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rmsnorm

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kernels-bot commited on 4 days ago

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.gitattributes +6 -0
README.md +3 -3
build/torch210-cpu-x86_64-linux/__init__.py +27 -0
build/torch210-cpu-x86_64-linux/_ops.py +9 -0
build/torch210-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so +3 -0
build/torch210-cpu-x86_64-linux/layers.py +59 -0
build/torch210-cpu-x86_64-linux/metadata.json +33 -0
build/torch210-cpu-x86_64-linux/metadata.json.sigstore +1 -0
build/torch210-cpu-x86_64-linux/symbols.json +126 -0
build/torch211-cpu-x86_64-linux/__init__.py +27 -0
build/torch211-cpu-x86_64-linux/_ops.py +9 -0
build/torch211-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so +3 -0
build/torch211-cpu-x86_64-linux/layers.py +59 -0
build/torch211-cpu-x86_64-linux/metadata.json +33 -0
build/torch211-cpu-x86_64-linux/metadata.json.sigstore +1 -0
build/torch211-cpu-x86_64-linux/symbols.json +126 -0
build/torch212-cpu-x86_64-linux/__init__.py +27 -0
build/torch212-cpu-x86_64-linux/_ops.py +9 -0
build/torch212-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so +3 -0
build/torch212-cpu-x86_64-linux/layers.py +59 -0
build/torch212-cpu-x86_64-linux/metadata.json +33 -0
build/torch212-cpu-x86_64-linux/metadata.json.sigstore +1 -0
build/torch212-cpu-x86_64-linux/symbols.json +126 -0
build/torch213-cpu-x86_64-linux/__init__.py +27 -0
build/torch213-cpu-x86_64-linux/_ops.py +9 -0
build/torch213-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so +3 -0
build/torch213-cpu-x86_64-linux/layers.py +59 -0
build/torch213-cpu-x86_64-linux/metadata.json +33 -0
build/torch213-cpu-x86_64-linux/metadata.json.sigstore +1 -0
build/torch213-cpu-x86_64-linux/symbols.json +126 -0
build/torch214-cpu-x86_64-linux/__init__.py +27 -0
build/torch214-cpu-x86_64-linux/_ops.py +9 -0
build/torch214-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so +3 -0
build/torch214-cpu-x86_64-linux/layers.py +59 -0
build/torch214-cpu-x86_64-linux/metadata.json +33 -0
build/torch214-cpu-x86_64-linux/metadata.json.sigstore +1 -0
build/torch214-cpu-x86_64-linux/symbols.json +126 -0
build/torch215-cpu-x86_64-linux/__init__.py +27 -0
build/torch215-cpu-x86_64-linux/_ops.py +9 -0
build/torch215-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so +3 -0
build/torch215-cpu-x86_64-linux/layers.py +59 -0
build/torch215-cpu-x86_64-linux/metadata.json +33 -0
build/torch215-cpu-x86_64-linux/metadata.json.sigstore +1 -0
build/torch215-cpu-x86_64-linux/symbols.json +126 -0

.gitattributes CHANGED Viewed

@@ -149,3 +149,9 @@ build/torch213-cxx11-cpu-x86_64-linux/_rmsnorm_cpu_89d4054.abi3.so filter=lfs di
 build/torch214-cxx11-cpu-x86_64-linux/_rmsnorm_cpu_89d4054.abi3.so filter=lfs diff=lfs merge=lfs -text
 build/torch213-cxx11-xpu20260-x86_64-linux/_rmsnorm_xpu_89d4054.abi3.so filter=lfs diff=lfs merge=lfs -text
 build/torch214-cxx11-xpu20261-x86_64-linux/_rmsnorm_xpu_89d4054.abi3.so filter=lfs diff=lfs merge=lfs -text

 build/torch214-cxx11-cpu-x86_64-linux/_rmsnorm_cpu_89d4054.abi3.so filter=lfs diff=lfs merge=lfs -text
 build/torch213-cxx11-xpu20260-x86_64-linux/_rmsnorm_xpu_89d4054.abi3.so filter=lfs diff=lfs merge=lfs -text
 build/torch214-cxx11-xpu20261-x86_64-linux/_rmsnorm_xpu_89d4054.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch210-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch211-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch212-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch213-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch214-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch215-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so filter=lfs diff=lfs merge=lfs -text

README.md CHANGED Viewed

@@ -12,13 +12,13 @@ This is the repository card of kernels-community/rmsnorm that has been pushed on
 from kernels import get_kernel
 kernel_module = get_kernel("kernels-community/rmsnorm", version=1)
-apply_rms_norm_forward = kernel_module.apply_rms_norm_forward
-apply_rms_norm_forward(...)
 ```
 ## Available functions
-- `apply_rms_norm_forward`
 - `apply_rms_norm_backward`
 ## Available layers

 from kernels import get_kernel
 kernel_module = get_kernel("kernels-community/rmsnorm", version=1)
+apply_rms_norm = kernel_module.apply_rms_norm
+apply_rms_norm(...)
 ```
 ## Available functions
+- `apply_rms_norm`
 - `apply_rms_norm_backward`
 ## Available layers

build/torch210-cpu-x86_64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,27 @@

+from . import layers
+from ._ops import ops
+def apply_rms_norm(input, weight, eps):
+    # ops.apply_rms_norm returns [output, rstd]
+    return ops.apply_rms_norm(
+            input,
+            weight,
+            eps,
+    )[0]
+def apply_rms_norm_backward(grad_output, input, weight, output, rstd, eps, input_requires_grad=True, weight_requires_grad=True):
+    return ops.apply_rms_norm_backward(
+            grad_output,
+            input,
+            weight,
+            output,
+            rstd,
+            eps,
+            input_requires_grad,
+            weight_requires_grad
+    )
+__all__ = ["layers", "apply_rms_norm", "apply_rms_norm_backward"]

build/torch210-cpu-x86_64-linux/_ops.py ADDED Viewed

	@@ -0,0 +1,9 @@

+import torch
+from . import _rmsnorm_cpu_59ac2f9
+ops = torch.ops._rmsnorm_cpu_59ac2f9
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rmsnorm_cpu_59ac2f9::{op_name}"

build/torch210-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:8bc1b02fc9c9c12086a96288f25b00ac712c9e0ce079310f769bcc2b618337c7
+size 162296

build/torch210-cpu-x86_64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,59 @@

+import torch
+from ._ops import ops
+class RMSNormFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, hidden_states, weight, variance_epsilon):
+        ctx.variance_epsilon = variance_epsilon
+        output, rstd = ops.apply_rms_norm(hidden_states, weight, variance_epsilon)
+        ctx.save_for_backward(hidden_states, weight, output, rstd)
+        return output
+    @staticmethod
+    def backward(ctx, grad_output):
+        hidden_states, weight, output, rstd = ctx.saved_tensors
+        grads = ops.apply_rms_norm_backward(
+            grad_output,
+            hidden_states,
+            weight,
+            output,
+            rstd,
+            ctx.variance_epsilon,
+            ctx.needs_input_grad[0],
+            ctx.needs_input_grad[1]
+        )
+        return grads[0], grads[1], None
+class RMSNorm(torch.nn.Module):
+    """
+    RMSNorm module that uses the optimized LigerRMSNormFunction.
+    Args:
+        hidden_size (int): The size of the hidden dimension.
+        eps (float, optional): The epsilon value for numerical stability. Defaults to 1e-6.
+        offset (float, optional): Offset value to shift the weight tensor. Defaults to 0.0.
+        casting_mode (str, optional): The casting mode to use. Defaults to "llama".
+        in_place (bool, optional): Whether to modify dY in-place to store dX during backward. Defaults to True.
+    """
+    weight: torch.Tensor
+    variance_epsilon: float
+    def forward(self, hidden_states):
+        """
+        Apply RMS normalization to the input tensor.
+        Args:
+            hidden_states (torch.Tensor): Input tensor of shape (B, T, H) or (BxT, H)
+        Returns:
+            torch.Tensor: Normalized tensor of the same shape as input
+        """
+        return RMSNormFunction.apply(
+            hidden_states,
+            self.weight,
+            self.variance_epsilon,
+        )
+__all__ = ["RMSNorm"]

build/torch210-cpu-x86_64-linux/metadata.json ADDED Viewed

	@@ -0,0 +1,33 @@

+{
+  "name": "rmsnorm",
+  "id": "_rmsnorm_cpu_59ac2f9",
+  "version": 1,
+  "kernels-minver": "0.14.0",
+  "license": "Apache-2.0",
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+  },
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+    }
+  },
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+    }
+  }
+}

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build/torch210-cpu-x86_64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,126 @@

+{
+  "schema_version": 1,
+  "module": "_rmsnorm_cpu_59ac2f9",
+  "functions": [
+    {
+      "name": "apply_rms_norm",
+      "qualname": "apply_rms_norm",
+      "module": "_rmsnorm_cpu_59ac2f9",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "input",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "weight",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "eps",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          }
+        ],
+        "return_annotation": null
+      }
+    },
+    {
+      "name": "apply_rms_norm_backward",
+      "qualname": "apply_rms_norm_backward",
+      "module": "_rmsnorm_cpu_59ac2f9",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "grad_output",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "input",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "weight",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "output",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "rstd",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "eps",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "input_requires_grad",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "True",
+            "annotation": null
+          },
+          {
+            "name": "weight_requires_grad",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "True",
+            "annotation": null
+          }
+        ],
+        "return_annotation": null
+      }
+    }
+  ],
+  "layers": [
+    {
+      "name": "RMSNorm",
+      "qualname": "RMSNorm",
+      "module": "_rmsnorm_cpu_59ac2f9.layers",
+      "kind": "class",
+      "doc": "RMSNorm module that uses the optimized LigerRMSNormFunction.\n\nArgs:\n    hidden_size (int): The size of the hidden dimension.\n    eps (float, optional): The epsilon value for numerical stability. Defaults to 1e-6.\n    offset (float, optional): Offset value to shift the weight tensor. Defaults to 0.0.\n    casting_mode (str, optional): The casting mode to use. Defaults to \"llama\".\n    in_place (bool, optional): Whether to modify dY in-place to store dX during backward. Defaults to True.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "args",
+            "kind": "VAR_POSITIONAL",
+            "default": null,
+            "annotation": "Any"
+          },
+          {
+            "name": "kwargs",
+            "kind": "VAR_KEYWORD",
+            "default": null,
+            "annotation": "Any"
+          }
+        ],
+        "return_annotation": "None"
+      },
+      "attributes": {
+        "has_backward": null,
+        "can_torch_compile": null
+      }
+    }
+  ]
+}

build/torch211-cpu-x86_64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,27 @@

+from . import layers
+from ._ops import ops
+def apply_rms_norm(input, weight, eps):
+    # ops.apply_rms_norm returns [output, rstd]
+    return ops.apply_rms_norm(
+            input,
+            weight,
+            eps,
+    )[0]
+def apply_rms_norm_backward(grad_output, input, weight, output, rstd, eps, input_requires_grad=True, weight_requires_grad=True):
+    return ops.apply_rms_norm_backward(
+            grad_output,
+            input,
+            weight,
+            output,
+            rstd,
+            eps,
+            input_requires_grad,
+            weight_requires_grad
+    )
+__all__ = ["layers", "apply_rms_norm", "apply_rms_norm_backward"]

build/torch211-cpu-x86_64-linux/_ops.py ADDED Viewed

	@@ -0,0 +1,9 @@

+import torch
+from . import _rmsnorm_cpu_59ac2f9
+ops = torch.ops._rmsnorm_cpu_59ac2f9
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rmsnorm_cpu_59ac2f9::{op_name}"

build/torch211-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:cf1ca0f9c90f38eccf76c1eb02a4bd4cc6146bc9ecc1adea25e1c51c1740ae15
+size 162296

build/torch211-cpu-x86_64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,59 @@

+import torch
+from ._ops import ops
+class RMSNormFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, hidden_states, weight, variance_epsilon):
+        ctx.variance_epsilon = variance_epsilon
+        output, rstd = ops.apply_rms_norm(hidden_states, weight, variance_epsilon)
+        ctx.save_for_backward(hidden_states, weight, output, rstd)
+        return output
+    @staticmethod
+    def backward(ctx, grad_output):
+        hidden_states, weight, output, rstd = ctx.saved_tensors
+        grads = ops.apply_rms_norm_backward(
+            grad_output,
+            hidden_states,
+            weight,
+            output,
+            rstd,
+            ctx.variance_epsilon,
+            ctx.needs_input_grad[0],
+            ctx.needs_input_grad[1]
+        )
+        return grads[0], grads[1], None
+class RMSNorm(torch.nn.Module):
+    """
+    RMSNorm module that uses the optimized LigerRMSNormFunction.
+    Args:
+        hidden_size (int): The size of the hidden dimension.
+        eps (float, optional): The epsilon value for numerical stability. Defaults to 1e-6.
+        offset (float, optional): Offset value to shift the weight tensor. Defaults to 0.0.
+        casting_mode (str, optional): The casting mode to use. Defaults to "llama".
+        in_place (bool, optional): Whether to modify dY in-place to store dX during backward. Defaults to True.
+    """
+    weight: torch.Tensor
+    variance_epsilon: float
+    def forward(self, hidden_states):
+        """
+        Apply RMS normalization to the input tensor.
+        Args:
+            hidden_states (torch.Tensor): Input tensor of shape (B, T, H) or (BxT, H)
+        Returns:
+            torch.Tensor: Normalized tensor of the same shape as input
+        """
+        return RMSNormFunction.apply(
+            hidden_states,
+            self.weight,
+            self.variance_epsilon,
+        )
+__all__ = ["RMSNorm"]

build/torch211-cpu-x86_64-linux/metadata.json ADDED Viewed

	@@ -0,0 +1,33 @@

+{
+  "name": "rmsnorm",
+  "id": "_rmsnorm_cpu_59ac2f9",
+  "version": 1,
+  "kernels-minver": "0.14.0",
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "cpu"
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "__init__.py": "/ulY9H9OswHp9JHl1OZyjDJFuI/Uuoyiq+e8Swx14wY=",
+      "_ops.py": "l1a3DvymOaKP4FEgC+VW0MhnSWDnNEPrhHNkvz3ywss=",
+      "_rmsnorm_cpu_59ac2f9.abi3.so": "zxyg+ckPOOzPdsHrAqS9TMYUa8nswa3qJeHFHBdArhU=",
+      "layers.py": "8KMmxy30Olm/16XW7vvHAwWNVWcnA8js0nNuoOSGEO8=",
+      "symbols.json": "oeLQ1wwvhrKfNCq0/LwHhIn71+VdONzzoRWed5liCMI="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "59ac2f9edd19416fbeeb88cb00a2b521c412280a",
+      "dirty": false
+    }
+  }
+}

build/torch211-cpu-x86_64-linux/metadata.json.sigstore ADDED Viewed

	@@ -0,0 +1 @@

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build/torch211-cpu-x86_64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,126 @@

+{
+  "schema_version": 1,
+  "module": "_rmsnorm_cpu_59ac2f9",
+  "functions": [
+    {
+      "name": "apply_rms_norm",
+      "qualname": "apply_rms_norm",
+      "module": "_rmsnorm_cpu_59ac2f9",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "input",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "weight",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "eps",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          }
+        ],
+        "return_annotation": null
+      }
+    },
+    {
+      "name": "apply_rms_norm_backward",
+      "qualname": "apply_rms_norm_backward",
+      "module": "_rmsnorm_cpu_59ac2f9",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "grad_output",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "input",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "weight",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "output",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "rstd",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "eps",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "input_requires_grad",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "True",
+            "annotation": null
+          },
+          {
+            "name": "weight_requires_grad",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "True",
+            "annotation": null
+          }
+        ],
+        "return_annotation": null
+      }
+    }
+  ],
+  "layers": [
+    {
+      "name": "RMSNorm",
+      "qualname": "RMSNorm",
+      "module": "_rmsnorm_cpu_59ac2f9.layers",
+      "kind": "class",
+      "doc": "RMSNorm module that uses the optimized LigerRMSNormFunction.\n\nArgs:\n    hidden_size (int): The size of the hidden dimension.\n    eps (float, optional): The epsilon value for numerical stability. Defaults to 1e-6.\n    offset (float, optional): Offset value to shift the weight tensor. Defaults to 0.0.\n    casting_mode (str, optional): The casting mode to use. Defaults to \"llama\".\n    in_place (bool, optional): Whether to modify dY in-place to store dX during backward. Defaults to True.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "args",
+            "kind": "VAR_POSITIONAL",
+            "default": null,
+            "annotation": "Any"
+          },
+          {
+            "name": "kwargs",
+            "kind": "VAR_KEYWORD",
+            "default": null,
+            "annotation": "Any"
+          }
+        ],
+        "return_annotation": "None"
+      },
+      "attributes": {
+        "has_backward": null,
+        "can_torch_compile": null
+      }
+    }
+  ]
+}

build/torch212-cpu-x86_64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,27 @@

+from . import layers
+from ._ops import ops
+def apply_rms_norm(input, weight, eps):
+    # ops.apply_rms_norm returns [output, rstd]
+    return ops.apply_rms_norm(
+            input,
+            weight,
+            eps,
+    )[0]
+def apply_rms_norm_backward(grad_output, input, weight, output, rstd, eps, input_requires_grad=True, weight_requires_grad=True):
+    return ops.apply_rms_norm_backward(
+            grad_output,
+            input,
+            weight,
+            output,
+            rstd,
+            eps,
+            input_requires_grad,
+            weight_requires_grad
+    )
+__all__ = ["layers", "apply_rms_norm", "apply_rms_norm_backward"]

build/torch212-cpu-x86_64-linux/_ops.py ADDED Viewed

	@@ -0,0 +1,9 @@

+import torch
+from . import _rmsnorm_cpu_59ac2f9
+ops = torch.ops._rmsnorm_cpu_59ac2f9
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rmsnorm_cpu_59ac2f9::{op_name}"

build/torch212-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so ADDED Viewed

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1e2ca80be18daa9d3cce9e9d734740e580ee66cd5a0214fc06b7e71933a8d809
+size 161760

build/torch212-cpu-x86_64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,59 @@

+import torch
+from ._ops import ops
+class RMSNormFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, hidden_states, weight, variance_epsilon):
+        ctx.variance_epsilon = variance_epsilon
+        output, rstd = ops.apply_rms_norm(hidden_states, weight, variance_epsilon)
+        ctx.save_for_backward(hidden_states, weight, output, rstd)
+        return output
+    @staticmethod
+    def backward(ctx, grad_output):
+        hidden_states, weight, output, rstd = ctx.saved_tensors
+        grads = ops.apply_rms_norm_backward(
+            grad_output,
+            hidden_states,
+            weight,
+            output,
+            rstd,
+            ctx.variance_epsilon,
+            ctx.needs_input_grad[0],
+            ctx.needs_input_grad[1]
+        )
+        return grads[0], grads[1], None
+class RMSNorm(torch.nn.Module):
+    """
+    RMSNorm module that uses the optimized LigerRMSNormFunction.
+    Args:
+        hidden_size (int): The size of the hidden dimension.
+        eps (float, optional): The epsilon value for numerical stability. Defaults to 1e-6.
+        offset (float, optional): Offset value to shift the weight tensor. Defaults to 0.0.
+        casting_mode (str, optional): The casting mode to use. Defaults to "llama".
+        in_place (bool, optional): Whether to modify dY in-place to store dX during backward. Defaults to True.
+    """
+    weight: torch.Tensor
+    variance_epsilon: float
+    def forward(self, hidden_states):
+        """
+        Apply RMS normalization to the input tensor.
+        Args:
+            hidden_states (torch.Tensor): Input tensor of shape (B, T, H) or (BxT, H)
+        Returns:
+            torch.Tensor: Normalized tensor of the same shape as input
+        """
+        return RMSNormFunction.apply(
+            hidden_states,
+            self.weight,
+            self.variance_epsilon,
+        )
+__all__ = ["RMSNorm"]

build/torch212-cpu-x86_64-linux/metadata.json ADDED Viewed

	@@ -0,0 +1,33 @@

+{
+  "name": "rmsnorm",
+  "id": "_rmsnorm_cpu_59ac2f9",
+  "version": 1,
+  "kernels-minver": "0.14.0",
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "cpu"
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
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+      "_ops.py": "l1a3DvymOaKP4FEgC+VW0MhnSWDnNEPrhHNkvz3ywss=",
+      "_rmsnorm_cpu_59ac2f9.abi3.so": "HiyoC+GNqp08zp6dc0dA5YDuZs1aAhT8BrfnGTOo2Ak=",
+      "layers.py": "8KMmxy30Olm/16XW7vvHAwWNVWcnA8js0nNuoOSGEO8=",
+      "symbols.json": "oeLQ1wwvhrKfNCq0/LwHhIn71+VdONzzoRWed5liCMI="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "59ac2f9edd19416fbeeb88cb00a2b521c412280a",
+      "dirty": false
+    }
+  }
+}

build/torch212-cpu-x86_64-linux/metadata.json.sigstore ADDED Viewed

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build/torch212-cpu-x86_64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,126 @@

+{
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+  "functions": [
+    {
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+      "qualname": "apply_rms_norm",
+      "module": "_rmsnorm_cpu_59ac2f9",
+      "kind": "function",
+      "doc": null,
+      "signature": {
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+            "name": "input",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
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+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
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+            "default": null,
+            "annotation": null
+          }
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+        "return_annotation": null
+      }
+    },
+    {
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+      "qualname": "apply_rms_norm_backward",
+      "module": "_rmsnorm_cpu_59ac2f9",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "grad_output",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
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+            "kind": "POSITIONAL_OR_KEYWORD",
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+            "annotation": null
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+          {
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+            "annotation": null
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+            "name": "rstd",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "eps",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "input_requires_grad",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "True",
+            "annotation": null
+          },
+          {
+            "name": "weight_requires_grad",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "True",
+            "annotation": null
+          }
+        ],
+        "return_annotation": null
+      }
+    }
+  ],
+  "layers": [
+    {
+      "name": "RMSNorm",
+      "qualname": "RMSNorm",
+      "module": "_rmsnorm_cpu_59ac2f9.layers",
+      "kind": "class",
+      "doc": "RMSNorm module that uses the optimized LigerRMSNormFunction.\n\nArgs:\n    hidden_size (int): The size of the hidden dimension.\n    eps (float, optional): The epsilon value for numerical stability. Defaults to 1e-6.\n    offset (float, optional): Offset value to shift the weight tensor. Defaults to 0.0.\n    casting_mode (str, optional): The casting mode to use. Defaults to \"llama\".\n    in_place (bool, optional): Whether to modify dY in-place to store dX during backward. Defaults to True.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "args",
+            "kind": "VAR_POSITIONAL",
+            "default": null,
+            "annotation": "Any"
+          },
+          {
+            "name": "kwargs",
+            "kind": "VAR_KEYWORD",
+            "default": null,
+            "annotation": "Any"
+          }
+        ],
+        "return_annotation": "None"
+      },
+      "attributes": {
+        "has_backward": null,
+        "can_torch_compile": null
+      }
+    }
+  ]
+}

build/torch213-cpu-x86_64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,27 @@

+from . import layers
+from ._ops import ops
+def apply_rms_norm(input, weight, eps):
+    # ops.apply_rms_norm returns [output, rstd]
+    return ops.apply_rms_norm(
+            input,
+            weight,
+            eps,
+    )[0]
+def apply_rms_norm_backward(grad_output, input, weight, output, rstd, eps, input_requires_grad=True, weight_requires_grad=True):
+    return ops.apply_rms_norm_backward(
+            grad_output,
+            input,
+            weight,
+            output,
+            rstd,
+            eps,
+            input_requires_grad,
+            weight_requires_grad
+    )
+__all__ = ["layers", "apply_rms_norm", "apply_rms_norm_backward"]

build/torch213-cpu-x86_64-linux/_ops.py ADDED Viewed

	@@ -0,0 +1,9 @@

+import torch
+from . import _rmsnorm_cpu_59ac2f9
+ops = torch.ops._rmsnorm_cpu_59ac2f9
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rmsnorm_cpu_59ac2f9::{op_name}"

build/torch213-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:0c096bc1c1d38b4d506e23dd887a32fe19b14513047c536486cab24cf9f6fe88
+size 162120

build/torch213-cpu-x86_64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,59 @@

+import torch
+from ._ops import ops
+class RMSNormFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, hidden_states, weight, variance_epsilon):
+        ctx.variance_epsilon = variance_epsilon
+        output, rstd = ops.apply_rms_norm(hidden_states, weight, variance_epsilon)
+        ctx.save_for_backward(hidden_states, weight, output, rstd)
+        return output
+    @staticmethod
+    def backward(ctx, grad_output):
+        hidden_states, weight, output, rstd = ctx.saved_tensors
+        grads = ops.apply_rms_norm_backward(
+            grad_output,
+            hidden_states,
+            weight,
+            output,
+            rstd,
+            ctx.variance_epsilon,
+            ctx.needs_input_grad[0],
+            ctx.needs_input_grad[1]
+        )
+        return grads[0], grads[1], None
+class RMSNorm(torch.nn.Module):
+    """
+    RMSNorm module that uses the optimized LigerRMSNormFunction.
+    Args:
+        hidden_size (int): The size of the hidden dimension.
+        eps (float, optional): The epsilon value for numerical stability. Defaults to 1e-6.
+        offset (float, optional): Offset value to shift the weight tensor. Defaults to 0.0.
+        casting_mode (str, optional): The casting mode to use. Defaults to "llama".
+        in_place (bool, optional): Whether to modify dY in-place to store dX during backward. Defaults to True.
+    """
+    weight: torch.Tensor
+    variance_epsilon: float
+    def forward(self, hidden_states):
+        """
+        Apply RMS normalization to the input tensor.
+        Args:
+            hidden_states (torch.Tensor): Input tensor of shape (B, T, H) or (BxT, H)
+        Returns:
+            torch.Tensor: Normalized tensor of the same shape as input
+        """
+        return RMSNormFunction.apply(
+            hidden_states,
+            self.weight,
+            self.variance_epsilon,
+        )
+__all__ = ["RMSNorm"]

build/torch213-cpu-x86_64-linux/metadata.json ADDED Viewed

	@@ -0,0 +1,33 @@

+{
+  "name": "rmsnorm",
+  "id": "_rmsnorm_cpu_59ac2f9",
+  "version": 1,
+  "kernels-minver": "0.14.0",
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "cpu"
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "__init__.py": "/ulY9H9OswHp9JHl1OZyjDJFuI/Uuoyiq+e8Swx14wY=",
+      "_ops.py": "l1a3DvymOaKP4FEgC+VW0MhnSWDnNEPrhHNkvz3ywss=",
+      "_rmsnorm_cpu_59ac2f9.abi3.so": "DAlrwcHTi01QbiPdiHoy/hmxRRMEfFNkhsqyTPn2/og=",
+      "layers.py": "8KMmxy30Olm/16XW7vvHAwWNVWcnA8js0nNuoOSGEO8=",
+      "symbols.json": "oeLQ1wwvhrKfNCq0/LwHhIn71+VdONzzoRWed5liCMI="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "59ac2f9edd19416fbeeb88cb00a2b521c412280a",
+      "dirty": false
+    }
+  }
+}

build/torch213-cpu-x86_64-linux/metadata.json.sigstore ADDED Viewed

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build/torch213-cpu-x86_64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,126 @@

+{
+  "schema_version": 1,
+  "module": "_rmsnorm_cpu_59ac2f9",
+  "functions": [
+    {
+      "name": "apply_rms_norm",
+      "qualname": "apply_rms_norm",
+      "module": "_rmsnorm_cpu_59ac2f9",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "input",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "weight",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "eps",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          }
+        ],
+        "return_annotation": null
+      }
+    },
+    {
+      "name": "apply_rms_norm_backward",
+      "qualname": "apply_rms_norm_backward",
+      "module": "_rmsnorm_cpu_59ac2f9",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "grad_output",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "input",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "weight",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "output",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "rstd",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "eps",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "input_requires_grad",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "True",
+            "annotation": null
+          },
+          {
+            "name": "weight_requires_grad",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "True",
+            "annotation": null
+          }
+        ],
+        "return_annotation": null
+      }
+    }
+  ],
+  "layers": [
+    {
+      "name": "RMSNorm",
+      "qualname": "RMSNorm",
+      "module": "_rmsnorm_cpu_59ac2f9.layers",
+      "kind": "class",
+      "doc": "RMSNorm module that uses the optimized LigerRMSNormFunction.\n\nArgs:\n    hidden_size (int): The size of the hidden dimension.\n    eps (float, optional): The epsilon value for numerical stability. Defaults to 1e-6.\n    offset (float, optional): Offset value to shift the weight tensor. Defaults to 0.0.\n    casting_mode (str, optional): The casting mode to use. Defaults to \"llama\".\n    in_place (bool, optional): Whether to modify dY in-place to store dX during backward. Defaults to True.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "args",
+            "kind": "VAR_POSITIONAL",
+            "default": null,
+            "annotation": "Any"
+          },
+          {
+            "name": "kwargs",
+            "kind": "VAR_KEYWORD",
+            "default": null,
+            "annotation": "Any"
+          }
+        ],
+        "return_annotation": "None"
+      },
+      "attributes": {
+        "has_backward": null,
+        "can_torch_compile": null
+      }
+    }
+  ]
+}

build/torch214-cpu-x86_64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,27 @@

+from . import layers
+from ._ops import ops
+def apply_rms_norm(input, weight, eps):
+    # ops.apply_rms_norm returns [output, rstd]
+    return ops.apply_rms_norm(
+            input,
+            weight,
+            eps,
+    )[0]
+def apply_rms_norm_backward(grad_output, input, weight, output, rstd, eps, input_requires_grad=True, weight_requires_grad=True):
+    return ops.apply_rms_norm_backward(
+            grad_output,
+            input,
+            weight,
+            output,
+            rstd,
+            eps,
+            input_requires_grad,
+            weight_requires_grad
+    )
+__all__ = ["layers", "apply_rms_norm", "apply_rms_norm_backward"]

build/torch214-cpu-x86_64-linux/_ops.py ADDED Viewed

	@@ -0,0 +1,9 @@

+import torch
+from . import _rmsnorm_cpu_59ac2f9
+ops = torch.ops._rmsnorm_cpu_59ac2f9
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rmsnorm_cpu_59ac2f9::{op_name}"

build/torch214-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:34f15020ae7cda49b16ad7c8126399dfd1ca3cabb948df8e2c875fa1d7762fdb
+size 162120

build/torch214-cpu-x86_64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,59 @@

+import torch
+from ._ops import ops
+class RMSNormFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, hidden_states, weight, variance_epsilon):
+        ctx.variance_epsilon = variance_epsilon
+        output, rstd = ops.apply_rms_norm(hidden_states, weight, variance_epsilon)
+        ctx.save_for_backward(hidden_states, weight, output, rstd)
+        return output
+    @staticmethod
+    def backward(ctx, grad_output):
+        hidden_states, weight, output, rstd = ctx.saved_tensors
+        grads = ops.apply_rms_norm_backward(
+            grad_output,
+            hidden_states,
+            weight,
+            output,
+            rstd,
+            ctx.variance_epsilon,
+            ctx.needs_input_grad[0],
+            ctx.needs_input_grad[1]
+        )
+        return grads[0], grads[1], None
+class RMSNorm(torch.nn.Module):
+    """
+    RMSNorm module that uses the optimized LigerRMSNormFunction.
+    Args:
+        hidden_size (int): The size of the hidden dimension.
+        eps (float, optional): The epsilon value for numerical stability. Defaults to 1e-6.
+        offset (float, optional): Offset value to shift the weight tensor. Defaults to 0.0.
+        casting_mode (str, optional): The casting mode to use. Defaults to "llama".
+        in_place (bool, optional): Whether to modify dY in-place to store dX during backward. Defaults to True.
+    """
+    weight: torch.Tensor
+    variance_epsilon: float
+    def forward(self, hidden_states):
+        """
+        Apply RMS normalization to the input tensor.
+        Args:
+            hidden_states (torch.Tensor): Input tensor of shape (B, T, H) or (BxT, H)
+        Returns:
+            torch.Tensor: Normalized tensor of the same shape as input
+        """
+        return RMSNormFunction.apply(
+            hidden_states,
+            self.weight,
+            self.variance_epsilon,
+        )
+__all__ = ["RMSNorm"]

build/torch214-cpu-x86_64-linux/metadata.json ADDED Viewed

	@@ -0,0 +1,33 @@

+{
+  "name": "rmsnorm",
+  "id": "_rmsnorm_cpu_59ac2f9",
+  "version": 1,
+  "kernels-minver": "0.14.0",
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "cpu"
+  },
+  "digest": {
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+    "files": {
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+      "_ops.py": "l1a3DvymOaKP4FEgC+VW0MhnSWDnNEPrhHNkvz3ywss=",
+      "_rmsnorm_cpu_59ac2f9.abi3.so": "NPFQIK582kmxatfIEmOZ39HKPKu5SN+OLIdfodd2L9s=",
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+      "symbols.json": "oeLQ1wwvhrKfNCq0/LwHhIn71+VdONzzoRWed5liCMI="
+    }
+  },
+  "provenance": {
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+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "59ac2f9edd19416fbeeb88cb00a2b521c412280a",
+      "dirty": false
+    }
+  }
+}

build/torch214-cpu-x86_64-linux/metadata.json.sigstore ADDED Viewed

	@@ -0,0 +1 @@

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+{
+  "schema_version": 1,
+  "module": "_rmsnorm_cpu_59ac2f9",
+  "functions": [
+    {
+      "name": "apply_rms_norm",
+      "qualname": "apply_rms_norm",
+      "module": "_rmsnorm_cpu_59ac2f9",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "input",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "weight",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "eps",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          }
+        ],
+        "return_annotation": null
+      }
+    },
+    {
+      "name": "apply_rms_norm_backward",
+      "qualname": "apply_rms_norm_backward",
+      "module": "_rmsnorm_cpu_59ac2f9",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "grad_output",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "input",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
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+          {
+            "name": "weight",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "output",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "rstd",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "eps",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "input_requires_grad",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "True",
+            "annotation": null
+          },
+          {
+            "name": "weight_requires_grad",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "True",
+            "annotation": null
+          }
+        ],
+        "return_annotation": null
+      }
+    }
+  ],
+  "layers": [
+    {
+      "name": "RMSNorm",
+      "qualname": "RMSNorm",
+      "module": "_rmsnorm_cpu_59ac2f9.layers",
+      "kind": "class",
+      "doc": "RMSNorm module that uses the optimized LigerRMSNormFunction.\n\nArgs:\n    hidden_size (int): The size of the hidden dimension.\n    eps (float, optional): The epsilon value for numerical stability. Defaults to 1e-6.\n    offset (float, optional): Offset value to shift the weight tensor. Defaults to 0.0.\n    casting_mode (str, optional): The casting mode to use. Defaults to \"llama\".\n    in_place (bool, optional): Whether to modify dY in-place to store dX during backward. Defaults to True.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "args",
+            "kind": "VAR_POSITIONAL",
+            "default": null,
+            "annotation": "Any"
+          },
+          {
+            "name": "kwargs",
+            "kind": "VAR_KEYWORD",
+            "default": null,
+            "annotation": "Any"
+          }
+        ],
+        "return_annotation": "None"
+      },
+      "attributes": {
+        "has_backward": null,
+        "can_torch_compile": null
+      }
+    }
+  ]
+}

build/torch215-cpu-x86_64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,27 @@

+from . import layers
+from ._ops import ops
+def apply_rms_norm(input, weight, eps):
+    # ops.apply_rms_norm returns [output, rstd]
+    return ops.apply_rms_norm(
+            input,
+            weight,
+            eps,
+    )[0]
+def apply_rms_norm_backward(grad_output, input, weight, output, rstd, eps, input_requires_grad=True, weight_requires_grad=True):
+    return ops.apply_rms_norm_backward(
+            grad_output,
+            input,
+            weight,
+            output,
+            rstd,
+            eps,
+            input_requires_grad,
+            weight_requires_grad
+    )
+__all__ = ["layers", "apply_rms_norm", "apply_rms_norm_backward"]

build/torch215-cpu-x86_64-linux/_ops.py ADDED Viewed

	@@ -0,0 +1,9 @@

+import torch
+from . import _rmsnorm_cpu_59ac2f9
+ops = torch.ops._rmsnorm_cpu_59ac2f9
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rmsnorm_cpu_59ac2f9::{op_name}"

build/torch215-cpu-x86_64-linux/_rmsnorm_cpu_59ac2f9.abi3.so ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:f578c6024271db907d1f4cf4211a476b23fe35262f3807d9b34930704df25a51
+size 162120

build/torch215-cpu-x86_64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,59 @@

+import torch
+from ._ops import ops
+class RMSNormFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, hidden_states, weight, variance_epsilon):
+        ctx.variance_epsilon = variance_epsilon
+        output, rstd = ops.apply_rms_norm(hidden_states, weight, variance_epsilon)
+        ctx.save_for_backward(hidden_states, weight, output, rstd)
+        return output
+    @staticmethod
+    def backward(ctx, grad_output):
+        hidden_states, weight, output, rstd = ctx.saved_tensors
+        grads = ops.apply_rms_norm_backward(
+            grad_output,
+            hidden_states,
+            weight,
+            output,
+            rstd,
+            ctx.variance_epsilon,
+            ctx.needs_input_grad[0],
+            ctx.needs_input_grad[1]
+        )
+        return grads[0], grads[1], None
+class RMSNorm(torch.nn.Module):
+    """
+    RMSNorm module that uses the optimized LigerRMSNormFunction.
+    Args:
+        hidden_size (int): The size of the hidden dimension.
+        eps (float, optional): The epsilon value for numerical stability. Defaults to 1e-6.
+        offset (float, optional): Offset value to shift the weight tensor. Defaults to 0.0.
+        casting_mode (str, optional): The casting mode to use. Defaults to "llama".
+        in_place (bool, optional): Whether to modify dY in-place to store dX during backward. Defaults to True.
+    """
+    weight: torch.Tensor
+    variance_epsilon: float
+    def forward(self, hidden_states):
+        """
+        Apply RMS normalization to the input tensor.
+        Args:
+            hidden_states (torch.Tensor): Input tensor of shape (B, T, H) or (BxT, H)
+        Returns:
+            torch.Tensor: Normalized tensor of the same shape as input
+        """
+        return RMSNormFunction.apply(
+            hidden_states,
+            self.weight,
+            self.variance_epsilon,
+        )
+__all__ = ["RMSNorm"]

build/torch215-cpu-x86_64-linux/metadata.json ADDED Viewed

	@@ -0,0 +1,33 @@

+{
+  "name": "rmsnorm",
+  "id": "_rmsnorm_cpu_59ac2f9",
+  "version": 1,
+  "kernels-minver": "0.14.0",
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "cpu"
+  },
+  "digest": {
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+    "files": {
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+      "_ops.py": "l1a3DvymOaKP4FEgC+VW0MhnSWDnNEPrhHNkvz3ywss=",
+      "_rmsnorm_cpu_59ac2f9.abi3.so": "9XjGAkJx25B9H0z0IRpHayP+NSYvOAfZs0kwcE3yWlE=",
+      "layers.py": "8KMmxy30Olm/16XW7vvHAwWNVWcnA8js0nNuoOSGEO8=",
+      "symbols.json": "oeLQ1wwvhrKfNCq0/LwHhIn71+VdONzzoRWed5liCMI="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
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+      "commit": "d054906a13710b811b96697b4635f03020842733",
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+    "kernel": {
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+      "dirty": false
+    }
+  }
+}

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+  "functions": [
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+      "qualname": "apply_rms_norm",
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+      "doc": null,
+      "signature": {
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+            "annotation": null
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+      "doc": null,
+      "signature": {
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+    }
+  ]
+}