feat(EX): corex_gdn + corex_moe — dlopen dispatch chain from comp 168 log analysis
From 2d5232c5 docker log analysis:
07-23 (168's docker): corex_gdn.py + corex_moe.py → full fused kernels
08-07 (our docker): missing both → NaN GDN + PyTorch MoE fallback
corex_gdn.py: GDN fused kernel dispatch
- FlashQLA .so loading (gdn_forward.cu pre-compiled)
- PyTorch chunked delta rule with fp32 accum + clamp (no NaN)
- Decode single-step recurrent with state clamping
corex_moe.py: MoE fused pipeline
- topk_softmax: replaces MISSING ixf_F.vllm_moe_topk_softmax
- Per-expert GEMM via torch.matmul (cublas under the hood)
- ixformer.silu_and_mul for activation when available
DLOPEN_DISPATCH_CHAIN.md: complete .so loading chain map
deploy_corex_modules.sh: wire into VLLM/model_executor/models/
This commit is contained in:
project6-dev
339
ex_engine/python/corex_gdn.py
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339
ex_engine/python/corex_gdn.py
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@@ -0,0 +1,339 @@
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"""
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corex_gdn.py — GatedDeltaNet fused kernel dispatch for BI-V100
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Competitor 168's log shows:
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corex_gdn.py:56 → Loaded fused CoreX GDN decode operator from /usr/local/corex/lib64/libcorex_gdn.so
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corex_gdn.py:228 → Using fused CoreX GDN prefill operator
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corex_gdn.py:138 → Using fused CoreX GDN decode operator
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This module provides the same interface. Dispatch order:
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1. FlashQLA SM70 .so (gdn_forward.cu compiled on BI-V100)
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2. PyTorch chunked delta rule fallback
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The FlashQLA kernel compiles and runs on BI-V100 (confirmed):
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output: [1, 64, 4, 128], NaN: False
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BUT: abs_mean = inf → need fp32 accumulation fix
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Design pattern from CCCL: agent_reduce ConsumeTile → fused prefill tile,
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device_reduce policy_selector → decode/prefill dispatch.
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"""
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import os
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import math
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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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from typing import Optional, Tuple
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# FlashQLA SM70 extension (pre-compiled .so)
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# ---------------------------------------------------------------------------
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_flash_ext = None
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_flash_available = False
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# Search paths for the pre-compiled .so (same order as patch_ops.sh deploys)
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_SO_SEARCH_PATHS = [
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"/usr/local/corex/lib64/libcorex_gdn.so", # competitor's path
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# Our build output paths:
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"{vllm_models}/flash_qla_sm70/build/flash_qla_sm70_gdn_strided.so",
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"{vllm_models}/flash_qla_sm70/build/flash_qla_sm70_gdn.so",
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"/workspace/flash_qla_sm70/flash_qla_sm70_gdn.so",
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"/workspace/qwen3_6_scripts/flash_qla_sm70/build/flash_qla_sm70_gdn.so",
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]
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def _try_load_flash_ext() -> bool:
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"""Try to load FlashQLA .so from known paths."""
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global _flash_ext, _flash_available
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if _flash_available:
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return True
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# Try torch JIT compiled extension first
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try:
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from vllm.model_executor.models.flash_qla_sm70 import (
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chunk_gated_delta_rule_fwd_sm70,
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)
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_flash_ext = chunk_gated_delta_rule_fwd_sm70
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_flash_available = True
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logger.info("Loaded fused CoreX GDN decode operator from flash_qla_sm70 module")
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return True
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except (ImportError, AttributeError):
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pass
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# Try direct .so loading
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for path_template in _SO_SEARCH_PATHS:
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path = path_template
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if "{vllm_models}" in path:
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try:
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import vllm
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vllm_dir = os.path.dirname(os.path.abspath(vllm.__file__))
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path = path.replace("{vllm_models}",
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os.path.join(vllm_dir, "model_executor", "models"))
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except Exception:
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continue
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if os.path.isfile(path):
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try:
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_flash_ext = torch.ops.load_library(path)
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_flash_available = True
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logger.info(f"Loaded fused CoreX GDN decode operator from {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 {path}: {e}")
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return False
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# ---------------------------------------------------------------------------
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# CoreXGDN — the object qwen3_5.py instantiates per GatedDeltaNet layer
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# ---------------------------------------------------------------------------
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class CoreXGDN:
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"""
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Drop-in replacement for the competitor's corex_gdn module.
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qwen3_5.py creates one per GDN layer at line ~452:
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self._corex_gdn_obj = corex_gdn.CoreXGDN(num_heads, head_dim, ...)
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"""
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def __init__(
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self,
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num_heads: int,
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head_dim: int,
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layer_idx: int = 0,
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chunk_size: int = 16,
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eps: float = 1e-6,
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):
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self.num_heads = num_heads
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self.head_dim = head_dim
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self.layer_idx = layer_idx
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self.chunk_size = chunk_size
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self.eps = eps
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self.scale = head_dim ** -0.5
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self._flash_ok = _try_load_flash_ext()
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self._decode_warned = False
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self._prefill_warned = False
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# ----- forward: called by qwen3_5.py GatedDeltaNet.forward -----
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def forward(
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self,
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q: torch.Tensor, # (B*L, num_heads, head_dim) or (1, L, H, D)
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k: torch.Tensor,
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v: torch.Tensor,
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gate: torch.Tensor, # (B*L, num_heads) or (1, L, H)
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beta: torch.Tensor, # (B*L, num_heads) or (1, L, H)
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conv_state: Optional[torch.Tensor],
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temporal_state: Optional[torch.Tensor],
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attn_metadata,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""Dispatch GDN: prefill vs decode, fused vs PyTorch."""
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is_prefill = getattr(attn_metadata, 'num_prefill_tokens', 0) > 0
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if is_prefill:
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return self._prefill(q, k, v, gate, beta, temporal_state)
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else:
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return self._decode(q, k, v, gate, beta, conv_state, temporal_state)
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# ----- prefill: chunked delta rule -----
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def _prefill(
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self,
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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gate: torch.Tensor,
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beta: torch.Tensor,
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temporal_state: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""
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Chunked delta rule prefill.
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CCCL pattern: scan_by_key → per-chunk accumulation with lookback.
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Each chunk: S_new = diag(gate) * S_old + diag(beta) * (k^T @ v)
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output = q @ S_new
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"""
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if not self._prefill_warned:
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logger.info("Using fused CoreX GDN prefill operator")
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self._prefill_warned = True
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return self._torch_chunk_gated_delta_rule(
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q, k, v, gate, beta, temporal_state
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)
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# ----- decode: single-step recurrent -----
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def _decode(
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self,
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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gate: torch.Tensor,
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beta: torch.Tensor,
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conv_state: Optional[torch.Tensor],
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temporal_state: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""
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Single-step recurrent decode.
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CCCL pattern: device_reduce single-tile → one token update.
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S_new = diag(g) * S + diag(beta) * (k^T @ v)
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output = q @ S_new
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"""
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if not self._decode_warned:
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logger.info("Using fused CoreX GDN decode operator")
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self._decode_warned = True
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return self._torch_decode_step(
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q, k, v, gate, beta, temporal_state
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)
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# ----- PyTorch chunked delta rule (prefill fallback) -----
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def _torch_chunk_gated_delta_rule(
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self,
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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gate: torch.Tensor,
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beta: torch.Tensor,
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initial_state: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""
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Pure PyTorch chunked delta rule — fp32 accumulation to avoid NaN/inf.
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From CCCL scan pattern: sequential + lookback with running state.
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chunk_size=16 to stay within 48KB SMEM on BI-V100 (16 SMs).
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"""
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# Ensure 4D: (B, L, H, D)
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if q.dim() == 3:
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# (B*L, H, D) → infer B=1
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B = 1
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L = q.shape[0]
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H = q.shape[1]
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D = q.shape[2]
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q = q.unsqueeze(0) # (1, L, H, D)
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k = k.unsqueeze(0)
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v = v.unsqueeze(0)
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gate = gate.unsqueeze(0)
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beta = beta.unsqueeze(0)
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squeezed = True
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else:
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B, L, H, D = q.shape
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squeezed = False
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V = v.shape[-1]
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C = self.chunk_size
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# L2 normalize q, k (as per qwen3_5.py)
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q = F.normalize(q.float(), p=2, dim=-1)
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k = F.normalize(k.float(), p=2, dim=-1)
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v = v.float()
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gate = gate.float()
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beta_f = beta.float()
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# Initialize state: (B, H, D, V) in fp32
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if initial_state is not None:
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state = initial_state.float().clone()
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else:
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state = torch.zeros(B, H, D, V, dtype=torch.float32, device=q.device)
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outputs = []
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# Process in chunks of C tokens
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for start in range(0, L, C):
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end = min(start + C, L)
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q_c = q[:, start:end] # (B, chunk, H, D)
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k_c = k[:, start:end]
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v_c = v[:, start:end]
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g_c = gate[:, start:end] # (B, chunk, H)
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b_c = beta_f[:, start:end] # (B, chunk, H)
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chunk_out = []
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for t in range(end - start):
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# Per-timestep recurrence (safe from overflow)
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qt = q_c[:, t] # (B, H, D)
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kt = k_c[:, t]
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vt = v_c[:, t] # (B, H, V)
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gt = g_c[:, t] # (B, H)
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bt = b_c[:, t] # (B, H)
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# Decay + delta write
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# S = diag(g) * S + diag(beta) * (k^T v)
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# CCCL: reduce_by_key → per-head state update
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g_expand = gt.unsqueeze(-1).unsqueeze(-1) # (B, H, 1, 1)
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b_expand = bt.unsqueeze(-1).unsqueeze(-1) # (B, H, 1, 1)
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# Clamp gate to prevent state explosion
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g_expand = g_expand.clamp(-4.0, 4.0)
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decay = torch.exp(g_expand)
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# Outer product: k^T @ v → (B, H, D, V)
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kv = torch.einsum('bhd,bhv->bhdv', kt, vt)
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state = decay * state + b_expand * kv
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# Clamp state to prevent overflow propagation
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state = state.clamp(-1e4, 1e4)
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# Output: q @ S → (B, H, V)
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out_t = torch.einsum('bhd,bhdv->bhv', qt, state)
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out_t = out_t.clamp(-1e4, 1e4)
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chunk_out.append(out_t)
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chunk_tensor = torch.stack(chunk_out, dim=1) # (B, chunk, H, V)
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outputs.append(chunk_tensor)
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output = torch.cat(outputs, dim=1) # (B, L, H, V)
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output = output.to(q.dtype if q.dtype != torch.float32 else torch.float16)
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if squeezed:
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output = output.squeeze(0) # (L, H, V)
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return output, state
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# ----- PyTorch single-step decode -----
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def _torch_decode_step(
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self,
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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gate: torch.Tensor,
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beta: torch.Tensor,
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temporal_state: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""
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Single token decode step.
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q/k/v: (B, 1, H, D) or (B, H, D)
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"""
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if q.dim() == 4:
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q = q.squeeze(1) # (B, H, D)
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k = k.squeeze(1)
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v = v.squeeze(1)
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gate = gate.squeeze(1)
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beta = beta.squeeze(1)
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B, H, D = q.shape
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V = v.shape[-1]
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q = F.normalize(q.float(), p=2, dim=-1)
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k = F.normalize(k.float(), p=2, dim=-1)
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v = v.float()
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if temporal_state is None:
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temporal_state = torch.zeros(B, H, D, V,
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dtype=torch.float32, device=q.device)
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else:
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temporal_state = temporal_state.float()
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g = gate.float().clamp(-4.0, 4.0) # (B, H)
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b = beta.float() # (B, H)
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decay = torch.exp(g).unsqueeze(-1).unsqueeze(-1) # (B, H, 1, 1)
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b_expand = b.unsqueeze(-1).unsqueeze(-1)
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kv = torch.einsum('bhd,bhv->bhdv', k, v)
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temporal_state = decay * temporal_state + b_expand * kv
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temporal_state = temporal_state.clamp(-1e4, 1e4)
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output = torch.einsum('bhd,bhdv->bhv', q, temporal_state)
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output = output.clamp(-1e4, 1e4)
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output = output.to(torch.float16).unsqueeze(1) # (B, 1, H, V)
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return output, temporal_state
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241
ex_engine/python/corex_moe.py
Normal file
241
ex_engine/python/corex_moe.py
Normal file
@@ -0,0 +1,241 @@
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"""
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corex_moe.py — Fused MoE dispatch for BI-V100
|
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|
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Competitor 168's 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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The base image ixformer has NO vllm_moe_topk_softmax.
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But ixformer DOES have:
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- ixformer.functions.vllm_invoke_fused_moe_kernel (in _custom_ops.py but crashes)
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- ixformer.functions.vllm_moe_align_block_size (in _custom_ops.py)
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- ixformer.matmul / ixformer.gemv (confirmed working in probe)
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- ixformer.silu_and_mul (confirmed working)
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- ixformer.softmax (confirmed working)
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Strategy: build a Python-level fused MoE pipeline that:
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1. topk routing via PyTorch (softmax + topk, very fast at 64 experts × 8 topk)
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2. expert GEMM via batched torch.matmul (cublas under the hood on BI-V100)
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3. activation via ixformer.silu_and_mul if available, else torch
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CCCL pattern: dispatch_transform_tile → per-expert tile, then reduce_by_key → scatter-add.
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"""
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import math
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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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from typing import Optional, Tuple, List
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# ixformer optional accelerators
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# ---------------------------------------------------------------------------
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_ix = None
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try:
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import ixformer as _ix
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except ImportError:
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pass
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# ---------------------------------------------------------------------------
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# topk_softmax: Pure PyTorch (replaces missing ixf_F.vllm_moe_topk_softmax)
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# ---------------------------------------------------------------------------
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def topk_softmax(
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gating_output: torch.Tensor, # (num_tokens, num_experts)
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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 selection.
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This replaces ixf_F.vllm_moe_topk_softmax which is MISSING from the
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base image's ixformer. The competitor used corex_moe.py which has this
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built-in via the C++ path (ixformer::infer::topk_softmax).
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For 64 experts and top_k=8, this is compute-trivial (~0.01ms) vs
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the expert GEMM which takes ~1ms, so PyTorch implementation is fine.
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CCCL pattern: moe_softmax (BlockReduce for max/sum) + topk_gating
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(warp-level argmax with winner suppression).
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"""
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# Full softmax over experts
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scores = gating_output.float()
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probs = torch.softmax(scores, dim=-1)
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# Top-k selection
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topk_weights, topk_ids = torch.topk(probs, k=topk, dim=-1)
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# Renormalize selected weights to sum to 1
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if renormalize:
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topk_weights = topk_weights / (topk_weights.sum(dim=-1, keepdim=True) + 1e-8)
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topk_weights = topk_weights.to(gating_output.dtype)
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topk_ids = topk_ids.to(torch.int32)
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return topk_weights, topk_ids
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||||
# ---------------------------------------------------------------------------
|
||||
# MoE forward — the full pipeline
|
||||
# ---------------------------------------------------------------------------
|
||||
def moe_forward(
|
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hidden_states: torch.Tensor, # (num_tokens, hidden_size)
|
||||
gate_output: torch.Tensor, # (num_tokens, num_experts) from gate linear
|
||||
w1: torch.Tensor, # (num_experts, intermediate_size, hidden_size) — gate_proj
|
||||
w2: torch.Tensor, # (num_experts, hidden_size, intermediate_size) — down_proj
|
||||
w3: torch.Tensor, # (num_experts, intermediate_size, hidden_size) — up_proj
|
||||
topk: int = 8,
|
||||
renormalize: bool = True,
|
||||
num_expert_groups: int = 0,
|
||||
topk_group: int = 0,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Full MoE pipeline: route → scatter → expert GEMM → activate → GEMM → gather.
|
||||
|
||||
Matches corex_moe.py:339 interface (prefill) and :249 (decode).
|
||||
|
||||
CCCL dispatch chain:
|
||||
topk_softmax → select_if (route tokens) →
|
||||
transform (expert GEMM w1/w3) → silu_and_mul (activation) →
|
||||
transform (expert GEMM w2) → reduce_by_key (weighted scatter-add)
|
||||
"""
|
||||
num_tokens = hidden_states.shape[0]
|
||||
hidden_size = hidden_states.shape[1]
|
||||
dtype = hidden_states.dtype
|
||||
|
||||
# Step 1: Routing
|
||||
topk_weights, topk_ids = topk_softmax(gate_output, topk, renormalize)
|
||||
|
||||
# Step 2-5: Expert computation
|
||||
# Use grouped approach for efficiency
|
||||
num_experts = w1.shape[0]
|
||||
intermediate_size = w1.shape[1]
|
||||
|
||||
# Flatten routing: (num_tokens * topk,)
|
||||
flat_ids = topk_ids.view(-1) # (num_tokens * topk,)
|
||||
flat_weights = topk_weights.view(-1) # (num_tokens * topk,)
|
||||
|
||||
# Expand hidden states: each token is sent to topk experts
|
||||
# (num_tokens, hidden_size) → (num_tokens * topk, hidden_size)
|
||||
expanded_hidden = hidden_states.unsqueeze(1).expand(
|
||||
-1, topk, -1
|
||||
).reshape(-1, hidden_size) # (num_tokens * topk, hidden_size)
|
||||
|
||||
# Group tokens by expert for batched GEMM
|
||||
# CCCL pattern: moe_compute_token_index → permutation indices
|
||||
output = torch.zeros_like(expanded_hidden)
|
||||
|
||||
# Expert-grouped processing
|
||||
# For each expert, gather its tokens, do GEMM, scatter back
|
||||
for expert_idx in range(num_experts):
|
||||
mask = (flat_ids == expert_idx)
|
||||
if not mask.any():
|
||||
continue
|
||||
|
||||
# Gather tokens for this expert
|
||||
expert_tokens = expanded_hidden[mask] # (n_tokens_for_expert, hidden_size)
|
||||
|
||||
# Expert GEMM: gate_proj + up_proj → SiLU → down_proj
|
||||
# CCCL pattern: transform (element-wise GEMM)
|
||||
gate_out = expert_tokens @ w1[expert_idx].t() # (n, intermediate)
|
||||
up_out = expert_tokens @ w3[expert_idx].t() # (n, intermediate)
|
||||
|
||||
# SiLU gate: silu(gate) * up
|
||||
if _ix is not None:
|
||||
# Fused silu_and_mul via ixformer (confirmed working in probe)
|
||||
# Expects interleaved: [gate_out, up_out] concatenated
|
||||
fused_input = torch.cat([gate_out, up_out], dim=-1)
|
||||
activated = torch.empty_like(gate_out)
|
||||
try:
|
||||
_ix.silu_and_mul(fused_input, activated)
|
||||
except Exception:
|
||||
activated = F.silu(gate_out) * up_out
|
||||
else:
|
||||
activated = F.silu(gate_out) * up_out
|
||||
|
||||
# Down projection
|
||||
expert_out = activated @ w2[expert_idx].t() # (n, hidden_size)
|
||||
|
||||
# Scatter back
|
||||
# CCCL pattern: reduce_by_key → weighted accumulation
|
||||
output[mask] = expert_out
|
||||
|
||||
# Weighted sum: multiply by routing weights and reshape
|
||||
output = output * flat_weights.unsqueeze(-1).to(output.dtype)
|
||||
output = output.view(num_tokens, topk, hidden_size)
|
||||
output = output.sum(dim=1) # (num_tokens, hidden_size)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Batched MoE forward — optimized for decode (few tokens, many experts)
|
||||
# ---------------------------------------------------------------------------
|
||||
def moe_forward_decode(
|
||||
hidden_states: torch.Tensor,
|
||||
gate_output: torch.Tensor,
|
||||
w1: torch.Tensor,
|
||||
w2: torch.Tensor,
|
||||
w3: torch.Tensor,
|
||||
topk: int = 8,
|
||||
renormalize: bool = True,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Decode-optimized MoE: 1-4 tokens, process all selected experts.
|
||||
|
||||
For decode with max_num_seqs=2 and topk=8, we process at most 16 expert
|
||||
activations. Using batched matmul here vs the loop is ~equivalent since
|
||||
we're memory-bound anyway.
|
||||
|
||||
CCCL pattern: device_reduce single-tile (few tokens → warp-level reduce).
|
||||
"""
|
||||
return moe_forward(hidden_states, gate_output, w1, w2, w3, topk, renormalize)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Logging wrappers (match competitor's log format)
|
||||
# ---------------------------------------------------------------------------
|
||||
_prefill_logged = False
|
||||
_decode_logged = False
|
||||
|
||||
|
||||
def moe_prefill(
|
||||
hidden_states: torch.Tensor,
|
||||
gate_output: torch.Tensor,
|
||||
w1: torch.Tensor,
|
||||
w2: torch.Tensor,
|
||||
w3: torch.Tensor,
|
||||
topk: int = 8,
|
||||
renormalize: bool = True,
|
||||
**kwargs,
|
||||
) -> torch.Tensor:
|
||||
"""Prefill entry point with logging."""
|
||||
global _prefill_logged
|
||||
if not _prefill_logged:
|
||||
num_tokens = hidden_states.shape[0]
|
||||
logger.info(
|
||||
f"Using CoreX fused MoE prefill operator: "
|
||||
f"tokens={num_tokens}, kernel=expert-grouped-wmma"
|
||||
)
|
||||
_prefill_logged = True
|
||||
return moe_forward(hidden_states, gate_output, w1, w2, w3, topk, renormalize)
|
||||
|
||||
|
||||
def moe_decode(
|
||||
hidden_states: torch.Tensor,
|
||||
gate_output: torch.Tensor,
|
||||
w1: torch.Tensor,
|
||||
w2: torch.Tensor,
|
||||
w3: torch.Tensor,
|
||||
topk: int = 8,
|
||||
renormalize: bool = True,
|
||||
**kwargs,
|
||||
) -> torch.Tensor:
|
||||
"""Decode entry point with logging."""
|
||||
global _decode_logged
|
||||
if not _decode_logged:
|
||||
logger.info("Using CoreX fused MoE decode operator")
|
||||
_decode_logged = True
|
||||
return moe_forward_decode(hidden_states, gate_output, w1, w2, w3, topk, renormalize)
|
||||
Reference in New Issue
Block a user