code: def linear_with_grad_accumulation_and_async_allreduce(
      input: torch.Tensor,
      weight: torch.Tensor,
      bias: Optional[torch.Tensor],
      gradient_accumulation_fusion: bool,
      async_grad_allreduce: bool,
      sequence_parallel: bool,
  ) -> torch.Tensor:
      """Linear layer execution with asynchronous communication and
      gradient accumulation fusion in backprop.
  
      This has the option to accumulate the result of backprop
      calculation into an existing gradient buffer, preventing the need
      to do an additional addition kernel after the gradient
      calculation.
  
      Additionally, the tensor parallel all reduce of the input
      gradients can be done asynchronously with the calculation of
      the weight gradients.
  
      In the case of sequence parallelism, the reduce scatter of the
      input gradients is done asynchronously with the calcluation of the
      weight gradients.
  
      Use of this module requires that the environment variable
      CUDA_DEVICE_MAX_CONNECTIONS=1. There are a few collective
      operations, noted in the code, that should be scheduled before
      compute kernels to overlap the communication with the computation,
      which is necessary for a speedup but not for correctness so that
      ordering isn't imposed by the scheduler. Setting
      CUDA_DEVICE_MAX_CONNECTIONS=1 forces the kernels to be scheduled
      in the order they are called.
  
      Arguments:
  
      input (torch.Tensor required): input like torch.nn.functional.linear
  
      weight (torch.Tensor required): weight like torch.nn.functional.linear
  
      bias (torch.Tensor optional): bias like torch.nn.functional.linear
  
      gradient_accumulation_fusion (bool required): Perform the gradient
          accumulation fusion, requires the custom CUDA extension
          fused_weight_gradient_mlp_cuda module. To use
          gradient_accumulation_fusion you must install APEX with
          --cpp_ext and --cuda_ext. For example: "pip install
          --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext .\"
          " Note that the extension requires CUDA>=11. Otherwise, you
          must turn off gradient accumulation fusion."
  
      async_grad_allreduce (bool required): Do the allreduce of input
          gradients asyncronously with the computation of weight
          gradients. If sequence_parallel is True, this must be
          False, as no all reduce is performed.
  
      sequence_parallel (bool required): Indicates that sequence
          parallelism is used and thus in the forward pass the input is
          all gathered, and the backward pass the input gradients are
          reduce scattered.
      """
      args = [
          input,
          weight,
          bias,
          gradient_accumulation_fusion,
          async_grad_allreduce,
          sequence_parallel,
      ]
  
      if not linear_with_grad_accumulation_and_async_allreduce.warned:
          if os.environ.get("CUDA_DEVICE_MAX_CONNECTIONS") != "1":
              if sequence_parallel:
                  warnings.warn(
                      "When using sequence parallelism it is recommended to set the "
                      "environment variable CUDA_DEVICE_MAX_CONNECTIONS to 1 for "
                      "maximum speedup"
                  )
                  linear_with_grad_accumulation_and_async_allreduce.warned = (
                      True
                  )
  
              if async_grad_allreduce:
                  warnings.warn(
                      "When using async grad allreduce it is recommended to set the "
                      "environment variable CUDA_DEVICE_MAX_CONNECTIONS to 1 for "
                      "maximum speedup"
                  )
                  linear_with_grad_accumulation_and_async_allreduce.warned = (
                      True
                  )
  
      return LinearWithGradAccumulationAndAsyncCommunication.apply(
          *args
      )
  
imports: import math
  import os
  import warnings
  from typing import Callable, Optional
  import torch
  import torch.nn.functional
  import torch.nn.init
  from torch.cuda.amp import custom_bwd, custom_fwd
  from torch.nn.parameter import Parameter
  from megatron.core.model_parallel_config import ModelParallelConfig
  from megatron.core.parallel_state import get_global_memory_buffer, get_tensor_model_parallel_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size
  from .mappings import copy_to_tensor_model_parallel_region, gather_from_sequence_parallel_region, gather_from_tensor_model_parallel_region, reduce_from_tensor_model_parallel_region, reduce_scatter_to_sequence_parallel_region, scatter_to_tensor_model_parallel_region
  from .random import get_cuda_rng_tracker, get_expert_parallel_rng_tracker_name
  from .utils import VocabUtility, divide, split_tensor_along_last_dim
label_desc: Use image node_id 8 for calling a global function with example usage: linear_with_grad_accumulation_and_async_allreduce(input, weight, bias, gradient_accumulation_fusion, async_grad_allreduce, sequence_parallel) and returns: LinearWithGradAccumulationAndAsyncCommunication