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
Normal file
150
ENGINE_CODEPATH_TIMELINE.md
Normal file
@@ -0,0 +1,150 @@
|
||||
# Engine Code Path Timeline: Sub168 vs Our Sub508/509
|
||||
|
||||
**Purpose**: Anyone reading this repo can understand the exact runtime difference in 2 minutes instead of re-deriving from raw logs.
|
||||
|
||||
## 1. Boot Sequence Comparison
|
||||
|
||||
```
|
||||
TIME SUB168 (07-23, score=60194) OUR SUB508 (08-07, score=0)
|
||||
──────────────────────────────────────────────────────────────────────────────────
|
||||
+0s api_server.py:530 → vLLM 0.6.3 api_server.py:530 → vLLM 0.6.3
|
||||
max_model_len=256000 max_model_len=256000 (same)
|
||||
max_num_seqs=2, gpu_mem=0.95 max_num_seqs=2, gpu_mem=0.95 (same)
|
||||
chunked_prefill=True chunked_prefill=True (same)
|
||||
|
||||
+10s model_runner.py:1074 load start model_runner.py:1119 load start
|
||||
↑ DIFFERENT line number ↑ DIFFERENT line number
|
||||
↑ (base image native model_runner) ↑ (our patched model_runner)
|
||||
|
||||
+18s weights = 17.3529 GB weights = 16.2303 GB
|
||||
↑ 1.1GB MORE (corex state buffers) ↑ 1.1GB LESS (no corex buffers)
|
||||
|
||||
+180s corex_gdn.py:56 → load libcorex_gdn.so qwen3_5.py:445 → NaN in prefill layer 0
|
||||
corex_gdn.py:228 → GDN prefill OK ↑ PyTorch GDN produces NaN (99.98%)
|
||||
corex_moe.py:339 → MoE prefill OK qwen3_5.py:913 → FusedMoE FAILED
|
||||
corex_fa2.py:333 → FA2 prefill OK ↑ ixformer.functions missing topk_softmax
|
||||
↑ ALL THREE CoreX accelerators loaded ↑ ZERO accelerators, all fallback
|
||||
|
||||
+182s GPU blocks: 19259 GPU blocks: ~19000 (similar)
|
||||
Ready to serve Ready to serve (but 10x slower)
|
||||
```
|
||||
|
||||
## 2. Call Chain During Inference
|
||||
|
||||
### Sub168 (with CoreX) — d01_basic_nostream: 8.49s
|
||||
```
|
||||
serving_chat.py → create_chat_completion()
|
||||
→ engine.generate()
|
||||
→ model_runner.py:1074 execute_model()
|
||||
→ qwen3_5.py:1421 Qwen3_5ForCausalLM.forward()
|
||||
→ qwen3_5.py:1165 Qwen3_5Model.forward() (decoder layers loop)
|
||||
→ qwen3_5.py:1086 Qwen3_5DecoderLayer.forward()
|
||||
├─ GatedDeltaNet layers (4 of 36):
|
||||
│ ├─ PREFILL: corex_gdn.py:228 → libcorex_gdn.so (fused CUDA kernel)
|
||||
│ └─ DECODE: corex_gdn.py:138 → libcorex_gdn.so (fused CUDA kernel)
|
||||
├─ MoE layers (all 36):
|
||||
│ ├─ PREFILL: corex_moe.py:339 → libcorex_moe.so (expert-grouped-wmma)
|
||||
│ └─ DECODE: corex_moe.py:249 → libcorex_moe.so (fused MoE decode)
|
||||
└─ Attention (32 of 36 layers):
|
||||
├─ PREFILL: corex_fa2.py:333 → libcorex_fa2.so (packed FA2)
|
||||
└─ DECODE: corex_fa2.py:225 → libcorex_fa2.so (paged decode)
|
||||
```
|
||||
|
||||
### Our Sub508 (no CoreX) — d01_basic_nostream: 95.87s (11.3x slower)
|
||||
```
|
||||
serving_chat.py → create_chat_completion()
|
||||
→ engine.generate()
|
||||
→ model_runner.py:1119 execute_model()
|
||||
→ qwen3_5.py:1369 Qwen3_5ForCausalLM.forward() (52 lines shorter!)
|
||||
→ qwen3_5.py:???? Qwen3_5Model.forward()
|
||||
→ qwen3_5.py:???? Qwen3_5DecoderLayer.forward()
|
||||
├─ GatedDeltaNet layers (4 of 36):
|
||||
│ ├─ PREFILL: pure PyTorch conv1d → matmul → softmax (NaN!)
|
||||
│ └─ DECODE: pure PyTorch _torch_causal_conv1d_update
|
||||
├─ MoE layers (all 36):
|
||||
│ ├─ PREFILL: PyTorch loop over unique_eids (SLOW)
|
||||
│ └─ DECODE: PyTorch batched GEMM fallback
|
||||
└─ Attention (32 of 36 layers):
|
||||
├─ PREFILL: xformers _run_sdpa_fallback (patched, matmul+softmax)
|
||||
└─ DECODE: xformers _run_sdpa_fallback
|
||||
```
|
||||
|
||||
## 3. The Crash Chain (Sub508/509 → Score 0)
|
||||
|
||||
```
|
||||
FUNCTIONAL TEST SEQUENCE:
|
||||
d01_basic_nostream ✓ PASS (95.87s — slow but works)
|
||||
d02_stream_usage ✓ PASS (1.84s)
|
||||
d03_tool_call ✗ FAIL (49.04s — model thinks instead of emitting tool XML)
|
||||
d04_reasoning ✓ PASS (128.74s)
|
||||
... more tests pass ...
|
||||
t2_n_2 ✗ FAIL → HTTP 500 → ENGINE PROCESS DIES
|
||||
↓
|
||||
t3_max_tokens_none ✗ FAIL → HTTP 500 (engine dead, Connection Refused)
|
||||
t3_max_tokens_1 ✗ FAIL → HTTP 500
|
||||
t3_max_tokens_64 ✗ FAIL → HTTP 500
|
||||
... 25 more tests ...
|
||||
t16c_empty_messages ✗ FAIL → HTTP 500
|
||||
───────────────────────────────────────
|
||||
functional score: 21/51 = 0.412 (passed before crash)
|
||||
|
||||
case_truncation → Connection Refused → score=0.0
|
||||
replay_tencent → 881/881 Connection Refused → score=0.0
|
||||
opencompass → Connection Refused → score=0.0
|
||||
───────────────────────────────────────
|
||||
TOTAL: 0.0 (engine was dead for 90% of evaluation)
|
||||
```
|
||||
|
||||
## 4. CoreX Dispatch Gap — The 52-Line Difference
|
||||
|
||||
Sub168's qwen3_5.py has ~1421 lines. Ours has 1369.
|
||||
The missing ~52 lines are CoreX dispatch wrappers:
|
||||
|
||||
```python
|
||||
# WHAT SUB168 HAS (reconstructed from log evidence):
|
||||
|
||||
# In GatedDeltaNet.__init__:
|
||||
try:
|
||||
from vllm.model_executor.models.corex_gdn import CoreXGDN
|
||||
self._corex_gdn = CoreXGDN(...) # loads libcorex_gdn.so
|
||||
except ImportError:
|
||||
self._corex_gdn = None
|
||||
|
||||
# In GatedDeltaNet.forward() prefill path:
|
||||
if self._corex_gdn is not None:
|
||||
result = self._corex_gdn.prefill(...) # → corex_gdn.py:228
|
||||
else:
|
||||
result = self._pytorch_prefill(...) # our current pure PyTorch
|
||||
|
||||
# In Qwen3_5MoE.forward():
|
||||
try:
|
||||
from vllm.model_executor.models.corex_moe import corex_moe_forward
|
||||
result = corex_moe_forward(...) # → corex_moe.py:339
|
||||
except:
|
||||
result = self._pytorch_moe_forward(...) # our current loop
|
||||
```
|
||||
|
||||
## 5. Environment Variables (already set in YAML)
|
||||
|
||||
```yaml
|
||||
VLLM_COREX_GDN_LIBRARY: /usr/local/corex/lib64/libcorex_gdn.so
|
||||
VLLM_COREX_MOE_LIBRARY: /usr/local/corex/lib64/libcorex_moe.so
|
||||
VLLM_COREX_FA2_LIBRARY: /usr/local/corex/lib64/libcorex_fa2.so
|
||||
```
|
||||
|
||||
These .so files exist in the base image. The Python wrappers
|
||||
(`corex_gdn.py`, `corex_moe.py`, `corex_fa2.py`) also exist in
|
||||
the base image at:
|
||||
`/usr/local/corex/lib/python3/dist-packages/vllm/model_executor/models/`
|
||||
|
||||
**Our qwen3_5.py simply never imports them.**
|
||||
|
||||
## 6. What Needs To Happen
|
||||
|
||||
Add try/except CoreX dispatch in 3 places in qwen3_5.py:
|
||||
1. `GatedDeltaNet.forward()` — prefill + decode paths
|
||||
2. `Qwen3_5MoE.forward()` — prefill + decode MoE dispatch
|
||||
3. Attention — already handled by xformers patches (corex_fa2 is separate)
|
||||
|
||||
CCCL pattern: `dispatch_with_env` — try native kernel first, fallback on error.
|
||||
Our Python equivalent: `try: corex_forward() except: pytorch_forward()`
|
||||
Reference in New Issue
Block a user