# this code is inspired by the DeepSpeed library and implemented with our own design from scratch
from functools import partial
from typing import Optional

import torch
import torch.distributed as dist
from torch.optim import Optimizer

from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler
from colossalai.context import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.logging import get_dist_logger
from colossalai.nn.optimizer import ColossalaiOptimizer
from colossalai.tensor import ColoParameter, ProcessGroup
from colossalai.utils.cuda import get_current_device

from ._utils import (
    calculate_global_norm_from_list,
    compute_norm,
    flatten,
    has_inf_or_nan,
    reduce_tensor_dp_group,
    release_param_grad,
    split_by_dtype,
    sync_param,
)
from .bookkeeping import BucketStore, GradientStore, ParameterStore, TensorBucket


class LowLevelZeroOptimizer(ColossalaiOptimizer):
    """Optimizer used for ZeRO-1 and ZeRO-2.
    """

    def __init__(
            self,
            optimizer: Optimizer,
            initial_scale: int = 2**16,    # grad scaler config
            min_scale: int = 1,
            growth_factor: float = 2.,
            backoff_factor: float = .5,
            growth_interval: int = 2000,
            hysteresis: int = 2,
            max_scale: int = 2**24,
            clip_grad_norm: float = 0.0,    # grad clipping
            verbose: bool = False,
            reduce_bucket_size: int = 1024 * 1024,    # communication
            communication_dtype: Optional[torch.dtype] = None,
            overlap_communication: bool = False,
            partition_grad: bool = False,    # stage 2 flag
            cpu_offload: bool = False,    # cpu offload
            forced_dtype: Optional[torch.dtype] = None):

        # TODO: add support for
        # 1. fp16 master weights
        # 2. contiguous gradients
        # 3. cpu offload
        # 4. support when some parameters requires_grad = False
        # 5. support layer drop
        super(LowLevelZeroOptimizer, self).__init__(optim=optimizer)
        self._dtype = self.optim.param_groups[0]['params'][0].dtype
        self._logger = get_dist_logger()
        self._verbose = verbose

        # stage 2
        self._partition_grads = partition_grad

        self._cpu_offload = cpu_offload

        colo_pg = self._search_colo_process_group()
        if isinstance(colo_pg, ProcessGroup):
            self._local_rank = colo_pg.dp_local_rank()
            self._world_size = colo_pg.dp_world_size()
            self._dp_global_ranks = colo_pg.get_ranks_in_dp()
            self._dp_torch_group = colo_pg.dp_process_group()
            self._mp_torch_group = None
            if colo_pg.tp_world_size() > 1:
                self._mp_torch_group = colo_pg.tp_process_group()
        elif colo_pg is None:
            dp_parallel_mode = ParallelMode.DATA
            mp_parallel_mode = ParallelMode.MODEL

            self._dp_parallel_mode = dp_parallel_mode
            self._mp_parallel_mode = mp_parallel_mode
            self._local_rank = gpc.get_local_rank(dp_parallel_mode)
            self._world_size = gpc.get_world_size(dp_parallel_mode)
            self._dp_global_ranks = gpc.get_ranks_in_group(dp_parallel_mode)
            self._dp_torch_group = gpc.get_group(dp_parallel_mode)
            self._mp_torch_group = None
            if gpc.is_initialized(mp_parallel_mode) and gpc.get_world_size(mp_parallel_mode) > 1:
                self._mp_torch_group = gpc.get_group(mp_parallel_mode)
        else:
            raise NotImplementedError

        # working and master params for mixed precision training
        self._working_param_groups = dict()
        self._master_flat_param_groups_of_current_rank = dict()

        # communication params
        self._overlap_communication = overlap_communication
        self._reduce_bucket_size = reduce_bucket_size
        self._communication_dtype = communication_dtype

        # gradient scaler
        self.grad_scaler = DynamicGradScaler(initial_scale=initial_scale,
                                             min_scale=min_scale,
                                             growth_factor=growth_factor,
                                             backoff_factor=backoff_factor,
                                             growth_interval=growth_interval,
                                             hysteresis=hysteresis,
                                             max_scale=max_scale,
                                             verbose=verbose)
        self._found_overflow = torch.FloatTensor([0]).to(get_current_device())

        # gradient clipping
        self._clip_grad_norm = clip_grad_norm

        if forced_dtype:
            for group in self.optim.param_groups:
                group_params = group['params']
                for param in group_params:
                    param.data = param.data.to(forced_dtype)
            self._dtype = forced_dtype

        # check argument conflict
        self._sanity_checks()

        # ParameterStore will manage the tensor buffers used for zero
        # it will not manage the tensors used by mixed precision training
        self._param_store = ParameterStore(self._dp_torch_group)
        self._grad_store = GradientStore(self._dp_torch_group)
        self._bucket_store = BucketStore(self._dp_torch_group)

        # iterate over the param group in the optimizer
        # partition these param groups for data parallel training
        # and add buffers to parameter store for future access
        for group_id, param_group in enumerate(self.optim.param_groups):
            group_params = list()
            for param in param_group['params']:
                if param.requires_grad:
                    group_params.append(param)

            # add the working params to working_param_groups for bookkeeping
            self._working_param_groups[group_id] = group_params

            # assign parameters to ranks
            # the params in the list are sorted
            params_per_rank = self._partition_param_list(group_params)

            # store the mapping between param to rank
            # each param should belong to only one rank
            for rank, params in enumerate(params_per_rank):
                self._param_store.add_param_list_by_rank_group(rank, group_id, params)
                for param in params:
                    self._param_store.set_param_to_rank(param, rank)

            # move to cpu to make room to create the flat tensor
            # move_tensor(params, device='cpu')
            for param in group_params:
                param.data = param.data.cpu()

            # flatten the reordered tensors
            for rank in range(self._world_size):
                tensor_list = self._param_store.get_params_by_rank_group(rank, group_id)
                with torch.no_grad():
                    flat_tensor = flatten(tensor_list)
                flat_tensor = flat_tensor.data.cuda()
                self._param_store.add_flat_param_by_rank_group(rank, group_id, flat_tensor)

            # sync parameters
            for rank in range(self._world_size):
                flat_tensor = self._param_store.get_flat_param_by_rank_group(rank, group_id)
                tensor_list = self._param_store.get_params_by_rank_group(rank, group_id)
                sync_param(flat_tensor=flat_tensor, tensor_list=tensor_list)

            # create a copy of fp32 master weights of the parameters for which this rank is responsible
            working_flat_current_rank = self._param_store.get_flat_param_by_rank_group(self._local_rank, group_id)
            master_flat_current_rank = working_flat_current_rank.float()
            device = 'cpu' if self._cpu_offload else get_current_device()
            master_flat_current_rank = master_flat_current_rank.to(device)
            master_flat_current_rank.requires_grad = True
            self._master_flat_param_groups_of_current_rank[group_id] = master_flat_current_rank

            # need to replace the params in the `params` field in the optimizer
            # so that when the optimizer calls step(), it only updates the tensors
            # managed by this data parallel rank
            param_group['params'] = [master_flat_current_rank]

            # set reduction state
            for param in self._working_param_groups[group_id]:
                self._param_store.set_param_reduction_state(param, False)

        # intialize communication stream for
        # communication-compuation overlapping
        if self._overlap_communication:
            self._comm_stream = torch.cuda.Stream()

        # reduction hook is only used if overlapping communication
        # or stage 2 is used
        # if it is stage 1 without overlapping, no hook will be attached
        if self._overlap_communication or self._partition_grads:
            self._attach_reduction_hook()

    @property
    def dtype(self):
        return self._dtype

    @property
    def loss_scale(self):
        return self.grad_scaler.scale

    @property
    def num_param_groups(self):
        return len(self._working_param_groups)

    def _sanity_checks(self):
        assert torch.cuda.is_available(), 'CUDA is required'
        for param_group in self.optim.param_groups:
            group_params = param_group['params']
            for param in group_params:
                assert param.dtype == self._dtype, \
                    f"Parameters are expected to have the same dtype `{self._dtype}`, but got `{param.dtype}`"

    def _search_colo_process_group(self):
        colo_flag = False
        colo_pg = None
        for param_group in self.optim.param_groups:
            group_params = param_group['params']
            for param in group_params:
                if isinstance(param, ColoParameter):
                    colo_flag = True
                    if colo_pg is None:
                        colo_pg = param.get_process_group()
                    else:
                        assert colo_pg == param.get_process_group(), "All parameters should be in a same process group"
                elif colo_flag:
                    raise RuntimeError("All parameters should be ColoParameter if you use ColoParameter.")
        return colo_pg

    def _partition_param_list(self, param_list):
        params_per_rank = [[] for _ in range(self._world_size)]
        numel_per_rank = [0 for _ in range(self._world_size)]

        # partititon the parameters in a greedy fashion
        sorted_params = sorted(param_list, key=lambda x: x.numel(), reverse=True)
        for param in sorted_params:
            # allocate this parameter to the rank with
            # the smallest numel for load balancing purpose
            rank_to_go = numel_per_rank.index(min(numel_per_rank))
            params_per_rank[rank_to_go].append(param)
            numel_per_rank[rank_to_go] += param.numel()

        if self._verbose:
            self._logger.info(f'Number of elements on ranks: {numel_per_rank}', ranks=[0])
        return params_per_rank

    ###########################
    # Backward Reduction Hook #
    ###########################

    def _grad_handler(self, param, grad, reduce_rank):
        self._add_to_reduction_bucket(param, reduce_rank)
        return grad

    def _attach_reduction_hook(self):
        # we iterate over the working params
        # on each param, we register a hook to its AccumulateGrad object
        for group_id in range(self.num_param_groups):
            param_group = self._working_param_groups[group_id]
            for param in param_group:
                if param.requires_grad:
                    # determines the reduction destionation rank
                    # this is only valid for stage 2
                    # dst_rank = None means using all-reduce
                    # else using reduce
                    if self._partition_grads:
                        reduce_rank = self._param_store.get_param_rank(param)
                    else:
                        reduce_rank = None

                    param.register_hook(partial(self._grad_handler, param, reduce_rank=reduce_rank))

    def _reduce_tensor_bucket(self, bucket: TensorBucket, reduce_rank):
        if self._overlap_communication:
            torch.cuda.synchronize()
            self._param_store.clear_grads_of_previous_reduced_params()
            stream = self._comm_stream
        else:
            stream = torch.cuda.current_stream()

        with torch.cuda.stream(stream):
            flat = bucket.flatten()
            reduce_global_rank = None
            if reduce_rank is not None:
                reduce_global_rank = self._dp_global_ranks[reduce_rank]
            reduced_flat = reduce_tensor_dp_group(tensor=flat,
                                                  dtype=self._communication_dtype,
                                                  dst_local_rank=reduce_rank,
                                                  dst_global_rank=reduce_global_rank,
                                                  group=self._dp_torch_group)

            # update the reduced tensor
            if reduce_rank is None or reduce_rank == self._local_rank:
                bucket.unflatten_and_copy(reduced_flat)

    def _reduce_tensor_list_with_one_dtype(self, tensor_list, bucket_size, reduce_rank):
        param_bucket = TensorBucket(size=bucket_size)

        for tensor in tensor_list:
            param_bucket.add_to_bucket(tensor, allow_oversize=True)

            if param_bucket.is_full_or_oversized():
                self._reduce_tensor_bucket(bucket=param_bucket, reduce_rank=reduce_rank)
                param_bucket.empty()

        if not param_bucket.is_empty():
            self._reduce_tensor_bucket(bucket=param_bucket, reduce_rank=reduce_rank)

    def _reduce_grads(self, reduce_rank, grads, bucket_size):
        grad_buckets_by_dtype = split_by_dtype(grads)

        for tensor_list in grad_buckets_by_dtype:
            self._reduce_tensor_list_with_one_dtype(tensor_list=tensor_list,
                                                    bucket_size=bucket_size,
                                                    reduce_rank=reduce_rank)

    #######################
    # Reduction Functions #
    #######################

    def _run_reduction(self, reduce_rank=None):
        # reduce grads
        self._reduce_grads(reduce_rank=reduce_rank,
                           grads=self._bucket_store.get_grad(reduce_rank=reduce_rank),
                           bucket_size=self._bucket_store.num_elements_in_bucket(reduce_rank))

        # use communication stream if overlapping
        # communication with computation
        if self._overlap_communication:
            stream = self._comm_stream
        else:
            stream = torch.cuda.current_stream()

        with torch.cuda.stream(stream):
            params_in_bucket = self._bucket_store.get_param(reduce_rank=reduce_rank)

            for param in params_in_bucket:
                # the is_param_reduced flag should be False showing that
                # this param is not reduced before calling self._reduce_grads_by_rank
                is_param_reduced = self._param_store.is_param_reduced(param)

                if is_param_reduced:
                    msg = f'Parameter of size ({param.size()}) has been reduced, ' + \
                          'duplicate reduction will lead to arithmetic incorrectness'
                    raise RuntimeError(msg)

                # update the flag
                self._param_store.set_param_reduction_state(param, True)

                # if partition grads = True
                # we do not keep the gradient after reduction
                if self._partition_grads and not self._param_store.belongs_to_current_rank(param):
                    if self._overlap_communication:
                        # we need to keep this gradient for now as reduction may
                        # be completed yet since it is using a different cuda stream
                        self._param_store.add_previous_reduced_param(param)
                    else:
                        param.grad = None

        self._bucket_store.reset_by_rank(reduce_rank)

    def _add_to_reduction_bucket(self, param, reduce_rank=None):
        param_size = param.numel()

        # check if the bucket is full
        # if full, will reduce the grads already in the bucket
        # after reduction, the bucket will be empty
        if self._bucket_store.num_elements_in_bucket(reduce_rank) + param_size > self._reduce_bucket_size:
            self._run_reduction(reduce_rank)

        # the param must not be reduced to ensure correctness
        is_param_reduced = self._param_store.is_param_reduced(param)
        if is_param_reduced:
            msg = f'Parameter of size ({param.size()}) has already been reduced, ' \
                  + 'duplicate reduction will lead to arithmetic incorrectness'
            raise RuntimeError(msg)

        self._bucket_store.add_num_elements_in_bucket(param_size, reduce_rank)
        self._bucket_store.add_param(param, reduce_rank)

    ################################
    # torch.optim.Optimizer methods
    ################################

    def backward(self, loss, retain_graph=False, sync_grad=True):
        loss = self.loss_scale * loss
        loss.backward(retain_graph=retain_graph)

        # finish gradient reduction
        if not self._partition_grads:
            self._reduce_grad_stage1()
        else:
            # TODO: support async comm in reduce
            self._reduce_grad_stage2()

        # clear reduced grads
        if self._overlap_communication:
            torch.cuda.synchronize()
            self._param_store.clear_grads_of_previous_reduced_params()

        # gradient synchronization
        if sync_grad:
            self._sync_grad()

    def zero_grad(self, set_to_none=True):
        """
        Set parameter gradients to zero. If set_to_none = True, gradient
        will be set to None to save memory.

        :param set_to_none: Whether set the gradient to None. Default value is True.
        :type set_to_none: bool
        """
        for _, param_group in self._working_param_groups.items():
            for param in param_group:
                if set_to_none:
                    param.grad = None
                else:
                    if param.grad is not None:
                        param.grad.detach()
                        param.grad.zero_()

    ####################
    # Update Parameter #
    ####################

    def step(self, closure=None):
        assert closure is None, 'closure is not supported by step()'

        # check for overflow
        found_inf = self._check_overflow()
        self.grad_scaler.update(found_inf)

        # update loss scale if overflow occurs
        if found_inf:
            self._grad_store.reset_all_average_gradients()
            if self._verbose:
                self._logger.info(f'Found overflow. Skip step')
            self.zero_grad()
            return

        # copy the grad of working param to master param
        single_grad_partition_groups = []
        norm_groups = []

        for group_id in range(self.num_param_groups):
            # compute norm
            norm_group = compute_norm(gradients=self._grad_store.get_averaged_gradients_by_group(group_id),
                                      params=self._param_store.get_params_by_rank_group(group_id=group_id,
                                                                                        rank=self._local_rank),
                                      dp_group=self._dp_torch_group,
                                      mp_group=self._mp_torch_group)
            norm_groups.append(norm_group)

            # create flat gradient for the flat fp32 master params
            working_avg_grads = self._grad_store.get_averaged_gradients_by_group(group_id)
            flat_working_avg_grads = flatten(working_avg_grads)

            dtype = self._master_flat_param_groups_of_current_rank[group_id].dtype
            flat_master_avg_grads = flat_working_avg_grads.to(dtype)

            param_shape = self._master_flat_param_groups_of_current_rank[group_id].shape
            assert param_shape == flat_master_avg_grads.shape, \
                f'fp32 param and grad have different shape {param_shape} vs {flat_master_avg_grads.shape}'

            single_grad_partition_groups.append(flat_master_avg_grads)
            device = self._master_flat_param_groups_of_current_rank[group_id].device
            self._master_flat_param_groups_of_current_rank[group_id].grad = flat_master_avg_grads.to(device)
            self._grad_store.reset_average_gradients_by_group(group_id)

        # unscale and clip grads
        global_norm = calculate_global_norm_from_list(norm_list=norm_groups)
        self._unscale_and_clip_grads(single_grad_partition_groups, global_norm)

        # update the parameters
        self.optim.step()
        # release the master grad
        release_param_grad(self._master_flat_param_groups_of_current_rank.values())

        # update working partition updated by the current rank
        for group_id in range(len(self._working_param_groups)):
            working_param = self._param_store.get_flat_param_by_rank_group(rank=self._local_rank, group_id=group_id)
            master_param = self._master_flat_param_groups_of_current_rank[group_id]
            working_param.data.copy_(master_param)

        # broadcast the updated model weights
        handles = []
        for group_id in range(self.num_param_groups):
            for index in range(self._world_size):
                rank = self._dp_global_ranks[index]
                working_param = self._param_store.get_flat_param_by_rank_group(rank=index, group_id=group_id)
                handle = dist.broadcast(working_param, src=rank, group=self._dp_torch_group, async_op=True)
                handles.append(handle)

        for handle in handles:
            handle.wait()

    #############################
    # Mixed Precision Utilities #
    #############################

    def _check_overflow(self):
        # clear previous overflow record
        self._found_overflow.fill_(0.0)

        # check for overflow
        for group_id in range(len(self._working_param_groups)):
            for avg_grad in self._grad_store.get_averaged_gradients_by_group(group_id):
                if avg_grad is not None and has_inf_or_nan(avg_grad):
                    self._found_overflow.fill_(1.0)
                    break

        # all-reduce across dp group
        dist.all_reduce(self._found_overflow, op=dist.ReduceOp.MAX, group=self._dp_torch_group)

        # all-reduce over model parallel group
        if self._mp_torch_group:
            dist.all_reduce(self._found_overflow, op=dist.ReduceOp.MAX, group=self._mp_torch_group)

        if self._found_overflow.item() > 0:
            return True
        else:
            return False

    def _unscale_and_clip_grads(self, grad_groups_flat, total_norm):
        # compute combined scale factor for this group
        combined_scale = self.loss_scale

        if self._clip_grad_norm > 0.:
            # norm is in fact norm*scale
            clip = ((total_norm / self.loss_scale) + 1e-6) / self._clip_grad_norm
            if clip > 1:
                combined_scale = clip * self.loss_scale

        for grad in grad_groups_flat:
            grad.data.mul_(1. / combined_scale)

    ############################
    # Gradient Synchronization #
    ############################

    def _sync_grad(self):
        # update param already reduced flag
        reduction_states = self._param_store.get_param_reduction_states()
        for tensor, _ in reduction_states.items():
            reduction_states[tensor] = False

        # accumulate gradient
        for group_id in range(self.num_param_groups):
            param_group = self._param_store.get_params_by_rank_group(self._local_rank, group_id)

            avg_gradients_group = self._grad_store.get_averaged_gradients_by_group(group_id)

            param_idx = 0
            for param in param_group:
                if param.grad is not None:
                    if len(avg_gradients_group) == param_idx:
                        self._grad_store.append_average_gradient_by_group(group_id, param.grad)
                    else:
                        self._grad_store.add_average_gradient_by_group(group_id, param_idx, param.grad)
                    param_idx += 1

        # the gradients needed are stored in the avg_gradients buffer
        # thus, can clear this
        self.zero_grad()

    def _reduce_grad_stage1(self):
        # if not overlapping communication (no reduction hook is attached)
        # we need to manually reduce these gradients
        if not self._overlap_communication:
            for group_id in range(len(self._working_param_groups)):
                param_group = self._working_param_groups[group_id]
                for param in param_group:
                    if param.grad is not None:
                        self._add_to_reduction_bucket(param)

        # we need to reduce the gradients
        # left in the communication bucket
        self._run_reduction()

    def _reduce_grad_stage2(self):
        # when partition_grads is True, reduction hooks
        # are attached in the __init__ function, so we
        # only need to reduce the gradients
        # left in the communication bucket
        for reduce_rank in range(self._world_size):
            self._run_reduction(reduce_rank)
