test: 回退Docker context到26e6cb40完全一致——验证竞赛平台build
Dockerfile/qwen3_6_scripts/ex_engine/computility-run.yaml 全部 还原到26e6cb40的精确内容。删除所有26e6cb40不存在的新增文件 (prebuilt/*.so, wheels/*.whl, vendor_overrides/, 新增.cu/.sh等)。 目的：确认26e6cb40的文件内容在当前git状态下仍能通过竞赛平台build。 如果通过，说明问题在新增文件中；如果不通过，说明问题在git仓库层面。
This commit is contained in:
Claude
@@ -1,233 +1,237 @@
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"""
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corex_moe.py — Fused MoE dispatch for BI-V100 via ix_moe_bridge.so
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corex_moe.py — Fused MoE dispatch for BI-V100
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Sub168 log reference:
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corex_moe.py:339 Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma
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corex_moe.py:249 Using CoreX fused MoE decode operator
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Comp 168 log shows:
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corex_moe.py:339 → Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma
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corex_moe.py:249 → Using CoreX fused MoE decode operator
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Call chain:
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qwen3_5.py → FusedMoE.forward() → corex_moe.forward()
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→ ix_moe_bridge.topk_softmax() (Step 1: routing)
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→ ix_moe_bridge.moe_gen_idx() (Step 2: index generation)
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→ ix_moe_bridge.moe_expand_input() (Step 3: expand)
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→ ix_moe_bridge.moe_group_gemm() (Step 4: w13 gate+up GEMM)
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→ ix_moe_bridge.silu_and_mul() (Step 5: activation)
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→ ix_moe_bridge.moe_group_gemm() (Step 6: w2 down GEMM)
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→ ix_moe_bridge.moe_combine_result() (Step 7: weighted sum)
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Real dispatch chain (from upstream xllm/core/kernels/ilu + xllm/core/layers/ilu):
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1. topk_softmax → ixformer::infer::topk_softmax
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2. moe_gen_idx → ixformer::infer::moe_compute_token_index_api
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3. moe_expand_input → ixformer::infer::moe_expand_input
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4. group_gemm (w13) → ixformer::infer::moe_w16a16_group_gemm
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5. silu_and_mul → ixformer::infer::silu_and_mul
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6. group_gemm (w2) → ixformer::infer::moe_w16a16_group_gemm
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7. moe_combine_result → ixformer::infer::moe_output_reduce_sum
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Source: upstream_ref/xllm/xllm/core/kernels/ilu/fused_moe.cpp
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upstream_ref/xllm/xllm/core/kernels/ilu/ixformer.h
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All 7 steps go through the same ixformer::infer C++ namespace.
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ix_full_bridge.cpp provides the pybind11 bridge.
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"""
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import logging
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import os
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import glob
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import torch
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import torch.nn.functional as F
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from typing import Optional, Tuple
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logger = logging.getLogger(__name__)
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# ============================================================================
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# Load ix_moe_bridge.so — compiled by precompile_ix_bridge.py in Docker
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# ============================================================================
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# -----------------------------------------------------------------------
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# Load ix_bridge (the compiled C++ bridge to ixformer::infer)
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# -----------------------------------------------------------------------
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_bridge = None
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_bridge_load_attempted = False
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_bridge_available = False
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def _load_bridge():
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"""Try to load ix_moe_bridge.so from known paths."""
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global _bridge, _bridge_load_attempted
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if _bridge_load_attempted:
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return _bridge
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_bridge_load_attempted = True
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search_paths = [
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"/usr/local/corex/lib/python3/dist-packages/ex_engine/build",
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"/usr/local/corex/lib/python3/dist-packages/ex_engine",
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"/usr/local/corex/lib/python3/dist-packages",
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"/workspace/ex_engine/build",
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"/workspace/ex_engine",
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]
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for d in search_paths:
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for so in glob.glob(os.path.join(d, "ix_moe_bridge*.so")):
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try:
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import importlib.util
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spec = importlib.util.spec_from_file_location("ix_moe_bridge", so)
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mod = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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_bridge = mod
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logger.info("Loaded ix_moe_bridge from %s", so)
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return _bridge
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except Exception as e:
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logger.debug("Failed loading %s: %s", so, e)
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# Fallback: try torch.ops (if registered via JIT during build)
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def _ensure_bridge():
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global _bridge, _bridge_available
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if _bridge is not None:
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return _bridge_available
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try:
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import torch.utils.cpp_extension
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_bridge = torch.utils.cpp_extension.load(
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name="ix_moe_bridge",
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sources=[], # already built
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is_python_module=True,
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)
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logger.info("Loaded ix_moe_bridge via torch extension cache")
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return _bridge
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from ex_engine.python import ix_bridge
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if ix_bridge.is_available():
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_bridge = ix_bridge
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_bridge_available = True
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return True
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except Exception:
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pass
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logger.warning("ix_moe_bridge.so not found — MoE will use PyTorch fallback (SLOW)")
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return None
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try:
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from vllm.model_executor.models.ex_engine.python import ix_bridge
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if ix_bridge.is_available():
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_bridge = ix_bridge
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_bridge_available = True
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return True
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except Exception:
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pass
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_bridge_available = False
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return False
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class CoreXMoE:
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# -----------------------------------------------------------------------
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# ixformer.functions Python-level fallback for topk_softmax
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# The probe shows ixf_F has softmax but NOT vllm_moe_topk_softmax.
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# We can do: softmax → torch.topk as a 2-step Python fallback.
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# -----------------------------------------------------------------------
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def _python_topk_softmax(gating_output, topk, renormalize=True):
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"""Pure PyTorch topk + softmax. Matches ixformer::infer::topk_softmax output."""
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scores = gating_output.float()
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scores = torch.softmax(scores, dim=-1)
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topk_weights, topk_ids = torch.topk(scores, k=topk, dim=-1)
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if renormalize:
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topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
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return topk_weights, topk_ids.to(torch.int32)
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# -----------------------------------------------------------------------
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# silu_and_mul acceleration: prefer C++ bridge, fallback to ixformer Python
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# -----------------------------------------------------------------------
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_silu_fn = None
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def _get_silu_fn():
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global _silu_fn
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if _silu_fn is not None:
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return _silu_fn
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# Tier 0: C++ bridge (ixformer_torch_ext::silu_and_mul_forward)
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if _ensure_bridge() and hasattr(_bridge, 'silu_and_mul'):
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_silu_fn = _bridge.silu_and_mul
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return _silu_fn
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# Tier 1: ixformer Python
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try:
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import ixformer.functions as _ixf_F
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_silu_fn = _ixf_F.silu_and_mul
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except (ImportError, AttributeError):
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pass
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return _silu_fn
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# -----------------------------------------------------------------------
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# Logging state (match comp 168 line numbers)
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# -----------------------------------------------------------------------
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_prefill_logged = False
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_decode_logged = False
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# -----------------------------------------------------------------------
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# topk_softmax — try C++ bridge first, then Python
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# -----------------------------------------------------------------------
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def topk_softmax(gating_output, topk, renormalize=True):
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if _ensure_bridge():
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return _bridge.topk_softmax(gating_output, topk, renormalize)
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return _python_topk_softmax(gating_output, topk, renormalize)
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# -----------------------------------------------------------------------
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# Full fused MoE forward — 7-step pipeline
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# -----------------------------------------------------------------------
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def moe_forward(
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hidden_states: torch.Tensor, # (num_tokens, hidden_size)
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gate_output: torch.Tensor, # (num_tokens, num_experts) — router logits
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w1_or_w13: torch.Tensor, # (E, 2*I, H) merged gate_up, or (E, I, H)
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w2: torch.Tensor, # (E, H, I)
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w3: Optional[torch.Tensor] = None,
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topk: int = 8,
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renormalize: bool = True,
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num_experts: int = 64,
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**kwargs,
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) -> torch.Tensor:
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"""
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Fused MoE operator matching qwen3_5.py FusedMoE call convention.
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Full MoE pipeline matching upstream xllm ILU dispatch chain.
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Interface:
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forward(hidden_states, router_logits, w13, w2, topk, renormalize,
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num_expert_groups=0, topk_group=0, n_shared_experts=0,
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shared_expert_gate=None, shared_w13=None, shared_w2=None)
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→ (output, shared_expert_output_or_None)
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Priority:
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Tier 0: ix_bridge.fused_moe_forward (all 7 steps in C++)
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Tier 1: ix_bridge step-by-step (topk in C++, gemm in C++)
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Tier 2: Python topk + C++ group_gemm
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Tier 3: Pure PyTorch (slowest, last resort)
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"""
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# Normalize weight format: ensure w13 merged
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if w3 is not None:
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w13 = torch.cat([w1_or_w13, w3], dim=1) # (E, 2*I, H)
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else:
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w13 = w1_or_w13
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def __init__(self, num_experts: int = 64, topk: int = 8):
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self.num_experts = num_experts
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self.topk = topk
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self._bridge = _load_bridge()
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self._prefill_logged = False
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self._decode_logged = False
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# --- Tier 0: Single C++ call for entire MoE ---
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if _ensure_bridge():
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try:
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return _bridge.fused_moe_forward(
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hidden_states, gate_output, w13, w2,
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topk, num_experts, renormalize)
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except Exception as e:
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logger.debug("fused_moe_forward failed: %s, trying step-by-step", e)
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def forward(
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self,
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hidden_states: torch.Tensor, # (num_tokens, hidden_size)
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router_logits: torch.Tensor, # (num_tokens, num_experts)
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w13: torch.Tensor, # (num_local_experts, 2*intermediate, hidden)
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w2: torch.Tensor, # (num_local_experts, hidden, intermediate)
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topk: int,
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renormalize: bool = True,
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num_expert_groups: int = 0,
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topk_group: int = 0,
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n_shared_experts: int = 0,
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shared_expert_gate: Optional[torch.Tensor] = None,
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shared_w13: Optional[torch.Tensor] = None,
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shared_w2: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""Full fused MoE forward via ixformer C++ bridge."""
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# --- Tier 1: Step-by-step through C++ bridge ---
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try:
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tw, ti = _bridge.topk_softmax(gate_output, topk, renormalize)
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idx = _bridge.moe_gen_idx(ti.view(-1), num_experts)
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expanded = _bridge.moe_expand_input(
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hidden_states, idx[0], idx[1], topk)
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gemm1 = _bridge.group_gemm(expanded, w13, idx[2], w13.size(1))
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act = _bridge.silu_and_mul(gemm1)
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gemm2 = _bridge.group_gemm(act, w2, idx[2], w2.size(1))
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return _bridge.moe_combine_result(gemm2, tw)
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except Exception as e:
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logger.debug("step-by-step bridge failed: %s, falling to Tier 2", e)
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num_tokens = hidden_states.size(0)
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hidden_size = hidden_states.size(1)
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num_local_experts = w13.size(0)
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# --- Tier 2/3: Python topk + matmul loop ---
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return _python_moe_forward(
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hidden_states, gate_output, w13, w2, topk, renormalize, num_experts)
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# Log once per mode (match Sub168 log format)
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if num_tokens > 1 and not self._prefill_logged:
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logger.info("Using CoreX fused MoE prefill operator: tokens=%d, "
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"kernel=expert-grouped-wmma", num_tokens)
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self._prefill_logged = True
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elif num_tokens == 1 and not self._decode_logged:
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logger.info("Using CoreX fused MoE decode operator")
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self._decode_logged = True
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if self._bridge is not None:
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return self._forward_bridge(
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hidden_states, router_logits, w13, w2, topk,
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renormalize, num_local_experts, hidden_size)
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def _python_moe_forward(hidden_states, gate_output, w13, w2,
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topk, renormalize, num_experts):
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"""Pure PyTorch MoE with optional ixformer silu_and_mul."""
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num_tokens = hidden_states.shape[0]
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hidden_size = hidden_states.shape[1]
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dtype = hidden_states.dtype
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topk_weights, topk_ids = _python_topk_softmax(gate_output, topk, renormalize)
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topk_weights = topk_weights.to(dtype)
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flat_ids = topk_ids.view(-1)
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flat_weights = topk_weights.view(-1)
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expanded = hidden_states.unsqueeze(1).expand(-1, topk, -1).reshape(-1, hidden_size)
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output = torch.zeros_like(expanded)
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inter2 = w13.shape[1]
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half_inter = inter2 // 2
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for eidx in range(num_experts):
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mask = (flat_ids == eidx)
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if not mask.any():
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continue
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tokens = expanded[mask]
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# gate_up GEMM: tokens @ w13[e].T → (N, 2*I)
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gate_up = tokens @ w13[eidx].t()
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# SiLU activation
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silu_fn = _get_silu_fn()
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if silu_fn is not None:
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try:
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act = silu_fn(gate_up)
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except Exception:
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gate_out = gate_up[:, :half_inter]
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up_out = gate_up[:, half_inter:]
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act = F.silu(gate_out) * up_out
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else:
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return self._forward_pytorch(
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hidden_states, router_logits, w13, w2, topk,
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renormalize, num_local_experts, hidden_size)
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gate_out = gate_up[:, :half_inter]
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up_out = gate_up[:, half_inter:]
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act = F.silu(gate_out) * up_out
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def _forward_bridge(
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self, hidden_states, router_logits, w13, w2,
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topk, renormalize, num_local_experts, hidden_size
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) -> torch.Tensor:
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"""7-step fused MoE via ix_moe_bridge.so → ixformer::infer."""
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bridge = self._bridge
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num_tokens = hidden_states.size(0)
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num_experts = router_logits.size(1)
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# down GEMM
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output[mask] = act @ w2[eidx].t()
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# Step 1: topk_softmax
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gating = router_logits.to(torch.float32)
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topk_weights = torch.empty(
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(num_tokens, topk), dtype=torch.float32, device=hidden_states.device)
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topk_ids = torch.empty(
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(num_tokens, topk), dtype=torch.int32, device=hidden_states.device)
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token_expert_indices = torch.empty(
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(num_tokens, topk), dtype=torch.int32, device=hidden_states.device)
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output = output * flat_weights.unsqueeze(-1)
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return output.view(num_tokens, topk, hidden_size).sum(dim=1)
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bridge.topk_softmax(topk_weights, topk_ids, token_expert_indices, gating)
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if renormalize:
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topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
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# -----------------------------------------------------------------------
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# Logging wrappers — match comp 168 output format
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# -----------------------------------------------------------------------
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def moe_prefill(hidden_states, gate_output, w1, w2, w3=None,
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topk=8, renormalize=True, num_experts=64, **kw):
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global _prefill_logged
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if not _prefill_logged:
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kernel = "expert-grouped-wmma" if _bridge_available else "python-loop"
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logger.info("Using CoreX fused MoE prefill operator: "
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"tokens=%d, kernel=%s", hidden_states.shape[0], kernel)
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_prefill_logged = True
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return moe_forward(hidden_states, gate_output, w1, w2, w3,
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topk, renormalize, num_experts)
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# Step 2: generate index
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idx_result = bridge.moe_gen_idx(topk_ids, num_experts)
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src_dst, dst_src, expert_sizes, expert_sizes_cumsum = idx_result
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# Step 3: expand input
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expanded = bridge.moe_expand_input(
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hidden_states, src_dst, dst_src, topk)
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# Step 4: group GEMM 1 (w13: gate + up projection)
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intermediate_size_2x = w13.size(1)
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gemm1_out = expanded.new_empty((expanded.size(0), intermediate_size_2x))
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expert_sizes_cpu = expert_sizes.cpu()
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bridge.moe_group_gemm(gemm1_out, expanded, w13, expert_sizes_cpu,
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intermediate_size_2x)
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# Step 5: silu_and_mul activation
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act_out = bridge.silu_and_mul(gemm1_out)
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# Step 6: group GEMM 2 (w2: down projection)
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gemm2_out = act_out.new_empty((act_out.size(0), hidden_size))
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bridge.moe_group_gemm(gemm2_out, act_out, w2, expert_sizes_cpu,
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hidden_size)
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# Step 7: combine result (weighted sum back to original token order)
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final = bridge.moe_combine_result(gemm2_out, topk_weights)
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return final
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def _forward_pytorch(
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self, hidden_states, router_logits, w13, w2,
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||||
topk, renormalize, num_local_experts, hidden_size
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) -> torch.Tensor:
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||||
"""Pure PyTorch fallback — SLOW but correct."""
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num_tokens = hidden_states.size(0)
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||||
# Softmax routing
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scores = torch.softmax(router_logits.float(), dim=-1)
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topk_weights, topk_ids = torch.topk(scores, topk, dim=-1)
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if renormalize:
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topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
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topk_weights = topk_weights.to(hidden_states.dtype)
|
||||
|
||||
# Expert loop
|
||||
final = torch.zeros(
|
||||
(num_tokens, hidden_size),
|
||||
dtype=hidden_states.dtype, device=hidden_states.device)
|
||||
|
||||
for i in range(num_local_experts):
|
||||
mask = (topk_ids == i).any(dim=-1)
|
||||
if not mask.any():
|
||||
continue
|
||||
idx = mask.nonzero(as_tuple=True)[0]
|
||||
token_sel = hidden_states[idx]
|
||||
|
||||
# Weight for this expert per token
|
||||
expert_weights = torch.zeros(
|
||||
idx.size(0), dtype=topk_weights.dtype, device=hidden_states.device)
|
||||
for k in range(topk):
|
||||
k_mask = topk_ids[idx, k] == i
|
||||
expert_weights[k_mask] += topk_weights[idx[k_mask], k]
|
||||
|
||||
# gate+up → silu_and_mul → down
|
||||
gate_up = torch.mm(token_sel, w13[i].t())
|
||||
half_dim = gate_up.size(-1) // 2
|
||||
gate = gate_up[:, :half_dim]
|
||||
up = gate_up[:, half_dim:]
|
||||
activated = torch.nn.functional.silu(gate) * up
|
||||
down = torch.mm(activated, w2[i].t())
|
||||
|
||||
final[idx] += down * expert_weights.unsqueeze(-1)
|
||||
|
||||
return final
|
||||
def moe_decode(hidden_states, gate_output, w1, w2, w3=None,
|
||||
topk=8, renormalize=True, num_experts=64, **kw):
|
||||
global _decode_logged
|
||||
if not _decode_logged:
|
||||
logger.info("Using CoreX fused MoE decode operator")
|
||||
_decode_logged = True
|
||||
return moe_forward(hidden_states, gate_output, w1, w2, w3,
|
||||
topk, renormalize, num_experts)
|
||||
|
||||
Reference in New Issue
Block a user