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| from typing import Any, Optional | |
| import lightning as L | |
| import torch | |
| import torch.nn.functional as F | |
| from lightning.pytorch.utilities.types import OptimizerLRScheduler | |
| import fish_speech.utils as utils | |
| from fish_speech.conversation import CODEBOOK_PAD_TOKEN_ID | |
| from fish_speech.models.text2semantic.llama import NaiveTransformer | |
| log = utils.RankedLogger(__name__, rank_zero_only=True) | |
| class TextToSemantic(L.LightningModule): | |
| def __init__( | |
| self, | |
| model: NaiveTransformer, | |
| optimizer: Any, | |
| lr_scheduler: Any, | |
| ): | |
| super().__init__() | |
| self.model = model | |
| self.optimizer_builder = optimizer | |
| self.lr_scheduler_builder = lr_scheduler | |
| def forward(self, x): | |
| return self.model(x) | |
| def on_save_checkpoint(self, checkpoint): | |
| # Save only LoRA parameters | |
| state_dict = checkpoint["state_dict"] | |
| use_lora = any("lora" in name for name in state_dict.keys()) | |
| if not use_lora: | |
| return | |
| for name in list(state_dict.keys()): | |
| if "lora" not in name: | |
| state_dict.pop(name) | |
| def configure_optimizers(self) -> OptimizerLRScheduler: | |
| # Get weight decay parameters | |
| weight_decay_parameters, other_parameters = [], [] | |
| for name, param in self.named_parameters(): | |
| if ".bias" in name or "norm.weight" in name or ".embeddings." in name: | |
| other_parameters.append(param) | |
| else: | |
| weight_decay_parameters.append(param) | |
| optimizer = self.optimizer_builder( | |
| [ | |
| {"params": weight_decay_parameters}, | |
| {"params": other_parameters, "weight_decay": 0.0}, | |
| ] | |
| ) | |
| # Print the parameters and their weight decay | |
| for i in optimizer.param_groups: | |
| log.info( | |
| f"Set weight decay: {i['weight_decay']} for {len(i['params'])} parameters" | |
| ) | |
| lr_scheduler = self.lr_scheduler_builder(optimizer) | |
| return { | |
| "optimizer": optimizer, | |
| "lr_scheduler": { | |
| "scheduler": lr_scheduler, | |
| "interval": "step", | |
| }, | |
| } | |
| # Copied from https://github.com/eric-mitchell/direct-preference-optimization/blob/main/trainers.py#L90 | |
| def get_batch_logps( | |
| self, | |
| logits: torch.FloatTensor, | |
| labels: torch.LongTensor, | |
| average_log_prob: bool = False, | |
| ) -> torch.FloatTensor: | |
| """Compute the log probabilities of the given labels under the given logits. | |
| Args: | |
| logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, codebook_size, vocab_size) | |
| labels: Labels for which to compute the log probabilities. Label tokens with a value of -100 are ignored. Shape: (batch_size, sequence_length, codebook_size) | |
| average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens. | |
| Returns: | |
| A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits. | |
| """ | |
| assert logits.shape[:-1] == labels.shape | |
| labels = labels.clone() | |
| loss_mask = labels != -100 | |
| # dummy token; we'll ignore the losses on these tokens later | |
| labels[labels == -100] = 0 | |
| per_token_logps = torch.gather( | |
| logits.log_softmax(-1), dim=-1, index=labels.unsqueeze(-1) | |
| ).squeeze(-1) | |
| if average_log_prob: | |
| return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1) | |
| else: | |
| return (per_token_logps * loss_mask).sum(-1) | |
| def _step(self, batch, batch_idx, stage: str): | |
| is_train = stage == "train" | |
| if is_train: | |
| # Key part to make lora work | |
| # Otherwise the parameters are merged, which lead to incorrect gradients | |
| self.model.train() | |
| # Do positive and negative samples in the same batch to speed up training | |
| labels = batch["labels"] | |
| outputs = self.model( | |
| inp=batch["inputs"], | |
| key_padding_mask=batch["attention_masks"], | |
| ) | |
| token_logits = outputs.token_logits | |
| codebook_logits = outputs.codebook_logits | |
| # Generate labels | |
| base_loss = F.cross_entropy( | |
| token_logits.view(-1, token_logits.size(-1)), | |
| labels[:, 0].reshape(-1), | |
| ignore_index=-100, | |
| ) | |
| codebook_labels = labels[:, 1 : 1 + self.model.config.num_codebooks].mT | |
| semantic_loss = F.cross_entropy( | |
| codebook_logits.view(-1, codebook_logits.size(-1)), | |
| codebook_labels.reshape(-1), | |
| ignore_index=-100, | |
| ) | |
| loss = base_loss + semantic_loss | |
| self.log( | |
| f"{stage}/loss", | |
| loss, | |
| on_step=is_train, | |
| on_epoch=not is_train, | |
| prog_bar=True, | |
| logger=True, | |
| sync_dist=not is_train, | |
| ) | |
| self.log( | |
| f"{stage}/base_loss", | |
| base_loss, | |
| on_step=is_train, | |
| on_epoch=not is_train, | |
| prog_bar=False, | |
| logger=True, | |
| sync_dist=not is_train, | |
| ) | |
| self.log( | |
| f"{stage}/semantic_loss", | |
| semantic_loss, | |
| on_step=is_train, | |
| on_epoch=not is_train, | |
| prog_bar=False, | |
| logger=True, | |
| sync_dist=not is_train, | |
| ) | |
| # Top-5 accuracy | |
| accuracy = self.get_accuracy(codebook_logits, codebook_labels) | |
| self.log( | |
| f"{stage}/top_5_accuracy", | |
| accuracy, | |
| on_step=is_train, | |
| on_epoch=not is_train, | |
| prog_bar=True, | |
| logger=True, | |
| sync_dist=not is_train, | |
| ) | |
| return loss | |
| def get_accuracy(self, logits, labels): | |
| mask = (labels != -100) & (labels != CODEBOOK_PAD_TOKEN_ID) | |
| if mask.sum() == 0: | |
| return torch.tensor(0.0, device=logits.device) | |
| _, indices = logits.topk(5, dim=-1) | |
| correct = indices.eq(labels.unsqueeze(-1)) | |
| correct[~mask] = 0 | |
| correct = correct.sum() | |
| accuracy = correct / mask.sum() | |
| return accuracy | |
| def training_step(self, batch, batch_idx): | |
| return self._step(batch, batch_idx, "train") | |
| def validation_step(self, batch, batch_idx): | |
| return self._step(batch, batch_idx, "val") | |