Use cudaMemcpyBatchAsync for RMM copies by bdice · Pull Request #2511 · rapidsai/rmm

bdice · 2026-08-13T22:38:19Z

Description

Closes #2509.

Route RMM's C++ asynchronous copy paths through an internal helper that uses cudaMemcpyBatchAsync on CUDA 13+ non-default streams. It retains cudaMemcpyAsync for legacy default streams and older CUDART builds, and leaves zero-byte copies as no-ops.

Use cudaMemcpyFlagPreferOverlapWithCompute for copies of 128 KiB or less and cudaMemcpyFlagDefault for larger copies. The choice of 128 KiB was determined by benchmarking on several devices (GB300, GB10, RTX PRO A6000) and minimizing the time cost modeled with latency and bandwidth.

Checklist

I am familiar with the Contributing Guidelines.
New or existing tests cover these changes.
The documentation is up to date with these changes.

coderabbitai · 2026-08-13T22:42:40Z

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📝 Walkthrough

Summary by CodeRabbit

New Features
- Added asynchronous CUDA memory-copy support with optimized handling for newer CUDA versions.
- Improved asynchronous copying for device buffers and vectors while preserving stream-based behavior.
- Added reliable detection of default, legacy, and per-thread CUDA streams.
Bug Fixes
- Ensured zero-length asynchronous copies complete safely without unnecessary CUDA operations.
Tests
- Added coverage for resizing buffers and reading/writing vector elements on non-default CUDA streams.
- Added coverage for identifying CUDA stream types consistently.

Walkthrough

RMM adds CUDA stream detection and an asynchronous copy helper. CUDA 13+ non-default streams use cudaMemcpyBatchAsync; other supported configurations use cudaMemcpyAsync. Device buffer and vector operations now use the helper, with tests added.

Changes

CUDA asynchronous copy integration

Layer / File(s)	Summary
Default stream detection `cpp/include/rmm/detail/cuda_stream.hpp`, `cpp/src/cuda_stream_view.cpp`, `cpp/tests/cuda_stream_tests.cpp`	Adds shared default-stream detection and tests for legacy, default, per-thread, and ordinary streams.
Copy helper contract and implementation `cpp/include/rmm/detail/cuda_memcpy.hpp`, `cpp/src/cuda_memcpy.cpp`, `cpp/CMakeLists.txt`	Adds `rmm::detail::memcpy_async`. It handles zero-byte copies, CUDA 13+ batched copies on non-default streams, and the fallback path.
Device copy path migration and validation `cpp/include/rmm/device_uvector.hpp`, `cpp/src/device_buffer.cpp`, `cpp/tests/device_buffer_tests.cu`, `cpp/tests/device_uvector_tests.cpp`	Routes asynchronous vector and buffer copies through `memcpy_async`. Tests cover non-default stream transfers and buffer resizing.

Estimated code review effort: 3 (Moderate) | ~20 minutes

Merge Risk: 🟡 Moderate · up to 6f745

The new CUDA batch-copy path does not compile on affected CUDA 13.1+ builds because of an incompatible pointer type, and the special-stream tests may miss classification regressions by relying on the same helper being tested. The PR is not merge-ready until the build issue is fixed and the test oracle is made independent.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 6.67% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	The changes satisfy the coding objectives in [`#2509`], including helper routing, CUDA 13 batching, fallbacks, zero-byte handling, and stream detection.
Out of Scope Changes check	✅ Passed	The changes remain within [`#2509`]; stream detection utilities, call-site updates, tests, and copyright updates support the stated objectives.
Title check	✅ Passed	The title clearly summarizes the main change: using cudaMemcpyBatchAsync for RMM asynchronous copies.
Description check	✅ Passed	The description directly explains the copy helper, fallback behavior, flags, thresholds, and test coverage.

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Inline comments:
In `@cpp/src/cuda_memcpy.cpp`:
- Line 21: Update the cudaMemcpyBatchAsync call to use a const void* destination
pointer: declare void const* dst_ptr initialized from dst, then pass &dst_ptr
instead of &dst while preserving the existing source, count, attributes, and
stream arguments.

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cpp/CMakeLists.txt
cpp/include/rmm/detail/cuda_memcpy.hpp
cpp/include/rmm/device_uvector.hpp
cpp/src/cuda_memcpy.cpp
cpp/src/device_buffer.cpp
cpp/tests/device_buffer_tests.cu
cpp/tests/device_uvector_tests.cpp

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Inline comments:
In `@cpp/tests/cuda_stream_tests.cpp`:
- Around line 57-70: Update the IsDefaultStream test to assert explicit
classifications: cuda_stream_legacy must be default, cuda_stream_default must
depend on CUDA_API_PER_THREAD_DEFAULT_STREAM, and cuda_stream_per_thread must
never be default. Replace the current self-comparison expectations while
preserving the non-default assertion for the ordinary stream.

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cpp/include/rmm/detail/cuda_memcpy.hpp
cpp/include/rmm/detail/cuda_stream.hpp
cpp/src/cuda_memcpy.cpp
cpp/src/cuda_stream_view.cpp
cpp/tests/cuda_stream_tests.cpp

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cpp/src/cuda_memcpy.cpp

wence- · 2026-08-14T07:22:06Z

+  if (!is_default_stream(stream)) {
+    cudaMemcpyAttributes attrs{};
+    attrs.srcAccessOrder = cudaMemcpySrcAccessOrderStream;
+    attrs.flags          = cudaMemcpyFlagPreferOverlapWithCompute;


followup: per felipeblazing/cudf#4

we should consider a size-guarded flag choice

Good call. I did a study and decided to threshold at 128 KiB. NVIDIA/cudf#23674

Signed-off-by: Bradley Dice <bdice@bradleydice.com>

bdice · 2026-08-16T16:39:42Z

I'll wait to merge this until we settle on a threshold choice in the other cuDF/rapidsmpf PRs since those libraries already use cudaMemcpyBatchAsync.

## Description Fix asynchronous host-to-device copies whose host source could be destroyed or mutated before the copy completed. The CUDA 13 `cudaMemcpyBatchAsync` changes in rapidsai/rmm#2511 exposed these invalid lifetime assumptions as nondeterministic failures in pylibcudf, cudf-polars, and hybrid scan tests. Synchronize affected copies at the ownership boundary, and preserve backing storage for Python buffer slices until queued copies can consume them. Also recognize `cudaMemcpyBatchAsync` in the stream-usage checker. This does not introduce a new API or change source ownership semantics. This borrows some lifetime fixes from #23517 and #23561 that we observed were necessary on GB300 but haven't been merged upstream yet. ## Checklist - [x] I am familiar with the [Contributing Guidelines](https://github.com/NVIDIA/cudf/blob/HEAD/CONTRIBUTING.md). - [x] New or existing tests cover these changes. - [x] The documentation is up to date with these changes. --------- Co-authored-by: Nghia Truong <7416935+ttnghia@users.noreply.github.com>

While #2513 fixed all issues in rmm, we had another inflight PR (#2511) that used the old include. Found by downstream failures in rapids-cmake ( rapidsai/rapids-cmake#1079 )

…value device_scalar::value() performs its device-to-host read through rmm::detail::memcpy_async, which dispatches to cudaMemcpyBatchAsync on CUDA 13 (rapidsai/rmm#2511). max_active_nodes sizes the route buffers, so a stale read here overflows them later in block_copy, tripping the device-side assert in test_batch_solve_varying_sizes on CUDA 13.3. raft::copy uses cudaMemcpyAsync and is otherwise equivalent (both copy then synchronize). See NVIDIA#1756. Signed-off-by: Ramakrishna Prabhu <ramakrishnap@nvidia.com>

…#1753) Every CUDA 13 test job in the 2026-08-19 nightly ([run 32220613929](https://github.com/NVIDIA/cuopt/actions/runs/32220613929)) failed — 19 jobs across C++, Python, server and notebooks — while every CUDA 12 job passed. All of them share one error: ``` CUDA error at: .../rmm/device_uvector.hpp:220: cudaErrorStreamCaptureUnsupported operation not permitted when stream is capturing ``` ### Root cause [rapidsai/rmm#2511](rapidsai/rmm#2511) (merged 2026-08-18) made `rmm::detail::memcpy_async` dispatch to `cudaMemcpyBatchAsync` on CUDA 13 for non-default streams. **That API cannot be captured into a CUDA graph.** `rmm::device_scalar::set_value_async` routes through it, so any such write issued inside one of the routing local-search capture regions now fails. Confirmed with a standalone program (no RAPIDS), same stream and same capture, only the API differing: ``` cudaMemcpyAsync during capture -> 0 (cudaSuccess) cudaMemcpyBatchAsync during capture -> 900 (cudaErrorStreamCaptureUnsupported) ``` The failing call chain, from a gdb backtrace against nightly packages: ``` vrp_search.cu:672 find_kernel_graph.start_capture(stream) vrp_search.cu:673 vrp_move_candidates.reset(sol_handle) vrp_move_candidates.cuh:58 max_added_size.set_value_async(max_fragment_size, stream) -> device_scalar::set_value_async -> device_uvector::set_element_async (device_uvector.hpp:220) -> rmm::detail::memcpy_async -> cudaMemcpyBatchAsync ``` The upstream fix is tracked in [rapidsai/rmm#2518](rapidsai/rmm#2518). This PR makes cuOpt's capture regions independent of which memcpy RMM picks. ### Changes - `vrp_move_candidates.cuh` — the failing write now uses `raft::copy`, which is plain `cudaMemcpyAsync` and is capturable. `max_fragment_size` has static storage duration, so the graph may safely re-read it on each launch. - `cycle_finder.hpp`, `random_move_candidates.cuh` — writes whose value is zero now use a memset instead of a host-to-device copy. This needs no host source at all, so it sidesteps both the capture restriction and the source-lifetime requirement below. - `cuda_graph.cuh` — `cudaStreamBeginCapture`, `cudaStreamEndCapture`, `cudaGraphInstantiate`, `cudaGraphExecDestroy`, `cudaGraphDestroy` and `cudaGraphLaunch` return codes are now checked, and a null graph from an invalidated capture is rejected at the point it happens. Previously all of these were discarded, so the original failure surfaced as a cascade of downstream `cudaErrorStreamCaptureInvalidated` errors rather than one clear message. `cudaGraphExecUpdate` is still allowed to fail (that is a normal path) but its error is now consumed so it cannot leak into a later `cudaGetLastError()`. ### A note on host-source lifetime Worth recording, since it constrains how these sites may be written. A memcpy captured into a graph reads its **host** source at *launch* time, not at capture time: ``` captured 42, mutated host to 99 before launch -> device has 99 ``` These graphs are reused across iterations via `cudaGraphExecUpdate`, so any host source must outlive the graph — a stack local would dangle. That is why the zero-valued sites use a memset rather than `raft::copy`, and why the `raft::copy` site is safe (its source is a namespace-scope `constexpr`). ## Issue Fixes the CUDA 13 nightly failures in #1748. Upstream: rapidsai/rmm#2518. ## Verification status Please treat this as **not yet verified end-to-end** — hence draft. - `libcuopt` builds clean against `librmm 26.10.00a30` (i.e. post-#2511, the build that exhibits the bug). - The root cause, the call site, and the capture-legality of `raft::copy` are each independently confirmed (standalone reproducer, gdb backtrace, nightly-package reproduction). - **What is not confirmed is that this patch closes the failure.** My local GPU is sm_75, and `vrp_search.cu:671` returns early when the kernel's shared-memory requirement does not fit, so the C++ tests never enter the affected capture region on this hardware — verified with a breakpoint on `cudaStreamBeginCapture`, which is never reached. The Python path does reach it, but the local Python build is not yet working. CI on A100/H100/GB300 is what will actually exercise this. I will move it out of draft once the CUDA 13 jobs are green, or update it if they are not. Authors: - Ramakrishna Prabhu (https://github.com/ramakrishnap-nv) Approvers: - Trevor McKay (https://github.com/tmckayus) - Ishika Roy (https://github.com/Iroy30) - Akif ÇÖRDÜK (https://github.com/akifcorduk) URL: #1753

…23789) Split out from #23517. `DeviceBuffer.to_device(bytes, stream)` reads host memory asynchronously (rapidsai/rmm#2511). If the bytes object isn't kept alive past the copy, Python can free it before the GPU reads it. The PR keeps those bytes alive through the copy by making an explicit reference. This fixes the immediate problem but it not the end of this problem. Let's use rapidsai/rmm#2521 to discuss a more general solution or at minimum document the current behavior. I tested these changes in #23517 (which reliably reproduced the bug). And the failing tests fixed in this PR are now passing. Authors: - Matthew Murray (https://github.com/Matt711) Approvers: - Bradley Dice (https://github.com/bdice) URL: #23789

These three locations involve a RMM API that is async, this means the values need to be kept alive until the stream is sync'ed. The fix was created by AI. From looking at when the failures started appearing in the nightly CI it looked like github.com/rapidsai/rmm/pull/2511 was a candidate for the failures we see. Reading rapidsai/rmm#2521 makes me think these APIs were always used incorrectly by cuml, but the implementation on the inside was not taking full advantage of all the async'ness that it could. Hence we didn't see this until now. This is also what the AI came up with and it had a plausible explanation of why this explains the failures. For example in this snippet the value in `val` is changed before `set_value_async` has used it. ```c++ value_t val = std::numeric_limits<value_t>::max(); min_d.set_value_async(val, stream); // deferred read val = std::numeric_limits<value_t>::lowest(); // val overwritten before the copy runs max_d.set_value_async(val, stream); ``` The fix in `cd.cuh` makes sense as well. The fix in `algo.cuh` looks sensible, but I'd have to do a bit more thinking to be able to explain why/what it exactly does. I'm inclined to believe my friend AI on this though. AI also had to do quite a lot of trickery (for a novice like me) to reproduce this issue locally on a non GB300. Which makes some amount of sense given we don't see this for jobs that don't use GB300. I can share the snippet it came up with in order to reproduce this locally. Not sure it is that useful. Fixes part of #8510 Closes #8509 #8508 (I couldn't come up wit ha good title for this PR :( ) Authors: - Tim Head (https://github.com/betatim) Approvers: - Lawrence Mitchell (https://github.com/wence-) - Bradley Dice (https://github.com/bdice) - Jim Crist-Harif (https://github.com/jcrist) URL: #8518

Use cudaMemcpyBatchAsync for RMM copies

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bdice requested review from a team as code owners August 13, 2026 22:38

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Use stream_ref for asynchronous copies

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wence- approved these changes Aug 14, 2026

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Replace <cuda/stream_ref> includes with <cuda/stream> #2513

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Select batched memcpy flag by copy size

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Signed-off-by: Bradley Dice <bdice@bradleydice.com>

bdice self-assigned this Aug 16, 2026

bdice added non-breaking Non-breaking change improvement Improvement / enhancement to an existing function labels Aug 16, 2026

bdice added the DO NOT MERGE Hold off on merging; see PR for details label Aug 16, 2026

Use the CUDA attribute type for memcpy flags

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bdice removed the DO NOT MERGE Hold off on merging; see PR for details label Aug 18, 2026

bdice merged commit 8a8dcfe into rapidsai:main Aug 18, 2026
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bdice mentioned this pull request Aug 19, 2026

Fix host-source lifetime races NVIDIA/cudf#23718

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This was referenced Aug 19, 2026

[BUG] cudaMemcpyBatchAsync path from #2511 breaks CUDA graph capture on CUDA 13 (silently drops the copy) #2518

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fix: avoid uncapturable RMM copies inside routing CUDA graph captures NVIDIA/cuopt#1753

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robertmaynard mentioned this pull request Aug 20, 2026

Replace <cuda/stream_ref> includes with <cuda/stream> #2520

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This was referenced Aug 20, 2026

[BUG] test_batch_solve_varying_sizes: block_copy destination too small on CUDA 13.3.0 (blocks all PR wheel CI) NVIDIA/cuopt#1756

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fix: keep host sources alive for async route-view copies (#1756) NVIDIA/cuopt#1759

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Matt711 mentioned this pull request Aug 21, 2026

to_device()/copy_host_to_ptr() can read freed host memory on a non-default stream #2521

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bdice mentioned this pull request Aug 21, 2026

perf: prefer copy bandwidth over compute overlap for large batch memcpys felipeblazing/cudf#4

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Matt711 mentioned this pull request Aug 24, 2026

Fix RMM race conditions from deferred H2D copies in pylibcudf tests NVIDIA/cudf#23789

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This was referenced Aug 25, 2026

Fix incorrect async initialisations NVIDIA/cuml#8518

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device_scalar(value, stream) launders a temporary past the guard that set_value_async has #2526

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ramakrishnap-nv mentioned this pull request Aug 25, 2026

[BUG] All CUDA 13 test jobs fail: RMM allocation during CUDA graph capture (cudaErrorStreamCaptureUnsupported) NVIDIA/cuopt#1748

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Use cudaMemcpyBatchAsync for RMM copies - #2511

Use cudaMemcpyBatchAsync for RMM copies#2511
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