code: def forward(ctx, vocab_parallel_logits, target, label_smoothing=0.0):
      # Maximum value along vocab dimension across all GPUs.
      logits_max = torch.max(vocab_parallel_logits, dim=-1)[0]
      torch.distributed.all_reduce(
          logits_max,
          op=torch.distributed.ReduceOp.MAX,
          group=get_tensor_model_parallel_group(),
      )
      # Subtract the maximum value.
      vocab_parallel_logits = (
          vocab_parallel_logits - logits_max.unsqueeze(dim=-1)
      )
  
      # Get the partition's vocab indecies
      get_vocab_range = (
          VocabUtility.vocab_range_from_per_partition_vocab_size
      )
      partition_vocab_size = vocab_parallel_logits.size()[-1]
      rank = get_tensor_model_parallel_rank()
      world_size = get_tensor_model_parallel_world_size()
      vocab_start_index, vocab_end_index = get_vocab_range(
          partition_vocab_size, rank, world_size
      )
  
      # Create a mask of valid vocab ids (1 means it needs to be masked).
      target_mask = (target < vocab_start_index) | (
          target >= vocab_end_index
      )
      masked_target = target.clone() - vocab_start_index
      masked_target[target_mask] = 0
  
      # Get predicted-logits = logits[target].
      # For Simplicity, we convert logits to a 2-D tensor with size
      # [*, partition-vocab-size] and target to a 1-D tensor of size [*].
      logits_2d = vocab_parallel_logits.view(-1, partition_vocab_size)
      masked_target_1d = masked_target.view(-1)
      arange_1d = torch.arange(
          start=0, end=logits_2d.size()[0], device=logits_2d.device
      )
      predicted_logits_1d = logits_2d[arange_1d, masked_target_1d]
      predicted_logits_1d = predicted_logits_1d.clone().contiguous()
      predicted_logits = predicted_logits_1d.view_as(target)
      predicted_logits[target_mask] = 0.0
      # All reduce is needed to get the chunks from other GPUs.
      torch.distributed.all_reduce(
          predicted_logits,
          op=torch.distributed.ReduceOp.SUM,
          group=get_tensor_model_parallel_group(),
      )
  
      # Sum of exponential of logits along vocab dimension across all GPUs.
      exp_logits = vocab_parallel_logits
      torch.exp(vocab_parallel_logits, out=exp_logits)
      sum_exp_logits = exp_logits.sum(dim=-1)
      torch.distributed.all_reduce(
          sum_exp_logits,
          op=torch.distributed.ReduceOp.SUM,
          group=get_tensor_model_parallel_group(),
      )
  
      # Loss = log(sum(exp(logits))) - predicted-logit.
      loss = torch.log(sum_exp_logits) - predicted_logits
  
      # Store softmax, target-mask and masked-target for backward pass.
      exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
  
      vocab_size = exp_logits.size(-1)
      if label_smoothing > 0:
          """
          We'd like to assign 1 / (K - 1) probability mass to every index that is not the ground truth.
          = (1 - alpha) * y_gt + alpha * mean(y_{i for i != gt})
          = (1 - alpha) * y_gt + (alpha / (K - 1)) * \sum_{i != gt} y_i
          = ((K - 1) * (1 - alpha) / (K - 1)) * y_gt + (alpha / (K - 1)) * \sum_{i != gt} y_i
          = (K * (1 - alpha) - 1) / (K - 1)) * y_gt  + (alpha / (K - 1)) * \sum_{i} y_i
          = (1 - (alpha * K) / (K - 1)) * y_gt + ( (alpha * K) / (K - 1) ) * \sum_{i} y_i / K
          From: https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/losses/smoothed_cross_entropy.py
          """
          assert 1.0 > label_smoothing > 0.0
          smoothing = label_smoothing * vocab_size / (vocab_size - 1)
  
          # Exp logits at this point are normalized probabilities. So we can just take the log to get log-probs.
          log_probs = torch.log(exp_logits)
          mean_log_probs = log_probs.mean(dim=-1)
          loss = (1.0 - smoothing) * loss - smoothing * mean_log_probs
  
      ctx.save_for_backward(
          exp_logits,
          target_mask,
          masked_target_1d,
          torch.Tensor([label_smoothing]),
          torch.LongTensor([vocab_size]),
      )
  
      return loss
  
imports: import torch
label_desc: Use image node_id 1 for calling the _VocabParallelCrossEntropy obj's underlying member method code with example usage: obj.forward(ctx, vocab_parallel_logits, target, label_smoothing) and returns: loss