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WalkthroughThe change replaces RAFT handle-based execution with caller-supplied CUDA streams. C++ inference, GPU buffer ownership, Python bindings, compatibility handling, build configuration, dependencies, documentation, and stream validation tests are updated. ChangesCUDA stream migration
Priority: ➖ Normal Estimated code review effort: 4 (Complex) | ~45 minutes Suggested reviewers: Merge Risk: 🟠 High · up to Valid multi-GPU inference and cross-memory per-tree or leaf-ID inference can fail or access incorrectly sized memory, and the documented example teaches an invalid stream lifetime. These issues should be resolved before merge. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 23.08% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 39 functions across 13 files. (2 skipped: 2 unsupported.) ✨ Finishing Touches🧪 Generate unit tests (beta)
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Actionable comments posted: 5
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Inline comments:
In `@cpp/include/nvforest/detail/owning_buffer/gpu.hpp`:
- Line 44: Select device_id with device_setter before constructing
cuda::stream_ref in owning_buffer and inference::infer; update both
cpp/include/nvforest/detail/owning_buffer/gpu.hpp:44-44 and
cpp/include/nvforest/detail/infer/gpu.cuh:117-117. Add a regression test
covering cuda_stream{} on a non-current GPU, ensuring stream wrapping and kernel
launch use the requested device.
In `@cpp/include/nvforest/forest_model.hpp`:
- Around line 165-168: Update the intermediate buffer initialization in the
inference path to size buffer_out using the caller-provided output.size()
instead of row_count * num_outputs(). Preserve the existing memory type, device,
stream, and output wrapping behavior while ensuring per_tree and leaf_id
predictions have sufficient capacity.
In `@python/nvforest/nvforest/__init__.py`:
- Line 27: Restore the public Handle export in the nvforest package initializer
and mark it as deprecated while loaders continue accepting handle. Preserve
compatibility for existing from nvforest import Handle callers until the
documented removal release, using the project’s established deprecation
mechanism.
In `@python/nvforest/nvforest/detail/forest_inference.pyx`:
- Around line 304-305: Update the CPU branch near the device assertion to
explicitly set stream to None after confirming device == "cpu", ensuring
supplied streams are discarded before ForestInference_impl.__cinit__ while
preserving CUDA stream handling.
In `@python/nvforest/nvforest/detail/raft_stream.pyx`:
- Line 14: Update _get_stream_from_raft_handle so the CUDA stream handle
returned by handle.c_obj.get_stream() is stored in uintptr_t rather than int,
preserving the pointer-sized value before passing it to Stream.from_handle().
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conda/environments/all_cuda-129_arch-aarch64.yamlconda/environments/all_cuda-129_arch-x86_64.yamlconda/environments/all_cuda-133_arch-aarch64.yamlconda/environments/all_cuda-133_arch-x86_64.yamlcpp/CMakeLists.txtcpp/cmake/thirdparty/get_rmm.cmakecpp/include/nvforest/detail/device_id/gpu.hppcpp/include/nvforest/detail/device_setter/gpu.hppcpp/include/nvforest/detail/infer/gpu.cuhcpp/include/nvforest/detail/owning_buffer/gpu.hppcpp/include/nvforest/forest_model.hppcpp/include/nvforest/handle.hppcpp/include/nvforest/treelite_importer.hppcpp/src/detail/device_buffer.cucpp/tests/CMakeLists.txtcpp/tests/invalid_stream.cucpp/tests/treelite_importer.cppdependencies.yamlpython/nvforest/CMakeLists.txtpython/nvforest/nvforest/__init__.pypython/nvforest/nvforest/_factory.pypython/nvforest/nvforest/_forest_inference.pypython/nvforest/nvforest/_typing.pypython/nvforest/nvforest/detail/cuda_stream.pxdpython/nvforest/nvforest/detail/forest_inference.pyxpython/nvforest/nvforest/detail/handle.pxdpython/nvforest/nvforest/detail/raft_stream.pyxpython/nvforest/pyproject.tomlpython/nvforest/tests/test_nvforest.py
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- cpp/include/nvforest/treelite_importer.hpp
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- cpp/include/nvforest/handle.hpp
- cpp/cmake/thirdparty/get_rmm.cmake
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Currently blocked by #221 |
csadorf
requested changes
Sep 10, 2026
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After reviewing this again, I have some concerns with the changes in this PR; see the inline comments. I think we should address those and keep this targeted for 26.12, rather than cherry-picking it into 26.10.
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Inline comments:
In `@cpp/include/nvforest/README.md`:
- Line 101: Update the example’s stream handling so prediction reuses the stream
created during model import instead of initializing a null/default stream; also
remove or rename the duplicate stream declaration so combined examples compile
without redeclaration errors.
In `@docs/source/getting_started.rst`:
- Line 256: Update the CUDA stream creation in the getting-started example to
use the same device_id value passed to import_from_treelite_model, ensuring the
stream and model are bound to the same device before fm.predict.
- Line 286: Adjust the scope around the nvforest::forest_model instance and
inference work so fm is fully destroyed before cudaStreamDestroy(stream)
executes; keep stream alive through all model cleanup and destroy it only
afterward.
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Re-submitting #195 (which was previously reverted to unblock the cuML CI).
#195 originally targeted 26.10, whereas this PR targets 26.12. Pending the team discussion to decide whether to cherry-pick this for the 26.10 release.
Extracted from #193
raft::handle_tandnvforest::handle_tand use CUDA streams directly.cuda::bufferinstead ofrmm::device_buffer. Sincecuda::bufferrequires NVCC to build, we need to use PIMPL with type erasure.handleparameter is now deprecated and its use will trigger aFutureWarning. Users are directed to usestreaminstead.cpp/cmake/thirdparty/get_rmm.cmake. For now, we retaincpp/cmake/thirdparty/get_raft.cmake, since we need to wait one release cycle until we can remove the deprecatedhandleparameter.After this PR, #193 will update
dependencies.yamlto remove the RMM dependency and update the docs.