#!/usr/bin/env python

import torch.distributed as dist
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors

from colossalai.core import global_context as gpc
from colossalai.registry import GRADIENT_HANDLER
from ._base_gradient_handler import BaseGradientHandler
from collections import defaultdict


@GRADIENT_HANDLER.register_module
class PipelineSharedModuleGradientHandler(BaseGradientHandler):
    """A helper class to handle all-reduce operations in sub parallel groups.
    A all-reduce collective communication will be operated in 
    :func:`handle_gradient` among all sub pipeline parallel groups.
    For better performance, it bucketizes the gradients of all parameters that are 
    the same type to improve the efficiency of communication.
    """

    def handle_gradient(self):
        """A method running a all-reduce operation in sub pipeline parallel groups.
        """
        if gpc.pipeline_parallel_size > 1:
            # bucketize and all-reduce
            buckets = defaultdict(lambda: defaultdict(list))
            # Pack the buckets.
            for param in self._model.parameters():
                group = getattr(param, 'pipeline_shared_module_pg', None)
                if param.requires_grad and param.grad is not None and group is not None:
                    tp = param.data.type()
                    buckets[group][tp].append(param)

            # For each bucket, all-reduce and copy all-reduced grads.
            for group, group_buckets in buckets.items():
                for tp, bucket in group_buckets.items():
                    grads = [param.grad.data for param in bucket]
                    coalesced = _flatten_dense_tensors(grads)
                    dist.all_reduce(coalesced, op=dist.ReduceOp.SUM, group=group)
                    for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
                        buf.copy_(synced)