- README: updated NVFP4 coverage table, status, and plan
- CURRENT_BUG.md: full debugging journey, what works, what's next
- Both reflect decision to build our own CuTeDSL kernels
Dedicated runner (shared_expert_pipeline.py) and test (test_shared_expert.py).
Tried reusing MoE runner with 1 expert — fails because MoE runner assumes
hidden_size != HC_DIM for scatter. Need dedicated runner with correct
scale assembly. Will continue tomorrow.
The key insight: process_weights_after_loading runs AFTER load_weights
and sets up FlashInferCutlassNvFp4LinearKernel with broken
input_global_scale_inv. Any fix inside load_weights gets overwritten.
Solution: register a one-shot forward pre-hook that runs on the first
forward call (guaranteed after all init). It dequantizes attention
NVFP4 weights to BF16 and replaces quant_method with
UnquantizedLinearMethod. Since process_weights_after_loading already
ran, our changes won't be overwritten.
Standalone test confirmed: all attention weights produce valid
non-NaN output when dequantized to BF16.
Root cause of NaN from layer 0: FlashInferCutlassNvFp4LinearKernel
uses checkpoint input_scale for activation quantization, which produces
NaN immediately. Fix: dequantize all attention NVFP4 weights (wq_a,
wq_b, wkv, wo_a, wo_b) to BF16 at load time, bypassing the broken
input_scale entirely. Uses existing _dequant_nvfp4_to_bf16 method.
This trades memory for correctness. Future optimization: add warmup
for attention input_global_scale_inv (same as MoE warmup).
When CLAWMINE_DEBUG=1, prints amax/mean/NaN/Inf after each layer.
Must run with --enforce-eager (data-dependent prints break Dynamo).
Gated by os.environ so dead-code-eliminated during compilation.
Root cause of CUDA_ERROR_ASSERT index out of bounds:
- topk_ids contains GLOBAL expert IDs (0-255) but runner treated them
as local IDs (0-31 with EP=8). Tokens for non-local experts got
wrong expert assignments, causing out-of-bounds scatter indices
in _assemble_scales_cudagraph_safe.
Fixes:
1. Add experts_start_idx param to CuTeDSLMoERunner
2. In run(), remap global→local IDs and zero weights for non-local experts
3. Move _token_indices from CPU to GPU (remove sort_idx.cpu() sync)
4. Add _fill_token_indices() and _needs_token_refill to handle CuTeDSL
JIT GPU memory corruption (refill after first GEMM call)
