- Fix interleave_l1_weights: remove //2 bug (g=granularity_bf16 for N-axis)
- Apply L1 weight+SF interleave in runner._ensure_stacked() and moe_pipeline
- De-interleave L1 GEMM output before gate/up split
- Fused SwiGLU kernel: epi_tile=(128,8) for subtile-level pairing
- Even subtiles = gate: SiLU in FP32 registers, save to register buffer
- Odd subtiles = up: silu(gate)*up from buffer
- Both branches produce same BF16 tensor type (CuTeDSL constraint)
- run_nvfp4_moe_fused() pipeline: fused L1 + PyTorch L2
- Runner: fused_swiglu=True option for CuTeDSLMoERunner
- Layertest: both fused and non-fused paths PASS (cosine 0.988)
- README.md updated with current status and lessons learned
SiLU in registers: PASS (0.034% error, Step 1 stable)
Gate/up subtile detection: blocked by CuTeDSL type system
CuTeDSL compiles the kernel for ALL subtile iterations at once.
Runtime conditionals (if is_gate_subtile) that affect:
- Register tensor assignment → DSLRuntimeError (type structure mismatch)
- TMA store skipping → corrupted output
- Mask blending → wrong results
Path forward: use const_expr debug flag for the BF16 side output,
or process gate/up in a separate post-GEMM kernel.
Verified that our NVFP4 packing convention (odd<<4|even, round-half-to-even)
matches the DeepSeek-V4 checkpoint exactly: 100% byte-identical round-trip
across all tested experts. The dequantize->requantize path is lossless in
practice but wasteful. Marked both prepare_weights_from_dequantized and
prepare_weights_direct as deprecated in favor of prepare_weights_from_stacked
which loads checkpoint FP4 bytes directly via .view().
Also added test_fp4_roundtrip.py for future reference.
Replaces vLLM's broken FlashMLA sparse attention which doesn't work on
SM100 (Blackwell). Uses torch.nn.functional.scaled_dot_product_attention
which works on all GPUs.
Architecture:
- CSA (C128A): Batched sparse gather + SDPA on top-k positions
- HCA (C4A): Same with compressed KV + per-layer indexer
- SWA: Sliding window attention
- Full reference: standard SDPA for testing without compression
Also adds test_csa_attention_b200.py to verify the full attention path.
The original attention forward uses fused_inv_rope_fp8_quant +
deepseek_v4_fp8_einsum which requires wo_a to have FP8 weights
and weight_scale_inv. Our checkpoint has wo_a in BF16, so the
original path crashes (produces empty output).
Replace O projection with:
1. _apply_inv_rope_bf16: pure PyTorch inverse RoPE (no FP8)
2. BMM grouped linear for wo_a (BF16)
3. NVFP4 wo_b via CuTeDSL
Also fixes activation global scale bug from previous commit:
- input_global_scale_inv IS the activation gs, don't re-invert
- w13_input_scale_orig (after undoing convert) IS the MoE gs
Test: tests/test_o_projection.py validates inv RoPE roundtrip
and wo_a BMM correctness.
