# Copyright The PyTorch Lightning team.
#
# 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.
from contextlib import ExitStack
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
import torch.nn as nn
from lightning_utilities.core.imports import module_available
from torch import Tensor
from torch.optim import Optimizer

import pytorch_lightning as pl
from lightning_fabric.plugins import CheckpointIO
from lightning_fabric.utilities.distributed import _distributed_available
from lightning_fabric.utilities.distributed import group as dist_group
from lightning_fabric.utilities.types import ReduceOp
from pytorch_lightning.core.optimizer import LightningOptimizer
from pytorch_lightning.plugins.precision import PrecisionPlugin
from pytorch_lightning.strategies.parallel import ParallelStrategy
from pytorch_lightning.strategies.strategy import TBroadcast
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation, rank_zero_only

_HOROVOD_AVAILABLE = module_available("horovod.torch")
if _HOROVOD_AVAILABLE:
    import horovod.torch as hvd


class HorovodStrategy(ParallelStrategy):
    """Plugin for Horovod distributed training integration."""

    strategy_name = "horovod"

    def __init__(
        self,
        accelerator: Optional["pl.accelerators.Accelerator"] = None,
        parallel_devices: Optional[List[torch.device]] = None,
        checkpoint_io: Optional[CheckpointIO] = None,
        precision_plugin: Optional[PrecisionPlugin] = None,
    ):
        rank_zero_deprecation(
            "`The `HorovodStrategy`: `Trainer(strategy='horovod')` has been deprecated in v1.9.0 and will be removed"
            " in v2.0.0. You can try using the `Trainer(strategy='ddp')` instead."
        )
        if not _HOROVOD_AVAILABLE:
            raise MisconfigurationException(
                'Requested `strategy="horovod"`, but Horovod is not installed.'
                " Install with `HOROVOD_WITH_PYTORCH=1 pip install horovod[pytorch]`"
            )
        super().__init__(
            accelerator=accelerator,
            parallel_devices=parallel_devices,
            cluster_environment=None,
            checkpoint_io=checkpoint_io,
            precision_plugin=precision_plugin,
        )
        rank_zero_only.rank = self.global_rank
        self._exit_stack: Optional[ExitStack] = None

    @property
    def global_rank(self) -> int:
        return hvd.rank()

    @property
    def local_rank(self) -> int:
        return hvd.local_rank()

    @property
    def world_size(self) -> int:
        return hvd.size()

    @property
    def root_device(self) -> torch.device:
        assert isinstance(self.parallel_devices, list)
        return self.parallel_devices[self.local_rank]

    @property
    def distributed_sampler_kwargs(self) -> Dict[str, Any]:
        distributed_sampler_kwargs = dict(num_replicas=self.world_size, rank=self.global_rank)
        return distributed_sampler_kwargs

    @property
    def handles_gradient_accumulation(self) -> bool:
        """Whether the plugin handles gradient accumulation internally."""
        return True

    def setup(self, trainer: "pl.Trainer") -> None:
        self.model_to_device()

        super().setup(trainer)

        self._exit_stack = ExitStack()
        self._exit_stack.__enter__()

        if not trainer.training:
            # no need to setup optimizers
            return

        def _unpack_lightning_optimizer(opt: Optimizer) -> Optimizer:
            return opt._optimizer if isinstance(opt, LightningOptimizer) else opt

        optimizers = self.optimizers
        optimizers = [_unpack_lightning_optimizer(opt) for opt in optimizers]

        # Horovod: scale the learning rate by the number of workers to account for
        # increased total batch size
        for optimizer in optimizers:
            for param_group in optimizer.param_groups:
                param_group["lr"] *= self.world_size

        # Horovod: adjust base LR used by schedulers to match scaled optimizer initial LR
        lr_scheduler_configs = self.lr_scheduler_configs
        for config in lr_scheduler_configs:
            scheduler = config.scheduler
            if hasattr(scheduler, "base_lrs"):
                scheduler.base_lrs = [lr * self.world_size for lr in scheduler.base_lrs]  # type: ignore[union-attr]

        assert self.lightning_module is not None
        # Horovod: broadcast parameters & optimizer state to ensure consistent initialization
        hvd.broadcast_parameters(self.lightning_module.state_dict(), root_rank=0)
        for optimizer in optimizers:
            hvd.broadcast_optimizer_state(optimizer, root_rank=0)

        accumulation_scheduler = trainer.accumulation_scheduler
        if accumulation_scheduler.epochs != [0]:
            raise MisconfigurationException(
                "Horovod currently does not support different `accumulate_grad_batches` at different epochs."
            )

        self.optimizers = self._wrap_optimizers(optimizers, trainer.accumulate_grad_batches)
        for optimizer in self.optimizers:
            # Synchronization will be performed explicitly following backward()
            self._exit_stack.enter_context(optimizer.skip_synchronize())

    def barrier(self, *args: Any, **kwargs: Any) -> None:
        if _distributed_available():
            self.join()

    def broadcast(self, obj: TBroadcast, src: int = 0) -> TBroadcast:
        obj = hvd.broadcast_object(obj, src)
        return obj

    def model_to_device(self) -> None:
        if self.root_device.type == "cuda":
            # this can potentially be removed after #8312. Not done due to lack of horovod testing
            torch.cuda.set_device(self.root_device)
        assert self.model is not None
        self.model.to(self.root_device)

    def join(self) -> None:
        if self.root_device.type == "cuda":
            hvd.join(self.local_rank)
        else:
            hvd.join()

    def reduce(
        self,
        tensor: Union[Any, Tensor],
        group: Optional[Any] = None,
        reduce_op: Optional[Union[ReduceOp, str]] = "mean",
    ) -> Union[Any, Tensor]:
        """Reduces a tensor from several distributed processes to one aggregated tensor.

        Args:
            tensor: the tensor to sync and reduce
            group: the process group to gather results from. Defaults to all processes (world)
            reduce_op: the reduction operation. Defaults to 'mean'/'avg'.
                Can also be a string 'sum' to calculate the sum during reduction.

        Return:
            reduced value, except when the input was not a tensor the output remains is unchanged
        """
        if group is not None:
            raise ValueError("Horovod does not support allreduce using a subcommunicator at this time. Unset `group`.")

        if reduce_op in (None, "avg", "mean"):
            reduce_op = hvd.Average
        elif reduce_op in ("sum", ReduceOp.SUM):
            reduce_op = hvd.Sum
        else:
            raise ValueError(f"unrecognized `reduce_op`: {reduce_op}")

        # sync all processes before reduction
        self.join()
        return hvd.allreduce(tensor, op=reduce_op)

    def all_gather(self, result: Tensor, group: Optional[Any] = dist_group.WORLD, sync_grads: bool = False) -> Tensor:
        if group is not None and group != dist_group.WORLD:
            raise ValueError("Horovod does not support allgather using a subcommunicator at this time. Unset `group`.")

        if len(result.shape) == 0:
            # Convert scalars to single dimension tensors
            result = result.reshape(1)

        # sync and gather all
        self.join()
        return hvd.allgather(result)

    def post_backward(self, closure_loss: Tensor) -> None:
        # synchronize all horovod optimizers.
        for optimizer in self.optimizers:
            optimizer.synchronize()

    def _wrap_optimizers(
        self, optimizers: List[Optimizer], accumulate_grad_batches: int
    ) -> List["hvd.DistributedOptimizer"]:
        """Wraps optimizers to perform gradient aggregation via allreduce."""
        assert self.lightning_module is not None
        return [
            hvd.DistributedOptimizer(
                opt,
                backward_passes_per_step=accumulate_grad_batches,
                named_parameters=self._filter_named_parameters(self.lightning_module, opt),
            )
            if "horovod" not in str(opt.__class__)
            else opt
            for opt in optimizers
        ]

    @staticmethod
    def _filter_named_parameters(model: nn.Module, optimizer: Optimizer) -> List[Tuple[str, nn.Parameter]]:
        opt_params = {p for group in optimizer.param_groups for p in group.get("params", [])}
        return [(name, p) for name, p in model.named_parameters() if p in opt_params]

    @classmethod
    def register_strategies(cls, strategy_registry: Dict) -> None:
        strategy_registry.register(
            cls.strategy_name,
            cls,
            description=f"{cls.__class__.__name__}",
        )

    def teardown(self) -> None:
        # teardown may be called before `_exit_stack` is set
        if self._exit_stack:
            self._exit_stack.__exit__(None, None, None)
            self._exit_stack = None
        # Make sure all workers have finished training before returning to the user
        self.join()
        super().teardown()