physics_mcp/source/physicsnemo/distributed/__init__.py

# 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