Add RAFT option to make_blobs

[NIne-WIngEd]

Adds an opt-in RAFT path to cuml.datasets.make_blobs.

The existing CuPy path stays the default.

Added focused tests for seeded reproducibility and C/F layouts.

Fixes #7363.

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📥 Commits

Reviewing files that changed from the base of the PR and between 0d3a802 and 8bcdb37.

📒 Files selected for processing (4)

• python/cuml/cuml/datasets/CMakeLists.txt
• python/cuml/cuml/datasets/_blobs.pyx
• python/cuml/cuml/datasets/blobs.py
• python/cuml/tests/test_make_blobs.py

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📝 Walkthrough

Summary by CodeRabbit

• New Features
  • Added an optional RAFT-backed execution path for make_blobs dataset generation.
  • Supports generated or explicitly provided centers, configurable dtypes and memory layouts, reproducible random states, and optional center output.
  • Added GPU-backed dataset generation with validation for supported inputs and clear errors for unsupported configurations.
• Bug Fixes
  • Improved consistency and parity between supported dataset-generation paths.

Walkthrough

Adds an optional RAFT-backed make_blobs path. The change includes a Cython GPU bridge, input validation, native dispatch, documentation, and tests for parity, reproducibility, layouts, dtypes, centers, and invalid inputs.

Changes

RAFT-backed make_blobs

Layer / File(s)	Summary
GPU bridge and build integration python/cuml/cuml/datasets/CMakeLists.txt, python/cuml/cuml/datasets/_blobs.pyx	Adds the Cython module to the build. The wrapper allocates CuPy outputs, dispatches float32 and float64 native overloads, synchronizes the handle, and returns generated data and labels.
RAFT API path and validation python/cuml/cuml/datasets/blobs.py, python/cuml/tests/test_make_blobs.py	Adds use_raft=False, validates RAFT-supported arguments, invokes native generation, documents restrictions, and tests parity, reproducibility, centers, layouts, dtypes, and validation errors.

Estimated code review effort: 3 (Moderate) | ~25 minutes

Merge Risk: ⚪ Minimal · up to 8bcdb

The PR adds an opt-in RAFT path while preserving the existing default behavior; no actionable merge-blocking risk remains after normal checks and review.

Possibly related PRs

• NVIDIA/cuml#8481: Implements the same RAFT-backed make_blobs path in the same dataset files and tests.

Suggested reviewers: dantegd

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and concisely identifies the main change: adding a RAFT option to make_blobs.
Description check	✅ Passed	The description accurately covers the opt-in RAFT path, default CuPy behavior, tests, and linked issue.
Linked Issues check	✅ Passed	The changes implement the RAFT option, add a Python wrapper, and preserve the existing CuPy path as required by issue #7363.
Out of Scope Changes check	✅ Passed	The build configuration, implementation, documentation, tests, and copyright update support the stated objectives without unrelated code changes.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.

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[csadorf]

Thanks a lot for the contribution! I have a few change requests and questions before we can move forward.

[csadorf]

⚠️ Whether we use RAFT or CuPy for the actual implementation is an implementation detail and should not be exposed within the Python API. The routing should be purely internal. I ran some preliminary benchmarks and it seems that the RAFT implementation is generally much faster.

We should maintain the CuPy implementation only to overcome any present limitations or if there is a non-trivial regime where CuPy is generally faster (which does not seem to be the case).

[csadorf]

The native API already accepts per-cluster standard deviations through the cluster_std device pointer. Could we pass an array here instead of restricting the RAFT path to a scalar cluster_std? That would preserve the existing Python API and eliminate one fallback case.

[csadorf]

Could we preserve return_centers=True by generating the centers in the Python compatibility layer and passing them explicitly to RAFT? That would allow generated-center calls to use RAFT without narrowing the existing API.

[csadorf]

To be clear, we should only do that for this specific condition.

[csadorf]

make_blobs is decorated with mlfunc, so its CuPy operations run on Stream.ptds, and the default RAFT handle also uses cudaStreamPerThread. Why do we need to sync here?

[csadorf]

This in-line comment seems to be at the wrong spot.