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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 typing import List, Optional
import torch
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from cosmos_predict1.utils import distributed
from cosmos_predict1.utils.callback import Callback
@torch.jit.script
def _fused_nan_to_num(params: List[torch.Tensor]):
for param in params:
torch.nan_to_num(param, nan=0.0, posinf=0.0, neginf=0.0, out=param)
class GradClip(Callback):
def __init__(
self, clip_norm=1.0, force_finite: bool = True, model_key: Optional[str] = None, fsdp_enabled: bool = False
):
self.clip_norm = clip_norm
self.force_finite = force_finite
self.model_key = model_key
self.fsdp_enabled = fsdp_enabled
def on_before_optimizer_step(
self,
model_ddp: distributed.DistributedDataParallel,
optimizer: torch.optim.Optimizer,
scheduler: torch.optim.lr_scheduler.LRScheduler,
grad_scaler: torch.amp.GradScaler,
iteration: int = 0,
) -> None:
del optimizer, scheduler
if isinstance(model_ddp, distributed.DistributedDataParallel):
model = model_ddp.module
else:
model = model_ddp
# select sub-network if specified
if self.model_key is not None:
items = self.model_key.split(".")
for item in items:
model = getattr(model, item)
if self.force_finite:
params = []
for param in model.parameters():
if param.grad is not None:
params.append(param.grad)
# torch.nan_to_num(param.grad, nan=0, posinf=0, neginf=0, out=param.grad)
_fused_nan_to_num(params)
# check if FSDP is used
# total_norm
if isinstance(model, FSDP) and self.fsdp_enabled:
model.clip_grad_norm_(self.clip_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), self.clip_norm, foreach=True)
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