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Remove RMM dependency and prepare for RAFT removal, Part 1 - #219
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📝 Summary
Merge Risk: 🟠 High · up to
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| Check name | Status | Explanation | Resolution |
|---|---|---|---|
| Docstring Coverage |  |
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:… | Write docstrings for the functions missing them to satisfy the coverage threshold. |
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Check name Status Explanation Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request. Title check The title clearly summarizes the main changes: removing RMM usage and preparing nvForest for RAFT removal. Description check The description directly explains the RMM and RAFT changes, CUDA stream migration, Python compatibility behavior, test updates, and removed CMake integration.
Full details: Docstring Coverage
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Explanation
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.)
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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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📒 Files selected for processing (29)
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
💤 Files with no reviewable changes (4)
- cpp/include/nvforest/treelite_importer.hpp
- python/nvforest/nvforest/detail/handle.pxd
- cpp/include/nvforest/handle.hpp
- cpp/cmake/thirdparty/get_rmm.cmake
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Currently blocked by #221 |
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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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Actionable comments posted: 3
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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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README.mdcpp/include/nvforest/README.mddocs/source/getting_started.rst
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csadorf
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I’d like to address the remaining correctness and test-coverage gaps before merging, and agree on the stream-pool performance tradeoff. See the inline comments.
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@csadorf Can I get another round of review? |
# Conflicts: # conda/environments/all_cuda-129_arch-aarch64.yaml # conda/environments/all_cuda-129_arch-x86_64.yaml # conda/environments/all_cuda-133_arch-aarch64.yaml # conda/environments/all_cuda-133_arch-x86_64.yaml # python/nvforest/pyproject.toml
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Approving. Thank you, @chyunsu3, for working through the review feedback. With your approval, I pushed additional fixes to preserve buffer device metadata and stream ordering, propagate the prediction width in the pool overload, and reject a zero pool size. Both overloads now take plain std::size_t. I also expanded the partitioned inference tests and fixed the wrong-device-stream test setup.
Local and multi-GPU validation passed, including sanitizer checks for inference and wrong-device-stream rejection. The restored four-stream path also recovers the originally reported benchmark regression.
Please hold off on merging until next week, when we can coordinate the landing with NVIDIA/cuml#8601. Merging these independently could disrupt cuML CI. I’ve applied DO NOT MERGE to prevent an accidental merge in the meantime.
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/merge |
Updates cuML’s nvForest integration to use CUDA streams following rapidsai/nvforest#219. - Passes streams directly when exporting RandomForest and IsolationForest models to nvForest. - Updates C++ RF tests to reuse the same stream for model import and inference. - Forwards cuML’s static Treelite setting when building nvForest from source. Companion to rapidsai/nvforest#219. Authors: - Philip Hyunsu Cho (https://github.com/chyunsu3) - Simon Adorf (https://github.com/csadorf) Approvers: - Simon Adorf (https://github.com/csadorf) URL: #8601
Companion PR: NVIDIA/cuml#8601. To minimize the disruption in the cuML CI, we should merge this PR together with NVIDIA/cuml#8601 .
raft::handle_tandnvforest::handle_tand use CUDA streams directly.predict()overloads that takes in either a single CUDA stream or a pool of multiple CUDA streams.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.History