feat(rocm): grouped + non-grouped keep-quant expert GEMM — the MoE-path terminus (issue #41) - #523
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feat(rocm): grouped + non-grouped keep-quant expert GEMM — the MoE-path terminus (issue #41)#523VikashLoomba wants to merge 2 commits into
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…mbineGate) (mudler#41) The next links in the generic MoE path after the router/silu-mul. Hand- translated from cuda_moe.cu (MoeCombineKernel :473, MoeCombineGateKernel :555) and the SharedExpertGate CPU oracle (cpu_ops.cpp:2387). Grid-stride, f32 math, bf16/f32 dtype arms via boundary conversions; the combine-gate folds the shared-expert sigmoid gate rounded through bf16 exactly as the donor. Evidence (4x gfx1100, ROCm 7.14, Release): - new MoE combine/gate cross-device case: 9/9 assertions (MoeCombineGate's oracle is the host-computed composite — no CPU op registration exists) - ctest -R 'rocm|cross_device': 4/4 - full ctest: pre-existing failure set shrinks 7 -> 5; test_bench and test_capi now PASS (they failed at op 77 / the router dtype before the chain). test_loaded_engine_dense now fails only on the async-scheduling assertion (a lane capability gap, not a kernel throw). - Named remaining blocker: the grouped quant expert GEMM (kMatmulBTQuantGrouped), the DeepSeek-V4 keep-quant family. FOLLOWING_AGENTS_PROTOCOL Following-Agents-Protocol: true AI-Assisted: true Assisted-by: pi:kimi-k3 [pi]
…ant, kMatmulBTQuantGrouped) — the MoE-path terminus (mudler#41) The last kernel blocker for GDN-MoE models on discrete ROCm. Ports the cuda_quant_dot.cu grouped + plain keep-quant GEMMs (QuantDotGemmGroupedKernel :746 / QuantDotGemmKernel :706 / the Q8_0 + Q8_K activation quantizers) and the DotQ8_0 / DotQ4K / DotQ5K / DotQ6K superblocks 1:1. Registers kMatmulBTQuant (op 74) + kMatmulBTQuantGrouped (op 75) — registering the non-grouped op flips GgufQuantComputeAvailable() so the keep-quant path actually activates on ROCm. Integer dot cores via a portable Dp4a (bit-identical to __dp4a); the HW dot instruction is a named perf lever. Evidence (4x gfx1100, ROCm 7.14, Release): - cross-device grouped-quant case: 16/16 (Q8_0/Q4_K/Q5_K/Q6_K, valid random blocks + real f32 activations, NMSE <= 5e-4 vs the CPU keep-quant oracle) - ctest -R 'rocm|cross_device': 4/4; full ctest: the 5 pre-existing host/lane failures only (test_bench + test_capi pass since the MoE chain). - E2E: Qwen3.6-35B-A3B Q4_K_M (21GB GDN-MoE GGUF) runs end to end on one gfx1100 with keep-quant active — ops 74 AND 75 resolve vt-native, zero CPU-ref fallback, correct output. Needs --max-num-seqs 1 to fit one 24GB card (the GDN state pool otherwise pushes past; residency note, not a kernel defect). A backend-Alloc instrumentation run confirmed the OOM was cumulative ~23.8 GiB = genuine capacity, not a kernel bug. FOLLOWING_AGENTS_PROTOCOL Following-Agents-Protocol: true AI-Assisted: true Assisted-by: pi:kimi-k3 [pi]
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BACKEND-ROCM— the last kernel blocker for MoE-bearing models on discrete ROCm. Issue #41. Stacked on #509 (MoE combine/gate chain).What changed
NEW
src/vt/rocm/rocm_grouped_gemm.hip— ports the keep-quant expert GEMM family fromcuda_quant_dot.cu1:1:kMatmulBTQuant(op 74, non-grouped) +kMatmulBTQuantGrouped(op 75, grouped overexpert_ids)DotQ8_0/DotQ4K/DotQ5K/DotQ6Ksuperblocks,__dp4a→ portableDp4a(bit-identical integer core),__shfl_down_syncreductionGgufQuantComputeAvailable()on ROCm — the grouped op alone leaves the loader dequantizing experts to bf16.Cross-device case: all four formats vs the CPU keep-quant oracle, valid random blocks + real activations.
Evidence (4× gfx1100, ROCm 7.14, Release)
ctest -R 'rocm|cross_device': 4/4; full ctest: only the 5 pre-existing host/lane failures (zero new)vt-native, zero CPU-ref fallback, correct output. Requires--max-num-seqs 1to fit one 24GB card (the GDN state pool otherwise pushes past — a residency note, not a kernel defect; confirmed via a backend-Alloc instrumentation run showing genuine cumulative ~23.8 GiB).Speed claims
Honest gaps
hipMalloc/hipFreeactivation scratch is correctness-grade; a queue-owned grow-only pool is a perf lever (decode-step churn).--max-num-seqs 1on 24GB; multi-GPU expert sharding or host streaming is the follow-on for bigger MoE / longer context.