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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 os
import threading
from typing import TYPE_CHECKING
import torch
from cosmos_predict1.utils import callback, distributed, log, misc
from cosmos_predict1.utils.model import Model
if TYPE_CHECKING:
from cosmos_predict1.utils.config import CheckpointConfig, JobConfig
class Checkpointer:
"""The checkpointer class. Supports checkpoint saving/loading to local disk."""
def __init__(self, config_checkpoint: CheckpointConfig, config_job: JobConfig, callbacks: callback.CallBackGroup):
"""Constructor of the checkpointer.
Args:
config_checkpoint (CheckpointConfig): The config object for the checkpointer.
"""
# Set the callback functions.
self.callbacks = callbacks
self.checkpoint_dir_local = f"{config_job.path_local}/checkpoints"
self.strict_resume = config_checkpoint.strict_resume
self.load_path = config_checkpoint.load_path or None
self.load_training_state = config_checkpoint.load_training_state
self.only_load_scheduler_state = config_checkpoint.only_load_scheduler_state
self.save_thread = None
def save(
self,
model: Model,
optimizer: torch.optim.Optimizer,
scheduler: torch.optim.lr_scheduler.LRScheduler,
grad_scaler: torch.amp.GradScaler,
iteration: int,
) -> None:
"""Save network weights, optimizer parameters, scheduler parameters to a checkpoint.
Args:
model (Model): The PyTorch model.
optimizer (torch.optim.Optimizer): The model optimizer.
scheduler (torch.optim.lr_scheduler.LRScheduler): The optimization scheduler.
grad_scaler (torch.amp.GradScaler): The gradient scaler (for mixed precision training).
iteration (int): Current iteration number.
"""
self.callbacks.on_save_checkpoint_start(model, iteration)
checkpoint_file = f"iter_{iteration:09}.pt"
if distributed.get_rank() == 0:
state_dict = dict(
model=model.state_dict(),
optimizer=optimizer.state_dict(),
scheduler=scheduler.state_dict(),
grad_scaler=grad_scaler.state_dict(),
iteration=iteration,
)
state_dict = misc.to(state_dict, device="cpu")
self.callbacks.on_save_checkpoint(model, state_dict=state_dict)
# Wait for previous saver thread to end.
if self.save_thread:
self.save_thread.join()
# Run the checkpoint saver in a separate thread.
self.save_thread = threading.Thread(
target=self._save_worker_local,
daemon=False,
args=(state_dict, checkpoint_file, distributed.get_rank()),
)
self.save_thread.start()
# Note: Checkpoints are saved on a separate thread and this callback is not accurate.
# Please check logs from on_save_checkpoint_success() for better accuracy
self.callbacks.on_save_checkpoint_end(model=None, iteration=iteration)
@misc.timer("checkpoint saving (local)")
def _save_worker_local(self, state_dict: dict[str, torch.Tensor], checkpoint_file: str, rank: int = 0) -> None:
"""Worker to save checkpoint to local disk, spawned with a child thread (runs in parallel with the training).
Args:
state_dict (dict[str, torch.Tensor]): The state dict of the model/optimizer/scheduler.
checkpoint_file (str): The file name of the model checkpoint.
rank (int): GPU device (default: 0).
"""
checkpoint_path = os.path.join(self.checkpoint_dir_local, checkpoint_file)
os.makedirs(self.checkpoint_dir_local, exist_ok=True)
try:
torch.save(state_dict, checkpoint_path)
if rank == 0:
self._write_latest_checkpoint_file(checkpoint_file)
log.success(f"Saved checkpoint (local): {checkpoint_path}")
iteration = int(checkpoint_file.replace("iter_", "").replace(".pt", ""))
self.callbacks.on_save_checkpoint_success(iteration=iteration)
except Exception as e: # noqa: BLE001
log.exception(f"Checkpoint failed to save (local): {e}")
@misc.timer("checkpoint loading")
def load(
self,
model: Model,
optimizer: torch.optim.Optimizer | None = None,
scheduler: torch.optim.lr_scheduler.LRScheduler | None = None,
grad_scaler: torch.amp.GradScaler | None = None,
) -> int:
"""Load network weights and optimizer states from a checkpoint in a single process.
The priority of the checkpoint loading logic is:
1. Attempt to resume training if possible by looking for latest_checkpoint.txt under the same name.
2. If no latest checkpoint were found, it loads the model weights specified by config_checkpoint.path.
- This is typically used for inference mode.
- If config_checkpoint.load_optimizer_state is True, then also load the optimizer and scheduler states.
3. If none of the above, randomly initialize the model parameters and train from scratch.
Args:
model (Model): The PyTorch model.
optimizer (torch.optim.Optimizer | None): The model optimizer (default: None).
scheduler (torch.optim.lr_scheduler.LRScheduler | None): The optimization scheduler (default: None).
grad_scaler (torch.amp.GradScaler | None): The gradient scaler (for mixed precision training).
Returns:
iteration (int): the iteration number to start/resume from.
"""
self.callbacks.on_load_checkpoint_start(model)
latest_checkpoint_file = self._read_latest_checkpoint_file()
if latest_checkpoint_file is not None:
# 1. Resume training from latest_checkpoint.txt under the same name.
checkpoint_dir = self.checkpoint_dir_local
checkpoint_path = os.path.join(checkpoint_dir, latest_checkpoint_file)
resume = True
only_resume_scheduler = True
else:
if self.load_path:
# 2. Load the module weights specified by config_checkpoint.path.
checkpoint_path = self.load_path
resume = self.load_training_state
only_resume_scheduler = self.only_load_scheduler_state
else:
# 3. Randomly initialize the model parameters and train from scratch.
checkpoint_path = None
resume = False
only_resume_scheduler = False
# Load checkpoint.
if checkpoint_path is not None:
self._check_checkpoint_exists(checkpoint_path)
log.info(f"Loading checkpoint (local): {checkpoint_path}")
state_dict = torch.load(checkpoint_path, map_location=lambda storage, loc: storage, weights_only=False)
log.success(f"Complete loading checkpoint (local): {checkpoint_path}")
self.callbacks.on_load_checkpoint(model, state_dict=state_dict)
# Load the state dicts.
log.info("- Loading the model...")
if "model" in state_dict:
model.load_state_dict(state_dict["model"], strict=self.strict_resume)
else:
model.load_state_dict(state_dict, strict=self.strict_resume)
if resume or only_resume_scheduler:
iteration = state_dict["iteration"]
assert scheduler
log.info("- Loading the scheduler...")
scheduler.load_state_dict(state_dict["scheduler"])
scheduler.last_epoch = iteration
else:
iteration = 0
if resume:
assert optimizer
log.info("- Loading the optimizer...")
optimizer.load_state_dict(state_dict["optimizer"])
log.info("- Loading the gradient scaler...")
grad_scaler.load_state_dict(state_dict["grad_scaler"])
log.success(f"Done with loading the checkpoint (iteration {iteration}).")
else:
log.success("Done with loading the checkpoint.")
else:
# Checkpoint not found and not specified. We will train everything from scratch.
iteration = 0
log.info("Training from scratch.")
torch.cuda.empty_cache()
self.callbacks.on_load_checkpoint_end(model)
return iteration
def _read_latest_checkpoint_file(self) -> str | None:
"""Get the file name of the latest saved checkpoint. If it doesn't exist, return None.
Returns:
checkpoint_file (str | None): file name of the latest saved checkpoint.
"""
checkpoint_file = None
latest_path = os.path.join(self.checkpoint_dir_local, "latest_checkpoint.txt")
if os.path.isfile(latest_path):
checkpoint_file = open(latest_path).read().strip()
return checkpoint_file
def _write_latest_checkpoint_file(self, checkpoint_file: str) -> None:
"""Track the file name of the latest saved checkpoint.
Args:
checkpoint_file (str): file name of the latest saved checkpoint.
"""
content = f"{checkpoint_file}\n"
latest_path = os.path.join(self.checkpoint_dir_local, "latest_checkpoint.txt")
with open(latest_path, "w") as file:
file.write(content)
def _check_checkpoint_exists(self, checkpoint_path: str) -> None:
"""If the file checkpoint_path does not exist, raise an error.
Args:
checkpoint_path (str): full path to the checkpoint.
"""
if not os.path.exists(checkpoint_path):
raise FileNotFoundError(f"File not found (local): {checkpoint_path}")
def finalize(self) -> None:
"""Finalize the checkpointer."""
if self.save_thread:
self.save_thread.join()
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