code: def gptj_sequence_parallel_forward_fn(shard_config: ShardConfig):
      def forward(
          self,
          input_ids: Optional[torch.LongTensor] = None,
          past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
          attention_mask: Optional[torch.FloatTensor] = None,
          token_type_ids: Optional[torch.LongTensor] = None,
          position_ids: Optional[torch.LongTensor] = None,
          head_mask: Optional[torch.FloatTensor] = None,
          inputs_embeds: Optional[torch.FloatTensor] = None,
          use_cache: Optional[bool] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
      ) -> Union[Tuple, BaseModelOutputWithPast]:
          output_attentions = (
              output_attentions
              if output_attentions is not None
              else self.config.output_attentions
          )
          output_hidden_states = (
              output_hidden_states
              if output_hidden_states is not None
              else self.config.output_hidden_states
          )
          use_cache = (
              use_cache
              if use_cache is not None
              else self.config.use_cache
          )
          return_dict = (
              return_dict
              if return_dict is not None
              else self.config.use_return_dict
          )
  
          if input_ids is not None and inputs_embeds is not None:
              raise ValueError(
                  "You cannot specify both input_ids and inputs_embeds at the same time"
              )
          elif input_ids is not None:
              input_shape = input_ids.size()
              input_ids = input_ids.view(-1, input_shape[-1])
              batch_size = input_ids.shape[0]
          elif inputs_embeds is not None:
              input_shape = inputs_embeds.size()[:-1]
              batch_size = inputs_embeds.shape[0]
          else:
              raise ValueError(
                  "You have to specify either input_ids or inputs_embeds"
              )
  
          device = (
              input_ids.device
              if input_ids is not None
              else inputs_embeds.device
          )
  
          if token_type_ids is not None:
              token_type_ids = token_type_ids.view(-1, input_shape[-1])
  
          if position_ids is not None:
              position_ids = position_ids.view(
                  -1, input_shape[-1]
              ).long()
  
          if past_key_values is None:
              past_length = 0
              past_key_values = tuple([None] * len(self.h))
          else:
              past_length = past_key_values[0][0].size(-2)
  
          if position_ids is None:
              position_ids = torch.arange(
                  past_length,
                  input_shape[-1] + past_length,
                  dtype=torch.long,
                  device=device,
              )
              position_ids = position_ids.unsqueeze(0).view(
                  -1, input_shape[-1]
              )
  
          # Attention mask.
          if attention_mask is not None:
              if batch_size <= 0:
                  raise ValueError(
                      "batch_size has to be defined and > 0"
                  )
              attention_mask = attention_mask.view(batch_size, -1)
              # We create a 3D attention mask from a 2D tensor mask.
              # Sizes are [batch_size, 1, 1, to_seq_length]
              # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
              # this attention mask is more simple than the triangular masking of causal attention
              # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
              attention_mask = attention_mask[:, None, None, :]
  
              # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
              # masked positions, this operation will create a tensor which is 0.0 for
              # positions we want to attend and the dtype's smallest value for masked positions.
              # Since we are adding it to the raw scores before the softmax, this is
              # effectively the same as removing these entirely.
              attention_mask = attention_mask.to(
                  dtype=self.dtype
              )  # fp16 compatibility
              attention_mask = (1.0 - attention_mask) * torch.finfo(
                  self.dtype
              ).min
  
          # Prepare head mask if needed
          # 1.0 in head_mask indicate we keep the head
          # attention_probs has shape bsz x num_attention_heads x N x N
          # head_mask has shape n_layer x batch x num_attention_heads x N x N
          head_mask = self.get_head_mask(head_mask, self.config.n_layer)
  
          if inputs_embeds is None:
              inputs_embeds = self.wte(input_ids)
  
          hidden_states = inputs_embeds
  
          if token_type_ids is not None:
              token_type_embeds = self.wte(token_type_ids)
              hidden_states = hidden_states + token_type_embeds
  
          hidden_states = self.drop(hidden_states)
  
          output_shape = input_shape + (hidden_states.size(-1),)
  
          if self.gradient_checkpointing and self.training:
              if use_cache:
                  logger.warning_once(
                      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                  )
                  use_cache = False
  
          presents = () if use_cache else None
          all_self_attentions = () if output_attentions else None
          all_hidden_states = () if output_hidden_states else None
  
          # split the input tensor along sequence dimension
          # [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
          hidden_states = split_forward_gather_backward(
              hidden_states,
              dim=1,
              process_group=shard_config.tensor_parallel_process_group,
          )
  
          for i, (block, layer_past) in enumerate(
              zip(self.h, past_key_values)
          ):
              # Model parallel
              if self.model_parallel:
                  torch.cuda.set_device(hidden_states.device)
                  # Ensure layer_past is on same device as hidden_states (might not be correct)
                  if layer_past is not None:
                      layer_past = tuple(
                          past_state.to(hidden_states.device)
                          for past_state in layer_past
                      )
                  # Ensure that attention_mask is always on the same device as hidden_states
                  if attention_mask is not None:
                      attention_mask = attention_mask.to(
                          hidden_states.device
                      )
                  if isinstance(head_mask, torch.Tensor):
                      head_mask = head_mask.to(hidden_states.device)
              if output_hidden_states:
                  all_hidden_states = all_hidden_states + (
                      hidden_states,
                  )
  
              if self.gradient_checkpointing and self.training:
  
                  def create_custom_forward(module):
                      def custom_forward(*inputs):
                          # None for past_key_value
                          return module(
                              *inputs, use_cache, output_attentions
                          )
  
                      return custom_forward
  
                  outputs = torch.utils.checkpoint.checkpoint(
                      create_custom_forward(block),
                      hidden_states,
                      None,
                      attention_mask,
                      position_ids,
                      head_mask[i],
                  )
              else:
                  outputs = block(
                      hidden_states=hidden_states,
                      layer_past=layer_past,
                      attention_mask=attention_mask,
                      position_ids=position_ids,
                      head_mask=head_mask[i],
                      use_cache=use_cache,
                      output_attentions=output_attentions,
                  )
  
              hidden_states = outputs[0]
              if use_cache is True:
                  presents = presents + (outputs[1],)
  
              if output_attentions:
                  all_self_attentions = all_self_attentions + (
                      outputs[2 if use_cache else 1],
                  )
  
              # Model Parallel: If it's the last layer for that device, put things on the next device
              if self.model_parallel:
                  for k, v in self.device_map.items():
                      if (
                          i == v[-1]
                          and "cuda:" + str(k) != self.last_device
                      ):
                          hidden_states = hidden_states.to(
                              "cuda:" + str(k + 1)
                          )
  
          # When sequence parallelism done, gather the output tensor in forward and split it in backward
          hidden_states = gather_forward_split_backward(
              hidden_states,
              dim=1,
              process_group=shard_config.tensor_parallel_process_group,
          )
  
          hidden_states = self.ln_f(hidden_states)
  
          hidden_states = hidden_states.view(output_shape)
          # Add last hidden state
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          if not return_dict:
              return tuple(
                  v
                  for v in [
                      hidden_states,
                      presents,
                      all_hidden_states,
                      all_self_attentions,
                  ]
                  if v is not None
              )
  
          return BaseModelOutputWithPast(
              last_hidden_state=hidden_states,
              past_key_values=presents,
              hidden_states=all_hidden_states,
              attentions=all_self_attentions,
          )
  
      return forward
  
file_path: ColossalAI/colossalai/shardformer/modeling/gptj.py
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast
  from transformers.models.gptj.modeling_gptj import GPTJForCausalLM, GPTJForQuestionAnswering, GPTJForSequenceClassification, GPTJModel, apply_rotary_pos_emb, get_embed_positions
  from transformers.utils import is_torch_fx_proxy, logging
  from colossalai.pipeline.stage_manager import PipelineStageManager
  from colossalai.shardformer.layer._operation import gather_forward_split_backward, split_forward_gather_backward
  from colossalai.shardformer.shard import ShardConfig
label_desc: Use image node_id 2 for calling a global function with example usage: gptj_sequence_parallel_forward_fn(shard_config) and returns: forward, BaseModelOutputWithPast, tuple, custom_forward, module