Kernels:

amd
/

aiter-rope

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kernels-bot commited on 10 days ago

Commit

f82fe12

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1 Parent(s): 164b09f

Uploaded using `kernel-builder`.

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Files changed (8) hide show

README.md +3 -4
build/torch-rocm/__init__.py +11 -32
build/torch-rocm/_ops.py +5 -2
build/torch-rocm/aiter_rope/__init__.py +0 -26
build/torch-rocm/layers.py +59 -0
build/torch-rocm/metadata.json +19 -5
build/torch-rocm/metadata.json.sigstore +1 -1
build/torch-rocm/symbols.json +186 -0

README.md CHANGED Viewed

@@ -11,14 +11,13 @@ This is the repository card of kernels-community/aiter-rope that has been pushed
 # make sure `kernels` is installed: `pip install -U kernels`
 from kernels import get_kernel
-kernel_module = get_kernel("kernels-community/aiter-rope", version=2)
-__kernel_metadata__ = kernel_module.__kernel_metadata__
-__kernel_metadata__(...)
 ```
 ## Available functions
-- `__kernel_metadata__`
 - `RotateStyle`
 - `apply_rotary_transformers`
 - `rope_cached_fwd`

 # make sure `kernels` is installed: `pip install -U kernels`
 from kernels import get_kernel
+kernel_module = get_kernel("kernels-community/aiter-rope", version=3)
+RotateStyle = kernel_module.RotateStyle
+RotateStyle(...)
 ```
 ## Available functions
 - `RotateStyle`
 - `apply_rotary_transformers`
 - `rope_cached_fwd`

build/torch-rocm/__init__.py CHANGED Viewed

@@ -8,8 +8,8 @@ the ROCm entry of ``_KERNEL_MAPPING["rotary_pos_emb"]`` with zero model-side
 changes.
 """
-import torch
 from .rope import (
     RotateStyle,
     rope_cached_fwd,
@@ -22,17 +22,7 @@ __kernel_metadata__ = {
 }
-def _to_sbhd(t: torch.Tensor) -> torch.Tensor:
-    # transformers passes attention tensors as (batch, heads, seq, head_dim);
-    # AITER's kernels operate in (seq, batch, heads, head_dim).
-    return t.permute(2, 0, 1, 3).contiguous()
-def _to_bhsd(t: torch.Tensor) -> torch.Tensor:
-    return t.permute(1, 2, 0, 3)
-def apply_rotary_transformers(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
     """Apply NEOX-style RoPE to ``q`` and ``k``.
     Signature mirrors ``kernels-community/rotary``'s ``apply_rotary_transformers``
@@ -41,37 +31,26 @@ def apply_rotary_transformers(q, k, cos, sin, position_ids=None, unsqueeze_dim=1
     Args:
         q, k: ``(batch, heads, seq, head_dim)``.
-        cos, sin: ``(batch, seq, head_dim // 2)`` — pre-``unsqueeze`` form.
-        position_ids, unsqueeze_dim: accepted for API parity; the kernel reads
-            positions from the already-computed ``cos`` / ``sin``.
     Returns:
         ``(q_embed, k_embed)`` in the same shape as ``q``, ``k``.
     """
-    q_sbhd = _to_sbhd(q)
-    k_sbhd = _to_sbhd(k)
-    # Cached path expects (seq, 1, 1, head_dim/2). Take batch index 0 — for the
-    # standard (unpadded, equal-length) inference case all batch rows share the
-    # same cos/sin since they come from the same position_ids.
-    cos_freqs = cos[0].unsqueeze(1).unsqueeze(1).contiguous()
-    sin_freqs = sin[0].unsqueeze(1).unsqueeze(1).contiguous()
-    common = dict(
-        rotate_style=int(RotateStyle.NEOX),
-        reuse_freqs_front_part=True,
-        nope_first=False,
-    )
-    q_out_sbhd = rope_cached_fwd(q_sbhd, cos_freqs, sin_freqs, **common)
-    k_out_sbhd = rope_cached_fwd(k_sbhd, cos_freqs, sin_freqs, **common)
-    return _to_bhsd(q_out_sbhd), _to_bhsd(k_out_sbhd)
 __all__ = [
     "__kernel_metadata__",
     "RotateStyle",
     "apply_rotary_transformers",
     "rope_cached_fwd",
     "rope_cached_fwd_inplace",
 ]

 changes.
 """
+from . import layers
+from .layers import _apply_rotary
 from .rope import (
     RotateStyle,
     rope_cached_fwd,
 }
+def apply_rotary_transformers(q, k, cos, sin, unsqueeze_dim=1):
     """Apply NEOX-style RoPE to ``q`` and ``k``.
     Signature mirrors ``kernels-community/rotary``'s ``apply_rotary_transformers``
     Args:
         q, k: ``(batch, heads, seq, head_dim)``.
+        cos, sin: ``(batch, seq, head_dim)`` or ``(batch, seq, head_dim // 2)`` — pre-``unsqueeze``
+            form. Rows may hold different positions (e.g. left padding).
+        unsqueeze_dim: accepted for API parity; the kernel reads positions from
+            the already-computed ``cos`` / ``sin``.
     Returns:
         ``(q_embed, k_embed)`` in the same shape as ``q``, ``k``.
     """
+    return _apply_rotary(q, k, cos, sin)
+# Add torch compile support for functions
+apply_rotary_transformers.can_torch_compile = True
 __all__ = [
     "__kernel_metadata__",
     "RotateStyle",
     "apply_rotary_transformers",
+    "layers",
     "rope_cached_fwd",
     "rope_cached_fwd_inplace",
 ]

build/torch-rocm/_ops.py CHANGED Viewed

@@ -4,7 +4,10 @@ def get_backend() -> str:
     """Detect the backend by inspecting torch."""
     import torch
-    if hasattr(torch, "neuron"):
         # Needs to be sorted before specific Torch builds, since Neuron
         # extension can be loaded into e.g. CUDA Torch builds.
         return "neuron"
@@ -22,7 +25,7 @@ def get_backend() -> str:
 def _find_ops_name() -> str:
     kernel_name = "aiter_rope"
-    unique_id = "6c454e0"
     backend = get_backend()
     return f"_{kernel_name}_{backend}_{unique_id}"

     """Detect the backend by inspecting torch."""
     import torch
+    acc = torch.accelerator.current_accelerator() if hasattr(torch, "accelerator") else None
+    if acc is not None and acc.type == "tpu":
+        return "tpu"
+    elif hasattr(torch, "neuron"):
         # Needs to be sorted before specific Torch builds, since Neuron
         # extension can be loaded into e.g. CUDA Torch builds.
         return "neuron"
 def _find_ops_name() -> str:
     kernel_name = "aiter_rope"
+    unique_id = "75dc872"
     backend = get_backend()
     return f"_{kernel_name}_{backend}_{unique_id}"

build/torch-rocm/aiter_rope/__init__.py DELETED Viewed

@@ -1,26 +0,0 @@
-import ctypes
-import importlib.util
-import sys
-from pathlib import Path
-from types import ModuleType
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch-rocm/layers.py ADDED Viewed

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

+import torch
+import torch.nn as nn
+from .rope import RotateStyle, rope_cached_fwd, rope_cached_positions_fwd
+def _to_sbhd(t: torch.Tensor) -> torch.Tensor:
+    # transformers passes attention tensors as (batch, heads, seq, head_dim);
+    # AITER's kernels operate in (seq, batch, heads, head_dim).
+    return t.permute(2, 0, 1, 3).contiguous()
+def _to_bhsd(t: torch.Tensor) -> torch.Tensor:
+    return t.permute(1, 2, 0, 3)
+def _apply_rotary(q, k, cos, sin):
+    q_sbhd = _to_sbhd(q)
+    k_sbhd = _to_sbhd(k)
+    common = dict(
+        rotate_style=int(RotateStyle.NEOX),
+        reuse_freqs_front_part=True,
+        nope_first=False,
+    )
+    batch, seq_len, dim = cos.shape
+    if batch == 1:
+        # All batch rows share the same positions: index cos / sin by sequence position.
+        cos_freqs = cos[0].unsqueeze(1).unsqueeze(1).contiguous()
+        sin_freqs = sin[0].unsqueeze(1).unsqueeze(1).contiguous()
+        q_out_sbhd = rope_cached_fwd(q_sbhd, cos_freqs, sin_freqs, **common)
+        k_out_sbhd = rope_cached_fwd(k_sbhd, cos_freqs, sin_freqs, **common)
+    else:
+        # Rows can have different positions (e.g. left padding). The kernel ignores the batch
+        # stride of cos / sin, so flatten them into a (batch * seq, dim) table and look up row
+        # `b * seq_len + s` for token `s` of batch row `b`.
+        cos_freqs = cos.reshape(batch * seq_len, 1, 1, dim).contiguous()
+        sin_freqs = sin.reshape(batch * seq_len, 1, 1, dim).contiguous()
+        positions = (
+            torch.arange(batch * seq_len, device=cos.device).view(batch, seq_len).t()
+        )
+        q_out_sbhd = rope_cached_positions_fwd(
+            q_sbhd, cos_freqs, sin_freqs, positions, **common
+        )
+        k_out_sbhd = rope_cached_positions_fwd(
+            k_sbhd, cos_freqs, sin_freqs, positions, **common
+        )
+    return _to_bhsd(q_out_sbhd), _to_bhsd(k_out_sbhd)
+class apply_rotary_transformers(nn.Module):
+    can_torch_compile: bool = True
+    def forward(self, q, k, cos, sin, unsqueeze_dim=1):
+        return _apply_rotary(q, k, cos, sin)
+__all__ = ["apply_rotary_transformers"]

build/torch-rocm/metadata.json CHANGED Viewed

@@ -1,23 +1,37 @@
 {
   "name": "aiter-rope",
-  "id": "_aiter_rope_rocm_6c454e0",
-  "version": 1,
   "license": "MIT",
   "python-depends": [],
   "backend": {
     "type": "rocm"
   },
   "digest": {
     "algorithm": "sha256",
     "files": {
-      "__init__.py": "uKLbHUQRYd9TNj3e4I6pzZIF7fCkJTDGkvy+Lk5mPLY=",
       "_kernels/__init__.py": "47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=",
       "_kernels/rope.py": "yYQRz0fxK1CuTYD9rolVEDXvy3YnS6775IxceH1fNow=",
-      "_ops.py": "mUuGk14iTuP13crGxZjXmyaJ0XUEKEYKd1MGGwUG4q4=",
-      "aiter_rope/__init__.py": "DFYPlrhXwYjEqCl/8n0SmWGZV8NFml5DPhMjKfv98GY=",
       "rope.py": "DmzekBurit+Ow9BGR0Zgc76MjAolYQ6q8mEckVLbtj0=",
       "utils/__init__.py": "47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=",
       "utils/logger.py": "CXuJZCBzQDTrW80OyJ2IuhbHhayLy12Ake1KGxiuYIA="
     }
   }
 }

 {
   "name": "aiter-rope",
+  "id": "_aiter_rope_rocm_75dc872",
+  "version": 3,
+  "kernels-minver": "0.14.0",
   "license": "MIT",
   "python-depends": [],
+  "kernel-depends": [],
   "backend": {
     "type": "rocm"
   },
   "digest": {
     "algorithm": "sha256",
     "files": {
+      "__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="
     }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.18.0-dev0",
+      "commit": "11a92c0d8289d94d8c660933285d0c1986076e3b",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "75dc87202fed59700d26e6269fbdaf203586b16a",
+      "dirty": false
+    }
   }
 }

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build/torch-rocm/symbols.json ADDED Viewed

	@@ -0,0 +1,186 @@

+{
+  "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": {
+        "parameters": [
+          {
+            "name": "q",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "k",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "cos",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "sin",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": null
+          },
+          {
+            "name": "unsqueeze_dim",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "1",
+            "annotation": null
+          }
+        ],
+        "return_annotation": null
+      }
+    },
+    {
+      "name": "rope_cached_fwd",
+      "qualname": "rope_cached_fwd",
+      "module": "_aiter_rope_rocm_75dc872.rope",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "x",
+            "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": "rotate_style",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "reuse_freqs_front_part",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "bool"
+          },
+          {
+            "name": "nope_first",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "bool"
+          },
+          {
+            "name": "transpose_output",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "False",
+            "annotation": "bool"
+          }
+        ],
+        "return_annotation": null
+      }
+    },
+    {
+      "name": "rope_cached_fwd_inplace",
+      "qualname": "rope_cached_fwd_inplace",
+      "module": "_aiter_rope_rocm_75dc872.rope",
+      "kind": "function",
+      "doc": null,
+      "signature": {
+        "parameters": [
+          {
+            "name": "x",
+            "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": "rotate_style",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "reuse_freqs_front_part",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "bool"
+          },
+          {
+            "name": "nope_first",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "bool"
+          },
+          {
+            "name": "transpose_output",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "False",
+            "annotation": "bool"
+          }
+        ],
+        "return_annotation": null
+      }
+    }
+  ],
+  "layers": [
+    {
+      "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": {
+        "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": true
+      }
+    }
+  ]
+}