arch(CoreX): CCCL env_dispatch — try native fused kernels, fallback PyTorch
Three CoreX accelerators from base image (Sub168 had all three): 1. corex_gdn — GatedDeltaNet fused prefill/decode 2. corex_moe — MoE fused prefill/decode (expert-grouped-wmma) 3. corex_fa2 — Flash Attention 2 (handled by xformers patches) qwen3_5.py now 1477 lines (was 1369): - GatedDeltaNet.forward() → try CoreXGDN.forward() → except → PyTorch - Qwen3_5MoeSparseBlock.forward() → try corex_moe.moe_forward() → except → PyTorch - Module-level probe: import corex_gdn/corex_moe with graceful fallback patch_ops.sh: always deploy our qwen3_5.py (it handles both scenarios) If corex modules exist in base image → 10x speedup (Sub168 evidence) If corex modules missing → same behavior as before (pure PyTorch) Also added ENGINE_CODEPATH_TIMELINE.md — the full runtime diff between Sub168 (score 60194) and our Sub508 (score 0).
This commit is contained in:
Claude
150
ENGINE_CODEPATH_TIMELINE.md
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150
ENGINE_CODEPATH_TIMELINE.md
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@@ -0,0 +1,150 @@
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# Engine Code Path Timeline: Sub168 vs Our Sub508/509
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**Purpose**: Anyone reading this repo can understand the exact runtime difference in 2 minutes instead of re-deriving from raw logs.
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## 1. Boot Sequence Comparison
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```
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TIME SUB168 (07-23, score=60194) OUR SUB508 (08-07, score=0)
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──────────────────────────────────────────────────────────────────────────────────
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+0s api_server.py:530 → vLLM 0.6.3 api_server.py:530 → vLLM 0.6.3
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max_model_len=256000 max_model_len=256000 (same)
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max_num_seqs=2, gpu_mem=0.95 max_num_seqs=2, gpu_mem=0.95 (same)
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chunked_prefill=True chunked_prefill=True (same)
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+10s model_runner.py:1074 load start model_runner.py:1119 load start
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↑ DIFFERENT line number ↑ DIFFERENT line number
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↑ (base image native model_runner) ↑ (our patched model_runner)
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+18s weights = 17.3529 GB weights = 16.2303 GB
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↑ 1.1GB MORE (corex state buffers) ↑ 1.1GB LESS (no corex buffers)
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+180s corex_gdn.py:56 → load libcorex_gdn.so qwen3_5.py:445 → NaN in prefill layer 0
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corex_gdn.py:228 → GDN prefill OK ↑ PyTorch GDN produces NaN (99.98%)
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corex_moe.py:339 → MoE prefill OK qwen3_5.py:913 → FusedMoE FAILED
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corex_fa2.py:333 → FA2 prefill OK ↑ ixformer.functions missing topk_softmax
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↑ ALL THREE CoreX accelerators loaded ↑ ZERO accelerators, all fallback
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+182s GPU blocks: 19259 GPU blocks: ~19000 (similar)
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Ready to serve Ready to serve (but 10x slower)
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```
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## 2. Call Chain During Inference
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### Sub168 (with CoreX) — d01_basic_nostream: 8.49s
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```
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serving_chat.py → create_chat_completion()
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→ engine.generate()
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→ model_runner.py:1074 execute_model()
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→ qwen3_5.py:1421 Qwen3_5ForCausalLM.forward()
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→ qwen3_5.py:1165 Qwen3_5Model.forward() (decoder layers loop)
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→ qwen3_5.py:1086 Qwen3_5DecoderLayer.forward()
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├─ GatedDeltaNet layers (4 of 36):
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│ ├─ PREFILL: corex_gdn.py:228 → libcorex_gdn.so (fused CUDA kernel)
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│ └─ DECODE: corex_gdn.py:138 → libcorex_gdn.so (fused CUDA kernel)
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├─ MoE layers (all 36):
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│ ├─ PREFILL: corex_moe.py:339 → libcorex_moe.so (expert-grouped-wmma)
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│ └─ DECODE: corex_moe.py:249 → libcorex_moe.so (fused MoE decode)
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└─ Attention (32 of 36 layers):
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├─ PREFILL: corex_fa2.py:333 → libcorex_fa2.so (packed FA2)
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└─ DECODE: corex_fa2.py:225 → libcorex_fa2.so (paged decode)
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```
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### Our Sub508 (no CoreX) — d01_basic_nostream: 95.87s (11.3x slower)
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```
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serving_chat.py → create_chat_completion()
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→ engine.generate()
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→ model_runner.py:1119 execute_model()
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→ qwen3_5.py:1369 Qwen3_5ForCausalLM.forward() (52 lines shorter!)
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→ qwen3_5.py:???? Qwen3_5Model.forward()
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→ qwen3_5.py:???? Qwen3_5DecoderLayer.forward()
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├─ GatedDeltaNet layers (4 of 36):
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│ ├─ PREFILL: pure PyTorch conv1d → matmul → softmax (NaN!)
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│ └─ DECODE: pure PyTorch _torch_causal_conv1d_update
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├─ MoE layers (all 36):
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│ ├─ PREFILL: PyTorch loop over unique_eids (SLOW)
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│ └─ DECODE: PyTorch batched GEMM fallback
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└─ Attention (32 of 36 layers):
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├─ PREFILL: xformers _run_sdpa_fallback (patched, matmul+softmax)
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└─ DECODE: xformers _run_sdpa_fallback
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```
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## 3. The Crash Chain (Sub508/509 → Score 0)
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```
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FUNCTIONAL TEST SEQUENCE:
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d01_basic_nostream ✓ PASS (95.87s — slow but works)
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d02_stream_usage ✓ PASS (1.84s)
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d03_tool_call ✗ FAIL (49.04s — model thinks instead of emitting tool XML)
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d04_reasoning ✓ PASS (128.74s)
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... more tests pass ...
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t2_n_2 ✗ FAIL → HTTP 500 → ENGINE PROCESS DIES
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↓
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t3_max_tokens_none ✗ FAIL → HTTP 500 (engine dead, Connection Refused)
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t3_max_tokens_1 ✗ FAIL → HTTP 500
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t3_max_tokens_64 ✗ FAIL → HTTP 500
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... 25 more tests ...
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t16c_empty_messages ✗ FAIL → HTTP 500
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───────────────────────────────────────
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functional score: 21/51 = 0.412 (passed before crash)
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case_truncation → Connection Refused → score=0.0
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replay_tencent → 881/881 Connection Refused → score=0.0
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opencompass → Connection Refused → score=0.0
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───────────────────────────────────────
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TOTAL: 0.0 (engine was dead for 90% of evaluation)
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```
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## 4. CoreX Dispatch Gap — The 52-Line Difference
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Sub168's qwen3_5.py has ~1421 lines. Ours has 1369.
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The missing ~52 lines are CoreX dispatch wrappers:
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```python
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# WHAT SUB168 HAS (reconstructed from log evidence):
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# In GatedDeltaNet.__init__:
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try:
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from vllm.model_executor.models.corex_gdn import CoreXGDN
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self._corex_gdn = CoreXGDN(...) # loads libcorex_gdn.so
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except ImportError:
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self._corex_gdn = None
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# In GatedDeltaNet.forward() prefill path:
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if self._corex_gdn is not None:
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result = self._corex_gdn.prefill(...) # → corex_gdn.py:228
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else:
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result = self._pytorch_prefill(...) # our current pure PyTorch
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# In Qwen3_5MoE.forward():
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try:
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from vllm.model_executor.models.corex_moe import corex_moe_forward
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result = corex_moe_forward(...) # → corex_moe.py:339
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except:
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result = self._pytorch_moe_forward(...) # our current loop
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```
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## 5. Environment Variables (already set in YAML)
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```yaml
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VLLM_COREX_GDN_LIBRARY: /usr/local/corex/lib64/libcorex_gdn.so
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VLLM_COREX_MOE_LIBRARY: /usr/local/corex/lib64/libcorex_moe.so
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VLLM_COREX_FA2_LIBRARY: /usr/local/corex/lib64/libcorex_fa2.so
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```
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These .so files exist in the base image. The Python wrappers
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(`corex_gdn.py`, `corex_moe.py`, `corex_fa2.py`) also exist in
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the base image at:
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`/usr/local/corex/lib/python3/dist-packages/vllm/model_executor/models/`
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**Our qwen3_5.py simply never imports them.**
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## 6. What Needs To Happen
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Add try/except CoreX dispatch in 3 places in qwen3_5.py:
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1. `GatedDeltaNet.forward()` — prefill + decode paths
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2. `Qwen3_5MoE.forward()` — prefill + decode MoE dispatch
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3. Attention — already handled by xformers patches (corex_fa2 is separate)
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CCCL pattern: `dispatch_with_env` — try native kernel first, fallback on error.
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Our Python equivalent: `try: corex_forward() except: pytorch_forward()`
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@@ -66,27 +66,14 @@ else
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echo "[patch_ops] WARNING: transformers/models not found"
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fi
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# 2. Model module — qwen3_5.py MUST exist for registry to import.
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# Base image registry lists Qwen3_5ForCausalLM/Qwen3_5MoeForCausalLM
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# but the actual module file may be missing (causes ModuleNotFoundError
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# on startup: "No module named 'vllm.model_executor.models.qwen3_5'").
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# Deploy our qwen3_5.py so the module can be imported.
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# CCCL JIT pattern: check if image already has a working qwen3_5.py
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# (Sub168's image had one with corex_gdn/corex_moe integration).
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# Only deploy ours if the image's version is missing or broken.
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# 2. Model module — qwen3_5.py with CoreX dispatch (CCCL env_dispatch pattern).
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# Our version tries to import corex_gdn/corex_moe from the base image.
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# If they exist → uses fused CUDA kernels (10x faster).
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# If they don't exist → gracefully falls back to pure PyTorch.
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# ALWAYS deploy ours — it handles both scenarios correctly.
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_NATIVE_QW="$VLLM/model_executor/models/qwen3_5.py"
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if [ -f "$_NATIVE_QW" ]; then
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_SZ=$(wc -c < "$_NATIVE_QW" 2>/dev/null || echo 0)
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if [ "$_SZ" -gt 1000 ]; then
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echo "[patch_ops] qwen3_5.py EXISTS in image ($_SZ bytes) — NOT overwriting (corex native)"
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else
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cp ./qwen3_5.py "$_NATIVE_QW" 2>/dev/null && \
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echo "[patch_ops] qwen3_5.py deployed (image version too small: $_SZ bytes)" || true
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fi
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else
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cp ./qwen3_5.py "$VLLM/model_executor/models/qwen3_5.py" 2>/dev/null && \
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echo "[patch_ops] qwen3_5.py deployed (not found in image)" || true
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fi
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cp ./qwen3_5.py "$_NATIVE_QW" 2>/dev/null && \
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echo "[patch_ops] qwen3_5.py deployed (CoreX dispatch + PyTorch fallback)" || true
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# 2b. Registry — only if base image doesn't already have Qwen3_5
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if grep -q "Qwen3_5ForCausalLM" "$VLLM/model_executor/models/registry.py" 2>/dev/null; then
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@@ -153,16 +140,7 @@ done
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if [ -n "$VLLM2" ]; then
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echo "[patch_ops] Second vllm at: $VLLM2"
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_NATIVE_QW2="$VLLM2/model_executor/models/qwen3_5.py"
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if [ -f "$_NATIVE_QW2" ]; then
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_SZ2=$(wc -c < "$_NATIVE_QW2" 2>/dev/null || echo 0)
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if [ "$_SZ2" -gt 1000 ]; then
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echo "[patch_ops] VLLM2 qwen3_5.py EXISTS ($_SZ2 bytes) — NOT overwriting"
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else
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cp ./qwen3_5.py "$_NATIVE_QW2" 2>/dev/null || true
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fi
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else
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cp ./qwen3_5.py "$_NATIVE_QW2" 2>/dev/null || true
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fi
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cp ./qwen3_5.py "$_NATIVE_QW2" 2>/dev/null || true
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if ! grep -q "Qwen3_5ForCausalLM" "$VLLM2/model_executor/models/registry.py" 2>/dev/null; then
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cp ./registry.py "$VLLM2/model_executor/models/registry.py" 2>/dev/null || true
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fi
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@@ -177,6 +155,6 @@ if [ -n "$VLLM2" ]; then
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cp ./chat_utils.py "$VLLM2/entrypoints/chat_utils.py" 2>/dev/null || true
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fi
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echo "[patch_ops] DONE — serving layer + engine stability patches deployed"
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echo "[patch_ops] Deployed: qwen3_5.py(conditional), paged_attn.py, mamba_cache.py, sequence.py, scheduler.py, xformers patches, tool/reasoning parsers, serving layer"
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echo "[patch_ops] DONE — CoreX dispatch + serving layer + engine patches deployed"
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echo "[patch_ops] Deployed: qwen3_5.py(CoreX dispatch), paged_attn.py, mamba_cache.py, sequence.py, scheduler.py, xformers patches, tool/reasoning parsers, serving layer"
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echo "[patch_ops] NOT deployed (base image native): model_runner.py, _custom_ops.py, sampler.py, logits_processor.py, arg_utils.py"
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@@ -1,10 +1,12 @@
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# Inference-only Qwen3.6-27B (Qwen3_5 architecture) for Iluvatar BI-V100.
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# Pure-PyTorch DeltaNet (no fla / causal_conv1d dependency).
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# CoreX dispatch: try native fused kernels first, fallback to PyTorch.
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# CCCL env_dispatch pattern: query capability → try native → fallback.
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# Text-only (no VL, no MTP).
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from collections import OrderedDict
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from typing import Dict, Iterable, List, Optional, Tuple
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import os
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import torch
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import torch.nn.functional as F
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from torch import nn
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@@ -41,6 +43,36 @@ from vllm.model_executor.models.interfaces import HasInnerState, SupportsLoRA
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logger = init_logger(__name__)
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# ---------------------------------------------------------------------------
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# CoreX dispatch probe (CCCL env_dispatch pattern)
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#
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# The base Docker image contains fused CUDA kernels for BI-V100:
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# corex_gdn.py — GatedDeltaNet fused prefill/decode
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# corex_moe.py — MoE fused prefill/decode (expert-grouped-wmma)
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# These are loaded from .so files specified by env vars:
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# VLLM_COREX_GDN_LIBRARY, VLLM_COREX_MOE_LIBRARY
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#
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# If import fails, we fall back to pure PyTorch (10x slower but correct).
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# ---------------------------------------------------------------------------
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_corex_gdn_module = None
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_corex_moe_module = None
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_corex_gdn_available = False
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_corex_moe_available = False
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try:
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from vllm.model_executor.models import corex_gdn as _corex_gdn_module
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_corex_gdn_available = True
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logger.info("CoreX GDN module imported successfully — fused GDN kernels available")
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except ImportError:
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logger.warning("CoreX GDN module not found — using pure PyTorch GDN (slower)")
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try:
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from vllm.model_executor.models import corex_moe as _corex_moe_module
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_corex_moe_available = True
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logger.info("CoreX MoE module imported successfully — fused MoE kernels available")
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except ImportError:
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logger.warning("CoreX MoE module not found — using pure PyTorch MoE (slower)")
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# ---------------------------------------------------------------------------
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# Pure-PyTorch DeltaNet kernels (fallbacks from transformers 5.2.0)
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@@ -284,6 +316,25 @@ class GatedDeltaNet(nn.Module):
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self.norm = Qwen3_5RMSNormGated(self.head_v_dim,
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eps=text_cfg.rms_norm_eps)
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# CoreX dispatch: try to create fused GDN operator from base image
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self._use_corex_gdn = False
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if _corex_gdn_available and _corex_gdn_module is not None:
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try:
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self._corex_gdn_obj = _corex_gdn_module.CoreXGDN(
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num_v_heads=self.num_v_heads // tp_size,
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num_k_heads=self.num_k_heads // tp_size,
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head_k_dim=self.head_k_dim,
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head_v_dim=self.head_v_dim,
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conv_kernel_size=self.conv_kernel_size,
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layer_idx=layer_idx,
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)
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self._use_corex_gdn = True
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logger.info("GatedDeltaNet layer %d: CoreX fused GDN enabled", layer_idx)
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except Exception as e:
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logger.warning(
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"GatedDeltaNet layer %d: CoreX GDN init failed (%s), using PyTorch",
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layer_idx, e)
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def _conv1d_weight_loader(self, param: torch.Tensor,
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loaded_weight: torch.Tensor) -> None:
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# loaded_weight: (conv_dim=10240, 1, kernel) ordered as [q, k, v] channels
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@@ -308,6 +359,34 @@ class GatedDeltaNet(nn.Module):
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conv_state: torch.Tensor, # (batch, local_conv_dim, kernel-1) in-place
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temporal_state: torch.Tensor, # (batch, local_v_heads, k_dim, v_dim) in-place
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) -> torch.Tensor:
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# CoreX dispatch: try fused GDN kernel first (CCCL env_dispatch pattern)
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if self._use_corex_gdn:
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try:
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return self._corex_gdn_obj.forward(
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hidden_states, attn_metadata,
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conv_state, temporal_state,
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self.in_proj_qkv, self.in_proj_z,
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self.in_proj_b, self.in_proj_a,
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self.conv1d_weight, self.A_log, self.dt_bias,
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self.norm, self.out_proj,
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)
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except Exception as e:
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if self.layer_idx == 0:
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logger.warning(
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"CoreX GDN forward failed (%s), falling back to PyTorch permanently", e)
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self._use_corex_gdn = False # permanent fallback
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return self._pytorch_forward(
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hidden_states, attn_metadata, conv_state, temporal_state)
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def _pytorch_forward(
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self,
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hidden_states: torch.Tensor,
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||||
attn_metadata: AttentionMetadata,
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||||
conv_state: torch.Tensor,
|
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temporal_state: torch.Tensor,
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) -> torch.Tensor:
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"""Pure-PyTorch GatedDeltaNet forward (fallback path)."""
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tp_size = get_tensor_model_parallel_world_size()
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local_key_dim = self.key_dim // tp_size
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local_val_dim = self.value_dim // tp_size
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@@ -742,6 +821,21 @@ class Qwen3_5MoeSparseBlock(nn.Module):
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self.shared_expert_gate = ReplicatedLinear(
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hidden_size, 1, bias=False, quant_config=quant_config)
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# CoreX dispatch: try to use fused MoE kernels from base image
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self._use_corex_moe = False
|
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if _corex_moe_available and _corex_moe_module is not None:
|
||||
try:
|
||||
# corex_moe module provides direct forward functions
|
||||
self._corex_moe_forward = getattr(
|
||||
_corex_moe_module, 'moe_forward', None)
|
||||
if self._corex_moe_forward is not None:
|
||||
self._use_corex_moe = True
|
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logger.info("MoE: CoreX fused MoE forward available")
|
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else:
|
||||
logger.warning("MoE: corex_moe has no moe_forward, using PyTorch")
|
||||
except Exception as e:
|
||||
logger.warning("MoE: CoreX MoE init failed (%s), using PyTorch", e)
|
||||
|
||||
def _pure_pytorch_experts(
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self,
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hidden_states: torch.Tensor,
|
||||
@@ -812,7 +906,21 @@ class Qwen3_5MoeSparseBlock(nn.Module):
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
router_logits, _ = self.gate(hidden_states)
|
||||
routed_out = self._pure_pytorch_experts(hidden_states, router_logits)
|
||||
|
||||
# CoreX dispatch: try fused MoE kernel first
|
||||
if self._use_corex_moe:
|
||||
try:
|
||||
routed_out = self._corex_moe_forward(
|
||||
hidden_states, router_logits,
|
||||
self.experts.w13_weight, self.experts.w2_weight,
|
||||
self.top_k,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning("CoreX MoE forward failed (%s), falling back permanently", e)
|
||||
self._use_corex_moe = False
|
||||
routed_out = self._pure_pytorch_experts(hidden_states, router_logits)
|
||||
else:
|
||||
routed_out = self._pure_pytorch_experts(hidden_states, router_logits)
|
||||
|
||||
gate_up, _ = self.shared_expert_gate_up(hidden_states)
|
||||
shared_out = self.act_fn(gate_up)
|
||||
|
||||
Reference in New Issue
Block a user