c152bd5a89
1. ex_topk_bridge.py (100行): ctypes.CDLL加载ex_factor_0.so → ex_dispatch_moe_topk_softmax() CCCL warp-shuffle kernel, 零SMEM, 64 experts × topk=8 2. _custom_ops.py topk_softmax调用链新增Priority 1: P0: ix_bridge → ixformer::infer P1: ex_factor_0.so → CCCL warp kernel ← NEW P2: _moe_C.so → vllm v0.5.5 kernel P3: moe_topk_softmax_v3.so → 自编译kernel 3. patch_ops.sh补齐ex_engine package部署: ex_engine/python/*.py + build/*.so → site-packages/ex_engine/
101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
"""ex_topk_bridge.py — ctypes bridge for ex_factor_0.so topk_softmax
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CCCL pattern: ex_registry → ex_dispatch → kernel
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Python bridge: ctypes.CDLL → ex_dispatch_moe_topk_softmax()
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Usage:
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from ex_engine.python.ex_topk_bridge import ex_topk_softmax
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ex_topk_softmax(topk_weights, topk_ids, token_expert_indices, gating_output)
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"""
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import ctypes
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import os
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import glob
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import logging
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import torch
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logger = logging.getLogger("ex_topk_bridge")
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_lib = None
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_dispatch_fn = None
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def _load():
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global _lib, _dispatch_fn
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if _dispatch_fn is not None:
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return True
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# Search for ex_factor_0.so
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search = [
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os.path.join(os.path.dirname(__file__), "..", "build"),
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"/workspace/ex_engine/build",
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os.path.join(os.path.dirname(__file__), ".."),
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]
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# Also check vllm model path (where build.sh factor compile puts it)
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for p in ["/usr/local/corex/lib64/python3/dist-packages/vllm/model_executor/models/ex_engine",
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"/usr/local/corex/lib/python3/dist-packages/vllm/model_executor/models/ex_engine"]:
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search.append(p)
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for d in search:
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so = os.path.join(d, "ex_factor_0.so")
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if os.path.isfile(so):
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try:
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_lib_local = ctypes.CDLL(so)
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fn = _lib_local.ex_dispatch_moe_topk_softmax
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fn.restype = ctypes.c_int
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fn.argtypes = [
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ctypes.c_void_p, # float* topk_weights
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ctypes.c_void_p, # int32_t* topk_ids
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ctypes.c_void_p, # const float* logits
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ctypes.c_int, # T
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ctypes.c_int, # E
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ctypes.c_int, # top_k
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ctypes.c_void_p, # stream
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]
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_lib = _lib_local
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_dispatch_fn = fn
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logger.info("ex_factor_0.so loaded from %s", so)
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return True
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except Exception as e:
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logger.warning("Failed to load %s: %s", so, e)
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return False
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def ex_topk_softmax(topk_weights: torch.Tensor,
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topk_ids: torch.Tensor,
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token_expert_indices: torch.Tensor,
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gating_output: torch.Tensor) -> None:
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"""Drop-in replacement for _custom_ops.topk_softmax using ex_factor_0.so.
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Same interface as vllm._custom_ops.topk_softmax:
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topk_weights: (T, K) float32, output
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topk_ids: (T, K) int32, output
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token_expert_indices: (T, K) int32, output (ignored by ex kernel)
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gating_output: (T, E) float32, input
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"""
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if not _load():
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raise RuntimeError("ex_factor_0.so not available")
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T, E = gating_output.shape
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K = topk_weights.shape[1]
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# Get CUDA stream
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stream = torch.cuda.current_stream().cuda_stream
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ret = _dispatch_fn(
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topk_weights.data_ptr(),
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topk_ids.data_ptr(),
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gating_output.data_ptr(),
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T, E, K,
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stream,
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)
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if ret != 0:
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raise RuntimeError(f"ex_dispatch_moe_topk_softmax returned {ret}")
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# token_expert_indices: vllm expects (T, K) with values k_idx * T + t_idx
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# ex kernel doesn't write this, fill it here
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if token_expert_indices is not None:
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T_t = torch.arange(T, device=topk_ids.device, dtype=torch.int32)
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for k in range(K):
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token_expert_indices[:, k] = k * T + T_t
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