From 206f4002821062427212cd4071ace1776347de1e Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <maltanar@gmail.com>
Date: Fri, 22 Jul 2022 19:25:39 +0200
Subject: [PATCH] [VVAU] binaryXnorMode support (untested)
---
.../fpgadataflow/vectorvectoractivation.py | 57 +++++++++++++++++--
1 file changed, 52 insertions(+), 5 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py b/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py
index 9d0a9ee52..24cb3101f 100644
--- a/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py
+++ b/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py
@@ -96,6 +96,9 @@ class VectorVectorActivation(HLSCustomOp):
"auto",
{"auto", "block", "distributed", "ultra"},
),
+ # use xnor-popcount for binary weights/inputs, thus treating them
+ # as bipolar
+ "binaryXnorMode": ("i", False, 0, {0, 1}),
}
my_attrs.update(super().get_nodeattr_types())
return my_attrs
@@ -289,13 +292,31 @@ class VectorVectorActivation(HLSCustomOp):
ret = dict()
inp_hls_str = self.get_input_datatype().get_hls_datatype_str()
out_hls_str = self.get_output_datatype().get_hls_datatype_str()
+ inp_is_binary = self.get_input_datatype() == DataType["BINARY"]
+ # out_is_binary = self.get_output_datatype() == DataType["BINARY"]
+ wt_is_binary = self.get_weight_datatype() == DataType["BINARY"]
+ bin_xnor_mode = self.get_nodeattr("binaryXnorMode") == 1
+ if (inp_is_binary or wt_is_binary) and (not bin_xnor_mode):
+ raise Exception("True binary (non-bipolar) inputs not yet supported")
inp_is_bipolar = self.get_input_datatype() == DataType["BIPOLAR"]
+ # out_is_bipolar = self.get_output_datatype() == DataType["BIPOLAR"]
wt_is_bipolar = self.get_weight_datatype() == DataType["BIPOLAR"]
+ # reinterpret inp/wt as bipolar if bin_xnor_mode is iset
+ 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)
# fill in TSrcI and TWeightI
- # TODO handle bipolar inputs
- if inp_is_bipolar or wt_is_bipolar:
- raise Exception("VVAU node doesn't support bipolar values yet.")
- else:
+ # TODO check these with Giulio
+ # TODO handle non-bipolar binary inputs
+ if inp_is_bipolar and wt_is_bipolar:
+ ret["TSrcI"] = "Recast<XnorMul>"
+ ret["TWeightI"] = "Identity"
+ elif (not inp_is_bipolar) and wt_is_bipolar:
+ ret["TSrcI"] = "Slice<%s>" % inp_hls_str
+ ret["TWeightI"] = "Recast<Binary>"
+ elif inp_is_bipolar and (not wt_is_bipolar):
+ ret["TSrcI"] = "Recast<Binary>"
+ ret["TWeightI"] = "Identity"
+ elif (not inp_is_bipolar) and (not wt_is_bipolar):
ret["TSrcI"] = "Slice<%s>" % inp_hls_str
ret["TWeightI"] = "Identity"
@@ -324,6 +345,13 @@ class VectorVectorActivation(HLSCustomOp):
return ret
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:
+ * ensure MH % PE == 0
+ * for bipolar weights&inputs, ensure thresholds are positive
+ * interleave rows between PEs
+ * reshape into (PE, TMEM, n_thres_steps) and return
+ """
ch = self.get_nodeattr("Channels")
pe = self.get_nodeattr("PE")
tmem = self.calc_tmem()
@@ -333,14 +361,33 @@ class VectorVectorActivation(HLSCustomOp):
), """Threshold matrix dimension is
not as expected (2)."""
n_thres_steps = orig_thres_matrix.shape[1]
+ inp_is_bipolar = self.get_input_datatype() == DataType["BIPOLAR"]
+ wt_is_bipolar = self.get_weight_datatype() == DataType["BIPOLAR"]
+ # reinterpret inp/wt as bipolar if bin_xnor_mode is iset
+ inp_is_binary = self.get_input_datatype() == DataType["BINARY"]
+ wt_is_binary = self.get_weight_datatype() == DataType["BINARY"]
+ 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:
+ # ensure all thresholds are nonnegative
+ assert (orig_thres_matrix >= 0).all()
+ # ensure all thresholds are integer
+ assert (orig_thres_matrix.astype(np.int32) == orig_thres_matrix).all()
ret = orig_thres_matrix
# workaround for vivado_hls threshold bug
- if ret[0][0] == 0:
+ if ret[0][0] == 0 and n_thres_steps == 1:
ret = np.copy(ret)
ret[0][0] = 1
warnings.warn(
"Setting 0-valued first threshold to 1 to avoid vivado_hls bug"
)
+ # ensure channels = mh , duplicating if necessary
+ if ret.shape[0] == 1:
+ ret = np.tile(ret, (ch, 1))
+ assert (
+ ret.shape[0] == ch
+ ), "Channels of threshold matrix are not as expected (ch)"
# distribute rows between PEs
ret = interleave_matrix_outer_dim_from_partitions(ret, pe)
assert (
--
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