Simplify canonicalizeReduction and generalize it for multi-dimensional sharding

[wujingyue]

More thoroughly tested by #5806

[wujingyue]

!test

[github-actions]

Review updated until commit fafcea9

Description

• Simplify canonicalizeReduction by removing internal helper functions mergeReduction and mergeNonReduction

• Generalize canonicalizeReduction to support multi-dimensional sharding with better parallel type ordering

• Update rankOfParallelType to handle DIDz, DIDy, DIDx ordering for multi-dimensional sharding

• Replace specific merge logic with generic merge_all predicate-based approach

Changes walkthrough

Relevant files

Enhancement

utils.cpp Update parallel type ranking for multi-dimensional sharding csrc/multidevice/utils.cpp Update rankOfParallelType function to reorder parallel types as Stream=0, DIDz=1, DIDy=2, DIDx=3 Update comments to clarify ordering rationale for multi-dimensional sharding support +7/-4      utils.cpp Simplify and generalize canonicalizeReduction for multi-dimensional sharding csrc/scheduler/utils.cpp Remove mergeReduction and mergeNonReduction helper functions (internalized into canonicalizeReduction) Completely rewrite canonicalizeReduction with generic merge_all predicate-based approach Add support for multi-dimensional sharding through reorderParallelizedToFront integration Maintain same functional behavior while simplifying implementation +44/-72

PR Reviewer Guide

Here are some key observations to aid the review process:

🧪 No relevant tests

⚡ Recommended focus areas for review

Lambda capture safety The lambda function merge_all captures tv and num_ordered_dims by reference. While tv remains constant, num_ordered_dims is modified within the lambda's scope (lines 1200, 1206), which could lead to unexpected behavior. Consider capturing by value or restructuring to avoid potential issues. auto merge_all = [&](auto pred) -> int64_t { int64_t merged = -1; for (int64_t i : arange(num_ordered_dims, tv->nDims()) | std::views::reverse) { if (pred(tv->axis(i))) { if (merged >= 0) { tv->merge(i, merged); } merged = i; } } return merged; }; Missing test coverage This is a significant refactoring that changes the core logic of canonicalizeReduction. The PR should include comprehensive tests covering both the 2D and 3D scheduling cases, as well as edge cases with different parallel types and dimension configurations. std::pair<bool, bool> canonicalizeReduction( Fusion* fusion, TensorView* tv, bool schedule_3d) { NVF_ERROR(tv != nullptr); if (schedule_3d) { NVF_ERROR_EQ(merge_3d(tv), 3, "Tried 3D merge, but result is not 3D."); if (tv->axis(1)->isBroadcast()) { NVF_ERROR( !tv->axis(0)->isBroadcast(), "3D reduction with first two merged axes broadcast should be 2D " "reduction."); tv->reorder({{0, 1}}); } return {true, true}; } // Merge all reductions and all non-reductions, and reorder them to // [DIDs/Streams..., merged non-reduction, merged reduction]. Merging happens // incrementally -- first the parallel IterDomains, then the non-reductions, // then the reductions. // // At this stage of scheduling, they can only be DIDs or Streams. std::unordered_map<int64_t, int64_t> reorder_map = reorderParallelizedToFront(tv); auto num_ordered_dims = std::ssize(reorder_map); // This helper function merges not-yet-ordered IterDomains that satisfy the // predicate from back to front. Returns the index of the last merged // IterDomain, or -1 if no IterDomains were merged. auto merge_all = [&](auto pred) -> int64_t { int64_t merged = -1; for (int64_t i : arange(num_ordered_dims, tv->nDims()) | std::views::reverse) { if (pred(tv->axis(i))) { if (merged >= 0) { tv->merge(i, merged); } merged = i; } } return merged; }; int64_t merged_non_reduction = merge_all([](IterDomain* id) { return !id->isReduction(); }); if (merged_non_reduction >= 0) { tv->reorder({{merged_non_reduction, num_ordered_dims}}); num_ordered_dims++; } int64_t merged_reduction = merge_all([](IterDomain* id) { return true; }); if (merged_reduction >= 0) { tv->reorder({{merged_reduction, num_ordered_dims}}); num_ordered_dims++; } NVF_ERROR_EQ(num_ordered_dims, tv->nDims(), "Did not merge all IterDomains."); return {merged_non_reduction >= 0, merged_reduction >= 0}; }

Test failures

• (High, 95) CUDA driver version too old for runtime – nvFuser matmul/top-k test suites on dlcluster_h100

  Test Name	H100	Source
  Ampere/MmaTest.SingleTile/Ampere_16_8_16__bfloat	❌	Link
  ArgsortParameterizedWithBlockAndBatch.SharedMemoryRequirement/2048_1_1_0	❌	Link
  BlockSizeAndItemsPerThread/ArgSortComprehensiveTest.ComprehensiveValidation/BlockSize32_ItemsPerThread4	❌	Link
  ClusterReductionTest.SimpleFusionNotAllReduce/cluster_15_dtype_double	❌	Link
  ClusterReductionTest.SimpleFusionNotAllReduce/cluster_4_dtype_double	❌	Link
  CutlassExecutorTest.Nvfp4Matmul_BiasEpilogue	❌	Link
  General/HopperPlusMatmulSchedulerTest.FusedMultiplySum/KK_512_256_128_MmaMacro_m64_n128_k16_splitk_2	❌	Link
  General/HopperPlusMatmulSchedulerTest.FusedMultiplySum/MK_512_256_128_MmaMacro_m128_n128_k16_tma_store	❌	Link
  General/HopperPlusMatmulSchedulerTest.FusedMultiplySumBiasNeg/MN_512_256_128_MmaMacro_m64_n128_k16_tma_store_splitk_2	❌	Link
  GreedySchedulerTest.ScanNonLocalOutput	❌	Link
  ... with 85 more test failures omitted. Check internal logs.

• (High, 1) Outdated NVIDIA driver on dlcluster_h100 causes CUDA initialization failure in RNG tests

  Test Name	H100	Source
  RNGTest.BroadcastingRNG	❌	Link

[greptile-apps]

Greptile Overview

Greptile Summary

This PR successfully refactors canonicalizeReduction in scheduler/utils.cpp, eliminating approximately 65 lines of code by consolidating two separate helper functions (mergeReduction and mergeNonReduction) into a single elegant merge_all lambda function.

Key Improvements

1. Bug Fix - Stream Handling

The old code only checked isDeviceDim() when preserving parallel dimensions, which excludes Stream-parallelized IterDomains. This would cause Stream dimensions to be incorrectly merged with other non-reduction dimensions. The new code correctly uses reorderParallelizedToFront, which handles both DIDs and Streams as parallelized dimensions that should remain at the front.

2. Multi-Dimensional Sharding Support

The change in multidevice/utils.cpp establishes a clear ordering between DIDs: Stream(0) < DIDz(1) < DIDy(2) < DIDx(3). Previously, all DIDs had the same rank (1), which would not work correctly with multiple DIDs present simultaneously. This ordering enables proper support for multi-dimensional sharding scenarios.

3. Code Simplification

The refactored implementation introduces a reusable merge_all lambda that accepts a predicate to selectively merge IterDomains, eliminating code duplication between the old mergeReduction and mergeNonReduction functions.

Implementation Details

The new canonicalizeReduction works in three phases:

1. Reorder parallel dimensions to front: Uses reorderParallelizedToFront to move all parallelized IterDomains (Stream, DIDs) to the beginning in rank order
2. Merge non-reduction dimensions: Merges all non-reduction IterDomains that haven't been ordered yet
3. Merge reduction dimensions: Merges all remaining IterDomains (reductions)

The final layout is: [DIDs/Streams..., merged non-reduction, merged reduction]

Edge Cases Verified

The implementation correctly handles:

• Empty tensors (0 dimensions)
• Tensors with only reductions or only iterations
• Multiple parallel dimensions of different types
• Broadcast dimensions (treated as non-reductions)
• Parallel dimensions that are also reductions

Confidence Score: 5/5

• This PR is safe to merge with high confidence - it fixes a real bug while simplifying the codebase.
• Score of 5 reflects a well-executed refactoring that improves code quality while fixing a Stream handling bug. The logic has been thoroughly analyzed across multiple edge cases, the implementation is elegant and maintainable, and the changes are backward compatible with existing tests. The new ordering of DIDs properly supports multi-dimensional sharding as intended.
• No files require special attention - both changes are well-implemented and correct.

Important Files Changed

File Analysis

Filename	Score	Overview
csrc/scheduler/utils.cpp	5/5	Refactored canonicalizeReduction to fix Stream handling bug and support multi-dimensional sharding. The new implementation is cleaner and more maintainable.
csrc/multidevice/utils.cpp	5/5	Updated rankOfParallelType to establish clear ordering between DIDs (DIDz < DIDy < DIDx) to support multi-dimensional sharding properly.

Sequence Diagram

sequenceDiagram
    participant Caller
    participant canonicalizeReduction
    participant reorderParallelizedToFront
    participant merge_all
    participant TensorView

    Caller->>canonicalizeReduction: canonicalizeReduction(fusion, tv, schedule_3d)
    
    alt schedule_3d == true
        canonicalizeReduction->>TensorView: merge_3d(tv)
        TensorView-->>canonicalizeReduction: 3D merged result
        canonicalizeReduction->>TensorView: handle broadcast reordering
        canonicalizeReduction-->>Caller: {true, true}
    else schedule_3d == false
        canonicalizeReduction->>reorderParallelizedToFront: reorderParallelizedToFront(tv)
        Note over reorderParallelizedToFront: Orders dims by rank:<br/>Stream(0) < DIDz(1) < DIDy(2) < DIDx(3)
        reorderParallelizedToFront->>TensorView: reorder parallel dims to front
        reorderParallelizedToFront-->>canonicalizeReduction: reorder_map (num_ordered_dims)
        
        Note over canonicalizeReduction: First merge_all: non-reductions
        canonicalizeReduction->>merge_all: merge_all(pred: !isReduction)
        loop For each unordered dim (reverse)
            merge_all->>TensorView: Check if !isReduction()
            alt Satisfies predicate
                merge_all->>TensorView: merge(i, merged)
            end
        end
        merge_all-->>canonicalizeReduction: merged_non_reduction index
        
        alt merged_non_reduction >= 0
            canonicalizeReduction->>TensorView: reorder to num_ordered_dims
            Note over canonicalizeReduction: Increment num_ordered_dims
        end
        
        Note over canonicalizeReduction: Second merge_all: all remaining
        canonicalizeReduction->>merge_all: merge_all(pred: always true)
        loop For each remaining dim (reverse)
            merge_all->>TensorView: merge(i, merged)
        end
        merge_all-->>canonicalizeReduction: merged_reduction index
        
        alt merged_reduction >= 0
            canonicalizeReduction->>TensorView: reorder to num_ordered_dims
            Note over canonicalizeReduction: Increment num_ordered_dims
        end
        
        canonicalizeReduction->>canonicalizeReduction: Assert num_ordered_dims == nDims()
        canonicalizeReduction-->>Caller: {has_iter, has_reduction}
    end

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[wujingyue]

!test

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[liqiangxl]

LGTM.

[liqiangxl]

Please fix failed tests.

[wujingyue]

!test

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[wujingyue]

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[wujingyue]

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[wujingyue]

!test

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[wujingyue]

There’s temporal coupling between mergeReduction and mergeNonReduction. For example:

1. mergeReduction must be called before mergeNonReduction.
2. mergeNonReduction needs to reorder DIDs, while mergeReduction does not.

This kind of coupling is much easier to reason about if both steps are inlined into a single function.

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[wujingyue]

!test

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[liqiangxl]

LGTM.