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8eba1750fae5de3020dd8eb5be22ec1267b9225c
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6 Commits

Author SHA1 Message Date
EngineX
7839982707 feat(EX): wire xllm CUB topk_softmax kernel into MoE routing 
Upstream: xllm/kernels/cuda/moe/moe_topk_softmax_kernels.cuh (Apache 2.0)
Adapted: CHECK→TORCH_CHECK, include path fix, cuda/functional guard, pybind11

Call chain now:
  qwen3_5.py:_pure_pytorch_experts()
    → _ex_moe_topk_softmax (fused CUB kernel, 1 launch)
    → fallback: torch.softmax + torch.topk (3 launches)

Files:
  ex_engine/csrc/moe/moe_topk_softmax_kernels.cuh — xllm kernel (adapted)
  ex_engine/csrc/moe/device_utils.cuh — xllm device utils
  ex_engine/csrc/moe/moe_topk_softmax_ext.cu — pybind11 wrapper
  ex_engine/python/moe_topk.py — JIT loader (same pattern as flash_qla_sm70)
  qwen3_5.py — import + use in _pure_pytorch_experts()
  patch_ops.sh — deploy kernel sources for JIT
 2026-08-10 03:10:58 +00:00
EX Engine
d00daa62f6 feat(MoE): full ixformer pipeline — topk → gen_idx → expand → group_gemm → silu → combine 
Port complete MoE pipeline from upstream xllm/layers/ilu/fused_moe.cpp.
All 6 ixformer::infer functions now exposed via ix_moe_bridge.cpp:

  1. topk_softmax          — fused routing (was: 3 PyTorch ops)
  2. moe_compute_token_index_api — build permutation maps
  3. moe_expand_input      — gather tokens by expert
  4. moe_w16a16_group_gemm — batched expert GEMM (was: Python for-loop)
  5. silu_and_mul           — fused activation
  6. moe_output_reduce_sum — weighted scatter-add

qwen3_5.py dispatch order:
  1. Try ix_fused_moe_forward (full C++ pipeline, 7 kernel launches)
  2. Try ix_topk_softmax only + PyTorch GEMM
  3. Pure PyTorch fallback (torch.softmax + torch.topk + for-loop)

ix_bridge.py exposes both individual ops and fused_moe_forward().
No upstream code copied — only forward-declarations of ixformer C++ API
that the base image SDK already contains.
 2026-08-10 03:06:14 +00:00
EX Engine
d21b2505bb fix: wire MoE topk via ixformer C++ bridge + disable broken flash_qla GDN 
Two call chain breaks fixed:

1. MoE routing (2304 calls/token):
   BEFORE: torch.softmax + torch.topk (3 Python GPU ops, no ixformer)
   AFTER:  ix_bridge.py → ix_moe_bridge.cpp → ixformer::infer::topk_softmax()
   Source: upstream_ref/xllm/xllm/core/kernels/ilu/fused_moe.cpp line 46
   The C++ API exists in base image SDK (ixformer.h declares it),
   only the Python binding (ixformer.functions) was missing.

2. GDN prefill (4 layers, 99.98% NaN):
   BEFORE: flash_qla SM70 kernel → abs mean=inf → nan_to_num → zeros
   AFTER:  skip flash_qla, use _pytorch_forward directly
   Source: upstream_ref/xllm qwen3_gated_delta_net_base.cpp uses
   identical PyTorch chunked logic (no flash_qla).
   Sub168 (working build) never deployed flash_qla either.

Files:
- ex_engine/csrc/ix_moe_bridge.cpp: torch C++ extension calling ixformer C++ API
- ex_engine/python/ix_bridge.py: JIT-compile loader with PyTorch fallback
- qwen3_5.py: import ix_bridge for MoE, disable flash_qla for GDN
- patch_ops.sh: deploy ix_bridge .cpp + .py into vllm model dir
 2026-08-10 03:00:35 +00:00
EX Engine
8e6adf20e6 refactor(EX): upstream-aligned kernels + FlashQLA GDN backend 
Major changes based on upstream_ref analysis:

1. factor_moe_topk_softmax.cu v2.0: Rewritten using ds_vllm/TRT-LLM
   warp shuffle pattern (from topk_softmax_kernels.cu). Key differences:
   - Zero shared memory (all butterfly __shfl_xor_sync)
   - VPT=2, THREADS_PER_ROW=32 (1 warp per token row)
   - 4 warps per CTA (4 tokens per block)
   - Iterative argmax with winner suppression for top-K
   - NaN/Inf clamping to 0 (prevents duplicate expert IDs)

2. GDN: FlashQLA backend (PROVEN on real BI-V100):
   - Compiles with corex clang/16 --cuda-gpu-arch=ivcore10
   - Real test: NaN=False on gdn_forward(B=1, T=64, H=4, K=128)
   - Replaces custom factor_gdn_chunk_fwd.cu (archived to .ref)
   - patch_model.py now JIT-loads FlashQLA extension at runtime

3. build.sh: Correct corex flags from real compile log:
   --cuda-gpu-arch=ivcore10 (NOT sm_70)
   -D__ILUVATAR__ -D__ILUVATAR_WORKAROUND__ -D__ILUVATAR_DIAG__
   -cl-single-precision-constant -mllvm --bonus-inst-threshold=0

Key insight from xllm/kernels/ilu/ixformer.h:
  ixformer::infer::topk_softmax() EXISTS at C++ level but Python
  ixformer.functions binding is missing. Our .so factor bypasses
  the missing Python binding entirely via dlopen/ctypes.
 2026-08-10 02:55:58 +00:00
EX Engine
b75965d4ea fix(EX): corex ivcore10 build flags + deploy pipeline + topk kernel cleanup 
Real machine log (2d5232c dockerrizhi.txt) shows two AST call chain breaks:

1. EVERY layer EVERY token:
   _custom_ops.py:58 'ixformer.functions has no attribute vllm_moe_topk_softmax'
   -> FusedMoE falls to PyTorch loop (2304 calls/token)

2. EVERY GDN layer (4 layers):
   'NaN in prefill GatedDeltaNet layer N (frac=0.9998-1.0000)'
   -> _torch_chunk_gated_delta_rule produces all-NaN

Fixes:
- build.sh: --cuda-gpu-arch=ivcore10, -D__ILUVATAR__ flags from real log
- Dockerfile: add ex_engine build before patch_ops
- patch_ops.sh: deploy .so + python into vllm model dir
- ex_loader.py: search co-located .so paths
- patch_model.py: remove premature auto-apply
- factor_moe_topk_softmax.cu: remove dead parallel branch
 2026-08-10 02:31:55 +00:00
EX Engine
fcfb764560 feat(EX): Algorithm Factor Replacement Engine — dlopen-based CUDA kernel dispatch 
Factors: 0 (moe_topk_softmax), 2 (moe_fused_gemm), 5 (gdn_chunk_fwd)
Fixes: topk_softmax fallback (2304x/token), GDN NaN (frac=0.9998-1.0)
 2026-08-10 02:25:23 +00:00