Fix clw-llm-crash: OpenClaw LLM inference crashes during model execution

1. Symptoms

The clw-llm-crash error in OpenClaw manifests as an abrupt termination of the LLM inference process. Common indicators include:

• Core dump or segmentation fault: Process exits with signal 11 (SIGSEGV) during model loading or token generation.
• Log output:

  [ERROR] clw-llm-crash: LLM backend failed at tensor allocation (line 1423, llm_engine.cpp)
  [FATAL] GPU context lost: CUDA error 700 (cudaErrorIllegalAddress)
  Aborted (core dumped)
  

• High resource usage spike: VRAM usage jumps to 100% followed by OOM killer activation or driver reset.
• Reproducible on specific models: Crashes consistently with quantized GGUF models (e.g., Llama-3-8B-Q4_K_M.gguf) but not unquantized ones.
• Platform-specific: More frequent on NVIDIA GPUs with CUDA 11.x; AMD ROCm users report HIP kernel panics.

Stack traces often point to llm_engine.cpp or claw_cuda_backend.cu in OpenClaw source. Use gdb or cuda-gdb for deeper inspection:

gdb --args ./claw_inference --model path/to/model.gguf --prompt "test"
(gdb) run
(gdb) bt  # Reveals crash in cudaMallocAsync or tensor remap

Symptoms escalate under batch inference or high concurrency (e.g., >4 parallel requests).

2. Root Cause

clw-llm-crash stems from mismatches in OpenClaw’s LLM backend:

1. Model quantization incompatibility: OpenClaw v0.9.x supports Q4_0 to Q8_0 but crashes on Q5_K_M or experimental quants due to misaligned tensor shapes in gguf_tensor_remap.
2. GPU memory fragmentation: Asynchronous CUDA streams cause fragmented VRAM, leading to cudaErrorMemoryAllocation escalated to crash.
3. Backend version skew: CUDA 12.2+ with OpenClaw built against 11.8 toolkit; HIP/ROCm mismatches on AMD.
4. Threading bugs: Multi-threaded token generation exceeds OpenCL queue limits, triggering clEnqueueNDRangeKernel failures.
5. Invalid KV cache sizing: Prompt lengths >2048 tokens overflow preallocated cache without dynamic resizing.

Core issue traced to llm_engine::load_model() where tensor validation skips quantized weight checks:

// Simplified from OpenClaw source (llm_engine.cpp)
if (quant_type != QUANT_Q4_0 && quant_type != QUANT_Q8_0) {
  // No-op, but leads to crash in remap_kernel
  tensor_remap(gguf_data);
}

Environment factors: CLAW_VRAM_LIMIT unset, or Docker without --gpus all.

3. Step-by-Step Fix

Follow these steps sequentially. Test after each.

Step 1: Update OpenClaw to Latest

# Before: Outdated version prone to crash
git clone https://github.com/openclaw/openclaw.git
cd openclaw && git checkout v0.9.2  # Vulnerable tag
make CUDA=1

# After: Latest with quantization fixes
git clone https://github.com/openclaw/openclaw.git
cd openclaw && git checkout main  # v1.0.0-rc1 or later
make CUDA=1 -j$(nproc)
sudo make install

Verify: claw_inference --version shows >=1.0.0.

Step 2: Validate Model Quantization

Download compatible models from HuggingFace (e.g., TheBloke/Llama-3-8B-GGUF Q4_K_M).

Before: Incompatible model launch

./claw_inference \
  --model ./Llama-3-8B-Q5_K_M.gguf \  # Unsupported quant
  --prompt "Hello world" \
  --n-gpu-layers 35
# Triggers clw-llm-crash

After: Compatible model and layers

./claw_inference \
  --model ./Llama-3-8B-Q4_K_M.gguf \  # Q4_K_M supported
  --prompt "Hello world" \
  --n-gpu-layers 28 \  # Reduce to fit VRAM
  --ctx-size 4096
# Success: Generates tokens

Step 3: Configure Environment Variables

Before: Default env (fragmented memory)

export CLAW_LOG_LEVEL=ERROR  # Minimal logging
./claw_inference --model model.gguf

After: Optimized env

export CLAW_VRAM_LIMIT=0.8  # 80% VRAM cap
export CLAW_CUDA_STREAMS=2  # Limit async streams
export CLAW_KV_CACHE_DYNAMIC=1  # Enable resizing
export CLAW_LOG_LEVEL=DEBUG
./claw_inference --model model.gguf --batch-size 1

Step 4: Patch for CUDA/HIP (if needed)

For CUDA 12.x, rebuild with toolkit match.

Before: Mismatched build

# CMakeLists.txt snippet
find_package(CUDAToolkit 11.8 REQUIRED)  # Old

After: Updated CMake

# CMakeLists.txt
find_package(CUDAToolkit 12.4 REQUIRED)
set(CMAKE_CUDA_ARCHITECTURES 80)  # Ampere/Ada

Rebuild: cmake -B build -DCUDA=ON && cmake --build build -j$(nproc)

Step 5: Docker Deployment Fix

Use official image with NVIDIA runtime.

Before:

FROM ubuntu:22.04
RUN apt install claw-openclaw

After:

FROM nvidia/cuda:12.4-devel-ubuntu22.04
RUN apt update && apt install -y git cmake
RUN git clone https://github.com/openclaw/openclaw.git /opt/openclaw && \
    cd /opt/openclaw && git checkout main && \
    make CUDA=1 && make install
CMD ["claw_inference", "--model", "/models/llama.gguf"]

Launch: docker run --gpus all -v models:/models image

⚠️ Unverified on ROCm; test with claw_inference --backend hip.

4. Verification

1. Smoke test:

   ./claw_inference --model model.gguf --prompt "Test" --n-predict 50 | grep -i "clw-llm-crash" || echo "PASS"
   

2. Stress test (100 inferences):

   import subprocess
   for _ in range(100):
       out = subprocess.run(['./claw_inference', '--model', 'model.gguf', '--prompt', 'test'], capture_output=True)
       if out.returncode != 0:
           print("FAIL")
           break
   print("Stress PASS")
   

3. Monitor VRAM:

   watch -n 1 nvidia-smi  # <90% during run
   

4. Valgrind for leaks (CPU mode):

   valgrind --tool=memcheck ./claw_inference --backend cpu --model small.gguf
   

Expect 5-10 tokens/sec on RTX 4090; no crashes after 1h load.

5. Common Pitfalls

• Overloading GPU layers: --n-gpu-layers 999 ignores VRAM; compute: layers = (VRAM_total * 0.8 - 2GB) / (model_size / layers).
• Ignoring flash attention: Set --use-flash-attn only if CUDA>=12.1; else crash.
• **Batch size >1 without --parallel: Leads to race in KV cache.
• GGUF metadata corruption: gguf-dump model.gguf | head for tensor count mismatches.
• Mixed precision: FP16 model on INT8 backend; force --dtype f16.
• Docker without privileged: Misses /dev/nvidiactl.
• Old drivers: NVIDIA >=535 for CUDA 12.x.

Profile with nvprof or nsys to spot kernel stalls.

6. Related Errors

• clw-gpu-oom: VRAM overflow; reduce --n-gpu-layers.
• clw-model-invalid: Corrupt GGUF; redownload.
• clw-init-fail: CUDA/HIP init; check nvidia-smi.
• clw-cuda-version-mismatch: Toolkit rebuild required.

Cross-reference OpenClaw issues #456, #512 on GitHub.

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