concrete.ml.quantization.quantized_module_passes.md

module concrete.ml.quantization.quantized_module_passes

Optimization passes for QuantizedModules.

---

class PowerOfTwoScalingRoundPBSAdapter

Detect neural network patterns that can be optimized with round PBS.

method __init__

__init__(qmodule: QuantizedModule) → None

---

property num_ignored_valid_patterns

Get the number of optimizable patterns that were ignored.

Patterns could be ignored since a number of rounding bits was set manually through the compilation function.

Returns:

• result (int): number of patterns that could be optimized but were not

---

method compute_op_predecessors

compute_op_predecessors() → DefaultDict[Union[QuantizedOp, NoneType], List[Tuple[Union[QuantizedOp, NoneType], str]]]

Compute the predecessors for each QuantizedOp in a QuantizedModule.

Stores, for each quantized op, a list of quantized ops that produce its inputs. Currently only the first input of the operations is considered as it is, usually, the encrypted input.

Returns:

• result (PredecessorsType): a dictionary containing a hierarchy of op predecessors

---

method detect_patterns

detect_patterns(
    predecessors: DefaultDict[Optional[QuantizedOp], List[Tuple[Optional[QuantizedOp], str]]]
) → Dict[QuantizedMixingOp, Tuple[List[Union[QuantizedOp, NoneType]], Union[QuantizedOp, NoneType]]]

Detect the patterns that can be optimized with roundPBS in the QuantizedModule.

Args:

• predecessors (PredecessorsType): Module predecessor operation list

Returns:

• result (PatternDict): list of optimizable patterns

---

method match_path_pattern

match_path_pattern(
    predecessors: DefaultDict[Optional[QuantizedOp], List[Tuple[Optional[QuantizedOp], str]]],
    nodes_in_path: List[Optional[QuantizedOp]],
    input_producer_of_path: Optional[QuantizedOp]
) → bool

Determine if a pattern has the structure that makes it viable for roundPBS.

Args:

• predecessors (PredecessorsType): Module predecessor operation list

• nodes_in_path (List[QuantizedOp]): list of quantized ops in the pattern

• input_producer_of_path (Optional[QuantizedOp]): operation that produces the input

Returns:

• result (bool): whether the pattern can be optimized

---

method process

process() → Dict[QuantizedMixingOp, Tuple[List[Union[QuantizedOp, NoneType]], Union[QuantizedOp, NoneType]]]

Analyze an ONNX graph and detect Gemm/Conv patterns that can use RoundPBS.

We want to detect a gemm/conv node whose weights/bias are Brevitas QAT, and whose input is produced by a Brevitas QAT node that is applied on the output of another Gemm/conv node. Optionally a Relu can be placed before this input quantization node.

Nothing will be done if rounding is already specified.

Returns:

• result (PatternDict): a dictionary containing for each Conv/Gemm node for which round PBS can be applied based on power-of-two scaling factors

---

method process_patterns

process_patterns(
    valid_paths: Dict[QuantizedMixingOp, Tuple[List[Optional[QuantizedOp]], Optional[QuantizedOp]]]
) → Dict[QuantizedMixingOp, Tuple[List[Union[QuantizedOp, NoneType]], Union[QuantizedOp, NoneType]]]

Configure the rounding bits of roundPBS for the optimizable operations.

Args:

• valid_paths (PatternDict): list of optimizable patterns

Returns:

• result (PatternDict): list of patterns actually optimized with roundPBS