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

Browse Source 
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")

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"""
xllm_ops.py — NO-FALLBACK xllm kernel loader for vllm hot path
Architecture (matching xllm/core/kernels/ilu/ dispatch):
xllm C++: kernels/ilu/*.cpp → ixformer::infer::* (dlopen ixformer .so)
Our Python: xllm_ops.py → xllm_*.so (dlopen our compiled .so)
→ ix_full_bridge.so (dlopen ixformer bridge)
Source mapping (upstream → us):
xllm/core/kernels/ilu/norm.cpp → xllm_norm.so
xllm/core/kernels/ilu/rope.cpp → xllm_rope.so
xllm/core/kernels/ilu/activation.cpp → xllm_activation.so
xllm/core/kernels/ilu/attention.cpp → ix_full_bridge.so (paged_attention, flash_attn)
xllm/core/kernels/ilu/fused_moe.cpp → xllm_moe.so + ix_full_bridge.so
xllm/core/kernels/ilu/matmul.cpp → ix_full_bridge.so (ixformer_linear)
xllm/core/layers/ilu/fused_moe.cpp → corex_moe.py (Python orchestrator)
xllm/core/layers/ilu/attention.cpp → corex_fa2.py (Python orchestrator)
NO FALLBACK: If a .so fails to load, we raise immediately.
The comp 168 log shows that fallback = pure PyTorch = 683 score.
We need 8000. Every kernel MUST go through hardware-accelerated path.
"""
import os
import sys
import importlib.util
import logging
from typing import Optional, Dict, Any
logger = logging.getLogger("ex_engine.xllm_ops")
# =========================================================================
# .so search paths
# =========================================================================
_SEARCH_DIRS = []
def _init_search_dirs():
"""Build list of directories to search for .so files."""
global _SEARCH_DIRS
if _SEARCH_DIRS:
return
here = os.path.dirname(os.path.abspath(__file__))
# 1. vllm package dir (deployed by patch_ops.sh)
try:
import vllm
_SEARCH_DIRS.append(os.path.dirname(vllm.__file__))
except ImportError:
pass
# 2. prebuilt dir
_SEARCH_DIRS.append(os.path.join(here, "..", "..", "qwen3_6_scripts",
"prebuilt", "corex-3.2.3-ivcore10"))
# 3. build output dir
_SEARCH_DIRS.append(os.path.join(here, "..", "build"))
# 4. /workspace paths (inside docker)
_SEARCH_DIRS.append("/workspace/qwen3_6_scripts/prebuilt/corex-3.2.3-ivcore10")
_SEARCH_DIRS.append("/workspace/ex_engine/build")
# Normalize
_SEARCH_DIRS = [os.path.normpath(d) for d in _SEARCH_DIRS if os.path.isdir(d)]
def _load_so(name: str) -> Any:
"""Load a .so by name. Raises RuntimeError if not found."""
_init_search_dirs()
for d in _SEARCH_DIRS:
path = os.path.join(d, f"{name}.so")
if not os.path.isfile(path):
continue
try:
spec = importlib.util.spec_from_file_location(name, path)
mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)
fns = [x for x in dir(mod) if not x.startswith("_")]
logger.info("xllm_ops: loaded %s from %s (%d functions: %s)",
name, path, len(fns), ", ".join(fns[:8]))
return mod
except Exception as e:
logger.warning("xllm_ops: %s at %s failed: %s", name, path, e)
continue
raise RuntimeError(
f"xllm_ops: CANNOT load {name}.so — searched {_SEARCH_DIRS}. "
f"Build with: bash ex_engine/build_xllm_kernels.sh"
)
# =========================================================================
# Module registry — lazy-loaded, no fallback
# =========================================================================
_modules: Dict[str, Any] = {}
def _get(name: str) -> Any:
if name not in _modules:
_modules[name] = _load_so(name)
return _modules[name]
# =========================================================================
# Public API — matches xllm/core/kernels/ilu/ function signatures
# =========================================================================
# --- Norm (xllm/core/kernels/ilu/norm.cpp) ---
def rms_norm(input, weight, epsilon):
"""RMSNorm. Maps to ixformer::infer::rms_norm."""
return _get("xllm_norm").rms_norm(input, weight, epsilon)
def residual_rms_norm(input, residual, weight, epsilon):
"""Fused residual + RMSNorm. Maps to ixformer::infer::residual_rms_norm."""
return _get("xllm_norm").residual_rms_norm(input, residual, weight, epsilon)
# --- RoPE (xllm/core/kernels/ilu/rope.cpp) ---
def rotary_embedding(positions, query, key, cos_sin_cache, is_neox=True):
"""Fused rotary embedding. Maps to ixformer::infer::xllm_rotary_embedding."""
return _get("xllm_rope").rotary_embedding(positions, query, key,
cos_sin_cache, is_neox)
# --- Activation (xllm/core/kernels/ilu/activation.cpp) ---
def silu_and_mul(input, output=None):
"""Fused SiLU activation. Maps to ixformer::infer::silu_and_mul."""
return _get("xllm_activation").silu_and_mul(input, output)
def gelu_and_mul(input, output=None):
"""Fused GeLU activation."""
return _get("xllm_activation").gelu_and_mul(input, output)
# --- Cache (xllm/core/kernels/ilu/attention.cpp reshape part) ---
def reshape_and_cache(key, value, key_cache, value_cache, slot_mapping):
"""Write KV to paged cache. Maps to ixformer::infer::xllm_reshape_and_cache."""
return _get("xllm_cache").reshape_and_cache(key, value, key_cache,
value_cache, slot_mapping)
# --- Attention (xllm/core/kernels/ilu/attention.cpp) ---
def paged_attention(out, query, key_cache, value_cache,
num_kv_heads, scale, block_tables, context_lens,
block_size, max_context_len, alibi_slopes=None):
"""Paged attention decode. Maps to ixformer::infer::xllm_paged_attention."""
bridge = _get("ix_full_bridge")
return bridge.ix_paged_attention(
out, query, key_cache, value_cache,
num_kv_heads, scale, block_tables, context_lens,
block_size, max_context_len, alibi_slopes
)
def flash_attn_prefill(query, key_cache, value_cache, out,
block_tables, cu_seq_q, cu_seq_k,
max_seq_q, max_seq_k, scale,
is_causal=True):
"""Flash attention prefill. Maps to ixformer::infer::ixinfer_flash_attn_unpad."""
bridge = _get("ix_full_bridge")
return bridge.ix_flash_attn_prefill(
query, key_cache, value_cache, out,
block_tables, cu_seq_q, cu_seq_k,
max_seq_q, max_seq_k, is_causal, scale
)
# --- MoE (xllm/core/kernels/ilu/fused_moe.cpp) ---
def topk_softmax(topk_weights, topk_ids, token_expert_ids, gating_output, topk):
"""MoE topk + softmax. Maps to ixformer::infer::topk_softmax."""
return _get("xllm_moe").topk_softmax(
topk_weights, topk_ids, token_expert_ids, gating_output, topk
)
def moe_compute_token_index(sorted_token_ids, expert_ids, num_tokens_post_padded,
token_expert_ids, num_experts, block_size):
"""MoE token routing. Maps to ixformer::infer::moe_compute_token_index_api."""
return _get("xllm_moe").moe_compute_token_index(
sorted_token_ids, expert_ids, num_tokens_post_padded,
token_expert_ids, num_experts, block_size
)
# --- Linear (xllm/core/kernels/ilu/matmul.cpp) ---
def ixformer_linear(input, weight, act_type=0, bias=None, out=None):
"""GEMM via ixformer. Maps to ixformer::infer::ixformer_linear."""
bridge = _get("ix_full_bridge")
return bridge.ix_linear(input, weight, act_type, bias, out)
# --- Fused QK-Norm + RoPE ---
def fused_qknorm_rope(query, key, cos_sin_cache, positions,
qk_norm_weight, epsilon, interleave=False):
"""Fused QK normalization + rotary embedding (saves 128 kernel launches)."""
return _get("xllm_fused_qknorm_rope").fused_qknorm_rope(
query, key, cos_sin_cache, positions, qk_norm_weight, epsilon, interleave
)
# =========================================================================
# Availability check — call at startup to verify ALL .so are loadable
# =========================================================================
def check_all(strict=True):
"""Verify all required .so files are loadable.
Args:
strict: If True, raise on any missing .so (NO FALLBACK mode).
If False, return dict of {name: loaded_bool}.
"""
required = [
"ix_full_bridge", # attention + linear + MoE bridge
"xllm_norm", # rms_norm, residual_rms_norm
"xllm_rope", # rotary_embedding
"xllm_activation", # silu_and_mul
"xllm_cache", # reshape_and_cache
"xllm_moe", # topk_softmax, moe_compute_token_index
]
optional = [
"xllm_fused_qknorm_rope", # nice-to-have: fused QK-norm + RoPE
]
results = {}
missing = []
for name in required:
try:
_get(name)
results[name] = True
except RuntimeError:
results[name] = False
missing.append(name)
for name in optional:
try:
_get(name)
results[name] = True
except RuntimeError:
results[name] = False
logger.info("xllm_ops: optional %s not available", name)
if strict and missing:
raise RuntimeError(
f"xllm_ops: {len(missing)} required .so MISSING: {missing}. "
f"Score will be ~683 without these. Build with: "
f"bash ex_engine/build_xllm_kernels.sh"
)
loaded = sum(1 for v in results.values() if v)
total = len(results)
logger.info("xllm_ops: %d/%d .so loaded", loaded, total)
return results