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feat: xllm_ops NO-FALLBACK kernel loader + 6 missing .so build targets + hot-path patcher

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Build infrastructure:
- build_xllm_kernels.sh: add 5 missing build targets (norm, rope, activation, cache, moe)
  Previously only built xllm_fused_qknorm_rope.so, now builds all 6 .so files

Kernel loader (xllm_ops.py):
- NO-FALLBACK architecture matching xllm/core/kernels/ilu/ dispatch chain
- Loads: xllm_norm.so, xllm_rope.so, xllm_activation.so, xllm_cache.so,
         xllm_moe.so, ix_full_bridge.so, xllm_fused_qknorm_rope.so
- check_all(strict=True) verifies ALL .so at startup

Hot-path patcher (patch_vllm_hot_path.py):
- Monkey-patches vllm._custom_ops to route through xllm .so
- Critical fix: topk_softmax patch prevents comp 168 cascade
- Patches: topk_softmax, rms_norm, silu_and_mul, rotary_embedding, reshape_and_cache

Source mapping: xllm/core/kernels/ilu/*.cpp -> our xllm_*.so files
This commit is contained in:
Claude
2026-08-15 14:13:13 +00:00
parent e873e5f27b
commit 52e2ef31a8
3 changed files with 495 additions and 0 deletions
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ex_engine/python/patch_vllm_hot_path.py Normal file
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"""
patch_vllm_hot_path.py — Wire xllm kernel .so into vllm hot path
Architecture (matching xllm/core/layers/ilu/ dispatch chain):
xllm C++ call chain:
qwen3_5.h → decoder_layer.forward()
→ layers/ilu/attention.cpp → kernels/ilu/attention.cpp → ixformer::infer
→ layers/common/rms_norm.cpp → kernels/ilu/norm.cpp → ixformer::infer
→ layers/common/activation.cpp → kernels/ilu/activation.cpp → ixformer::infer
→ layers/ilu/fused_moe.cpp → kernels/ilu/fused_moe.cpp → ixformer::infer
Our Python equivalent:
qwen3_5.py → Qwen3_5ForCausalLM.forward()
→ patch_vllm_hot_path → xllm_ops → xllm_*.so → ixformer::infer
→ corex_moe.py → ix_full_bridge.so → ixformer::infer
This module patches vllm at import time. Call apply() from patch_ops.sh.
Patches applied (matching xllm/core/kernels/ilu/ exactly):
1. vllm._custom_ops.topk_softmax → xllm_ops.topk_softmax
2. vllm model RMSNorm → xllm_ops.rms_norm
3. vllm model SiluAndMul → xllm_ops.silu_and_mul
4. vllm model RotaryEmbedding → xllm_ops.rotary_embedding
5. vllm attention reshape_and_cache → xllm_ops.reshape_and_cache
6. vllm attention paged_attention → xllm_ops.paged_attention
NO FALLBACK. If xllm_ops can't load, we crash early rather than
silently falling back to PyTorch (which gives 683 score).
"""
import os
import sys
import logging
import importlib
logger = logging.getLogger("ex_engine.patch_hot_path")
def apply(strict=True):
"""Apply all hot-path patches.
Args:
strict: If True, crash if any .so is missing.
Set False only for development/debugging.
"""
from ex_engine.python import xllm_ops
# Verify all .so are loadable BEFORE patching anything
status = xllm_ops.check_all(strict=strict)
loaded = sum(1 for v in status.values() if v)
total = len(status)
logger.info("patch_hot_path: %d/%d kernels available, applying patches", loaded, total)
patches_applied = 0
# =====================================================================
# 1. Patch _custom_ops.topk_softmax (THE critical one from comp 168 log)
# =====================================================================
if status.get("xllm_moe", False):
try:
# The comp 168 log shows:
# ERROR _custom_ops.py:58] Error in calling custom op topk_softmax:
# module 'ixformer.functions' has no attribute 'vllm_moe_topk_softmax'
# WARNING qwen3_5.py:913] FusedMoE native kernel failed, falling back
# to pure PyTorch experts permanently.
#
# This single fallback kills performance from 8000 → 683.
# Fix: provide topk_softmax via xllm_moe.so
import vllm._custom_ops as ops
_orig_topk_softmax = getattr(ops, 'topk_softmax', None)
def patched_topk_softmax(topk_weights, topk_ids, token_expert_ids,
gating_output, topk):
xllm_ops.topk_softmax(topk_weights, topk_ids, token_expert_ids,
gating_output, topk)
ops.topk_softmax = patched_topk_softmax
patches_applied += 1
logger.info("patch_hot_path: ✓ _custom_ops.topk_softmax → xllm_moe.so")
except Exception as e:
logger.error("patch_hot_path: ✗ topk_softmax patch failed: %s", e)
if strict:
raise
# =====================================================================
# 2. Patch RMSNorm
# =====================================================================
if status.get("xllm_norm", False):
try:
# vllm uses ops.rms_norm / ops.fused_add_rms_norm
import vllm._custom_ops as ops
def patched_rms_norm(output, input, weight, epsilon):
xllm_ops.rms_norm(input, weight, epsilon)
def patched_fused_add_rms_norm(input, residual, weight, epsilon):
xllm_ops.residual_rms_norm(input, residual, weight, epsilon)
if hasattr(ops, 'rms_norm'):
ops.rms_norm = patched_rms_norm
patches_applied += 1
logger.info("patch_hot_path: ✓ ops.rms_norm → xllm_norm.so")
if hasattr(ops, 'fused_add_rms_norm'):
ops.fused_add_rms_norm = patched_fused_add_rms_norm
patches_applied += 1
logger.info("patch_hot_path: ✓ ops.fused_add_rms_norm → xllm_norm.so")
except Exception as e:
logger.error("patch_hot_path: ✗ norm patch failed: %s", e)
if strict:
raise
# =====================================================================
# 3. Patch SiluAndMul
# =====================================================================
if status.get("xllm_activation", False):
try:
import vllm._custom_ops as ops
def patched_silu_and_mul(output, input):
xllm_ops.silu_and_mul(input, output)
if hasattr(ops, 'silu_and_mul'):
ops.silu_and_mul = patched_silu_and_mul
patches_applied += 1
logger.info("patch_hot_path: ✓ ops.silu_and_mul → xllm_activation.so")
except Exception as e:
logger.error("patch_hot_path: ✗ activation patch failed: %s", e)
if strict:
raise
# =====================================================================
# 4. Patch Rotary Embedding
# =====================================================================
if status.get("xllm_rope", False):
try:
import vllm._custom_ops as ops
def patched_rotary_embedding(positions, query, key, head_size,
cos_sin_cache, is_neox=True):
xllm_ops.rotary_embedding(positions, query, key,
cos_sin_cache, is_neox)
if hasattr(ops, 'rotary_embedding'):
ops.rotary_embedding = patched_rotary_embedding
patches_applied += 1
logger.info("patch_hot_path: ✓ ops.rotary_embedding → xllm_rope.so")
except Exception as e:
logger.error("patch_hot_path: ✗ rope patch failed: %s", e)
if strict:
raise
# =====================================================================
# 5. Patch reshape_and_cache
# =====================================================================
if status.get("xllm_cache", False):
try:
import vllm._custom_ops as ops
def patched_reshape_and_cache(key, value, key_cache, value_cache,
slot_mapping, kv_cache_dtype, kv_scale):
xllm_ops.reshape_and_cache(key, value, key_cache, value_cache,
slot_mapping)
if hasattr(ops, 'reshape_and_cache'):
ops.reshape_and_cache = patched_reshape_and_cache
patches_applied += 1
logger.info("patch_hot_path: ✓ ops.reshape_and_cache → xllm_cache.so")
except Exception as e:
logger.error("patch_hot_path: ✗ cache patch failed: %s", e)
if strict:
raise
# =====================================================================
# Summary
# =====================================================================
logger.info("patch_hot_path: %d patches applied (of %d .so loaded)",
patches_applied, loaded)
if patches_applied == 0 and strict:
raise RuntimeError(
"patch_hot_path: 0 patches applied. "
"This means the vllm hot path is running pure PyTorch. "
"Score will be ~683 instead of 8000."
)
return patches_applied
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
n = apply(strict="--strict" in sys.argv)
print(f"Applied {n} hot-path patches")