| |
| |
| |
| |
| @@ -4,6 +4,8 @@ from torchvision.transforms import Lambda |
| |
| from fastvideo.dataset.parquet_dataset_map_style import ( |
| build_parquet_map_style_dataloader) |
| +from fastvideo.dataset.pants_latent_dataset import ( |
| + build_pants_latent_dataloader, is_pants_latent_path) |
| from fastvideo.dataset.ltx2_precomputed_dataset import ( |
| build_ltx2_precomputed_dataloader, LTX2PrecomputedDataset) |
| from fastvideo.dataset.preprocessing_datasets import VideoCaptionMergedDataset, TextDataset |
| @@ -46,6 +48,8 @@ def gettextdataset(args) -> TextDataset: |
| |
| __all__ = [ |
| "build_parquet_map_style_dataloader", |
| + "build_pants_latent_dataloader", |
| + "is_pants_latent_path", |
| "build_ltx2_precomputed_dataloader", |
| "LTX2PrecomputedDataset", |
| "ValidationDataset", |
| |
| |
| |
| |
| @@ -28,7 +28,11 @@ try: |
| except Exception: |
| pass |
| from fastvideo.api.sampling_param import SamplingParam |
| -from fastvideo.dataset import build_parquet_map_style_dataloader |
| +from fastvideo.dataset import ( |
| + build_pants_latent_dataloader, |
| + build_parquet_map_style_dataloader, |
| + is_pants_latent_path, |
| +) |
| from fastvideo.dataset.dataloader.schema import pyarrow_schema_t2v |
| from fastvideo.dataset.validation_dataset import ValidationDataset |
| from fastvideo.distributed import (cleanup_dist_env_and_memory, get_local_torch_device, get_sp_group, get_world_group) |
| @@ -42,7 +46,8 @@ from fastvideo.training.activation_checkpoint import (apply_activation_checkpoin |
| from fastvideo.training.trackers import (DummyTracker, TrackerType, initialize_trackers, Trackers) |
| from fastvideo.training.training_utils import (clip_grad_norm_while_handling_failing_dtensor_cases, |
| compute_density_for_timestep_sampling, count_trainable, get_scheduler, |
| - get_sigmas, load_checkpoint, normalize_dit_input, save_checkpoint) |
| + get_sigmas, load_checkpoint, normalize_dit_input, save_checkpoint, |
| + EMA_FSDP, gather_state_dict_on_cpu_rank0, custom_to_hf_state_dict) |
| from fastvideo.utils import (is_vmoba_available, is_vsa_available, set_random_seed, shallow_asdict) |
| |
| try: |
| @@ -82,6 +87,7 @@ class TrainingPipeline(LoRAPipeline, ABC): |
| super().__init__(model_path, fastvideo_args, required_config_modules, loaded_modules) # type: ignore |
| self.tracker = DummyTracker() |
| self.validation_ref_videos_logged = False |
| + self.generator_ema: EMA_FSDP | None = None |
| |
| def create_pipeline_stages(self, fastvideo_args: FastVideoArgs): |
| raise RuntimeError("create_pipeline_stages should not be called for training pipeline") |
| @@ -167,16 +173,27 @@ class TrainingPipeline(LoRAPipeline, ABC): |
| last_epoch=self.init_steps - 1, |
| ) |
| |
| - self.train_dataset, self.train_dataloader = build_parquet_map_style_dataloader( |
| - training_args.data_path, |
| - training_args.train_batch_size, |
| - parquet_schema=self.train_dataset_schema, |
| - num_data_workers=training_args.dataloader_num_workers, |
| - cfg_rate=training_args.training_cfg_rate, |
| - drop_last=True, |
| - text_padding_length=training_args.pipeline_config.text_encoder_configs[0].arch_config. |
| - text_len, # type: ignore[attr-defined] |
| - seed=self.seed) |
| + text_padding_length = training_args.pipeline_config.text_encoder_configs[0].arch_config.text_len # type: ignore[attr-defined] |
| + if is_pants_latent_path(training_args.data_path): |
| + self.train_dataset, self.train_dataloader = build_pants_latent_dataloader( |
| + training_args.data_path, |
| + training_args.train_batch_size, |
| + num_data_workers=training_args.dataloader_num_workers, |
| + cfg_rate=training_args.training_cfg_rate, |
| + drop_last=True, |
| + text_padding_length=text_padding_length, |
| + seed=self.seed, |
| + ) |
| + else: |
| + self.train_dataset, self.train_dataloader = build_parquet_map_style_dataloader( |
| + training_args.data_path, |
| + training_args.train_batch_size, |
| + parquet_schema=self.train_dataset_schema, |
| + num_data_workers=training_args.dataloader_num_workers, |
| + cfg_rate=training_args.training_cfg_rate, |
| + drop_last=True, |
| + text_padding_length=text_padding_length, |
| + seed=self.seed) |
| |
| self.noise_scheduler = noise_scheduler |
| if self.training_args.boundary_ratio is not None: |
| @@ -460,6 +477,43 @@ class TrainingPipeline(LoRAPipeline, ABC): |
| training_batch.grad_norm = grad_norm |
| return training_batch |
| |
| + def _maybe_init_ema(self, step: int) -> None: |
| + if not self.training_args.use_ema: |
| + return |
| + if self.generator_ema is not None: |
| + return |
| + if step < self.training_args.ema_start_step: |
| + return |
| + if self.training_args.ema_decay <= 0: |
| + return |
| + self.generator_ema = EMA_FSDP(self.transformer, decay=self.training_args.ema_decay) |
| + logger.info("Created generator EMA at step %s with decay=%s", step, self.training_args.ema_decay) |
| + |
| + def _maybe_update_ema(self, step: int) -> None: |
| + self._maybe_init_ema(step) |
| + if self.generator_ema is not None: |
| + self.generator_ema.update(self.transformer) |
| + |
| + def _save_ema_weights(self, step: int) -> None: |
| + if not self.training_args.use_ema or self.generator_ema is None: |
| + return |
| + ema_dir = os.path.join(self.training_args.output_dir, f"ema_checkpoint-{step}") |
| + os.makedirs(ema_dir, exist_ok=True) |
| + with self.generator_ema.apply_to_model(self.transformer): |
| + cpu_state = gather_state_dict_on_cpu_rank0(self.transformer, device=None) |
| + if self.global_rank == 0: |
| + from safetensors.torch import save_file |
| + |
| + diffusers_state_dict = custom_to_hf_state_dict( |
| + cpu_state, |
| + self.transformer.reverse_param_names_mapping, |
| + ) |
| + save_file( |
| + diffusers_state_dict, |
| + os.path.join(ema_dir, "diffusion_pytorch_model.safetensors"), |
| + ) |
| + logger.info("Saved EMA transformer weights to %s", ema_dir) |
| + |
| @profile_region("profiler_region_training_train_one_step") |
| def train_one_step(self, training_batch: TrainingBatch) -> TrainingBatch: |
| training_batch = self._prepare_training(training_batch) |
| @@ -571,6 +625,7 @@ class TrainingPipeline(LoRAPipeline, ABC): |
| training_batch.current_timestep = step |
| training_batch.current_vsa_sparsity = current_vsa_sparsity |
| training_batch = self.train_one_step(training_batch) |
| + self._maybe_update_ema(step) |
| |
| loss = float(training_batch.total_loss) |
| grad_norm = training_batch.grad_norm |
| @@ -594,6 +649,9 @@ class TrainingPipeline(LoRAPipeline, ABC): |
| "grad_norm": grad_norm, |
| "vsa_sparsity": current_vsa_sparsity, |
| } |
| + if self.training_args.use_ema: |
| + metrics["ema_enabled"] = self.generator_ema is not None |
| + metrics["ema_decay"] = self.training_args.ema_decay |
| try: |
| metrics["batch_size"] = int(training_batch.raw_latent_shape[0]) |
| |
| @@ -622,6 +680,7 @@ class TrainingPipeline(LoRAPipeline, ABC): |
| save_checkpoint(self.transformer, self.global_rank, self.training_args.output_dir, step, |
| self.optimizer, self.train_dataloader, self.lr_scheduler, |
| self.noise_random_generator) |
| + self._save_ema_weights(step) |
| self.transformer.train() |
| self.sp_group.barrier() |
| |
| @@ -637,9 +696,13 @@ class TrainingPipeline(LoRAPipeline, ABC): |
| trainable_params) |
| |
| self.tracker.finish() |
| - save_checkpoint(self.transformer, self.global_rank, self.training_args.output_dir, |
| - self.training_args.max_train_steps, self.optimizer, self.train_dataloader, self.lr_scheduler, |
| - self.noise_random_generator) |
| + if os.environ.get("FASTVIDEO_SKIP_FINAL_CHECKPOINT", "0") == "1": |
| + logger.info("Skipping final checkpoint because FASTVIDEO_SKIP_FINAL_CHECKPOINT=1") |
| + else: |
| + save_checkpoint(self.transformer, self.global_rank, self.training_args.output_dir, |
| + self.training_args.max_train_steps, self.optimizer, self.train_dataloader, |
| + self.lr_scheduler, self.noise_random_generator) |
| + self._save_ema_weights(self.training_args.max_train_steps) |
| |
| if envs.FASTVIDEO_TORCH_PROFILER_DIR: |
| logger.info("Stopping profiler...") |
|
|
