ollama37/tests/testcases/inference/TC-INFERENCE-001.yml

id: TC-INFERENCE-001
name: Model Pull
suite: inference
priority: 1
timeout: 600000

dependencies:
  - TC-RUNTIME-003

steps:
  - name: Check if model exists
    command: docker exec ollama37 ollama list | grep -q "gemma3:4b" && echo "Model exists" || echo "Model not found"

  - name: Pull model if needed
    command: docker exec ollama37 ollama list | grep -q "gemma3:4b" || docker exec ollama37 ollama pull gemma3:4b
    timeout: 600000

  - name: Verify model available
    command: docker exec ollama37 ollama list

  - name: Warmup model (preload into GPU)
    command: |
      curl -s http://localhost:11434/api/generate \
        -d '{"model":"gemma3:4b","prompt":"hi","stream":false}' \
        | jq -r '.response' | head -c 100
    timeout: 300000

  - name: Verify model loading in logs
    command: |
      # Use log collector file if available, fallback to docker compose logs
      if [ -f "/tmp/test-${TEST_ID}-logs.txt" ]; then
        LOGS=$(cat /tmp/test-${TEST_ID}-logs.txt)
      else
        LOGS=$(cd docker && docker compose logs 2>&1)
      fi

      echo "=== Model Loading Check ==="

      # Check for model loading message
      if echo "$LOGS" | grep -q 'msg="loading model"'; then
        echo "SUCCESS: Model loading initiated"
        echo "$LOGS" | grep 'msg="loading model"' | tail -1
      else
        echo "WARNING: Model loading message not found"
      fi

      # Check for layer offloading to GPU
      if echo "$LOGS" | grep -q "offloaded.*layers to GPU"; then
        echo "SUCCESS: Model layers offloaded to GPU"
        echo "$LOGS" | grep "offloaded.*layers to GPU" | tail -1
      else
        echo "ERROR: Model layers not offloaded to GPU"
        exit 1
      fi

      # Check model weights loaded
      if echo "$LOGS" | grep -q 'msg="model weights loaded successfully"'; then
        echo "SUCCESS: Model weights loaded"
      else
        echo "WARNING: Model weights loaded message not found"
      fi

  - name: Verify llama runner started
    command: |
      # Use log collector file if available, fallback to docker compose logs
      if [ -f "/tmp/test-${TEST_ID}-logs.txt" ]; then
        LOGS=$(cat /tmp/test-${TEST_ID}-logs.txt)
      else
        LOGS=$(cd docker && docker compose logs 2>&1)
      fi

      echo "=== Llama Runner Check ==="

      # Check llama runner started
      if echo "$LOGS" | grep -q "llama runner started"; then
        echo "SUCCESS: Llama runner started"
        echo "$LOGS" | grep "llama runner started" | tail -1
      else
        echo "ERROR: Llama runner not started"
        exit 1
      fi

  - name: Check for model loading errors
    command: |
      # Use log collector file if available, fallback to docker compose logs
      if [ -f "/tmp/test-${TEST_ID}-logs.txt" ]; then
        LOGS=$(cat /tmp/test-${TEST_ID}-logs.txt)
      else
        LOGS=$(cd docker && docker compose logs 2>&1)
      fi

      echo "=== Model Loading Error Check ==="

      # Check for CUDA/CUBLAS errors during model load
      if echo "$LOGS" | grep -qE "(CUBLAS_STATUS_|CUDA error|cudaMalloc failed)"; then
        echo "CRITICAL CUDA ERRORS during model load:"
        echo "$LOGS" | grep -E "(CUBLAS_STATUS_|CUDA error|cudaMalloc failed)"
        exit 1
      fi

      # Check for out of memory
      if echo "$LOGS" | grep -qi "out of memory"; then
        echo "ERROR: Out of memory during model load"
        echo "$LOGS" | grep -i "out of memory"
        exit 1
      fi

      echo "SUCCESS: No model loading errors"

  - name: Display model memory allocation from logs
    command: |
      # Use log collector file if available, fallback to docker compose logs
      if [ -f "/tmp/test-${TEST_ID}-logs.txt" ]; then
        LOGS=$(cat /tmp/test-${TEST_ID}-logs.txt)
      else
        LOGS=$(cd docker && docker compose logs 2>&1)
      fi

      echo "=== Model Memory Allocation ==="
      echo "$LOGS" | grep -E '(model weights|kv cache|compute graph|total memory).*device=' | tail -8

criteria: |
  The gemma3:4b model should be available for inference.

  Expected:
  - Model is either already present or successfully downloaded
  - "ollama list" shows gemma3:4b in the output
  - No download errors
  - Logs show "offloaded X/Y layers to GPU"
  - Logs show "llama runner started"
  - Logs show model weights on CUDA device (not CPU only)
  - NO CUBLAS_STATUS_ errors during model load
  - NO out of memory errors

  Accept if model already exists (skip download).
  Model size is ~3GB, download may take time.
  First inference loads model into VRAM - subsequent inferences will be fast.