fix(build): 回退qwen3_6_scripts+ex_engine到26e6cb40(能得分版本)
唯一改动: computility-run.yaml max_model_len 80000→100000 26e6cb40是Sub520能在竞赛平台docker build成功并得分的版本 之后所有commit都导致docker build失败 根因: 新增的65个文件(vendor_overrides/prebuilt/*.so/wheels等) 可能触发了竞赛平台docker build的某个限制 本次回退: - qwen3_6_scripts/: 110→45文件(删掉65个新增文件) - ex_engine/: 恢复到26e6cb40完全一致 - Dockerfile: 恢复5个RUN步骤结构(已验证能build) - computility-run.yaml: max_model_len=100000(避免replay 400拒绝)
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Claude
@@ -974,73 +974,14 @@ def invoke_fused_moe_kernel(
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_moe_topk_ext = None
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_moe_topk_init_done = False
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_ix_bridge_mod = None
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_ix_bridge_init_done = False
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def _init_ix_bridge():
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"""Try to load ix_bridge which calls ixformer::infer::topk_softmax() via C++ pybind."""
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global _ix_bridge_mod, _ix_bridge_init_done
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_ix_bridge_init_done = True
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try:
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from ex_engine.python.ix_bridge import is_available, topk_softmax as _ix_ts
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if is_available():
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_ix_bridge_mod = True
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logger.info("topk_softmax: ix_bridge → ixformer::infer::topk_softmax() LOADED")
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return
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except Exception as e:
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logger.info("topk_softmax: ix_bridge unavailable (%s)", e)
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# Also try direct import from workspace
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try:
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import sys
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for p in ['/workspace/ex_engine/python', '/workspace/ex_engine',
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'/usr/local/corex/lib/python3/dist-packages/ex_engine/python']:
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if p not in sys.path:
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sys.path.insert(0, p)
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from ix_bridge import is_available, topk_softmax as _ix_ts
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if is_available():
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_ix_bridge_mod = True
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logger.info("topk_softmax: ix_bridge (direct) → ixformer::infer LOADED")
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return
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except Exception as e:
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logger.info("topk_softmax: ix_bridge direct import failed (%s)", e)
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def _init_moe_topk():
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global _moe_topk_ext, _moe_topk_init_done
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_moe_topk_init_done = True
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# 0. Try _moe_C (CUB-based, proven on BI-V100 real hardware 2026-08-11)
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try:
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import _moe_C as ext
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if hasattr(ext, 'topk_softmax'):
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_moe_topk_ext = ext
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logger.info("topk_softmax: loaded _moe_C (CUB BlockReduce, WARP_SIZE=64)")
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return
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except ImportError:
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pass
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# 0b. Try loading from torch cache
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import glob as _glob
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for pattern in [
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"/root/.cache/torch_extensions/py310_cu102/_moe_C/_moe_C.so",
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"/root/.cache/torch_extensions/*/_moe_C/*.so",
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]:
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for so_path in _glob.glob(pattern):
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try:
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torch.ops.load_library(so_path)
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import _moe_C as ext
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_moe_topk_ext = ext
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logger.info("topk_softmax: loaded _moe_C from %s", so_path)
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return
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except Exception:
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pass
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# 0c. Try ix_bridge (calls ixformer C++ SDK if available)
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_init_ix_bridge()
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if _ix_bridge_mod:
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return
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# 1. Try import old precompiled module (torch cache from Docker build)
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# 1. Try import precompiled module (torch cache from Docker build)
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try:
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import moe_topk_softmax_v3 as ext
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_moe_topk_ext = ext
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logger.info("topk_softmax: loaded precompiled moe_topk_softmax_v3")
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logger.info("topk_softmax: loaded precompiled CUDA kernel")
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return
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except ImportError:
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pass
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@@ -1054,11 +995,9 @@ def _init_moe_topk():
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for pattern in so_patterns:
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for so_path in glob.glob(pattern):
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try:
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import importlib.util
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spec = importlib.util.spec_from_file_location(
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"moe_topk_softmax_v3", so_path)
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ext = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(ext)
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torch.ops.load_library(so_path)
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# After load_library, the pybind module should be importable
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import moe_topk_softmax_v3 as ext
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_moe_topk_ext = ext
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logger.info("topk_softmax: loaded CUDA kernel from %s", so_path)
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return
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@@ -1099,47 +1038,15 @@ def topk_softmax(topk_weights: torch.Tensor, topk_ids: torch.Tensor,
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if not _moe_topk_init_done:
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_init_moe_topk()
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# Priority 0: ix_bridge → ixformer::infer::topk_softmax() (fastest, uses SDK)
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if _ix_bridge_mod:
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try:
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from ex_engine.python.ix_bridge import topk_softmax as _ix_topk
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gating = gating_output if isinstance(gating_output, torch.Tensor) else gating_output
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topk_k = topk_weights.shape[1]
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weights, ids = _ix_topk(gating, topk_k, renormalize=False)
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topk_weights.copy_(weights.to(topk_weights.dtype))
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topk_ids.copy_(ids.to(topk_ids.dtype))
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# token_expert_indicies not produced by ix_bridge, fill with topk_ids
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token_expert_indicies.copy_(ids.to(token_expert_indicies.dtype))
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return
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except Exception as e:
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logger.warning("topk_softmax ix_bridge failed (%s), trying CUDA kernel", e)
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# Priority 1: ex_factor_0.so → CCCL warp-shuffle topk kernel (compiled for BI-V100)
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try:
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from ex_engine.python.ex_topk_bridge import ex_topk_softmax as _ex_topk
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gating = gating_output if isinstance(gating_output, torch.Tensor) else gating_output
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_ex_topk(topk_weights, topk_ids, token_expert_indicies, gating.float())
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return
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except Exception as e:
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if not getattr(topk_softmax, '_ex_warned', False):
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logger.warning("ex_factor_0 topk failed (%s), trying _moe_C", e)
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topk_softmax._ex_warned = True
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# Priority 2: CUDA kernel (_moe_C or moe_topk_softmax_v3)
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# Priority 1: Our CUDA kernel (fused warp-shuffle, ~5x faster than PyTorch)
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if _moe_topk_ext is not None:
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try:
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gating = gating_output if isinstance(gating_output, torch.Tensor) else gating_output
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if hasattr(_moe_topk_ext, 'topk_softmax'):
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# _moe_C style: in-place (vllm standard API)
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_moe_topk_ext.topk_softmax(topk_weights, topk_ids,
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token_expert_indicies, gating.float())
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elif hasattr(_moe_topk_ext, 'moe_topk_softmax'):
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# old v3 style: returns tuple
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topk_k = topk_weights.shape[1]
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results = _moe_topk_ext.moe_topk_softmax(gating, topk_k, False)
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topk_weights.copy_(results[0].to(topk_weights.dtype))
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topk_ids.copy_(results[1].to(topk_ids.dtype))
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token_expert_indicies.copy_(results[2].to(token_expert_indicies.dtype))
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topk_k = topk_weights.shape[1]
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results = _moe_topk_ext.moe_topk_softmax(gating, topk_k, False)
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topk_weights.copy_(results[0].to(topk_weights.dtype))
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topk_ids.copy_(results[1].to(topk_ids.dtype))
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token_expert_indicies.copy_(results[2].to(token_expert_indicies.dtype))
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return
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except Exception as e:
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logger.warning("topk_softmax CUDA kernel failed (%s), falling back to PyTorch", e)
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