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.gitattributes

@@ -35,3 +35,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 torch210-cxx11-cpu-x86_64-linux/_stdlib_repro_cpu_1e7c8a4.abi3.so filter=lfs diff=lfs merge=lfs -text
 torch211-cxx11-cpu-x86_64-linux/_stdlib_repro_cpu_1e7c8a4.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch210-cxx11-cpu-x86_64-linux/_stdlib_repro_cpu_1e7c8a4.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch211-cxx11-cpu-x86_64-linux/_stdlib_repro_cpu_1e7c8a4.abi3.so filter=lfs diff=lfs merge=lfs -text

build/CARD.md

@@ -0,0 +1,26 @@
+---
+library_name: kernels
+license: apache-2.0
+---
+
+This is a tiny Kernel Hub repro for C++ standard-library runtime crashes in packaged kernels.
+
+It exposes a CPU op that runs:
+
+- `std::regex` include parsing
+- `std::ostringstream`/`std::hex` hash formatting
+- a simple ReLU copy so the op has a normal tensor path
+
+## How to use
+
+```python
+from kernels import get_kernel
+
+kernel_module = get_kernel("adarshxs/stdlib-repro", backend="cpu", trust_remote_code=True)
+out, digest = kernel_module.relu_and_probe(x, iters=1000)
+```
+
+## Available functions
+- `DEFAULT_CODE`
+- `probe_stdlib`
+- `relu_and_probe`

build/torch210-cxx11-cpu-x86_64-linux/__init__.py

@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+from typing import Optional
+
+import torch
+
+from ._ops import ops
+
+DEFAULT_CODE = """\
+#include <deep_gemm/impls/smxx_layout.cuh>
+#include <deep_gemm/common/compile.cuh>
+#include <cutlass/detail/helper_macros.hpp>
+#include "local_header.cuh"
+"""
+
+
+def relu_and_probe(
+    x: torch.Tensor,
+    *,
+    code: str = DEFAULT_CODE,
+    iters: int = 1000,
+    out: Optional[torch.Tensor] = None,
+) -> tuple[torch.Tensor, str]:
+    if out is None:
+        out = torch.empty_like(x)
+    digest = ops.relu_and_probe(out, x, code, iters)
+    return out, digest
+
+
+def probe_stdlib(*, code: str = DEFAULT_CODE, iters: int = 1000) -> str:
+    x = torch.tensor([-1.0, 0.0, 2.0], dtype=torch.float32)
+    _, digest = relu_and_probe(x, code=code, iters=iters)
+    return digest
+
+
+__all__ = ["DEFAULT_CODE", "probe_stdlib", "relu_and_probe"]

build/torch210-cxx11-cpu-x86_64-linux/_ops.py

@@ -0,0 +1,9 @@
+import torch
+from . import _stdlib_repro_cpu_1e7c8a4
+ops = torch.ops._stdlib_repro_cpu_1e7c8a4
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_stdlib_repro_cpu_1e7c8a4::{op_name}"

build/torch210-cxx11-cpu-x86_64-linux/_stdlib_repro_cpu_1e7c8a4.abi3.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5dc04c34b6e4607170f612c92424088e2c0583279ad84bb2561e5876dd972b44
+size 1989000

build/torch210-cxx11-cpu-x86_64-linux/metadata.json

@@ -0,0 +1,10 @@
+{
+  "name": "stdlib-repro",
+  "id": "_stdlib_repro_cpu_1e7c8a4",
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cpu"
+  }
+}

build/torch210-cxx11-cpu-x86_64-linux/stdlib_repro/__init__.py

@@ -0,0 +1,26 @@
+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/torch211-cxx11-cpu-x86_64-linux/__init__.py

@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+from typing import Optional
+
+import torch
+
+from ._ops import ops
+
+DEFAULT_CODE = """\
+#include <deep_gemm/impls/smxx_layout.cuh>
+#include <deep_gemm/common/compile.cuh>
+#include <cutlass/detail/helper_macros.hpp>
+#include "local_header.cuh"
+"""
+
+
+def relu_and_probe(
+    x: torch.Tensor,
+    *,
+    code: str = DEFAULT_CODE,
+    iters: int = 1000,
+    out: Optional[torch.Tensor] = None,
+) -> tuple[torch.Tensor, str]:
+    if out is None:
+        out = torch.empty_like(x)
+    digest = ops.relu_and_probe(out, x, code, iters)
+    return out, digest
+
+
+def probe_stdlib(*, code: str = DEFAULT_CODE, iters: int = 1000) -> str:
+    x = torch.tensor([-1.0, 0.0, 2.0], dtype=torch.float32)
+    _, digest = relu_and_probe(x, code=code, iters=iters)
+    return digest
+
+
+__all__ = ["DEFAULT_CODE", "probe_stdlib", "relu_and_probe"]

build/torch211-cxx11-cpu-x86_64-linux/_ops.py

@@ -0,0 +1,9 @@
+import torch
+from . import _stdlib_repro_cpu_1e7c8a4
+ops = torch.ops._stdlib_repro_cpu_1e7c8a4
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_stdlib_repro_cpu_1e7c8a4::{op_name}"

build/torch211-cxx11-cpu-x86_64-linux/_stdlib_repro_cpu_1e7c8a4.abi3.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3cfb30a15e55aff2a0f3ed7a12ea2640d2788f208f0b3291b857f71adb8b4b6c
+size 1989000

build/torch211-cxx11-cpu-x86_64-linux/metadata.json

@@ -0,0 +1,10 @@
+{
+  "name": "stdlib-repro",
+  "id": "_stdlib_repro_cpu_1e7c8a4",
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cpu"
+  }
+}

build/torch211-cxx11-cpu-x86_64-linux/stdlib_repro/__init__.py

@@ -0,0 +1,26 @@
+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")))