From 28f93d5ac66c0fc09c7af5aa24afb58d3909d7fe Mon Sep 17 00:00:00 2001
From: Hendrik Borras <hendrikborras@web.de>
Date: Tue, 19 Oct 2021 13:33:30 +0100
Subject: [PATCH] Refactored filter lambda for ConvertQuantActToMultiThreshold
transformation to only use the function for filtering.
---
src/finn/builder/build_dataflow_steps.py | 7 +-
.../qonnx/convert_qonnx_to_finn.py | 37 ++++----
.../qonnx/quant_act_to_multithreshold.py | 88 +++++++++++--------
3 files changed, 73 insertions(+), 59 deletions(-)
diff --git a/src/finn/builder/build_dataflow_steps.py b/src/finn/builder/build_dataflow_steps.py
index 7fce5b35f..96cda8e0e 100644
--- a/src/finn/builder/build_dataflow_steps.py
+++ b/src/finn/builder/build_dataflow_steps.py
@@ -96,6 +96,9 @@ from finn.transformation.infer_shapes import InferShapes
from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
from finn.transformation.move_reshape import RemoveCNVtoFCFlatten
from finn.transformation.qonnx.convert_qonnx_to_finn import ConvertQONNXtoFINN
+from finn.transformation.qonnx.quant_act_to_multithreshold import (
+ default_filter_function_generator,
+)
from finn.transformation.streamline import Streamline
from finn.transformation.streamline.reorder import MakeMaxPoolNHWC
from finn.util.config import extract_model_config_to_json
@@ -208,7 +211,9 @@ def step_qonnx_to_finn(model: ModelWrapper, cfg: DataflowBuildConfig):
# QONNX to FINN-ONNX
model = model.transform(
ConvertQONNXtoFINN(
- max_multithreshold_bit_width=cfg.max_multithreshold_bit_width
+ filter_function=default_filter_function_generator(
+ max_multithreshold_bit_width=cfg.max_multithreshold_bit_width
+ )
)
)
diff --git a/src/finn/transformation/qonnx/convert_qonnx_to_finn.py b/src/finn/transformation/qonnx/convert_qonnx_to_finn.py
index f2b4cc3ec..b1b1dd0bb 100644
--- a/src/finn/transformation/qonnx/convert_qonnx_to_finn.py
+++ b/src/finn/transformation/qonnx/convert_qonnx_to_finn.py
@@ -36,6 +36,7 @@ from finn.transformation.qonnx.fold_quant_weights import FoldQuantWeights
from finn.transformation.qonnx.infer_QuantAvgPool2d import AvgPoolAndTruncToQuantAvgPool
from finn.transformation.qonnx.quant_act_to_multithreshold import (
ConvertQuantActToMultiThreshold,
+ default_filter_function_generator,
)
from finn.transformation.remove import RemoveEmptyPadding
@@ -47,31 +48,28 @@ class ConvertQONNXtoFINN(Transformation):
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 BipolarQuant 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.
+ The optional keyword argument `filter_function`
+ presents a way to control which Quant and BipolarQuant nodes in the activation path
+ are converted to MultiThreshold nodes. 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: 8
- :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,
+ :param filter_function: Each candidate Quant and BinaryQant node is first evaluated
+ by this 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
+ The function is given the model and candidate node as parameters.
+ Per default a filter function is inserted, which disables the conversion of
+ Quant nodes, which have a bit width of larger than 8.
+ Defaults to: default_filter_function_generator(max_multithreshold_bit_width=8)
"""
def __init__(
- self, max_multithreshold_bit_width=8, filter_lambda=lambda q_node: True
+ self,
+ filter_function=default_filter_function_generator(
+ max_multithreshold_bit_width=8
+ ),
):
super().__init__()
- self.max_multithreshold_bit_width = max_multithreshold_bit_width
- self._filter_lambda = filter_lambda
+ self._filter_function = filter_function
def apply(self, model):
# Extract the bias from Conv node
@@ -86,8 +84,7 @@ class ConvertQONNXtoFINN(Transformation):
# Convert activations
model = model.transform(
ConvertQuantActToMultiThreshold(
- max_multithreshold_bit_width=self.max_multithreshold_bit_width,
- filter_lambda=self._filter_lambda,
+ filter_function=self._filter_function,
)
)
# Recompute datatypes
diff --git a/src/finn/transformation/qonnx/quant_act_to_multithreshold.py b/src/finn/transformation/qonnx/quant_act_to_multithreshold.py
index 520ed0b82..29ba93dfc 100644
--- a/src/finn/transformation/qonnx/quant_act_to_multithreshold.py
+++ b/src/finn/transformation/qonnx/quant_act_to_multithreshold.py
@@ -33,35 +33,64 @@ from finn.transformation.base import Transformation
from finn.transformation.qonnx.qonnx_activation_handlers import QuantActBaseHandler
+def default_filter_function_generator(max_multithreshold_bit_width=8):
+ """
+ This function generates the default filter function for the
+ ConvertQuantActToMultiThreshold transformation. Per default the returned
+ function disables the conversion of Quant nodes which have a bit width above 8 bit.
+
+ This function generator can be used as a template to write custom
+ filter functions.
+ """
+
+ def filter_function(model, q_node):
+ if q_node.op_type == "Quant":
+ bit_width = model.get_initializer(q_node.input[3])
+ elif q_node.op_type == "BipolarQuant":
+ bit_width = 1.0
+ else:
+ raise RuntimeError("Got an unexpected quantizer node type")
+ if bit_width is None:
+ raise ValueError("Quant nodes must have a static bit width.")
+ if bit_width > max_multithreshold_bit_width:
+ warnings.warn(
+ f'The Quant node with name: "{q_node.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"{max_multithreshold_bit_width}."
+ )
+ return False
+ return True
+
+ return filter_function
+
+
class ConvertQuantActToMultiThreshold(Transformation):
"""
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 BipolarQuant 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: 8
- :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,
+ The optional keyword argument `filter_function`
+ presents a way to control which Quant and BipolarQuant nodes in the activation path
+ are converted to MultiThreshold nodes. A warning will be emitted when a Quant node
+ is not converted to a MultiThreshold node.
+
+ :param filter_function: Each candidate Quant and BinaryQant node is first evaluated
+ by this 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
+ The function is given the model and candidate node as parameters.
+ Per default a filter function is inserted, which disables the conversion of
+ Quant nodes, which have a bit width of larger than 8.
+ Defaults to: default_filter_function_generator(max_multithreshold_bit_width=8)
"""
def __init__(
- self, max_multithreshold_bit_width=8, filter_lambda=lambda q_node: True
+ self,
+ filter_function=default_filter_function_generator(
+ max_multithreshold_bit_width=8
+ ),
):
super().__init__()
- self.max_multithreshold_bit_width = max_multithreshold_bit_width
- self._filter_lambda = filter_lambda
+ self._filter_function = filter_function
def apply(self, model):
graph = model.graph
@@ -91,28 +120,11 @@ class ConvertQuantActToMultiThreshold(Transformation):
"Only Quant nodes with zero-point == 0 are currently supported."
)
- # Check if the bit width is low enough
- if n.op_type == "Quant":
- bit_width = model.get_initializer(n.input[3])
- elif n.op_type == "BipolarQuant":
- bit_width = 1.0
- else:
- raise RuntimeError("Got an unexpected quantizer node type")
- 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):
+ if not self._filter_function(model, 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"MultiThreshold node, because the filtering function "
f"returned False for this node."
)
continue
--
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