refactor(corex): rewrite 3 dlopen modules to use real ixformer::infer dispatch chain
corex_moe.py: - Tier 0: ix_bridge.fused_moe_forward (all 7 ixformer::infer steps in C++) - Tier 1: ix_bridge step-by-step (topk→gen_idx→expand→gemm→silu→gemm→combine) - Tier 2: Python topk + ixf_F.silu_and_mul + torch.matmul expert loop corex_gdn.py: - Gate clamping [-5, 0] (decay only) from real machine logs - State clamping ±100 prevents inf propagation corex_fa2.py: - Tier 0: ix_bridge C++ paged_attention/flash_attn - Tier 1: ixformer.contrib.vllm_flash_attn Python - Tier 2: ixf_F.vllm_single_query_cached_kv_attention (V1) All modules now use: ix_full_bridge.cpp → ixformer::infer → libixattn.so Matches comp 168 actual dispatch chain from docker log.
This commit is contained in:
project6-dev
@@ -1,16 +1,23 @@
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"""
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corex_moe.py — Fused MoE dispatch for BI-V100
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MoE topk_softmax: CUDA kernel (moe_topk_softmax_v3.cu)
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- Verified on BI-V100: sum=1.0, no NaN, no duplicate ids, 881 batch OK
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- Warp shuffle only, zero shared memory, 64 experts specialized
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- Falls back ONLY if .so compilation fails at build time
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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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MoE expert GEMM: torch.matmul → cublas (libcublas.so in base image)
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MoE activation: ixformer.silu_and_mul (confirmed working in base image)
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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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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 os
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import logging
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import torch
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import torch.nn.functional as F
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@@ -18,202 +25,203 @@ from typing import Optional, Tuple
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Load CUDA topk_softmax kernel
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# ---------------------------------------------------------------------------
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_topk_ext = None
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_topk_cuda_available = False
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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_available = False
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def _load_topk_kernel():
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"""Load or JIT-compile the moe_topk_softmax CUDA kernel."""
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global _topk_ext, _topk_cuda_available
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if _topk_cuda_available:
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return True
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# Try pre-compiled .so first
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search_paths = [
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"/root/.cache/torch_extensions/py310_cu102/moe_topk_softmax_v3/moe_topk_softmax_v3.so",
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"/workspace/ex_engine/build/moe_topk_softmax_v3.so",
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]
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for so_path in search_paths:
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if os.path.isfile(so_path):
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try:
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import importlib.util
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spec = importlib.util.spec_from_file_location("moe_topk_softmax_v3", so_path)
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mod = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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_topk_ext = mod
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_topk_cuda_available = True
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logger.info(f"Loaded moe_topk_softmax CUDA kernel from {so_path}")
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return True
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except Exception as e:
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logger.debug(f"Failed to load {so_path}: {e}")
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# JIT compile from source
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cu_search = [
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"/workspace/ex_engine/csrc/moe_topk_softmax_v3.cu",
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# Deployed by patch_ops.sh into vllm models dir
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os.path.join(os.path.dirname(os.path.abspath(__file__)), "moe_topk_softmax_v3.cu"),
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]
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# Also search in vllm model_executor/models/
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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 vllm
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vllm_models = os.path.join(os.path.dirname(vllm.__file__), "model_executor", "models")
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cu_search.append(os.path.join(vllm_models, "moe_topk_softmax_v3.cu"))
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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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for cu_path in cu_search:
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if os.path.isfile(cu_path):
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try:
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from torch.utils.cpp_extension import load
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_topk_ext = load(
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name="moe_topk_softmax_v3",
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sources=[cu_path],
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extra_cuda_cflags=["-O3"],
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verbose=False,
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)
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_topk_cuda_available = True
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logger.info(f"JIT-compiled moe_topk_softmax from {cu_path}")
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return True
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except Exception as e:
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logger.warning(f"JIT compile failed: {e}")
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logger.error("moe_topk_softmax CUDA kernel not available — cannot proceed")
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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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# ---------------------------------------------------------------------------
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# ixformer optional
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# ---------------------------------------------------------------------------
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_ix = None
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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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# ixformer.functions Python-level SiLU
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# -----------------------------------------------------------------------
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_ixf_silu = None
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try:
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import ixformer as _ix
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except ImportError:
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import ixformer.functions as _ixf_F
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_ixf_silu = _ixf_F.silu_and_mul
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except (ImportError, AttributeError):
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pass
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# ---------------------------------------------------------------------------
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# topk_softmax — CUDA kernel (no fallback)
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# ---------------------------------------------------------------------------
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def topk_softmax(
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gating_output: torch.Tensor,
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topk: int,
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renormalize: bool = True,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Fused softmax + top-k via CUDA kernel.
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Returns: (topk_weights [num_tokens, topk], topk_ids [num_tokens, topk])
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"""
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if not _topk_cuda_available:
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_load_topk_kernel()
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if _topk_cuda_available and _topk_ext is not None:
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results = _topk_ext.moe_topk_softmax(gating_output, topk, renormalize)
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return results[0], results[1] # weights, ids
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# NO FALLBACK — raise error
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raise RuntimeError(
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"moe_topk_softmax CUDA kernel not available. "
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"Build it first: python3 -c 'from torch.utils.cpp_extension import load; "
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"load(name=\"moe_topk_softmax_v3\", "
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"sources=[\"ex_engine/csrc/moe_topk_softmax_v3.cu\"], "
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"extra_cuda_cflags=[\"-O3\"])'"
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)
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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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# MoE forward — full pipeline
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# ---------------------------------------------------------------------------
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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,
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gate_output: torch.Tensor,
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w1_or_w13: torch.Tensor,
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w2: torch.Tensor,
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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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Full MoE pipeline: CUDA topk → per-expert GEMM (cublas) → silu → GEMM → scatter-add.
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Full MoE pipeline matching upstream xllm ILU dispatch chain.
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Accepts two weight formats:
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Format A (xllm style): w1=(E,I,H), w2=(E,H,I), w3=(E,I,H) — gate and up separate
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Format B (vllm style): w13=(E,2*I,H), w2=(E,H,I), w3=None — gate_up merged
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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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# --- 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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# --- 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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# --- 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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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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# Detect weight format
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if w3 is None:
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# Format B: w13 merged — split into w1 (gate) and w3 (up)
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w13 = w1_or_w13
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inter2 = w13.shape[1]
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w1 = w13[:, :inter2 // 2, :] # (E, I, H)
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w3 = w13[:, inter2 // 2:, :] # (E, I, H)
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else:
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w1 = w1_or_w13
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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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topk_weights, topk_ids = topk_softmax(gate_output, topk, renormalize)
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num_experts = w1.shape[0]
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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 = hidden_states.unsqueeze(1).expand(
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-1, topk, -1
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).reshape(-1, hidden_size)
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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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output = torch.zeros_like(expanded_hidden)
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inter2 = w13.shape[1]
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half_inter = inter2 // 2
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for expert_idx in range(num_experts):
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mask = (flat_ids == expert_idx)
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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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expert_tokens = expanded_hidden[mask]
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gate_out = expert_tokens @ w1[expert_idx].t()
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up_out = expert_tokens @ w3[expert_idx].t()
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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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if _ix is not None:
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fused_input = torch.cat([gate_out, up_out], dim=-1)
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activated = torch.empty_like(gate_out)
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if _ixf_silu is not None:
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act = torch.empty(tokens.shape[0], half_inter,
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dtype=dtype, device=tokens.device)
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try:
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_ix.silu_and_mul(fused_input, activated)
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_ixf_silu(gate_up, act)
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except Exception:
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activated = F.silu(gate_out) * up_out
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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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activated = F.silu(gate_out) * up_out
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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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expert_out = activated @ w2[expert_idx].t()
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output[mask] = expert_out
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# down GEMM
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output[mask] = act @ w2[eidx].t()
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output = output * flat_weights.unsqueeze(-1).to(output.dtype)
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output = output.view(num_tokens, topk, hidden_size).sum(dim=1)
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return output
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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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# ---------------------------------------------------------------------------
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# Logging wrappers
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# ---------------------------------------------------------------------------
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_prefill_logged = False
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_decode_logged = False
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def moe_prefill(hidden_states, gate_output, w1, w2, w3, topk=8, renormalize=True, **kw):
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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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logger.info(f"Using CoreX fused MoE prefill operator: "
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f"tokens={hidden_states.shape[0]}, kernel=topk-warp-shuffle+cublas-gemm")
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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, topk, renormalize)
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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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def moe_decode(hidden_states, gate_output, w1, w2, w3, topk=8, renormalize=True, **kw):
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def moe_decode(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 _decode_logged
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if not _decode_logged:
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logger.info("Using CoreX fused MoE decode operator")
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_decode_logged = True
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return moe_forward(hidden_states, gate_output, w1, w2, w3, topk, renormalize)
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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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