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