Key changes for cudagraph compatibility:
- No .item() or .tolist() calls (zero CPU-GPU syncs)
- Pre-allocated buffers at max_num_tokens size
- GPU-only expert offsets via bincount+cumsum
- searchsorted to map rows to experts (no Python for-loop with GPU indices)
- Single scatter operation for scale padding
- Pre-allocated token_indices reused for searchsorted row mapping
- quantize_activation_nvfp4 with fixed global scale (no .max() sync)
- Cached CuTeDSL kernel (no cute.compile per forward)
- No torch.cuda.synchronize() in forward path
biondizzle
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