diff --git a/src/finn/builder/build_dataflow_config.py b/src/finn/builder/build_dataflow_config.py
index 49f800f62a6f9c646c5223504d09cb90d3d144f7..4d91845455be67b6e27c48f2adc60bb2b2b4c363 100644
--- a/src/finn/builder/build_dataflow_config.py
+++ b/src/finn/builder/build_dataflow_config.py
@@ -295,6 +295,14 @@ class DataflowBuildConfig:
#: If given, stop at this step.
stop_step: Optional[str] = None
+ #: The optional argument `max_multithreshold_bit_width` affects which Quant nodes
+ #: of the QONNX format get converted to the MultiThreshold nodes of FINN. This
+ #: only affects Quant nodes in the activation path. Quant nodes, which define a
+ #: bit width larger than `max_multithreshold_bit_width` are not converted to
+ #: MultiThreshold nodes and a warning is raised instead.
+ #: If not given `max_multithreshold_bit_width` defaults to 4.
+ max_multithreshold_bit_width: Optional[int] = 4
+
def _resolve_hls_clk_period(self):
if self.hls_clk_period_ns is None:
# use same clk for synth and hls if not explicitly specified
diff --git a/src/finn/builder/build_dataflow_steps.py b/src/finn/builder/build_dataflow_steps.py
index 2216fcb3535360b6da5a43942bc46e81780fa341..7fce5b35faedb823254cd513e1c4e844d129a277 100644
--- a/src/finn/builder/build_dataflow_steps.py
+++ b/src/finn/builder/build_dataflow_steps.py
@@ -206,7 +206,11 @@ def step_qonnx_to_finn(model: ModelWrapper, cfg: DataflowBuildConfig):
# QONNX cleanup
model = cleanup_model(model)
# QONNX to FINN-ONNX
- model = model.transform(ConvertQONNXtoFINN())
+ model = model.transform(
+ ConvertQONNXtoFINN(
+ max_multithreshold_bit_width=cfg.max_multithreshold_bit_width
+ )
+ )
if VerificationStepType.QONNX_TO_FINN_PYTHON in cfg._resolve_verification_steps():
verify_step(model, cfg, "initial_python", need_parent=False)
diff --git a/src/finn/transformation/qonnx/convert_qonnx_to_finn.py b/src/finn/transformation/qonnx/convert_qonnx_to_finn.py
index 184a9785322fa9603f7f20dcc914dc68d41462a8..d4eba367c305d4228d572e9f082b191c4081166e 100644
--- a/src/finn/transformation/qonnx/convert_qonnx_to_finn.py
+++ b/src/finn/transformation/qonnx/convert_qonnx_to_finn.py
@@ -43,8 +43,33 @@ class ConvertQONNXtoFINN(Transformation):
then the ConvertQuantActToMultiThreshold transformation is used to convert
the activations.
If incompatibilities are found a ValueError or RuntimeError is raised.
+
+ The optional keyword arguments `max_multithreshold_bit_width` and `filter_lambda`
+ present a way to control which Quant and BinaryQuant nodes in the activation path
+ are converted to MultiThreshold nodes.
+ The filters which are represented by `max_multithreshold_bit_width` and
+ `filter_lambda` are internally connected by an `AND` operation. A warning
+ will be emitted when a Quant node is not converted to a MultiThreshold node.
+
+ :param max_multithreshold_bit_width: The value of max_multithreshold_bit_width is
+ checked against the bit width of any given Quant node and the transformation to a
+ MultiTrheshold node is rejected, when the bitwidth of the Quant node is larger
+ than value of max_multithreshold_bit_with. Defaults to: 4
+ :type max_multithreshold_bit_width: `int`, optional
+ :param filter_lambda: Each candidate Quant and BinaryQant node is first evaluated
+ by this lambda function. If the function returns False,
+ then the node is not converted to a MultiTrheshold node.
+ Defaults to: lambda q_node: True
+ :type filter_lambda: `lambda`, optional
"""
+ def __init__(
+ self, max_multithreshold_bit_width=4, filter_lambda=lambda q_node: True
+ ):
+ super().__init__()
+ self.max_multithreshold_bit_width = max_multithreshold_bit_width
+ self._filter_lambda = filter_lambda
+
def apply(self, model):
# Gemm operations are not supported by FINN, so we convert them to MatMul
model = model.transform(GemmToMatMul())
@@ -54,7 +79,12 @@ class ConvertQONNXtoFINN(Transformation):
# Fold weights
model = model.transform(FoldQuantWeights())
# Convert activations
- model = model.transform(ConvertQuantActToMultiThreshold())
+ model = model.transform(
+ ConvertQuantActToMultiThreshold(
+ max_multithreshold_bit_width=self.max_multithreshold_bit_width,
+ filter_lambda=self._filter_lambda,
+ )
+ )
# Recompute datatypes
model = model.transform(InferDataTypes())
diff --git a/src/finn/transformation/qonnx/quant_act_to_multithreshold.py b/src/finn/transformation/qonnx/quant_act_to_multithreshold.py
index 641cef0c3aaaf5b452dac7c8831a35242bab7313..cd203e63f88323c867e5235a2a2f774b505a205a 100644
--- a/src/finn/transformation/qonnx/quant_act_to_multithreshold.py
+++ b/src/finn/transformation/qonnx/quant_act_to_multithreshold.py
@@ -27,12 +27,41 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+import warnings
+
from finn.transformation.base import Transformation
from finn.transformation.qonnx.qonnx_activation_handlers import QuantActBaseHandler
class ConvertQuantActToMultiThreshold(Transformation):
- """Converts Quant nodes in the activation path to MultiThreshold nodes."""
+ """
+ Converts Quant nodes in the activation path to MultiThreshold nodes.
+
+ The optional keyword arguments `max_multithreshold_bit_width` and `filter_lambda`
+ present a way to control which Quant and BinaryQuant nodes in the activation path
+ are converted to MultiThreshold nodes.
+ The filters which are represented by `max_multithreshold_bit_width` and
+ `filter_lambda` are internally connected by an `AND` operation. A warning
+ will be emitted when a Quant node is not converted to a MultiThreshold node.
+
+ :param max_multithreshold_bit_width: The value of max_multithreshold_bit_width is
+ checked against the bit width of any given Quant node and the transformation to a
+ MultiTrheshold node is rejected, when the bitwidth of the Quant node is larger
+ than value of max_multithreshold_bit_with. Defaults to: 4
+ :type max_multithreshold_bit_width: `int`, optional
+ :param filter_lambda: Each candidate Quant and BinaryQant node is first evaluated
+ by this lambda function. If the function returns False,
+ then the node is not converted to a MultiTrheshold node.
+ Defaults to: lambda q_node: True
+ :type filter_lambda: `lambda`, optional
+ """
+
+ def __init__(
+ self, max_multithreshold_bit_width=4, filter_lambda=lambda q_node: True
+ ):
+ super().__init__()
+ self.max_multithreshold_bit_width = max_multithreshold_bit_width
+ self._filter_lambda = filter_lambda
def apply(self, model):
graph = model.graph
@@ -61,6 +90,27 @@ class ConvertQuantActToMultiThreshold(Transformation):
"Only Quant nodes with zero-point == 0 are currently supported."
)
+ # Check if the bit width is low enough
+ bit_width = model.get_initializer(n.input[3])
+ if bit_width is None:
+ raise ValueError("Quant nodes must have a static bit width.")
+ if bit_width > self.max_multithreshold_bit_width:
+ warnings.warn(
+ f'The Quant node with name: "{n.name}" was not converted to a '
+ f"MultiThreshold node, because its bit width of {bit_width} is "
+ f"higher than the configured maximum bit width of "
+ f"{self.max_multithreshold_bit_width}."
+ )
+ continue
+ # Check that this node passes the user filter
+ if not self._filter_lambda(n):
+ warnings.warn(
+ f'The Quant node with name: "{n.name}" was not converted to a '
+ f"MultiThreshold node, because the filtering lambda function "
+ f"returned False for this node."
+ )
+ continue
+
# Check for possible ambiguity in handler selection
valid_predecessors = []
for cls in QuantActBaseHandler.__subclasses__():