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* Unmodified copies of: * https://github.com/NVIDIA/numba-cuda/blob/bf487d78a40eea87f009d636882a5000a7524c95/numba_cuda/numba/cuda/cuda_paths.py * https://github.com/numba/numba/blob/f0d24824fcd6a454827e3c108882395d00befc04/numba/misc/findlib.py * Add Forked from URLs. * Strip down cuda_paths.py to minimum required for `_get_nvvm_path()` Tested interactively with: ``` import cuda_paths nvvm_path = cuda_paths._get_nvvm_path() print(f"{nvvm_path=}") ``` * ruff auto-fixes (NO manual changes) * Make `get_nvvm_path()` a pubic API (i.e. remove leading underscore). * Fetch numba-cuda/numba_cuda/numba/cuda/cuda_paths.py from NVIDIA/numba-cuda#155 AS-IS * ruff format NO MANUAL CHANGES * Minimal changes to adapt numba-cuda/numba_cuda/numba/cuda/cuda_paths.py from NVIDIA/numba-cuda#155 * Rename ecosystem/cuda_paths.py -> path_finder.py * Plug cuda.bindings.path_finder into cuda/bindings/_internal/nvvm_linux.pyx * Plug cuda.bindings.path_finder into cuda/bindings/_internal/nvjitlink_linux.pyx * Fix `os.path.exists(None)` issue: ``` ______________________ ERROR collecting test_nvjitlink.py ______________________ tests/test_nvjitlink.py:62: in <module> not check_nvjitlink_usable(), reason="nvJitLink not usable, maybe not installed or too old (<12.3)" tests/test_nvjitlink.py:58: in check_nvjitlink_usable return inner_nvjitlink._inspect_function_pointer("__nvJitLinkVersion") != 0 cuda/bindings/_internal/nvjitlink.pyx:257: in cuda.bindings._internal.nvjitlink._inspect_function_pointer ??? cuda/bindings/_internal/nvjitlink.pyx:260: in cuda.bindings._internal.nvjitlink._inspect_function_pointer ??? cuda/bindings/_internal/nvjitlink.pyx:208: in cuda.bindings._internal.nvjitlink._inspect_function_pointers ??? cuda/bindings/_internal/nvjitlink.pyx:102: in cuda.bindings._internal.nvjitlink._check_or_init_nvjitlink ??? cuda/bindings/_internal/nvjitlink.pyx:59: in cuda.bindings._internal.nvjitlink.load_library ??? /opt/hostedtoolcache/Python/3.13.2/x64/lib/python3.13/site-packages/cuda/bindings/path_finder.py:312: in get_cuda_paths "nvvm": _get_nvvm_path(), /opt/hostedtoolcache/Python/3.13.2/x64/lib/python3.13/site-packages/cuda/bindings/path_finder.py:285: in _get_nvvm_path by, path = _get_nvvm_path_decision() /opt/hostedtoolcache/Python/3.13.2/x64/lib/python3.13/site-packages/cuda/bindings/path_finder.py:96: in _get_nvvm_path_decision if os.path.exists(nvvm_ctk_dir): <frozen genericpath>:19: in exists ??? E TypeError: stat: path should be string, bytes, os.PathLike or integer, not NoneType ``` * Fix another `os.path.exists(None)` issue: ``` ______________________ ERROR collecting test_nvjitlink.py ______________________ tests/test_nvjitlink.py:62: in <module> not check_nvjitlink_usable(), reason="nvJitLink not usable, maybe not installed or too old (<12.3)" tests/test_nvjitlink.py:58: in check_nvjitlink_usable return inner_nvjitlink._inspect_function_pointer("__nvJitLinkVersion") != 0 cuda/bindings/_internal/nvjitlink.pyx:257: in cuda.bindings._internal.nvjitlink._inspect_function_pointer ??? cuda/bindings/_internal/nvjitlink.pyx:260: in cuda.bindings._internal.nvjitlink._inspect_function_pointer ??? cuda/bindings/_internal/nvjitlink.pyx:208: in cuda.bindings._internal.nvjitlink._inspect_function_pointers ??? cuda/bindings/_internal/nvjitlink.pyx:102: in cuda.bindings._internal.nvjitlink._check_or_init_nvjitlink ??? cuda/bindings/_internal/nvjitlink.pyx:59: in cuda.bindings._internal.nvjitlink.load_library ??? /opt/hostedtoolcache/Python/3.13.2/x64/lib/python3.13/site-packages/cuda/bindings/path_finder.py:313: in get_cuda_paths "libdevice": _get_libdevice_paths(), /opt/hostedtoolcache/Python/3.13.2/x64/lib/python3.13/site-packages/cuda/bindings/path_finder.py:126: in _get_libdevice_paths by, libdir = _get_libdevice_path_decision() /opt/hostedtoolcache/Python/3.13.2/x64/lib/python3.13/site-packages/cuda/bindings/path_finder.py:73: in _get_libdevice_path_decision if os.path.exists(libdevice_ctk_dir): <frozen genericpath>:19: in exists ??? E TypeError: stat: path should be string, bytes, os.PathLike or integer, not NoneType ``` * Change "/lib64/" → "/lib/" in nvjitlink_linux.pyx * nvjitlink_linux.pyx load_library() enhancements, mainly to avoid os.path.join(None, "libnvJitLink.so") * Add missing f-string f * Add back get_nvjitlink_dso_version_suffix() call. * pytest -ra -s -v * Rewrite nvjitlink_linux.pyx load_library() to produce detailed error messages. * Attach listdir output to "Unable to load" exception message. * Guard os.listdir() call with os.path.isdir() * Fix logic error in nvjitlink_linux.pyx load_library() * Move path_finder.py to _path_finder_utils/cuda_paths.py, import only public functions from new path_finder.py * Add find_nvidia_dynamic_library() and use from nvjitlink_linux.pyx, nvvm_linux.pyx * Fix oversight in _find_using_lib_dir() * Also look for versioned library in _find_using_nvidia_lib_dirs() * glob.glob() Python 3.9 compatibility * Reduce build-and-test.yml to Windows-only, Python 3.12 only. * Comment out `if: ${{ github.repository_owner == nvidia }}` * Revert "Comment out `if: ${{ github.repository_owner == nvidia }}`" This reverts commit b0db24f. * Add back `linux-64` `host-platform` * Rewrite load_library() in nvjitlink_windows.pyx to use path_finder.find_nvidia_dynamic_library() * Revert "Rewrite load_library() in nvjitlink_windows.pyx to use path_finder.find_nvidia_dynamic_library()" This reverts commit 1bb7151. * Add _inspect_environment() in find_nvidia_dynamic_library.py, call from nvjitlink_windows.pyx, nvvm_windows.pyx * Add & use _find_dll_using_nvidia_bin_dirs(), _find_dll_using_cudalib_dir() * Fix silly oversight: forgot to undo experimental change. * Also reduce test test-linux matrix. * Reimplement load_library() functions in nvjitlink_windows.pyx, nvvm_windows.pyx to actively use path_finder.find_nvidia_dynamic_library() * Factor out load_nvidia_dynamic_library() from _internal/nvjitlink_linux.pyx, nvvm_linux.pyx * Generalize load_nvidia_dynamic_library.py to also work under Windows. * Add `void*` return type to load_library() implementations in _internal/nvjitlink_windows.pyx, nvvm_windows.pyx * Resolve cython error: object handle vs `void*` handle ``` Error compiling Cython file: ------------------------------------------------------------ ... err = (<int (*)(int*) nogil>__cuDriverGetVersion)(&driver_ver) if err != 0: raise RuntimeError('something went wrong') # Load library handle = load_library(driver_ver) ^ ------------------------------------------------------------ cuda\bindings\_internal\nvjitlink.pyx:72:29: Cannot convert 'void *' to Python object ``` * Resolve another cython error: `void*` handle vs `intptr_t` handle ``` Error compiling Cython file: ------------------------------------------------------------ ... handle = load_library(driver_ver) # Load function global __nvJitLinkCreate try: __nvJitLinkCreate = <void*><intptr_t>win32api.GetProcAddress(handle, 'nvJitLinkCreate') ^ ------------------------------------------------------------ cuda\bindings\_internal\nvjitlink.pyx:78:73: Cannot convert 'void *' to Python object ``` * Resolve signed/unsigned runtime error. Use uintptr_t consistently. https://github.com/NVIDIA/cuda-python/actions/runs/14224673173/job/39861750852?pr=447#logs ``` =================================== ERRORS ==================================== _____________________ ERROR collecting test_nvjitlink.py ______________________ tests\test_nvjitlink.py:62: in <module> not check_nvjitlink_usable(), reason="nvJitLink not usable, maybe not installed or too old (<12.3)" tests\test_nvjitlink.py:58: in check_nvjitlink_usable return inner_nvjitlink._inspect_function_pointer("__nvJitLinkVersion") != 0 cuda\\bindings\\_internal\\nvjitlink.pyx:221: in cuda.bindings._internal.nvjitlink._inspect_function_pointer ??? cuda\\bindings\\_internal\\nvjitlink.pyx:224: in cuda.bindings._internal.nvjitlink._inspect_function_pointer ??? cuda\\bindings\\_internal\\nvjitlink.pyx:172: in cuda.bindings._internal.nvjitlink._inspect_function_pointers ??? cuda\\bindings\\_internal\\nvjitlink.pyx:73: in cuda.bindings._internal.nvjitlink._check_or_init_nvjitlink ??? cuda\\bindings\\_internal\\nvjitlink.pyx:46: in cuda.bindings._internal.nvjitlink.load_library ??? E OverflowError: can't convert negative value to size_t ``` * Change <void*><uintptr_t>win32api.GetProcAddress` back to `intptr_t`. Changing load_nvidia_dynamic_library() to also use to-`intptr_t` conversion, for compatibility with win32api.GetProcAddress. Document that CDLL behaves differently (it uses to-`uintptr_t`). * Use win32api.LoadLibrary() instead of ctypes.windll.kernel32.LoadLibraryW(), to be more similar to original (and working) cython code. Hoping to resolve this kind of error: ``` _ ERROR at setup of test_c_or_v_program_fail_bad_option[txt-compile_program] __ request = <SubRequest 'minimal_nvvmir' for <Function test_c_or_v_program_fail_bad_option[txt-compile_program]>> @pytest.fixture(params=MINIMAL_NVVMIR_FIXTURE_PARAMS) def minimal_nvvmir(request): for pass_counter in range(2): nvvmir = MINIMAL_NVVMIR_CACHE.get(request.param, -1) if nvvmir != -1: if nvvmir is None: pytest.skip(f"UNAVAILABLE: {request.param}") return nvvmir if pass_counter: raise AssertionError("This code path is meant to be unreachable.") # Build cache entries, then try again (above). > major, minor, debug_major, debug_minor = nvvm.ir_version() tests\test_nvvm.py:148: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cuda\bindings\nvvm.pyx:95: in cuda.bindings.nvvm.ir_version cpdef tuple ir_version(): cuda\bindings\nvvm.pyx:113: in cuda.bindings.nvvm.ir_version status = nvvmIRVersion(&major_ir, &minor_ir, &major_dbg, &minor_dbg) cuda\bindings\cynvvm.pyx:19: in cuda.bindings.cynvvm.nvvmIRVersion return _nvvm._nvvmIRVersion(majorIR, minorIR, majorDbg, minorDbg) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ > ??? E cuda.bindings._internal.utils.FunctionNotFoundError: function nvvmIRVersion is not found ``` * Remove debug print statements. * Remove some cruft. * Trivial renaming of variables. No functional changes. * Revert debug changes under .github/workflows * Rename _path_finder_utils → _path_finder * Remove LD_LIBRARY_PATH in fetch_ctk/action.yml * Linux: First try using the platform-specific dynamic loader search mechanisms * Add _windows_load_with_dll_basename() * Revert "Revert debug changes under .github/workflows" This reverts commit cc6113c. * Add debug prints in load_nvidia_dynamic_library() * Report dlopen error for libnvrtc.so.12 * print("\nLOOOK dlfcn.dlopen('libnvrtc.so.12', dlfcn.RTLD_NOW)", flush=True) * Revert "Remove LD_LIBRARY_PATH in fetch_ctk/action.yml" This reverts commit 1b1139c. * Only remove ${CUDA_PATH}/nvvm/lib64 from LD_LIBRARY_PATH * Use path_finder.load_nvidia_dynamic_library("nvrtc") from cuda/bindings/_bindings/cynvrtc.pyx.in * Somewhat ad hoc heuristics for nvidia_cuda_nvrtc wheels. * Remove LD_LIBRARY_PATH entirely from .github/actions/fetch_ctk/action.yml * Remove CUDA_PATH\nvvm\bin in .github/workflows/test-wheel-windows.yml * Revert "Remove LD_LIBRARY_PATH entirely from .github/actions/fetch_ctk/action.yml" This reverts commit bff8cf0. * Revert "Somewhat ad hoc heuristics for nvidia_cuda_nvrtc wheels." This reverts commit 43abec8. * Restore cuda/bindings/_bindings/cynvrtc.pyx.in as-is on main * Remove debug print from load_nvidia_dynamic_library.py * Reapply "Revert debug changes under .github/workflows" This reverts commit aaa6aff.
* Revert "Restore cuda/bindings/_bindings/cynvrtc.pyx.in as-is on main" This reverts commit ba093f5. * Revert "Reapply "Revert debug changes under .github/workflows"" This reverts commit 8f69f83. * Also load nvrtc from cuda_bindings/tests/path_finder.py * Add heuristics for nvidia_cuda_nvrtc Windows wheels. Also fix a couple bugs discovered by ChatGPT: * `glob.glob()` in this code return absolute paths. * stray `error_messages = []` * Add debug prints, mostly for `os.add_dll_directory(bin_dir)` * Fix unfortunate silly oversight (import os missing under Windows) * Use `win32api.LoadLibraryEx()` with suitable `flags`; also update `os.environ["PATH"]` * Hard-wire WinBase.h constants (they are not exposed by win32con) * Remove debug prints * Reapply "Reapply "Revert debug changes under .github/workflows"" This reverts commit b002ff6.
* Revert "Reapply "Revert debug changes under .github/workflows"" This reverts commit 8f69f83. * Add names of all CTK 12.8.1 x86_64-linux libraries (.so) as `path_finder.SUPPORTED_LIBNAMES` https://chatgpt.com/share/67f98d0b-148c-8008-9951-9995cf5d860c * Add `SUPPORTED_WINDOWS_DLLS` * Add copyright notice * Move SUPPORTED_LIBNAMES, SUPPORTED_WINDOWS_DLLS to _path_finder/supported_libs.py * Use SUPPORTED_WINDOWS_DLLS in _windows_load_with_dll_basename() * Change "Set up mini CTK" to use `method: local`, remove `sub-packages` line. * Use Jimver/[email protected] also under Linux, `method: local`, no `sub-packages`. * Add more `nvidia-*-cu12` wheels to get as many of the supported shared libraries as possible. * Revert "Use Jimver/[email protected] also under Linux, `method: local`, no `sub-packages`." This reverts commit d499806. Problem observed: ``` /usr/bin/docker exec 1b42cd4ea3149ac3f2448eae830190ee62289b7304a73f8001e90cead5005102 sh -c "cat /etc/*release | grep ^ID" Warning: Failed to restore: Cache service responded with 422 /usr/bin/tar --posix -cf cache.tgz --exclude cache.tgz -P -C /__w/cuda-python/cuda-python --files-from manifest.txt -z Failed to save: Unable to reserve cache with key cuda_installer-linux-5.15.0-135-generic-x64-12.8.0, another job may be creating this cache. More details: This legacy service is shutting down, effective April 15, 2025. Migrate to the new service ASAP. For more information: https://gh.io/gha-cache-sunset Warning: Error during installation: Error: Unable to locate executable file: sudo. Please verify either the file path exists or the file can be found within a directory specified by the PATH environment variable. Also check the file mode to verify the file is executable. Error: Error: Unable to locate executable file: sudo. Please verify either the file path exists or the file can be found within a directory specified by the PATH environment variable. Also check the file mode to verify the file is executable. ``` * Change test_path_finder::test_find_and_load() to skip cufile on Windows, and report exceptions as failures, except for cudart * Add nvidia-cuda-runtime-cu12 to pyproject.toml (for libname cudart) * test_path_finder.py: before loading cusolver, load nvJitLink, cusparse, cublas (experiment to see if that resolves the only Windows failure) Test (win-64, Python 3.12, CUDA 12.8.0, Runner default, CTK wheels) / test ``` ================================== FAILURES =================================== ________________________ test_find_and_load[cusolver] _________________________ libname = 'cusolver' @pytest.mark.parametrize("libname", path_finder.SUPPORTED_LIBNAMES) def test_find_and_load(libname): if sys.platform == "win32" and libname == "cufile": pytest.skip(f'test_find_and_load("{libname}") not supported on this platform') print(f'\ntest_find_and_load("{libname}")') failures = [] for algo, func in ( ("find", path_finder.find_nvidia_dynamic_library), ("load", path_finder.load_nvidia_dynamic_library), ): try: out = func(libname) except Exception as e: out = f"EXCEPTION: {type(e)} {str(e)}" failures.append(algo) print(out) print() > assert not failures E AssertionError: assert not ['load'] tests\test_path_finder.py:29: AssertionError ``` * test_path_finder.py: load *only* nvJitLink before loading cusolver * Run each test_find_or_load_nvidia_dynamic_library() subtest in a subprocess * Add cublasLt to supported_libs.py and load deps for cusolver, cusolverMg, cusparse in test_path_finder.py. Also restrict test_path_finder.py to test load only for now. * Add supported_libs.DIRECT_DEPENDENCIES * Remove cufile_rdma from supported libs (comment out). https://chatgpt.com/share/68033a33-385c-8008-a293-4c8cc3ea23ae * Split out `PARTIALLY_SUPPORTED_LIBNAMES`. Fix up test code. * Reduce public API to only load_nvidia_dynamic_library, SUPPORTED_LIBNAMES * Set CUDA_BINDINGS_PATH_FINDER_TEST_ALL_LIBNAMES=1 to match expected availability of nvidia shared libraries. * Refactor as `class _find_nvidia_dynamic_library` * Strict wheel, conda, system rule: try using the platform-specific dynamic loader search mechanisms only last * Introduce _load_and_report_path_linux(), add supported_libs.EXPECTED_LIB_SYMBOLS * Plug in ctypes.windll.kernel32.GetModuleFileNameW() * Keep track of nvrtc-related GitHub comment * Factor out `_find_dll_under_dir(dirpath, file_wild)` and reuse from `_find_dll_using_nvidia_bin_dirs()`, `_find_dll_using_cudalib_dir()` (to fix loading nvrtc64_120_0.dll from local CTK) * Minimal "is already loaded" code. * Add THIS FILE NEEDS TO BE REVIEWED/UPDATED FOR EACH CTK RELEASE comment in _path_finder/supported_libs.py * Add SUPPORTED_LINUX_SONAMES in _path_finder/supported_libs.py * Update SUPPORTED_WINDOWS_DLLS in _path_finder/supported_libs.py based on DLLs found in cuda_*win*.exe files. * Remove `os.add_dll_directory()` and `os.environ["PATH"]` manipulations from find_nvidia_dynamic_library.py. Add `supported_libs.LIBNAMES_REQUIRING_OS_ADD_DLL_DIRECTORY` and use from `load_nvidia_dynamic_library()`. * Move nvrtc-specific code from find_nvidia_dynamic_library.py to `supported_libs.is_suppressed_dll_file()` * Introduce dataclass LoadedDL as return type for load_nvidia_dynamic_library() * Factor out _abs_path_for_dynamic_library_* and use on handle obtained through "is already loaded" checks * Factor out _load_nvidia_dynamic_library_no_cache() and use for exercising LoadedDL.was_already_loaded_from_elsewhere * _check_nvjitlink_usable() in test_path_finder.py * Undo changes in .github/workflows/ and cuda_bindings/pyproject.toml * Move cuda_bindings/tests/path_finder.py -> toolshed/run_cuda_bindings_path_finder.py * Add bandit suppressions in test_path_finder.py * Add pytest info_summary_append fixture and use from test_path_finder.py to report the absolute paths of the loaded libraries.
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/ok to test 17478da |
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/ok to test 7da74bd |
leofang
added
cuda.bindings
…ions 12.0, 12.1, 12.2
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/ok to test a649e7d |
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For completeness: I used these command while working on commit a649e7d: |
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Test results for commit a649e7d:
import sys
def get_info_abs_path(filename):
print_buffer = [filename]
done = set()
for line in open(filename).read().splitlines():
if "Z INFO " in line:
flds = line.split(": abs_path=", 1)
assert len(flds) == 2
abs_path = eval(flds[1]) # eval undoes repr double backslashes
if abs_path not in done:
print_buffer.append(f" {abs_path}")
done.add(abs_path)
return print_buffer
def run(args):
no_info = []
has_info = []
for filename in sorted(args):
print_buffer = get_info_abs_path(filename)
assert print_buffer
if len(print_buffer) == 1:
no_info.append(print_buffer)
else:
has_info.append(print_buffer)
for print_buffer in no_info + has_info:
print("\n".join(print_buffer))
if __name__ == "__main__":
run(args=sys.argv[1:]) |
…d_dl_windows.py with the help of Cursor.
rwgk
force-pushed
the
path_finder_review1
branch
from
2452fdb to
b5cef1b
Compare
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/ok to test b5cef1b |
… passed), followed by ruff auto-fixes
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/ok to test 001a6a2 |
… maximally robust.
I think I'm through. I'll run the CI again to be sure the PR is still in working condition. If I overlooked something, could you please flag it again? |
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/ok to test 92e7b42a3e43328b9da3d6944c8ce15f6645aa3f |
kkraus14
reviewed
May 1, 2025
I'm not sure if this behavior is limited to just the system search, but certain package managers, i.e. conda split packages into runtime and dev packages. I.e. conda has We should probably just make sure we're universally using the |
The versioned names will be found, too, with the code as-is. See
We have
The only advantage would be that we'd pick out e.g. |
👍 I didn't follow this properly. https://github.com/rwgk/cuda-python/blob/aeaf4f02278b62befb0e380e9f6f97a50b848fb3/cuda_bindings/cuda/bindings/_path_finder/find_nvidia_dynamic_library.py#L33-L37 This will be potentially problematic. If there's no I think the only way this could happen today is by installing wheels since there's both Would it maybe make more sense to just looking for the specific version we'd expect based on cuda 11 vs cuda 12? |
It's definitely more predictable, but I'll need to add in determining the CUDA driver version. I'll work on that. To explain why I was hesitating before: Maximize portability Currently this code would work as pure Python code, even without all the rest of cuda.bindings. I believe that determining the CUDA driver version will either introduce a a dependency on cuda.bindings.driver, or if portability is (or becomes at some point) important, we'd need to reimplement a minimalistic version. |
We shouldn't need to determine the CUDA driver version, we just need the major version which we're already capturing in the We just need a map of CUDA major version --> soname per library I think? |
Oh! Thanks, I need to get my head around that. |
…ced by perplexity
There is a catch here, which is what the current
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…ual inspection of cuda_paths.py. Minor additional edits.
Question, scoped to the Currently I'm looking through If we're now targeting, e.g., "just 11" or "12 or 11", do we want to fully traverse |
2 tasks
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(btw I merged #593) |
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/ok to test aeaf4f0 |
We want to find the library as if the extension module was normally built and linked against the library we're finding, which means that it would only search for the |
leofang
reviewed
May 1, 2025
| @@ -0,0 +1,78 @@ | |||
| #!/usr/bin/env python3 | |||
| @@ -0,0 +1,88 @@ | |||
| #!/usr/bin/env python3 | |||
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Description
Major milestone for the work tracked under #451
This PR introduces only two public APIs:
cuda.bindings.path_finder.SUPPORTED_LIBNAMES(currently('nvJitLink', 'nvrtc', 'nvvm'))cuda.bindings.path_finder.load_nvidia_dynamic_library(libname: str) -> LoadedDLWith:
However, the implementations were actually thoroughly tested (under #558) for all
enumerated under
cuda.bindings._path_finder.supported_libs(note that this module is private).To make this PR easier to review, the changes to the
nvJitLink,nvrtc, andnvvmbindings are NOT included in this PR. Those changes were also already tested under #558. They will be merged intomainwith two follow-on PRs (one for thenvrtcbindings, one fornvJitLinkandnvvm).Thorough testing of all
SUPPORTED_LIBNAMES + PARTIALLY_SUPPORTED_LIBNAMESrequires changes to the GitHub Actions configs, to set up suitable CTK installations. This will also be handled separately in follow-on PRs.Suggested order for reviewing files:
cuda/bindings/_path_finder/supported_libs.pycuda/bindings/_path_finder/load_nvidia_dynamic_library.pycuda/bindings/_path_finder/load_dl_common.pycuda/bindings/_path_finder/load_dl_linux.pycuda/bindings/_path_finder/load_dl_windows.pytests/test_path_finder.pycuda/bindings/_path_finder/find_nvidia_dynamic_library.pyDiscussion points:
cuda/bindings/_path_finder/cuda_paths.py(the original file under numba-cuda does not have one)SUPPORTED_LIBNAMES + PARTIALLY_SUPPORTED_LIBNAMESas new CTK versions are released