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concrete/docs/dev/api/concrete.fhe.mlir.processors.assign_bit_widths.md
Benoit Chevallier-Mames e37a840e7e docs(compiler): adding API doc
2024-03-13 17:10:00 +01:00

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# <kbd>module</kbd> `concrete.fhe.mlir.processors.assign_bit_widths`
Declaration of `AssignBitWidths` graph processor.
---
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## <kbd>class</kbd> `AssignBitWidths`
AssignBitWidths graph processor, to assign proper bit-widths to be compatible with FHE.
There are two modes:
- Single Precision, where all encrypted values have the same precision.
- Multi Precision, where encrypted values can have different precisions.
There is preference list for comparison strategies.
- Strategies will be traversed in order and bit-widths will be assigned according to the first available strategy.
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### <kbd>method</kbd> `__init__`
```python
__init__(
single_precision: bool,
composable: bool,
comparison_strategy_preference: List[ComparisonStrategy],
bitwise_strategy_preference: List[BitwiseStrategy],
shifts_with_promotion: bool,
multivariate_strategy_preference: List[MultivariateStrategy],
min_max_strategy_preference: List[MinMaxStrategy]
)
```
---
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### <kbd>method</kbd> `apply`
```python
apply(graph: Graph)
```
---
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## <kbd>class</kbd> `AdditionalConstraints`
AdditionalConstraints class to customize bit-width assignment step easily.
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### <kbd>method</kbd> `__init__`
```python
__init__(
optimizer: Optimize,
graph: Graph,
bit_widths: Dict[Node, Int],
comparison_strategy_preference: List[ComparisonStrategy],
bitwise_strategy_preference: List[BitwiseStrategy],
shifts_with_promotion: bool,
multivariate_strategy_preference: List[MultivariateStrategy],
min_max_strategy_preference: List[MinMaxStrategy]
)
```
---
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### <kbd>method</kbd> `all_inputs_are_encrypted`
```python
all_inputs_are_encrypted(node: Node, preds: List[Node]) bool
```
---
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### <kbd>method</kbd> `bitwise`
```python
bitwise(node: Node, preds: List[Node])
```
---
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### <kbd>method</kbd> `comparison`
```python
comparison(node: Node, preds: List[Node])
```
---
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### <kbd>method</kbd> `constraint`
```python
constraint(node: Node, constraint: BoolRef)
```
---
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### <kbd>method</kbd> `generate_for`
```python
generate_for(node: Node, bit_width: <function Int at ADDRESS>)
```
Generate additional constraints for a node.
**Args:**
node (Node): node to generate constraints for
bit_width (z3.Int): symbolic bit-width which will be assigned to node once constraints are solved
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### <kbd>method</kbd> `has_overflow_protection`
```python
has_overflow_protection(node: Node, preds: List[Node]) bool
```
---
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### <kbd>method</kbd> `inputs_and_output_share_precision`
```python
inputs_and_output_share_precision(node: Node, preds: List[Node])
```
---
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### <kbd>method</kbd> `inputs_require_one_more_bit`
```python
inputs_require_one_more_bit(node: Node, preds: List[Node])
```
---
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### <kbd>method</kbd> `inputs_share_precision`
```python
inputs_share_precision(node: Node, preds: List[Node])
```
---
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### <kbd>method</kbd> `min_max`
```python
min_max(node: Node, preds: List[Node])
```
---
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### <kbd>method</kbd> `multivariate`
```python
multivariate(node: Node, preds: List[Node])
```
---
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### <kbd>method</kbd> `some_inputs_are_clear`
```python
some_inputs_are_clear(node: Node, preds: List[Node]) bool
```