cafd34fe4a
THE OTHER CLAUDE'S COMMIT (5b8d7c6) IS WRONG. IT DEPLOYS ALL CUSTOM FILES.
Docker log evidence proves this is the root cause of ALL our failures:
Sub168 (07-23, PASS all d-tests):
corex_gdn.py:56 'Loaded fused CoreX GDN decode operator'
corex_moe.py:339 'Using CoreX fused MoE prefill: expert-grouped-wmma'
model_runner.py:1074 (BASE IMAGE native)
weights: 17.3529 GB
NaN: 0 times
Our Sub508 (08-07, 41.2%):
NO corex_gdn loading
model_runner.py:1119 (OUR CUSTOM — wrong)
weights: 16.2303 GB (1.1GB MISSING)
NaN: 16 times, FusedMoE fail: 19 times
Our custom qwen3_5.py REPLACES the base image's CoreX-accelerated model
with pure-PyTorch code that:
- Produces 99.98% NaN in every GatedDeltaNet layer
- Falls back to Python MoE loop (base image uses WMMA hardware)
- Loses 1.1GB of weights (broken load_weights function)
THIS COMMIT: deploy ONLY serving layer, keep base image model intact.
computility-run.yaml: exact Sub168 params (256K, 0.95, seqs=2, chunked).
118 lines
5.1 KiB
Bash
Executable File
118 lines
5.1 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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# Our custom qwen3_5.py/model_runner/etc BREAKS CoreX acceleration.
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#
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# DO NOT deploy: qwen3_5.py, 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 "$TMODELS" ]; then
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cp -r ./qwen3_5 "$TMODELS/" 2>/dev/null && echo "[patch_ops] qwen3_5 config copied" || true
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cp -r ./qwen3_5_moe "$TMODELS/" 2>/dev/null && echo "[patch_ops] qwen3_5_moe config copied" || true
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python3 ./patch_transformers_qwen3_5.py 2>&1 || echo "[patch_ops] WARNING: transformers patch failed (non-fatal)"
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else
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echo "[patch_ops] WARNING: transformers/models not found"
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fi
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# 2. Registry — only if base image doesn't already have Qwen3_5
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if grep -q "Qwen3_5ForCausalLM" "$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 "$VLLM/model_executor/models/registry.py" 2>/dev/null && \
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echo "[patch_ops] registry.py deployed" || true
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fi
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# 3. Tool parser
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mkdir -p "$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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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: $VLLM2"
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if ! grep -q "Qwen3_5ForCausalLM" "$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 — serving-only patches, CoreX native model PRESERVED"
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echo "[patch_ops] NOT deployed (base image native): qwen3_5.py, model_runner.py, _custom_ops.py, sampler.py, scheduler.py, sequence.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, mamba_cache.py, arg_utils.py"
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