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Algorithm-level GPU test suite (matmul, attention, shared tiling) #72

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Algorithm-level GPU test suite (matmul, attention, shared tiling)#72
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enhancementNew feature or request

Description

@tetsuo-cpp
tetsuo-cpp
opened on Aug 9, 2026

Summary

Extend gpu_test/ beyond arithmetic/stack micro-ops with end-to-end numerical tests for real algorithms: naive matmul, shared-memory tiled matmul (when applicable), and scaled-dot-product attention.

Motivation

LIT covers translation and conversion thoroughly. gpu_test/test_kernels.py mostly checks +, DUP, simple stores, etc. Regressions in address spaces, barriers, float paths, or multi-param kernels can slip through until the GPT-2 demo is run by hand.

Algorithm tests also give a baseline for performance work (#10, #11, #12, stack flattening).

Proposed cases

TestValidates
Naive matmul (small MNK)multi-buffer params, GLOBAL-ID, loops
Tiled matmul + shared + BARRIERS@/S!, synchronization
Attention (tiny seq/head)f32 global, f64 shared, softmax, causal mask
Scalar + array mixheader + runner path
Reduced-width roundtripF32@/F32! correctness

Use numpy/torch reference implementations; assert allclose with documented tolerances.

Acceptance criteria

  • At least matmul + attention GPU tests green on Vast (or documented local CUDA)
  • Failures print expected vs actual diffs usefully
  • Tests marked @pytest.mark.gpu and skip cleanly without API key / GPU
  • Optional timing print under an env flag (not flaky asserts on perf)

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Algorithm-level GPU test suite (matmul, attention, shared tiling) · Issue #72 · tetsuo-cpp/warpforth · GitHub
    Skip to content
    Algorithm-level GPU test suite (matmul, attention, shared tiling) #72
    Closed
    Closed
    Algorithm-level GPU test suite (matmul, attention, shared tiling)#72
    Labels
    enhancementNew feature or request
    Description
    @tetsuo-cpp
    tetsuo-cpp
    opened on Aug 9, 2026
    Summary
    Extend gpu_test/ beyond arithmetic/stack micro-ops with end-to-end numerical tests for real algorithms: naive matmul, shared-memory tiled matmul (when applicable), and scaled-dot-product attention.
    Motivation
    LIT covers translation and conversion thoroughly. gpu_test/test_kernels.py mostly checks +, DUP, simple stores, etc. Regressions in address spaces, barriers, float paths, or multi-param kernels can slip through until the GPT-2 demo is run by hand.
    Algorithm tests also give a baseline for performance work (#10, #11, #12, stack flattening).
    Proposed cases
    TestValidates
    Naive matmul (small MNK)multi-buffer params, GLOBAL-ID, loops
    Tiled matmul + shared + BARRIERS@/S!, synchronization
    Attention (tiny seq/head)f32 global, f64 shared, softmax, causal mask
    Scalar + array mixheader + runner path
    Reduced-width roundtripF32@/F32! correctness
    Use numpy/torch reference implementations; assert allclose with documented tolerances.
    Acceptance criteria
    •  At least matmul + attention GPU tests green on Vast (or documented local CUDA)
    •  Failures print expected vs actual diffs usefully
    •  Tests marked @pytest.mark.gpu and skip cleanly without API key / GPU
    •  Optional timing print under an env flag (not flaky asserts on perf)
    Related
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Algorithm-level GPU test suite (matmul, attention, shared tiling) · Issue #72 · tetsuo-cpp/warpforth · GitHub
      Skip to content
      Algorithm-level GPU test suite (matmul, attention, shared tiling) #72
      Closed
      Closed
      Algorithm-level GPU test suite (matmul, attention, shared tiling)#72
      Labels
      enhancementNew feature or request
      Description
      @tetsuo-cpp
      tetsuo-cpp
      opened on Aug 9, 2026
      Summary
      Extend gpu_test/ beyond arithmetic/stack micro-ops with end-to-end numerical tests for real algorithms: naive matmul, shared-memory tiled matmul (when applicable), and scaled-dot-product attention.
      Motivation
      LIT covers translation and conversion thoroughly. gpu_test/test_kernels.py mostly checks +, DUP, simple stores, etc. Regressions in address spaces, barriers, float paths, or multi-param kernels can slip through until the GPT-2 demo is run by hand.
      Algorithm tests also give a baseline for performance work (#10, #11, #12, stack flattening).
      Proposed cases
      TestValidates
      Naive matmul (small MNK)multi-buffer params, GLOBAL-ID, loops
      Tiled matmul + shared + BARRIERS@/S!, synchronization
      Attention (tiny seq/head)f32 global, f64 shared, softmax, causal mask
      Scalar + array mixheader + runner path
      Reduced-width roundtripF32@/F32! correctness
      Use numpy/torch reference implementations; assert allclose with documented tolerances.
      Acceptance criteria
      •  At least matmul + attention GPU tests green on Vast (or documented local CUDA)
      •  Failures print expected vs actual diffs usefully
      •  Tests marked @pytest.mark.gpu and skip cleanly without API key / GPU
      •  Optional timing print under an env flag (not flaky asserts on perf)
      Related
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, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Algorithm-level GPU test suite (matmul, attention, shared tiling) · Issue #72 · tetsuo-cpp/warpforth · GitHub
        Skip to content
        Algorithm-level GPU test suite (matmul, attention, shared tiling) #72
        Closed
        Closed
        Algorithm-level GPU test suite (matmul, attention, shared tiling)#72
        Labels
        enhancementNew feature or request
        Description
        @tetsuo-cpp
        tetsuo-cpp
        opened on Aug 9, 2026
        Summary
        Extend gpu_test/ beyond arithmetic/stack micro-ops with end-to-end numerical tests for real algorithms: naive matmul, shared-memory tiled matmul (when applicable), and scaled-dot-product attention.
        Motivation
        LIT covers translation and conversion thoroughly. gpu_test/test_kernels.py mostly checks +, DUP, simple stores, etc. Regressions in address spaces, barriers, float paths, or multi-param kernels can slip through until the GPT-2 demo is run by hand.
        Algorithm tests also give a baseline for performance work (#10, #11, #12, stack flattening).
        Proposed cases
        TestValidates
        Naive matmul (small MNK)multi-buffer params, GLOBAL-ID, loops
        Tiled matmul + shared + BARRIERS@/S!, synchronization
        Attention (tiny seq/head)f32 global, f64 shared, softmax, causal mask
        Scalar + array mixheader + runner path
        Reduced-width roundtripF32@/F32! correctness
        Use numpy/torch reference implementations; assert allclose with documented tolerances.
        Acceptance criteria
        •  At least matmul + attention GPU tests green on Vast (or documented local CUDA)
        •  Failures print expected vs actual diffs usefully
        •  Tests marked @pytest.mark.gpu and skip cleanly without API key / GPU
        •  Optional timing print under an env flag (not flaky asserts on perf)
        Related
        Metadata
        Metadata
        Assignees
        No one assigned
          Labels
          enhancementNew feature or request
          Projects
          No projects
          Milestone
          No milestone
          Relationships
          None yet
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          No branches or pull requests
          Issue actions
          
, 'i');
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var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
Algorithm-level GPU test suite (matmul, attention, shared tiling) · Issue #72 · tetsuo-cpp/warpforth · GitHub
          Skip to content
          Algorithm-level GPU test suite (matmul, attention, shared tiling) #72
          Closed
          Closed
          Algorithm-level GPU test suite (matmul, attention, shared tiling)#72
          Labels
          enhancementNew feature or request
          Description
          @tetsuo-cpp
          tetsuo-cpp
          opened on Aug 9, 2026
          Summary
          Extend gpu_test/ beyond arithmetic/stack micro-ops with end-to-end numerical tests for real algorithms: naive matmul, shared-memory tiled matmul (when applicable), and scaled-dot-product attention.
          Motivation
          LIT covers translation and conversion thoroughly. gpu_test/test_kernels.py mostly checks +, DUP, simple stores, etc. Regressions in address spaces, barriers, float paths, or multi-param kernels can slip through until the GPT-2 demo is run by hand.
          Algorithm tests also give a baseline for performance work (#10, #11, #12, stack flattening).
          Proposed cases
          TestValidates
          Naive matmul (small MNK)multi-buffer params, GLOBAL-ID, loops
          Tiled matmul + shared + BARRIERS@/S!, synchronization
          Attention (tiny seq/head)f32 global, f64 shared, softmax, causal mask
          Scalar + array mixheader + runner path
          Reduced-width roundtripF32@/F32! correctness
          Use numpy/torch reference implementations; assert allclose with documented tolerances.
          Acceptance criteria
          •  At least matmul + attention GPU tests green on Vast (or documented local CUDA)
          •  Failures print expected vs actual diffs usefully
          •  Tests marked @pytest.mark.gpu and skip cleanly without API key / GPU
          •  Optional timing print under an env flag (not flaky asserts on perf)
          Related
          Metadata
          Metadata
          Assignees
          No one assigned
            Labels
            enhancementNew feature or request
            Projects
            No projects
            Milestone
            No milestone
            Relationships
            None yet
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            No branches or pull requests
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, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Algorithm-level GPU test suite (matmul, attention, shared tiling) · Issue #72 · tetsuo-cpp/warpforth · GitHub
            Skip to content
            Algorithm-level GPU test suite (matmul, attention, shared tiling) #72
            Closed
            Closed
            Algorithm-level GPU test suite (matmul, attention, shared tiling)#72
            Labels
            enhancementNew feature or request
            Description
            @tetsuo-cpp
            tetsuo-cpp
            opened on Aug 9, 2026
            Summary
            Extend gpu_test/ beyond arithmetic/stack micro-ops with end-to-end numerical tests for real algorithms: naive matmul, shared-memory tiled matmul (when applicable), and scaled-dot-product attention.
            Motivation
            LIT covers translation and conversion thoroughly. gpu_test/test_kernels.py mostly checks +, DUP, simple stores, etc. Regressions in address spaces, barriers, float paths, or multi-param kernels can slip through until the GPT-2 demo is run by hand.
            Algorithm tests also give a baseline for performance work (#10, #11, #12, stack flattening).
            Proposed cases
            TestValidates
            Naive matmul (small MNK)multi-buffer params, GLOBAL-ID, loops
            Tiled matmul + shared + BARRIERS@/S!, synchronization
            Attention (tiny seq/head)f32 global, f64 shared, softmax, causal mask
            Scalar + array mixheader + runner path
            Reduced-width roundtripF32@/F32! correctness
            Use numpy/torch reference implementations; assert allclose with documented tolerances.
            Acceptance criteria
            •  At least matmul + attention GPU tests green on Vast (or documented local CUDA)
            •  Failures print expected vs actual diffs usefully
            •  Tests marked @pytest.mark.gpu and skip cleanly without API key / GPU
            •  Optional timing print under an env flag (not flaky asserts on perf)
            Related
            Metadata
            Metadata
            Assignees
            No one assigned
              Labels
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              Projects
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              Relationships
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, 'i');
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})();
Algorithm-level GPU test suite (matmul, attention, shared tiling) · Issue #72 · tetsuo-cpp/warpforth · GitHub
              Skip to content
              Algorithm-level GPU test suite (matmul, attention, shared tiling) #72
              Closed
              Closed
              Algorithm-level GPU test suite (matmul, attention, shared tiling)#72
              Labels
              enhancementNew feature or request
              Description
              @tetsuo-cpp
              tetsuo-cpp
              opened on Aug 9, 2026
              Summary
              Extend gpu_test/ beyond arithmetic/stack micro-ops with end-to-end numerical tests for real algorithms: naive matmul, shared-memory tiled matmul (when applicable), and scaled-dot-product attention.
              Motivation
              LIT covers translation and conversion thoroughly. gpu_test/test_kernels.py mostly checks +, DUP, simple stores, etc. Regressions in address spaces, barriers, float paths, or multi-param kernels can slip through until the GPT-2 demo is run by hand.
              Algorithm tests also give a baseline for performance work (#10, #11, #12, stack flattening).
              Proposed cases
              TestValidates
              Naive matmul (small MNK)multi-buffer params, GLOBAL-ID, loops
              Tiled matmul + shared + BARRIERS@/S!, synchronization
              Attention (tiny seq/head)f32 global, f64 shared, softmax, causal mask
              Scalar + array mixheader + runner path
              Reduced-width roundtripF32@/F32! correctness
              Use numpy/torch reference implementations; assert allclose with documented tolerances.
              Acceptance criteria
              •  At least matmul + attention GPU tests green on Vast (or documented local CUDA)
              •  Failures print expected vs actual diffs usefully
              •  Tests marked @pytest.mark.gpu and skip cleanly without API key / GPU
              •  Optional timing print under an env flag (not flaky asserts on perf)
              Related
              Metadata
              Metadata
              Assignees
              No one assigned
                Labels
                enhancementNew feature or request
                Projects
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                No milestone
                Relationships
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                No branches or pull requests
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