NVIDIA
diff --git a/‎.spdx-ignore‎
Lines changed: 2 additions & 0 deletions b/‎.spdx-ignore‎
Lines changed: 2 additions & 0 deletions
diff --git a/‎cuda_core/build_hooks.py‎
Lines changed: 21 additions & 1 deletion b/‎cuda_core/build_hooks.py‎
Lines changed: 21 additions & 1 deletion
diff --git a/‎cuda_core/cuda/core/_include/aoti_shim.def‎
Lines changed: 37 additions & 0 deletions b/‎cuda_core/cuda/core/_include/aoti_shim.def‎
Lines changed: 37 additions & 0 deletions
diff --git a/‎cuda_core/cuda/core/_include/aoti_shim.h‎
Lines changed: 117 additions & 0 deletions b/‎cuda_core/cuda/core/_include/aoti_shim.h‎
Lines changed: 117 additions & 0 deletions
diff --git a/‎cuda_core/cuda/core/_memoryview.pyx‎
Lines changed: 94 additions & 0 deletions b/‎cuda_core/cuda/core/_memoryview.pyx‎
Lines changed: 94 additions & 0 deletions
@@ -10,5 +10,7 @@ cuda_bindings/examples/*
 
 # Vendored
 cuda_core/cuda/core/_include/dlpack.h
+cuda_core/cuda/core/_include/aoti_shim.h
+cuda_core/cuda/core/_include/aoti_shim.def
 
 qa/ctk-next.drawio.svg
@@ -11,6 +11,7 @@
 import glob
 import os
 import re
+import subprocess
 import sys
 import tempfile
 import zipfile
@@ -182,6 +183,25 @@ def get_sources(mod_name):
         # related to free-threading builds.
         extra_compile_args += ["-DCYTHON_TRACE_NOGIL=1", "-DCYTHON_USE_SYS_MONITORING=0"]
 
+    # On Windows, _tensor_bridge.pyx needs a stub import library so the MSVC
+    # linker can resolve the AOTI symbols (they live in torch_cpu.dll at
+    # runtime).  We generate the .lib from a .def file at build time.
+    _aoti_extra_link_args = []
+    if sys.platform == "win32":
+        _def_file = os.path.join("cuda", "core", "_include", "aoti_shim.def")
+        _lib_file = os.path.join("build", "aoti_shim.lib")
+        os.makedirs("build", exist_ok=True)
+        subprocess.check_call(  # noqa: S603
+            ["lib", f"/DEF:{_def_file}", f"/OUT:{_lib_file}", "/MACHINE:X64"],  # noqa: S607
+            stdout=subprocess.DEVNULL,
+        )
+        _aoti_extra_link_args = [_lib_file]
+
+    def get_extra_link_args(mod_name):
+        if mod_name == "_tensor_bridge" and _aoti_extra_link_args:
+            return extra_link_args + _aoti_extra_link_args
+        return extra_link_args
+
     ext_modules = tuple(
         Extension(
             f"cuda.core.{mod.replace(os.path.sep, '.')}",
@@ -193,7 +213,7 @@ def get_sources(mod_name):
             + all_include_dirs,
             language="c++",
             extra_compile_args=extra_compile_args,
-            extra_link_args=extra_link_args,
+            extra_link_args=get_extra_link_args(mod),
         )
         for mod in module_names()
     )
 
@@ -0,0 +1,37 @@
+; Stub import library definition for PyTorch's AOTI stable C ABI symbols.
+; Used on Windows only: 'lib /DEF:aoti_shim.def /OUT:aoti_shim.lib /MACHINE:X64'
+; generates a minimal import library that satisfies the MSVC linker.
+; At runtime the symbols resolve from torch_cpu.dll (loaded by 'import torch').
+;
+; IMPORTANT: Keep this export list in sync with the AOTI_SHIM_API declarations
+; in aoti_shim.h. build_hooks.py turns this file into the stub import library
+; that MSVC uses to link _tensor_bridge, so any added/removed/renamed AOTI
+; symbol must be updated in both files.
+LIBRARY torch_cpu.dll
+EXPORTS
+    aoti_torch_get_data_ptr
+    aoti_torch_get_dim
+    aoti_torch_get_sizes
+    aoti_torch_get_strides
+    aoti_torch_get_dtype
+    aoti_torch_dtype_float16
+    aoti_torch_dtype_float32
+    aoti_torch_dtype_float64
+    aoti_torch_dtype_bfloat16
+    aoti_torch_dtype_uint8
+    aoti_torch_dtype_uint16
+    aoti_torch_dtype_uint32
+    aoti_torch_dtype_uint64
+    aoti_torch_dtype_int8
+    aoti_torch_dtype_int16
+    aoti_torch_dtype_int32
+    aoti_torch_dtype_int64
+    aoti_torch_dtype_bool
+    aoti_torch_dtype_complex32
+    aoti_torch_dtype_complex64
+    aoti_torch_dtype_complex128
+    aoti_torch_get_device_type
+    aoti_torch_get_device_index
+    aoti_torch_device_type_cpu
+    aoti_torch_device_type_cuda
+    aoti_torch_get_current_cuda_stream
@@ -0,0 +1,117 @@
+/*
+ * Vendored subset of PyTorch's AOT Inductor (AOTI) stable C ABI.
+ * Original: torch/csrc/inductor/aoti_torch/c/shim.h
+ *
+ * These are declarations only -- no definitions are provided.  The actual
+ * symbols are exported by libtorch (loaded via torch._C with RTLD_GLOBAL)
+ * and resolved at runtime by the dynamic linker.  This means PyTorch is
+ * NOT required at compile time.
+ *
+ * From PyTorch:
+ *
+ * Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
+ * Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
+ * Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
+ * Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
+ * Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
+ * Copyright (c) 2011-2013 NYU                      (Clement Farabet)
+ * Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
+ * Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
+ * Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
+ *
+ * SPDX-License-Identifier: BSD-3-Clause
+ * See https://github.com/pytorch/pytorch/blob/main/LICENSE
+ */
+
+#ifndef CUDA_CORE_AOTI_SHIM_H
+#define CUDA_CORE_AOTI_SHIM_H
+
+#include <stdint.h>
+
+/*
+ * On Windows the AOTI symbols live in torch_cpu.dll.  We consume them
+ * via __declspec(dllimport) and a stub import library generated from
+ * aoti_shim.def at build time.  On Linux/macOS the symbols are made
+ * visible at runtime through ctypes.CDLL(torch._C, RTLD_GLOBAL).
+ */
+#ifdef _WIN32
+#  define AOTI_SHIM_API __declspec(dllimport)
+#else
+#  define AOTI_SHIM_API
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+typedef int32_t AOTITorchError;
+
+/* Opaque tensor handle -- corresponds to at::Tensor on the C++ side. */
+struct AtenTensorOpaque;
+typedef struct AtenTensorOpaque* AtenTensorHandle;
+
+/*
+ * IMPORTANT: Keep the AOTI_SHIM_API declaration list below in sync with
+ * aoti_shim.def. On Windows, build_hooks.py turns that .def file into the
+ * stub import library that MSVC needs to link _tensor_bridge without making
+ * PyTorch a build-time dependency. If you add, remove, or rename an imported
+ * AOTI symbol here, update aoti_shim.def in the same change.
+ */
+
+/* ---- tensor metadata --------------------------------------------------- */
+
+AOTI_SHIM_API AOTITorchError aoti_torch_get_data_ptr(
+    AtenTensorHandle tensor, void** ret_data_ptr);
+
+AOTI_SHIM_API AOTITorchError aoti_torch_get_dim(
+    AtenTensorHandle tensor, int64_t* ret_dim);
+
+AOTI_SHIM_API AOTITorchError aoti_torch_get_sizes(
+    AtenTensorHandle tensor, int64_t** ret_sizes);
+
+AOTI_SHIM_API AOTITorchError aoti_torch_get_strides(
+    AtenTensorHandle tensor, int64_t** ret_strides);
+
+/* ---- dtype ------------------------------------------------------------- */
+
+AOTI_SHIM_API AOTITorchError aoti_torch_get_dtype(
+    AtenTensorHandle tensor, int32_t* ret_dtype);
+
+AOTI_SHIM_API int32_t aoti_torch_dtype_float16(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_float32(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_float64(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_bfloat16(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_uint8(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_uint16(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_uint32(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_uint64(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_int8(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_int16(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_int32(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_int64(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_bool(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_complex32(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_complex64(void);
+AOTI_SHIM_API int32_t aoti_torch_dtype_complex128(void);
+
+/* ---- device ------------------------------------------------------------ */
+
+AOTI_SHIM_API AOTITorchError aoti_torch_get_device_type(
+    AtenTensorHandle tensor, int32_t* ret_device_type);
+
+AOTI_SHIM_API AOTITorchError aoti_torch_get_device_index(
+    AtenTensorHandle tensor, int32_t* ret_device_index);
+
+AOTI_SHIM_API int32_t aoti_torch_device_type_cpu(void);
+AOTI_SHIM_API int32_t aoti_torch_device_type_cuda(void);
+
+/* ---- stream -------------------------------------------------------------- */
+
+AOTI_SHIM_API AOTITorchError aoti_torch_get_current_cuda_stream(
+    int32_t device_index, void** ret_stream);
+
+#ifdef __cplusplus
+}  /* extern "C" */
+#endif
+
+#endif /* CUDA_CORE_AOTI_SHIM_H */
@@ -10,7 +10,9 @@ from libc.stdint cimport intptr_t
 from cuda.core._layout cimport _StridedLayout, get_strides_ptr
 from cuda.core._stream import Stream
 
+import ctypes
 import functools
+import sys
 import warnings
 
 import numpy
@@ -29,6 +31,73 @@ from cuda.core._utils.cuda_utils cimport HANDLE_RETURN
 from cuda.core._memory import Buffer
 
 
+# ---------------------------------------------------------------------------
+# Lazy tensor bridge (avoids loading _tensor_bridge.so until torch is used)
+# ---------------------------------------------------------------------------
+
+cdef object _tensor_bridge = None
+# Cache: type(obj) -> True/False for the torch tensor check.
+# Once a type is seen, we never re-check.
+cdef dict _torch_type_cache = {}
+# Tri-state: None = not checked, True/False = result of version check
+cdef object _torch_version_ok = None
+
+cdef inline bint _torch_version_check():
+    """Return True if 2.3 <= torch <= 2.11 (known AOTI ABI range). Memoized.
+
+    Lower bound: AOTI functions we use were introduced in PyTorch 2.3.
+    Upper bound: the ``pyobj_to_aten_handle`` trick relies on the
+    THPVariable struct layout (PyObject_HEAD followed by at::Tensor cdata)
+    and the identity ``AtenTensorHandle == at::Tensor*``.  Both are
+    undocumented internals that could change in a future PyTorch version.
+    We cap at the latest version we have tested against; unknown versions
+    fall back to the standard DLPack/CAI paths.  Bump the upper bound
+    after verifying a new PyTorch release.
+    """
+    global _torch_version_ok
+    if _torch_version_ok is not None:
+        return <bint>_torch_version_ok
+    torch = sys.modules.get("torch")
+    if torch is None:
+        _torch_version_ok = False
+        return False
+    try:
+        major, minor = int(torch.__version__.split(".")[0]), \
+                       int(torch.__version__.split(".")[1])
+        _torch_version_ok = (2, 3) <= (major, minor) <= (2, 11)
+    except (ValueError, IndexError):
+        _torch_version_ok = False
+    return <bint>_torch_version_ok
+
+
+cdef inline bint _is_torch_tensor(object obj):
+    cdef type tp = type(obj)
+    cdef object cached = _torch_type_cache.get(tp)
+    if cached is not None:
+        return <bint>cached
+    cdef str mod = tp.__module__ or ""
+    cdef bint result = mod.startswith("torch") and hasattr(obj, "data_ptr") \
+        and _torch_version_check()
+    _torch_type_cache[tp] = result
+    return result
+
+
+cdef object _get_tensor_bridge():
+    """Bootstrap AOTI symbols, then import _tensor_bridge on first use."""
+    global _tensor_bridge
+    if _tensor_bridge is not None:
+        return _tensor_bridge
+    torch_C = sys.modules.get("torch._C")
+    if torch_C is None:
+        raise RuntimeError(
+            "torch._C is not loaded; cannot initialise the tensor bridge. "
+            "Make sure PyTorch is imported before passing a torch.Tensor.")
+    ctypes.CDLL(torch_C.__file__, mode=ctypes.RTLD_GLOBAL)
+    from cuda.core import _tensor_bridge as tb
+    _tensor_bridge = tb
+    return _tensor_bridge
+
+
 try:
     from ml_dtypes import bfloat16
 except ImportError:
@@ -150,6 +219,9 @@ cdef class StridedMemoryView:
             Stream pointer for synchronization. If ``None``, no synchronization is performed.
         """
         cdef StridedMemoryView buf = StridedMemoryView.__new__(cls)
+        if _is_torch_tensor(obj):
+            _get_tensor_bridge().view_as_torch_tensor(obj, stream_ptr, buf)
+            return buf
         view_as_dlpack(obj, stream_ptr, buf)
         return buf
 
@@ -165,6 +237,9 @@ cdef class StridedMemoryView:
             Stream pointer for synchronization. If ``None``, no synchronization is performed.
         """
         cdef StridedMemoryView buf = StridedMemoryView.__new__(cls)
+        if _is_torch_tensor(obj):
+            _get_tensor_bridge().view_as_torch_tensor(obj, stream_ptr, buf)
+            return buf
         view_as_cai(obj, stream_ptr, buf)
         return buf
 
@@ -178,6 +253,9 @@ cdef class StridedMemoryView:
             An object implementing the `__array_interface__ <https://numpy.org/doc/stable/reference/arrays.interface.html>`_ protocol (e.g., a numpy array).
         """
         cdef StridedMemoryView buf = StridedMemoryView.__new__(cls)
+        if _is_torch_tensor(obj):
+            _get_tensor_bridge().view_as_torch_tensor(obj, None, buf)
+            return buf
         view_as_array_interface(obj, buf)
         return buf
 
@@ -187,6 +265,8 @@ cdef class StridedMemoryView:
 
         Tries `DLPack <https://dmlc.github.io/dlpack/latest/>`_ first, then falls back to
         `__cuda_array_interface__ <https://numba.readthedocs.io/en/stable/cuda/cuda_array_interface.html>`_.
+        ``torch.Tensor`` objects are transparently handled via a fast AOTI path
+        regardless of which protocol is selected.
 
         Parameters
         ----------
@@ -480,6 +560,10 @@ cdef class StridedMemoryView:
         if self._dtype is None:
             if self.dl_tensor != NULL:
                 self._dtype = dtype_dlpack_to_numpy(&self.dl_tensor.dtype)
+            elif isinstance(self.metadata, int):
+                # AOTI dtype code stored by the torch tensor bridge
+                self._dtype = _get_tensor_bridge().resolve_aoti_dtype(
+                    self.metadata)
             elif self.metadata is not None:
                 self._dtype = _typestr2dtype(self.metadata["typestr"])
         return self._dtype
@@ -1122,6 +1206,16 @@ cpdef StridedMemoryView view_as_cai(obj, stream_ptr, view=None):
                         as_cu(h_event), <cydriver.CUstream>producer_s))
                     HANDLE_RETURN(cydriver.cuStreamWaitEvent(
                         <cydriver.CUstream>consumer_s, as_cu(h_event), 0))
+        elif _is_torch_tensor(obj):
+            # PyTorch's __cuda_array_interface__ reports version 2 and
+            # omits the "stream" field, so the standard CAI sync path
+            # above is a no-op for torch tensors.  This is unsafe: the
+            # consumer has no guarantee that the producer's work is
+            # visible.  We fix this by querying PyTorch's current CUDA
+            # stream via the AOTI stable C ABI and performing the same
+            # event-based stream ordering.
+            _get_tensor_bridge().sync_torch_stream(
+                buf.device_id, <intptr_t>(stream_ptr))
 
     return buf