Build

build/torch25-cxx11-cu118-x86_64-linux/activation/__init__.py

@@ -1,52 +0,0 @@
-import torch
-
-from ._ops import ops
-
-from . import layers
-
-
-def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.silu_and_mul(out, x)
-    return out
-
-
-def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_and_mul(out, x)
-    return out
-
-
-def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_tanh_and_mul(out, x)
-    return out
-
-
-def fatrelu_and_mul(out: torch.Tensor, x: torch.Tensor, threshold: float = 0.0) -> None:
-    ops.fatrelu_and_mul(out, x, threshold)
-    return out
-
-
-def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_fast(out, x)
-    return out
-
-
-def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_new(out, x)
-    return out
-
-
-def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_quick(out, x)
-    return out
-
-
-__all__ = [
-    "silu_and_mul",
-    "gelu_and_mul",
-    "gelu_tanh_and_mul",
-    "fatrelu_and_mul",
-    "gelu_fast",
-    "gelu_new",
-    "gelu_quick",
-    "layers",
-]

build/torch25-cxx11-cu118-x86_64-linux/activation/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _activation_78448fa
-ops = torch.ops._activation_78448fa
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_activation_78448fa::{op_name}"

build/torch25-cxx11-cu118-x86_64-linux/activation/layers.py

@@ -1,65 +0,0 @@
-import torch
-import torch.nn as nn
-
-from ._ops import ops
-
-
-class SiluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.silu_and_mul(out, x)
-        return out
-
-
-class GeluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_and_mul(out, x)
-        return out
-
-
-class GeluTanhAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_tanh_and_mul(out, x)
-        return out
-
-
-class FatreluAndMul(nn.Module):
-    def __init__(self, threshold: float = 0.0):
-        super().__init__()
-        self.threshold = threshold
-
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.fatrelu_and_mul(out, x, self.threshold)
-        return out
-
-
-class FastGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_fast(out, x)
-        return out
-
-
-class NewGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_new(out, x)
-        return out
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_quick(out, x)
-        return out

build/torch25-cxx11-cu121-x86_64-linux/activation/__init__.py

@@ -1,52 +0,0 @@
-import torch
-
-from ._ops import ops
-
-from . import layers
-
-
-def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.silu_and_mul(out, x)
-    return out
-
-
-def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_and_mul(out, x)
-    return out
-
-
-def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_tanh_and_mul(out, x)
-    return out
-
-
-def fatrelu_and_mul(out: torch.Tensor, x: torch.Tensor, threshold: float = 0.0) -> None:
-    ops.fatrelu_and_mul(out, x, threshold)
-    return out
-
-
-def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_fast(out, x)
-    return out
-
-
-def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_new(out, x)
-    return out
-
-
-def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_quick(out, x)
-    return out
-
-
-__all__ = [
-    "silu_and_mul",
-    "gelu_and_mul",
-    "gelu_tanh_and_mul",
-    "fatrelu_and_mul",
-    "gelu_fast",
-    "gelu_new",
-    "gelu_quick",
-    "layers",
-]

build/torch25-cxx11-cu121-x86_64-linux/activation/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _activation_78448fa
-ops = torch.ops._activation_78448fa
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_activation_78448fa::{op_name}"

build/torch25-cxx11-cu121-x86_64-linux/activation/layers.py

@@ -1,65 +0,0 @@
-import torch
-import torch.nn as nn
-
-from ._ops import ops
-
-
-class SiluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.silu_and_mul(out, x)
-        return out
-
-
-class GeluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_and_mul(out, x)
-        return out
-
-
-class GeluTanhAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_tanh_and_mul(out, x)
-        return out
-
-
-class FatreluAndMul(nn.Module):
-    def __init__(self, threshold: float = 0.0):
-        super().__init__()
-        self.threshold = threshold
-
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.fatrelu_and_mul(out, x, self.threshold)
-        return out
-
-
-class FastGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_fast(out, x)
-        return out
-
-
-class NewGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_new(out, x)
-        return out
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_quick(out, x)
-        return out

build/torch25-cxx11-cu124-x86_64-linux/activation/__init__.py

@@ -1,52 +0,0 @@
-import torch
-
-from ._ops import ops
-
-from . import layers
-
-
-def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.silu_and_mul(out, x)
-    return out
-
-
-def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_and_mul(out, x)
-    return out
-
-
-def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_tanh_and_mul(out, x)
-    return out
-
-
-def fatrelu_and_mul(out: torch.Tensor, x: torch.Tensor, threshold: float = 0.0) -> None:
-    ops.fatrelu_and_mul(out, x, threshold)
-    return out
-
-
-def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_fast(out, x)
-    return out
-
-
-def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_new(out, x)
-    return out
-
-
-def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_quick(out, x)
-    return out
-
-
-__all__ = [
-    "silu_and_mul",
-    "gelu_and_mul",
-    "gelu_tanh_and_mul",
-    "fatrelu_and_mul",
-    "gelu_fast",
-    "gelu_new",
-    "gelu_quick",
-    "layers",
-]

build/torch25-cxx11-cu124-x86_64-linux/activation/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _activation_78448fa
-ops = torch.ops._activation_78448fa
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_activation_78448fa::{op_name}"

build/torch25-cxx11-cu124-x86_64-linux/activation/layers.py

@@ -1,65 +0,0 @@
-import torch
-import torch.nn as nn
-
-from ._ops import ops
-
-
-class SiluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.silu_and_mul(out, x)
-        return out
-
-
-class GeluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_and_mul(out, x)
-        return out
-
-
-class GeluTanhAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_tanh_and_mul(out, x)
-        return out
-
-
-class FatreluAndMul(nn.Module):
-    def __init__(self, threshold: float = 0.0):
-        super().__init__()
-        self.threshold = threshold
-
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.fatrelu_and_mul(out, x, self.threshold)
-        return out
-
-
-class FastGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_fast(out, x)
-        return out
-
-
-class NewGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_new(out, x)
-        return out
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_quick(out, x)
-        return out

build/torch25-cxx98-cu118-x86_64-linux/activation/__init__.py

@@ -1,52 +0,0 @@
-import torch
-
-from ._ops import ops
-
-from . import layers
-
-
-def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.silu_and_mul(out, x)
-    return out
-
-
-def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_and_mul(out, x)
-    return out
-
-
-def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_tanh_and_mul(out, x)
-    return out
-
-
-def fatrelu_and_mul(out: torch.Tensor, x: torch.Tensor, threshold: float = 0.0) -> None:
-    ops.fatrelu_and_mul(out, x, threshold)
-    return out
-
-
-def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_fast(out, x)
-    return out
-
-
-def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_new(out, x)
-    return out
-
-
-def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_quick(out, x)
-    return out
-
-
-__all__ = [
-    "silu_and_mul",
-    "gelu_and_mul",
-    "gelu_tanh_and_mul",
-    "fatrelu_and_mul",
-    "gelu_fast",
-    "gelu_new",
-    "gelu_quick",
-    "layers",
-]

build/torch25-cxx98-cu118-x86_64-linux/activation/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _activation_78448fa
-ops = torch.ops._activation_78448fa
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_activation_78448fa::{op_name}"

build/torch25-cxx98-cu118-x86_64-linux/activation/layers.py

@@ -1,65 +0,0 @@
-import torch
-import torch.nn as nn
-
-from ._ops import ops
-
-
-class SiluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.silu_and_mul(out, x)
-        return out
-
-
-class GeluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_and_mul(out, x)
-        return out
-
-
-class GeluTanhAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_tanh_and_mul(out, x)
-        return out
-
-
-class FatreluAndMul(nn.Module):
-    def __init__(self, threshold: float = 0.0):
-        super().__init__()
-        self.threshold = threshold
-
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.fatrelu_and_mul(out, x, self.threshold)
-        return out
-
-
-class FastGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_fast(out, x)
-        return out
-
-
-class NewGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_new(out, x)
-        return out
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_quick(out, x)
-        return out

build/torch25-cxx98-cu121-x86_64-linux/activation/__init__.py

@@ -1,52 +0,0 @@
-import torch
-
-from ._ops import ops
-
-from . import layers
-
-
-def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.silu_and_mul(out, x)
-    return out
-
-
-def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_and_mul(out, x)
-    return out
-
-
-def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_tanh_and_mul(out, x)
-    return out
-
-
-def fatrelu_and_mul(out: torch.Tensor, x: torch.Tensor, threshold: float = 0.0) -> None:
-    ops.fatrelu_and_mul(out, x, threshold)
-    return out
-
-
-def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_fast(out, x)
-    return out
-
-
-def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_new(out, x)
-    return out
-
-
-def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_quick(out, x)
-    return out
-
-
-__all__ = [
-    "silu_and_mul",
-    "gelu_and_mul",
-    "gelu_tanh_and_mul",
-    "fatrelu_and_mul",
-    "gelu_fast",
-    "gelu_new",
-    "gelu_quick",
-    "layers",
-]

build/torch25-cxx98-cu121-x86_64-linux/activation/_activation_78448fa.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7e6475ed603ad2cb565bd19ad2554484bd6c00d0d3f02decff60f2285df2546f
-size 2463232

build/torch25-cxx98-cu121-x86_64-linux/activation/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _activation_78448fa
-ops = torch.ops._activation_78448fa
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_activation_78448fa::{op_name}"

build/torch25-cxx98-cu121-x86_64-linux/activation/layers.py

@@ -1,65 +0,0 @@
-import torch
-import torch.nn as nn
-
-from ._ops import ops
-
-
-class SiluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.silu_and_mul(out, x)
-        return out
-
-
-class GeluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_and_mul(out, x)
-        return out
-
-
-class GeluTanhAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_tanh_and_mul(out, x)
-        return out
-
-
-class FatreluAndMul(nn.Module):
-    def __init__(self, threshold: float = 0.0):
-        super().__init__()
-        self.threshold = threshold
-
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.fatrelu_and_mul(out, x, self.threshold)
-        return out
-
-
-class FastGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_fast(out, x)
-        return out
-
-
-class NewGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_new(out, x)
-        return out
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_quick(out, x)
-        return out

build/torch25-cxx98-cu124-x86_64-linux/activation/__init__.py

@@ -1,52 +0,0 @@
-import torch
-
-from ._ops import ops
-
-from . import layers
-
-
-def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.silu_and_mul(out, x)
-    return out
-
-
-def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_and_mul(out, x)
-    return out
-
-
-def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_tanh_and_mul(out, x)
-    return out
-
-
-def fatrelu_and_mul(out: torch.Tensor, x: torch.Tensor, threshold: float = 0.0) -> None:
-    ops.fatrelu_and_mul(out, x, threshold)
-    return out
-
-
-def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_fast(out, x)
-    return out
-
-
-def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_new(out, x)
-    return out
-
-
-def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_quick(out, x)
-    return out
-
-
-__all__ = [
-    "silu_and_mul",
-    "gelu_and_mul",
-    "gelu_tanh_and_mul",
-    "fatrelu_and_mul",
-    "gelu_fast",
-    "gelu_new",
-    "gelu_quick",
-    "layers",
-]

build/torch25-cxx98-cu124-x86_64-linux/activation/_activation_78448fa.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a0767f6dba00c543d3cb77e2044bccd32ef569abc55b921231112c8a1ddfb187
-size 2502088

build/torch25-cxx98-cu124-x86_64-linux/activation/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _activation_78448fa
-ops = torch.ops._activation_78448fa
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_activation_78448fa::{op_name}"

build/torch25-cxx98-cu124-x86_64-linux/activation/layers.py

@@ -1,65 +0,0 @@
-import torch
-import torch.nn as nn
-
-from ._ops import ops
-
-
-class SiluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.silu_and_mul(out, x)
-        return out
-
-
-class GeluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_and_mul(out, x)
-        return out
-
-
-class GeluTanhAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_tanh_and_mul(out, x)
-        return out
-
-
-class FatreluAndMul(nn.Module):
-    def __init__(self, threshold: float = 0.0):
-        super().__init__()
-        self.threshold = threshold
-
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.fatrelu_and_mul(out, x, self.threshold)
-        return out
-
-
-class FastGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_fast(out, x)
-        return out
-
-
-class NewGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_new(out, x)
-        return out
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_quick(out, x)
-        return out

build/torch26-cxx11-cu118-x86_64-linux/activation/_activation_78448fa.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e0c04d860454cc565113a3c93ff755fe9cbba0578c4604b89ad89e47c2503932
-size 2448056

build/{torch25-cxx11-cu121-x86_64-linux/activation/_activation_78448fa.abi3.so → torch26-cxx11-cu118-x86_64-linux/activation/_activation_e99cc09_dirty.abi3.so}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6146ac6e77cbd458560bf67c46d93217833f2caf08260cc80a4aa62ba5645ee9
-size 2471056
+oid sha256:b925dc27b6a9afd5b6d11e454275222c531a92f7ca27958ac81a78c580665e4d
+size 2448088

build/torch26-cxx11-cu118-x86_64-linux/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_78448fa
-ops = torch.ops._activation_78448fa
+from . import _activation_e99cc09_dirty
+ops = torch.ops._activation_e99cc09_dirty
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_78448fa::{op_name}"
+    return f"_activation_e99cc09_dirty::{op_name}"

build/torch26-cxx11-cu118-x86_64-linux/activation/layers.py

@@ -5,6 +5,8 @@ from ._ops import ops
 
 
 class SiluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -14,6 +16,8 @@ class SiluAndMul(nn.Module):
 
 
 class GeluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -23,6 +27,8 @@ class GeluAndMul(nn.Module):
 
 
 class GeluTanhAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -32,6 +38,8 @@ class GeluTanhAndMul(nn.Module):
 
 
 class FatreluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def __init__(self, threshold: float = 0.0):
         super().__init__()
         self.threshold = threshold
@@ -45,6 +53,8 @@ class FatreluAndMul(nn.Module):
 
 
 class FastGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_fast(out, x)
@@ -52,6 +62,8 @@ class FastGELU(nn.Module):
 
 
 class NewGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_new(out, x)
@@ -59,6 +71,8 @@ class NewGELU(nn.Module):
 
 
 class QuickGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_quick(out, x)

build/torch26-cxx11-cu124-x86_64-linux/activation/_activation_78448fa.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:48d7b0d190af1dd0366dbaeb0690b9c7cd1dfdc9aeda9b0b23bce56c70f5cbae
-size 2509928

build/{torch25-cxx11-cu118-x86_64-linux/activation/_activation_78448fa.abi3.so → torch26-cxx11-cu124-x86_64-linux/activation/_activation_e99cc09_dirty.abi3.so}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:323dbf69b89390fd46b207abc1314a4cbe27491e1bb9f026c840bc3bff43b7d3
-size 2447952
+oid sha256:cfdbe510752b57a8dc4671f744bb0a2da5b1646e0b9a19fec02f1505ba044c8c
+size 2509960

build/torch26-cxx11-cu124-x86_64-linux/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_78448fa
-ops = torch.ops._activation_78448fa
+from . import _activation_e99cc09_dirty
+ops = torch.ops._activation_e99cc09_dirty
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_78448fa::{op_name}"
+    return f"_activation_e99cc09_dirty::{op_name}"

build/torch26-cxx11-cu124-x86_64-linux/activation/layers.py

@@ -5,6 +5,8 @@ from ._ops import ops
 
 
 class SiluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -14,6 +16,8 @@ class SiluAndMul(nn.Module):
 
 
 class GeluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -23,6 +27,8 @@ class GeluAndMul(nn.Module):
 
 
 class GeluTanhAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -32,6 +38,8 @@ class GeluTanhAndMul(nn.Module):
 
 
 class FatreluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def __init__(self, threshold: float = 0.0):
         super().__init__()
         self.threshold = threshold
@@ -45,6 +53,8 @@ class FatreluAndMul(nn.Module):
 
 
 class FastGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_fast(out, x)
@@ -52,6 +62,8 @@ class FastGELU(nn.Module):
 
 
 class NewGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_new(out, x)
@@ -59,6 +71,8 @@ class NewGELU(nn.Module):
 
 
 class QuickGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_quick(out, x)

build/torch26-cxx11-cu126-aarch64-linux/activation/__init__.py

@@ -1,52 +0,0 @@
-import torch
-
-from ._ops import ops
-
-from . import layers
-
-
-def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.silu_and_mul(out, x)
-    return out
-
-
-def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_and_mul(out, x)
-    return out
-
-
-def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_tanh_and_mul(out, x)
-    return out
-
-
-def fatrelu_and_mul(out: torch.Tensor, x: torch.Tensor, threshold: float = 0.0) -> None:
-    ops.fatrelu_and_mul(out, x, threshold)
-    return out
-
-
-def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_fast(out, x)
-    return out
-
-
-def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_new(out, x)
-    return out
-
-
-def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_quick(out, x)
-    return out
-
-
-__all__ = [
-    "silu_and_mul",
-    "gelu_and_mul",
-    "gelu_tanh_and_mul",
-    "fatrelu_and_mul",
-    "gelu_fast",
-    "gelu_new",
-    "gelu_quick",
-    "layers",
-]

build/torch26-cxx11-cu126-aarch64-linux/activation/_activation_bbdc1b4_dirty.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:558e4499ad3c09d02633488cfdc802a228b78a8cd51d963c92239d44744298c7
-size 2631936

build/torch26-cxx11-cu126-aarch64-linux/activation/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _activation_bbdc1b4_dirty
-ops = torch.ops._activation_bbdc1b4_dirty
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_activation_bbdc1b4_dirty::{op_name}"

build/torch26-cxx11-cu126-aarch64-linux/activation/layers.py

@@ -1,65 +0,0 @@
-import torch
-import torch.nn as nn
-
-from ._ops import ops
-
-
-class SiluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.silu_and_mul(out, x)
-        return out
-
-
-class GeluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_and_mul(out, x)
-        return out
-
-
-class GeluTanhAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_tanh_and_mul(out, x)
-        return out
-
-
-class FatreluAndMul(nn.Module):
-    def __init__(self, threshold: float = 0.0):
-        super().__init__()
-        self.threshold = threshold
-
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.fatrelu_and_mul(out, x, self.threshold)
-        return out
-
-
-class FastGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_fast(out, x)
-        return out
-
-
-class NewGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_new(out, x)
-        return out
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_quick(out, x)
-        return out

build/torch26-cxx11-cu126-x86_64-linux/activation/_activation_78448fa.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:11a11d0f4119edc5c637bab04ebd5669750a0e4f4000f58ab1bf5be2d8d9ab0b
-size 2518568

build/{torch27-cxx11-cu126-x86_64-linux/activation/_activation_78448fa.abi3.so → torch26-cxx11-cu126-x86_64-linux/activation/_activation_e99cc09_dirty.abi3.so}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:22ed530294eb70c8261e581615bd9da0d2dc1ba8c3f0dcc3696cff9be62580cb
+oid sha256:70e544ad6448a5576d26147f48403f3e9e593f4a2e24167dc8acb81ce3b7932e
 size 2518600

build/torch26-cxx11-cu126-x86_64-linux/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_78448fa
-ops = torch.ops._activation_78448fa
+from . import _activation_e99cc09_dirty
+ops = torch.ops._activation_e99cc09_dirty
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_78448fa::{op_name}"
+    return f"_activation_e99cc09_dirty::{op_name}"

build/torch26-cxx11-cu126-x86_64-linux/activation/layers.py

@@ -5,6 +5,8 @@ from ._ops import ops
 
 
 class SiluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -14,6 +16,8 @@ class SiluAndMul(nn.Module):
 
 
 class GeluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -23,6 +27,8 @@ class GeluAndMul(nn.Module):
 
 
 class GeluTanhAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -32,6 +38,8 @@ class GeluTanhAndMul(nn.Module):
 
 
 class FatreluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def __init__(self, threshold: float = 0.0):
         super().__init__()
         self.threshold = threshold
@@ -45,6 +53,8 @@ class FatreluAndMul(nn.Module):
 
 
 class FastGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_fast(out, x)
@@ -52,6 +62,8 @@ class FastGELU(nn.Module):
 
 
 class NewGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_new(out, x)
@@ -59,6 +71,8 @@ class NewGELU(nn.Module):
 
 
 class QuickGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_quick(out, x)

build/torch26-cxx98-cu118-x86_64-linux/activation/_activation_78448fa.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:56dcc985761e309cbef3fc2a201f26e800583128d6e5a3fc1b23800fb0b8b48c
-size 2440544

build/{torch25-cxx11-cu124-x86_64-linux/activation/_activation_78448fa.abi3.so → torch26-cxx98-cu118-x86_64-linux/activation/_activation_e99cc09_dirty.abi3.so}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:28eea3907055742f99bc9d7d4260add848adc2f6464e97029f37cd42a5c6bd0a
-size 2509832
+oid sha256:60fd224c33657558f03be5be57cc8d35ade23225b1abd71557b170c8a7010cd1
+size 2440576

build/torch26-cxx98-cu118-x86_64-linux/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_78448fa
-ops = torch.ops._activation_78448fa
+from . import _activation_e99cc09_dirty
+ops = torch.ops._activation_e99cc09_dirty
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_78448fa::{op_name}"
+    return f"_activation_e99cc09_dirty::{op_name}"

build/torch26-cxx98-cu118-x86_64-linux/activation/layers.py

@@ -5,6 +5,8 @@ from ._ops import ops
 
 
 class SiluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -14,6 +16,8 @@ class SiluAndMul(nn.Module):
 
 
 class GeluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -23,6 +27,8 @@ class GeluAndMul(nn.Module):
 
 
 class GeluTanhAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -32,6 +38,8 @@ class GeluTanhAndMul(nn.Module):
 
 
 class FatreluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def __init__(self, threshold: float = 0.0):
         super().__init__()
         self.threshold = threshold
@@ -45,6 +53,8 @@ class FatreluAndMul(nn.Module):
 
 
 class FastGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_fast(out, x)
@@ -52,6 +62,8 @@ class FastGELU(nn.Module):
 
 
 class NewGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_new(out, x)
@@ -59,6 +71,8 @@ class NewGELU(nn.Module):
 
 
 class QuickGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_quick(out, x)

build/torch26-cxx98-cu124-x86_64-linux/activation/_activation_78448fa.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:03c5f08322796d0736024412babe5d7f13bb1126387976ae12a80485a40d3883
-size 2502240

build/{torch25-cxx98-cu118-x86_64-linux/activation/_activation_78448fa.abi3.so → torch26-cxx98-cu124-x86_64-linux/activation/_activation_e99cc09_dirty.abi3.so}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d5609ad07903b98c83c297bfb64f0d944df5edfe1c611fee23ec6c8fbd952604
-size 2440392
+oid sha256:e364773259dc1b91f3c0d3b076da83c5a9c6ee18ffdace30315c602dffd1dabe
+size 2502264

build/torch26-cxx98-cu124-x86_64-linux/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_78448fa
-ops = torch.ops._activation_78448fa
+from . import _activation_e99cc09_dirty
+ops = torch.ops._activation_e99cc09_dirty
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_78448fa::{op_name}"
+    return f"_activation_e99cc09_dirty::{op_name}"

build/torch26-cxx98-cu124-x86_64-linux/activation/layers.py

@@ -5,6 +5,8 @@ from ._ops import ops
 
 
 class SiluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -14,6 +16,8 @@ class SiluAndMul(nn.Module):
 
 
 class GeluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -23,6 +27,8 @@ class GeluAndMul(nn.Module):
 
 
 class GeluTanhAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor):
         d = x.shape[-1] // 2
         output_shape = x.shape[:-1] + (d,)
@@ -32,6 +38,8 @@ class GeluTanhAndMul(nn.Module):
 
 
 class FatreluAndMul(nn.Module):
+    can_torch_compile: bool = True
+
     def __init__(self, threshold: float = 0.0):
         super().__init__()
         self.threshold = threshold
@@ -45,6 +53,8 @@ class FatreluAndMul(nn.Module):
 
 
 class FastGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_fast(out, x)
@@ -52,6 +62,8 @@ class FastGELU(nn.Module):
 
 
 class NewGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_new(out, x)
@@ -59,6 +71,8 @@ class NewGELU(nn.Module):
 
 
 class QuickGELU(nn.Module):
+    can_torch_compile: bool = True
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         ops.gelu_quick(out, x)

build/torch26-cxx98-cu126-aarch64-linux/activation/__init__.py

@@ -1,52 +0,0 @@
-import torch
-
-from ._ops import ops
-
-from . import layers
-
-
-def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.silu_and_mul(out, x)
-    return out
-
-
-def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_and_mul(out, x)
-    return out
-
-
-def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_tanh_and_mul(out, x)
-    return out
-
-
-def fatrelu_and_mul(out: torch.Tensor, x: torch.Tensor, threshold: float = 0.0) -> None:
-    ops.fatrelu_and_mul(out, x, threshold)
-    return out
-
-
-def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_fast(out, x)
-    return out
-
-
-def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_new(out, x)
-    return out
-
-
-def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.gelu_quick(out, x)
-    return out
-
-
-__all__ = [
-    "silu_and_mul",
-    "gelu_and_mul",
-    "gelu_tanh_and_mul",
-    "fatrelu_and_mul",
-    "gelu_fast",
-    "gelu_new",
-    "gelu_quick",
-    "layers",
-]

build/torch26-cxx98-cu126-aarch64-linux/activation/_activation_bbdc1b4_dirty.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f6afd50526ff4221cddd52cb947900cdf6bb95ad0a6bffcd1a86bda4d3f52349
-size 2628128

build/torch26-cxx98-cu126-aarch64-linux/activation/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _activation_bbdc1b4_dirty
-ops = torch.ops._activation_bbdc1b4_dirty
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_activation_bbdc1b4_dirty::{op_name}"

build/torch26-cxx98-cu126-aarch64-linux/activation/layers.py

@@ -1,65 +0,0 @@
-import torch
-import torch.nn as nn
-
-from ._ops import ops
-
-
-class SiluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.silu_and_mul(out, x)
-        return out
-
-
-class GeluAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_and_mul(out, x)
-        return out
-
-
-class GeluTanhAndMul(nn.Module):
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.gelu_tanh_and_mul(out, x)
-        return out
-
-
-class FatreluAndMul(nn.Module):
-    def __init__(self, threshold: float = 0.0):
-        super().__init__()
-        self.threshold = threshold
-
-    def forward(self, x: torch.Tensor):
-        d = x.shape[-1] // 2
-        output_shape = x.shape[:-1] + (d,)
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        ops.fatrelu_and_mul(out, x, self.threshold)
-        return out
-
-
-class FastGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_fast(out, x)
-        return out
-
-
-class NewGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_new(out, x)
-        return out
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_quick(out, x)
-        return out

build/torch26-cxx98-cu126-x86_64-linux/activation/_activation_78448fa.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f6eae5c895c564fbd2524ce488f4e91e65dc63402cd41a8bc74474b7437b2e62
-size 2506784

build/torch26-cxx98-cu126-x86_64-linux/activation/_activation_e99cc09_dirty.abi3.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ac88cc0d3c65ab283d20608f3a097be29ee572e7856f10f8d7919536efd95b4
+size 2506808