Fix flaky Lion optimizer test on HIP/AMD GPUs - #95
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Scale max_error_count with tensor size instead of hardcoding 10. Lion uses sgn() on a lerp value, and when it's near zero the fused bnb kernel and separate-op PyTorch reference disagree on the sign due to different FMA contraction by HIP clang vs nvcc. Each flip causes exactly 2*lr error. Larger tensors have more boundary cases, so the tolerance should scale accordingly. For dim2=4097 x dim1=1024 (4.19M elements), the new limit is 20 instead of 10. The test was failing ~30% of runs on MI300X with 12 errors — just 2 over the old limit. Co-Authored-By: Claude Opus 4 (1M context) <noreply@anthropic.com>
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Summary
max_error_countintest_optimizer32bitwith tensor size instead of hardcoding 10test_optimizer32bit[dim2=4097-dim1=1024-fp32-opt=lion]on MI300X (~30% failure rate)Root Cause
Lion uses
sgn()on a lerp of momentum and gradient. When the lerp value is near zero, the fused bnb kernel and the separate-op PyTorch reference can disagree on the sign due to different FMA contraction by HIP's clang vs nvcc. Each sign flip causes exactly2 * lr = 0.0002error. Larger tensors have more boundary cases — the 4.19M element case was hitting ~12 errors against a limit of 10.New formula:
max(10, p1.numel() // 200000)— keeps the floor at 10 for small tensors, scales up for larger ones.Test plan
dim2=4097-dim1=1024-fp32-opt=lion: 10/10 passes (was 7/10 before)test_optim.py: 151 passed, 26 skipped, 0 failedtest_functional.py: 411 passed, 339 skipped, 0 failures related to this change🤖 Generated with Claude Code