feat(cccl): integrate missing CCCL directories — python/, ci/, .agent/, docs/, test/
Sparse-checkout from NVIDIA/cccl main branch to complete cccl_upstream: Added: - python/cuda_cccl/ (226 files) — Python bindings for device-level algorithms Critical for muh toolchain: cuda.compute.reduce_into, scan, radix_sort, etc. Includes 204 .py files with full test coverage for all 27 algorithms - ci/ (163 files) — Build/test infrastructure build_cub.sh, test_cub.sh, build_and_test_targets.sh, matrix.yaml Directly maps to our [INFRA-CI] and [INFRA-BUILD] items - .agent/skills/ (7 files) — NVIDIA's own agent skills for CCCL cccl-style/SKILL.md, cccl-test/SKILL.md, sass-diff/SKILL.md - docs/ (491 files) — Official CCCL documentation CI references, CMake guides, Python compute docs, libcudacxx PTX docs - test/ (12 files) — Top-level integration tests (cuda_smoke, stdpar) - Root configs: .clang-format, .clang-tidy, CONTRIBUTING.md, pyproject.toml - CLAUDE.md symlink → AGENTS.md (NVIDIA's standard) cccl_upstream now mirrors full NVIDIA/cccl structure: Before: 42M (cub + thrust + libcudacxx + cudax + c + examples + benchmarks) After: 53M (+python +ci +docs +.agent +test +configs) This completes the CCCL base needed for: - [muh-bench] items: ci/util/build_and_test_targets.sh for targeted builds - [CCCL-verify] items: python/cuda_cccl/tests/ as reference implementations - [CCCL-test] items: ci/test_cub.sh, ci/test_thrust.sh - Agent workflow: .agent/skills/ for consistent style and test patterns
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muh-bot
169
cccl_upstream/python/cuda_cccl/tests/compute/conftest.py
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169
cccl_upstream/python/cuda_cccl/tests/compute/conftest.py
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import builtins
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from collections.abc import Generator
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import numpy as np
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import pytest
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from cuda.core import Device, Stream
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try:
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from cuda.compute._build_info import USING_V2
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except ImportError:
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USING_V2 = False
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check_ldl_stl_in_sass = False
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# Define a pytest fixture that returns random arrays with different dtypes
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@pytest.fixture(
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params=[
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np.int8,
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np.int16,
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np.int32,
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np.int64,
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np.uint8,
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np.uint16,
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np.uint32,
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np.uint64,
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np.float16,
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np.float32,
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np.float64,
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np.complex64,
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np.complex128,
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]
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)
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def input_array(request):
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dtype = np.dtype(request.param)
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sample_size = 1000
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# Generate random values based on the dtype
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if np.issubdtype(dtype, np.integer):
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is_unsigned = dtype.kind == "u"
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# For integer types, use np.random.randint for random integers
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if is_unsigned:
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low_inclusive, high_exclusive = 0, 8
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else:
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low_inclusive, high_exclusive = -5, 6
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array = np.random.randint(
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low=low_inclusive, high=high_exclusive, size=sample_size, dtype=dtype
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)
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elif np.issubdtype(dtype, np.floating):
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# For floating-point types, use np.random.random and cast to the required dtype
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array = np.random.random(sample_size).astype(dtype)
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elif np.issubdtype(dtype, np.complexfloating):
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# For complex types, generate random real and imaginary parts
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packed = np.random.random(2 * sample_size)
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real_part = packed[:sample_size]
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imag_part = packed[sample_size:]
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array = (real_part + 1j * imag_part).astype(dtype)
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return array
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# Define a pytest fixture that returns random floating-point arrays only
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@pytest.fixture(
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params=[
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np.float32,
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np.float64,
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]
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)
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def floating_array(request):
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dtype = np.dtype(request.param)
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sample_size = 1000
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# Generate random floating-point values
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array = np.random.random(sample_size).astype(dtype)
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return array
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@pytest.fixture(scope="function")
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def cuda_stream() -> Generator[Stream, None, None]:
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device = Device()
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device.set_current()
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stream = device.create_stream()
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try:
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yield stream
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finally:
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stream.close()
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@pytest.fixture(scope="function", autouse=True)
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def verify_sass(request):
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if request.node.get_closest_marker("no_verify_sass"):
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return
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if not check_ldl_stl_in_sass:
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return
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# Pull monkeypatch dynamically rather than as a fixture parameter so this
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# autouse fixture does not add monkeypatch to every test's static fixture
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# closure. pytest-run-parallel treats monkeypatch as thread-unsafe based on
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# that closure, so a parameter here would serialize the entire free-threaded
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# parallel sweep -- even though this fixture only patches on the opt-in
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# SASS-check path (check_ldl_stl_in_sass, off by default and in CI).
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monkeypatch = request.getfixturevalue("monkeypatch")
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import cuda.compute._cccl_interop
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monkeypatch.setattr(
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cuda.compute._cccl_interop,
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"_check_sass",
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True,
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)
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@pytest.fixture
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def raise_on_numba_import(monkeypatch):
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"""This fixture will raise if a test attempts to import numba"""
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real_import = builtins.__import__
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def guarded_import(name, *args, **kwargs):
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if name == "numba" or name.startswith("numba."):
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raise ModuleNotFoundError(
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"This test is marked 'no_numba' but attempted to import it"
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)
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return real_import(name, *args, **kwargs)
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monkeypatch.setattr(builtins, "__import__", guarded_import)
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def pytest_collection_modifyitems(config, items):
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"""Runs after pytest collects the tests. Makes a test marked no_numba fail
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if it imports numba, and skips a test marked serialization when running on
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the v2 (HostJIT) backend."""
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serialization_skip = pytest.mark.skip(
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reason="serialization not supported on v2 (HostJIT) backend"
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)
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# Tests marked no_numba must not import numba. We enforce that by attaching
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# the raise_on_numba_import fixture defined above to each one; it raises if
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# numba is imported.
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#
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# We skip attaching it during a real pytest-run-parallel sweep of more than
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# one thread: the fixture uses monkeypatch, which pytest-run-parallel
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# serializes as thread-unsafe, so attaching it to every no_numba test would
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# make the whole sweep run serially and defeat its purpose. A single-threaded
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# run has no sweep to protect, so we attach it and keep the check.
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#
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# config.getoption gives the --parallel-threads value as an int for the
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# default but a str when passed on the command line; normalize to str and
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# count the run as parallel only for an explicit number > 1:
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#
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# pytest ... -> 1 single-threaded, attach
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# pytest --parallel-threads=1 ... -> "1" single-threaded, attach
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# pytest --parallel-threads=8 ... -> "8" parallel, skip (CI sweep)
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# pytest --parallel-threads=auto ... -> "auto" single-threaded, attach
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#
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# "auto" counts as single-threaded on purpose: it can resolve to one CPU, so
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# keeping the check is safer than dropping it on a non-parallel run. isdigit
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# also stops int() from raising on the non-numeric "auto".
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parallel_threads = str(config.getoption("parallel_threads", 1))
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running_parallel = parallel_threads.isdigit() and int(parallel_threads) > 1
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for item in items:
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# no_numba: add raise_on_numba_import unless we skip it for the sweep
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if item.get_closest_marker("no_numba") and not running_parallel:
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if "raise_on_numba_import" not in item.fixturenames:
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item.fixturenames.append("raise_on_numba_import")
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# serialization is unsupported on v2 (HostJIT); skip those tests there
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if USING_V2 and item.get_closest_marker("serialization"):
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item.add_marker(serialization_skip)
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