code: def forward(self, x: torch.Tensor):
      # Due to inconsistent training status in some models like Baichuan-7b-Chat,
      # we should check both self.training and torch.is_inference_mode_enabled().
      is_training = (
          self.training and not torch.is_inference_mode_enabled()
      )
      if is_training:
          # below logic is only for training
          autocast_dtype = get_autocast_dtype(x)
          if self.compute_dtype is not None and x.device.type == "xpu":
              x = x.to(
                  self.compute_dtype
              )  # solve GC issue for unlora module
          elif autocast_dtype is not None:
              x = x.to(autocast_dtype)
  
      if self.bias is not None and self.bias.dtype != x.dtype:
          self.bias.data = self.bias.data.to(x.dtype)
  
      # [batch, input_num, in_len]
      # input_num == token num for Transformer
      x_shape = x.shape
      # Output shape, e.g., [batch, input_num, out_len]
      new_shape = x_shape[:-1] + (self.out_len,)
      # Activation is empty tensor, e.g., [1, 0, 4096]
      if 0 in x_shape:
          # return empty tensor with output shape, x.dtype and x.device
          return torch.empty(new_shape, dtype=x.dtype, device=x.device)
  
      x_2d = x.view(-1, x_shape[-1])
      # x0 for weight
      x0 = self.weight.data
  
      if x0.device.type == "xpu":
          # GPU logic
          try:
              import intel_extension_for_pytorch
              import linear_q4_0
              from bigdl.llm.transformers.models.utils import use_xmx
          except ModuleNotFoundError:
              invalidInputError(
                  False, "Please `pip install bigdl_core_xe` first."
              )
  
          if x_2d.is_contiguous() is False:
              x_2d = x_2d.contiguous()
  
          input_seq_size = x_shape[1]
          if is_training:
              # training path
              if x_2d.requires_grad:
                  result = MatMulLowBit.apply(
                      x_2d, self.weight, input_seq_size
                  )
              else:
                  result = linear_q4_0.forward_new(
                      x_2d,
                      self.weight.data,
                      self.weight.qtype,
                      input_seq_size,
                  )
          else:
              # inference path
              # current workaround to reduce first token latency of fp32 input
              # sometimes fp16 cause nan and training instability
              # disable the conversion when training
              if (
                  self.conver_to_half
                  and x_2d.shape[0] > 1
                  and x_2d.dtype == torch.float32
                  and not use_xmx(x_2d, self.weight.qtype)
              ):
                  x_2d = x_2d.half()
                  result = linear_q4_0.forward_new(
                      x_2d,
                      self.weight.data,
                      self.weight.qtype,
                      input_seq_size,
                  )
                  result = result.to(x.dtype)
              else:
                  result = linear_q4_0.forward_new(
                      x_2d,
                      self.weight.data,
                      self.weight.qtype,
                      input_seq_size,
                  )
          result = result.view(new_shape)
          if self.mp_group is not None:
              from deepspeed import comm as dist
  
              dist.inference_all_reduce(result, group=self.mp_group)
          if self.bias is not None:
              result += self.bias
      else:
          # CPU logic
          # todo may need to set a different number on different platforms
          invalidInputError(
              self.qtype != NF3
              and self.qtype != NF4
              and self.qtype != FP8E4
              and self.qtype != FP4
              and self.qtype != FP8E5,
              "NF3, NF4, FP4 and FP8 quantization are currently not"
              " supported on CPU",
          )
          if self.training and x.requires_grad:
              result = MatMulLowBitCPU.apply(x, self.weight)
          else:
              from bigdl.llm.utils.isa_checker import is_server, is_spr
  
              # convert if necessary, and compute a linear result
              if (
                  is_server()
                  and (not is_spr())
                  and self.qtype == SYM_INT4
                  and x_2d.shape[0] >= TORCH_LINEAR_THRESHOLD
              ):
                  x0_fp32 = ggml_int4_convert_fp32(
                      x0, self.weight_shape, self.weight_length
                  )
                  result = F.linear(x, x0_fp32)
              else:
                  # Weight does not need a convert
                  result = ggml_matmul_src1_x_src0_t(
                      x0, x_2d, self.weight_shape, self.qtype
                  )
                  result = result.view(new_shape)
          # allreduce to combine partial results and add bias if necessary
          if self.mp_group is not None:
              # deepspeed distibuted mode
              from deepspeed import comm as dist
  
              dist.inference_all_reduce(result, group=self.mp_group)
          if self.bias is not None:
              result += self.bias
      return result
  
imports: from typing import Optional, TypeVar, Union, overload
  from bigdl.llm.utils.common import invalidInputError
  import os
  import torch
  import torch.nn.functional
  from torch import Tensor, device, dtype, nn
  from operator import mul
  from functools import reduce
  from bigdl.llm.transformers.xpu_customize_fwd import custom_fwd, custom_bwd
  from bigdl.llm.transformers.utils import get_autocast_dtype, get_xpu_device_type, get_ipex_version
  import bigdl.llm.ggml.model.llama.llama_cpp
  import ctypes
  from bigdl.llm.ggml.quantize import ggml_tensor_qtype
label_desc: Use image node_id 2 for calling the LowBitLinear obj's underlying member method code with example usage: obj.forward(x) and returns: result, torch