5cd2780320
Three fixes derived from CCCL source code patterns: 1. CCCL accumulator_t pattern (dispatch_segmented_scan.cuh): - Clamp g to [-5, 2] BEFORE cumsum (was: no pre-clamp, post-clamp ±80) - Tighten post-cumsum clamp to ±20 (was ±80) - Clamp A_log to [-8, 4] before exp() (was: unclamped) - Clamp softplus output to max=10 (was: unclamped) - Clamp g before exp_() in decode path (was: NO clamp at all) 2. CCCL error isolation pattern: - Catch-all exception handler around engine.generate() - max-num-seqs 1→2 to prevent t2_n_2 crash cascade 3. Reduce _DNN_CHUNK 4096→2048 (fewer cumsum steps = less overflow) Root cause: Sub508/509 scored 0 because t2_n_2 killed engine process. NaN (99.98-100% per GatedDeltaNet layer) from unclamped cumsum→exp overflow.
181 lines
8.8 KiB
Bash
Executable File
181 lines
8.8 KiB
Bash
Executable File
#!/bin/bash
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# ==========================================================================
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# SERVING-LAYER-ONLY PATCHES
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#
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# EVIDENCE FROM SUB168 DOCKER LOG (07-23, competition reference):
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# - corex_gdn.py:56 "Loaded fused CoreX GDN decode operator" ✓
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# - corex_moe.py:339 "Using CoreX fused MoE prefill operator" ✓
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# - model_runner.py:1074 (base image's line number)
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# - "Loading model weights took 17.3529 GB"
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# - ZERO NaN warnings
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# - d01: 8.49s, d03_tool_call: PASS in 2.12s
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#
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# EVIDENCE FROM OUR SUB508 DOCKER LOG (08-07):
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# - NO corex_gdn loading
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# - model_runner.py:1119 (our custom code)
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# - "Loading model weights took 16.2303 GB" (1.1GB MISSING)
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# - 16 NaN in prefill, 19 FusedMoE failures
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# - d01: 95.87s, d03_tool_call: FAIL in 49s
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#
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# CONCLUSION: Sub168 succeeds by using BASE IMAGE native model code.
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# qwen3_5.py MUST be deployed — base image registry references it but
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# the module file is missing (causes ModuleNotFoundError on startup).
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#
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# DO NOT deploy: model_runner.py, _custom_ops.py,
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# sampler.py, scheduler.py, sequence.py, xformers.py, paged_attn.py,
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# prefix_prefill.py, logits_processor.py, mamba_cache.py, arg_utils.py
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# ==========================================================================
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cd "$(dirname "$0")"
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echo "[patch_ops] START — working directory: $(pwd)"
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# Find vllm installation
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VLLM=""
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for P in /usr/local/corex/lib/python3/dist-packages/vllm \
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/usr/local/corex/lib64/python3/dist-packages/vllm; do
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if [ -d "$P" ]; then
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VLLM="$P"
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echo "[patch_ops] Found vllm at: $VLLM"
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break
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fi
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done
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if [ -z "$VLLM" ]; then
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echo "[patch_ops] ERROR: vllm not found"
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exit 1
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fi
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# 1. Transformers config registration (config only, NOT model code)
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TMODELS=""
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for P in /usr/local/lib/python3.10/site-packages/transformers/models \
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/usr/local/corex/lib/python3/dist-packages/transformers/models \
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/usr/local/corex/lib64/python3/dist-packages/transformers/models; do
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if [ -d "$P" ]; then
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TMODELS="$P"
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break
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fi
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done
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if [ -n "$TMODELS" ]; then
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# Base engine requires transformers 4.55.3 for Qwen3_5Config support
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pip install transformers==4.55.3 -i https://pypi.tuna.tsinghua.edu.cn/simple --timeout 30 2>&1 || \
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echo "[patch_ops] WARNING: transformers install failed (may already be correct version)"
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cp -r ./qwen3_5 "$TMODELS/" 2>/dev/null && echo "[patch_ops] qwen3_5 config copied" || true
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cp -r ./qwen3_5_moe "$TMODELS/" 2>/dev/null && echo "[patch_ops] qwen3_5_moe config copied" || true
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python3 ./patch_transformers_qwen3_5.py 2>&1 || echo "[patch_ops] WARNING: transformers patch failed (non-fatal)"
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else
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echo "[patch_ops] WARNING: transformers/models not found"
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fi
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# 1b. CoreX probe — direct shell, guaranteed to show in build log
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echo "[probe] === CoreX .so files ==="
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ls -la /usr/local/corex/lib64/libcorex_*.so 2>/dev/null || echo "[probe] NO .so files in /usr/local/corex/lib64/"
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echo "[probe] === CoreX Python wrappers ==="
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ls -la "$VLLM/model_executor/models/corex_"*.py 2>/dev/null || echo "[probe] NO corex_*.py in $VLLM/model_executor/models/"
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echo "[probe] === Native qwen3_5.py ==="
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if [ -f "$VLLM/model_executor/models/qwen3_5.py" ]; then
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wc -lc "$VLLM/model_executor/models/qwen3_5.py"
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grep -c "corex_gdn\|corex_moe\|CoreXGDN" "$VLLM/model_executor/models/qwen3_5.py" || echo "[probe] no corex refs"
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else
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echo "[probe] qwen3_5.py NOT in base image"
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fi
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echo "[probe] === All model files (corex related) ==="
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find "$VLLM" -name "*corex*" -type f 2>/dev/null || echo "[probe] zero corex files anywhere in vllm"
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echo "[probe] === LD_LIBRARY_PATH ==="
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echo "$LD_LIBRARY_PATH"
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echo "[probe] === /usr/local/corex/ tree ==="
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find /usr/local/corex/lib64/ -name "*.so" 2>/dev/null | head -20 || echo "[probe] no .so in corex lib64"
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echo "[probe] ==========================="
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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="$VLLM/model_executor/models/qwen3_5.py"
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cp ./qwen3_5.py "$_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" "$VLLM/model_executor/models/registry.py" 2>/dev/null; then
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echo "[patch_ops] registry already has Qwen3_5 — NOT overwriting"
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else
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cp ./registry.py "$VLLM/model_executor/models/registry.py" 2>/dev/null && \
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echo "[patch_ops] registry.py deployed" || true
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fi
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# 2c. paged_attn.py — CRITICAL: Triton context_attention_fwd hangs BI-V100.
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# Base engine comment: "The Triton context_attention_fwd kernel hangs BI-V100
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# GPUs permanently. Our paged_attn.py bypasses it via _forward_prefix_pytorch."
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cp ./paged_attn.py "$VLLM/attention/ops/paged_attn.py" 2>/dev/null && \
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echo "[patch_ops] paged_attn.py deployed (Triton hang bypass)" || true
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# 2d. patch_model_runner.py — fix prefix_cache_hit in chunked-prefill chunk 2+
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python3 ./patch_model_runner.py 2>&1 || echo "[patch_ops] WARNING: model_runner patch failed (non-fatal)"
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# 2e. mamba_cache.py — required for GatedDeltaNet state management
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cp ./mamba_cache.py "$VLLM/model_executor/models/mamba_cache.py" 2>/dev/null && \
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echo "[patch_ops] mamba_cache.py deployed" || true
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# 2f. sequence.py — fix completion_tokens inflation under chunked prefill
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cp ./sequence.py "$VLLM/sequence.py" 2>/dev/null && \
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echo "[patch_ops] sequence.py deployed (token count fix)" || true
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# 2g. scheduler.py — record num_cached_tokens in RequestMetrics
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cp ./scheduler.py "$VLLM/core/scheduler.py" 2>/dev/null && \
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echo "[patch_ops] scheduler.py deployed (cache metrics)" || true
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# 2h. xformers — bypass cudnnFlashAttn (head_dim=256 > 128 limit)
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python3 ./patch_xformers_sdpa_seq.py 2>&1 || echo "[patch_ops] WARNING: xformers seq patch failed"
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python3 ./patch_xformers_sdpa_batch.py 2>&1 || echo "[patch_ops] WARNING: xformers batch patch failed"
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echo "[patch_ops] xformers patches applied"
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# 3. Tool parser
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mkdir -p "$VLLM/entrypoints/openai/tool_parsers" 2>/dev/null || true
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cp ./qwen3coder_tool_parser.py "$VLLM/entrypoints/openai/tool_parsers/" 2>/dev/null || true
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cp ./tool_parsers_init.py "$VLLM/entrypoints/openai/tool_parsers/__init__.py" 2>/dev/null || true
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python3 ./patch_vllm_tool_parser.py 2>&1 || echo "[patch_ops] WARNING: tool parser registry patch failed"
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echo "[patch_ops] tool parser deployed"
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# 4. Reasoning parser
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cp -r ./reasoning "$VLLM/" 2>/dev/null || true
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echo "[patch_ops] reasoning parser deployed"
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# 5. Serving layer ONLY
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cp ./protocol.py "$VLLM/entrypoints/openai/protocol.py" 2>/dev/null || true
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cp ./cli_args.py "$VLLM/entrypoints/openai/cli_args.py" 2>/dev/null || true
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cp ./serving_chat.py "$VLLM/entrypoints/openai/serving_chat.py" 2>/dev/null || true
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cp ./api_server.py "$VLLM/entrypoints/openai/api_server.py" 2>/dev/null || true
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cp ./chat_utils.py "$VLLM/entrypoints/chat_utils.py" 2>/dev/null || true
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echo "[patch_ops] serving layer deployed"
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# 6. Mirror to second vllm path if exists
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VLLM2=""
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for P in /usr/local/corex/lib/python3/dist-packages/vllm \
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/usr/local/corex/lib64/python3/dist-packages/vllm; do
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if [ -d "$P" ] && [ "$P" != "$VLLM" ]; then
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VLLM2="$P"
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break
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fi
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done
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if [ -n "$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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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 "$VLLM2/model_executor/models/registry.py" 2>/dev/null || true
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fi
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mkdir -p "$VLLM2/entrypoints/openai/tool_parsers" 2>/dev/null || true
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cp ./qwen3coder_tool_parser.py "$VLLM2/entrypoints/openai/tool_parsers/" 2>/dev/null || true
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cp ./tool_parsers_init.py "$VLLM2/entrypoints/openai/tool_parsers/__init__.py" 2>/dev/null || true
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cp -r ./reasoning "$VLLM2/" 2>/dev/null || true
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cp ./protocol.py "$VLLM2/entrypoints/openai/protocol.py" 2>/dev/null || true
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cp ./cli_args.py "$VLLM2/entrypoints/openai/cli_args.py" 2>/dev/null || true
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cp ./serving_chat.py "$VLLM2/entrypoints/openai/serving_chat.py" 2>/dev/null || true
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cp ./api_server.py "$VLLM2/entrypoints/openai/api_server.py" 2>/dev/null || true
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cp ./chat_utils.py "$VLLM2/entrypoints/chat_utils.py" 2>/dev/null || true
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fi
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echo "[patch_ops] DONE — all patches deployed"
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echo "[patch_ops] Deployed: qwen3_5.py, 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 (using base image native): model_runner.py, _custom_ops.py, sampler.py, logits_processor.py, arg_utils.py"
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