From bb5a4a425d860740535affc043d4191751fa6eea Mon Sep 17 00:00:00 2001
From: Lucian Petrica <lucianp@xilinx.com>
Date: Mon, 27 Jul 2020 08:57:19 +0000
Subject: [PATCH] Bitwidth optimizations to accumulators and thresholds,
through clipping of too-large threshold values and explicit calculation of
the bitwidth
---
.../fpgadataflow/streamingfclayer_batch.py | 37 +++++++++++++++++--
.../fpgadataflow/thresholding_batch.py | 17 ++++++++-
.../streamline/round_thresholds.py | 12 +++++-
3 files changed, 61 insertions(+), 5 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index 9c3bd3ac8..686975c8c 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -594,7 +594,6 @@ class StreamingFCLayer_Batch(HLSCustomOp):
thresholds = model.get_initializer(self.onnx_node.input[2])
if thresholds is not None:
threshold_tensor = self.get_hls_compatible_threshold_tensor(thresholds)
- tdt = DataType.INT32
# use UINT32 threshold export for bipolar times bipolar
inp_is_bipolar = self.get_input_datatype() == DataType.BIPOLAR
wt_is_bipolar = self.get_weight_datatype() == DataType.BIPOLAR
@@ -604,8 +603,40 @@ 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)
- if inp_is_bipolar and wt_is_bipolar:
- tdt = DataType.UINT32
+ # 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)
+
thresholds_hls_code = numpy_to_hls_code(
threshold_tensor, tdt, "thresholds", False, True
)
diff --git a/src/finn/custom_op/fpgadataflow/thresholding_batch.py b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
index fa33c7021..cf19a2534 100644
--- a/src/finn/custom_op/fpgadataflow/thresholding_batch.py
+++ b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
@@ -279,7 +279,22 @@ class Thresholding_Batch(HLSCustomOp):
thresholds = model.get_initializer(self.onnx_node.input[1])
threshold_tensor = self.get_hls_compatible_threshold_tensor(thresholds)
- tdt = DataType.INT32
+
+ min_threshold = thresholds.min()
+ max_threshold = thresholds.max()
+ min_input = self.get_input_datatype().min()
+ max_input = self.get_input_datatype().max()
+ # get range required by threshold values
+ tdt_min = min(min_input, min_threshold)
+ tdt_max = max(max_input, 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 - 1)
+ else:
+ tdt = DataType.get_smallest_possible(tdt_max)
+
thresholds_hls_code = numpy_to_hls_code(
threshold_tensor, tdt, "thresholds", False, True
)
diff --git a/src/finn/transformation/streamline/round_thresholds.py b/src/finn/transformation/streamline/round_thresholds.py
index c33281d85..8626ef406 100644
--- a/src/finn/transformation/streamline/round_thresholds.py
+++ b/src/finn/transformation/streamline/round_thresholds.py
@@ -51,10 +51,20 @@ class RoundAndClipThresholds(Transformation):
model.set_tensor_datatype(n.input[1], idtype)
graph_modified = True
if idtype.is_integer() and not idtype.signed() and (Tnew < 0).any():
- # clip any negative thresholds
+ # clip any negative thresholds if input is unsigned
Tnew = np.clip(Tnew, 0, None)
model.set_initializer(n.input[1], Tnew)
# use same datatype as inputs for thresholds
model.set_tensor_datatype(n.input[1], idtype)
graph_modified = True
+ if idtype.is_integer() and (
+ (Tnew < (idtype.min() - 1)).any()
+ or (Tnew > (idtype.max() + 1)).any()
+ ):
+ # clip any large thresholds to input range + 1
+ Tnew = np.clip(Tnew, idtype.min() - 1, idtype.max() + 1)
+ model.set_initializer(n.input[1], Tnew)
+ # use same datatype as inputs for thresholds
+ model.set_tensor_datatype(n.input[1], idtype)
+ graph_modified = True
return (model, graph_modified)
--
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