Build (x86_64-linux)

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

@@ -1,128 +0,0 @@
-import torch
-import torch.nn as nn
-
-from ._ops import ops
-
-
-class SiluAndMul(nn.Module):
-    """An activation function for SwiGLU.
-
-    The function computes x -> silu(x[:d]) * x[d:] where d = x.shape[-1] // 2.
-
-    Shapes:
-        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
-        return: (num_tokens, d) or (batch_size, seq_len, d)
-    """
-
-    can_torch_compile: bool = True
-
-    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 MulAndSilu(nn.Module):
-    """An activation function for SwiGLU.
-
-    The function computes x -> x[:d] * silu(x[d:]) where d = x.shape[-1] // 2.
-
-    Shapes:
-        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
-        return: (num_tokens, d) or (batch_size, seq_len, d)
-    """
-
-    can_torch_compile: bool = True
-
-    def forward(self, x: torch.Tensor) -> 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.mul_and_silu(out, x)
-        return out
-
-
-class GeluAndMul(nn.Module):
-    """An activation function for GeGLU.
-
-    The function computes x -> GELU(x[:d]) * x[d:] where d = x.shape[-1] // 2.
-
-    Shapes:
-        x: (batch_size, seq_len, 2 * d) or (num_tokens, 2 * d)
-        return: (batch_size, seq_len, d) or (num_tokens, d)
-    """
-
-    can_torch_compile: bool = True
-
-    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):
-    can_torch_compile: bool = True
-
-    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):
-    """An activation function for FATReLU.
-
-    The function computes x -> FATReLU(x[:d]) * x[d:] where
-    d = x.shape[-1] // 2.
-    This is used in openbmb/MiniCPM-S-1B-sft.
-
-    Shapes:
-        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
-        return: (num_tokens, d) or (batch_size, seq_len, d)
-    """
-
-    can_torch_compile: bool = True
-
-    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):
-    can_torch_compile: bool = True
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_fast(out, x)
-        return out
-
-
-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)
-        return out
-
-
-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)
-        return out

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

@@ -1,57 +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 mul_and_silu(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.mul_and_silu(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-cu118-x86_64-linux/activation/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _activation_be5bedb
-ops = torch.ops._activation_be5bedb
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_activation_be5bedb::{op_name}"

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

@@ -1,57 +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 mul_and_silu(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.mul_and_silu(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-cu124-x86_64-linux/activation/_activation_be5bedb.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:53ddfb42466bfe01feb98348f5c2d6beefd589aeb3dec4c5c36609e11a6bde4c
-size 2605136

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

@@ -1,9 +0,0 @@
-import torch
-from . import _activation_be5bedb
-ops = torch.ops._activation_be5bedb
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_activation_be5bedb::{op_name}"

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

@@ -1,128 +0,0 @@
-import torch
-import torch.nn as nn
-
-from ._ops import ops
-
-
-class SiluAndMul(nn.Module):
-    """An activation function for SwiGLU.
-
-    The function computes x -> silu(x[:d]) * x[d:] where d = x.shape[-1] // 2.
-
-    Shapes:
-        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
-        return: (num_tokens, d) or (batch_size, seq_len, d)
-    """
-
-    can_torch_compile: bool = True
-
-    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 MulAndSilu(nn.Module):
-    """An activation function for SwiGLU.
-
-    The function computes x -> x[:d] * silu(x[d:]) where d = x.shape[-1] // 2.
-
-    Shapes:
-        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
-        return: (num_tokens, d) or (batch_size, seq_len, d)
-    """
-
-    can_torch_compile: bool = True
-
-    def forward(self, x: torch.Tensor) -> 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.mul_and_silu(out, x)
-        return out
-
-
-class GeluAndMul(nn.Module):
-    """An activation function for GeGLU.
-
-    The function computes x -> GELU(x[:d]) * x[d:] where d = x.shape[-1] // 2.
-
-    Shapes:
-        x: (batch_size, seq_len, 2 * d) or (num_tokens, 2 * d)
-        return: (batch_size, seq_len, d) or (num_tokens, d)
-    """
-
-    can_torch_compile: bool = True
-
-    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):
-    can_torch_compile: bool = True
-
-    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):
-    """An activation function for FATReLU.
-
-    The function computes x -> FATReLU(x[:d]) * x[d:] where
-    d = x.shape[-1] // 2.
-    This is used in openbmb/MiniCPM-S-1B-sft.
-
-    Shapes:
-        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
-        return: (num_tokens, d) or (batch_size, seq_len, d)
-    """
-
-    can_torch_compile: bool = True
-
-    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):
-    can_torch_compile: bool = True
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_fast(out, x)
-        return out
-
-
-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)
-        return out
-
-
-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)
-        return out

build/torch26-cxx98-cu126-x86_64-linux/activation/__init__.py

@@ -1,57 +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 mul_and_silu(out: torch.Tensor, x: torch.Tensor) -> None:
-    ops.mul_and_silu(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-x86_64-linux/activation/_activation_be5bedb.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ac7174352dea307231f308c84ca32ee001cdbcefd976de860e76501c52aae591
-size 2613776

build/torch26-cxx98-cu126-x86_64-linux/activation/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _activation_be5bedb
-ops = torch.ops._activation_be5bedb
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_activation_be5bedb::{op_name}"

build/torch26-cxx98-cu126-x86_64-linux/activation/layers.py

@@ -1,128 +0,0 @@
-import torch
-import torch.nn as nn
-
-from ._ops import ops
-
-
-class SiluAndMul(nn.Module):
-    """An activation function for SwiGLU.
-
-    The function computes x -> silu(x[:d]) * x[d:] where d = x.shape[-1] // 2.
-
-    Shapes:
-        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
-        return: (num_tokens, d) or (batch_size, seq_len, d)
-    """
-
-    can_torch_compile: bool = True
-
-    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 MulAndSilu(nn.Module):
-    """An activation function for SwiGLU.
-
-    The function computes x -> x[:d] * silu(x[d:]) where d = x.shape[-1] // 2.
-
-    Shapes:
-        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
-        return: (num_tokens, d) or (batch_size, seq_len, d)
-    """
-
-    can_torch_compile: bool = True
-
-    def forward(self, x: torch.Tensor) -> 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.mul_and_silu(out, x)
-        return out
-
-
-class GeluAndMul(nn.Module):
-    """An activation function for GeGLU.
-
-    The function computes x -> GELU(x[:d]) * x[d:] where d = x.shape[-1] // 2.
-
-    Shapes:
-        x: (batch_size, seq_len, 2 * d) or (num_tokens, 2 * d)
-        return: (batch_size, seq_len, d) or (num_tokens, d)
-    """
-
-    can_torch_compile: bool = True
-
-    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):
-    can_torch_compile: bool = True
-
-    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):
-    """An activation function for FATReLU.
-
-    The function computes x -> FATReLU(x[:d]) * x[d:] where
-    d = x.shape[-1] // 2.
-    This is used in openbmb/MiniCPM-S-1B-sft.
-
-    Shapes:
-        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
-        return: (num_tokens, d) or (batch_size, seq_len, d)
-    """
-
-    can_torch_compile: bool = True
-
-    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):
-    can_torch_compile: bool = True
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
-        ops.gelu_fast(out, x)
-        return out
-
-
-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)
-        return out
-
-
-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)
-        return out

build/torch27-cxx11-cu118-x86_64-linux/activation/_activation_20250917153858.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:618cdba5f19eabc1f9c1d33e130ef03ab1b11b52f1e7b00b73f2a10d5cf1e62f
-size 2773664

build/{torch26-cxx11-cu124-x86_64-linux/activation/_activation_be5bedb.abi3.so → torch27-cxx11-cu118-x86_64-linux/activation/_activation_beeaae6.abi3.so}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:331dcb3900d5e47a11d3577cdbac54f15a0b6e14910239293323c1d9e4eb9f49
-size 2616928
+oid sha256:ce06ec284ecd4ac5423d3822a60cd9eeb686d0054b38d66567de73e1137b0567
+size 2773632

build/torch27-cxx11-cu118-x86_64-linux/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_20250917153858
-ops = torch.ops._activation_20250917153858
+from . import _activation_beeaae6
+ops = torch.ops._activation_beeaae6
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_20250917153858::{op_name}"
+    return f"_activation_beeaae6::{op_name}"

build/torch27-cxx11-cu126-x86_64-linux/activation/_activation_20250917153858.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:87ee9280b670b3323378c17d75ee7082f419987a568769fe8479bf509ee6c245
-size 2852232

build/{torch26-cxx11-cu118-x86_64-linux/activation/_activation_be5bedb.abi3.so → torch27-cxx11-cu126-x86_64-linux/activation/_activation_beeaae6.abi3.so}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9b6ba32ecc6fc898df3b0cebee85e9afc6881749fe58142280f051ca3332d913
-size 2546864
+oid sha256:a529bd105aca5081398d63329e829b6b159570424cd654d3a9f275ca9a720e82
+size 2852200

build/torch27-cxx11-cu126-x86_64-linux/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_20250917153858
-ops = torch.ops._activation_20250917153858
+from . import _activation_beeaae6
+ops = torch.ops._activation_beeaae6
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_20250917153858::{op_name}"
+    return f"_activation_beeaae6::{op_name}"

build/torch27-cxx11-cu128-x86_64-linux/activation/_activation_20250917153858.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:28ca9a3e35c49ae49694d7c6c77f85f3664622cad9c857bf13dfbf3bc144ae1b
-size 4127912

build/{torch26-cxx11-cu126-x86_64-linux/activation/_activation_be5bedb.abi3.so → torch27-cxx11-cu128-x86_64-linux/activation/_activation_beeaae6.abi3.so}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1ce11492b9675a44afb3b896ed80e425f2a47e29481c4aad9c4a6ac59520f011
-size 2621472
+oid sha256:0f2cffcb6b5b9a49f03a2df46fc2ad36765676edecb468c233e78e1f5e21e206
+size 4127872

build/torch27-cxx11-cu128-x86_64-linux/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_20250917153858
-ops = torch.ops._activation_20250917153858
+from . import _activation_beeaae6
+ops = torch.ops._activation_beeaae6
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_20250917153858::{op_name}"
+    return f"_activation_beeaae6::{op_name}"

build/torch28-cxx11-cu126-x86_64-linux/activation/_activation_20250917153858.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0fcd47dd765bba10bb09f65388f6c1b357b117b2611c17bae5bf8214499a9e39
-size 2837224

build/{torch26-cxx98-cu118-x86_64-linux/activation/_activation_be5bedb.abi3.so → torch28-cxx11-cu126-x86_64-linux/activation/_activation_beeaae6.abi3.so}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:434bd1ae43b7cbdb10d86b82da9a237ec05ef9d9fb4fc15cdc9096d3d5ed3fa7
-size 2539352
+oid sha256:972be0b2b7ce4f771028406367437488743dc81d70e6316e7a2694df1422b23d
+size 2837192

build/torch28-cxx11-cu126-x86_64-linux/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_20250917153858
-ops = torch.ops._activation_20250917153858
+from . import _activation_beeaae6
+ops = torch.ops._activation_beeaae6
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_20250917153858::{op_name}"
+    return f"_activation_beeaae6::{op_name}"

build/torch28-cxx11-cu128-x86_64-linux/activation/_activation_20250917153858.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0e6d88c71eebabc842f6a566de7cfaf24d3d90a30572eae584a3b51dcb7e838e
-size 4117000

build/torch28-cxx11-cu128-x86_64-linux/activation/_activation_beeaae6.abi3.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ec0756eb56dab9c57cc1aa01cfc2301d508fdf11ac4d02d015f7c16dd2246f2f
+size 4116960

build/torch28-cxx11-cu128-x86_64-linux/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_20250917153858
-ops = torch.ops._activation_20250917153858
+from . import _activation_beeaae6
+ops = torch.ops._activation_beeaae6
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_20250917153858::{op_name}"
+    return f"_activation_beeaae6::{op_name}"

build/torch28-cxx11-cu129-x86_64-linux/activation/_activation_20250917153858.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f3c1b86db31b04bd5fe75b0c9d6915ba2766a2456ea9bd1a20f2d75c4b1acf35
-size 4154880

build/torch28-cxx11-cu129-x86_64-linux/activation/_activation_beeaae6.abi3.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de346c02f046cbb177556580efc9994632adad1439bb90f451f2f690e326c39c
+size 4154840

build/torch28-cxx11-cu129-x86_64-linux/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_20250917153858
-ops = torch.ops._activation_20250917153858
+from . import _activation_beeaae6
+ops = torch.ops._activation_beeaae6
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_20250917153858::{op_name}"
+    return f"_activation_beeaae6::{op_name}"

build/{torch26-cxx11-cu118-x86_64-linux → torch29-cxx11-cu126-x86_64-linux}/activation/__init__.py

@@ -30,6 +30,20 @@ def fatrelu_and_mul(out: torch.Tensor, x: torch.Tensor, threshold: float = 0.0)
     return out
 
 
+def gelu(out: torch.Tensor, x: torch.Tensor) -> None:
+    ops.gelu(out, x)
+    return out
+
+def silu(out: torch.Tensor, x: torch.Tensor) -> None:
+    ops.silu(out, x)
+    return out
+
+
+def gelu_tanh(out: torch.Tensor, x: torch.Tensor) -> None:
+    ops.gelu_tanh(out, x)
+    return out
+
+
 def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None:
     ops.gelu_fast(out, x)
     return out
@@ -47,11 +61,15 @@ def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> None:
 
 __all__ = [
     "silu_and_mul",
+    "mul_and_silu",
     "gelu_and_mul",
     "gelu_tanh_and_mul",
     "fatrelu_and_mul",
     "gelu_fast",
     "gelu_new",
     "gelu_quick",
+    "gelu_tanh",
+    "silu",
+    "gelu",
     "layers",
 ]