# Backends

A backend bundles the pattern lists that drive each pipeline stage. When
`transform()` is called without an explicit `patterns` argument, the active
backend supplies the conversion and fusion patterns automatically. The
quantization pipeline similarly reads the backend's `quantized_patterns` and
`smooth_patterns`.

## Built-in backends

| Backend | Package | Patterns |
|---------|---------|----------|
| `"tensorrt"` | `embedl_deploy._internal.tensorrt` | Full set: conversions, fusions, smooth, quantized. Ships as `embedl-deploy-tensorrt`. |
| `"lattice"` | `embedl_deploy._internal.lattice` | Conversions (fusions, smooth, quantized TBA). Ships as `embedl-deploy-lattice`. |

## Selecting a backend

```python
from embedl_deploy.backend import set_backend

set_backend("tensorrt")
```

After this call, `transform(model, (example_input,))` uses the TensorRT
conversion and fusion patterns — equivalent to passing
`patterns=TENSORRT_PATTERNS` explicitly.

If only one backend is installed, `get_backend()` auto-selects it. With
multiple backends installed you must call `set_backend()` before any
pipeline function that relies on implicit pattern selection.

## API reference

```python
from embedl_deploy.backend import (
    Backend,
    discover_backends,
    get_backend,
    reset_backends,
    set_backend,
)
```

### set_backend(name)

Select the active backend by name. Raises `ValueError` if the name does
not match any discovered backend.

### get_backend()

Return the active `Backend`. If none has been set, auto-discovers backends
and selects the only one found. Raises `RuntimeError` when zero or multiple
backends are installed without an explicit `set_backend()` call.

### discover_backends()

Scan for installed backend packages and return a list of `Backend`
instances. Results are cached after the first call.

### reset_backends()

Clear the cached discovery results and deselect the active backend. Useful
in test suites to isolate backend state between tests.

### Backend

```python
@dataclass(frozen=True)
class Backend:
    name: str
    conversion_patterns: Sequence[type[Pattern]] = ()
    fusion_patterns: Sequence[type[Pattern]] = ()
    smooth_patterns: Sequence[type[Pattern]] = ()
    quantized_patterns: Sequence[type[Pattern]] = ()
```

Each field holds pattern classes for the corresponding pipeline stage.
Validation in `__post_init__` ensures every pattern's `phase` matches the
field it is assigned to.