code: def forward(ctx, logits, targets):
      # logits: [b/q, h/q]
      # labels: [b/q]
      # loss: [b/q]
      # vocab_parallel_logits: [b/q, s, v/q]
      # target: [b/q, s]
      logits_max = torch.max(logits, dim=-1)[0]
      torch.distributed.all_reduce(
          logits_max,
          op=torch.distributed.ReduceOp.MAX,
          group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW),
      )
      # Subtract the maximum value.
      # vocab_parallel_logits.sub_(logits_max.unsqueeze(dim=-1))
      logits = logits - logits_max.unsqueeze(dim=-1)
  
      vocab_size = logits.size(-1)
      rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
      vocab_start = rank * (vocab_size)
      vocab_end = (rank + 1) * (vocab_size) - 1
  
      target_mask = (targets < vocab_start) | (targets > vocab_end)
  
      masked_target = targets.clone() - vocab_start
      masked_target[target_mask] = 0
      arange_1d = torch.arange(
          start=0,
          end=logits.size()[0],
      )
      predicted_logits = logits[arange_1d, masked_target]
      predicted_logits[target_mask] = 0.0
      dist.all_reduce(
          predicted_logits,
          group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW),
      )
  
      exp_logits = torch.exp(logits)
      sum_exp_logits = exp_logits.sum(dim=1)
      dist.all_reduce(
          sum_exp_logits,
          group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW),
      )
  
      loss = torch.log(sum_exp_logits) - predicted_logits
  
      exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
      ctx.save_for_backward(exp_logits, target_mask, masked_target)
  
      return loss
  
imports: import torch
  import torch.distributed
  from torch.cuda.amp import custom_bwd, custom_fwd
  from torch.nn.functional import cross_entropy
  from torch.nn.modules.loss import _Loss
  from colossalai.legacy.context import ParallelMode
  from colossalai.legacy.core import global_context
  from colossalai.legacy.nn.layer.parallel_2d import reduce_by_batch_2d, split_batch_2d
  from colossalai.legacy.nn.layer.parallel_2d._utils import assert_summa_initialization
  from colossalai.legacy.registry import LOSSES
  from colossalai.utils import get_current_device
label_desc: Use image node_id 1 for calling the _VocabParallelCrossEntropy2D obj's underlying member method code with example usage: obj.forward(ctx, logits, targets) and returns: loss