This PR makes exo engine-agnostic by adding PyTorch as an inference backend,
enabling Linux systems with NVIDIA GPUs to run inference.

## Architecture Changes

- **Engine abstraction**: Created base_engine.py with Engine interface
- **MlxEngine**: Moved all MLX-specific patches into MlxEngine.generate()
  - Includes KV prefix cache for faster prefill (upstream feature exo-explore#1262)
  - Properly passes kv_prefix_cache to mlx_generate()
- **PytorchEngine**: New engine for HuggingFace transformers on NVIDIA GPUs
- **Engine-agnostic runner**: runner.py no longer imports MLX at top level
- **Conditional imports**: bootstrap.py selects engine based on instance type

## New Files

- src/exo/worker/engines/base_engine.py - Abstract Engine interface
- src/exo/worker/engines/pytorch/__init__.py - PyTorch engine implementation
- src/exo/worker/engines/pytorch/auto_parallel.py - Pipeline parallelism for PyTorch
- src/exo/worker/engines/mlx/patches.py - Extracted MLX-specific helpers
- src/exo/utils/info_gatherer/linux_metrics.py - nvidia-smi GPU metrics

## Upstream Features Preserved

- KV prefix cache (exo-explore#1262) - integrated into MlxEngine
- Empty message fix (exo-explore#1292) - in utils_mlx.py
- Model shard loading fix (exo-explore#1291) - in auto_parallel.py

## Dashboard Changes

- Model-engine compatibility filtering
- Reordered instance type buttons (MLX above PyTorch)
- Fixed matchesSelectedRuntime() for PyTorch
- Model dropdown reset on instance type change

## Bug Fixes

- placement.py: Added missing logger import
- api.py: Model/engine compatibility validation
- api.py: Fixed tags hardcoding (tags=card.tags or [])
- test_event_ordering.py: Updated to use MockEngine instead of MLX patches

## Testing

- 20+ tests for nvidia-smi parsing edge cases
- PyTorch engine tests
- All existing tests pass (137 passed)