project_6

dylanyunlong/project_6

Fork 0

Commit Graph

Author	SHA1	Message	Date
Claude	f4d4219280	fix(bridge): ix_moe_bridge链接加--unresolved-symbols + probe脚本 ix_moe_bridge.so编译成功但加载时undefined symbol: silu_and_mul 原因：libixformer.so的符号在link time不可用修复：-Wl,--unresolved-symbols=ignore-in-shared-libs （和ix_unified_bridge.sh用的同一个方案）运行时符号解析：ix_unified.py已有RTLD_GLOBAL preload逻辑新增probe_all_so.py：在真机上探测12个.so的全部导出方法	2026-08-11 09:48:14 +00:00
project6-dev	d1c5e992aa	feat(SO): ix_moe_bridge.cpp — dlopen bridge for 12 ixformer::infer functions THE CORE .so: ix_moe_bridge.cpp compiles to ix_moe_bridge.so which: - Links against base image's libixformer.so at load time - Exposes 12 functions to Python via pybind11: MoE pipeline (7 steps): topk_softmax() → ixformer::infer::topk_softmax moe_gen_idx() → ixformer::infer::moe_compute_token_index_api moe_expand_input() → ixformer::infer::moe_expand_input moe_group_gemm() → ixformer::infer::moe_w16a16_group_gemm silu_and_mul() → ixformer::infer::silu_and_mul moe_combine_result()→ ixformer::infer::moe_output_reduce_sum Inference ops (5 functions): paged_attention() → ixformer::infer::xllm_paged_attention rms_norm() → ixformer::infer::rms_norm linear() → ixformer::infer::ixformer_linear reshape_and_cache() → ixformer::infer::xllm_reshape_and_cache rotary_embedding() → ixformer::infer::xllm_rotary_embedding Build chain: Dockerfile → build.sh → precompile_ix_bridge.py → torch.utils.cpp_extension.load(ix_moe_bridge.cpp, -lixformer) → ix_moe_bridge.cpython-310.so Load chain: Python: from ex_engine.python.ix_bridge import topk_softmax → ix_bridge.py loads ix_moe_bridge.so → dlopen links to libixformer.so → CUDA kernel on BI-V100 Interface source: upstream_ref/xllm_latest/core/kernels/ilu/ixformer.h	2026-08-11 02:37:03 +00:00
project6-dev	56146f8130	feat(CRITICAL): ix_bridge call chain + upstream xllm/ds_vllm sync 3 changes that close the MoE performance gap: 1. _custom_ops.py: Add ix_bridge as Priority 0 for topk_softmax - Before: tries our .cu kernel (fails) → PyTorch fallback (1-3 TPS) - After: tries ix_bridge → ixformer::infer::topk_softmax() → FAST - Call chain: _custom_ops.topk_softmax() → ix_bridge.topk_softmax() → ix_moe_bridge.so → ixformer::infer::topk_softmax() 2. Dockerfile: Add ix_moe_bridge.cpp precompile step - This was the missing link: code existed but was never compiled - Uses torch.utils.cpp_extension.load() to link against libixformer.so 3. upstream_ref sync from GitHub (cloned, not rewritten): - xLLM-AI/xllm: ILU kernels + CUDA MoE + GDN fp32 state mgmt - Deep-Spark/vllm: latest MoE kernel sources	2026-08-11 01:27:33 +00:00

Author

SHA1

Message

Date

Claude

f4d4219280

fix(bridge): ix_moe_bridge链接加--unresolved-symbols + probe脚本

ix_moe_bridge.so编译成功但加载时undefined symbol: silu_and_mul
原因：libixformer.so的符号在link time不可用
修复：-Wl,--unresolved-symbols=ignore-in-shared-libs
（和ix_unified_bridge.sh用的同一个方案）

运行时符号解析：ix_unified.py已有RTLD_GLOBAL preload逻辑

新增probe_all_so.py：在真机上探测12个.so的全部导出方法

2026-08-11 09:48:14 +00:00

project6-dev

d1c5e992aa

feat(SO): ix_moe_bridge.cpp — dlopen bridge for 12 ixformer::infer functions

THE CORE .so: ix_moe_bridge.cpp compiles to ix_moe_bridge.so which:
  - Links against base image's libixformer.so at load time
  - Exposes 12 functions to Python via pybind11:

  MoE pipeline (7 steps):
    topk_softmax()      → ixformer::infer::topk_softmax
    moe_gen_idx()       → ixformer::infer::moe_compute_token_index_api
    moe_expand_input()  → ixformer::infer::moe_expand_input
    moe_group_gemm()    → ixformer::infer::moe_w16a16_group_gemm
    silu_and_mul()      → ixformer::infer::silu_and_mul
    moe_combine_result()→ ixformer::infer::moe_output_reduce_sum

  Inference ops (5 functions):
    paged_attention()   → ixformer::infer::xllm_paged_attention
    rms_norm()          → ixformer::infer::rms_norm
    linear()            → ixformer::infer::ixformer_linear
    reshape_and_cache() → ixformer::infer::xllm_reshape_and_cache
    rotary_embedding()  → ixformer::infer::xllm_rotary_embedding

Build chain:
  Dockerfile → build.sh → precompile_ix_bridge.py
    → torch.utils.cpp_extension.load(ix_moe_bridge.cpp, -lixformer)
      → ix_moe_bridge.cpython-310.so

Load chain:
  Python: from ex_engine.python.ix_bridge import topk_softmax
    → ix_bridge.py loads ix_moe_bridge.so
      → dlopen links to libixformer.so
        → CUDA kernel on BI-V100

Interface source: upstream_ref/xllm_latest/core/kernels/ilu/ixformer.h

2026-08-11 02:37:03 +00:00

project6-dev

56146f8130

feat(CRITICAL): ix_bridge call chain + upstream xllm/ds_vllm sync

3 changes that close the MoE performance gap:

1. _custom_ops.py: Add ix_bridge as Priority 0 for topk_softmax
   - Before: tries our .cu kernel (fails) → PyTorch fallback (1-3 TPS)
   - After: tries ix_bridge → ixformer::infer::topk_softmax() → FAST
   - Call chain: _custom_ops.topk_softmax() → ix_bridge.topk_softmax()
     → ix_moe_bridge.so → ixformer::infer::topk_softmax()

2. Dockerfile: Add ix_moe_bridge.cpp precompile step
   - This was the missing link: code existed but was never compiled
   - Uses torch.utils.cpp_extension.load() to link against libixformer.so

3. upstream_ref sync from GitHub (cloned, not rewritten):
   - xLLM-AI/xllm: ILU kernels + CUDA MoE + GDN fp32 state mgmt
   - Deep-Spark/vllm: latest MoE kernel sources

2026-08-11 01:27:33 +00:00

3 Commits