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[CUDA][Reduce] Fix non-finite constants and reject sub-byte accumulators - #3189
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reduce_max/min build their -INFINITY/+INFINITY init via make_const, bypassing T.infinity()'s guard. e4m3/float4 have no inf encoding, so PrintConst printed it as the invalid literal `inff`. Clamp to the format's max-finite value instead, since (unlike e5m2 in tile-ai#2671) there's no bit pattern to bitcast to. Fixes tile-ai#2998
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| Check name | Status | Explanation | Resolution |
|---|---|---|---|
| Docstring Coverage |  |
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Check name Status Explanation Linked Issues check Issue #2998requires valid CUDA constants forfloat8_e4m3andfloat4, plus compilingreduce_maxandreduce_minfor those destinations.PrintConstnow usesCUDART_INF_FandCUDART_NAN_Ft…Out of Scope Changes check The FP4 shuffle, numerical extrema, and workspace changes support the FP4 reduction requirement in issue #2998. The E8M0 identity and conversion changes extend the same non-finite constant and reducti…Title check The title correctly identifies the CUDA reduction context and the fix for non-finite constants, which is a main change. However, the changes do not reject sub-byte accumulators; they add FP4 reduction… Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Full details: Linked Issues check
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Explanation
Issue
#2998requires valid CUDA constants forfloat8_e4m3andfloat4, plus compilingreduce_maxandreduce_minfor those destinations.PrintConstnow usesCUDART_INF_FandCUDART_NAN_Fthrough destination conversion constructors. E4M3 uses a representable finite reduction identity. FP4 shuffles use raw bits, extrema compare numerical values, and workspace allocation reserves sufficient scalar storage. Focused reduction, conversion, cross-warp, and code-generation tests cover these paths. The change does not alter the statedfloat8_e5m2scope.
Full details: Out of Scope Changes check
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Explanation
The FP4 shuffle, numerical extrema, and workspace changes support the FP4 reduction requirement in issue
#2998. The E8M0 identity and conversion changes extend the same non-finite constant and reduction-initialization handling, with focused tests. No unrelated change is demonstrated.
Full details: Title check
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Explanation
The title correctly identifies the CUDA reduction context and the fix for non-finite constants, which is a main change. However, the changes do not reject sub-byte accumulators; they add FP4 reduction support and corrections.
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In `@src/cuda/codegen/codegen_cuda.cc`:
- Line 6137: Update PrintConst so float8_e8m0fnu constants use an E8M0-specific
maximum finite value and explicit negative non-finite conversion policy before
reaching MaxFiniteFp8Fp4; retain the existing FP4 fallback for supported E4M3
types.
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@LeiWang1999 @SiriusNEO could one of you take a look when you have a moment? Following @Hughshine's updated analysis on #2998, this now also fixes fp4 reduce:
Added fp4 max/min runtime tests and an e8m0 codegen test. Verified on sm_120. |
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Inline comments:
Review comments at @src/cuda/codegen/codegen_cuda.cc:
- Around line 6314-6315: Update the NaN handling around MaxFiniteFp8Fp4 to
preserve reserved NaN encodings for the supported CUDA E4M3 types and set the
E8M0 object’s raw __x value to 0xFF. Keep finite saturation for infinities, and
do not add encoding behavior for the unsupported float8_e4m3fnuz path.
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The FP4 runtime coverage needs a cross-warp case; I will address the workspace allocation issue described below.
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The current CI build failure comes from the newer main commit 741bf8b (#3226), which is included in the CI merge commit 4add86a. In src/transform/loop_vectorize.cc:1411, VectorizeLoop now receives const arith::Analyzer*, but VectorizeRewriter still requires arith::Analyzer*. The CUDA and Metal install jobs both fail on this conversion before tests run. This file is outside the current PR diff, so I am recording the build issue here.
I will merge the current main into this branch and give VectorizeRewriter a cloned analyzer. This keeps the caller's analysis context read-only, as intended by #3226. Validation will include reproducing the compile failure, rebuilding the wheel, and running the existing loop-analysis/vectorization and focused FP8/FP4 coverage.
Merge main through 741bf8b. Clone the read-only analyzer before constructing VectorizeRewriter so the updated VectorizeLoop signature compiles while preserving the caller analysis context.
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The build failure recorded in #3189 (review) is fixed in a1d1805, which is now on this PR branch.
The commit merges main through 741bf8b and gives VectorizeRewriter a cloned analyzer. I reproduced the original const-pointer compilation error after the main sync, then verified the fix with a native GCC build, a Clang compilation check, and an isolated wheel build and install from an empty build directory. Formatting passed.
Validation: 139 existing loop-analysis/vectorization tests passed. Another 24 tests passed against the installed wheel: 22 focused FP8/FP4 cases plus the nested-serial-bounds and nonzero-loop-minimum regressions. The test entry point verified that TileLang and both TVM native libraries loaded from the installed wheel. New CI run: https://github.com/tile-ai/tilelang/actions/runs/39776527794; checks are pending.
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Thanks for the updates @LeiWang1999 . For any further changes or fixes, please feel free to leave review comments as well. I'd be more than happy to address them. |
LeiWang1999
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Thanks for the original fix. I've pushed some improvements and narrowed the supported scope, most recently in a3a030c. The main design decision is that FP4 accumulation is an artificial requirement for this PR: we have no concrete workload that justifies maintaining it. FP4 input data can be converted exactly to FP16/FP32 for reduction, with the final result converted back to FP4 if needed. In the current API, the output/reducer dtype also determines accumulation precision and the type passed through the collective. Choosing FP4 therefore brings additional shuffle, comparison, and packed-workspace requirements; for sums, it also introduces severe rounding and range limitations. Even though FP4 max/min is mathematically possible, that alone does not justify maintaining a separate FP4 collective path. The resulting changes and constraints are:
Validation on B300 with CUDA 13.1: native rebuild and formatting passed; all 29 focused tests passed, including the rejection diagnostics and FP4 inputs with FP16/FP32 accumulation. The complete reduction, reducer v2, and bitwise-reduction test files had 233 passes and the same 3 pre-existing batch-validation failures. |
T.reduce_maxandT.reduce_minwith FP8 E4M3 output emitted invalidinffliterals. Non-finite E4M3, E8M0, and FP4 constants now pass validCUDART_INF_F/CUDART_NAN_Fexpressions to the existing conversion constructors, preserving their saturation and NaN behavior.Regular and deferred reductions now reject sub-byte numeric accumulator dtypes such as FP4 and int4 with an early diagnostic. Use FP16/FP32 or a wider integer output/reducer buffer; FP4 inputs with FP16/FP32 accumulation remain supported. Boolean reductions are exempt. The FP4 shuffle/comparison overloads and workspace-sizing special case are removed. E8M0 reduction support remains outside this change.
Fixes #2998
Validation on B300 with CUDA 13.1:
sumfloat16/bfloat16 with batch=4 > N=2, andminfloat16 with batch=8 > N=4.target="cuda". The accumulator policy is unchanged.