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import os
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from typing import Callable, Optional
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import warnings
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from torch import Tensor, nn
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import torch.nn.functional as F
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class SwiGLUFFN(nn.Module):
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def __init__(
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self,
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in_features: int,
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hidden_features: Optional[int] = None,
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out_features: Optional[int] = None,
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act_layer: Callable[..., nn.Module] = None,
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drop: float = 0.0,
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bias: bool = True,
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) -> None:
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)
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self.w3 = nn.Linear(hidden_features, out_features, bias=bias)
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def forward(self, x: Tensor) -> Tensor:
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x12 = self.w12(x)
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x1, x2 = x12.chunk(2, dim=-1)
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hidden = F.silu(x1) * x2
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return self.w3(hidden)
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XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
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try:
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if XFORMERS_ENABLED:
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from xformers.ops import SwiGLU
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XFORMERS_AVAILABLE = True
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warnings.warn("xFormers is available (SwiGLU)")
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else:
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warnings.warn("xFormers is disabled (SwiGLU)")
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raise ImportError
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except ImportError:
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SwiGLU = SwiGLUFFN
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XFORMERS_AVAILABLE = False
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warnings.warn("xFormers is not available (SwiGLU)")
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class SwiGLUFFNFused(SwiGLU):
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def __init__(
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self,
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in_features: int,
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hidden_features: Optional[int] = None,
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out_features: Optional[int] = None,
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act_layer: Callable[..., nn.Module] = None,
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drop: float = 0.0,
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bias: bool = True,
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) -> None:
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
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super().__init__(
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in_features=in_features,
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hidden_features=hidden_features,
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out_features=out_features,
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bias=bias,
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)
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