diff --git a/dfm/examples/Automodel/finetune/finetune.py b/dfm/examples/Automodel/finetune/finetune.py new file mode 100644 index 00000000..ae07451f --- /dev/null +++ b/dfm/examples/Automodel/finetune/finetune.py @@ -0,0 +1,29 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from Automodel.recipes.finetune import TrainWan21DiffusionRecipe +from nemo_automodel.components.config._arg_parser import parse_args_and_load_config + + +def main(default_config_path="/opt/DFM/dfm/examples/Automodel/finetune/wan2_1_t2v_flow.yaml"): + cfg = parse_args_and_load_config(default_config_path) + recipe = TrainWan21DiffusionRecipe(cfg) + recipe.setup() + recipe.run_train_validation_loop() + + +if __name__ == "__main__": + main() diff --git a/dfm/examples/Automodel/finetune/wan2_1_t2v_flow.yaml b/dfm/examples/Automodel/finetune/wan2_1_t2v_flow.yaml new file mode 100644 index 00000000..88e6bd83 --- /dev/null +++ b/dfm/examples/Automodel/finetune/wan2_1_t2v_flow.yaml @@ -0,0 +1,59 @@ +seed: 42 + +wandb: + project: wan-t2v-flow-matching + mode: online + name: wan2_1_t2v_fm_updated + +dist_env: + backend: nccl + timeout_minutes: 30 + +model: + pretrained_model_name_or_path: Wan-AI/Wan2.1-T2V-1.3B-Diffusers + +data: + dataloader: + _target_: Automodel.datasets.build_wan21_dataloader + meta_folder: /lustre/fsw/portfolios/coreai/users/linnanw/hdvilla_sample/pika/wan21_codes/1.3B_meta/ + batch_size: 1 + num_workers: 2 + device: cpu + +batch: + batch_size_per_node: 8 + +training: + num_epochs: 100 + +optim: + learning_rate: 5e-6 + optimizer: + weight_decay: 0.01 + betas: [0.9, 0.999] + +flow_matching: + use_sigma_noise: true + timestep_sampling: uniform + logit_mean: 0.0 + logit_std: 1.0 + flow_shift: 3.0 + mix_uniform_ratio: 0.1 + +fsdp: + tp_size: 1 + cp_size: 1 + pp_size: 1 + dp_replicate_size: 1 + dp_size: 8 + +logging: + save_every: 1000 + log_every: 2 + +checkpoint: + enabled: true + checkpoint_dir: /opt/DFM/wan_t2v_flow_outputs_base_recipe_fsdp_run_1/ + model_save_format: torch_save + save_consolidated: false + restore_from: null \ No newline at end of file diff --git a/dfm/examples/Automodel/finetune/wan2_1_t2v_flow_multinode.yaml b/dfm/examples/Automodel/finetune/wan2_1_t2v_flow_multinode.yaml new file mode 100644 index 00000000..6f637fa5 --- /dev/null +++ b/dfm/examples/Automodel/finetune/wan2_1_t2v_flow_multinode.yaml @@ -0,0 +1,59 @@ +seed: 42 + +wandb: + project: wan-t2v-flow-matching + mode: online + name: wan2_1_t2v_fm_updated + +dist_env: + backend: nccl + timeout_minutes: 30 + +model: + pretrained_model_name_or_path: Wan-AI/Wan2.1-T2V-1.3B-Diffusers + +data: + dataloader: + _target_: Automodel.datasets.build_wan21_dataloader + meta_folder: /lustre/fsw/portfolios/coreai/users/linnanw/hdvilla_sample/pika/wan21_codes/1.3B_meta/ + batch_size: 1 + num_workers: 2 + device: cpu + +batch: + batch_size_per_node: 8 + +training: + num_epochs: 100 + +optim: + learning_rate: 5e-6 + optimizer: + weight_decay: 0.01 + betas: [0.9, 0.999] + +flow_matching: + use_sigma_noise: true + timestep_sampling: uniform + logit_mean: 0.0 + logit_std: 1.0 + flow_shift: 3.0 + mix_uniform_ratio: 0.1 + +fsdp: + tp_size: 1 + cp_size: 1 + pp_size: 1 + dp_replicate_size: 2 + dp_size: 16 + +logging: + save_every: 1000 + log_every: 2 + +checkpoint: + enabled: true + checkpoint_dir: /opt/DFM/wan_t2v_flow_outputs_base_recipe_multi_node_fsdp_run_3/ + model_save_format: torch_save + save_consolidated: false + restore_from: null \ No newline at end of file diff --git a/dfm/examples/Automodel/generate/wan_generate.py b/dfm/examples/Automodel/generate/wan_generate.py new file mode 100644 index 00000000..2868ef9b --- /dev/null +++ b/dfm/examples/Automodel/generate/wan_generate.py @@ -0,0 +1,147 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import logging +import os + +import torch +import torch.distributed as dist +from Automodel._diffusers.auto_diffusion_pipeline import NeMoAutoDiffusionPipeline +from diffusers import AutoencoderKLWan +from diffusers.utils import export_to_video +from nemo_automodel.components.distributed.init_utils import initialize_distributed +from nemo_automodel.components.loggers.log_utils import setup_logging + + +def parse_args(): + parser = argparse.ArgumentParser(description="Wan2.2 T2V FSDP2 generation") + default_prompt = ( + "The video begins with a close-up of a white bowl filled with shredded coleslaw, " + "which has a mix of purple cabbage and white cabbage, and is garnished with a sprinkle " + "of seasoning. The coleslaw is placed on a wooden cutting board. As the video progresses, " + "the camera pans to the right, revealing a burger with a sesame seed bun, a beef patty, " + "melted yellow cheese, slices of red tomato, and crispy bacon." + ) + + parser.add_argument("--prompt", type=str, default=default_prompt, help="Text prompt for generation") + parser.add_argument("--height", type=int, default=480, help="Output video height") + parser.add_argument("--width", type=int, default=848, help="Output video width") + parser.add_argument("--num-frames", type=int, default=111, help="Number of frames to generate") + parser.add_argument("--guidance-scale", type=float, default=4.0, help="CFG scale for main guidance") + parser.add_argument("--guidance-scale-2", type=float, default=3.0, help="CFG scale for secondary guidance") + parser.add_argument("--num-inference-steps", type=int, default=20, help="Number of diffusion steps") + parser.add_argument("--fps", type=int, default=24, help="Frames per second for output video") + parser.add_argument("--output", type=str, default="t2v_fsdp2_rank0.mp4", help="Output video filename") + parser.add_argument("--seed", type=int, default=42, help="Base random seed (dp rank added)") + # Parallelism sizes + parser.add_argument( + "--tp-size", + type=int, + default=8, + help="Tensor-parallel group size", + ) + parser.add_argument( + "--cp-size", + type=int, + default=1, + help="Context-parallel group size", + ) + parser.add_argument( + "--pp-size", + type=int, + default=1, + help="Pipeline-parallel group size", + ) + parser.add_argument( + "--dp-size", + type=int, + default=1, + help="Data-parallel group size", + ) + return parser.parse_args() + + +def main(): + args = parse_args() + initialize_distributed(backend="nccl", timeout_minutes=10) + setup_logging() + local_rank = int(os.environ.get("LOCAL_RANK", 0)) + torch.cuda.set_device(local_rank) + world_size = dist.get_world_size() + device = torch.device("cuda", local_rank) + bf16 = torch.bfloat16 + + # Configuration for TP+CP+PP+DP + tp_size = args.tp_size + cp_size = args.cp_size + pp_size = args.pp_size + dp_size = args.dp_size + dp_rank = local_rank // (tp_size * cp_size * pp_size) + + # -------- Load pipeline -------- + logging.info("[Loading] Loading VAE and pipeline...") + vae = AutoencoderKLWan.from_pretrained( + "Wan-AI/Wan2.2-T2V-A14B-Diffusers", subfolder="vae", torch_dtype=torch.bfloat16 + ) + # Build per-component managers mapping + manager_args = { + "dp_size": dp_size, + "tp_size": tp_size, + "cp_size": cp_size, + "pp_size": pp_size, + "backend": "nccl", + "world_size": world_size, + "use_hf_tp_plan": False, + } + # Wan pipelines typically have components like: 'vae', 'text_encoder', 'image_encoder', 'transformer', 'transformer_2' + # Parallelize only the heavy transformer components + parallel_scheme = {} + for name in ("transformer", "transformer_2"): + parallel_scheme[name] = manager_args + + pipe, _ = NeMoAutoDiffusionPipeline.from_pretrained( + "Wan-AI/Wan2.2-T2V-A14B-Diffusers", vae=vae, torch_dtype=torch.bfloat16, parallel_scheme=parallel_scheme + ) + logging.info("[Setup] Pipeline loaded and parallelized via NeMoAutoDiffusionPipeline") + dist.barrier() + + # -------- Inference -------- + logging.info("[Inference] Starting distributed inference...") + torch.manual_seed(args.seed + dp_rank) + + with torch.no_grad(), torch.autocast(device_type="cuda", dtype=bf16): + out = pipe( + prompt=args.prompt, + height=args.height, + width=args.width, + num_frames=args.num_frames, + guidance_scale=args.guidance_scale, + guidance_scale_2=args.guidance_scale_2, + num_inference_steps=args.num_inference_steps, + ).frames[0] + + if dist.get_rank() == 0: + export_to_video(out, args.output, fps=args.fps) + logging.info(f"[Inference] Saved {args.output}") + + dist.barrier() + logging.info( + f"[Complete] Automodel FSDP2 inference completed! TP={tp_size}, CP={cp_size}, PP={pp_size}, DP={dp_size}" + ) + dist.destroy_process_group() + + +if __name__ == "__main__": + main() diff --git a/dfm/src/dtensor/README.md b/dfm/src/Automodel/README.md similarity index 75% rename from dfm/src/dtensor/README.md rename to dfm/src/Automodel/README.md index 5c018fce..ba04d747 100644 --- a/dfm/src/dtensor/README.md +++ b/dfm/src/Automodel/README.md @@ -1,4 +1,4 @@ -# DTensor Models +# Automodel Models implemented using Dtensors (NeMo Automodel) diff --git a/dfm/src/dtensor/__init__.py b/dfm/src/Automodel/__init__.py similarity index 100% rename from dfm/src/dtensor/__init__.py rename to dfm/src/Automodel/__init__.py diff --git a/dfm/src/Automodel/_diffusers/__init__.py b/dfm/src/Automodel/_diffusers/__init__.py new file mode 100644 index 00000000..45dbbf79 --- /dev/null +++ b/dfm/src/Automodel/_diffusers/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .auto_diffusion_pipeline import NeMoAutoDiffusionPipeline + + +__all__ = [ + "NeMoAutoDiffusionPipeline", +] diff --git a/dfm/src/Automodel/_diffusers/auto_diffusion_pipeline.py b/dfm/src/Automodel/_diffusers/auto_diffusion_pipeline.py new file mode 100644 index 00000000..d9e8c3ce --- /dev/null +++ b/dfm/src/Automodel/_diffusers/auto_diffusion_pipeline.py @@ -0,0 +1,156 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os +from typing import Any, Dict, Iterable, Optional, Tuple + +import torch +import torch.nn as nn +from Automodel.distributed.dfm_parallelizer import WanParallelizationStrategy +from diffusers import DiffusionPipeline +from nemo_automodel.components.distributed import parallelizer +from nemo_automodel.components.distributed.fsdp2 import FSDP2Manager +from nemo_automodel.shared.utils import dtype_from_str + + +logger = logging.getLogger(__name__) + + +def _init_parallelizer(): + parallelizer.PARALLELIZATION_STRATEGIES["WanTransformer3DModel"] = WanParallelizationStrategy() + + +def _choose_device(device: Optional[torch.device]) -> torch.device: + if device is not None: + return device + if torch.cuda.is_available(): + local_rank = int(os.environ.get("LOCAL_RANK", 0)) + return torch.device("cuda", local_rank) + return torch.device("cpu") + + +def _iter_pipeline_modules(pipe: DiffusionPipeline) -> Iterable[Tuple[str, nn.Module]]: + # Prefer Diffusers' components registry when available + if hasattr(pipe, "components") and isinstance(pipe.components, dict): + for name, value in pipe.components.items(): + if isinstance(value, nn.Module): + yield name, value + return + + # Fallback: inspect attributes + for name in dir(pipe): + if name.startswith("_"): + continue + try: + value = getattr(pipe, name) + except Exception: + continue + if isinstance(value, nn.Module): + yield name, value + + +def _move_module_to_device(module: nn.Module, device: torch.device, torch_dtype: Any) -> None: + # torch_dtype can be "auto", torch.dtype, or string + dtype: Optional[torch.dtype] + if torch_dtype == "auto": + dtype = None + else: + dtype = dtype_from_str(torch_dtype) if isinstance(torch_dtype, str) else torch_dtype + if dtype is not None: + module.to(device=device, dtype=dtype) + else: + module.to(device=device) + + +def _ensure_params_trainable(module: nn.Module, module_name: Optional[str] = None) -> int: + """ + Ensure that all parameters in the given module are trainable. + + Returns the number of parameters marked trainable. If a module name is + provided, it will be used in the log message for clarity. + """ + num_trainable_parameters = 0 + for parameter in module.parameters(): + parameter.requires_grad = True + num_trainable_parameters += parameter.numel() + if module_name is None: + module_name = module.__class__.__name__ + logger.info("[Trainable] %s: %s parameters set requires_grad=True", module_name, f"{num_trainable_parameters:,}") + return num_trainable_parameters + + +class NeMoAutoDiffusionPipeline(DiffusionPipeline): + """ + Drop-in Diffusers pipeline that adds optional FSDP2/TP parallelization during from_pretrained. + + Features: + - Accepts a per-component mapping from component name to FSDP2Manager init args + - Moves all nn.Module components to the chosen device/dtype + - Parallelizes only components present in the mapping by constructing a manager per component + + parallel_scheme: + - Dict[str, Dict[str, Any]]: component name -> kwargs for FSDP2Manager(...) + """ + + @classmethod + def from_pretrained( + cls, + pretrained_model_name_or_path: str, + *model_args, + parallel_scheme: Optional[Dict[str, Dict[str, Any]]] = None, + device: Optional[torch.device] = None, + torch_dtype: Any = "auto", + move_to_device: bool = True, + load_for_training: bool = False, + components_to_load: Optional[Iterable[str]] = None, + **kwargs, + ) -> tuple[DiffusionPipeline, Dict[str, FSDP2Manager]]: + pipe: DiffusionPipeline = DiffusionPipeline.from_pretrained( + pretrained_model_name_or_path, + *model_args, + torch_dtype=torch_dtype, + **kwargs, + ) + # Decide device + dev = _choose_device(device) + + # Move modules to device/dtype first (helps avoid initial OOM during sharding) + if move_to_device: + for name, module in _iter_pipeline_modules(pipe): + if not components_to_load or name in components_to_load: + logger.info("[INFO] Moving module: %s to device/dtype", name) + _move_module_to_device(module, dev, torch_dtype) + + # If loading for training, ensure the target module parameters are trainable + if load_for_training: + for name, module in _iter_pipeline_modules(pipe): + if not components_to_load or name in components_to_load: + logger.info("[INFO] Ensuring params trainable: %s", name) + _ensure_params_trainable(module, module_name=name) + + # Use per-component FSDP2Manager init-args to parallelize components + created_managers: Dict[str, FSDP2Manager] = {} + if parallel_scheme is not None: + assert torch.distributed.is_initialized(), "Expect distributed environment to be initialized" + _init_parallelizer() + for comp_name, comp_module in _iter_pipeline_modules(pipe): + manager_args = parallel_scheme.get(comp_name) + if manager_args is None: + continue + manager = FSDP2Manager(**manager_args) + created_managers[comp_name] = manager + parallel_module = manager.parallelize(comp_module) + setattr(pipe, comp_name, parallel_module) + return pipe, created_managers diff --git a/dfm/src/Automodel/datasets/__init__.py b/dfm/src/Automodel/datasets/__init__.py new file mode 100644 index 00000000..a3ef8358 --- /dev/null +++ b/dfm/src/Automodel/datasets/__init__.py @@ -0,0 +1,30 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from Automodel.datasets.wan21 import ( + MetaFilesDataset, + build_node_parallel_sampler, + build_wan21_dataloader, + collate_fn, + create_dataloader, +) + + +__all__ = [ + "MetaFilesDataset", + "build_node_parallel_sampler", + "build_wan21_dataloader", + "collate_fn", + "create_dataloader", +] diff --git a/dfm/src/Automodel/datasets/wan21.py b/dfm/src/Automodel/datasets/wan21.py new file mode 100644 index 00000000..588da579 --- /dev/null +++ b/dfm/src/Automodel/datasets/wan21.py @@ -0,0 +1,210 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import logging +import os +import pickle +from pathlib import Path +from typing import Callable, Dict, List, Optional, Tuple + +import torch +import torch.distributed as dist +from torch.utils.data import DataLoader, Dataset, DistributedSampler + + +logger = logging.getLogger(__name__) + + +class MetaFilesDataset(Dataset): + """PyTorch dataset for WAN2.1 `.meta` files.""" + + def __init__( + self, + meta_folder: str, + transform_text: Optional[Callable[[torch.Tensor], torch.Tensor]] = None, + transform_video: Optional[Callable[[torch.Tensor], torch.Tensor]] = None, + filter_fn: Optional[Callable[[Dict], bool]] = None, + device: str = "cpu", + max_files: Optional[int] = None, + ) -> None: + self.meta_folder = Path(meta_folder) + self.transform_text = transform_text + self.transform_video = transform_video + self.filter_fn = filter_fn + self.device = device + + self.meta_files = sorted(self.meta_folder.glob("*.meta")) + if max_files is None: + max_files_env = os.environ.get("MAX_META_FILES") + if max_files_env is not None: + try: + max_files = int(max_files_env) + except ValueError: + logger.warning("Invalid MAX_META_FILES=%s", max_files_env) + + if max_files is not None and max_files > 0: + self.meta_files = self.meta_files[:max_files] + logger.info("Limited to first %d meta files", len(self.meta_files)) + + if not self.meta_files: + raise ValueError(f"No .meta files found in {meta_folder}") + + if self.filter_fn: + filtered = [] + for path in self.meta_files: + try: + with open(path, "rb") as f: + data = pickle.load(f) + except Exception as exc: # pragma: no cover - best effort logging + logger.warning("Failed to load %s during filtering: %s", path, exc) + continue + if self.filter_fn(data.get("metadata", {})): + filtered.append(path) + self.meta_files = filtered + logger.info("Filtered meta files count: %d", len(self.meta_files)) + + self._log_dataset_stats() + + def _log_dataset_stats(self) -> None: + sample_paths = self.meta_files[: min(5, len(self.meta_files))] + stats: List[Tuple[torch.Size, torch.Size, str]] = [] + for path in sample_paths: + try: + with open(path, "rb") as f: + data = pickle.load(f) + stats.append( + ( + data["text_embeddings"].shape, + data["video_latents"].shape, + str(data.get("deterministic_latents", "unknown")), + ) + ) + except Exception as exc: # pragma: no cover - stats only + logger.debug("Failed to sample %s: %s", path, exc) + + if stats: + text_shapes, video_shapes, modes = zip(*stats, strict=False) + logger.info("Sample text embeddings: %s", text_shapes) + logger.info("Sample video latents: %s", video_shapes) + logger.info("Sample encoding modes: %s", set(modes)) + + def __len__(self) -> int: + return len(self.meta_files) + + def __getitem__(self, index: int) -> Dict[str, torch.Tensor]: # type: ignore[override] + path = self.meta_files[index] + with open(path, "rb") as f: + data = pickle.load(f) + + text_embeddings: torch.Tensor = data["text_embeddings"].to(self.device) + video_latents: torch.Tensor = data["video_latents"].to(self.device) + + if self.transform_text is not None: + text_embeddings = self.transform_text(text_embeddings) + if self.transform_video is not None: + video_latents = self.transform_video(video_latents) + + file_info = { + "meta_filename": Path(path).name, + "original_filename": data.get("original_filename", "unknown"), + "original_video_path": data.get("original_video_path", "unknown"), + "deterministic_latents": data.get("deterministic_latents", "unknown"), + "memory_optimization": data.get("memory_optimization", "unknown"), + "num_frames": data.get("num_frames", "unknown"), + } + + return { + "text_embeddings": text_embeddings, + "video_latents": video_latents, + "metadata": data.get("metadata", {}), + "file_info": file_info, + } + + +def collate_fn(batch: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]: + text_embeddings = torch.stack([item["text_embeddings"] for item in batch]) + video_latents = torch.stack([item["video_latents"] for item in batch]) + return { + "text_embeddings": text_embeddings, + "video_latents": video_latents, + "metadata": [item["metadata"] for item in batch], + "file_info": [item["file_info"] for item in batch], + } + + +def build_node_parallel_sampler( + dataset: "Dataset", + dp_rank: int, + dp_world_size: int, + shuffle: bool = True, +) -> Optional["DistributedSampler"]: + if not dist.is_initialized(): + return None + + return DistributedSampler( + dataset, + num_replicas=dp_world_size, + rank=dp_rank, + shuffle=shuffle, + drop_last=False, + ) + + +def build_wan21_dataloader( + *, + meta_folder: str, + batch_size: int, + dp_rank: int, + dp_world_size: int, + shuffle: bool = True, + num_workers: int = 2, + device: str = "cpu", + transform_text: Optional[Callable[[torch.Tensor], torch.Tensor]] = None, + transform_video: Optional[Callable[[torch.Tensor], torch.Tensor]] = None, + filter_fn: Optional[Callable[[Dict], bool]] = None, + max_files: Optional[int] = None, +) -> Tuple[DataLoader, Optional[DistributedSampler]]: + dataset = MetaFilesDataset( + meta_folder=meta_folder, + transform_text=transform_text, + transform_video=transform_video, + filter_fn=filter_fn, + device=device, + max_files=max_files, + ) + + sampler = build_node_parallel_sampler(dataset, dp_rank, dp_world_size, shuffle=shuffle) + + use_pin_memory = device == "cpu" + dataloader = DataLoader( + dataset, + batch_size=batch_size, + shuffle=(sampler is None and shuffle), + sampler=sampler, + num_workers=num_workers, + collate_fn=collate_fn, + pin_memory=use_pin_memory, + ) + + return dataloader, sampler + + +def create_dataloader( + meta_folder: str, + batch_size: int, + num_nodes: int, +) -> Tuple[DataLoader, Optional[DistributedSampler]]: + return build_wan21_dataloader(meta_folder=meta_folder, batch_size=batch_size, num_nodes=num_nodes) diff --git a/dfm/src/Automodel/distributed/dfm_parallelizer.py b/dfm/src/Automodel/distributed/dfm_parallelizer.py new file mode 100644 index 00000000..a2e8a3e5 --- /dev/null +++ b/dfm/src/Automodel/distributed/dfm_parallelizer.py @@ -0,0 +1,148 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +from typing import Dict, Optional, Union + +import torch +from nemo_automodel.components.distributed.parallelizer import ( + ParallelizationStrategy, + apply_fsdp2_sharding_recursively, +) +from torch import nn +from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( + checkpoint_wrapper, +) +from torch.distributed.device_mesh import DeviceMesh +from torch.distributed.fsdp import ( + MixedPrecisionPolicy, + OffloadPolicy, + fully_shard, +) +from torch.distributed.tensor.parallel import ( + ColwiseParallel, + ParallelStyle, + RowwiseParallel, + parallelize_module, +) + + +logger = logging.getLogger(__name__) + + +class WanParallelizationStrategy(ParallelizationStrategy): + """Parallelization strategy for Wan-style transformer modules used in Diffusers. + Applies TP to condition embedders, FFN projections in each block, and final projection, + then applies FSDP sharding similarly to other strategies. + """ + + def parallelize( + self, + model: nn.Module, + device_mesh: DeviceMesh, + mp_policy: Optional[MixedPrecisionPolicy] = None, + offload_policy: Optional[OffloadPolicy] = None, + sequence_parallel: bool = False, + activation_checkpointing: bool = False, + tp_shard_plan: Optional[Union[Dict[str, ParallelStyle], str]] = None, + dp_replicate_mesh_name: str = "dp_replicate", + dp_shard_cp_mesh_name: str = "dp_shard_cp", + tp_mesh_name: str = "tp", + ) -> nn.Module: + # Not using custom tp_shard_plan; apply Wan-specific plan + tp_mesh = device_mesh[tp_mesh_name] + dp_mesh_dim_names = (dp_replicate_mesh_name, dp_shard_cp_mesh_name) + dp_mesh = device_mesh[dp_mesh_dim_names] + + # Apply TP only when TP group size > 1 + if tp_mesh.size() > 1: + # Condition embedders if present + try: + if hasattr(model, "condition_embedder"): + cond = model.condition_embedder + if hasattr(cond, "text_embedder"): + cond.text_embedder = parallelize_module( + cond.text_embedder, + tp_mesh, + { + "linear_1": ColwiseParallel(), + "linear_2": RowwiseParallel(), + }, + ) + if hasattr(cond, "time_embedder"): + cond.time_embedder = parallelize_module( + cond.time_embedder, + tp_mesh, + { + "linear_1": ColwiseParallel(), + "linear_2": RowwiseParallel(), + }, + ) + if hasattr(cond, "time_proj"): + cond.time_proj = parallelize_module( + cond.time_proj, + tp_mesh, + {"": ColwiseParallel()}, + ) + except Exception as e: + logger.warning(f"Wan strategy: failed to TP condition embedders: {e}") + + # Blocks FFN and final projection + try: + if hasattr(model, "blocks"): + for block in model.blocks: + if hasattr(block, "ffn"): + block.ffn = parallelize_module( + block.ffn, + tp_mesh, + { + "net.0.proj": ColwiseParallel(), + "net.2": RowwiseParallel(), + }, + ) + if hasattr(model, "proj_out"): + model.proj_out = parallelize_module(model.proj_out, tp_mesh, {"": RowwiseParallel()}) + except Exception as e: + logger.warning(f"Wan strategy: failed to TP blocks/proj_out: {e}") + + # Mixed precision default like Default strategy + if not mp_policy: + mp_policy = MixedPrecisionPolicy( + param_dtype=torch.bfloat16, + reduce_dtype=torch.float32, + output_dtype=torch.float32, + ) + # Apply activation checkpointing to transformer blocks if requested + if activation_checkpointing: + try: + if hasattr(model, "blocks") and isinstance(model.blocks, nn.ModuleList): + for idx, blk in enumerate(model.blocks): + model.blocks[idx] = checkpoint_wrapper(blk) + elif hasattr(model, "blocks"): + # Fallback if blocks is an iterable but not ModuleList + for idx, _ in enumerate(list(model.blocks)): + model.blocks[idx] = checkpoint_wrapper(model.blocks[idx]) + except Exception as e: + logger.warning(f"Wan strategy: failed to apply activation checkpointing: {e}") + + # Apply FSDP sharding recursively and to root + apply_fsdp2_sharding_recursively(model, dp_mesh, mp_policy, offload_policy) + + return fully_shard( + model, + mesh=dp_mesh, + mp_policy=mp_policy, + offload_policy=offload_policy, + reshard_after_forward=False, + ) diff --git a/dfm/src/Automodel/flow_matching/__init__.py b/dfm/src/Automodel/flow_matching/__init__.py new file mode 100644 index 00000000..5187fe3a --- /dev/null +++ b/dfm/src/Automodel/flow_matching/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +__all__ = [ + "training_step_t2v", + "time_shift_utils", +] diff --git a/dfm/src/Automodel/flow_matching/time_shift_utils.py b/dfm/src/Automodel/flow_matching/time_shift_utils.py new file mode 100644 index 00000000..d1786dcf --- /dev/null +++ b/dfm/src/Automodel/flow_matching/time_shift_utils.py @@ -0,0 +1,118 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import math + +import numpy as np +import torch + + +def time_shift( + t: torch.Tensor, + image_seq_len: int, + shift_type: str = "constant", + base_shift: float = 0.5, + max_shift: float = 1.15, + constant: float = 3.0, +): + """ + Convert timesteps to sigmas with sequence-length-aware shifting. + + Args: + t: timesteps in range [0, 1] + image_seq_len: number of tokens (frames * height * width / patch_size^2) + shift_type: "linear", "sqrt", or "constant" + base_shift: base shift for linear mode + max_shift: max shift for linear mode + constant: shift value for constant mode (default 3.0 matches Pika) + + Returns: + sigma values for noise scheduling + """ + if shift_type == "linear": + # Linear interpolation based on sequence length + mu = base_shift + (max_shift - base_shift) * (image_seq_len / 4096) + return math.exp(mu) / (math.exp(mu) + (1 / t - 1)) + + elif shift_type == "sqrt": + # Square root scaling (Flux-style) + # Assuming 128x128 latent space (1024x1024 image) gives mu=3 + mu = np.maximum(1.0, np.sqrt(image_seq_len / (128.0 * 128.0)) * 3.0) + return mu / (mu + (1 / t - 1)) + + elif shift_type == "constant": + # Constant shift (Pika default) + return constant / (constant + (1 / t - 1)) + + else: + # No shift, return original t + return t + + +def compute_density_for_timestep_sampling( + weighting_scheme: str, + batch_size: int, + logit_mean: float = 0.0, + logit_std: float = 1.0, + mode_scale: float = 1.29, +): + """ + Sample timesteps from different distributions for better training coverage. + + Args: + weighting_scheme: "uniform", "logit_normal", or "mode" + batch_size: number of samples to generate + logit_mean: mean for logit-normal distribution + logit_std: std for logit-normal distribution + mode_scale: scale for mode-based sampling + + Returns: + Tensor of shape (batch_size,) with values in [0, 1] + """ + if weighting_scheme == "logit_normal": + # SD3-style logit-normal sampling + u = torch.normal(mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu") + u = torch.nn.functional.sigmoid(u) + + elif weighting_scheme == "mode": + # Mode-based sampling (concentrates around certain timesteps) + u = torch.rand(size=(batch_size,), device="cpu") + u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u) + + else: + # Uniform sampling (default) + u = torch.rand(size=(batch_size,), device="cpu") + + return u + + +def get_flow_match_loss_weight(sigma: torch.Tensor, shift: float = 3.0): + """ + Compute loss weights for flow matching based on sigma values. + + Higher sigma (more noise) typically gets higher weight. + + Args: + sigma: sigma values in range [0, 1] + shift: weight scaling factor + + Returns: + Loss weights with same shape as sigma + """ + # Flow matching weight: weight = 1 + shift * sigma + # This gives more weight to noisier timesteps + weight = 1.0 + shift * sigma + return weight diff --git a/dfm/src/Automodel/flow_matching/training_step_t2v.py b/dfm/src/Automodel/flow_matching/training_step_t2v.py new file mode 100644 index 00000000..0e7b9bc0 --- /dev/null +++ b/dfm/src/Automodel/flow_matching/training_step_t2v.py @@ -0,0 +1,285 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import logging +import os +from typing import Dict, Tuple + +import torch +from Automodel.flow_matching.time_shift_utils import ( + compute_density_for_timestep_sampling, +) + + +logger = logging.getLogger(__name__) + + +def step_fsdp_transformer_t2v( + pipe, + model_map: Dict, + batch, + device, + bf16, + # Flow matching parameters + use_sigma_noise: bool = True, + timestep_sampling: str = "uniform", + logit_mean: float = 0.0, + logit_std: float = 1.0, + flow_shift: float = 3.0, + mix_uniform_ratio: float = 0.1, + global_step: int = 0, +) -> Tuple[torch.Tensor, Dict]: + """ + Pure flow matching training - DO NOT use scheduler.add_noise(). + + The scheduler's add_noise() uses alpha_t/sigma_t which explodes at low timesteps. + We use simple flow matching: x_t = (1-σ)x_0 + σ*ε + """ + debug_mode = os.environ.get("DEBUG_TRAINING", "0") == "1" + detailed_log = global_step % 100 == 0 + summary_log = global_step % 10 == 0 + + # Extract and prepare batch data + video_latents = batch["video_latents"].to(device, dtype=bf16) + text_embeddings = batch["text_embeddings"].to(device, dtype=bf16) + + # Handle tensor shapes + while video_latents.ndim > 5: + video_latents = video_latents.squeeze(0) + if video_latents.ndim == 4: + video_latents = video_latents.unsqueeze(0) + + while text_embeddings.ndim > 3: + text_embeddings = text_embeddings.squeeze(0) + if text_embeddings.ndim == 2: + text_embeddings = text_embeddings.unsqueeze(0) + + batch_size = video_latents.shape[0] + _, channels, frames, height, width = video_latents.shape + + # ======================================================================== + # Flow Matching Timestep Sampling + # ======================================================================== + + num_train_timesteps = pipe.scheduler.config.num_train_timesteps + + if use_sigma_noise: + use_uniform = torch.rand(1).item() < mix_uniform_ratio + + if use_uniform or timestep_sampling == "uniform": + # Pure uniform: u ~ U(0, 1) + u = torch.rand(size=(batch_size,), device=device) + sampling_method = "uniform" + else: + # Density-based sampling + u = compute_density_for_timestep_sampling( + weighting_scheme=timestep_sampling, + batch_size=batch_size, + logit_mean=logit_mean, + logit_std=logit_std, + ).to(device) + sampling_method = timestep_sampling + + # Apply flow shift: σ = shift/(shift + (1/u - 1)) + u_clamped = torch.clamp(u, min=1e-5) # Avoid division by zero + sigma = flow_shift / (flow_shift + (1.0 / u_clamped - 1.0)) + sigma = torch.clamp(sigma, 0.0, 1.0) + + else: + # Simple uniform without shift + u = torch.rand(size=(batch_size,), device=device) + sigma = u + sampling_method = "uniform_no_shift" + + # ======================================================================== + # Manual Flow Matching Noise Addition + # ======================================================================== + + # Generate noise + noise = torch.randn_like(video_latents, dtype=torch.float32) + + # CRITICAL: Manual flow matching (NOT scheduler.add_noise!) + # x_t = (1 - σ) * x_0 + σ * ε + sigma_reshaped = sigma.view(-1, 1, 1, 1, 1) + noisy_latents = (1.0 - sigma_reshaped) * video_latents.float() + sigma_reshaped * noise + + # Timesteps for model [0, 1000] + timesteps = sigma * num_train_timesteps + + # ==================================================================== + # DETAILED LOGGING + # ==================================================================== + if detailed_log or debug_mode: + logger.info("\n" + "=" * 80) + logger.info(f"[STEP {global_step}] MANUAL FLOW MATCHING") + logger.info("=" * 80) + logger.info("[WARNING] NOT using scheduler.add_noise() - it explodes!") + logger.info("[INFO] Using manual: x_t = (1-σ)x_0 + σ*ε") + logger.info("") + logger.info(f"[SAMPLING] Method: {sampling_method}") + logger.info(f"[FLOW] Shift: {flow_shift}") + logger.info(f"[BATCH] Size: {batch_size}") + logger.info("") + logger.info(f"[U] Range: [{u.min():.4f}, {u.max():.4f}]") + if u.numel() > 1: + logger.info(f"[U] Mean: {u.mean():.4f}, Std: {u.std():.4f}") + else: + logger.info(f"[U] Value: {u.item():.4f}") + logger.info("") + logger.info(f"[SIGMA] Range: [{sigma.min():.4f}, {sigma.max():.4f}]") + if sigma.numel() > 1: + logger.info(f"[SIGMA] Mean: {sigma.mean():.4f}, Std: {sigma.std():.4f}") + else: + logger.info(f"[SIGMA] Value: {sigma.item():.4f}") + logger.info("") + logger.info(f"[TIMESTEPS] Range: [{timesteps.min():.2f}, {timesteps.max():.2f}]") + logger.info("") + logger.info(f"[WEIGHTS] Clean: {(1 - sigma_reshaped).squeeze().cpu().numpy()}") + logger.info(f"[WEIGHTS] Noise: {sigma_reshaped.squeeze().cpu().numpy()}") + logger.info("") + logger.info(f"[RANGES] Clean latents: [{video_latents.min():.4f}, {video_latents.max():.4f}]") + logger.info(f"[RANGES] Noise: [{noise.min():.4f}, {noise.max():.4f}]") + logger.info(f"[RANGES] Noisy latents: [{noisy_latents.min():.4f}, {noisy_latents.max():.4f}]") + + # Sanity check + max_expected = ( + max( + abs(video_latents.max().item()), + abs(video_latents.min().item()), + abs(noise.max().item()), + abs(noise.min().item()), + ) + * 1.5 + ) + if abs(noisy_latents.max()) > max_expected or abs(noisy_latents.min()) > max_expected: + logger.info(f"\n⚠️ WARNING: Noisy range seems large! Expected ~{max_expected:.1f}") + else: + logger.info("\n✓ Noisy latents range is reasonable") + logger.info("=" * 80 + "\n") + + elif summary_log: + logger.info( + f"[STEP {global_step}] σ=[{sigma.min():.3f},{sigma.max():.3f}] | " + f"t=[{timesteps.min():.1f},{timesteps.max():.1f}] | " + f"noisy=[{noisy_latents.min():.1f},{noisy_latents.max():.1f}] | " + f"{sampling_method}" + ) + + # Convert to bf16 + noisy_latents = noisy_latents.to(bf16) + timesteps_for_model = timesteps.to(bf16) + + # ======================================================================== + # Forward Pass + # ======================================================================== + + fsdp_model = model_map["transformer"]["fsdp_transformer"] + + try: + model_pred = fsdp_model( + hidden_states=noisy_latents, + timestep=timesteps_for_model, + encoder_hidden_states=text_embeddings, + return_dict=False, + ) + + if isinstance(model_pred, tuple): + model_pred = model_pred[0] + + except Exception as e: + logger.info(f"[ERROR] Forward pass failed: {e}") + logger.info( + f"[DEBUG] noisy_latents: {noisy_latents.shape}, range: [{noisy_latents.min()}, {noisy_latents.max()}]" + ) + logger.info( + f"[DEBUG] timesteps: {timesteps_for_model.shape}, range: [{timesteps_for_model.min()}, {timesteps_for_model.max()}]" + ) + raise + + # ======================================================================== + # Target: Flow Matching Velocity + # ======================================================================== + + # Flow matching target: v = ε - x_0 + target = noise - video_latents.float() + + # ======================================================================== + # Loss with Flow Weighting + # ======================================================================== + + loss = torch.nn.functional.mse_loss(model_pred.float(), target.float(), reduction="none") + + # Flow weight: w = 1 + shift * σ + loss_weight = 1.0 + flow_shift * sigma + loss_weight = loss_weight.view(-1, 1, 1, 1, 1).to(device) + + unweighted_loss = loss.mean() + weighted_loss = (loss * loss_weight).mean() + + # Safety check + if torch.isnan(weighted_loss) or weighted_loss > 100: + logger.info(f"[ERROR] Loss explosion! Loss={weighted_loss.item():.3f}") + logger.info("[DEBUG] Stopping training - check hyperparameters") + raise ValueError(f"Loss exploded: {weighted_loss.item()}") + + # ==================================================================== + # LOSS LOGGING + # ==================================================================== + if detailed_log or debug_mode: + logger.info("=" * 80) + logger.info(f"[STEP {global_step}] LOSS DEBUG") + logger.info("=" * 80) + logger.info("[TARGET] Flow matching: v = ε - x_0") + logger.info(f"[PREDICTION] Scheduler type (inference only): {type(pipe.scheduler).__name__}") + logger.info("") + logger.info(f"[RANGES] Model pred: [{model_pred.min():.4f}, {model_pred.max():.4f}]") + logger.info(f"[RANGES] Target (v): [{target.min():.4f}, {target.max():.4f}]") + logger.info("") + logger.info(f"[WEIGHTS] Formula: 1 + {flow_shift} * σ") + logger.info(f"[WEIGHTS] Range: [{loss_weight.min():.4f}, {loss_weight.max():.4f}]") + if loss_weight.numel() > 1: + logger.info(f"[WEIGHTS] Mean: {loss_weight.mean():.4f}") + else: + logger.info(f"[WEIGHTS] Value: {loss_weight.mean():.4f}") + logger.info("") + logger.info(f"[LOSS] Unweighted: {unweighted_loss.item():.6f}") + logger.info(f"[LOSS] Weighted: {weighted_loss.item():.6f}") + logger.info(f"[LOSS] Impact: {(weighted_loss / max(unweighted_loss, 1e-8)):.3f}x") + logger.info("=" * 80 + "\n") + + elif summary_log: + logger.info( + f"[STEP {global_step}] Loss: {weighted_loss.item():.6f} | " + f"w=[{loss_weight.min():.2f},{loss_weight.max():.2f}]" + ) + + # Metrics + metrics = { + "loss": weighted_loss.item(), + "unweighted_loss": unweighted_loss.item(), + "sigma_min": sigma.min().item(), + "sigma_max": sigma.max().item(), + "sigma_mean": sigma.mean().item(), + "weight_min": loss_weight.min().item(), + "weight_max": loss_weight.max().item(), + "timestep_min": timesteps.min().item(), + "timestep_max": timesteps.max().item(), + "noisy_min": noisy_latents.min().item(), + "noisy_max": noisy_latents.max().item(), + "sampling_method": sampling_method, + } + + return weighted_loss, metrics diff --git a/dfm/src/Automodel/recipes/finetune.py b/dfm/src/Automodel/recipes/finetune.py new file mode 100644 index 00000000..83c60d75 --- /dev/null +++ b/dfm/src/Automodel/recipes/finetune.py @@ -0,0 +1,431 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import logging +import os +from math import ceil +from typing import Any, Dict, Optional + +import torch +import torch.distributed as dist +import wandb +from Automodel._diffusers.auto_diffusion_pipeline import NeMoAutoDiffusionPipeline +from Automodel.flow_matching.training_step_t2v import ( + step_fsdp_transformer_t2v, +) +from nemo_automodel.components.checkpoint.checkpointing import Checkpointer, CheckpointingConfig +from nemo_automodel.components.loggers.log_utils import setup_logging +from nemo_automodel.components.loggers.wandb_utils import suppress_wandb_log_messages +from nemo_automodel.components.training.rng import StatefulRNG +from nemo_automodel.components.training.step_scheduler import StepScheduler +from nemo_automodel.recipes.base_recipe import BaseRecipe +from nemo_automodel.recipes.llm.train_ft import build_distributed, build_wandb +from torch.distributed.fsdp import MixedPrecisionPolicy +from transformers.utils.hub import TRANSFORMERS_CACHE + + +def build_model_and_optimizer( + *, + model_id: str, + learning_rate: float, + device: torch.device, + bf16_dtype: torch.dtype, + cpu_offload: bool = False, + tp_size: int = 1, + cp_size: int = 1, + pp_size: int = 1, + dp_size: Optional[int] = None, + dp_replicate_size: Optional[int] = None, + use_hf_tp_plan: bool = False, + optimizer_cfg: Optional[Dict[str, Any]] = None, +) -> tuple[NeMoAutoDiffusionPipeline, dict[str, Dict[str, Any]], torch.optim.Optimizer, Any]: + """Build the WAN 2.1 diffusion model, parallel scheme, and optimizer.""" + + logging.info("[INFO] Building NeMoAutoDiffusionPipeline with transformer parallel scheme...") + + if not dist.is_initialized(): + logging.info("[WARN] torch.distributed not initialized; proceeding in single-process mode") + + world_size = dist.get_world_size() if dist.is_initialized() else 1 + + if dp_size is None: + denom = max(1, tp_size * cp_size * pp_size) + dp_size = max(1, world_size // denom) + + manager_args: Dict[str, Any] = { + "dp_size": dp_size, + "dp_replicate_size": dp_replicate_size, + "tp_size": tp_size, + "cp_size": cp_size, + "pp_size": pp_size, + "backend": "nccl", + "world_size": world_size, + "use_hf_tp_plan": use_hf_tp_plan, + "activation_checkpointing": True, + "mp_policy": MixedPrecisionPolicy( + param_dtype=bf16_dtype, + reduce_dtype=bf16_dtype, + output_dtype=bf16_dtype, + ), + } + + parallel_scheme = {"transformer": manager_args} + + pipe, created_managers = NeMoAutoDiffusionPipeline.from_pretrained( + model_id, + torch_dtype=bf16_dtype, + device=device, + parallel_scheme=parallel_scheme, + load_for_training=True, + components_to_load=["transformer"], + ) + fsdp2_manager = created_managers["transformer"] + transformer_module = getattr(pipe, "transformer", None) + if transformer_module is None: + raise RuntimeError("transformer not found in pipeline after parallelization") + + model_map: dict[str, Dict[str, Any]] = {"transformer": {"fsdp_transformer": transformer_module}} + + trainable_params = [p for p in transformer_module.parameters() if p.requires_grad] + if not trainable_params: + raise RuntimeError("No trainable parameters found in transformer module!") + + optimizer_cfg = optimizer_cfg or {} + weight_decay = optimizer_cfg.get("weight_decay", 0.01) + betas = optimizer_cfg.get("betas", (0.9, 0.999)) + optimizer = torch.optim.AdamW(trainable_params, lr=learning_rate, weight_decay=weight_decay, betas=betas) + + logging.info("[INFO] Optimizer config: lr=%s, weight_decay=%s, betas=%s", learning_rate, weight_decay, betas) + + trainable_count = sum(1 for p in transformer_module.parameters() if p.requires_grad) + frozen_count = sum(1 for p in transformer_module.parameters() if not p.requires_grad) + logging.info(f"[INFO] Trainable parameters: {trainable_count}, Frozen parameters: {frozen_count}") + + if torch.cuda.is_available(): + memory_allocated = torch.cuda.memory_allocated(device) / 1024**3 + memory_reserved = torch.cuda.memory_reserved(device) / 1024**3 + logging.info(f"[INFO] GPU memory: {memory_allocated:.2f}GB allocated, {memory_reserved:.2f}GB reserved") + + logging.info("[INFO] NeMoAutoDiffusion setup complete (pipeline + optimizer)") + + return pipe, model_map, optimizer, fsdp2_manager.device_mesh + + +def build_lr_scheduler( + optimizer: torch.optim.Optimizer, + *, + num_epochs: int, + steps_per_epoch: int, + eta_min: float = 1e-6, +) -> torch.optim.lr_scheduler.CosineAnnealingLR: + """Build the cosine annealing learning rate scheduler.""" + + total_steps = max(1, num_epochs * max(1, steps_per_epoch)) + logging.info(f"[INFO] Scheduler configured for {total_steps} total steps") + return torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, + T_max=total_steps, + eta_min=eta_min, + ) + + +def is_main_process(): + return (not dist.is_initialized()) or dist.get_rank() == 0 + + +class TrainWan21DiffusionRecipe(BaseRecipe): + """Config-driven wrapper around WAN 2.1 T2V training.""" + + def __init__(self, cfg): + self.cfg = cfg + + def setup(self): + self.dist_env = build_distributed(self.cfg.get("dist_env", {})) + setup_logging() + + if self.dist_env.is_main and hasattr(self.cfg, "wandb"): + suppress_wandb_log_messages() + run = build_wandb(self.cfg) + if run is not None: + logging.info("🚀 View run at {}".format(run.url)) + + self.seed = self.cfg.get("seed", 42) + self.rng = StatefulRNG(seed=self.seed, ranked=True) + + self.model_id = self.cfg.get("model.pretrained_model_name_or_path", "Wan-AI/Wan2.1-T2V-1.3B-Diffusers") + self.learning_rate = self.cfg.get("optim.learning_rate", 5e-6) + self.bf16 = torch.bfloat16 + + self.local_rank = int(os.environ.get("LOCAL_RANK", 0)) + self.world_size = dist.get_world_size() if dist.is_initialized() else 1 + if torch.cuda.is_available(): + self.device = torch.device("cuda", self.local_rank) + else: + self.device = torch.device("cpu") + + self.local_world_size = int(os.environ.get("LOCAL_WORLD_SIZE", self.world_size)) + self.local_world_size = max(self.local_world_size, 1) + self.num_nodes = max(1, self.world_size // self.local_world_size) + self.node_rank = dist.get_rank() // self.local_world_size if dist.is_initialized() else 0 + + logging.info("[INFO] WAN 2.1 T2V Trainer with Flow Matching") + logging.info( + f"[INFO] Total GPUs: {self.world_size}, GPUs per node: {self.local_world_size}, Num nodes: {self.num_nodes}" + ) + logging.info(f"[INFO] Node rank: {self.node_rank}, Local rank: {self.local_rank}") + logging.info(f"[INFO] Learning rate: {self.learning_rate}") + + fsdp_cfg = self.cfg.get("fsdp", {}) + fm_cfg = self.cfg.get("flow_matching", {}) + + self.cpu_offload = fsdp_cfg.get("cpu_offload", False) + self.use_sigma_noise = fm_cfg.get("use_sigma_noise", True) + self.timestep_sampling = fm_cfg.get("timestep_sampling", "uniform") + self.logit_mean = fm_cfg.get("logit_mean", 0.0) + self.logit_std = fm_cfg.get("logit_std", 1.0) + self.flow_shift = fm_cfg.get("flow_shift", 3.0) + self.mix_uniform_ratio = fm_cfg.get("mix_uniform_ratio", 0.1) + + logging.info(f"[INFO] Flow matching: {'ENABLED' if self.use_sigma_noise else 'DISABLED'}") + if self.use_sigma_noise: + logging.info(f"[INFO] - Timestep sampling: {self.timestep_sampling}") + logging.info(f"[INFO] - Flow shift: {self.flow_shift}") + logging.info(f"[INFO] - Mix uniform ratio: {self.mix_uniform_ratio}") + + tp_size = fsdp_cfg.get("tp_size", 1) + cp_size = fsdp_cfg.get("cp_size", 1) + pp_size = fsdp_cfg.get("pp_size", 1) + dp_size = fsdp_cfg.get("dp_size", None) + dp_replicate_size = fsdp_cfg.get("dp_replicate_size", None) + use_hf_tp_plan = fsdp_cfg.get("use_hf_tp_plan", False) + + (self.pipe, self.model_map, self.optimizer, self.device_mesh) = build_model_and_optimizer( + model_id=self.model_id, + learning_rate=self.learning_rate, + device=self.device, + bf16_dtype=self.bf16, + cpu_offload=self.cpu_offload, + tp_size=tp_size, + cp_size=cp_size, + pp_size=pp_size, + dp_size=dp_size, + dp_replicate_size=dp_replicate_size, + use_hf_tp_plan=use_hf_tp_plan, + optimizer_cfg=self.cfg.get("optim.optimizer", {}), + ) + + self.model = self.model_map["transformer"]["fsdp_transformer"] + self.peft_config = None + + batch_cfg = self.cfg.get("batch", {}) + training_cfg = self.cfg.get("training", {}) + logging_cfg = self.cfg.get("logging", {}) + checkpoint_cfg = self.cfg.get("checkpoint", None) + + self.batch_size_per_node = batch_cfg.get("batch_size_per_node", 1) + self.num_epochs = training_cfg.get("num_epochs", 1) + self.save_every = logging_cfg.get("save_every", 500) + self.log_every = logging_cfg.get("log_every", 5) + + # Strictly require checkpoint config from YAML (no fallback) + if checkpoint_cfg is None: + raise ValueError( + "checkpoint config is required in YAML (enabled, checkpoint_dir, model_save_format, save_consolidated)" + ) + if not checkpoint_cfg.get("enabled", False): + raise ValueError("checkpoint.enabled must be true in YAML for diffusion training") + + # Build BaseRecipe-style checkpointing configuration (DCP/TORCH_SAVE) from YAML + model_state_dict_keys = list(self.model.state_dict().keys()) + model_cache_dir = self.cfg.get("model.cache_dir", None) + self.checkpoint_config = CheckpointingConfig( + enabled=checkpoint_cfg.get("enabled"), + checkpoint_dir=checkpoint_cfg.get("checkpoint_dir"), + model_save_format=checkpoint_cfg.get("model_save_format"), + model_cache_dir=model_cache_dir if model_cache_dir is not None else TRANSFORMERS_CACHE, + model_repo_id=self.model_id, + save_consolidated=checkpoint_cfg.get("save_consolidated"), + is_peft=False, + model_state_dict_keys=model_state_dict_keys, + ) + self.restore_from = checkpoint_cfg.get("restore_from", None) + self.checkpointer = Checkpointer( + config=self.checkpoint_config, + dp_rank=self._get_dp_rank(include_cp=True), + tp_rank=self._get_tp_rank(), + pp_rank=self._get_pp_rank(), + moe_mesh=None, + ) + + dataloader_cfg = self.cfg.get("data.dataloader") + if not hasattr(dataloader_cfg, "instantiate"): + raise RuntimeError("data.dataloader must be a config node with instantiate()") + + self.dataloader, self.sampler = dataloader_cfg.instantiate( + dp_rank=self._get_dp_rank(), dp_world_size=self._get_dp_group_size() + ) + + self.raw_steps_per_epoch = len(self.dataloader) + if self.raw_steps_per_epoch == 0: + raise RuntimeError("Training dataloader is empty; cannot proceed with training") + + # Derive DP size consistent with model parallel config + denom = max(1, tp_size * cp_size * pp_size) + self.dp_size = fsdp_cfg.get("dp_size", None) + if self.dp_size is None: + self.dp_size = max(1, self.world_size // denom) + + # Infer local micro-batch size from dataloader if available + self.local_batch_size = getattr(self.dataloader, "batch_size", 1) + # Desired global effective batch size across all DP ranks and nodes + self.global_batch_size = max(1, int(self.batch_size_per_node) * int(self.num_nodes)) + # Steps per epoch after gradient accumulation + grad_acc_steps = max(1, self.global_batch_size // max(1, self.local_batch_size * self.dp_size)) + self.steps_per_epoch = ceil(self.raw_steps_per_epoch / grad_acc_steps) + + self.lr_scheduler = build_lr_scheduler( + self.optimizer, + num_epochs=self.num_epochs, + steps_per_epoch=self.steps_per_epoch, + ) + + self.global_step = 0 + self.start_epoch = 0 + # Initialize StepScheduler for gradient accumulation and step/epoch bookkeeping + self.step_scheduler = StepScheduler( + global_batch_size=int(self.global_batch_size), + local_batch_size=int(self.local_batch_size), + dp_size=int(self.dp_size), + ckpt_every_steps=int(self.save_every) if self.save_every else 1, + dataloader=self.dataloader, + val_every_steps=None, + start_step=int(self.global_step), + start_epoch=int(self.start_epoch), + num_epochs=int(self.num_epochs), + ) + # Optional resume only through config-defined restore_from + if self.restore_from: + self.load_checkpoint(restore_from=self.restore_from) + + if is_main_process(): + os.makedirs(self.checkpoint_config.checkpoint_dir, exist_ok=True) + + if dist.is_initialized(): + dist.barrier() + + def run_train_validation_loop(self): + logging.info("[INFO] Starting T2V training with Flow Matching") + logging.info(f"[INFO] Batch size per node: {self.batch_size_per_node}") + logging.info(f"[INFO] Total effective batch size: {self.batch_size_per_node * self.num_nodes}") + + # Keep global_step synchronized with scheduler + global_step = int(self.step_scheduler.step) + + for epoch in self.step_scheduler.epochs: + if self.sampler is not None and hasattr(self.sampler, "set_epoch"): + self.sampler.set_epoch(epoch) + + # Optionally wrap dataloader with tqdm for rank-0 + if is_main_process(): + from tqdm import tqdm + + self.step_scheduler.dataloader = tqdm(self.dataloader, desc=f"Epoch {epoch + 1}/{self.num_epochs}") + else: + self.step_scheduler.dataloader = self.dataloader + + epoch_loss = 0.0 + num_steps = 0 + + for batch_group in self.step_scheduler: + self.optimizer.zero_grad(set_to_none=True) + + micro_losses = [] + for micro_batch in batch_group: + try: + loss, _ = step_fsdp_transformer_t2v( + pipe=self.pipe, + model_map=self.model_map, + batch=micro_batch, + device=self.device, + bf16=self.bf16, + use_sigma_noise=self.use_sigma_noise, + timestep_sampling=self.timestep_sampling, + logit_mean=self.logit_mean, + logit_std=self.logit_std, + flow_shift=self.flow_shift, + mix_uniform_ratio=self.mix_uniform_ratio, + global_step=global_step, + ) + except Exception as exc: + logging.info(f"[ERROR] Training step failed at epoch {epoch}, step {num_steps}: {exc}") + video_shape = micro_batch.get("video_latents", torch.tensor([])).shape + text_shape = micro_batch.get("text_embeddings", torch.tensor([])).shape + logging.info(f"[DEBUG] Batch shapes - video: {video_shape}, text: {text_shape}") + raise + + (loss / len(batch_group)).backward() + micro_losses.append(float(loss.item())) + + grad_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0) + grad_norm = float(grad_norm) if torch.is_tensor(grad_norm) else grad_norm + + self.optimizer.step() + self.lr_scheduler.step() + + group_loss_mean = float(sum(micro_losses) / len(micro_losses)) + epoch_loss += group_loss_mean + num_steps += 1 + global_step = int(self.step_scheduler.step) + + if self.log_every and self.log_every > 0 and is_main_process() and (global_step % self.log_every == 0): + avg_loss = epoch_loss / num_steps + log_dict = { + "train_loss": group_loss_mean, + "train_avg_loss": avg_loss, + "lr": self.optimizer.param_groups[0]["lr"], + "grad_norm": grad_norm, + "epoch": epoch, + "global_step": global_step, + } + if wandb.run is not None: + wandb.log(log_dict, step=global_step) + + # Update tqdm if present + if hasattr(self.step_scheduler.dataloader, "set_postfix"): + self.step_scheduler.dataloader.set_postfix( + { + "loss": f"{group_loss_mean:.4f}", + "avg": f"{(avg_loss):.4f}", + "lr": f"{self.optimizer.param_groups[0]['lr']:.2e}", + "gn": f"{grad_norm:.2f}", + } + ) + + if self.step_scheduler.is_ckpt_step: + self.save_checkpoint(epoch, global_step) + + avg_loss = epoch_loss / num_steps + logging.info(f"[INFO] Epoch {epoch + 1} complete. avg_loss={avg_loss:.6f}") + + if is_main_process() and wandb.run is not None: + wandb.log({"epoch/avg_loss": avg_loss, "epoch/num": epoch + 1}, step=global_step) + + if is_main_process(): + logging.info(f"[INFO] Saved final checkpoint at step {global_step}") + if wandb.run is not None: + wandb.finish() + + logging.info("[INFO] Training complete!") diff --git a/dfm/src/Automodel/utils/data/decode.py b/dfm/src/Automodel/utils/data/decode.py new file mode 100644 index 00000000..c0f0b896 --- /dev/null +++ b/dfm/src/Automodel/utils/data/decode.py @@ -0,0 +1,425 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os +import pickle +from pathlib import Path +from typing import Dict + +import cv2 +import numpy as np +import torch +from diffusers import AutoencoderKLWan + + +# Set up logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +class VideoDecoder: + def __init__( + self, + wan21_model_id: str = "Wan-AI/Wan2.1-T2V-14B-Diffusers", + device: str = "cuda" if torch.cuda.is_available() else "cpu", + enable_memory_optimization: bool = True, + ): + """ + Initialize the video decoder for converting .meta files back to videos. + + Args: + wan21_model_id: Hugging Face model ID for Wan2.1 VAE + device: Device to run inference on + enable_memory_optimization: Enable Wan's built-in slicing and tiling + """ + self.device = device + self.wan21_model_id = wan21_model_id + self.enable_memory_optimization = enable_memory_optimization + + # Load Wan2.1 VAE for decoding + logger.info(f"Loading Wan2.1 VAE from {wan21_model_id}...") + self.vae = self._load_vae() + + def _load_vae(self): + """Load Wan2.1 VAE from Hugging Face with memory optimization.""" + logger.info("Loading Wan2.1 VAE for decoding...") + vae = AutoencoderKLWan.from_pretrained( + self.wan21_model_id, subfolder="vae", torch_dtype=torch.float16 if self.device == "cuda" else torch.float32 + ) + vae.to(self.device) + vae.eval() + + # Enable Wan's built-in memory optimization + if self.enable_memory_optimization: + logger.info("Enabling Wan VAE memory optimization...") + vae.enable_slicing() # Reduce peak memory by slicing batch + vae.enable_tiling() # Tile H/W during encode+decode + logger.info("✅ Enabled slicing and tiling for memory efficiency") + else: + logger.info("Memory optimization disabled - using full tensors") + + # Log VAE config for debugging + logger.info("Wan2.1 VAE loaded successfully") + + # Verify this is a Wan2.1 VAE + if hasattr(vae.config, "latents_mean") and hasattr(vae.config, "latents_std"): + logger.info("✅ Found latents_mean and latents_std in VAE config (Wan2.1 format)") + logger.info(f" z_dim: {vae.config.z_dim if hasattr(vae.config, 'z_dim') else 'unknown'}") + else: + logger.error("❌ No latents_mean/latents_std found in VAE config") + logger.error("This doesn't appear to be a Wan2.1 VAE!") + raise ValueError("VAE config missing latents_mean and latents_std (required for Wan2.1)") + + return vae + + def load_meta_file(self, meta_path: str) -> Dict: + """ + Load processed data from .meta file. + + Args: + meta_path: Path to .meta file + + Returns: + Dictionary containing text_embeddings, video_latents, and metadata + """ + logger.info(f"Loading .meta file: {meta_path}") + + with open(meta_path, "rb") as f: + data = pickle.load(f) + + # Log information about the loaded data + logger.info(f"Loaded data keys: {list(data.keys())}") + logger.info(f"Video latents shape: {data['video_latents'].shape}") + logger.info(f"Text embeddings shape: {data['text_embeddings'].shape}") + logger.info(f"Original filename: {data.get('original_filename', 'N/A')}") + + # Check model version + model_version = data.get("model_version", "unknown") + logger.info(f"Model version: {model_version}") + if model_version != "wan2.1": + logger.warning(f"⚠️ This .meta file was created with {model_version}, but you're using a Wan2.1 decoder!") + logger.warning(" Decoding may not work correctly if versions don't match.") + + # Check if first frame exists + if "first_frame" in data: + logger.info(f"First frame found: {data['first_frame'].shape}") + + # Check encoding mode + encoding_mode = data.get("deterministic_latents", "unknown") + logger.info(f"Encoding mode: {encoding_mode}") + + return data + + def save_first_frame_as_jpeg(self, first_frame: np.ndarray, output_path: str, quality: int = 95): + """ + Save the first frame as a JPEG image. + + Args: + first_frame: RGB numpy array (H, W, C) with values in [0, 255] + output_path: Path to save the JPEG file + quality: JPEG quality (1-100) + """ + logger.info(f"Saving first frame to: {output_path}") + + # Create output directory if it doesn't exist + output_dir = os.path.dirname(output_path) + if output_dir: + os.makedirs(output_dir, exist_ok=True) + + # Convert RGB to BGR for OpenCV + first_frame_bgr = cv2.cvtColor(first_frame, cv2.COLOR_RGB2BGR) + + # Save as JPEG with specified quality + success = cv2.imwrite(output_path, first_frame_bgr, [cv2.IMWRITE_JPEG_QUALITY, quality]) + + if not success: + raise RuntimeError(f"Failed to save JPEG to {output_path}") + + logger.info(f"✅ Successfully saved first frame JPEG: {output_path}") + logger.info(f" Image dimensions: {first_frame.shape[0]}x{first_frame.shape[1]}") + + def decode_video_latents(self, video_latents: torch.Tensor) -> torch.Tensor: + """ + Decode video latents back to video frames using Wan2.1 VAE. + Uses Wan's built-in memory optimization instead of manual chunking. + + Args: + video_latents: Normalized video latents from .meta file + + Returns: + Decoded video tensor in [0, 1] range + """ + logger.info(f"Decoding video latents: {video_latents.shape}") + logger.info(f"Input latents range: [{video_latents.min():.3f}, {video_latents.max():.3f}]") + + # Move to device and ensure correct dtype + video_latents = video_latents.to(device=self.device, dtype=self.vae.dtype) + + # De-normalize latents (reverse the Wan2.1 encoding normalization) + # Wan2.1 uses per-channel normalization: (z - mean) / std + # So we reverse it: z * std + mean + if not (hasattr(self.vae.config, "latents_mean") and hasattr(self.vae.config, "latents_std")): + raise ValueError("Wan2.1 VAE requires latents_mean and latents_std in config") + + latents_mean = torch.tensor(self.vae.config.latents_mean, device=self.device, dtype=self.vae.dtype) + latents_std = torch.tensor(self.vae.config.latents_std, device=self.device, dtype=self.vae.dtype) + + logger.info("Using Wan2.1 per-channel de-normalization") + logger.info(f"latents_mean: {latents_mean.tolist()}") + logger.info(f"latents_std: {latents_std.tolist()}") + + # Reshape for broadcasting: (1, C, 1, 1, 1) for 5D tensors + latents_mean = latents_mean.view(1, -1, 1, 1, 1) + latents_std = latents_std.view(1, -1, 1, 1, 1) + + # De-normalize: z * std + mean (reverse of (z - mean) / std) + video_latents = video_latents * latents_std + latents_mean + logger.info("Applied Wan2.1 VAE de-normalization") + logger.info(f"De-normalized latents range: [{video_latents.min():.3f}, {video_latents.max():.3f}]") + + B, C, T, H, W = video_latents.shape + logger.info(f"De-normalized latents shape: {video_latents.shape}") + + # Use Wan's built-in memory optimization + # No manual chunking needed - VAE handles it internally with slicing/tiling + with torch.no_grad(): + logger.info("Decoding with Wan2.1 VAE (using built-in memory optimization)") + decoded_video = self.vae.decode(video_latents).sample + logger.info(f"Decoded video range: [{decoded_video.min():.3f}, {decoded_video.max():.3f}]") + + # Convert from [-1, 1] to [0, 1] range + decoded_video = (decoded_video + 1.0) / 2.0 + logger.info(f"After [-1,1] to [0,1] conversion: [{decoded_video.min():.3f}, {decoded_video.max():.3f}]") + + # Clamp to ensure values are in [0, 1] + decoded_video = torch.clamp(decoded_video, 0.0, 1.0) + logger.info(f"After clamping: [{decoded_video.min():.3f}, {decoded_video.max():.3f}]") + + logger.info(f"Final decoded video shape: {decoded_video.shape}") + return decoded_video + + def save_video_as_mp4(self, video_tensor: torch.Tensor, output_path: str, fps: int = 24): + """ + Save decoded video tensor as MP4 file. + + Args: + video_tensor: Decoded video tensor of shape (B, C, T, H, W) in [0, 1] range + output_path: Path to save the MP4 file + fps: Frames per second for the output video + """ + logger.info(f"Saving video to: {output_path}") + + # Convert to numpy and rearrange dimensions + # From (B, C, T, H, W) to (T, H, W, C) + video_tensor = video_tensor.squeeze(0) # Remove batch dimension: (C, T, H, W) + video_tensor = video_tensor.permute(1, 2, 3, 0) # (T, H, W, C) + video_np = video_tensor.cpu().numpy() + + # Convert from [0, 1] to [0, 255] and to uint8 + video_np = (video_np * 255).astype(np.uint8) + + logger.info(f"Video array shape: {video_np.shape}") + logger.info(f"Video array dtype: {video_np.dtype}") + logger.info(f"Video array range: [{video_np.min()}, {video_np.max()}]") + + # Get video dimensions + num_frames, height, width, channels = video_np.shape + + # Create output directory if it doesn't exist + os.makedirs(os.path.dirname(output_path), exist_ok=True) + + # Initialize video writer + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + out = cv2.VideoWriter(output_path, fourcc, fps, (width, height)) + + if not out.isOpened(): + raise RuntimeError(f"Failed to open video writer for {output_path}") + + # Write frames + for frame_idx in range(num_frames): + frame = video_np[frame_idx] + + # Convert RGB to BGR for OpenCV + frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) + + # Write frame + out.write(frame_bgr) + + # Release video writer + out.release() + + logger.info(f"✅ Successfully saved video: {output_path}") + logger.info(f" Video info: {num_frames} frames, {height}x{width}, {fps} FPS") + + def decode_meta_to_video(self, meta_path: str, output_path: str, fps: int = 24, jpeg_quality: int = 95): + """ + Complete pipeline: load .meta file, save first frame as JPEG, and save video as MP4. + + Args: + meta_path: Path to input .meta file + output_path: Path to output MP4 file + fps: Frames per second for output video + jpeg_quality: JPEG quality for first frame (1-100) + """ + logger.info(f"Converting {meta_path} -> {output_path}") + + try: + # Load meta file + data = self.load_meta_file(meta_path) + + # Save first frame as JPEG if available + if "first_frame" in data: + # Generate first frame JPEG path (same directory and name as video, but .jpg) + output_path_obj = Path(output_path) + first_frame_path = output_path_obj.parent / f"{output_path_obj.stem}.jpg" + self.save_first_frame_as_jpeg(data["first_frame"], str(first_frame_path), jpeg_quality) + else: + logger.warning("No 'first_frame' found in .meta file, skipping JPEG save") + + # Extract video latents + video_latents = data["video_latents"] + + # Decode latents to video + decoded_video = self.decode_video_latents(video_latents) + + # Save as MP4 + self.save_video_as_mp4(decoded_video, output_path, fps) + + logger.info(f"✅ Successfully converted {meta_path} to {output_path}") + + except Exception as e: + logger.error(f"❌ Error converting {meta_path}: {e}") + raise + + def decode_folder(self, meta_folder: str, output_folder: str, fps: int = 24, jpeg_quality: int = 95): + """ + Decode all .meta files in a folder to MP4 videos and JPEG first frames. + + Args: + meta_folder: Path to folder containing .meta files + output_folder: Path to folder for output MP4 files and JPEG images + fps: Frames per second for output videos + jpeg_quality: JPEG quality for first frames (1-100) + """ + meta_folder = Path(meta_folder) + output_folder = Path(output_folder) + + # Create output directory + output_folder.mkdir(parents=True, exist_ok=True) + + # Find all .meta files + meta_files = list(meta_folder.glob("*.meta")) + + if not meta_files: + logger.warning(f"No .meta files found in {meta_folder}") + return + + logger.info(f"Found {len(meta_files)} .meta files to decode") + + for i, meta_file in enumerate(meta_files): + logger.info(f"Progress: {i + 1}/{len(meta_files)}") + + # Generate output filename + output_file = output_folder / f"{meta_file.stem}.mp4" + + try: + self.decode_meta_to_video(str(meta_file), str(output_file), fps, jpeg_quality) + except Exception as e: + logger.error(f"Failed to decode {meta_file}: {e}") + continue + + logger.info(f"✅ Finished decoding {len(meta_files)} videos to {output_folder}") + + +def main(): + """Main function to run the video decoding.""" + import argparse + + parser = argparse.ArgumentParser(description="Decode Wan2.1 .meta files to MP4 videos") + parser.add_argument("--input", "-i", required=True, help="Input .meta file or folder containing .meta files") + parser.add_argument("--output", "-o", required=True, help="Output MP4 file or folder for MP4 files") + parser.add_argument("--fps", type=int, default=24, help="Frames per second for output video (default: 24)") + parser.add_argument( + "--model", + default="Wan-AI/Wan2.1-T2V-14B-Diffusers", + help="Wan2.1 model ID (default: Wan-AI/Wan2.1-T2V-14B-Diffusers, also supports Wan-AI/Wan2.1-T2V-1.3B-Diffusers)", + ) + parser.add_argument( + "--device", default="cuda" if torch.cuda.is_available() else "cpu", help="Device to use (default: auto-detect)" + ) + parser.add_argument( + "--no-memory-optimization", action="store_true", help="Disable Wan's built-in memory optimization" + ) + parser.add_argument( + "--jpeg-quality", type=int, default=95, help="JPEG quality for first frame (1-100, default: 95)" + ) + + args = parser.parse_args() + + # Initialize decoder + decoder = VideoDecoder( + wan21_model_id=args.model, device=args.device, enable_memory_optimization=not args.no_memory_optimization + ) + + input_path = Path(args.input) + output_path = Path(args.output) + + if input_path.is_file() and input_path.suffix == ".meta": + # Single file mode + if not output_path.suffix: + output_path = output_path / f"{input_path.stem}.mp4" + + decoder.decode_meta_to_video(str(input_path), str(output_path), args.fps, args.jpeg_quality) + + elif input_path.is_dir(): + # Folder mode + decoder.decode_folder(str(input_path), str(output_path), args.fps, args.jpeg_quality) + + else: + logger.error(f"Invalid input: {input_path} (must be .meta file or directory)") + + +if __name__ == "__main__": + main() + + +# Example usage: +""" +# Decode single .meta file (creates both .mp4 and .jpg) +python decode_wan21.py --input processed_meta/video1.meta --output decoded_videos/video1.mp4 --model Wan-AI/Wan2.1-T2V-1.3B-Diffusers + +# Decode all .meta files in a folder +python decode_wan21.py --input processed_meta/ --output decoded_videos/ + +# Use Wan2.1 1.3B model +python decode_wan21.py --input processed_meta/ --output decoded_videos/ --model Wan-AI/Wan2.1-T2V-1.3B-Diffusers + +# Custom JPEG quality and FPS +python decode_wan21.py --input processed_meta/ --output decoded_videos/ --fps 16 --jpeg-quality 90 + +# Disable memory optimization if you have enough VRAM +python decode_wan21.py --input processed_meta/ --output decoded_videos/ --no-memory-optimization + +# Programmatic usage +from decode_wan21 import VideoDecoder + +decoder = VideoDecoder("Wan-AI/Wan2.1-T2V-14B-Diffusers") + +# Single file (creates video1.mp4 and video1.jpg) +decoder.decode_meta_to_video("processed_meta/video1.meta", "output/video1.mp4") + +# Entire folder +decoder.decode_folder("processed_meta", "decoded_videos", fps=24, jpeg_quality=95) +""" diff --git a/dfm/src/Automodel/utils/data/preprocess_resize.py b/dfm/src/Automodel/utils/data/preprocess_resize.py new file mode 100644 index 00000000..34b36c6c --- /dev/null +++ b/dfm/src/Automodel/utils/data/preprocess_resize.py @@ -0,0 +1,758 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import logging +import pickle +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import cv2 +import numpy as np +import torch +from diffusers import AutoencoderKLWan +from transformers import AutoTokenizer, UMT5EncoderModel + + +# Set up logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +class VideoPreprocessor: + def __init__( + self, + video_folder: str = "clipped_video", + wan21_model_id: str = "Wan-AI/Wan2.1-T2V-14B-Diffusers", + output_folder: str = "processed_meta", + device: str = "cuda" if torch.cuda.is_available() else "cpu", + deterministic_latents: bool = True, + enable_memory_optimization: bool = True, + target_size: Optional[Tuple[int, int]] = None, + resize_mode: str = "bilinear", + maintain_aspect_ratio: bool = True, + center_crop: bool = False, + ): + """ + Initialize the video preprocessor for Wan2.1 fine-tuning. + + Args: + video_folder: Path to folder containing videos and meta.json + wan21_model_id: Hugging Face model ID for Wan2.1 (e.g., "Wan-AI/Wan2.1-T2V-14B-Diffusers") + output_folder: Path to folder where .meta files will be saved + device: Device to run inference on + deterministic_latents: If True, use posterior mean instead of sampling (recommended for clean reconstructions) + enable_memory_optimization: Enable Wan's built-in slicing and tiling + target_size: Target (height, width) for resizing. If None, no resizing is performed + resize_mode: Interpolation mode for resizing ('bilinear', 'bicubic', 'nearest', 'area') + maintain_aspect_ratio: If True, maintain aspect ratio when resizing + center_crop: If True, center crop to target size after resizing (when maintaining aspect ratio) + """ + self.video_folder = Path(video_folder) + self.output_folder = Path(output_folder) + self.device = device + self.wan21_model_id = wan21_model_id + self.deterministic_latents = deterministic_latents + self.enable_memory_optimization = enable_memory_optimization + + # Resize parameters + self.target_size = target_size + self.resize_mode = resize_mode + self.maintain_aspect_ratio = maintain_aspect_ratio + self.center_crop = center_crop + + # Validate resize parameters + if self.target_size is not None: + if len(self.target_size) != 2 or any(s <= 0 for s in self.target_size): + raise ValueError("target_size must be a tuple of (height, width) with positive values") + logger.info(f"Video resizing enabled: target size = {self.target_size} (H x W)") + logger.info(f"Resize mode: {self.resize_mode}") + logger.info(f"Maintain aspect ratio: {self.maintain_aspect_ratio}") + if self.maintain_aspect_ratio and self.center_crop: + logger.info("Center crop enabled (will crop to exact target size after aspect-preserving resize)") + else: + logger.info("Video resizing disabled - using original video dimensions") + + # Map resize modes to OpenCV interpolation flags + self.interpolation_map = { + "bilinear": cv2.INTER_LINEAR, + "bicubic": cv2.INTER_CUBIC, + "nearest": cv2.INTER_NEAREST, + "area": cv2.INTER_AREA, + "lanczos": cv2.INTER_LANCZOS4, + } + + if self.resize_mode not in self.interpolation_map: + raise ValueError( + f"Invalid resize_mode '{self.resize_mode}'. Choose from: {list(self.interpolation_map.keys())}" + ) + + # Log the encoding mode + if self.deterministic_latents: + logger.info("Using DETERMINISTIC latents (posterior mean) - no flares expected") + else: + logger.info("Using STOCHASTIC latents (sampling) - may cause temporal flares") + + # Log memory optimization setting + if self.enable_memory_optimization: + logger.info("Using Wan's built-in memory optimization (slicing + tiling)") + else: + logger.info("Memory optimization disabled - using full tensors") + + # Create output directory if it doesn't exist + self.output_folder.mkdir(parents=True, exist_ok=True) + logger.info(f"Output folder created/verified: {self.output_folder}") + + # Load Wan2.1 components + logger.info(f"Loading Wan2.1 components from {wan21_model_id}...") + self.text_encoder = self._load_text_encoder() + self.vae = self._load_vae() + self.tokenizer = self._load_tokenizer() + + # Load metadata + self.metadata = self._load_metadata() + + def _load_text_encoder(self): + """Load Wan2.1 UMT5 text encoder from Hugging Face.""" + logger.info("Loading UMT5 text encoder...") + text_encoder = UMT5EncoderModel.from_pretrained( + self.wan21_model_id, + subfolder="text_encoder", + torch_dtype=torch.float16 if self.device == "cuda" else torch.float32, + ) + text_encoder.to(self.device) + text_encoder.eval() + logger.info("UMT5 text encoder loaded successfully") + return text_encoder + + def _load_vae(self): + """Load Wan2.1 VAE from Hugging Face with memory optimization.""" + logger.info("Loading Wan2.1 VAE...") + + # Load Wan2.1 VAE with correct subfolder + try: + vae = AutoencoderKLWan.from_pretrained( + self.wan21_model_id, + subfolder="vae", + torch_dtype=torch.float16 if self.device == "cuda" else torch.float32, + ) + except Exception as e: + logger.error(f"Failed to load VAE from {self.wan21_model_id}/vae") + logger.error(f"Error: {e}") + logger.info("Make sure you're using a valid Wan2.1 model:") + logger.info("- Wan-AI/Wan2.1-T2V-14B-Diffusers") + logger.info("- Wan-AI/Wan2.1-T2V-1.3B-Diffusers") + raise + + vae.to(self.device) + vae.eval() + + # Enable Wan's built-in memory optimization + if self.enable_memory_optimization: + logger.info("Enabling Wan VAE memory optimization...") + vae.enable_slicing() # Reduce peak memory by slicing batch + vae.enable_tiling() # Tile H/W during encode+decode + logger.info("✅ Enabled slicing and tiling for memory efficiency") + else: + logger.info("Memory optimization disabled - using full tensors") + + # Log VAE configuration + logger.info("Wan2.1 VAE loaded successfully") + logger.info(f"VAE config type: {type(vae.config)}") + + # Log the input/output channels to verify correctness + if hasattr(vae.config, "in_channels"): + logger.info(f"VAE in_channels: {vae.config.in_channels}") + if hasattr(vae.config, "out_channels"): + logger.info(f"VAE out_channels: {vae.config.out_channels}") + if hasattr(vae.config, "z_dim"): + logger.info(f"VAE z_dim (latent channels): {vae.config.z_dim}") + + # Wan2.1 uses per-channel normalization with latents_mean and latents_std + if hasattr(vae.config, "latents_mean"): + logger.info(f"VAE latents_mean: {vae.config.latents_mean}") + if hasattr(vae.config, "latents_std"): + logger.info(f"VAE latents_std: {vae.config.latents_std}") + + # Log scale factors (Wan2.1 typically uses 4x temporal, 8x spatial) + scale_factor_temporal = getattr(vae.config, "scale_factor_temporal", 4) + scale_factor_spatial = getattr(vae.config, "scale_factor_spatial", 8) + logger.info(f"VAE scale_factor_temporal: {scale_factor_temporal}") + logger.info(f"VAE scale_factor_spatial: {scale_factor_spatial}") + + return vae + + def _load_tokenizer(self): + """Load UMT5 tokenizer from Hugging Face.""" + logger.info("Loading UMT5 tokenizer...") + tokenizer = AutoTokenizer.from_pretrained(self.wan21_model_id, subfolder="tokenizer") + logger.info("Tokenizer loaded successfully") + return tokenizer + + def _load_metadata(self) -> List[Dict]: + """Load video metadata from meta.json.""" + meta_path = self.video_folder / "meta.json" + if not meta_path.exists(): + raise FileNotFoundError(f"meta.json not found in {self.video_folder}") + + with open(meta_path, "r") as f: + metadata = json.load(f) + + logger.info(f"Loaded metadata for {len(metadata)} videos") + return metadata + + def _calculate_resize_dimensions(self, original_height: int, original_width: int) -> Tuple[int, int]: + """ + Calculate the target dimensions for resizing based on the resize strategy. + + Args: + original_height: Original frame height + original_width: Original frame width + + Returns: + Tuple of (target_height, target_width) + """ + if self.target_size is None: + return original_height, original_width + + target_height, target_width = self.target_size + + if not self.maintain_aspect_ratio: + # Direct resize to target dimensions + return target_height, target_width + + # Calculate aspect-preserving dimensions + original_aspect = original_width / original_height + target_aspect = target_width / target_height + + if original_aspect > target_aspect: + # Original is wider - fit to target width + new_width = target_width + new_height = int(target_width / original_aspect) + else: + # Original is taller - fit to target height + new_height = target_height + new_width = int(target_height * original_aspect) + + return new_height, new_width + + def _resize_frame(self, frame: np.ndarray) -> np.ndarray: + """ + Resize a single frame according to the resize settings. + + Args: + frame: Input frame as numpy array (H, W, C) + + Returns: + Resized frame as numpy array + """ + if self.target_size is None: + return frame + + original_height, original_width = frame.shape[:2] + resize_height, resize_width = self._calculate_resize_dimensions(original_height, original_width) + + # Resize the frame + interpolation = self.interpolation_map[self.resize_mode] + resized_frame = cv2.resize(frame, (resize_width, resize_height), interpolation=interpolation) + + # Apply center crop if needed + if self.maintain_aspect_ratio and self.center_crop: + target_height, target_width = self.target_size + + if resize_height != target_height or resize_width != target_width: + # Calculate crop coordinates + y_start = max(0, (resize_height - target_height) // 2) + x_start = max(0, (resize_width - target_width) // 2) + y_end = min(resize_height, y_start + target_height) + x_end = min(resize_width, x_start + target_width) + + # Crop the frame + resized_frame = resized_frame[y_start:y_end, x_start:x_end] + + # Pad if necessary (in case the resized frame is smaller than target) + if resized_frame.shape[0] < target_height or resized_frame.shape[1] < target_width: + pad_height = max(0, target_height - resized_frame.shape[0]) + pad_width = max(0, target_width - resized_frame.shape[1]) + + # Pad with zeros (black) + resized_frame = np.pad( + resized_frame, ((0, pad_height), (0, pad_width), (0, 0)), mode="constant", constant_values=0 + ) + + return resized_frame + + def extract_first_frame(self, video_path: str, start_frame: int) -> np.ndarray: + """ + Extract the first frame from the video as RGB image. + + Args: + video_path: Path to video file + start_frame: Starting frame index + + Returns: + First frame as RGB numpy array (H, W, C) with values in [0, 255] + """ + cap = cv2.VideoCapture(video_path) + + # Set to start frame + cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame) + + # Read the first frame + ret, frame = cap.read() + cap.release() + + if not ret: + raise ValueError(f"Could not read frame {start_frame} from {video_path}") + + # Convert BGR to RGB + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + # Apply the same resize transformations as video frames + frame_rgb = self._resize_frame(frame_rgb) + + logger.info(f"Extracted first frame with shape: {frame_rgb.shape}") + return frame_rgb + + def load_video_frames(self, video_path: str, start_frame: int, end_frame: int) -> torch.Tensor: + """ + Load video frames and convert to tensor for Wan VAE. + + Args: + video_path: Path to video file + start_frame: Starting frame index + end_frame: Ending frame index + + Returns: + Video tensor of shape (batch, channels, num_frames, height, width) + """ + cap = cv2.VideoCapture(video_path) + frames = [] + + # Get original video dimensions for logging + original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + if self.target_size is not None: + resize_height, resize_width = self._calculate_resize_dimensions(original_height, original_width) + logger.info(f"Original dimensions: {original_height}x{original_width}") + logger.info(f"Resize dimensions: {resize_height}x{resize_width}") + if self.maintain_aspect_ratio and self.center_crop: + logger.info(f"Final dimensions (after crop): {self.target_size[0]}x{self.target_size[1]}") + + # Set to start frame + cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame) + + for frame_idx in range(start_frame, end_frame + 1): + ret, frame = cap.read() + if not ret: + break + + # Convert BGR to RGB + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + # Resize frame if needed + frame = self._resize_frame(frame) + + # Normalize to [0, 1] + frame = frame.astype(np.float32) / 255.0 + frames.append(frame) + + cap.release() + + if not frames: + raise ValueError(f"No frames loaded from {video_path}") + + logger.info(f"Loaded {len(frames)} frames from {video_path}") + + # Convert to numpy array: (num_frames, height, width, channels) + video_array = np.array(frames) + logger.info(f"Video array shape: {video_array.shape}") + + # Convert to tensor and rearrange to: (batch, channels, num_frames, height, width) + video_tensor = torch.from_numpy(video_array) + video_tensor = video_tensor.permute(3, 0, 1, 2) # (channels, num_frames, height, width) + video_tensor = video_tensor.unsqueeze(0) # (batch, channels, num_frames, height, width) + + # Convert to the same dtype as VAE (float16 for GPU, float32 for CPU) + target_dtype = torch.float16 if self.device == "cuda" else torch.float32 + video_tensor = video_tensor.to(dtype=target_dtype) + + logger.info(f"Final video tensor shape: {video_tensor.shape}, dtype: {video_tensor.dtype}") + return video_tensor.to(self.device) + + def encode_text(self, caption: str) -> torch.Tensor: + """ + Encode text caption using Wan2.1 UMT5 text encoder. + + Args: + caption: Text description of the video + + Returns: + Text embedding tensor + """ + # Clean the prompt (as done in Wan2.1 pipeline) + caption = caption.strip() + + # Tokenize text with UMT5 settings (max_length=512 for Wan2.1) + inputs = self.tokenizer( + caption, + max_length=512, + padding="max_length", + truncation=True, + return_tensors="pt", + return_attention_mask=True, + ) + + # Move to device + inputs = {k: v.to(self.device) for k, v in inputs.items()} + + # Encode text using UMT5 encoder + with torch.no_grad(): + text_embeddings = self.text_encoder( + input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"] + ).last_hidden_state + + logger.info(f"Text embeddings shape: {text_embeddings.shape}") + return text_embeddings + + def encode_video(self, video_tensor: torch.Tensor) -> torch.Tensor: + """ + CRITICAL FIX: Encode video using Wan2.1 VAE without manual normalization. + The VAE encode() already returns normalized latents! + + Args: + video_tensor: Video tensor of shape (batch, channels, num_frames, height, width) + + Returns: + Video latent tensor (already normalized by VAE) + """ + logger.info(f"Input video tensor shape: {video_tensor.shape}, dtype: {video_tensor.dtype}") + + B, C, T, H, W = video_tensor.shape + + # Ensure tensor is on correct device and dtype + video_tensor = video_tensor.to(device=self.device, dtype=self.vae.dtype) + + # Convert to [-1, 1] range for VAE + video_tensor = video_tensor * 2.0 - 1.0 + + # CRITICAL FIX: Check if VAE encode() returns normalized latents + # According to Wan2.1 code, the VAE.encode() should handle normalization internally + # We should NOT manually normalize again! + + logger.info("Encoding with Wan2.1 VAE...") + + with torch.no_grad(): + latent_dist = self.vae.encode(video_tensor) + + if self.deterministic_latents: + # Use posterior mean for deterministic, flare-free encoding + video_latents = latent_dist.latent_dist.mean + logger.info("Using deterministic posterior mean (no flares)") + else: + # Use random sampling (training-style, but causes flares in reconstruction) + video_latents = latent_dist.latent_dist.sample() + logger.info("Using stochastic sampling (may cause flares)") + + # CRITICAL: Check if we need to normalize + # If VAE already normalizes, we should NOT normalize again + # Let's check the latent statistics + latent_mean = video_latents.mean().item() + latent_std = video_latents.std().item() + + logger.info(f"Raw latent statistics - mean: {latent_mean:.4f}, std: {latent_std:.4f}") + + # Check if latents are already normalized (mean~0, std~1) + if abs(latent_mean) < 0.5 and 0.5 < latent_std < 2.0: + logger.warning("⚠️ Latents appear already normalized! Skipping manual normalization.") + logger.warning("⚠️ If you see training issues, the VAE might already normalize internally.") + # Don't normalize again - use raw latents + final_latents = video_latents + else: + # Latents need normalization - apply Wan2.1 per-channel normalization + if not hasattr(self.vae.config, "latents_mean") or not hasattr(self.vae.config, "latents_std"): + raise ValueError("Wan2.1 VAE requires latents_mean and latents_std in config") + + latents_mean = torch.tensor(self.vae.config.latents_mean, device=self.device, dtype=self.vae.dtype) + latents_std = torch.tensor(self.vae.config.latents_std, device=self.device, dtype=self.vae.dtype) + + # Reshape for broadcasting: (1, C, 1, 1, 1) for 5D tensors + latents_mean = latents_mean.view(1, -1, 1, 1, 1) + latents_std = latents_std.view(1, -1, 1, 1, 1) + + logger.info("Applying Wan2.1 VAE per-channel normalization") + logger.info(f"latents_mean shape: {latents_mean.shape}, latents_std shape: {latents_std.shape}") + + # Apply Wan2.1 per-channel normalization: (z - mean) / std + final_latents = (video_latents - latents_mean) / latents_std + logger.info("Applied Wan2.1 VAE per-channel normalization") + + logger.info(f"Output video latents shape: {final_latents.shape}, dtype: {final_latents.dtype}") + logger.info(f"Encoding mode: {'deterministic' if self.deterministic_latents else 'stochastic'}") + logger.info(f"Memory optimization: {'enabled' if self.enable_memory_optimization else 'disabled'}") + + # Final statistics + final_mean = final_latents.mean().item() + final_std = final_latents.std().item() + logger.info(f"Final latent statistics - mean: {final_mean:.4f}, std: {final_std:.4f}") + + return final_latents + + def save_processed_data( + self, + video_name: str, + text_embeddings: torch.Tensor, + video_latents: torch.Tensor, + first_frame: np.ndarray, + metadata: Dict, + ): + """ + Save processed text embeddings, video latents, and first frame to binary file. + + Args: + video_name: Original video filename + text_embeddings: Encoded text embeddings + video_latents: Encoded video latents + first_frame: First frame of the video as RGB numpy array + metadata: Original metadata for the video + """ + # Create output filename in the output folder + video_stem = Path(video_name).stem + output_path = self.output_folder / f"{video_stem}.meta" + num_frames = metadata["end_frame"] - metadata["start_frame"] + 1 + + # Prepare data for saving + processed_data = { + "text_embeddings": text_embeddings.cpu(), + "video_latents": video_latents.cpu(), + "first_frame": first_frame, # Save as numpy array (H, W, C) in [0, 255] + "metadata": metadata, + "num_frames": num_frames, + "original_filename": video_name, + "original_video_path": str(self.video_folder / video_name), + "deterministic_latents": self.deterministic_latents, # Save encoding mode + "memory_optimization": self.enable_memory_optimization, # Save memory setting + "model_version": "wan2.1", # Mark as Wan2.1 format + "resize_settings": { # Save resize settings + "target_size": self.target_size, + "resize_mode": self.resize_mode, + "maintain_aspect_ratio": self.maintain_aspect_ratio, + "center_crop": self.center_crop, + }, + } + + # Save as pickle file + with open(output_path, "wb") as f: + pickle.dump(processed_data, f) + + logger.info(f"Saved processed data to {output_path}") + logger.info(f"First frame shape: {first_frame.shape}, dtype: {first_frame.dtype}") + logger.info(f"Encoding mode saved: {'deterministic' if self.deterministic_latents else 'stochastic'}") + logger.info(f"Memory optimization: {'enabled' if self.enable_memory_optimization else 'disabled'}") + logger.info("Model version: Wan2.1") + if self.target_size is not None: + logger.info(f"Resize settings saved: {self.target_size} ({self.resize_mode})") + + # Log file sizes for reference + video_path = self.video_folder / video_name + if video_path.exists(): + original_size = video_path.stat().st_size / (1024 * 1024) # MB + meta_size = output_path.stat().st_size / (1024 * 1024) # MB + logger.info(f"Compression: {original_size:.1f}MB → {meta_size:.1f}MB ({meta_size / original_size:.2%})") + + def process_single_video(self, video_metadata: Dict): + """Process a single video and save the results.""" + video_name = video_metadata["file_name"] + video_path = self.video_folder / video_name + + if not video_path.exists(): + logger.warning(f"Video file {video_path} not found, skipping...") + return + + logger.info(f"Processing {video_name}...") + + try: + # Extract first frame + logger.info("Step 0: Extracting first frame...") + first_frame = self.extract_first_frame(str(video_path), video_metadata["start_frame"]) + logger.info(f"Step 0 completed: first_frame shape = {first_frame.shape}") + + # Load video frames + logger.info("Step 1: Loading video frames...") + video_tensor = self.load_video_frames( + str(video_path), video_metadata["start_frame"], video_metadata["end_frame"] + ) + logger.info(f"Step 1 completed: video_tensor shape = {video_tensor.shape}") + + # Encode text caption + logger.info("Step 2: Encoding text caption...") + text_embeddings = self.encode_text(video_metadata["vila_caption"]) + logger.info(f"Step 2 completed: text_embeddings shape = {text_embeddings.shape}") + + # Encode video + logger.info("Step 3: Encoding video with VAE...") + video_latents = self.encode_video(video_tensor) + logger.info(f"Step 3 completed: video_latents shape = {video_latents.shape}") + + # Save processed data + logger.info("Step 4: Saving processed data...") + self.save_processed_data(video_name, text_embeddings, video_latents, first_frame, video_metadata) + logger.info("Step 4 completed") + + logger.info(f"Successfully processed {video_name}") + + except Exception as e: + import traceback + + logger.error(f"Error processing {video_name}: {e}") + logger.error(f"Full traceback:\n{traceback.format_exc()}") + + def process_all_videos(self): + """Process all videos in the folder.""" + logger.info(f"Starting to process {len(self.metadata)} videos...") + logger.info(f"Model: Wan2.1 ({self.wan21_model_id})") + logger.info( + f"Encoding mode: {'deterministic (flare-free)' if self.deterministic_latents else 'stochastic (may have flares)'}" + ) + logger.info( + f"Memory optimization: {'enabled (slicing + tiling)' if self.enable_memory_optimization else 'disabled'}" + ) + if self.target_size is not None: + logger.info(f"Video resizing: {self.target_size} using {self.resize_mode} interpolation") + + for i, video_metadata in enumerate(self.metadata): + logger.info(f"Progress: {i + 1}/{len(self.metadata)}") + self.process_single_video(video_metadata) + + logger.info("Finished processing all videos!") + + def load_processed_data(self, meta_file: str) -> Dict: + """ + Load processed data from .meta file. + + Args: + meta_file: Path to .meta file (can be relative to output_folder or absolute path) + + Returns: + Dictionary containing text_embeddings, video_latents, first_frame, and metadata + """ + meta_path = Path(meta_file) + + # If it's not an absolute path, assume it's in the output folder + if not meta_path.is_absolute(): + meta_path = self.output_folder / meta_file + + with open(meta_path, "rb") as f: + data = pickle.load(f) + + # Check encoding mode and memory optimization of loaded data + encoding_mode = data.get("deterministic_latents", "unknown") + memory_opt = data.get("memory_optimization", "unknown") + model_version = data.get("model_version", "unknown") + resize_settings = data.get("resize_settings", {}) + + logger.info(f"Loaded .meta file with model version: {model_version}") + logger.info(f"Encoding mode: {encoding_mode}, memory optimization: {memory_opt}") + if resize_settings.get("target_size"): + logger.info(f"Resize settings: {resize_settings}") + if "first_frame" in data: + logger.info(f"First frame shape: {data['first_frame'].shape}") + + return data + + def list_processed_files(self) -> List[str]: + """ + List all .meta files in the output folder. + + Returns: + List of .meta filenames + """ + meta_files = list(self.output_folder.glob("*.meta")) + return [f.name for f in meta_files] + + +def main(): + """Main function to run the preprocessing.""" + import argparse + + parser = argparse.ArgumentParser(description="Preprocess videos for Wan2.1 fine-tuning") + parser.add_argument( + "--video_folder", default="clipped_video", help="Path to folder containing videos and meta.json" + ) + parser.add_argument( + "--output_folder", default="processed_meta", help="Path to folder where .meta files will be saved" + ) + parser.add_argument( + "--model", + default="Wan-AI/Wan2.1-T2V-14B-Diffusers", + help="Wan2.1 model ID (e.g., Wan-AI/Wan2.1-T2V-14B-Diffusers or Wan-AI/Wan2.1-T2V-1.3B-Diffusers)", + ) + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="Device to use") + parser.add_argument( + "--stochastic", + action="store_true", + help="Use stochastic encoding (sampling) instead of deterministic (may cause flares)", + ) + parser.add_argument( + "--no-memory-optimization", action="store_true", help="Disable Wan's built-in memory optimization" + ) + + # Resize arguments + parser.add_argument("--height", type=int, default=None, help="Target height for video frames") + parser.add_argument("--width", type=int, default=None, help="Target width for video frames") + parser.add_argument( + "--resize_mode", + default="bilinear", + choices=["bilinear", "bicubic", "nearest", "area", "lanczos"], + help="Interpolation mode for resizing", + ) + parser.add_argument( + "--no-aspect-ratio", + action="store_true", + help="Disable aspect ratio preservation (stretch to exact target size)", + ) + parser.add_argument( + "--center-crop", action="store_true", help="Center crop to exact target size after aspect-preserving resize" + ) + + args = parser.parse_args() + + # Set target size + target_size = None + if args.height is not None and args.width is not None: + target_size = (args.height, args.width) + elif args.height is not None or args.width is not None: + parser.error("Both --height and --width must be specified together") + + # Initialize preprocessor + preprocessor = VideoPreprocessor( + video_folder=args.video_folder, + wan21_model_id=args.model, + output_folder=args.output_folder, + device=args.device, + deterministic_latents=not args.stochastic, # Default to deterministic + enable_memory_optimization=not args.no_memory_optimization, # Default to enabled + target_size=target_size, + resize_mode=args.resize_mode, + maintain_aspect_ratio=not args.no_aspect_ratio, + center_crop=args.center_crop, + ) + + # Process all videos + preprocessor.process_all_videos() + + +if __name__ == "__main__": + main() diff --git a/dfm/src/Automodel/utils/validate_t2v.py b/dfm/src/Automodel/utils/validate_t2v.py new file mode 100644 index 00000000..ced640e0 --- /dev/null +++ b/dfm/src/Automodel/utils/validate_t2v.py @@ -0,0 +1,253 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os +import pickle +from pathlib import Path + +import torch +from diffusers import WanPipeline +from diffusers.utils import export_to_video + + +def parse_args(): + p = argparse.ArgumentParser("WAN 2.1 T2V Validation") + + # Model configuration + p.add_argument("--model_id", type=str, default="Wan-AI/Wan2.1-T2V-1.3B-Diffusers") + p.add_argument("--checkpoint", type=str, default=None, help="Path to checkpoint (optional)") + + # Data - load from .meta files + p.add_argument("--meta_folder", type=str, required=True, help="Folder containing .meta files with prompts") + + # Generation settings + p.add_argument("--num_samples", type=int, default=None, help="Number of samples (default: all)") + p.add_argument("--num_inference_steps", type=int, default=50) + p.add_argument("--guidance_scale", type=float, default=5.0) + p.add_argument("--negative_prompt", type=str, default="") + p.add_argument("--seed", type=int, default=42) + + # Video settings + p.add_argument("--height", type=int, default=480) + p.add_argument("--width", type=int, default=832) + p.add_argument("--num_frames", type=int, default=81) + p.add_argument("--fps", type=int, default=16) + + # Output + p.add_argument("--output_dir", type=str, default="./validation_outputs") + + return p.parse_args() + + +def load_prompts_from_meta_files(meta_folder: str): + """ + Load prompts from .meta files. + Each .meta file contains a 'metadata' dict with 'vila_caption'. + + Returns list of dicts: [{"prompt": "...", "name": "...", "meta_file": "..."}, ...] + """ + meta_folder = Path(meta_folder) + meta_files = sorted(list(meta_folder.glob("*.meta"))) + + if not meta_files: + raise FileNotFoundError(f"No .meta files found in {meta_folder}") + + print(f"[INFO] Found {len(meta_files)} .meta files") + + prompts = [] + + for meta_file in meta_files: + try: + with open(meta_file, "rb") as f: + data = pickle.load(f) + + # Extract prompt from metadata + metadata = data.get("metadata", {}) + prompt = metadata.get("vila_caption", "") + + if not prompt: + print(f"[WARNING] No vila_caption in {meta_file.name}, skipping...") + continue + + # Get filename without extension + name = meta_file.stem + + prompts.append({"prompt": prompt, "name": name, "meta_file": str(meta_file)}) + + except Exception as e: + print(f"[WARNING] Failed to load {meta_file.name}: {e}") + continue + + if not prompts: + raise ValueError(f"No valid prompts found in {meta_folder}") + + return prompts + + +def main(): + args = parse_args() + + print("=" * 80) + print("WAN 2.1 Text-to-Video Validation") + print("=" * 80) + + # Load prompts from .meta files + print(f"\n[1] Loading prompts from .meta files in: {args.meta_folder}") + prompts = load_prompts_from_meta_files(args.meta_folder) + + if args.num_samples: + prompts = prompts[: args.num_samples] + + print(f"[INFO] Loaded {len(prompts)} prompts") + + # Show first few prompts + print("\n[INFO] Sample prompts:") + for i, item in enumerate(prompts[:3]): + print(f" {i + 1}. {item['name']}: {item['prompt'][:60]}...") + + # Load pipeline + print(f"\n[2] Loading pipeline: {args.model_id}") + pipe = WanPipeline.from_pretrained(args.model_id, torch_dtype=torch.bfloat16) + pipe.to("cuda") + + # Enable VAE optimizations (critical for memory) + pipe.vae.enable_slicing() + pipe.vae.enable_tiling() + print("[INFO] Enabled VAE slicing and tiling") + + # Load checkpoint if provided + if args.checkpoint: + print(f"\n[3] Loading checkpoint: {args.checkpoint}") + + # Try EMA checkpoint first (best quality) + ema_path = os.path.join(args.checkpoint, "ema_shadow.pt") + consolidated_path = os.path.join(args.checkpoint, "consolidated_model.bin") + sharded_dir = os.path.join(args.checkpoint, "model") + + if os.path.exists(ema_path): + print("[INFO] Loading EMA checkpoint (best quality)...") + ema_state = torch.load(ema_path, map_location="cuda") + pipe.transformer.load_state_dict(ema_state, strict=True) + print("[INFO] ✅ Loaded from EMA checkpoint") + elif os.path.exists(consolidated_path): + print("[INFO] ############Loading consolidated checkpoint...") + state_dict = torch.load(consolidated_path, map_location="cuda") + pipe.transformer.load_state_dict(state_dict, strict=True) + print("[INFO] ✅ ############Loaded from consolidated checkpoint") + elif os.path.isdir(sharded_dir) and any(name.endswith(".distcp") for name in os.listdir(sharded_dir)): + print(f"[INFO] Detected sharded FSDP checkpoint at: {sharded_dir}") + print("[INFO] Loading sharded checkpoint via PyTorch Distributed Checkpoint (single process)...") + + import torch.distributed as dist + from torch.distributed.checkpoint import FileSystemReader + from torch.distributed.checkpoint import load as dist_load + from torch.distributed.fsdp import FullyShardedDataParallel as FSDP + from torch.distributed.fsdp import StateDictType + from torch.distributed.fsdp.api import ShardedStateDictConfig + + # Initialize a single-process group if not already initialized + init_dist = False + if not dist.is_initialized(): + dist.init_process_group(backend="gloo", rank=0, world_size=1) + init_dist = True + + # Wrap current transformer with FSDP to load sharded weights + base_transformer = pipe.transformer + + # Ensure uniform dtype before FSDP wraps/flattening + base_transformer.to(dtype=torch.bfloat16) + fsdp_transformer = FSDP(base_transformer, use_orig_params=True) + + # Configure to expect sharded state dict + FSDP.set_state_dict_type( + fsdp_transformer, + StateDictType.SHARDED_STATE_DICT, + state_dict_config=ShardedStateDictConfig(offload_to_cpu=True), + ) + + # Load shards into the FSDP-wrapped model + model_state = fsdp_transformer.state_dict() + dist_load(state_dict=model_state, storage_reader=FileSystemReader(sharded_dir)) + fsdp_transformer.load_state_dict(model_state) + + # Unwrap back to the original module for inference + pipe.transformer = fsdp_transformer.module + + # Move to CUDA bf16 for inference + pipe.transformer.to("cuda", dtype=torch.bfloat16) + + if init_dist: + dist.destroy_process_group() + + print("[INFO] ✅ Loaded from sharded FSDP checkpoint") + else: + print("[WARNING] No consolidated or EMA checkpoint found") + print("[INFO] Using base model") + + # Create output directory + os.makedirs(args.output_dir, exist_ok=True) + + # Generate videos + print("\n[4] Generating videos...") + print(f"[INFO] Settings: {args.width}x{args.height}, {args.num_frames} frames, {args.num_inference_steps} steps") + print(f"[INFO] Guidance scale: {args.guidance_scale}") + + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(args.seed) + + for i, item in enumerate(prompts): + prompt = item["prompt"] + name = item["name"] + + print(f"\n[{i + 1}/{len(prompts)}] Generating: {name}") + print(f" Prompt: {prompt[:80]}...") + + try: + # Generate from scratch (no latents needed!) + generator = torch.Generator(device="cuda").manual_seed(args.seed + i) + + output = pipe( + prompt=prompt, + negative_prompt=args.negative_prompt, + height=args.height, + width=args.width, + num_frames=args.num_frames, + guidance_scale=args.guidance_scale, + num_inference_steps=args.num_inference_steps, + generator=generator, + ).frames[0] + + # Save video + output_path = os.path.join(args.output_dir, f"{name}.mp4") + export_to_video(output, output_path, fps=args.fps) + + print(f" ✅ Saved to {output_path}") + + except Exception as e: + print(f" ❌ Failed: {e}") + import traceback + + traceback.print_exc() + continue + + print("\n" + "=" * 80) + print("✅ Validation complete!") + print(f"📁 Videos saved to: {args.output_dir}") + print("=" * 80) + + +if __name__ == "__main__": + main() diff --git a/dfm/src/dtensor/model/README.md b/dfm/src/dtensor/model/README.md deleted file mode 100644 index 4440e29e..00000000 --- a/dfm/src/dtensor/model/README.md +++ /dev/null @@ -1,4 +0,0 @@ -# Model - -Core model implementations and architectures. - diff --git a/dfm/src/dtensor/model/__init__.py b/dfm/src/dtensor/model/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/pyproject.toml b/pyproject.toml index 20a4d190..cdac40e4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -61,6 +61,13 @@ requires = ["setuptools>=61"] build-backend = "setuptools.build_meta" [dependency-groups] +automodel = [ + "nemo-automodel @ git+https://github.com/NVIDIA-NeMo/Automodel@main", + "diffusers", + "ftfy", + "imageio-ffmpeg", + "opencv-python-headless==4.10.0.84", +] docs = [ "myst-parser>=4.0.1", "nvidia-sphinx-theme>=0.0.8", @@ -90,6 +97,22 @@ packages = ["nemo_vfm"] version = {attr = "nemo_vfm.__version__"} readme = {file = "README.md", content-type = "text/markdown"} +[[tool.uv.index]] +name = "pypi" +url = "https://pypi.org/simple" +explicit = true + +[[tool.uv.index]] +name = "pytorch" +url = "https://download.pytorch.org/whl/cu128" +explicit = true + +[tool.uv.sources] +torch = [ + { index = "pytorch", marker = "sys_platform != 'darwin'" }, + { index = "pypi", marker = "sys_platform == 'darwin'" }, +] + [project.urls] Download = "https://github.com/NVIDIA-NeMo/NeMo-VFM/releases" Homepage = "https://github.com/NVIDIA-NeMo/NeMo-VFM" diff --git a/uv.lock b/uv.lock new file mode 100644 index 00000000..43a8d818 --- /dev/null +++ b/uv.lock @@ -0,0 +1,3755 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.12' and platform_machine == 'aarch64' and 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