## Problem
NaN values in input tensors (e.g., from attention softmax 0/0) cause NaN
block scales during FP4 quantization, which then contaminate 100% of the
token's output during GEMM.
## Solution
Mask NaN→0 before quantization in apply_nvfp4_linear(). This prevents
NaN from contaminating block scales while preserving clean data.
## Cost
~19us per layer (~0.6ms for 32-layer model, ~50% overhead on quantization).
Cannot fuse into custom CUDA op without kernel changes.
## Tests
- test_nvfp4_nan_block_contamination.py: Demonstrates bug (NaN→100% output)
- test_nvfp4_nan_integration.py: Validates fix through production code path
- test_nvfp4_nan_propagation.py: Comprehensive multi-scenario coverage
- All existing NVFP4 tests pass (no regression)
## Future Work
TODO in code notes proper fixes:
1. Integrate NaN check into scaled_fp4_quant CUDA kernel (zero-cost)
2. Fix upstream attention to not produce NaN
3. Integrate with check_tensor infrastructure
Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com>
