code: def forward(
      self,
      input_ids: torch.LongTensor,
      past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None,
      attention_mask: Optional[torch.ByteTensor] = None,
      prefix_mask: Optional[torch.ByteTensor] = None,
      sequence_id: Optional[torch.LongTensor] = None,
      return_dict: Optional[bool] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      use_cache: Optional[bool] = None,
  ):
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.return_dict
      )
      use_cache = (
          use_cache if use_cache is not None else self.config.use_cache
      )
      if attention_mask is not None:
          attention_mask = attention_mask.bool()
      if prefix_mask is not None:
          prefix_mask = prefix_mask.bool()
      if not return_dict:
          raise NotImplementedError(
              "return_dict False is not implemented yet for MPT"
          )
      if output_attentions:
          if self.attn_impl != "torch":
              raise NotImplementedError(
                  "output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`."
              )
      if (
          attention_mask is not None
          and attention_mask[:, 0].sum() != attention_mask.shape[0]
          and self.training
      ):
          raise NotImplementedError(
              "MPT does not support training with left padding."
          )
      if self.prefix_lm and prefix_mask is None:
          raise ValueError(
              "prefix_mask is a required argument when MPT is configured with prefix_lm=True."
          )
      if self.training:
          if self.attn_uses_sequence_id and sequence_id is None:
              raise ValueError(
                  "sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True "
                  + "and the model is in train mode."
              )
          elif (
              self.attn_uses_sequence_id is False
              and sequence_id is not None
          ):
              warnings.warn(
                  "MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. "
                  + "This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True."
              )
      S = input_ids.size(1)
      assert (
          S <= self.config.max_seq_len
      ), f"Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}"
      tok_emb = self.wte(input_ids)
      if self.alibi:
          x = tok_emb
      else:
          past_position = 0
          if past_key_values is not None:
              if len(past_key_values) != self.config.n_layers:
                  raise ValueError(
                      f"past_key_values must provide a past_key_value for each attention "
                      + f"layer in the network (len(past_key_values)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r})."
                  )
              past_position = past_key_values[0][0].size(1)
              if self.attn_impl == "torch":
                  past_position = past_key_values[0][0].size(3)
          if S + past_position > self.config.max_seq_len:
              raise ValueError(
                  f"Cannot forward input with past sequence length {past_position} and current sequence length {S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}."
              )
          pos = torch.arange(
              past_position,
              S + past_position,
              dtype=torch.long,
              device=input_ids.device,
          ).unsqueeze(0)
          if attention_mask is not None:
              pos = torch.clamp(
                  pos
                  - torch.cumsum(
                      (~attention_mask).to(torch.int32), dim=1
                  )[:, past_position:],
                  min=0,
              )
          pos_emb = self.wpe(pos)
          x = tok_emb + pos_emb
      (attn_bias, attention_mask) = self._attn_bias(
          device=x.device,
          dtype=torch.float32,
          attention_mask=attention_mask,
          prefix_mask=prefix_mask,
          sequence_id=sequence_id,
      )
      if use_cache and past_key_values is None:
          past_key_values = [() for _ in range(self.config.n_layers)]
      all_hidden_states = () if output_hidden_states else None
      all_self_attns = () if output_attentions else None
      for b_idx, block in enumerate(self.blocks):
          if output_hidden_states:
              assert all_hidden_states is not None
              all_hidden_states = all_hidden_states + (x,)
          past_key_value = (
              past_key_values[b_idx]
              if past_key_values is not None
              else None
          )
          (x, attn_weights, past_key_value) = block(
              x,
              past_key_value=past_key_value,
              attn_bias=attn_bias,
              attention_mask=attention_mask,
              is_causal=self.is_causal,
          )
          if past_key_values is not None:
              past_key_values[b_idx] = past_key_value
          if output_attentions:
              assert all_self_attns is not None
              all_self_attns = all_self_attns + (attn_weights,)
      x = self.norm_f(x)
      if output_hidden_states:
          assert all_hidden_states is not None
          all_hidden_states = all_hidden_states + (x,)
      return BaseModelOutputWithPast(
          last_hidden_state=x,
          past_key_values=past_key_values,
          hidden_states=all_hidden_states,
          attentions=all_self_attns,
      )
  
imports: import math
  import os
  import warnings
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from einops import rearrange
  from packaging import version
  from text_generation_server.utils.layers import TensorParallelEmbedding, TensorParallelColumnLinear, TensorParallelRowLinear, TensorParallelHead, get_linear
label_desc: Use image node_id 5 for calling the MPTModel obj's underlying member method code with example usage: obj.forward(input_ids, past_key_values, attention_mask, prefix_mask, sequence_id, return_dict, output_attentions, output_hidden_states, use_cache) and returns: BaseModelOutputWithPast