Open-Source Contributions — Microsoft, NVIDIA

Project Context

I contribute to established GPU and ML libraries by reproducing correctness bugs, tracing them through unfamiliar codebases, and submitting focused fixes with regression tests.

Contributions

Problem

A CUDA tensor-reduction path grouped values by byte size instead of numeric type, so same-sized types could use the wrong arithmetic and return incorrect results.

Action

I kept byte-based dispatch for raw assignment, moved arithmetic reductions to element-type dispatch, and added tests across numeric types and repeated indices.

Result

The patch passed maintainer review and was merged upstream.

semantic dispatch split (abridged)

if (args.operation == GatherScatterElementsArgs::Operation::NONE) {
  int dtype = GetElementType(input_tensor->DataType()->Size());
  utils::MLTypeCallDispatcher<int8_t, MLFloat16, float, double>
      t_disp(dtype);
  return t_disp.InvokeRet<Status, ComputeImpl>(
    Stream(context), input_tensor->DataRaw(),
    updates_tensor->DataRaw(), indices_tensor->DataRaw(),
    output_tensor->MutableDataRaw(),
    indices_tensor->DataType()->Size(), args);
}

utils::MLTypeCallDispatcher<bool, int8_t, uint8_t, int16_t,
  uint16_t, int32_t, uint32_t, int64_t, uint64_t,
  MLFloat16, BFloat16, float, double>
    t_disp(input_tensor->GetElementType());
return t_disp.InvokeRet<Status, ComputeImpl>(
  Stream(context), input_tensor->DataRaw(),
  updates_tensor->DataRaw(), indices_tensor->DataRaw(),
  output_tensor->MutableDataRaw(),
  indices_tensor->DataType()->Size(), args);

int32 semantic-dispatch regression

TEST(ScatterElements, AddReductionInt32SemanticDispatch) {
  OpTester test("ScatterElements", 18);
  test.AddAttribute("reduction", "add");
  test.AddInput<int32_t>("data", {1}, {0x3f800000});
  test.AddInput<int64_t>("indices", {1}, {0});
  test.AddInput<int32_t>("updates", {1}, {0x3f800000});
  test.AddOutput<int32_t>("y", {1}, {0x7f000000});
}

Problem

An FP8 GEMM kernel could read the wrong scaling values when thread blocks were reordered, producing incorrect output or invalid memory access.

Action

I traced the bug to a mismatch between physical block coordinates and logical matrix-tile coordinates, passed the logical tile offsets into the kernel, and added tests for reordered layouts.

Result

The fix restored correct FP8 scale indexing under swizzled layouts and was merged after upstream review.

logical scale indexing (abridged)

- params.scale_A, params.scale_B);
+ params.scale_A, params.scale_B,
+ threadblock_tile_offset.m(),
+ threadblock_tile_offset.n());

// ...

- int block_m_idx = (blockIdx.x * Shape::kM) / kScaleBlock;
+ int block_m_idx =
+   (threadblock_tile_m * Shape::kM) / kScaleBlock;

- int block_n_idx = (blockIdx.y * Shape::kN) / kScaleBlock;
+ int block_n_idx =
+   (threadblock_tile_n * Shape::kN) / kScaleBlock;

scale-sensitive swizzle regression (abridged)

scale_a.host_view().at({0, 0}) = 1.0f;
scale_a.host_view().at({1, 0}) = 4.0f;
scale_b.host_view().at({0, 0}) = 2.0f;
scale_b.host_view().at({1, 0}) = 8.0f;

float expected = float(kK) *
  scale_a_values[m / kScaleBlock] *
  scale_b_values[n / kScaleBlock];

// Identity<2> exercises a nontrivial physical-to-logical CTA mapping.
using Swizzle = GemmIdentityThreadblockSwizzle<2>;
EXPECT_TRUE(run_blockwise_gemm<Swizzle>());

Problem

CUB’s DeviceAdjacentDifference failed to compile with CUDA container iterators, even though the algorithm is intended to support them.

Action

I traced the failure to inconsistent iterator construction and routed it through the same cache-aware path used for iterator type selection. I also added a device-buffer regression test.

Result

The fix restored CUDA container iterator support and was merged after upstream compiler, sanitizer, and regression checks.

iterator construction

- load_it(LoadIt(input_it))
+ load_it(try_make_cache_modified_iterator<
+   Policy::LOAD_MODIFIER>(input_it))

regression coverage

auto input = cuda::make_device_buffer<type>(
  stream, cuda::devices[0], {2, 5, 9, 14, 20});
c2h::device_vector<type> output(input.size(), thrust::no_init);
const auto output_it = thrust::raw_pointer_cast(output.data());

adjacent_difference_subtract_left_copy(
  input.begin(), output_it, input.size(),
  cuda::std::minus<>{}, stream.get());
stream.sync();

const c2h::host_vector<type> expected{2, 3, 4, 5, 6};
REQUIRE(output == expected);

Result

Current contribution status:

The Microsoft ONNX Runtime patch has been merged upstream.
The NVIDIA CUTLASS and CCCL correctness fixes have been merged upstream.
Across these contributions, I reproduced bugs, traced root causes, implemented fixes, and added regression coverage.

Engineering / AI / 3D Systems