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# Source code for torch.distributed.algorithms.ddp_comm_hooks.default_hooks

    from typing import Any, Callable

    import torch
    import torch.distributed as dist

    __all__ = ["allreduce_hook", "fp16_compress_hook", "bf16_compress_hook", "fp16_compress_wrapper", "bf16_compress_wrapper"]

    def _allreduce_fut(
        process_group: dist.ProcessGroup, tensor: torch.Tensor
    ) -> torch.futures.Future[torch.Tensor]:
        "Averages the input gradient tensor by allreduce and returns a future."
        group_to_use = process_group if process_group is not None else dist.group.WORLD

        # Apply the division first to avoid overflow, especially for FP16.
        tensor.div_(group_to_use.size())

        return (
            dist.all_reduce(tensor, group=group_to_use, async_op=True)
            .get_future()
            .then(lambda fut: fut.value()[0])
        )

    def allreduce_hook(
        process_group: dist.ProcessGroup, bucket: dist.GradBucket
    ) -> torch.futures.Future[torch.Tensor]:
        """
        This DDP communication hook just calls ``allreduce`` using ``GradBucket``
        tensors. Once gradient tensors are aggregated across all workers, its ``then``
        callback takes the mean and returns the result. If user registers this hook,
        DDP results is expected to be same as the case where no hook was registered.
        Hence, this won't change behavior of DDP and user can use this as a reference
        or modify this hook to log useful information or any other purposes while
        unaffecting DDP behavior.

        Example::
            >>> # xdoctest: +SKIP
            >>> ddp_model.register_comm_hook(process_group, allreduce_hook)
        """
        return _allreduce_fut(process_group, bucket.buffer())

    def fp16_compress_hook(
        process_group: dist.ProcessGroup, bucket: dist.GradBucket
    ) -> torch.futures.Future[torch.Tensor]:
        """
        This DDP communication hook implements a simple gradient compression
        approach that casts ``GradBucket`` tensor to half-precision floating-point format (``torch.float16``)
        and then divides it by the process group size.
        It allreduces those ``float16`` gradient tensors. Once compressed gradient
        tensors are allreduced, the chained callback ``decompress`` casts it back to the input data type (such as ``float32``).

        Example::
            >>> # xdoctest: +SKIP
            >>> ddp_model.register_comm_hook(process_group, fp16_compress_hook)
        """
        group_to_use = process_group if process_group is not None else dist.group.WORLD
        world_size = group_to_use.size()

        compressed_tensor = bucket.buffer().to(torch.float16).div_(world_size)

        fut = dist.all_reduce(
            compressed_tensor, group=group_to_use, async_op=True
        ).get_future()

        def decompress(fut):
            decompressed_tensor = bucket.buffer()
            # Decompress in place to reduce the peak memory.
            # See: https://github.com/pytorch/pytorch/issues/45968
            decompressed_tensor.copy_(fut.value()[0])
            return decompressed_tensor

        return fut.then(decompress)

    # TODO: create an internal helper function and extract the duplicate code in FP16_compress and BF16_compress.
    def bf16_compress_hook(
        process_group: dist.ProcessGroup, bucket: dist.GradBucket
    ) -> torch.futures.Future[torch.Tensor]:
        """
        Warning: This API is experimental, and it requires NCCL version later than 2.9.6.

        This DDP communication hook implements a simple gradient compression
        approach that casts ``GradBucket`` tensor to half-precision
        `Brain floating point format <https://en.wikipedia.org/wiki/Bfloat16_floating-point_format>`_ (``torch.bfloat16``)
        and then divides it by the process group size.
        It allreduces those ``bfloat16`` gradient tensors. Once compressed gradient
        tensors are allreduced, the chained callback ``decompress`` casts it back to the input data type (such as ``float32``).

        Example::
            >>> # xdoctest: +SKIP
            >>> ddp_model.register_comm_hook(process_group, bf16_compress_hook)
        """
        group_to_use = process_group if process_group is not None else dist.group.WORLD
        world_size = group_to_use.size()

        compressed_tensor = bucket.buffer().to(torch.bfloat16).div_(world_size)

        fut = dist.all_reduce(
            compressed_tensor, group=group_to_use, async_op=True
        ).get_future()

        def decompress(fut):
            decompressed_tensor = bucket.buffer()
            # Decompress in place to reduce the peak memory.
            # See: https://github.com/pytorch/pytorch/issues/45968
            decompressed_tensor.copy_(fut.value()[0])
            return decompressed_tensor

        return fut.then(decompress)

    def fp16_compress_wrapper(
        hook: Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]
    ) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:
        """
        This wrapper casts the input gradient tensor of a given DDP communication hook to half-precision
        floating point format (``torch.float16``), and casts the resulting tensor of the given hook back to
        the input data type, such as ``float32``.

        Therefore, ``fp16_compress_hook`` is equivalent to ``fp16_compress_wrapper(allreduce_hook)``.

        Example::
            >>> # xdoctest: +SKIP
            >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, start_powerSGD_iter=10)
            >>> ddp_model.register_comm_hook(state, fp16_compress_wrapper(powerSGD_hook))
        """

        def fp16_compress_wrapper_hook(
            hook_state, bucket: dist.GradBucket
        ) -> torch.futures.Future[torch.Tensor]:
            # Cast bucket tensor to FP16.
            bucket.set_buffer(bucket.buffer().to(torch.float16))

            fut = hook(hook_state, bucket)

            def decompress(fut):
                decompressed_tensor = bucket.buffer()
                # Decompress in place to reduce the peak memory.
                # See: https://github.com/pytorch/pytorch/issues/45968
                decompressed_tensor.copy_(fut.value())
                return decompressed_tensor

            # Decompress after hook has run.
            return fut.then(decompress)

        return fp16_compress_wrapper_hook

    def bf16_compress_wrapper(
        hook: Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]
    ) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:
        """
        Warning: This API is experimental, and it requires NCCL version later than 2.9.6.

        This wrapper casts the input gradient tensor of a given DDP communication hook to half-precision
        `Brain floating point format <https://en.wikipedia.org/wiki/Bfloat16_floating-point_format> `_  (``torch.bfloat16``),
        and casts the resulting tensor of the given hook back to the input data type, such as ``float32``.

        Therefore, ``bf16_compress_hook`` is equivalent to ``bf16_compress_wrapper(allreduce_hook)``.

        Example::
            >>> # xdoctest: +SKIP
            >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, start_powerSGD_iter=10)
            >>> ddp_model.register_comm_hook(state, bf16_compress_wrapper(powerSGD_hook))
        """

        def bf16_compress_wrapper_hook(
            hook_state, bucket: dist.GradBucket
        ) -> torch.futures.Future[torch.Tensor]:
            # Cast bucket tensor to BF16.
            bucket.set_buffer(bucket.buffer().to(torch.bfloat16))

            fut = hook(hook_state, bucket)

            def decompress(fut):
                decompressed_tensor = bucket.buffer()
                # Decompress in place to reduce the peak memory.
                # See: https://github.com/pytorch/pytorch/issues/45968
                decompressed_tensor.copy_(fut.value())
                return decompressed_tensor

            # Decompress after hook has run.
            return fut.then(decompress)

        return bf16_compress_wrapper_hook

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