded = self.mask_token.expand(B, N, -1)
mask_bool = mask.bool()
encoder_input[mask_bool] = mask_tokens_expanded[mask_bool]
encoder_out = self.encoder(encoder_input, grid_size=current_grid_size)
with torch.no_grad():
spec = self.spectrogram(waveform)
spec = self._adjust_spectrogram(spec)
raw_patches = self._get_raw_patches(spec)
m = mask[0]
mask_indices = torch.nonzero(m).flatten()
target_input = raw_patches[:, mask_indices, :]
targets = self.quantizer(target_input)
predictions = encoder_out[:, mask_indices, :]
logits = self.output_proj(predictions)
loss = self.criterion(logits.reshape(-1, self.vocab_size), targets.reshape(-1))
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 configure_optimizers(self) -> Dict[str, Any]:
optimizer = self.optimizer_config(params=self.parameters())
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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