diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index 73bc573c5e01d332bb731d00d3b051a4fc583dbd..bc266e4934c41d6f5f1261e0a30e90cb72ba83a8 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -39,6 +39,7 @@ from finn.custom_op.fpgadataflow import HLSCustomOp
from finn.util.basic import (
interleave_matrix_outer_dim_from_partitions,
roundup_to_integer_multiple,
+ calculate_matvec_accumulator_range,
)
from finn.util.data_packing import (
npy_to_rtlsim_input,
@@ -75,6 +76,8 @@ class StreamingFCLayer_Batch(HLSCustomOp):
"inputDataType": ("s", True, ""),
"weightDataType": ("s", True, ""),
"outputDataType": ("s", True, ""),
+ # FINN DataType for accumulator -- auto-computed and updated
+ "accDataType": ("s", False, "DataType.INT32"),
# use xnor-popcount for binary weights/inputs, thus treating them
# as bipolar
"binaryXnorMode": ("i", False, 0),
@@ -444,6 +447,47 @@ class StreamingFCLayer_Batch(HLSCustomOp):
ret = np.flip(ret, axis=-1)
return ret
+ def minimize_accumulator_width(self, model):
+ weights = model.get_initializer(self.onnx_node.input[1])
+ if len(self.onnx_node.input) > 2:
+ thresholds = model.get_initializer(self.onnx_node.input[2])
+ else:
+ thresholds = None
+ idt = self.get_input_datatype()
+ # calculate minimum and maximum values of accumulator
+ (acc_min, acc_max) = calculate_matvec_accumulator_range(weights, idt)
+ if thresholds is not None:
+ threshold_tensor = self.get_hls_compatible_threshold_tensor(thresholds)
+ # set threshold datatype (and accumulator datatype implicitly)
+ min_threshold = thresholds.min()
+ max_threshold = thresholds.max()
+ # get range required by threshold values
+ tdt_min = min(acc_min, min_threshold)
+ tdt_max = max(acc_max, max_threshold)
+ if tdt_min < 0:
+ if abs(tdt_min) > tdt_max:
+ tdt = DataType.get_smallest_possible(tdt_min)
+ else:
+ tdt = DataType.get_smallest_possible(0 - tdt_max)
+ else:
+ tdt = DataType.get_smallest_possible(tdt_max)
+ assert np.vectorize(tdt.allowed)(
+ threshold_tensor
+ ).all(), "Thresholds can't be expressed with type %s" % str(tdt)
+ self.set_nodeattr("accDataType", tdt.name)
+ else:
+ if acc_min < 0:
+ if abs(acc_min) > acc_max:
+ adt = DataType.get_smallest_possible(acc_min)
+ else:
+ adt = DataType.get_smallest_possible(0 - acc_max)
+ else:
+ adt = DataType.get_smallest_possible(acc_max)
+ self.set_nodeattr("accDataType", adt.name)
+ # for no-activation nodes, output dt = acc dt
+ self.set_nodeattr("outputDataType", adt.name)
+ return DataType[self.get_nodeattr("accDataType")]
+
def get_hls_compatible_threshold_tensor(self, orig_thres_matrix):
"""Convert the original numpy weight matrix orig_weight_matrix into
a form suitable for passing to the hlslib call:
@@ -614,44 +658,12 @@ class StreamingFCLayer_Batch(HLSCustomOp):
bin_xnor_mode = self.get_nodeattr("binaryXnorMode") == 1
inp_is_bipolar = inp_is_bipolar or (inp_is_binary and bin_xnor_mode)
wt_is_bipolar = wt_is_bipolar or (wt_is_binary and bin_xnor_mode)
- # set threshold datatype (and accumulator datatype implicitly)
- min_threshold = thresholds.min()
- max_threshold = thresholds.max()
- min_weight = weights.min()
- max_weight = weights.max()
- perceptive_field_elems = self.get_nodeattr("MW")
- min_input = self.get_input_datatype().min()
- max_input = self.get_input_datatype().max()
- # calculate minimum and maximum values of accumulator
- # assume inputs span the whole range of the input datatype
- acc_min = perceptive_field_elems * min(
- min_weight * max_input,
- min_weight * min_input,
- max_weight * max_input,
- max_weight * min_input,
- )
- acc_max = perceptive_field_elems * max(
- min_weight * max_input,
- min_weight * min_input,
- max_weight * max_input,
- max_weight * min_input,
- )
-
- # get range required by threshold values
- tdt_min = min(acc_min, min_threshold)
- tdt_max = max(acc_max, max_threshold)
- if tdt_min < 0:
- if abs(tdt_min) > tdt_max:
- tdt = DataType.get_smallest_possible(tdt_min)
- else:
- tdt = DataType.get_smallest_possible(0 - tdt_max)
- else:
- tdt = DataType.get_smallest_possible(tdt_max)
+ # get computed threshold datatype from attribute
+ tdt = DataType[self.get_nodeattr("accDataType")]
assert np.vectorize(tdt.allowed)(
threshold_tensor
).all(), "Thresholds can't be expressed with type %s" % str(tdt)
-
thresholds_hls_code = numpy_to_hls_code(
threshold_tensor, tdt, "thresholds", False, True
)
diff --git a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
index e6dca0e4b05f943c971bc0f97af03f5038fd0dab..88f5fa926f73d5cb1919a02c83153cb8d1894711 100644
--- a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
+++ b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
@@ -40,6 +40,9 @@ from finn.transformation.general import SortGraph
import finn.core.data_layout as DataLayout
from finn.util.onnx import nchw_to_nhwc
from finn.util.basic import get_by_name
+from finn.transformation.fpgadataflow.minimize_accumulator_width import (
+ MinimizeAccumulatorWidth,
+)
class InferConvInpGen(Transformation):
@@ -489,6 +492,7 @@ class InferBinaryStreamingFCLayer(Transformation):
graph.node.remove(n)
graph_modified = True
if graph_modified:
+ model = model.transform(MinimizeAccumulatorWidth())
model = model.transform(InferShapes())
model = model.transform(InferDataTypes())
return (model, graph_modified)
@@ -623,6 +627,7 @@ class InferQuantizedStreamingFCLayer(Transformation):
graph.node.remove(n)
graph_modified = True
if graph_modified:
+ model = model.transform(MinimizeAccumulatorWidth())
model = model.transform(InferShapes())
model = model.transform(InferDataTypes())
return (model, graph_modified)
diff --git a/src/finn/transformation/fpgadataflow/minimize_accumulator_width.py b/src/finn/transformation/fpgadataflow/minimize_accumulator_width.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c54a5efbd3b28f0fbfd074b512929edab234e78
--- /dev/null
+++ b/src/finn/transformation/fpgadataflow/minimize_accumulator_width.py
@@ -0,0 +1,48 @@
+# Copyright (c) 2020, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+from finn.custom_op.registry import getCustomOp
+from finn.transformation import Transformation
+from finn.util.fpgadataflow import is_fpgadataflow_node
+
+
+class MinimizeAccumulatorWidth(Transformation):
+ """For relevant nodes, call the accumulator width minimization
+ functions to save on resources. May alter tensor DataType for
+ certain nodes if they produce an accumulator as result."""
+
+ def __init__(self):
+ super().__init__()
+
+ def apply(self, model):
+ for node in model.graph.node:
+ if is_fpgadataflow_node(node) is True:
+ inst = getCustomOp(node)
+ if hasattr(inst, "minimize_accumulator_width"):
+ inst.minimize_accumulator_width(model)
+ return (model, False)