Fused kernels (zero CPU sync, single kernel launch per projection):
- fused_amax_quantize.cu: amax→gsa→quantize in one pass. Replaces two-step
compute_amax_gsa_gpu + quantize_nvfp4_gpu (had .item() sync).
- fused_deinterleave_amax_quantize.cu: Same for MoE fused_swiglu L2 path.
Deinterleave + amax + quantize in one pass. Replaces compute_amax_gsa_gpu
+ deinterleave_quantize_nvfp4_cuda (had .item() sync).
All kernel loaders use dsv4/kernels/cuda/loader.py (compile-once cache).
Was JIT-compiling on every call via torch.utils.cpp_extension.load (~100ms/call,
~500 calls/token). Now compiles once and reuses the cached module.
Updated layers:
- linear.py Nvfp4Linear._run_impl: fused kernel, gsa via GPU buffer
- moe.py Nvfp4MoE._run_impl: fused for L1 and L2 (both fused_swiglu and
non-fused paths)
- shared_expert.py: fused for L1 and L2
- quantize.py: All functions use module loader cache
- sampler.py: Uses module loader cache
- indexer/score_topk.py: Uses module loader cache
P2: Vectorized KVCache.append_swa — index_copy_ instead of Python loop.
2 kernel launches instead of 2T. No .item() in comp_pos either.
P3: Pre-allocated comp_kv buffers — O(1) append instead of O(N) torch.cat.
max_comp=32768 per layer (32MB). No more quadratic memory growth.
~486 .item() syncs per decoded token → ~0 (only argmax + token decode remain).
- indexer/__init__.py: compute_index_scores_topk now calls
run_indexer_score_topk with proper tensor reshaping
- compressor/__init__.py: added torch import, fixed csa_compress_tail
and hca_compress_tail imports for flush.py
- Full flush pipeline now importable end-to-end
Both indexer files now use a constexpr LUT matching Python's
E2M1_MAGNITUDES = [0, 0.5, 1, 1.5, 2, 3, 4, 6].
This is cleaner and more auditable than bit-manipulation.
The dequant_fp4_scalar function was treating the magnitude bits as
a raw integer (0-6) instead of the E2M1 floating-point format:
Old (WRONG): val = (int)(nibble & 0x07) * scale
New (CORRECT): proper E2M1 decode with exponent + mantissa
E2M1 encoding (bias=1):
exp=0 subnormal: 0b000=0, 0b001=0.5
exp=1: 0b010=1, 0b011=1.5
exp=2: 0b100=2, 0b101=3
exp=3: 0b110=4, 0b111=6
Bug found by outside consultant. Affects indexer top-k selection
correctness — wrong FP4 key decoding would select wrong CSA blocks.
Fixed in both:
- dsv4/kernels/indexer/indexer_score_topk.cu
- dsv4/kernels/cuda/indexer_score_topk.cu
