code: def swiglu(
      x: torch.Tensor,
      w1: torch.Tensor,
      b1: Optional[torch.Tensor],
      w2: torch.Tensor,
      b2: Optional[torch.Tensor],
      w3: torch.Tensor,
      b3: Optional[torch.Tensor],
      *,
      op: Optional[SwiGLUOp] = None,
  ) -> torch.Tensor:
      """
      Computes a SwiGLU block given the weights/bias of the 3
      linear layers.
  
      - It is recommended to keep ``op=None`` so the best implementation \
      available for the inputs will be used.
  
  
      :Equivalent pytorch code:
  
      .. code-block:: python
  
          x1 = F.linear(x, w1, b1)
          x2 = F.linear(x, w2, b2)
          hidden = F.silu(x1) * x2
          return F.linear(hidden, w3, b3)
  
      :Packing weights:
  
      To allow faster implementations, it's recommended to have w1/w2 come from the same storage, as in:
          .. code-block:: python
  
              w1, w2 = xformers.ops.unbind(w12, 0)
  
      :Supported hardware:
  
      This operator is only optimized on A100+ on ``torch.half`` or ``torch.bfloat16`` \
          (autocast is supported), and will fallback to a functional pytorch \
          implementation otherwise.
      """
  
      batch_shape = x.shape[:-1]
      x = x.reshape([-1, x.shape[-1]])
      if w1.ndim != 2 or w1.shape != w2.shape:
          raise ValueError(
              f"Invalid shapes for w1: {w1.shape} / w2: {w2.shape}"
          )
      if b1 is not None:
          if b1.ndim != 1 or b1.shape[0] != w1.shape[0]:
              raise ValueError(f"Invalid shapes for b1: {b1.shape}")
      if b2 is not None:
          if b2.ndim != 1 or b2.shape[0] != w2.shape[0]:
              raise ValueError(f"Invalid shapes for b2: {b2.shape}")
      if w3.ndim != 2 or w3.shape[1] != w2.shape[0]:
          raise ValueError(f"Invalid shape for w3: {w3.shape}")
      if b3 is not None:
          if b3.ndim != 1 or b3.shape[0] != w3.shape[0]:
              raise ValueError(
                  f"Invalid shapes for w3: {w3.shape} / b3: {b3.shape}"
              )
  
      if op is None:
          op = SwiGLUOpDispatch.from_arguments(
              x, w1, b1, w2, b2, w3, b3
          ).op
  
      if not op.PACKED_WEIGHTS:
          return op(x, w1, b1, w2, b2, w3, b3).reshape(
              [*batch_shape, -1]
          )
      w1w2 = stack_or_none((w1, w2), dim=0)
      if b1 is not None and b2 is not None:
          b1b2: Optional[torch.Tensor] = stack_or_none((b1, b2), dim=0)
          if b1b2 is None:
              raise NotImplementedError(
                  "b1/b2 needs to be properly packed"
              )
      else:
          b1b2 = None
          assert b1 is None and b2 is None
  
      if w1w2 is None:
          raise NotImplementedError("w1/w2 needs to be properly packed")
      return op(x, w1w2, b1b2, w3, b3).reshape([*batch_shape, -1])
  
imports: from dataclasses import dataclass
  from typing import Dict, Optional, Sequence, Tuple, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from .common import BaseOperator, get_xformers_operator, register_operator
  from .unbind import stack_or_none, unbind
label_desc: Use image node_id 6 for calling a global function with example usage: swiglu(x, w1, b1, w2, b2, w3, b3) and returns: op, op