torch 2.8 support (#1)

- feat: upload torch28 build (7bc152adfb8413bf260a99b903dc0f90ec14d559)

build/torch26-cxx98-cu118-x86_64-linux/activation/_activation_605f22e_dirty.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:176b8610ed2b9650c68347ec2f1d9e99b653170b4fd4f6f3540731f3fd78e98b
-size 2949936

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

@@ -1,9 +0,0 @@
-import torch
-from . import _activation_605f22e_dirty
-ops = torch.ops._activation_605f22e_dirty
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_activation_605f22e_dirty::{op_name}"

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

@@ -1,30 +0,0 @@
-import torch
-
-from . import layers
-from ._ops import ops
-from .poly_norm import PolyNormFunction
-from .rms_norm import RMSNormFunction
-
-
-def poly_norm(
-    x: torch.Tensor,
-    weight: torch.Tensor,
-    bias: torch.Tensor,
-    eps: float = 1e-6,
-) -> None:
-    return PolyNormFunction.apply(x, weight, bias, eps)
-
-
-def rms_norm(
-    x: torch.Tensor,
-    weight: torch.Tensor,
-    eps: float = 1e-6,
-) -> None:
-    return RMSNormFunction.apply(x, weight, eps)
-
-
-__all__ = [
-    "poly_norm",
-    "layers",
-    "ops",
-]

build/torch26-cxx98-cu124-x86_64-linux/activation/_activation_605f22e_dirty.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8075bbb5b339e0305d353003eb86a2b6a4d8a468907d821cefbed29e6e439c19
-size 2974640

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

@@ -1,9 +0,0 @@
-import torch
-from . import _activation_605f22e_dirty
-ops = torch.ops._activation_605f22e_dirty
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_activation_605f22e_dirty::{op_name}"

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

@@ -1,46 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.nn import init
-
-from .poly_norm import PolyNormFunction
-from .rms_norm import RMSNormFunction
-
-
-class PolyNorm(nn.Module):
-    def __init__(self, eps=1e-6, dtype: torch.dtype = torch.float32):
-        super().__init__()
-        self.weight = torch.nn.Parameter(torch.ones(3, dtype=dtype) / 3)
-        self.bias = torch.nn.Parameter(torch.zeros(1, dtype=dtype))
-        self.eps = eps
-
-    def forward(
-        self,
-        x: torch.Tensor,
-    ):
-        return PolyNormFunction.apply(x, self.weight, self.bias, self.eps)
-
-    def reset_parameters(self) -> None:
-        """
-        Resets parameters based on their initialization used in __init__.
-        """
-        init.ones_(self.weight)
-        init.zeros_(self.bias)
-
-
-class RMSNorm(nn.Module):
-    def __init__(self, dim: int, eps=1e-6, dtype: torch.dtype = torch.float32):
-        super().__init__()
-        self.weight = torch.nn.Parameter(torch.ones(dim, dtype=dtype))
-        self.eps = eps
-
-    def forward(
-        self,
-        x: torch.Tensor,
-    ):
-        return RMSNormFunction.apply(x, self.weight, self.eps)
-
-    def reset_parameters(self) -> None:
-        """
-        Resets parameters based on their initialization used in __init__.
-        """
-        init.ones_(self.weight)

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

@@ -1,41 +0,0 @@
-import torch
-
-from ._ops import ops
-
-
-# Inherit from Function
-class PolyNormFunction(torch.autograd.Function):
-    # Note that forward, setup_context, and backward are @staticmethods
-    @staticmethod
-    def forward(input, weight, bias, eps):
-        output = torch.empty_like(input)
-        ops.poly_norm(output, input, weight, bias, eps)
-        return output
-
-    @staticmethod
-    # inputs is a Tuple of all of the inputs passed to forward.
-    # output is the output of the forward().
-    def setup_context(ctx, inputs, output):
-        input, weight, bias, eps = inputs
-        ctx.save_for_backward(input, weight)
-        ctx.eps = eps
-
-    # This function has only a single output, so it gets only one gradient
-    @staticmethod
-    def backward(ctx, output_grad):
-        input, weight = ctx.saved_tensors
-        eps = ctx.eps
-
-        input_grad = torch.empty_like(input) if ctx.needs_input_grad[0] else None
-        weight_grad = torch.empty_like(weight) if ctx.needs_input_grad[1] else None
-        bias_grad = (
-            torch.empty(1, dtype=weight.dtype, device=weight.device)
-            if ctx.needs_input_grad[2]
-            else None
-        )
-
-        ops.poly_norm_backward(
-            input_grad, weight_grad, bias_grad, output_grad, input, weight, eps
-        )
-
-        return input_grad, weight_grad, bias_grad, None

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

@@ -1,34 +0,0 @@
-import torch
-
-from ._ops import ops
-
-
-# Inherit from Function
-class RMSNormFunction(torch.autograd.Function):
-    # Note that forward, setup_context, and backward are @staticmethods
-    @staticmethod
-    def forward(input, weight, eps):
-        output = torch.empty_like(input)
-        ops.rms_norm(output, input, weight, eps)
-        return output
-
-    @staticmethod
-    # inputs is a Tuple of all of the inputs passed to forward.
-    # output is the output of the forward().
-    def setup_context(ctx, inputs, output):
-        input, weight, eps = inputs
-        ctx.save_for_backward(input, weight)
-        ctx.eps = eps
-
-    # This function has only a single output, so it gets only one gradient
-    @staticmethod
-    def backward(ctx, output_grad):
-        input, weight = ctx.saved_tensors
-        eps = ctx.eps
-
-        input_grad = torch.empty_like(input) if ctx.needs_input_grad[0] else None
-        weight_grad = torch.empty_like(weight) if ctx.needs_input_grad[1] else None
-
-        ops.rms_norm_backward(input_grad, weight_grad, output_grad, input, weight, eps)
-
-        return input_grad, weight_grad, None

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

@@ -1,30 +0,0 @@
-import torch
-
-from . import layers
-from ._ops import ops
-from .poly_norm import PolyNormFunction
-from .rms_norm import RMSNormFunction
-
-
-def poly_norm(
-    x: torch.Tensor,
-    weight: torch.Tensor,
-    bias: torch.Tensor,
-    eps: float = 1e-6,
-) -> None:
-    return PolyNormFunction.apply(x, weight, bias, eps)
-
-
-def rms_norm(
-    x: torch.Tensor,
-    weight: torch.Tensor,
-    eps: float = 1e-6,
-) -> None:
-    return RMSNormFunction.apply(x, weight, eps)
-
-
-__all__ = [
-    "poly_norm",
-    "layers",
-    "ops",
-]

build/torch26-cxx98-cu126-x86_64-linux/activation/_activation_605f22e_dirty.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:614ef2bf9867f65bf8e09d861def1c554d384676aa58dfbfd73bf96241cb7171
-size 2987456

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

@@ -1,9 +0,0 @@
-import torch
-from . import _activation_605f22e_dirty
-ops = torch.ops._activation_605f22e_dirty
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_activation_605f22e_dirty::{op_name}"

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

@@ -1,46 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.nn import init
-
-from .poly_norm import PolyNormFunction
-from .rms_norm import RMSNormFunction
-
-
-class PolyNorm(nn.Module):
-    def __init__(self, eps=1e-6, dtype: torch.dtype = torch.float32):
-        super().__init__()
-        self.weight = torch.nn.Parameter(torch.ones(3, dtype=dtype) / 3)
-        self.bias = torch.nn.Parameter(torch.zeros(1, dtype=dtype))
-        self.eps = eps
-
-    def forward(
-        self,
-        x: torch.Tensor,
-    ):
-        return PolyNormFunction.apply(x, self.weight, self.bias, self.eps)
-
-    def reset_parameters(self) -> None:
-        """
-        Resets parameters based on their initialization used in __init__.
-        """
-        init.ones_(self.weight)
-        init.zeros_(self.bias)
-
-
-class RMSNorm(nn.Module):
-    def __init__(self, dim: int, eps=1e-6, dtype: torch.dtype = torch.float32):
-        super().__init__()
-        self.weight = torch.nn.Parameter(torch.ones(dim, dtype=dtype))
-        self.eps = eps
-
-    def forward(
-        self,
-        x: torch.Tensor,
-    ):
-        return RMSNormFunction.apply(x, self.weight, self.eps)
-
-    def reset_parameters(self) -> None:
-        """
-        Resets parameters based on their initialization used in __init__.
-        """
-        init.ones_(self.weight)

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

@@ -1,41 +0,0 @@
-import torch
-
-from ._ops import ops
-
-
-# Inherit from Function
-class PolyNormFunction(torch.autograd.Function):
-    # Note that forward, setup_context, and backward are @staticmethods
-    @staticmethod
-    def forward(input, weight, bias, eps):
-        output = torch.empty_like(input)
-        ops.poly_norm(output, input, weight, bias, eps)
-        return output
-
-    @staticmethod
-    # inputs is a Tuple of all of the inputs passed to forward.
-    # output is the output of the forward().
-    def setup_context(ctx, inputs, output):
-        input, weight, bias, eps = inputs
-        ctx.save_for_backward(input, weight)
-        ctx.eps = eps
-
-    # This function has only a single output, so it gets only one gradient
-    @staticmethod
-    def backward(ctx, output_grad):
-        input, weight = ctx.saved_tensors
-        eps = ctx.eps
-
-        input_grad = torch.empty_like(input) if ctx.needs_input_grad[0] else None
-        weight_grad = torch.empty_like(weight) if ctx.needs_input_grad[1] else None
-        bias_grad = (
-            torch.empty(1, dtype=weight.dtype, device=weight.device)
-            if ctx.needs_input_grad[2]
-            else None
-        )
-
-        ops.poly_norm_backward(
-            input_grad, weight_grad, bias_grad, output_grad, input, weight, eps
-        )
-
-        return input_grad, weight_grad, bias_grad, None

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

@@ -1,34 +0,0 @@
-import torch
-
-from ._ops import ops
-
-
-# Inherit from Function
-class RMSNormFunction(torch.autograd.Function):
-    # Note that forward, setup_context, and backward are @staticmethods
-    @staticmethod
-    def forward(input, weight, eps):
-        output = torch.empty_like(input)
-        ops.rms_norm(output, input, weight, eps)
-        return output
-
-    @staticmethod
-    # inputs is a Tuple of all of the inputs passed to forward.
-    # output is the output of the forward().
-    def setup_context(ctx, inputs, output):
-        input, weight, eps = inputs
-        ctx.save_for_backward(input, weight)
-        ctx.eps = eps
-
-    # This function has only a single output, so it gets only one gradient
-    @staticmethod
-    def backward(ctx, output_grad):
-        input, weight = ctx.saved_tensors
-        eps = ctx.eps
-
-        input_grad = torch.empty_like(input) if ctx.needs_input_grad[0] else None
-        weight_grad = torch.empty_like(weight) if ctx.needs_input_grad[1] else None
-
-        ops.rms_norm_backward(input_grad, weight_grad, output_grad, input, weight, eps)
-
-        return input_grad, weight_grad, None

build/torch27-cxx11-cu118-x86_64-linux/activation/_activation_605f22e_dirty.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 2957448

build/{torch26-cxx11-cu118-x86_64-linux/activation/_activation_605f22e_dirty.abi3.so → torch27-cxx11-cu118-x86_64-linux/activation/_activation_cf68df1_dirty.abi3.so}

@@ -1,3 +1,3 @@
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+size 2994312

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

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_605f22e_dirty
-ops = torch.ops._activation_605f22e_dirty
+from . import _activation_cf68df1_dirty
+ops = torch.ops._activation_cf68df1_dirty
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_605f22e_dirty::{op_name}"
+    return f"_activation_cf68df1_dirty::{op_name}"

build/torch27-cxx11-cu126-x86_64-linux/activation/_activation_605f22e_dirty.abi3.so

@@ -1,3 +0,0 @@
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-size 2994736

build/{torch26-cxx11-cu126-x86_64-linux/activation/_activation_605f22e_dirty.abi3.so → torch27-cxx11-cu126-x86_64-linux/activation/_activation_cf68df1_dirty.abi3.so}

@@ -1,3 +1,3 @@
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+size 3027504

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

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_605f22e_dirty
-ops = torch.ops._activation_605f22e_dirty
+from . import _activation_cf68df1_dirty
+ops = torch.ops._activation_cf68df1_dirty
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_605f22e_dirty::{op_name}"
+    return f"_activation_cf68df1_dirty::{op_name}"

build/torch27-cxx11-cu128-x86_64-linux/activation/_activation_605f22e_dirty.abi3.so

@@ -1,3 +0,0 @@
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-size 3909704

build/{torch26-cxx11-cu124-x86_64-linux/activation/_activation_605f22e_dirty.abi3.so → torch27-cxx11-cu128-x86_64-linux/activation/_activation_cf68df1_dirty.abi3.so}

@@ -1,3 +1,3 @@
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-oid sha256:5baac6228e04fbb209cbc90a24702c14f4eb52d2698cea12a766d77412622096
-size 2981880
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+size 3987512

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

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_605f22e_dirty
-ops = torch.ops._activation_605f22e_dirty
+from . import _activation_cf68df1_dirty
+ops = torch.ops._activation_cf68df1_dirty
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_605f22e_dirty::{op_name}"
+    return f"_activation_cf68df1_dirty::{op_name}"

build/torch27-cxx11-rocm63-x86_64-linux/activation/_activation_605f22e_dirty.abi3.so

@@ -1,3 +0,0 @@
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-size 2642880

build/{torch26-cxx11-rocm62-x86_64-linux/activation/_activation_605f22e_dirty.abi3.so → torch27-cxx11-rocm63-x86_64-linux/activation/_activation_cf68df1_dirty.abi3.so}

@@ -1,3 +1,3 @@
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+size 2642976

build/torch27-cxx11-rocm63-x86_64-linux/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_605f22e_dirty
-ops = torch.ops._activation_605f22e_dirty
+from . import _activation_cf68df1_dirty
+ops = torch.ops._activation_cf68df1_dirty
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_605f22e_dirty::{op_name}"
+    return f"_activation_cf68df1_dirty::{op_name}"

build/torch28-cxx11-cu126-x86_64-linux/activation/_activation_cf68df1_dirty.abi3.so

@@ -0,0 +1,3 @@
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+size 3032488

build/{torch26-cxx11-cu126-x86_64-linux → torch28-cxx11-cu126-x86_64-linux}/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_605f22e_dirty
-ops = torch.ops._activation_605f22e_dirty
+from . import _activation_cf68df1_dirty
+ops = torch.ops._activation_cf68df1_dirty
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_605f22e_dirty::{op_name}"
+    return f"_activation_cf68df1_dirty::{op_name}"

build/torch28-cxx11-cu128-x86_64-linux/activation/_activation_cf68df1_dirty.abi3.so

@@ -0,0 +1,3 @@
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+size 4000920

build/{torch26-cxx11-cu118-x86_64-linux → torch28-cxx11-cu128-x86_64-linux}/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_605f22e_dirty
-ops = torch.ops._activation_605f22e_dirty
+from . import _activation_cf68df1_dirty
+ops = torch.ops._activation_cf68df1_dirty
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_605f22e_dirty::{op_name}"
+    return f"_activation_cf68df1_dirty::{op_name}"

build/torch28-cxx11-cu129-x86_64-linux/activation/_activation_cf68df1_dirty.abi3.so

@@ -0,0 +1,3 @@
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+size 4059256

build/{torch26-cxx11-rocm62-x86_64-linux → torch28-cxx11-cu129-x86_64-linux}/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_605f22e_dirty
-ops = torch.ops._activation_605f22e_dirty
+from . import _activation_cf68df1_dirty
+ops = torch.ops._activation_cf68df1_dirty
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_605f22e_dirty::{op_name}"
+    return f"_activation_cf68df1_dirty::{op_name}"

build/torch28-cxx11-rocm63-x86_64-linux/activation/_activation_cf68df1_dirty.abi3.so

@@ -0,0 +1,3 @@
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+size 2647872

build/{torch26-cxx11-cu124-x86_64-linux → torch28-cxx11-rocm63-x86_64-linux}/activation/_ops.py

@@ -1,9 +1,9 @@
 import torch
-from . import _activation_605f22e_dirty
-ops = torch.ops._activation_605f22e_dirty
+from . import _activation_cf68df1_dirty
+ops = torch.ops._activation_cf68df1_dirty
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_activation_605f22e_dirty::{op_name}"
+    return f"_activation_cf68df1_dirty::{op_name}"