feat(EX): Algorithm Factor Replacement Engine — dlopen-based CUDA kernel dispatch
Factors: 0 (moe_topk_softmax), 2 (moe_fused_gemm), 5 (gdn_chunk_fwd) Fixes: topk_softmax fallback (2304x/token), GDN NaN (frac=0.9998-1.0)
This commit is contained in:
EX Engine
3
ex_engine/python/__init__.py
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3
ex_engine/python/__init__.py
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@@ -0,0 +1,3 @@
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from .ex_loader import EXEngine, get_engine
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__all__ = ["EXEngine", "get_engine"]
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337
ex_engine/python/ex_loader.py
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337
ex_engine/python/ex_loader.py
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"""
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ex_engine/python/ex_loader.py — EX Engine Python loader
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Architecture:
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CCCL: compute_capability → policy_selector → kernel template instantiation
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EX: hardware_id → ctypes.dlopen → factor.kernel() via torch stream
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This module loads the compiled .so factors and provides torch-compatible
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wrappers that the vllm model code can call directly.
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Usage:
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from ex_engine.python.ex_loader import EXEngine
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engine = EXEngine("/workspace/ex_engine/build")
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engine.load_all()
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# Replace MoE topk+softmax (was: torch.softmax + torch.topk, 36× per layer)
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topk_w, topk_ids = engine.moe_topk_softmax(router_logits, top_k=8)
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# Replace GDN prefill (was: _torch_chunk_gated_delta_rule producing NaN)
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output, new_state = engine.gdn_chunk_fwd(q, k, v, gate, beta, state)
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"""
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import ctypes
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import os
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import logging
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import torch
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from typing import Optional, Tuple
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logger = logging.getLogger("ex_engine")
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# ---------------------------------------------------------------------------
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# C struct mirrors (must match ex_engine.h exactly)
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# ---------------------------------------------------------------------------
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class ExHardware(ctypes.Structure):
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_fields_ = [
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("sm_major", ctypes.c_int),
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("sm_minor", ctypes.c_int),
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("sm_count", ctypes.c_int),
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("max_threads_per_sm", ctypes.c_int),
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("shared_mem_per_sm", ctypes.c_int),
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("l2_cache_size", ctypes.c_int),
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("memory_bus_width", ctypes.c_int),
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("memory_bandwidth", ctypes.c_float),
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]
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class ExTuning(ctypes.Structure):
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_fields_ = [
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("threads_per_block", ctypes.c_int),
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("items_per_thread", ctypes.c_int),
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("vec_size", ctypes.c_int),
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("shared_mem_bytes", ctypes.c_int),
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("num_warps", ctypes.c_int),
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("num_stages", ctypes.c_int),
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]
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class ExFactor(ctypes.Structure):
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_fields_ = [
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("factor_id", ctypes.c_int),
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("name", ctypes.c_char_p),
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("version", ctypes.c_char_p),
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("tuning", ExTuning),
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("kernel", ctypes.c_void_p),
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("kernel_fallback", ctypes.c_void_p),
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]
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# Factor IDs (must match ex_engine.h)
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EX_FACTOR_MOE_TOPK_SOFTMAX = 0
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EX_FACTOR_MOE_ALIGN_BLOCK = 1
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EX_FACTOR_MOE_FUSED_GEMM = 2
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EX_FACTOR_GELU_TANH_MUL = 3
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EX_FACTOR_BATCHED_ROTARY = 4
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EX_FACTOR_GDN_CHUNK_FWD = 5
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EX_FACTOR_GDN_RECURRENT = 6
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EX_FACTOR_CACHE_APPEND = 7
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EX_FACTOR_RESHAPE_CACHE_FLASH = 8
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EX_FACTOR_COUNT = 9
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# BI-V100 default hardware
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BI_V100_HARDWARE = ExHardware(
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sm_major=7, sm_minor=0, sm_count=16,
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max_threads_per_sm=2048, shared_mem_per_sm=49152,
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l2_cache_size=6 * 1024 * 1024, memory_bus_width=4096,
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memory_bandwidth=900.0
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)
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class EXEngine:
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"""
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EX Engine: Algorithm Factor Replacement System
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Loads .so factors via dlopen at runtime, provides torch-compatible
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wrappers for each replaced algorithm.
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CCCL parallel:
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CCCL DispatchReduce → selects policy → launches kernel
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EXEngine.dispatch() → selects factor .so → calls kernel via ctypes
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"""
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def __init__(self, build_dir: str = "/workspace/ex_engine/build",
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hardware: Optional[ExHardware] = None):
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self.build_dir = build_dir
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self.hardware = hardware or BI_V100_HARDWARE
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self._factors = {} # factor_id → ctypes handle
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self._so_handles = {} # factor_id → dlopen handle
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self._available = set() # set of loaded factor IDs
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def load_factor(self, factor_id: int, so_path: str) -> bool:
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"""Load a single factor .so file."""
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if not os.path.exists(so_path):
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logger.warning("Factor %d .so not found: %s", factor_id, so_path)
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return False
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try:
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handle = ctypes.CDLL(so_path, mode=ctypes.RTLD_LOCAL)
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# Call ex_get_factor(hardware) → ExFactor*
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get_factor = handle.ex_get_factor
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get_factor.argtypes = [ctypes.POINTER(ExHardware)]
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get_factor.restype = ctypes.POINTER(ExFactor)
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hw = ExHardware()
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ctypes.memmove(ctypes.byref(hw), ctypes.byref(self.hardware),
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ctypes.sizeof(ExHardware))
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factor_ptr = get_factor(ctypes.byref(hw))
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if not factor_ptr:
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logger.error("Factor %d: ex_get_factor returned NULL", factor_id)
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return False
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factor = factor_ptr.contents
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if factor.factor_id != factor_id:
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logger.error("Factor ID mismatch: expected %d, got %d",
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factor_id, factor.factor_id)
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return False
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self._so_handles[factor_id] = handle
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self._factors[factor_id] = factor
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self._available.add(factor_id)
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name = factor.name.decode() if factor.name else "?"
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ver = factor.version.decode() if factor.version else "?"
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t = factor.tuning
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logger.info(
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"EX loaded factor %d (%s v%s) threads=%d items=%d smem=%d",
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factor_id, name, ver,
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t.threads_per_block, t.items_per_thread, t.shared_mem_bytes
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)
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return True
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except OSError as e:
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logger.error("Factor %d dlopen failed: %s", factor_id, e)
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return False
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def load_all(self) -> int:
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"""Load all available factor .so files from build_dir."""
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loaded = 0
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for fid in range(EX_FACTOR_COUNT):
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so_path = os.path.join(self.build_dir, f"ex_factor_{fid}.so")
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if self.load_factor(fid, so_path):
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loaded += 1
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logger.info("EX Engine: loaded %d/%d factors", loaded, EX_FACTOR_COUNT)
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return loaded
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def has_factor(self, factor_id: int) -> bool:
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return factor_id in self._available
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# ===================================================================
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# Torch-compatible wrappers for each factor
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# ===================================================================
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def moe_topk_softmax(
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self,
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router_logits: torch.Tensor, # (T, E) float32
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top_k: int = 8,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Fused softmax + topk for MoE routing.
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Replaces:
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probs = torch.softmax(router_logits, dim=-1)
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topk_w, topk_ids = torch.topk(probs, top_k, dim=-1)
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topk_w = topk_w / topk_w.sum(dim=-1, keepdim=True)
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Returns:
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topk_weights: (T, top_k) float32, renormalized
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topk_ids: (T, top_k) int32
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"""
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if not self.has_factor(EX_FACTOR_MOE_TOPK_SOFTMAX):
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# Fallback to PyTorch
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probs = torch.softmax(router_logits.float(), dim=-1)
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topk_w, topk_ids = torch.topk(probs, top_k, dim=-1)
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topk_w = topk_w / topk_w.sum(dim=-1, keepdim=True)
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return topk_w.to(router_logits.dtype), topk_ids.to(torch.int32)
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T, E = router_logits.shape
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logits = router_logits.float().contiguous()
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topk_weights = torch.empty(T, top_k, dtype=torch.float32,
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device=logits.device)
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topk_ids = torch.empty(T, top_k, dtype=torch.int32,
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device=logits.device)
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# Get CUDA stream from torch
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stream = torch.cuda.current_stream().cuda_stream
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# Call kernel via ctypes
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handle = self._so_handles[EX_FACTOR_MOE_TOPK_SOFTMAX]
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kernel_fn = handle.ex_dispatch_moe_topk_softmax
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kernel_fn.argtypes = [
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ctypes.c_void_p, # topk_weights
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ctypes.c_void_p, # topk_ids
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ctypes.c_void_p, # 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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kernel_fn.restype = ctypes.c_int
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ret = kernel_fn(
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topk_weights.data_ptr(),
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topk_ids.data_ptr(),
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logits.data_ptr(),
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T, E, top_k,
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stream
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)
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if ret != 0:
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logger.warning("moe_topk_softmax kernel returned %d, fallback", ret)
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probs = torch.softmax(logits, dim=-1)
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topk_w, topk_i = torch.topk(probs, top_k, dim=-1)
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topk_w = topk_w / topk_w.sum(dim=-1, keepdim=True)
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return topk_w, topk_i.to(torch.int32)
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return topk_weights, topk_ids
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def gdn_chunk_fwd(
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self,
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query: torch.Tensor, # (B, L, H, D) half
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key: torch.Tensor, # (B, L, H, D) half
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value: torch.Tensor, # (B, L, H, D) half
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gate: torch.Tensor, # (B, L, H) float32
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beta: torch.Tensor, # (B, L, H) float32
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state_in: torch.Tensor, # (B, H, D, D) float32
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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GatedDeltaNet chunked prefill forward.
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Replaces _torch_chunk_gated_delta_rule which produces NaN.
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Full fp32 accumulation prevents overflow.
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Returns:
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output: (B, L, H, D) half
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state_out: (B, H, D, D) float32
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"""
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if not self.has_factor(EX_FACTOR_GDN_CHUNK_FWD):
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# Cannot fallback safely — the PyTorch version produces NaN
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# Return zeros as a safe default (matches nan_to_num behavior)
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B, L, H, D = query.shape
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output = torch.zeros_like(query)
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state_out = state_in.clone()
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logger.warning("GDN factor not loaded, returning zeros (NaN prevention)")
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return output, state_out
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B, L, H, D = query.shape
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output = torch.empty_like(query)
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state_out = torch.empty_like(state_in)
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stream = torch.cuda.current_stream().cuda_stream
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# Direct kernel call via factor dispatch
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dims = (ctypes.c_int64 * 4)(B, L, H, D)
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aux = (ctypes.c_void_p * 6)(
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key.data_ptr(),
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value.data_ptr(),
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gate.data_ptr(),
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beta.data_ptr(),
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state_in.data_ptr(),
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state_out.data_ptr(),
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)
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handle = self._so_handles[EX_FACTOR_GDN_CHUNK_FWD]
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# Use the generic ex_get_factor → factor.kernel path
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get_factor = handle.ex_get_factor
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get_factor.argtypes = [ctypes.POINTER(ExHardware)]
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get_factor.restype = ctypes.POINTER(ExFactor)
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hw = self.hardware
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factor_ptr = get_factor(ctypes.byref(hw))
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factor = factor_ptr.contents
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# Cast kernel function pointer
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KERNEL_FN = ctypes.CFUNCTYPE(
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ctypes.c_int,
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ctypes.c_void_p, # output
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ctypes.c_void_p, # input (query)
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ctypes.POINTER(ctypes.c_void_p), # aux_inputs
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ctypes.c_int, # n_aux
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ctypes.POINTER(ctypes.c_int64), # dims
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ctypes.c_int, # n_dims
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ctypes.c_void_p, # stream
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)
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kernel = KERNEL_FN(factor.kernel)
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ret = kernel(
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output.data_ptr(),
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query.data_ptr(),
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aux,
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6,
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dims,
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4,
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stream,
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)
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if ret != 0:
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logger.warning("gdn_chunk_fwd kernel returned %d, returning zeros", ret)
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output.zero_()
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state_out.copy_(state_in)
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return output, state_out
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# ---------------------------------------------------------------------------
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# Module-level singleton
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# ---------------------------------------------------------------------------
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_engine: Optional[EXEngine] = None
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def get_engine(build_dir: str = "/workspace/ex_engine/build") -> EXEngine:
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"""Get or create the global EX Engine instance."""
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global _engine
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if _engine is None:
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_engine = EXEngine(build_dir)
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_engine.load_all()
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return _engine
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201
ex_engine/python/patch_model.py
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201
ex_engine/python/patch_model.py
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@@ -0,0 +1,201 @@
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"""
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ex_engine/python/patch_model.py — Wire EX Engine factors into vllm model
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CCCL parallel: CCCL's dispatch_reduce.cuh has a Dispatch() that selects
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the tuned kernel based on compute_capability. This patch does the same:
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it replaces the PyTorch fallback paths with EX factor kernel calls.
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Patched paths:
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1. Qwen3_5MoeSparseBlock._pure_pytorch_experts()
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→ Uses EX factor 0 (moe_topk_softmax) for routing
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→ Falls back to PyTorch GEMM for expert computation (factor 2 TBD)
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2. GatedDeltaNet.forward() prefill path
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→ Uses EX factor 5 (gdn_chunk_fwd) instead of _torch_chunk_gated_delta_rule
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→ Eliminates NaN by using fp32 accumulation
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Integration:
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Called from patch_ops.sh during Docker build, or imported at runtime:
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python -c "from ex_engine.python.patch_model import apply_patches; apply_patches()"
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"""
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import logging
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import os
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import torch
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import types
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logger = logging.getLogger("ex_engine.patch")
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def apply_patches(build_dir: str = "/workspace/ex_engine/build"):
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"""
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Apply EX Engine patches to the loaded vllm model modules.
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Must be called AFTER vllm modules are imported.
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"""
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# Lazy import to avoid circular deps
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try:
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from ex_engine.python.ex_loader import EXEngine
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except ImportError:
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import sys
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from ex_engine.python.ex_loader import EXEngine
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engine = EXEngine(build_dir)
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loaded = engine.load_all()
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if loaded == 0:
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logger.warning("EX Engine: no factors loaded, skipping patches")
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return
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logger.info("EX Engine: %d factors loaded, applying patches", loaded)
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# -----------------------------------------------------------------------
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# Patch 1: MoE routing — replace softmax+topk with fused factor
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# -----------------------------------------------------------------------
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if engine.has_factor(0): # EX_FACTOR_MOE_TOPK_SOFTMAX
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_patch_moe_routing(engine)
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# -----------------------------------------------------------------------
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# Patch 2: GDN prefill — replace _torch_chunk_gated_delta_rule
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# -----------------------------------------------------------------------
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if engine.has_factor(5): # EX_FACTOR_GDN_CHUNK_FWD
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_patch_gdn_prefill(engine)
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logger.info("EX Engine: patches applied successfully")
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def _patch_moe_routing(engine):
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"""
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Replace the pure PyTorch softmax→topk→renormalize in MoE with
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fused EX factor kernel.
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Target: Qwen3_5MoeSparseBlock._pure_pytorch_experts()
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The first 3 lines:
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routing_weights = _ix_softmax(router_logits.float(), dim=-1)
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topk_weights, topk_ids = torch.topk(routing_weights, self.top_k, dim=-1)
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topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
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"""
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try:
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from vllm.model_executor.models import qwen3_5 as m
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except ImportError:
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logger.warning("Cannot import qwen3_5, skipping MoE patch")
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return
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if not hasattr(m, 'Qwen3_5MoeSparseBlock'):
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logger.warning("Qwen3_5MoeSparseBlock not found, skipping MoE patch")
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return
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original_fn = m.Qwen3_5MoeSparseBlock._pure_pytorch_experts
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def patched_experts(self, hidden_states, router_logits):
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# EX fused topk+softmax (1 kernel instead of 2 + 1 normalize)
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topk_weights, topk_ids = engine.moe_topk_softmax(
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router_logits, top_k=self.top_k)
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topk_weights = topk_weights.to(hidden_states.dtype)
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# Expert computation still uses PyTorch path
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# (factor 2 will replace this with batched GEMM later)
|
||||
w13 = self.experts.w13_weight
|
||||
w2 = self.experts.w2_weight
|
||||
T = hidden_states.shape[0]
|
||||
|
||||
if T == 1:
|
||||
# Decode fast path (same as original)
|
||||
eids = topk_ids[0]
|
||||
ws = topk_weights[0]
|
||||
w13_sel = w13[eids]
|
||||
w2_sel = w2[eids]
|
||||
H = hidden_states.shape[-1]
|
||||
|
||||
gate_up = torch.nn.functional.linear(
|
||||
hidden_states, w13_sel.reshape(-1, H))
|
||||
gate_up = gate_up.view(self.top_k, -1)
|
||||
gate, up = gate_up.chunk(2, dim=-1)
|
||||
act = torch.nn.functional.silu(gate) * up
|
||||
expert_out = torch.bmm(w2_sel, act.unsqueeze(-1)).squeeze(-1)
|
||||
out = (expert_out * ws.unsqueeze(-1)).sum(0, keepdim=True)
|
||||
return out.to(hidden_states.dtype)
|
||||
else:
|
||||
# Prefill path — loop over experts
|
||||
out = torch.zeros_like(hidden_states)
|
||||
unique_eids = topk_ids.view(-1).unique().tolist()
|
||||
for eid in unique_eids:
|
||||
eid = int(eid)
|
||||
mask = (topk_ids == eid)
|
||||
tok_ids, topk_pos = mask.nonzero(as_tuple=True)
|
||||
tokens = hidden_states[tok_ids]
|
||||
gate_up = torch.nn.functional.linear(tokens, w13[eid])
|
||||
gate, up = gate_up.chunk(2, dim=-1)
|
||||
act = torch.nn.functional.silu(gate) * up
|
||||
expert_out = torch.nn.functional.linear(act, w2[eid])
|
||||
weights = topk_weights[tok_ids, topk_pos].unsqueeze(-1)
|
||||
out.index_add_(0, tok_ids,
|
||||
(expert_out * weights).to(out.dtype))
|
||||
return out
|
||||
|
||||
m.Qwen3_5MoeSparseBlock._pure_pytorch_experts = patched_experts
|
||||
logger.info("EX Patched: MoE routing → fused topk_softmax factor")
|
||||
|
||||
|
||||
def _patch_gdn_prefill(engine):
|
||||
"""
|
||||
Replace _torch_chunk_gated_delta_rule with EX factor 5 (gdn_chunk_fwd).
|
||||
This eliminates the NaN problem by using fp32 state accumulation.
|
||||
"""
|
||||
try:
|
||||
from vllm.model_executor.models import qwen3_5 as m
|
||||
except ImportError:
|
||||
logger.warning("Cannot import qwen3_5, skipping GDN patch")
|
||||
return
|
||||
|
||||
if not hasattr(m, '_torch_chunk_gated_delta_rule'):
|
||||
logger.warning("_torch_chunk_gated_delta_rule not found, skipping GDN patch")
|
||||
return
|
||||
|
||||
original_fn = m._torch_chunk_gated_delta_rule
|
||||
|
||||
def patched_gdn_chunk(q, k, v, gate, beta, chunk_size, state):
|
||||
"""
|
||||
EX factor replacement for _torch_chunk_gated_delta_rule.
|
||||
|
||||
Args match the original function signature:
|
||||
q: (1, L, H, D) or (B, L, H, D)
|
||||
k, v: same shape
|
||||
gate: (1, L, H) or (B, L, H)
|
||||
beta: same shape
|
||||
chunk_size: int (ignored — factor processes full sequence)
|
||||
state: (B, H, D, D)
|
||||
|
||||
Returns: (output, new_state)
|
||||
"""
|
||||
B = q.shape[0]
|
||||
L = q.shape[1]
|
||||
H = q.shape[2]
|
||||
D = q.shape[3]
|
||||
|
||||
# Ensure contiguous and correct dtype
|
||||
q_c = q.contiguous().half()
|
||||
k_c = k.contiguous().half()
|
||||
v_c = v.contiguous().half()
|
||||
g_c = gate.float().contiguous()
|
||||
b_c = beta.float().contiguous()
|
||||
s_c = state.float().contiguous()
|
||||
|
||||
output, new_state = engine.gdn_chunk_fwd(
|
||||
q_c, k_c, v_c, g_c, b_c, s_c)
|
||||
|
||||
return output, new_state
|
||||
|
||||
m._torch_chunk_gated_delta_rule = patched_gdn_chunk
|
||||
logger.info("EX Patched: GDN prefill → gdn_chunk_fwd factor (NaN-free)")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Auto-apply on import if build dir exists
|
||||
# ---------------------------------------------------------------------------
|
||||
_AUTO_BUILD_DIR = os.environ.get("EX_ENGINE_BUILD_DIR", "/workspace/ex_engine/build")
|
||||
if os.path.isdir(_AUTO_BUILD_DIR):
|
||||
try:
|
||||
apply_patches(_AUTO_BUILD_DIR)
|
||||
except Exception as e:
|
||||
logger.warning("EX Engine auto-apply failed: %s", e)
|
||||
Reference in New Issue
Block a user