content: # Copyright (c) Meta Platforms, Inc. and affiliates.
  #
  # This source code is licensed under the Apache License, Version 2.0
  # found in the LICENSE file in the root directory of this source tree.
  
  import os
  from typing import Callable, Optional
  import warnings
  
  from torch import Tensor, nn
  import torch.nn.functional as F
  
  
  class SwiGLUFFN(nn.Module):
      def __init__(
          self,
          in_features: int,
          hidden_features: Optional[int] = None,
          out_features: Optional[int] = None,
          act_layer: Callable[..., nn.Module] = None,
          drop: float = 0.0,
          bias: bool = True,
      ) -> None:
          super().__init__()
          out_features = out_features or in_features
          hidden_features = hidden_features or in_features
          self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)
          self.w3 = nn.Linear(hidden_features, out_features, bias=bias)
  
      def forward(self, x: Tensor) -> Tensor:
          x12 = self.w12(x)
          x1, x2 = x12.chunk(2, dim=-1)
          hidden = F.silu(x1) * x2
          return self.w3(hidden)
  
  
  XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
  try:
      if XFORMERS_ENABLED:
          from xformers.ops import SwiGLU
  
          XFORMERS_AVAILABLE = True
          warnings.warn("xFormers is available (SwiGLU)")
      else:
          warnings.warn("xFormers is disabled (SwiGLU)")
          raise ImportError
  except ImportError:
      SwiGLU = SwiGLUFFN
      XFORMERS_AVAILABLE = False
  
      warnings.warn("xFormers is not available (SwiGLU)")
  
  
  class SwiGLUFFNFused(SwiGLU):
      def __init__(
          self,
          in_features: int,
          hidden_features: Optional[int] = None,
          out_features: Optional[int] = None,
          act_layer: Callable[..., nn.Module] = None,
          drop: float = 0.0,
          bias: bool = True,
      ) -> None:
          out_features = out_features or in_features
          hidden_features = hidden_features or in_features
          hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
          super().__init__(
              in_features=in_features,
              hidden_features=hidden_features,
              out_features=out_features,
              bias=bias,
          )
  