CRITICAL REWRITE of nvfp4_fused_router_kernel.py:
- REMOVED: Raw pointer SMEM merge (storage.merge_scores.data_ptr()[idx] = val)
This crashed the CuTeDSL MLIR optimizer. Never use raw pointer indexing
inside CuTeDSL kernels.
- REMOVED: Per-thread top-k accumulation + 128-thread SMEM merge. Too complex
for MLIR, caused SIGABRT during compilation.
- ADDED: MoE-style epilogue (TMEM→regs→activation→SMEM→TMA store→GMEM)
using paired copy atoms from CUTLASS (epilogue_tmem_copy_and_partition +
epilogue_smem_copy_and_partition). Structurally identical to the proven
FusedSwiGLUScaledGroupedGemmKernel epilogue. This SHOULD compile.
- Activation: sqrt(softplus(logit)) in registers (replaces SwiGLU)
- Output: FP32 activated scores written to GMEM via TMA store
- Top-k handled by activation_topk CUDA kernel in Python wrapper
Other changes:
- _activation_topk.py: Added run_fused_activation_topk_pre_activated() for
top-k + renorm on pre-activated scores (PyTorch reference, not CUDA kernel)
- dense_router_dispatch_nvfp4_fused: Updated to match new kernel API
- Kernel now uses standard _compute_stages() for SMEM budget calculation
- Kernel now uses compute_epilogue_tile_shape() for epi_tile (not hardcoded)
- C pipeline (PipelineTmaStore) added for SMEM→GMEM overlap
biondizzle
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