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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.
import signal
import torch
import torch.distributed as dist
import torch.utils.data
from megatron.core import parallel_state
from cosmos_predict1.checkpointer.tp import Checkpointer as TensorParallelCheckpointer
from cosmos_predict1.utils import distributed, ema, log, misc
from cosmos_predict1.utils.checkpointer import Checkpointer
from cosmos_predict1.utils.fsdp_checkpointer import FSDPCheckpointer
from cosmos_predict1.utils.model import Model
from cosmos_predict1.utils.trainer import Trainer
class Trainer(Trainer):
def __init__(self, config):
super(Trainer, self).__init__(config)
if config.trainer.distributed_parallelism == "ddp":
if parallel_state.get_tensor_model_parallel_world_size() > 1:
self.checkpointer = TensorParallelCheckpointer(config.checkpoint, config.job, callbacks=self.callbacks)
log.critical("Using Tensor Parallelism Checkpointer")
else:
self.checkpointer = Checkpointer(config.checkpoint, config.job, callbacks=self.callbacks)
elif config.trainer.distributed_parallelism == "fsdp":
self.checkpointer = FSDPCheckpointer(config.checkpoint, config.job, callbacks=self.callbacks)
else:
raise ValueError(f"Unsupported distributed parallelism: {config.trainer.distributed_parallelism}")
"""
Modify the original trainer to log average loss (averaging across all devices and gradient accumulation)
"""
def train(
self,
model: Model,
dataloader_train: torch.utils.data.DataLoader,
dataloader_val: torch.utils.data.DataLoader,
) -> None:
"""The training function.
Args:
model (Model): The PyTorch model.
dataloader_train (torch.utils.data.DataLoader): The training data loader.
dataloader_val (torch.utils.data.DataLoader): The validation data loader.
"""
# Leaving this for backward compability for now, but we can think about moving this to model.on_train_start for all models.
model = model.to("cuda", memory_format=self.config.trainer.memory_format) # type: ignore
log.info(f"Model Architecture:\n {model}")
model.on_train_start(self.config.trainer.memory_format)
# Initialize the optimizer and scheduler.
self.callbacks.on_optimizer_init_start()
optimizer, scheduler = model.init_optimizer_scheduler(self.config.optimizer, self.config.scheduler)
grad_scaler = torch.amp.GradScaler("cuda", **self.config.trainer.grad_scaler_args)
self.callbacks.on_optimizer_init_end()
# Load the model checkpoint and get the starting iteration number.
iteration = self.checkpointer.load(model, optimizer, scheduler, grad_scaler)
# Set the scheduler to the current iteration.
scheduler.last_epoch = iteration
scheduler._step_count = iteration + 1
grad_accum_iter = 0
log.critical(f"Distributed parallelism mode: {self.config.trainer.distributed_parallelism}")
if self.config.trainer.distributed_parallelism == "ddp":
# Create a DDP model wrapper.
model_ddp = distributed.parallel_model_wrapper(self.config.trainer.ddp, model)
elif self.config.trainer.distributed_parallelism == "fsdp":
model_ddp = model
else:
raise ValueError(f"Unknown distributed parallelism mode: {self.config.trainer.distributed_parallelism}")
log.info("Starting training...")
self.callbacks.on_train_start(model, iteration=iteration)
# Initial validation.
if self.config.trainer.run_validation and iteration == 0:
self.validate(model, dataloader_val, iteration=iteration)
_end_training = False
self.callbacks.on_before_dataloading(iteration)
accumulated_loss = 0.0
while True:
dataloader_train_iter = iter(dataloader_train)
while True:
self.callbacks.on_before_dataloading(iteration)
try:
data_batch = next(dataloader_train_iter)
except StopIteration:
break
self.callbacks.on_after_dataloading(iteration)
# If max_iter is reached, exit the training loop.
if iteration >= self.config.trainer.max_iter:
_end_training = True
break
# Move all tensors in the data batch to GPU device.
data_batch = misc.to(data_batch, device="cuda")
# The actual training step.
self.callbacks.on_training_step_start(model, data_batch, iteration=iteration)
model_ddp.train()
output_batch, loss, grad_accum_iter = self.training_step(
model_ddp,
optimizer,
scheduler,
grad_scaler,
data_batch,
iteration=iteration,
grad_accum_iter=grad_accum_iter,
)
# Accumulate loss
accumulated_loss += loss.detach()
# If the gradients are still being accumulated, continue to load the next training batch.
if grad_accum_iter != 0:
if self.enable_one_logger:
# Callback for skipped OneLoggerCallback.on_training_step_end()
self.one_logger.on_train_batch_end(set_barrier=False)
continue
# Do the following when an actual optimizer (update) step has been made.
iteration += 1
# Average loss over accumulation steps
grad_accum_avg_loss = accumulated_loss / self.config.trainer.grad_accum_iter
# Average loss across all devices
device_avg_loss = grad_accum_avg_loss.clone()
dist.all_reduce(device_avg_loss, op=dist.ReduceOp.SUM)
device_avg_loss /= dist.get_world_size()
# Reset accumulation variables
accumulated_loss = 0.0
self.callbacks.on_training_step_end(
model, data_batch, output_batch, device_avg_loss, iteration=iteration
)
# self.callbacks.on_training_step_end(model, data_batch, output_batch, loss, iteration=iteration)
# Validation.
if self.config.trainer.run_validation and iteration % self.config.trainer.validation_iter == 0:
self.validate(model, dataloader_val, iteration=iteration)
# Save checkpoint.
if iteration % self.config.checkpoint.save_iter == 0:
self.checkpointer.save(model, optimizer, scheduler, grad_scaler, iteration=iteration)
# This iteration is successful; reset the timeout signal.
signal.alarm(self.config.trainer.timeout_period)
if _end_training:
break
log.success("Done with training.")
self.checkpointer.save(model, optimizer, scheduler, grad_scaler, iteration=iteration)
self.callbacks.on_train_end(model, iteration=iteration)
self.checkpointer.finalize()
distributed.barrier()
self.callbacks.on_app_end()
def training_step(
self,
model_ddp: torch.nn.Module | distributed.DistributedDataParallel,
optimizer: torch.optim.Optimizer,
scheduler: torch.optim.lr_scheduler.LRScheduler,
grad_scaler: torch.amp.GradScaler,
data: dict[str, torch.Tensor],
iteration: int = 0,
grad_accum_iter: int = 0,
) -> tuple[dict[str, torch.Tensor], torch.Tensor, int]:
"""The training step.
Args:
model_ddp (torch.nn.Module | distributed.DistributedDataParallel): The model with a DDP wrapper or, the bare
module, depending on whether distributed training is enabled or not.
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).
data (dict[str, torch.Tensor]): Data batch (dictionary of tensors).
iteration (int): Current iteration number.
grad_accum_iter (int): Number of gradient accumulation iterations.
Returns:
output (dict[str, torch.Tensor]): The model output from the training data batch (dictionary of tensors).
loss (torch.Tensor): The total loss of the training data batch.
"""
# Only let DDP sync gradient at the last iteration of the gradient accumulation window
with distributed.ddp_sync_grad(model_ddp, grad_accum_iter == self.config.trainer.grad_accum_iter - 1):
with self.training_timer("forward"):
output_batch, loss = model_ddp.training_step(data, iteration)
self.callbacks.on_before_backward(model_ddp, loss, iteration=iteration)
with self.training_timer("backward"):
loss_scaled = grad_scaler.scale(loss / self.config.trainer.grad_accum_iter)
loss_scaled.backward()
if self.config.trainer.distributed_parallelism == "ddp":
model_ddp.module.on_after_backward()
else:
model_ddp.on_after_backward()
self.callbacks.on_after_backward(model_ddp, iteration=iteration)
grad_accum_iter += 1
if grad_accum_iter == self.config.trainer.grad_accum_iter:
with self.training_timer("optimizer_step"):
self.callbacks.on_before_optimizer_step(
model_ddp, optimizer, scheduler, grad_scaler, iteration=iteration
)
grad_scaler.step(optimizer)
grad_scaler.update()
scheduler.step()
self.callbacks.on_before_zero_grad(model_ddp, optimizer, scheduler, iteration=iteration)
if self.config.trainer.distributed_parallelism == "ddp":
model_ddp.module.on_before_zero_grad(optimizer, scheduler, iteration=iteration)
else:
model_ddp.on_before_zero_grad(optimizer, scheduler, iteration=iteration)
optimizer.zero_grad(set_to_none=True)
grad_accum_iter = 0
return output_batch, loss, grad_accum_iter
@torch.no_grad()
def validate(self, model: Model, dataloader_val: torch.utils.data.DataLoader, iteration: int = 0) -> None:
"""Validate on the full validation dataset.
Args:
model (Model): The PyTorch model.
dataloader_val (torch.utils.data.DataLoader): The validation data loader.
iteration (int): Current iteration number.
"""
self.callbacks.on_validation_start(model, dataloader_val, iteration=iteration)
model.eval()
# Evaluate on the full validation set.
with ema.ema_scope(model, enabled=getattr(model.config.ema, "enabled", False)):
for val_iter, data_batch in enumerate(dataloader_val):
if self.config.trainer.max_val_iter is not None and val_iter >= self.config.trainer.max_val_iter:
break
data_batch = misc.to(data_batch, device="cuda")
self.callbacks.on_validation_step_start(model, data_batch, iteration=iteration)
output_batch, loss = model.validation_step(data_batch, iteration)
self.callbacks.on_validation_step_end(model, data_batch, output_batch, loss, iteration=iteration)
self.callbacks.on_validation_end(model, iteration=iteration)
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