# Copyright (c) 2023, NVIDIA CORPORATION.
#
# 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.

rmm_torch_allocator = None

try:
    from torch.cuda.memory import CUDAPluggableAllocator
except ImportError:
    pass
else:
    from torch.cuda import is_available

    if is_available():
        import pathlib

        # To support editable installs, we cannot search for the compiled torch
        # allocator .so relative to the current file because the current file
        # is pure Python and will therefore be in the source directory.
        # Instead, we search relative to an arbitrary file in the compiled
        # package. We use the _lib.lib module because it is small.
        from rmm._lib import lib

        sofile = pathlib.Path(lib.__file__).parent / "_torch_allocator.so"
        rmm_torch_allocator = CUDAPluggableAllocator(
            str(sofile.absolute()),
            alloc_fn_name="allocate",
            free_fn_name="deallocate",
        )
        del pathlib, sofile
    del is_available
