Kernels:

amd
/

aiter-rope

Trusted publisher

kernels-bot commited on 6 days ago

Commit

152ddd2

verified ·

1 Parent(s): f82fe12

Uploaded using `kernel-builder`.

Browse files

Files changed (4) hide show

build/torch-rocm/_ops.py +1 -1
build/torch-rocm/metadata.json +5 -5
build/torch-rocm/metadata.json.sigstore +1 -1
build/torch-rocm/symbols.json +5 -5

build/torch-rocm/_ops.py CHANGED Viewed

@@ -25,7 +25,7 @@ def get_backend() -> str:
 def _find_ops_name() -> str:
     kernel_name = "aiter_rope"
-    unique_id = "75dc872"
     backend = get_backend()
     return f"_{kernel_name}_{backend}_{unique_id}"

 def _find_ops_name() -> str:
     kernel_name = "aiter_rope"
+    unique_id = "fe2bb66"
     backend = get_backend()
     return f"_{kernel_name}_{backend}_{unique_id}"

build/torch-rocm/metadata.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "aiter-rope",
-  "id": "_aiter_rope_rocm_75dc872",
   "version": 3,
   "kernels-minver": "0.14.0",
   "license": "MIT",
@@ -15,10 +15,10 @@
       "__init__.py": "gAAL6hzdsMrvo9+VsICnOKdEmfcUz+36zXMsKIrqUaI=",
       "_kernels/__init__.py": "47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=",
       "_kernels/rope.py": "yYQRz0fxK1CuTYD9rolVEDXvy3YnS6775IxceH1fNow=",
-      "_ops.py": "gm05aB4Ir8V0bf2AtYzCQi9i17iXw2tuvvG/l0S2zj8=",
       "layers.py": "zBFYJlQHjvOEt4NinTCPWpq+koKgCmvleZBYxtHcqro=",
       "rope.py": "DmzekBurit+Ow9BGR0Zgc76MjAolYQ6q8mEckVLbtj0=",
-      "symbols.json": "K4bGp833woarg6DmDk5UiGGhchv+eqEFBok1Cj1vpN4=",
       "utils/__init__.py": "47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=",
       "utils/logger.py": "CXuJZCBzQDTrW80OyJ2IuhbHhayLy12Ake1KGxiuYIA="
     }
@@ -26,11 +26,11 @@
   "provenance": {
     "kernel-builder": {
       "version": "0.18.0-dev0",
-      "commit": "11a92c0d8289d94d8c660933285d0c1986076e3b",
       "dirty": false
     },
     "kernel": {
-      "commit": "75dc87202fed59700d26e6269fbdaf203586b16a",
       "dirty": false
     }
   }

 {
   "name": "aiter-rope",
+  "id": "_aiter_rope_rocm_fe2bb66",
   "version": 3,
   "kernels-minver": "0.14.0",
   "license": "MIT",
       "__init__.py": "gAAL6hzdsMrvo9+VsICnOKdEmfcUz+36zXMsKIrqUaI=",
       "_kernels/__init__.py": "47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=",
       "_kernels/rope.py": "yYQRz0fxK1CuTYD9rolVEDXvy3YnS6775IxceH1fNow=",
+      "_ops.py": "lStyjnNgbGXrhdLacg3LEIpSH1Kf2FW8XpLeE8S+tEU=",
       "layers.py": "zBFYJlQHjvOEt4NinTCPWpq+koKgCmvleZBYxtHcqro=",
       "rope.py": "DmzekBurit+Ow9BGR0Zgc76MjAolYQ6q8mEckVLbtj0=",
+      "symbols.json": "2wRQEQ7wJmCG29Tsk7ATWmo9NYmbxDmmaiKmgGXMU/I=",
       "utils/__init__.py": "47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=",
       "utils/logger.py": "CXuJZCBzQDTrW80OyJ2IuhbHhayLy12Ake1KGxiuYIA="
     }
   "provenance": {
     "kernel-builder": {
       "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
       "dirty": false
     },
     "kernel": {
+      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
       "dirty": false
     }
   }

build/torch-rocm/metadata.json.sigstore CHANGED Viewed

@@ -1 +1 @@

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build/torch-rocm/symbols.json CHANGED Viewed

@@ -1,11 +1,11 @@
 {
   "schema_version": 1,
-  "module": "_aiter_rope_rocm_75dc872",
   "functions": [
     {
       "name": "apply_rotary_transformers",
       "qualname": "apply_rotary_transformers",
-      "module": "_aiter_rope_rocm_75dc872",
       "kind": "function",
       "doc": "Apply NEOX-style RoPE to ``q`` and ``k``.\n\nSignature mirrors ``kernels-community/rotary``'s ``apply_rotary_transformers``\nso this kernel can be a drop-in replacement under the ``\"rocm\"`` entry of\ntransformers' ``_KERNEL_MAPPING[\"rotary_pos_emb\"]``.\n\nArgs:\n    q, k: ``(batch, heads, seq, head_dim)``.\n    cos, sin: ``(batch, seq, head_dim)`` or ``(batch, seq, head_dim // 2)`` — pre-``unsqueeze``\n        form. Rows may hold different positions (e.g. left padding).\n    unsqueeze_dim: accepted for API parity; the kernel reads positions from\n        the already-computed ``cos`` / ``sin``.\n\nReturns:\n    ``(q_embed, k_embed)`` in the same shape as ``q``, ``k``.",
       "signature": {
@@ -47,7 +47,7 @@
     {
       "name": "rope_cached_fwd",
       "qualname": "rope_cached_fwd",
-      "module": "_aiter_rope_rocm_75dc872.rope",
       "kind": "function",
       "doc": null,
       "signature": {
@@ -101,7 +101,7 @@
     {
       "name": "rope_cached_fwd_inplace",
       "qualname": "rope_cached_fwd_inplace",
-      "module": "_aiter_rope_rocm_75dc872.rope",
       "kind": "function",
       "doc": null,
       "signature": {
@@ -157,7 +157,7 @@
     {
       "name": "apply_rotary_transformers",
       "qualname": "apply_rotary_transformers",
-      "module": "_aiter_rope_rocm_75dc872.layers",
       "kind": "class",
       "doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n    import torch.nn as nn\n    import torch.nn.functional as F\n\n\n    class Model(nn.Module):\n        def __init__(self) -> None:\n            super().__init__()\n            self.conv1 = nn.Conv2d(1, 20, 5)\n            self.conv2 = nn.Conv2d(20, 20, 5)\n\n        def forward(self, x):\n            x = F.relu(self.conv1(x))\n            return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n    As per the example above, an ``__init__()`` call to the parent class\n    must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n                evaluation mode.\n:vartype training: bool",
       "signature": {

 {
   "schema_version": 1,
+  "module": "_aiter_rope_rocm_fe2bb66",
   "functions": [
     {
       "name": "apply_rotary_transformers",
       "qualname": "apply_rotary_transformers",
+      "module": "_aiter_rope_rocm_fe2bb66",
       "kind": "function",
       "doc": "Apply NEOX-style RoPE to ``q`` and ``k``.\n\nSignature mirrors ``kernels-community/rotary``'s ``apply_rotary_transformers``\nso this kernel can be a drop-in replacement under the ``\"rocm\"`` entry of\ntransformers' ``_KERNEL_MAPPING[\"rotary_pos_emb\"]``.\n\nArgs:\n    q, k: ``(batch, heads, seq, head_dim)``.\n    cos, sin: ``(batch, seq, head_dim)`` or ``(batch, seq, head_dim // 2)`` — pre-``unsqueeze``\n        form. Rows may hold different positions (e.g. left padding).\n    unsqueeze_dim: accepted for API parity; the kernel reads positions from\n        the already-computed ``cos`` / ``sin``.\n\nReturns:\n    ``(q_embed, k_embed)`` in the same shape as ``q``, ``k``.",
       "signature": {
     {
       "name": "rope_cached_fwd",
       "qualname": "rope_cached_fwd",
+      "module": "_aiter_rope_rocm_fe2bb66.rope",
       "kind": "function",
       "doc": null,
       "signature": {
     {
       "name": "rope_cached_fwd_inplace",
       "qualname": "rope_cached_fwd_inplace",
+      "module": "_aiter_rope_rocm_fe2bb66.rope",
       "kind": "function",
       "doc": null,
       "signature": {
     {
       "name": "apply_rotary_transformers",
       "qualname": "apply_rotary_transformers",
+      "module": "_aiter_rope_rocm_fe2bb66.layers",
       "kind": "class",
       "doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n    import torch.nn as nn\n    import torch.nn.functional as F\n\n\n    class Model(nn.Module):\n        def __init__(self) -> None:\n            super().__init__()\n            self.conv1 = nn.Conv2d(1, 20, 5)\n            self.conv2 = nn.Conv2d(20, 20, 5)\n\n        def forward(self, x):\n            x = F.relu(self.conv1(x))\n            return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n    As per the example above, an ``__init__()`` call to the parent class\n    must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n                evaluation mode.\n:vartype training: bool",
       "signature": {