[fusheng-ji]

What problem are you trying to solve?

Part of #2069.

bsr_mv requires the matrix and vectors to have the same scalar type, forcing callers to allocate a converted matrix when matrix storage precision differs from vector arithmetic precision. Allow these types to differ while computing in the vector scalar type.

This proposal comes from an independent correctness and performance investigation of #2069. No downstream production deployment or maintainer assignment is claimed.

What does this change?

Support different floating-point matrix and vector scalar types in the existing scalar, CUDA tiled, and transpose paths. Convert each coefficient to the vector scalar type before multiplication; products, accumulation, and scaling use that type. Both wider and narrower vector precision are supported. Mixed types involving integers are rejected before output allocation or modification; existing same-type behavior is preserved. Input and explicit output vectors must have matching scalar types, and an omitted output uses the input scalar type and the matrix's output block shape.

import warp as wp
from warp.sparse import bsr_identity, bsr_mv

A = bsr_identity(4, block_type=wp.float32)
x = wp.ones(4, dtype=wp.float64, device=A.device)
y = bsr_mv(A, x)  # FP64 output; no FP64 matrix copy.

The same rules cover BSR blocks and scaled matrix expressions. Arithmetic cannot recover precision lost when constructing the stored matrix or converting its coefficients to the vector scalar type. Solver dtype inference and autodiff support are outside this change. Row packing is a separate contribution.

Existing pull requests

• I searched open pull requests for duplicate or overlapping work.
• Related pull requests: sparse: make nnz_sync ordering sound by construction after graph replay [GH-1792] #1791 changes sparse nonzero-count synchronization; Fix assorted small issues across tiles, geometry, CUDA graphs, and Python utilities #1975 includes BSR transpose and tile fixes. Neither implements mixed-precision bsr_mv. Add optional row-packed CUDA BSR products (GH-2069) #2073 adds optional row packing and depends on this contribution.

The original overlap search was performed on 2026-10-10.

Validation

• Regression validation: Feature extension. Tests cover all nine matrix/vector combinations of float16, float32, and float64, plus all seven combinations involving bfloat16. Exact arithmetic diagnostics verify conversion before multiplication and accumulation in vector precision. New regressions verify that floating/integer and differing integer combinations are rejected without modifying provided or aliased arrays, while matching integer products remain supported.
• NVIDIA GPU: CPU and two NVIDIA B200 GPUs on Ubuntu 24.04, CUDA Toolkit 13.0, driver 580.126.20, Python 3.12, Warp 1.19.0.dev1.
• Other validation: All 144 sparse tests, including 17 mixed-precision tests, pass across CPU and both GPUs on the current revision. The prior revision at 51fddf96 also passed 129 linear-solver tests. Native libraries are unchanged from the fresh standard build at that revision; pre-commit and the documentation build with warnings treated as errors pass.

Coverage includes scalar and block matrices, compact and padded topology, transpose accumulation, forced CUDA tiling, coefficient downcasts, scaling, output allocation, aliasing, strided vectors, mismatched input/output types, and CUDA graph capture. CPU tests cover automatic/scalar and transpose paths; forced tiling is validated on CUDA.

Checklist

• I have read and followed the Contributing Guidelines.
• This pull request does not bundle unrelated changes unless a maintainer agreed to the broader scope.
• My branch is up to date with main and contains one commit, unless a maintainer agreed to keep the change as multiple commits.
• I included any required changelog fragments and used a + prefix only for changes that do not require a GitHub issue.

The branch includes the original implementation, the merge of upstream main at 0382a97e, and focused review follow-ups. The implementation and review follow-ups include DCO sign-off; the changelog fragment is changelog/2069.added.md.

Summary by CodeRabbit

• New Features

  • Added mixed-precision support for sparse BSR matrix-vector multiplication with floating-point matrix and vector types.
  • Supports transposed products, scaling, automatic output allocation, vector views, and bfloat16 where supported.
  • Computation and outputs use the vector’s scalar type, while matrix storage precision remains unchanged.

• Documentation

  • Documented mixed-precision behavior, type requirements, and precision considerations.

• Tests

  • Added coverage for supported data types, layouts, operations, edge cases, aliasing, and CUDA graph capture.

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• asv/benchmarks/sparse/matrix_vector.py

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Walkthrough

bsr_mv now supports mixed floating-point matrix and vector scalar types. The change updates validation, output allocation, scaling, and kernel coefficient conversion. It adds tests, documentation, a changelog entry, and a CUDA graph benchmark.

Changes: Mixed-precision BSR matrix-vector multiplication

Layer / File(s)	Summary
Mixed-precision API and computation warp/_src/sparse.py, docs/domain_modules/sparse.rst, changelog/2069.added.md	bsr_mv validates vector and output types, uses the vector type for allocation and arithmetic, and casts matrix coefficients before multiplication. The documentation and changelog describe the supported mixed-precision behavior.
Mixed-precision validation warp/tests/test_sparse.py	Tests cover floating-point and nonfloating types, transpose modes, layouts, arithmetic, edge cases, aliasing, and CUDA graph capture. Registration applies device and architecture conditions to selected tests.
CUDA graph benchmark asv/benchmarks/sparse/matrix_vector.py	The benchmark captures bsr_mv in a CUDA graph and measures graph replay across matrix dtype, blocks per row, tile size, and transpose mode.

Priority: Normal

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

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant Caller
  participant bsr_mv
  participant BSRKernels
  Caller->>bsr_mv: Submit matrix and vector with differing scalar types
  bsr_mv->>bsr_mv: Validate types and configure vector-typed output
  bsr_mv->>BSRKernels: Pass vector-typed views and scaling factors
  BSRKernels->>BSRKernels: Cast matrix coefficients and accumulate
  BSRKernels-->>Caller: Write vector-typed output

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Suggested reviewers: gdaviet

Merge Risk: Minimal · previous review 27e5e

No actionable merge-blocking risk is established for this change.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 10.53% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 19 functions across 3 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.
Title check		The title clearly and concisely describes the main change: adding mixed-precision support for BSR products. The issue reference is relevant and does not obscure the primary change.
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.

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Confidence Score: 5/5

[Medium impact] The PR appears safe to merge; no blocking issues were found.

Summary

Adds mixed floating-point storage and vector types to bsr_mv.

• BSR matrix-vector products can use a different floating-point type from the stored matrix.

No actionable issues were found. The previous and current commits have different merge bases with the target, so this review covered the full PR diff. No previous review threads were supplied.

_{Reviews (7) · Last reviewed commit: "Merge branch 'main' into feat/bsr-mv-mix..." · Reviewed by Greptile}

[gdaviet]

Implementation looks good to me but it should not be limited to float32/float64. The actually implementation is already generic, it is just doc / preconditions that need to be relaxed

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Actionable comments posted: 1

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Inline comments:
Review comments at @warp/_src/sparse.py:
- Around line 5083-5091: Reject unsupported mixed scalar types in the bsr_mv
path before combining scaling factors: import float_types from warp._src.types
and, inside the scalar_type != A.scalar_type branch, raise ValueError unless
both A.scalar_type and scalar_type are floating-point types. Preserve the
existing mixed-precision scaling behavior for supported float combinations.

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[fusheng-ji]

Implementation looks good to me but it should not be limited to float32/float64. The actually implementation is already generic, it is just doc / preconditions that need to be relaxed

Hi @gdaviet. Thanks for your review and time; agreed. I removed the FP32/FP64-specific restriction and updated the docstring, documentation, and changelog. The preconditions now allow all floating-point scalar combinations, including FP16 and BF16, while requiring matching x and y scalar types. Mixed types involving integers are rejected before modifying the output.
Tests cover widening and narrowing conversions, transpose, output allocation, and invalid mixed types. All 144 sparse tests pass on CPU and two B200 GPUs.

[gdaviet]

Thanks for the update; the implementation looks good. One request on the ASV benchmark: BsrMvMixedPrecision runs 54 parameter combinations, each in a fresh process with a cold kernel cache, so it adds significant compile time to every ASV run. Its setup also isn't decorated with @setup_once like the other sparse benchmarks, so the matrix construction and graph capture are redone before every sample. Could you add @setup_once (and drop the _setup indirection) and reduce the grid to something like [["float32", "float64"], [32], [0], [False, True]]?

[fusheng-ji]

Implementation looks good to me but it should not be limited to float32/float64. The actually implementation is already generic, it is just doc / preconditions that need to be relaxed

Thanks for the suggestion. Added @setup_once directly to setup, removed the _setup indirection, and reduced the parameter grid to [["float32", "float64"], [32], [0], [False, True]] (four combinations).

Verified all four combinations on GPU, including numerical correctness and reuse of the captured graph across repeated setup calls. Pre-commit checks also pass.