docstr: Allreduce partial derived gradients across the specified process group.
  
  This function performs gradient synchronization for parameters that are marked as partially derived in sequence parallelism.
  
  Args:
      tp_group (ProcessGroup): The process group for gradient synchronization.
      model (nn.Module): The model from which gradients will be synchronized.
      grads (List[torch.Tensor]): The list of gradients to be synchronized.
  
  Raises:
      AssertionError: If both `model` and `grads` are provided or neither is provided.
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code: def allreduce_partial_data_grad(
      tp_group: ProcessGroup,
      model: nn.Module = None,
      grads: List[torch.Tensor] = None,
  ):
      """
      Allreduce partial derived gradients across the specified process group.
  
      This function performs gradient synchronization for parameters that are marked as partially derived in sequence parallelism.
  
      Args:
          tp_group (ProcessGroup): The process group for gradient synchronization.
          model (nn.Module): The model from which gradients will be synchronized.
          grads (List[torch.Tensor]): The list of gradients to be synchronized.
  
      Raises:
          AssertionError: If both `model` and `grads` are provided or neither is provided.
      """
      # Ensure that exactly one of `model` and `grads` is provided for gradient synchronization.
      assert (model is not None) ^ (
          grads is not None
      ), "Exactly one of model and grads must be not None."
  
      # Get the size of the process group, which determines whether synchronization is needed.
      tp_size = get_world_size(tp_group) if tp_group is not None else 1
  
      if tp_size == 1:
          # If the process group size is 1, no synchronization is required.
          return
  
      if model is not None:
          # If `model` is provided, extract partial derived gradients from the model's parameters.
          grads = []
          for p in model.parameters():
              if (
                  p.grad is not None
                  and SeqParallelUtils.is_sp_partial_derived_param(p)
              ):
                  grads.append(p.grad.data)
  
          # Flatten and reduce the gradients using the specified process group.
          coalesced = _flatten_dense_tensors(grads)
          dist.all_reduce(
              coalesced, op=dist.ReduceOp.SUM, group=tp_group
          )
  
          # Unflatten the synchronized gradients and update the model's gradients.
          for buf, synced in zip(
              grads, _unflatten_dense_tensors(coalesced, grads)
          ):
              buf.copy_(synced)
      else:
          # If `grads` are provided explicitly, synchronize those gradients directly.
          coalesced = _flatten_dense_tensors(grads)
          dist.all_reduce(
              coalesced, op=dist.ReduceOp.SUM, group=tp_group
          )
          for buf, synced in zip(
              grads, _unflatten_dense_tensors(coalesced, grads)
          ):
              buf.copy_(synced)
  
imports: from contextlib import contextmanager
  from typing import List
  import torch
  import torch.distributed
  from torch import nn
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  from torch.distributed import ProcessGroup, get_world_size
  from colossalai.utils.device import get_current_device, get_rng_state, set_rng_state, manual_seed
label_desc: Use image node_id 3 for calling the SeqParallelUtils obj's underlying member method code with example usage: obj.allreduce_partial_data_grad(tp_group, model, grads) without return types