Fix int32 overflow in softmax_warp_forward offset arithmetic

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first_batch and stride/blockIdx.x are int32; their product can exceed INT32_MAX for large batch counts, silently wrapping before being added to src/dst. This causes out-of-bounds reads/writes, observed as an illegal memory access, silently unwritten output, or occasionally a hang, depending on allocator layout.

Fixes both softmax_warp_forward and softmax_warp_forward_resource_efficient, which have the identical pattern. Widened the offset computation to int64_t at the multiply site rather than changing first_batch/stride's types, since those are used elsewhere in signed-subtraction bounds checks that assume int.

Verified with a standalone repro: an isolated arithmetic test showing the exact expression wraps to a negative offset, and a full GPU kernel test (~4GB fp16 tensor, ~2.1M rows) showing the unfixed kernel faults with an illegal memory access at the exact predicted boundary row, and the fixed kernel produces correct output across the boundary.

Addresses #32299

Good — this is close, but it looks like the template's section headers (### Description, ### Motivation and Context) got left as empty placeholders below your actual content instead of your content going into them. Let's restructure it properly and fold in the test output as evidence. Here's the full corrected PR body:

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Description

Widens the offset computation in softmax_warp_forward and softmax_warp_forward_resource_efficient (onnxruntime/core/providers/cuda/math/softmax_warpwise_impl.cuh) from int32 to int64_t at the multiply site.

first_batch and stride (and blockIdx.x/stride in the resource-efficient variant) are both int32. Their product can exceed INT32_MAX for large batch counts, silently wrapping before being added to src/dst. This causes out-of-bounds reads/writes — observed as an illegal memory access, silently unwritten output, or occasionally a hang, depending on allocator layout.

Kept first_batch/stride/batch_size as int rather than widening their declared types, since local_batches = batch_size - first_batch relies on signed subtraction elsewhere in the function and widening those types risked an unrelated regression for no benefit — only the multiplication result needed widening.

Motivation and Context

Addresses #32299.

A tensor large enough to trigger this (batch_size × stride > 2^31) requires several GB of host memory to construct through the standard OpTester float-vector interface, which isn't practical to add as a CI unit test. Instead, I verified the fix two ways:

1. Isolated arithmetic test (no GPU): confirms first_batch * stride computed in int32 produces a wrapped/negative result for representative large inputs, and that the int64_t-cast version produces the correct value.

2. Standalone GPU repro: a minimal extraction of the kernel's offset logic and warp-reduction structure, run against a ~4GB fp16 tensor (~2.1M rows × 1024 elements) straddling the exact overflow boundary (predicted boundary row: 2,097,152, where first_batch × stride first exceeds INT32_MAX).

Before the fix, the kernel faults deterministically at the predicted boundary:

❯ ./gpu_repro buggy
Mode: BUGGY (int32 offset)
batch_size=2101248 stride=1024 total_elements=2151677952 (4.01 GiB)
overflow boundary row (int32 math): 2097152
Fill complete.
*** Kernel execution FAILED: an illegal memory access was encountered ***

After the fix, the same tensor — including the rows immediately straddling the boundary — produces correct output:

❯ ./gpu_repro fixed
Mode: FIXED (int64_t offset)
batch_size=2101248 stride=1024 total_elements=2151677952 (4.01 GiB)
overflow boundary row (int32 math): 2097152
Fill complete.
Kernel completed without a fault -- checking output correctness...

-- Sanity check (well below overflow boundary) --
row            0 | expected peak col  1023 | actual peak col  1023 | PASS
row            5 | expected peak col     0 | actual peak col     0 | PASS
row         1000 | expected peak col  1023 | actual peak col  1023 | PASS
row       500000 | expected peak col  1023 | actual peak col  1023 | PASS

-- Boundary check (around row 2097152) --
row      2097150 | expected peak col  1023 | actual peak col  1023 | PASS
row      2097151 | expected peak col     0 | actual peak col     0 | PASS
row      2097152 | expected peak col  1023 | actual peak col  1023 | PASS
row      2097153 | expected peak col     0 | actual peak col     0 | PASS
row      2097154 | expected peak col  1023 | actual peak col  1023 | PASS

Sanity rows:  ALL PASS
Boundary rows: ALL PASS

Existing Softmax op tests (softmax_test.cc) are unaffected by this change — it only touches the offset computation, not the reduction/arithmetic logic.

Note on Erf

The linked issue also reports Erf as silently producing wrong output above the same 2³¹-element boundary. I traced Erf's standalone CUDA path end-to-end, unary_elementwise_ops.cc::ComputeInternal (passes Tensor::Shape().Size(), which is int64_t) → Impl_Erf → UnaryElementWiseImpl → the templated _UnaryElementWise kernel in unary_elementwise_impl.cuh — and didn't find the same bug. The kernel's per-thread index is computed as static_cast<int64_t>(NumElementsPerThread) * NumThreadsPerBlock * blockIdx.x + threadIdx.x, which widens to int64_t before any multiplication happens, and the host-side grid-size calculation is already guarded with ORT_ENFORCE(blocksPerGridSize <= INT32_MAX, ...). This path appears correct as-is on main.

This PR only fixes the Softmax case (softmax_warp_forward / softmax_warp_forward_resource_efficient). I don't have a repro for the Erf case described in the issue, it's possible it goes through a fused path (e.g. GELU) or is specific to the ROCm backend (the issue's hardware trace is from ROCm/MI300A) rather than the standalone CUDA unary-elementwise dispatch I checked. Flagging this rather than guessing at a fix for code I couldn't confirm is broken, happy to dig further if a maintainer can point me at the right path, or if the issue author can confirm which path they hit.

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Pull request overview

Widens CUDA Softmax kernel offset arithmetic to prevent large-tensor out-of-bounds memory access.

Changes:

• Uses 64-bit offsets in both warpwise Softmax implementations.
• Documents the overflow rationale.

Suppressed comments (1)

onnxruntime/core/providers/cuda/math/softmax_warpwise_impl.cuh:185

• blockIdx.x is unsigned, so the old multiplication was evaluated in 32-bit unsigned arithmetic. It remains correct past INT32_MAX and wraps only past UINT32_MAX; the current explanation gives the wrong failure boundary and incorrectly describes this as identical to the signed first_batch case. Please document the unsigned threshold while retaining the cast.

  // blockIdx.x and stride are both int32; on large inputs their product can
  // exceed INT32_MAX and silently wrap before it's added to src/dst, causing
  // out-of-bounds reads/writes. Cast to int64_t before multiplying so the
  // arithmetic happens in 64-bit.

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