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[CCCL] 瘦身 + 补全: 移除 cudax/python/libcudacxx-tests 冗余文件, 新增 c2h 测试助手 + cmake 构建系统 + 8 个 CUDA thrust examples

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变更摘要:
- 删除: cudax/ (783 files, 7.2M) — 实验性组件，竞赛不需要
- 删除: python/ (226 files, 2.0M) — Python 绑定，竞赛不需要
- 删除: libcudacxx/{test,benchmarks,codegen,cmake,share} (4432 files, 31M)
  保留: libcudacxx/include/ (1463 headers, cuda::std 编译依赖)
- 新增: c2h/ (27 files) — CUB Catch2 测试辅助头文件，编译 243 个测试必需
- 新增: cmake/ (29 files) — CCCL 原生 CMake 构建系统
- 新增: thrust/examples/cuda/ (7 files) + cpp_integration/ (1 file)
  async_reduce, custom_temporary_allocation, explicit_cuda_stream,
  global_device_vector, range_view, unwrap_pointer, wrap_pointer, device

结果: cccl_upstream 从 74M→35M (瘦身 53%), 核心内容 100% 保留:
  27/27 tuning headers, 78 benchmarks, 243 tests,
  60 thrust examples, 18 CUB examples, 全部编译头文件
This commit is contained in:
muh-bot
2026-08-03 12:39:26 +00:00
parent a2a5dd8f00
commit 24ef6a91b5
5439 changed files with 0 additions and 719516 deletions
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5
cccl_upstream/python/cuda_cccl/tests/_utils/__init__.py
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# Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. ALL RIGHTS RESERVED.
#
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
"""Shared test utilities for cuda-cccl."""

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cccl_upstream/python/cuda_cccl/tests/_utils/device_array.py
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@@ -1,252 +0,0 @@
# Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. ALL RIGHTS RESERVED.
#
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
from __future__ import annotations
import math
import operator
from collections.abc import Iterable
import numpy as np
from numpy.typing import DTypeLike
from cuda.core import Buffer, Device, Stream
def get_compute_capability() -> tuple[int, int]:
return Device().compute_capability
def _normalize_shape(shape: int | Iterable[int]) -> tuple[int, ...]:
try:
dimensions = (operator.index(shape),) # type: ignore[arg-type]
except TypeError:
dimensions = tuple(operator.index(dimension) for dimension in shape) # type: ignore[union-attr]
if any(dimension < 0 for dimension in dimensions):
raise ValueError("negative dimensions are not allowed")
return dimensions
def _contiguous_strides(
shape: tuple[int, ...], itemsize: int, order: str
) -> tuple[int, ...]:
if any(dimension == 0 for dimension in shape):
return (0,) * len(shape)
strides = [0] * len(shape)
stride = itemsize
if order == "C":
for index in range(len(shape) - 1, -1, -1):
strides[index] = stride
stride *= shape[index]
else:
for index, dimension in enumerate(shape):
strides[index] = stride
stride *= dimension
return tuple(strides)
def _resolve_device_and_stream(
device: Device | None, stream: Stream | None
) -> tuple[Device, Stream]:
if device is None:
device = stream.device if stream is not None else Device()
if stream is not None and stream.device.device_id != device.device_id:
raise ValueError("device and stream must refer to the same device")
device.set_current()
return device, device.default_stream if stream is None else stream
class DeviceArray:
"""A small, Buffer-backed device array for cuda-cccl tests.
The class intentionally provides only allocation, NumPy transfers, array
metadata, and the CUDA Array Interface. Array operations and initialization
belong on the NumPy host arrays used by the tests.
"""
def __init__(
self,
buffer: Buffer,
device: Device,
stream: Stream,
shape: tuple[int, ...],
dtype: np.dtype,
strides: tuple[int, ...],
order: str,
) -> None:
self._buffer = buffer
self._device = device
self._stream = stream
self._order = order
self._shape = shape
self._dtype = dtype
self._strides = strides
@classmethod
def empty(
cls,
shape: int | Iterable[int],
dtype: DTypeLike,
*,
order: str = "C",
device: Device | None = None,
stream: Stream | None = None,
) -> DeviceArray:
"""Allocate an uninitialized device array."""
shape = _normalize_shape(shape)
dtype = np.dtype(dtype)
order = order.upper()
if order not in ("C", "F"):
raise ValueError("order must be either 'C' or 'F'")
if dtype.itemsize == 0:
raise ValueError("zero-sized dtypes are not supported")
device, stream = _resolve_device_and_stream(device, stream)
buffer = device.allocate(math.prod(shape) * dtype.itemsize, stream=stream)
result = cls(
buffer,
device,
stream,
shape,
dtype,
_contiguous_strides(shape, dtype.itemsize, order),
order,
)
# Device allocation is stream ordered. Synchronizing makes an empty array
# safe to hand to a test that subsequently uses a different stream.
stream.sync()
return result
@classmethod
def from_numpy(
cls,
array: np.ndarray,
*,
device: Device | None = None,
stream: Stream | None = None,
) -> DeviceArray:
"""Allocate a device array and initialize it from a NumPy array."""
host_array = np.asarray(array)
if host_array.dtype.itemsize == 0:
raise ValueError("zero-sized dtypes are not supported")
if host_array.flags.c_contiguous:
order = "C"
elif host_array.flags.f_contiguous:
order = "F"
else:
host_array = np.ascontiguousarray(host_array)
order = "C"
device, stream = _resolve_device_and_stream(device, stream)
buffer = device.allocate(host_array.nbytes, stream=stream)
result = cls(
buffer,
device,
stream,
host_array.shape,
host_array.dtype,
host_array.strides,
order,
)
result._copy_from_host_array(host_array, stream)
stream.sync()
return result
@property
def nbytes(self) -> int:
return self._buffer.size
@property
def dtype(self) -> np.dtype:
return self._dtype
def __len__(self) -> int:
if not self._shape:
raise TypeError("len() of unsized object")
return self._shape[0]
@property
def __cuda_array_interface__(self) -> dict[str, object]:
interface: dict[str, object] = {
"data": (0 if self.nbytes == 0 else int(self._buffer.handle), False),
"shape": self._shape,
"strides": None if self._is_c_contiguous() else self._strides,
"typestr": self._dtype.str,
"version": 3,
}
if self._dtype.fields is not None:
interface["descr"] = self._dtype.descr
return interface
def _is_c_contiguous(self) -> bool:
return (
self._order == "C"
or self.nbytes == 0
or sum(dimension > 1 for dimension in self._shape) <= 1
)
@staticmethod
def _host_buffer(array: np.ndarray) -> Buffer:
# Buffer.from_handle does not own the host memory. `owner` ties the NumPy
# allocation to this temporary Buffer; the caller also retains the array
# and synchronizes the copy stream before returning.
return Buffer.from_handle(
ptr=int(array.ctypes.data), size=array.nbytes, owner=array
)
def _copy_stream(self, stream: Stream | None) -> Stream:
if stream is None:
# The allocation stream is not necessarily the last stream to have
# used the array. Synchronize the device when that stream is unknown.
self._device.sync()
return self._stream
if stream.device.device_id != self._device.device_id:
raise ValueError("copy stream must belong to the array's device")
return stream
def _copy_from_host_array(self, array: np.ndarray, stream: Stream) -> None:
self._buffer.copy_from(self._host_buffer(array), stream=stream)
def copy_from_host(
self, array: np.ndarray, *, stream: Stream | None = None
) -> None:
"""Replace the array's contents from a shape- and dtype-matched NumPy array."""
host_array = np.asarray(array)
if host_array.shape != self._shape:
raise ValueError(
f"source shape {host_array.shape} does not match {self._shape}"
)
if host_array.dtype != self._dtype:
raise TypeError(
f"source dtype {host_array.dtype} does not match {self._dtype}"
)
if self._order == "F":
host_array = np.asfortranarray(host_array)
else:
host_array = np.ascontiguousarray(host_array)
self._device.set_current()
stream = self._copy_stream(stream)
self._copy_from_host_array(host_array, stream)
stream.sync()
def copy_to_host(self, *, stream: Stream | None = None) -> np.ndarray:
"""Return an owning NumPy copy of the array."""
self._device.set_current()
stream = self._copy_stream(stream)
result = np.empty(self._shape, dtype=self._dtype, order=self._order)
self._buffer.copy_to(self._host_buffer(result), stream=stream)
stream.sync()
return result