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kernels-bot commited on 10 days ago

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build/torch210-xpu20253-x86_64-linux/__init__.py +57 -0
build/torch210-xpu20253-x86_64-linux/_ops.py +9 -0
build/torch210-xpu20253-x86_64-linux/_rotary_xpu_fe2bb66.abi3.so +3 -0
build/torch210-xpu20253-x86_64-linux/layers.py +41 -0
build/torch210-xpu20253-x86_64-linux/metadata.json +33 -0
build/torch210-xpu20253-x86_64-linux/metadata.json.sigstore +1 -0
build/torch210-xpu20253-x86_64-linux/symbols.json +103 -0
build/torch211-xpu20253-x86_64-linux/__init__.py +57 -0
build/torch211-xpu20253-x86_64-linux/_ops.py +9 -0
build/torch211-xpu20253-x86_64-linux/_rotary_xpu_fe2bb66.abi3.so +3 -0
build/torch211-xpu20253-x86_64-linux/layers.py +41 -0
build/torch211-xpu20253-x86_64-linux/metadata.json +33 -0
build/torch211-xpu20253-x86_64-linux/metadata.json.sigstore +1 -0
build/torch211-xpu20253-x86_64-linux/symbols.json +103 -0
build/torch212-xpu20253-x86_64-linux/__init__.py +57 -0
build/torch212-xpu20253-x86_64-linux/_ops.py +9 -0
build/torch212-xpu20253-x86_64-linux/_rotary_xpu_fe2bb66.abi3.so +3 -0
build/torch212-xpu20253-x86_64-linux/layers.py +41 -0
build/torch212-xpu20253-x86_64-linux/metadata.json +33 -0
build/torch212-xpu20253-x86_64-linux/metadata.json.sigstore +1 -0
build/torch212-xpu20253-x86_64-linux/symbols.json +103 -0
build/torch213-xpu20260-x86_64-linux/__init__.py +57 -0
build/torch213-xpu20260-x86_64-linux/_ops.py +9 -0
build/torch213-xpu20260-x86_64-linux/_rotary_xpu_fe2bb66.abi3.so +3 -0
build/torch213-xpu20260-x86_64-linux/layers.py +41 -0
build/torch213-xpu20260-x86_64-linux/metadata.json +33 -0
build/torch213-xpu20260-x86_64-linux/metadata.json.sigstore +1 -0
build/torch213-xpu20260-x86_64-linux/symbols.json +103 -0
build/torch214-xpu20261-x86_64-linux/__init__.py +57 -0
build/torch214-xpu20261-x86_64-linux/_ops.py +9 -0
build/torch214-xpu20261-x86_64-linux/_rotary_xpu_fe2bb66.abi3.so +3 -0
build/torch214-xpu20261-x86_64-linux/layers.py +41 -0
build/torch214-xpu20261-x86_64-linux/metadata.json +33 -0
build/torch214-xpu20261-x86_64-linux/metadata.json.sigstore +1 -0
build/torch214-xpu20261-x86_64-linux/symbols.json +103 -0
build/torch215-xpu20261-x86_64-linux/__init__.py +57 -0
build/torch215-xpu20261-x86_64-linux/_ops.py +9 -0
build/torch215-xpu20261-x86_64-linux/_rotary_xpu_fe2bb66.abi3.so +3 -0
build/torch215-xpu20261-x86_64-linux/layers.py +41 -0
build/torch215-xpu20261-x86_64-linux/metadata.json +33 -0
build/torch215-xpu20261-x86_64-linux/metadata.json.sigstore +1 -0
build/torch215-xpu20261-x86_64-linux/symbols.json +103 -0

build/torch210-xpu20253-x86_64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,57 @@

+import torch
+from . import layers
+from ._ops import ops
+def apply_rotary(
+    x1: torch.Tensor,
+    x2: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    out1: torch.Tensor,
+    out2: torch.Tensor,
+    conj: bool,
+) -> None:
+    ops.apply_rotary(x1, x2, cos, sin, out1, out2, conj)
+def apply_rotary_transformers(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    unsqueeze_dim: int = 1,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Rotary kernel implementation wrapper
+    Adapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_rotated = q.clone()
+    k_rotated = k.clone()
+    # Get half dimension for rotation
+    half_dim = q.shape[-1] // 2
+    q1 = q_rotated[..., :half_dim]
+    q2 = q_rotated[..., half_dim:]
+    k1 = k_rotated[..., :half_dim]
+    k2 = k_rotated[..., half_dim:]
+    if cos.shape[-1] != half_dim:
+        # Trim cos/sin to match half_dim
+        cos = cos[..., :half_dim]
+        sin = sin[..., :half_dim]
+    apply_rotary(q1, q2, cos, sin, q1, q2, False)
+    apply_rotary(k1, k2, cos, sin, k1, k2, False)
+    return q_rotated, k_rotated
+# Add torch compile support for functions
+apply_rotary_transformers.can_torch_compile = True
+# Keeping `apply_rotary` and `apply_rotary_transformers` for BC
+__all__ = ["apply_rotary", "apply_rotary_transformers", "layers"]

build/torch210-xpu20253-x86_64-linux/_ops.py ADDED Viewed

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

+import torch
+from . import _rotary_xpu_fe2bb66
+ops = torch.ops._rotary_xpu_fe2bb66
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rotary_xpu_fe2bb66::{op_name}"

build/torch210-xpu20253-x86_64-linux/_rotary_xpu_fe2bb66.abi3.so ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:e0e88ecfb7c570f35312f8a42b997350a042a59a601f16c9e88a4191f753e786
+size 465032

build/torch210-xpu20253-x86_64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,41 @@

+import torch
+import torch.nn as nn
+from ._ops import ops
+class apply_rotary_transformers(nn.Module):
+    can_torch_compile = True
+    def forward(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+        unsqueeze_dim: int = 1,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Rotary kernel implementation wrapper
+        Adapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature
+        """
+        cos = cos.unsqueeze(unsqueeze_dim)
+        sin = sin.unsqueeze(unsqueeze_dim)
+        q_rotated = q.clone()
+        k_rotated = k.clone()
+        # Get half dimension for rotation
+        half_dim = q.shape[-1] // 2
+        q1 = q_rotated[..., :half_dim]
+        q2 = q_rotated[..., half_dim:]
+        k1 = k_rotated[..., :half_dim]
+        k2 = k_rotated[..., half_dim:]
+        if cos.shape[-1] != half_dim:
+            # Trim cos/sin to match half_dim
+            cos = cos[..., :half_dim]
+            sin = sin[..., :half_dim]
+        ops.apply_rotary(q1, q2, cos, sin, q1, q2, False)
+        ops.apply_rotary(k1, k2, cos, sin, k1, k2, False)
+        return q_rotated, k_rotated

build/torch210-xpu20253-x86_64-linux/metadata.json ADDED Viewed

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

+{
+  "name": "rotary",
+  "id": "_rotary_xpu_fe2bb66",
+  "version": 2,
+  "kernels-minver": "0.14.0",
+  "license": "BSD-3-Clause",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "xpu"
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "__init__.py": "qlvCb0Z6KMkcCxgjDRxjD1ofWaJ/Lqx0cTFlqrncWyE=",
+      "_ops.py": "Jeq0/zADY+Cq3MnRyzR/8jDpyYANlwKH71wgSME2/vA=",
+      "_rotary_xpu_fe2bb66.abi3.so": "4OiOz7fFcPNTEvikK5lzUKBCpZpgHxbJ6IpBkfdT54Y=",
+      "layers.py": "c8ZzUlsVi1lOGhM67JSViZdKBSbIKItQxmWkXPiTEy4=",
+      "symbols.json": "PxgUNQB1dU+NEARgssX8a05dn1+R6dbDC79RXAlVxqw="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
+      "dirty": false
+    }
+  }
+}

build/torch210-xpu20253-x86_64-linux/metadata.json.sigstore ADDED Viewed

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build/torch210-xpu20253-x86_64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,103 @@

+{
+  "schema_version": 1,
+  "module": "_rotary_xpu_fe2bb66",
+  "functions": [
+    {
+      "name": "apply_rotary",
+      "qualname": "apply_rotary",
+      "module": "_rotary_xpu_fe2bb66",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "x1",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x2",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "cos",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "sin",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "out1",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "out2",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "conj",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "bool"
+          }
+        ],
+        "return_annotation": "None"
+      }
+    },
+    {
+      "name": "apply_rotary_transformers",
+      "qualname": "apply_rotary_transformers",
+      "module": "_rotary_xpu_fe2bb66",
+      "kind": "function",
+      "doc": "Rotary kernel implementation wrapper\nAdapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature",
+      "signature": {
+        "parameters": [
+          {
+            "name": "q",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "k",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "cos",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "sin",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "unsqueeze_dim",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "1",
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "tuple[torch.Tensor, torch.Tensor]"
+      }
+    }
+  ],
+  "layers": []
+}

build/torch211-xpu20253-x86_64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,57 @@

+import torch
+from . import layers
+from ._ops import ops
+def apply_rotary(
+    x1: torch.Tensor,
+    x2: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    out1: torch.Tensor,
+    out2: torch.Tensor,
+    conj: bool,
+) -> None:
+    ops.apply_rotary(x1, x2, cos, sin, out1, out2, conj)
+def apply_rotary_transformers(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    unsqueeze_dim: int = 1,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Rotary kernel implementation wrapper
+    Adapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_rotated = q.clone()
+    k_rotated = k.clone()
+    # Get half dimension for rotation
+    half_dim = q.shape[-1] // 2
+    q1 = q_rotated[..., :half_dim]
+    q2 = q_rotated[..., half_dim:]
+    k1 = k_rotated[..., :half_dim]
+    k2 = k_rotated[..., half_dim:]
+    if cos.shape[-1] != half_dim:
+        # Trim cos/sin to match half_dim
+        cos = cos[..., :half_dim]
+        sin = sin[..., :half_dim]
+    apply_rotary(q1, q2, cos, sin, q1, q2, False)
+    apply_rotary(k1, k2, cos, sin, k1, k2, False)
+    return q_rotated, k_rotated
+# Add torch compile support for functions
+apply_rotary_transformers.can_torch_compile = True
+# Keeping `apply_rotary` and `apply_rotary_transformers` for BC
+__all__ = ["apply_rotary", "apply_rotary_transformers", "layers"]

build/torch211-xpu20253-x86_64-linux/_ops.py ADDED Viewed

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

+import torch
+from . import _rotary_xpu_fe2bb66
+ops = torch.ops._rotary_xpu_fe2bb66
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rotary_xpu_fe2bb66::{op_name}"

build/torch211-xpu20253-x86_64-linux/_rotary_xpu_fe2bb66.abi3.so ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:89027cff7db89d7fd5bd8b3f8a94c9c97aeafd3abb84df6145c610ba6c4c362d
+size 465032

build/torch211-xpu20253-x86_64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,41 @@

+import torch
+import torch.nn as nn
+from ._ops import ops
+class apply_rotary_transformers(nn.Module):
+    can_torch_compile = True
+    def forward(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+        unsqueeze_dim: int = 1,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Rotary kernel implementation wrapper
+        Adapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature
+        """
+        cos = cos.unsqueeze(unsqueeze_dim)
+        sin = sin.unsqueeze(unsqueeze_dim)
+        q_rotated = q.clone()
+        k_rotated = k.clone()
+        # Get half dimension for rotation
+        half_dim = q.shape[-1] // 2
+        q1 = q_rotated[..., :half_dim]
+        q2 = q_rotated[..., half_dim:]
+        k1 = k_rotated[..., :half_dim]
+        k2 = k_rotated[..., half_dim:]
+        if cos.shape[-1] != half_dim:
+            # Trim cos/sin to match half_dim
+            cos = cos[..., :half_dim]
+            sin = sin[..., :half_dim]
+        ops.apply_rotary(q1, q2, cos, sin, q1, q2, False)
+        ops.apply_rotary(k1, k2, cos, sin, k1, k2, False)
+        return q_rotated, k_rotated

build/torch211-xpu20253-x86_64-linux/metadata.json ADDED Viewed

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

+{
+  "name": "rotary",
+  "id": "_rotary_xpu_fe2bb66",
+  "version": 2,
+  "kernels-minver": "0.14.0",
+  "license": "BSD-3-Clause",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "xpu"
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "__init__.py": "qlvCb0Z6KMkcCxgjDRxjD1ofWaJ/Lqx0cTFlqrncWyE=",
+      "_ops.py": "Jeq0/zADY+Cq3MnRyzR/8jDpyYANlwKH71wgSME2/vA=",
+      "_rotary_xpu_fe2bb66.abi3.so": "iQJ8/324nX/VvYs/ipTJyXrq/Tq7hN9hRcYQumxMNi0=",
+      "layers.py": "c8ZzUlsVi1lOGhM67JSViZdKBSbIKItQxmWkXPiTEy4=",
+      "symbols.json": "PxgUNQB1dU+NEARgssX8a05dn1+R6dbDC79RXAlVxqw="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
+      "dirty": false
+    }
+  }
+}

build/torch211-xpu20253-x86_64-linux/metadata.json.sigstore ADDED Viewed

	@@ -0,0 +1 @@

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build/torch211-xpu20253-x86_64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,103 @@

+{
+  "schema_version": 1,
+  "module": "_rotary_xpu_fe2bb66",
+  "functions": [
+    {
+      "name": "apply_rotary",
+      "qualname": "apply_rotary",
+      "module": "_rotary_xpu_fe2bb66",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "x1",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x2",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "cos",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "sin",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "out1",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "out2",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "conj",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "bool"
+          }
+        ],
+        "return_annotation": "None"
+      }
+    },
+    {
+      "name": "apply_rotary_transformers",
+      "qualname": "apply_rotary_transformers",
+      "module": "_rotary_xpu_fe2bb66",
+      "kind": "function",
+      "doc": "Rotary kernel implementation wrapper\nAdapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature",
+      "signature": {
+        "parameters": [
+          {
+            "name": "q",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "k",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "cos",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "sin",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "unsqueeze_dim",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "1",
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "tuple[torch.Tensor, torch.Tensor]"
+      }
+    }
+  ],
+  "layers": []
+}

build/torch212-xpu20253-x86_64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,57 @@

+import torch
+from . import layers
+from ._ops import ops
+def apply_rotary(
+    x1: torch.Tensor,
+    x2: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    out1: torch.Tensor,
+    out2: torch.Tensor,
+    conj: bool,
+) -> None:
+    ops.apply_rotary(x1, x2, cos, sin, out1, out2, conj)
+def apply_rotary_transformers(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    unsqueeze_dim: int = 1,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Rotary kernel implementation wrapper
+    Adapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_rotated = q.clone()
+    k_rotated = k.clone()
+    # Get half dimension for rotation
+    half_dim = q.shape[-1] // 2
+    q1 = q_rotated[..., :half_dim]
+    q2 = q_rotated[..., half_dim:]
+    k1 = k_rotated[..., :half_dim]
+    k2 = k_rotated[..., half_dim:]
+    if cos.shape[-1] != half_dim:
+        # Trim cos/sin to match half_dim
+        cos = cos[..., :half_dim]
+        sin = sin[..., :half_dim]
+    apply_rotary(q1, q2, cos, sin, q1, q2, False)
+    apply_rotary(k1, k2, cos, sin, k1, k2, False)
+    return q_rotated, k_rotated
+# Add torch compile support for functions
+apply_rotary_transformers.can_torch_compile = True
+# Keeping `apply_rotary` and `apply_rotary_transformers` for BC
+__all__ = ["apply_rotary", "apply_rotary_transformers", "layers"]

build/torch212-xpu20253-x86_64-linux/_ops.py ADDED Viewed

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

+import torch
+from . import _rotary_xpu_fe2bb66
+ops = torch.ops._rotary_xpu_fe2bb66
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rotary_xpu_fe2bb66::{op_name}"

build/torch212-xpu20253-x86_64-linux/_rotary_xpu_fe2bb66.abi3.so ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:6360a05536cc020846583784052708933705d22ee95a113da9b31d3c11bb7977
+size 464744

build/torch212-xpu20253-x86_64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,41 @@

+import torch
+import torch.nn as nn
+from ._ops import ops
+class apply_rotary_transformers(nn.Module):
+    can_torch_compile = True
+    def forward(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+        unsqueeze_dim: int = 1,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Rotary kernel implementation wrapper
+        Adapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature
+        """
+        cos = cos.unsqueeze(unsqueeze_dim)
+        sin = sin.unsqueeze(unsqueeze_dim)
+        q_rotated = q.clone()
+        k_rotated = k.clone()
+        # Get half dimension for rotation
+        half_dim = q.shape[-1] // 2
+        q1 = q_rotated[..., :half_dim]
+        q2 = q_rotated[..., half_dim:]
+        k1 = k_rotated[..., :half_dim]
+        k2 = k_rotated[..., half_dim:]
+        if cos.shape[-1] != half_dim:
+            # Trim cos/sin to match half_dim
+            cos = cos[..., :half_dim]
+            sin = sin[..., :half_dim]
+        ops.apply_rotary(q1, q2, cos, sin, q1, q2, False)
+        ops.apply_rotary(k1, k2, cos, sin, k1, k2, False)
+        return q_rotated, k_rotated

build/torch212-xpu20253-x86_64-linux/metadata.json ADDED Viewed

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

+{
+  "name": "rotary",
+  "id": "_rotary_xpu_fe2bb66",
+  "version": 2,
+  "kernels-minver": "0.14.0",
+  "license": "BSD-3-Clause",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "xpu"
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "__init__.py": "qlvCb0Z6KMkcCxgjDRxjD1ofWaJ/Lqx0cTFlqrncWyE=",
+      "_ops.py": "Jeq0/zADY+Cq3MnRyzR/8jDpyYANlwKH71wgSME2/vA=",
+      "_rotary_xpu_fe2bb66.abi3.so": "Y2CgVTbMAghGWDeEBScIkzcF0i7pWhE9qbMdPBG7eXc=",
+      "layers.py": "c8ZzUlsVi1lOGhM67JSViZdKBSbIKItQxmWkXPiTEy4=",
+      "symbols.json": "PxgUNQB1dU+NEARgssX8a05dn1+R6dbDC79RXAlVxqw="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
+      "dirty": false
+    }
+  }
+}

build/torch212-xpu20253-x86_64-linux/metadata.json.sigstore ADDED Viewed

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build/torch212-xpu20253-x86_64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,103 @@

+{
+  "schema_version": 1,
+  "module": "_rotary_xpu_fe2bb66",
+  "functions": [
+    {
+      "name": "apply_rotary",
+      "qualname": "apply_rotary",
+      "module": "_rotary_xpu_fe2bb66",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "x1",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x2",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "cos",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "sin",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "out1",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "out2",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "conj",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "bool"
+          }
+        ],
+        "return_annotation": "None"
+      }
+    },
+    {
+      "name": "apply_rotary_transformers",
+      "qualname": "apply_rotary_transformers",
+      "module": "_rotary_xpu_fe2bb66",
+      "kind": "function",
+      "doc": "Rotary kernel implementation wrapper\nAdapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature",
+      "signature": {
+        "parameters": [
+          {
+            "name": "q",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "k",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "cos",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "sin",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "unsqueeze_dim",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "1",
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "tuple[torch.Tensor, torch.Tensor]"
+      }
+    }
+  ],
+  "layers": []
+}

build/torch213-xpu20260-x86_64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,57 @@

+import torch
+from . import layers
+from ._ops import ops
+def apply_rotary(
+    x1: torch.Tensor,
+    x2: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    out1: torch.Tensor,
+    out2: torch.Tensor,
+    conj: bool,
+) -> None:
+    ops.apply_rotary(x1, x2, cos, sin, out1, out2, conj)
+def apply_rotary_transformers(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    unsqueeze_dim: int = 1,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Rotary kernel implementation wrapper
+    Adapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_rotated = q.clone()
+    k_rotated = k.clone()
+    # Get half dimension for rotation
+    half_dim = q.shape[-1] // 2
+    q1 = q_rotated[..., :half_dim]
+    q2 = q_rotated[..., half_dim:]
+    k1 = k_rotated[..., :half_dim]
+    k2 = k_rotated[..., half_dim:]
+    if cos.shape[-1] != half_dim:
+        # Trim cos/sin to match half_dim
+        cos = cos[..., :half_dim]
+        sin = sin[..., :half_dim]
+    apply_rotary(q1, q2, cos, sin, q1, q2, False)
+    apply_rotary(k1, k2, cos, sin, k1, k2, False)
+    return q_rotated, k_rotated
+# Add torch compile support for functions
+apply_rotary_transformers.can_torch_compile = True
+# Keeping `apply_rotary` and `apply_rotary_transformers` for BC
+__all__ = ["apply_rotary", "apply_rotary_transformers", "layers"]

build/torch213-xpu20260-x86_64-linux/_ops.py ADDED Viewed

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

+import torch
+from . import _rotary_xpu_fe2bb66
+ops = torch.ops._rotary_xpu_fe2bb66
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rotary_xpu_fe2bb66::{op_name}"

build/torch213-xpu20260-x86_64-linux/_rotary_xpu_fe2bb66.abi3.so ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:43ea5990a0610abd90cbf844b3518baf84aff965f267dca4951edca5c93c0f8b
+size 399736

build/torch213-xpu20260-x86_64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,41 @@

+import torch
+import torch.nn as nn
+from ._ops import ops
+class apply_rotary_transformers(nn.Module):
+    can_torch_compile = True
+    def forward(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+        unsqueeze_dim: int = 1,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Rotary kernel implementation wrapper
+        Adapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature
+        """
+        cos = cos.unsqueeze(unsqueeze_dim)
+        sin = sin.unsqueeze(unsqueeze_dim)
+        q_rotated = q.clone()
+        k_rotated = k.clone()
+        # Get half dimension for rotation
+        half_dim = q.shape[-1] // 2
+        q1 = q_rotated[..., :half_dim]
+        q2 = q_rotated[..., half_dim:]
+        k1 = k_rotated[..., :half_dim]
+        k2 = k_rotated[..., half_dim:]
+        if cos.shape[-1] != half_dim:
+            # Trim cos/sin to match half_dim
+            cos = cos[..., :half_dim]
+            sin = sin[..., :half_dim]
+        ops.apply_rotary(q1, q2, cos, sin, q1, q2, False)
+        ops.apply_rotary(k1, k2, cos, sin, k1, k2, False)
+        return q_rotated, k_rotated

build/torch213-xpu20260-x86_64-linux/metadata.json ADDED Viewed

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

+{
+  "name": "rotary",
+  "id": "_rotary_xpu_fe2bb66",
+  "version": 2,
+  "kernels-minver": "0.14.0",
+  "license": "BSD-3-Clause",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "xpu"
+  },
+  "digest": {
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+      "symbols.json": "PxgUNQB1dU+NEARgssX8a05dn1+R6dbDC79RXAlVxqw="
+    }
+  },
+  "provenance": {
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+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
+      "dirty": false
+    }
+  }
+}

build/torch213-xpu20260-x86_64-linux/metadata.json.sigstore ADDED Viewed

	@@ -0,0 +1 @@

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build/torch213-xpu20260-x86_64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,103 @@

+{
+  "schema_version": 1,
+  "module": "_rotary_xpu_fe2bb66",
+  "functions": [
+    {
+      "name": "apply_rotary",
+      "qualname": "apply_rotary",
+      "module": "_rotary_xpu_fe2bb66",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "x1",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x2",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "cos",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "sin",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "out1",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "out2",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "conj",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "bool"
+          }
+        ],
+        "return_annotation": "None"
+      }
+    },
+    {
+      "name": "apply_rotary_transformers",
+      "qualname": "apply_rotary_transformers",
+      "module": "_rotary_xpu_fe2bb66",
+      "kind": "function",
+      "doc": "Rotary kernel implementation wrapper\nAdapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature",
+      "signature": {
+        "parameters": [
+          {
+            "name": "q",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "k",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "cos",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "sin",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "unsqueeze_dim",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "1",
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "tuple[torch.Tensor, torch.Tensor]"
+      }
+    }
+  ],
+  "layers": []
+}

build/torch214-xpu20261-x86_64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,57 @@

+import torch
+from . import layers
+from ._ops import ops
+def apply_rotary(
+    x1: torch.Tensor,
+    x2: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    out1: torch.Tensor,
+    out2: torch.Tensor,
+    conj: bool,
+) -> None:
+    ops.apply_rotary(x1, x2, cos, sin, out1, out2, conj)
+def apply_rotary_transformers(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    unsqueeze_dim: int = 1,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Rotary kernel implementation wrapper
+    Adapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_rotated = q.clone()
+    k_rotated = k.clone()
+    # Get half dimension for rotation
+    half_dim = q.shape[-1] // 2
+    q1 = q_rotated[..., :half_dim]
+    q2 = q_rotated[..., half_dim:]
+    k1 = k_rotated[..., :half_dim]
+    k2 = k_rotated[..., half_dim:]
+    if cos.shape[-1] != half_dim:
+        # Trim cos/sin to match half_dim
+        cos = cos[..., :half_dim]
+        sin = sin[..., :half_dim]
+    apply_rotary(q1, q2, cos, sin, q1, q2, False)
+    apply_rotary(k1, k2, cos, sin, k1, k2, False)
+    return q_rotated, k_rotated
+# Add torch compile support for functions
+apply_rotary_transformers.can_torch_compile = True
+# Keeping `apply_rotary` and `apply_rotary_transformers` for BC
+__all__ = ["apply_rotary", "apply_rotary_transformers", "layers"]

build/torch214-xpu20261-x86_64-linux/_ops.py ADDED Viewed

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

+import torch
+from . import _rotary_xpu_fe2bb66
+ops = torch.ops._rotary_xpu_fe2bb66
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rotary_xpu_fe2bb66::{op_name}"

build/torch214-xpu20261-x86_64-linux/_rotary_xpu_fe2bb66.abi3.so ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:a16e04413a720342f34283c8851abe878b537d188789d7dd44c2e99e7beda72d
+size 403832

build/torch214-xpu20261-x86_64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,41 @@

+import torch
+import torch.nn as nn
+from ._ops import ops
+class apply_rotary_transformers(nn.Module):
+    can_torch_compile = True
+    def forward(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+        unsqueeze_dim: int = 1,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Rotary kernel implementation wrapper
+        Adapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature
+        """
+        cos = cos.unsqueeze(unsqueeze_dim)
+        sin = sin.unsqueeze(unsqueeze_dim)
+        q_rotated = q.clone()
+        k_rotated = k.clone()
+        # Get half dimension for rotation
+        half_dim = q.shape[-1] // 2
+        q1 = q_rotated[..., :half_dim]
+        q2 = q_rotated[..., half_dim:]
+        k1 = k_rotated[..., :half_dim]
+        k2 = k_rotated[..., half_dim:]
+        if cos.shape[-1] != half_dim:
+            # Trim cos/sin to match half_dim
+            cos = cos[..., :half_dim]
+            sin = sin[..., :half_dim]
+        ops.apply_rotary(q1, q2, cos, sin, q1, q2, False)
+        ops.apply_rotary(k1, k2, cos, sin, k1, k2, False)
+        return q_rotated, k_rotated

build/torch214-xpu20261-x86_64-linux/metadata.json ADDED Viewed

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

+{
+  "name": "rotary",
+  "id": "_rotary_xpu_fe2bb66",
+  "version": 2,
+  "kernels-minver": "0.14.0",
+  "license": "BSD-3-Clause",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "xpu"
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "__init__.py": "qlvCb0Z6KMkcCxgjDRxjD1ofWaJ/Lqx0cTFlqrncWyE=",
+      "_ops.py": "Jeq0/zADY+Cq3MnRyzR/8jDpyYANlwKH71wgSME2/vA=",
+      "_rotary_xpu_fe2bb66.abi3.so": "oW4EQTpyA0LzQoPIhRq+h4tTfRiHidfdRMLpnnvtpy0=",
+      "layers.py": "c8ZzUlsVi1lOGhM67JSViZdKBSbIKItQxmWkXPiTEy4=",
+      "symbols.json": "PxgUNQB1dU+NEARgssX8a05dn1+R6dbDC79RXAlVxqw="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
+      "dirty": false
+    }
+  }
+}

build/torch214-xpu20261-x86_64-linux/metadata.json.sigstore ADDED Viewed

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build/torch214-xpu20261-x86_64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,103 @@

+{
+  "schema_version": 1,
+  "module": "_rotary_xpu_fe2bb66",
+  "functions": [
+    {
+      "name": "apply_rotary",
+      "qualname": "apply_rotary",
+      "module": "_rotary_xpu_fe2bb66",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "x1",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x2",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "cos",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "sin",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "out1",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "out2",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "conj",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "bool"
+          }
+        ],
+        "return_annotation": "None"
+      }
+    },
+    {
+      "name": "apply_rotary_transformers",
+      "qualname": "apply_rotary_transformers",
+      "module": "_rotary_xpu_fe2bb66",
+      "kind": "function",
+      "doc": "Rotary kernel implementation wrapper\nAdapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature",
+      "signature": {
+        "parameters": [
+          {
+            "name": "q",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "k",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "cos",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "sin",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "unsqueeze_dim",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "1",
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "tuple[torch.Tensor, torch.Tensor]"
+      }
+    }
+  ],
+  "layers": []
+}

build/torch215-xpu20261-x86_64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,57 @@

+import torch
+from . import layers
+from ._ops import ops
+def apply_rotary(
+    x1: torch.Tensor,
+    x2: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    out1: torch.Tensor,
+    out2: torch.Tensor,
+    conj: bool,
+) -> None:
+    ops.apply_rotary(x1, x2, cos, sin, out1, out2, conj)
+def apply_rotary_transformers(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    unsqueeze_dim: int = 1,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Rotary kernel implementation wrapper
+    Adapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_rotated = q.clone()
+    k_rotated = k.clone()
+    # Get half dimension for rotation
+    half_dim = q.shape[-1] // 2
+    q1 = q_rotated[..., :half_dim]
+    q2 = q_rotated[..., half_dim:]
+    k1 = k_rotated[..., :half_dim]
+    k2 = k_rotated[..., half_dim:]
+    if cos.shape[-1] != half_dim:
+        # Trim cos/sin to match half_dim
+        cos = cos[..., :half_dim]
+        sin = sin[..., :half_dim]
+    apply_rotary(q1, q2, cos, sin, q1, q2, False)
+    apply_rotary(k1, k2, cos, sin, k1, k2, False)
+    return q_rotated, k_rotated
+# Add torch compile support for functions
+apply_rotary_transformers.can_torch_compile = True
+# Keeping `apply_rotary` and `apply_rotary_transformers` for BC
+__all__ = ["apply_rotary", "apply_rotary_transformers", "layers"]

build/torch215-xpu20261-x86_64-linux/_ops.py ADDED Viewed

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

+import torch
+from . import _rotary_xpu_fe2bb66
+ops = torch.ops._rotary_xpu_fe2bb66
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rotary_xpu_fe2bb66::{op_name}"

build/torch215-xpu20261-x86_64-linux/_rotary_xpu_fe2bb66.abi3.so ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:6d99f245f290b0f6f0b6925dda5460f4778a2cb2766a2a3aeca9e83c709b8193
+size 403832

build/torch215-xpu20261-x86_64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,41 @@

+import torch
+import torch.nn as nn
+from ._ops import ops
+class apply_rotary_transformers(nn.Module):
+    can_torch_compile = True
+    def forward(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+        unsqueeze_dim: int = 1,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Rotary kernel implementation wrapper
+        Adapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature
+        """
+        cos = cos.unsqueeze(unsqueeze_dim)
+        sin = sin.unsqueeze(unsqueeze_dim)
+        q_rotated = q.clone()
+        k_rotated = k.clone()
+        # Get half dimension for rotation
+        half_dim = q.shape[-1] // 2
+        q1 = q_rotated[..., :half_dim]
+        q2 = q_rotated[..., half_dim:]
+        k1 = k_rotated[..., :half_dim]
+        k2 = k_rotated[..., half_dim:]
+        if cos.shape[-1] != half_dim:
+            # Trim cos/sin to match half_dim
+            cos = cos[..., :half_dim]
+            sin = sin[..., :half_dim]
+        ops.apply_rotary(q1, q2, cos, sin, q1, q2, False)
+        ops.apply_rotary(k1, k2, cos, sin, k1, k2, False)
+        return q_rotated, k_rotated

build/torch215-xpu20261-x86_64-linux/metadata.json ADDED Viewed

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

+{
+  "name": "rotary",
+  "id": "_rotary_xpu_fe2bb66",
+  "version": 2,
+  "kernels-minver": "0.14.0",
+  "license": "BSD-3-Clause",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "xpu"
+  },
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+      "_rotary_xpu_fe2bb66.abi3.so": "bZnyRfKQsPbwtpJd2lRg9HeKLLJ2aio67KnoPHCbgZM=",
+      "layers.py": "c8ZzUlsVi1lOGhM67JSViZdKBSbIKItQxmWkXPiTEy4=",
+      "symbols.json": "PxgUNQB1dU+NEARgssX8a05dn1+R6dbDC79RXAlVxqw="
+    }
+  },
+  "provenance": {
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+      "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
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+      "dirty": false
+    }
+  }
+}

build/torch215-xpu20261-x86_64-linux/metadata.json.sigstore ADDED Viewed

	@@ -0,0 +1 @@

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build/torch215-xpu20261-x86_64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,103 @@

+{
+  "schema_version": 1,
+  "module": "_rotary_xpu_fe2bb66",
+  "functions": [
+    {
+      "name": "apply_rotary",
+      "qualname": "apply_rotary",
+      "module": "_rotary_xpu_fe2bb66",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "x1",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x2",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "cos",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "sin",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "out1",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "out2",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "conj",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "bool"
+          }
+        ],
+        "return_annotation": "None"
+      }
+    },
+    {
+      "name": "apply_rotary_transformers",
+      "qualname": "apply_rotary_transformers",
+      "module": "_rotary_xpu_fe2bb66",
+      "kind": "function",
+      "doc": "Rotary kernel implementation wrapper\nAdapts rotary kernel implementation to match transformers apply_rotary_pos_emb signature",
+      "signature": {
+        "parameters": [
+          {
+            "name": "q",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "k",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "cos",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "sin",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "unsqueeze_dim",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "1",
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "tuple[torch.Tensor, torch.Tensor]"
+      }
+    }
+  ],
+  "layers": []
+}