Datasets:
File size: 9,446 Bytes
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index b82c653..f76077b 100644
--- a/fastvideo/dataset/__init__.py
+++ b/fastvideo/dataset/__init__.py
@@ -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",
diff --git a/fastvideo/training/training_pipeline.py b/fastvideo/training/training_pipeline.py
index 575d6dc..140bb31 100644
--- a/fastvideo/training/training_pipeline.py
+++ b/fastvideo/training/training_pipeline.py
@@ -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...")
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