code: def forward(
      self,
      input_ids,
      cache,
      last_id_only=True,
      preprocess_only=False,
      lora=None,
      output_device=None,
      input_mask=None,
  ):
      q_len = input_ids.shape[-1]
      remaining_q_len = q_len
      bsz = input_ids.shape[0]
  
      assert input_mask is None or (
          input_mask.shape[-1] >= input_ids.shape[-1]
          and input_mask.shape[-2] == input_ids.shape[-2]
      )
  
      # The buffers can only fit max_input_len tokens, so with larger batch sizes we reduce our work size correspondingly.
  
      effective_max_input_len = self.config.max_input_len // bsz
  
      # Split sequence
  
      result = None
  
      chunk_begin = 0
      while chunk_begin < q_len:
          # Limit chunk_size to max_input_len
  
          chunk_size = min(remaining_q_len, effective_max_input_len)
  
          # Limit chunk_size to keep size of attention operation <= max_attention_size, unless using flash-attn
  
          if not self.config.use_flash_attn_2 or chunk_begin > 0:
              past_len = cache.current_seq_len
              attn_size = (past_len + remaining_q_len) * remaining_q_len
              max_a = self.config.max_attention_size
              if attn_size > max_a:
                  cs = (
                      math.sqrt(past_len**2 + 4 * max_a) - past_len
                  ) / 2
                  chunk_size = min(chunk_size, math.floor(cs))
  
          # Process chunk
  
          chunk_end = min(chunk_begin + chunk_size, q_len)
  
          _last_id_only = last_id_only
          _preprocess_only = preprocess_only or (
              chunk_end < q_len and last_id_only
          )
  
          r = self._forward(
              input_ids[:, chunk_begin:chunk_end],
              cache,
              _last_id_only,
              _preprocess_only,
              lora,
              output_device,
              input_mask,
          )
  
          if not _preprocess_only:
              result = (
                  r if result is None else torch.cat((result, r), dim=1)
              )
  
          chunk_begin = chunk_end
          remaining_q_len -= chunk_size
  
      return result
  
imports: import sys
  import torch
  from torch import nn
  import torch.nn.functional
  from safetensors import safe_open
  import cuda_ext
  import json
  import math
  import gc
  from enum import Enum
label_desc: Use image node_id 2 for calling the ExLlama obj's underlying member method code with example usage: obj.forward(input_ids, cache, last_id_only, preprocess_only, lora, output_device, input_mask) and returns: result