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# Source code for torch.distributed.pipeline.sync.pipe

    # Copyright 2019 Kakao Brain
    #
    # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
    #
    # This source code is licensed under the BSD license found in the
    # LICENSE file in the root directory of this source tree.
    """The Pipe interface."""
    from collections import OrderedDict
    from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Union, Sequence, Tuple, cast

    import torch
    from torch import Tensor, nn
    from torch.distributed.rpc import RRef
    import torch.autograd
    import torch.cuda

    from . import microbatch
    from .batchnorm import DeferredBatchNorm
    from .pipeline import Pipeline
    from .skip.layout import inspect_skip_layout
    from .skip.skippable import verify_skippables
    from .stream import AbstractStream, new_stream

    __all__ = ["Pipe", "BalanceError", "PipeSequential", "WithDevice"]

    Device = Union[torch.device, int, str]
    Devices = Union[Iterable[Device], List[Device]]

    Tensors = Sequence[Tensor]
    TensorOrTensors = Union[Tensor, Tensors]

    if TYPE_CHECKING:
        # Typechecking: nn.Module is not a Generic
        Module = nn.Module[TensorOrTensors]  # type: ignore[type-arg]
        NamedModules = OrderedDict[str, Module]
    else:
        Module = nn.Module
        NamedModules = OrderedDict

    def _recommend_auto_balance(message: str) -> str:
        """Expands a message with recommendation to :mod:`torchpipe.balance`."""
        return f"""{message}

    If your model is still under development, its optimal balance would change
    frequently. In this case, we highly recommend 'torch.distributed.pipeline.sync.balance' for
    naive automatic balancing:

      from torch.distributed.pipeline.sync import Pipe
      from torch.distributed.pipeline.sync.balance import balance_by_time

      partitions = torch.cuda.device_count()
      sample = torch.empty(...)
      balance = balance_by_time(partitions, model, sample)

      model = Pipe(model, balance, ...)
    """

    def _verify_module(module: nn.Sequential) -> None:
        if not isinstance(module, nn.Sequential):
            raise TypeError("module must be nn.Sequential to be partitioned")

        named_children = list(module.named_children())
        if len(named_children) != len(module):
            raise ValueError("module with duplicate children is not supported")

    def _verify_splitting(
        module: nn.Sequential, partitions: List[nn.Sequential], devices: List[torch.device]
    ) -> None:
        num_parameters = len(list(module.parameters()))
        num_child_parameters = sum(len(list(child.parameters())) for child in module.children())
        if num_parameters == num_child_parameters:
            return

        for i in range(len(partitions)):
            for j in range(i + 1, len(partitions)):
                parti = partitions[i]
                partj = partitions[j]
                if devices[i] == devices[j]:
                    continue
                for p in parti.parameters():
                    for q in partj.parameters():
                        if p is q:
                            raise ValueError("module with duplicate parameters on distinct devices is not supported")

    class BalanceError(ValueError):
        pass

    def _retrieve_device(module: nn.Module) -> torch.device:
        """Validates all parameters in the Module have the same device and returns
        the appropriate device.

        Args:
            An ``nn.Module`` to process.

        Returns:
            ``torch.Device`` for the entire module.

        Raises:
            ValueError:
                If devices for ``nn.Module`` parameters are not all same.
        """

        device = None
        for parameter in module.parameters():
            if device is None:
                device = parameter.device
            elif device != parameter.device:
                raise ValueError(
                    f'nn.Module: {module}, should have all parameters on a single device,'
                    ' please use .to() to place the module on a single device')

        return device if device is not None else torch.device("cpu")

    class PipeSequential(nn.Sequential):
        """
        Pipe variant of ``nn.Sequential`` which supports multiple inputs.
        """

        def forward(self, *inputs):
            for module in self:
                if isinstance(inputs, Tuple):  # type: ignore[arg-type]
                    inputs = module(*inputs)
                else:
                    # Don't expand single variables (ex: lists/Tensor)
                    inputs = module(inputs)
            return inputs

    class WithDevice(nn.Module):
        """
        Wraps an ``nn.Module`` which is part of ``nn.Sequential`` passed into :class:`Pipe`
        that overrides the device for that module. In cases where :class:`Pipe`
        can't implicitly determine the device for the module and places it on CPU,
        this wrapper can be used to override the implicit behavior and explicitly
        specify which device a module should run on.

        The provided module is also moved to the given device via ``.to(device)``
        by :class:`Pipe`

        Args:
            module(:class:`torch.nn.Module`): The module to be wrapped.
            device(:class:`torch.device`): The device to run the module on.

        Example::
            >>> # xdoctest: +SKIP("distributed")
            >>> fc1 = nn.Linear(16, 8).cuda(0)
            >>> fc2 = nn.Linear(8, 4).cuda(1)
            >>> dropout = nn.Dropout()
            >>>
            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)
            >>> # Dropout does not have any parameters/buffers, but we want to
            >>> # run it on cuda:1 to avoid any GPU to CPU transfers.
            >>> model = nn.Sequential(fc1, fc2, WithDevice(dropout, 'cuda:1'))
            >>> # xdoctest: +SKIP("Needs RPC framework init")
            >>> model = Pipe(model, chunks=8)
        """
        def __init__(self, module: nn.Module, device: torch.device):
            super().__init__()
            self._module = module
            self._device = torch.device(device)

        def forward(self, *args, **kwargs):
            return self._module(*args, **kwargs)

        @property
        def module(self):
            return self._module

        @property
        def device(self):
            return self._device

    def _assemble_partition(modules: List[nn.Module]):
        modules_list: List[nn.Module] = []
        for module in modules:
            if isinstance(module, nn.Sequential):
                modules_list.extend(module.children())
            else:
                modules_list.append(module)
        return PipeSequential(*modules_list)

    def _split_module(modules: nn.Sequential) -> Tuple[List[nn.Sequential], List[torch.device]]:
        partitions = []
        devices = []

        current_partition = []
        current_device = None
        for name, module in modules.named_children():
            if isinstance(module, WithDevice):
                # Process device override and move module to appropriate device.
                device = module.device
                module = module.module
                module.to(device)
            else:
                device = _retrieve_device(module)
            if current_device is not None and (current_device != device or device.type == 'cpu'):
                partitions.append(_assemble_partition(current_partition))
                devices.append(current_device)
                current_partition = []
            current_device = device
            current_partition.append(module)

        if current_device is not None:
            partitions.append(_assemble_partition(current_partition))
            devices.append(current_device)

        partitions = cast(List[nn.Sequential], nn.ModuleList(partitions))

        return partitions, devices

    MOVING_DENIED = TypeError("denied to move parameters and buffers, because Pipe should manage device placement")

    class Pipe(Module):
        """Wraps an arbitrary :class:`nn.Sequential <torch.nn.Sequential>` module
        to train on using synchronous pipeline parallelism. If the module requires
        lots of memory and doesn't fit on a single GPU, pipeline parallelism is a
        useful technique to employ for training.

        The implementation is based on the torchgpipe_ paper.

        .. _torchgpipe: https://arxiv.org/abs/2004.09910

        Pipe combines pipeline parallelism with checkpointing to reduce peak
        memory required to train while minimizing device under-utilization.

        You should place all the modules on the appropriate devices and wrap them
        into an :class:`nn.Sequential <torch.nn.Sequential>` module defining the
        desired order of execution. If a module does not contain any
        parameters/buffers, it is assumed this module should be executed on CPU
        and appropriate input tensors to the module are moved to CPU before
        execution. This behavior can be overridden by the :class:`WithDevice`
        wrapper which can be used to explicitly specify which device a module
        should run on.

        Args:
            module (:class:`nn.Sequential <torch.nn.Sequential>`):
                sequential module to be parallelized using pipelining. Each module
                in the sequence has to have all of its parameters on a single
                device. Each module in the sequence has to either be an nn.Module
                or :class:`nn.Sequential <torch.nn.Sequential>` (to combine multiple
                sequential modules on a single device)
            chunks (int):
                number of micro-batches (default: ``1``)
            checkpoint (str):
                when to enable checkpointing, one of ``'always'``,
                ``'except_last'``, or ``'never'`` (default: ``'except_last'``).
                ``'never'`` disables checkpointing completely, ``'except_last'``
                enables checkpointing for all micro-batches except the last one
                and ``'always'`` enables checkpointing for all micro-batches.
            deferred_batch_norm (bool):
                whether to use deferred ``BatchNorm`` moving statistics (default:
                :data:`False`). If set to :data:`True`, we track statistics across
                multiple micro-batches to update the running statistics per
                mini-batch.

        Raises:
            TypeError:
                the module is not a :class:`nn.Sequential <torch.nn.Sequential>`.
            ValueError:
                invalid arguments

        Example::
            Pipeline of two FC layers across GPUs 0 and 1.

            >>> # Need to initialize RPC framework first.
            >>> # xdoctest: +SKIP
            >>> os.environ['MASTER_ADDR'] = 'localhost'
            >>> os.environ['MASTER_PORT'] = '29500'
            >>> torch.distributed.rpc.init_rpc('worker', rank=0, world_size=1)
            >>>
            >>> # Build pipe.
            >>> fc1 = nn.Linear(16, 8).cuda(0)
            >>> fc2 = nn.Linear(8, 4).cuda(1)
            >>> model = nn.Sequential(fc1, fc2)
            >>> model = Pipe(model, chunks=8)
            >>> input = torch.rand(16, 16).cuda(0)
            >>> output_rref = model(input)

        .. note::
            You can wrap a :class:`Pipe` model with
            :class:`torch.nn.parallel.DistributedDataParallel` only when the
            checkpoint parameter of :class:`Pipe` is ``'never'``.

        .. note::
            :class:`Pipe` only supports intra-node pipelining currently, but
            will be expanded to support inter-node pipelining in the future.
            The forward function returns an :class:`~torch.distributed.rpc.RRef`
            to allow for inter-node pipelining in the future, where the output
            might be on a remote host. For intra-node pipelining you can use
            :meth:`~torch.distributed.rpc.RRef.local_value` to retrieve the
            output locally.

        .. warning::
            :class:`Pipe` is experimental and subject to change.
        """

        def __init__(
            self,
            module: nn.Sequential,
            chunks: int = 1,
            checkpoint: str = "except_last",
            deferred_batch_norm: bool = False,
        ) -> None:
            super().__init__()

            # Check if RPC framework is initialized.
            if not torch.distributed.rpc._is_current_rpc_agent_set():
                raise RuntimeError(
                    'Please initialize RPC framework for Pipe using '
                    'torch.distributed.rpc.init_rpc')

            chunks = int(chunks)
            checkpoint = str(checkpoint)

            if chunks <= 0:
                raise ValueError("number of chunks must be positive integer")
            if checkpoint not in ["always", "except_last", "never"]:
                raise ValueError("checkpoint is not one of 'always', 'except_last', or 'never'")

            _verify_module(module)

            # Verify if the underlying skippable modules satisfy integrity. The
            # integrity can be verified before forward() because it is static.
            verify_skippables(module)

            self.chunks = chunks
            self.checkpoint = checkpoint

            if deferred_batch_norm:
                module = DeferredBatchNorm.convert_deferred_batch_norm(module, chunks)

            self.partitions, self.devices = _split_module(module)
            _verify_splitting(module, self.partitions, self.devices)

            self._copy_streams: List[List[AbstractStream]] = []
            self._skip_layout = inspect_skip_layout(self.partitions)

            # Separate CUDA streams for copy.
            copy_streams = self._ensure_copy_streams()

            # The micro-batch index where the checkpointing stops.
            checkpoint_stop = {"always": self.chunks, "except_last": self.chunks - 1, "never": 0}[self.checkpoint]

            self.pipeline = Pipeline(self.partitions, self.devices, copy_streams, self._skip_layout, checkpoint_stop)

        def __len__(self) -> int:
            """Counts the length of the underlying sequential module."""
            return sum(len(p) for p in self.partitions)

        def __getitem__(self, index: int) -> nn.Module:
            """Gets a layer in the underlying sequential module."""
            partitions = self.partitions
            if index < 0:
                partitions = partitions[::-1]

            for partition in partitions:
                try:
                    return partition[index]
                except IndexError:
                    pass

                shift = len(partition)

                if index < 0:
                    index += shift
                else:
                    index -= shift

            raise IndexError

        def __iter__(self) -> Iterable[nn.Module]:
            """Iterates over children of the underlying sequential module."""
            for partition in self.partitions:
                yield from partition

        # Pipe should manage the device of each partition.
        # Deny cuda(), cpu(), and to() with device, by TypeError.
        def cuda(self, device: Optional[Device] = None) -> "Pipe":
            raise MOVING_DENIED

        def cpu(self) -> "Pipe":
            raise MOVING_DENIED

        def to(self, *args: Any, **kwargs: Any) -> "Pipe":
            # Deny these usages:
            #
            # - to(device[, dtype, non_blocking])
            # - to(tensor[, non_blocking])
            #
            # But allow this:
            #
            # - to(dtype[, non_blocking])
            #
            if "device" in kwargs or "tensor" in kwargs:
                raise MOVING_DENIED

            if args:
                if isinstance(args[0], (torch.device, int, str)):
                    raise MOVING_DENIED
                if torch.is_tensor(args[0]):
                    raise MOVING_DENIED

            return super().to(*args, **kwargs)

        def _ensure_copy_streams(self) -> List[List[AbstractStream]]:
            """Ensures that :class:`Pipe` caches CUDA streams for copy.

            It's worth to cache CUDA streams although PyTorch already manages a
            pool of pre-allocated CUDA streams, because it may reduce GPU memory
            fragmentation when the number of micro-batches is small.

            """
            if not self._copy_streams:
                for device in self.devices:
                    self._copy_streams.append([new_stream(device) for _ in range(self.chunks)])

            return self._copy_streams

        def forward(self, *inputs) -> RRef:
            """
            Processes a single input mini-batch through the pipe and returns an
            :class:`~torch.distributed.rpc.RRef` pointing to the output.
            :class:`Pipe` is a fairly transparent module wrapper. It doesn't
            modify the input and output signature of the underlying module. But
            there's type restriction. Input and output have to contain at least one
            tensor. This restriction is applied at partition boundaries too.

            The sequence of inputs are fed into the first stage of the pipeline as
            ``*inputs``. As a result the positional args for this function should
            match the positional args for the first stage of the pipeline. The same
            condition applies for output of one stage of the pipeline which is the
            input for the next stage.

            The input tensor is split into multiple micro-batches based on the
            ``chunks`` parameter used to initialize :class:`Pipe`. The batch size
            is assumed to be the first dimension of the tensor and if the batch
            size is less than ``chunks``, the number of micro-batches is equal to
            the batch size.

            Only tensors are split into multiple micro-batches, non-Tensor inputs
            are just replicated as-is in each micro-batch. For non-Tensor outputs
            in the last stage of the pipeline, they are aggregated as a ``List``
            and returned the user. For example, if you have 2 micro-batches
            returning the integer 5, the user would receive the consolidated
            output of `[5, 5]`

            All the input tensors need to be on the same device as the first
            partition of the pipeline.

            If a tensor is wrapped with the :class:`NoChunk` wrapper, the tensor
            is not split across micro-batches and is replicated as-is similar to
            non-tensors.

            Args:
                inputs: input mini-batch

            Returns:
                :class:`~torch.distributed.rpc.RRef` to the output of the mini-batch

            Raises:
                TypeError: input doesn't contain at least one tensor

            """
            first_partition_device = self.devices[0] if len(self.devices) != 0 else torch.device("cpu")
            microbatch.check(first_partition_device, *inputs)

            if not self.devices:
                # Empty sequential module is not illegal.
                return RRef(*inputs)

            # Divide a mini-batch into micro-batches.
            batches = microbatch.scatter(*inputs, chunks=self.chunks)

            # Run pipeline parallelism.
            self.pipeline.run(batches)

            # Merge the micro-batches into one mini-batch.
            output = microbatch.gather(batches)
            return RRef(output)

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