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# SPDX-FileCopyrightText: Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES.
# SPDX-FileCopyrightText: 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.
# There is a minimum version of pytorch required for shard tensor.
# 2.6.0+ works
# 2.5.X and lower does not work
import torch
from physicsnemo.utils.version_check import check_module_requirements
from .autograd import all_gather_v, gather_v, indexed_all_to_all_v, scatter_v
from .config import ProcessGroupConfig, ProcessGroupNode
# Load and register custom ops:
from .manager import (
DistributedManager,
PhysicsNeMoUndefinedGroupError,
PhysicsNeMoUninitializedDistributedManagerWarning,
)
from .utils import (
mark_module_as_shared,
reduce_loss,
unmark_module_as_shared,
)
try:
check_module_requirements("physicsnemo.distributed.shard_tensor")
# In minumum versions are met, we can import the shard tensor and spec.
from ._shard_tensor_spec import ShardTensorSpec
from .shard_tensor import ShardTensor, scatter_tensor
def register_custom_ops():
# These imports will register the custom ops with the ShardTensor class.
# It's done here to avoid an import cycle.
from .custom_ops import (
mean_wrapper,
sum_wrapper,
unbind_rules,
)
from .shard_utils import register_shard_wrappers
register_shard_wrappers()
# Protect the automatic imports by checking cuda is available.
if torch.cuda.is_available():
register_custom_ops()
except ImportError:
pass
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