From 3f2d6d97aa15a9c33057cb5ab20f39aa1c3a0dd5 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <maltanar@gmail.com>
Date: Thu, 22 Sep 2022 12:02:24 +0200
Subject: [PATCH] [Refactor] add optional ind=0 argument to HLSCustomOp stream
properties
---
.../fpgadataflow/addstreams_batch.py | 12 ++++----
.../fpgadataflow/channelwise_op_batch.py | 16 +++++------
src/finn/custom_op/fpgadataflow/checksum.py | 14 +++++-----
src/finn/custom_op/fpgadataflow/concat.py | 8 +++---
.../fpgadataflow/convolutioninputgenerator.py | 14 +++++-----
.../convolutioninputgenerator1d.py | 16 +++++------
.../custom_op/fpgadataflow/downsampler.py | 16 +++++------
.../fpgadataflow/duplicatestreams_batch.py | 12 ++++----
.../custom_op/fpgadataflow/fmpadding_batch.py | 14 +++++-----
.../fpgadataflow/globalaccpool_batch.py | 16 +++++------
.../custom_op/fpgadataflow/hlscustomop.py | 28 ++++++++++++-------
src/finn/custom_op/fpgadataflow/iodma.py | 14 +++++-----
.../fpgadataflow/labelselect_batch.py | 16 +++++------
src/finn/custom_op/fpgadataflow/lookup.py | 16 +++++------
.../fpgadataflow/matrixvectoractivation.py | 12 ++++----
src/finn/custom_op/fpgadataflow/pool_batch.py | 16 +++++------
.../streamingdatawidthconverter_batch.py | 16 +++++------
.../custom_op/fpgadataflow/streamingfifo.py | 12 ++++----
.../fpgadataflow/streamingmaxpool_batch.py | 16 +++++------
.../fpgadataflow/thresholding_batch.py | 16 +++++------
.../custom_op/fpgadataflow/tlastmarker.py | 8 +++---
src/finn/custom_op/fpgadataflow/upsampler.py | 16 +++++------
.../fpgadataflow/vectorvectoractivation.py | 16 +++++------
23 files changed, 174 insertions(+), 166 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/addstreams_batch.py b/src/finn/custom_op/fpgadataflow/addstreams_batch.py
index 1190ad064..cd0af6b3a 100644
--- a/src/finn/custom_op/fpgadataflow/addstreams_batch.py
+++ b/src/finn/custom_op/fpgadataflow/addstreams_batch.py
@@ -73,10 +73,10 @@ class AddStreams_Batch(HLSCustomOp):
ishape = tuple(vecs + [ich // pe, pe])
return ishape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
return self.get_normal_input_shape()
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
return self.get_folded_input_shape()
def make_shape_compatible_op(self, model):
@@ -127,11 +127,11 @@ class AddStreams_Batch(HLSCustomOp):
return info_messages
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
return DataType[self.get_nodeattr("inputDataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output."""
# we need to set output datatype to the next larger int or uint
# enhancement: consider specifying w/ explicit outputDataType attribute
@@ -142,14 +142,14 @@ class AddStreams_Batch(HLSCustomOp):
else:
return DataType.get_smallest_possible(2 * idt.max())
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
"""Returns input stream width."""
ibits = self.get_input_datatype().bitwidth()
pe = self.get_nodeattr("PE")
in_width = pe * ibits
return in_width
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
"""Returns output stream width."""
obits = self.get_output_datatype().bitwidth()
pe = self.get_nodeattr("PE")
diff --git a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
index 9d08a2432..f2d9f1aeb 100644
--- a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
+++ b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
@@ -221,23 +221,23 @@ class ChannelwiseOp_Batch(HLSCustomOp):
# total cost
return comparator_cost + lutram_cost
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
return DataType[self.get_nodeattr("inputDataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output."""
return DataType[self.get_nodeattr("outputDataType")]
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
i_bits = self.get_input_datatype().bitwidth()
return i_bits * self.get_nodeattr("PE")
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
o_bits = self.get_output_datatype().bitwidth()
return o_bits * self.get_nodeattr("PE")
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
ich = self.get_nodeattr("NumChannels")
pe = self.get_nodeattr("PE")
fold = ich // pe
@@ -245,17 +245,17 @@ class ChannelwiseOp_Batch(HLSCustomOp):
folded_input_shape = tuple(vecs + [fold, pe])
return folded_input_shape
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
# same shape as input
return self.get_folded_input_shape()
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
ich = self.get_nodeattr("NumChannels")
vecs = list(self.get_nodeattr("numInputVectors"))
normal_input_shape = tuple(vecs + [ich])
return normal_input_shape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
# same shape as input
return self.get_normal_input_shape()
diff --git a/src/finn/custom_op/fpgadataflow/checksum.py b/src/finn/custom_op/fpgadataflow/checksum.py
index bde285eb0..21a09a1c5 100644
--- a/src/finn/custom_op/fpgadataflow/checksum.py
+++ b/src/finn/custom_op/fpgadataflow/checksum.py
@@ -77,31 +77,31 @@ class CheckSum(HLSCustomOp):
def verify_node(self):
pass
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
return DataType[self.get_nodeattr("inputDataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output."""
# here same as input data type
return DataType[self.get_nodeattr("inputDataType")]
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
dtype = DataType[self.get_nodeattr("inputDataType")]
folded_shape = self.get_nodeattr("folded_shape")
in_width = folded_shape[-1] * dtype.bitwidth()
return in_width
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
return self.get_instream_width()
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
return self.get_nodeattr("folded_shape")
def get_folded_output_shape(self):
return self.get_nodeattr("folded_shape")
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
# derive normal shape from folded shape
# checksum nodes are inserted in between fpgadataflow nodes
# the folded shape could be for example (1, nf, pe)
@@ -127,7 +127,7 @@ class CheckSum(HLSCustomOp):
def get_ap_int_max_w(self):
return max(super().get_ap_int_max_w(), 32)
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
# same shape as input
return self.get_normal_input_shape()
diff --git a/src/finn/custom_op/fpgadataflow/concat.py b/src/finn/custom_op/fpgadataflow/concat.py
index 5fcf9cf96..4437bcd19 100644
--- a/src/finn/custom_op/fpgadataflow/concat.py
+++ b/src/finn/custom_op/fpgadataflow/concat.py
@@ -74,12 +74,12 @@ class StreamingConcat(HLSCustomOp):
def get_folded_input_shape(self, ind=0):
return self.get_normal_input_shape(ind)
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
total_elems = self.get_total_elems()
vecs = list(self.get_nodeattr("numInputVectors"))
return tuple(vecs + [total_elems])
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
return self.get_normal_output_shape()
def make_shape_compatible_op(self, model):
@@ -106,7 +106,7 @@ class StreamingConcat(HLSCustomOp):
# input dt identical for all inputs
return DataType[self.get_nodeattr("inputDataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
return self.get_input_datatype()
def get_instream_width(self, ind=0):
@@ -115,7 +115,7 @@ class StreamingConcat(HLSCustomOp):
ibits = self.get_input_datatype().bitwidth()
return elems * ibits
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
obits = self.get_output_datatype().bitwidth()
total_elems = self.get_total_elems()
out_width = total_elems * obits
diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
index 251a9882c..6f039f7d6 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
@@ -99,13 +99,13 @@ class ConvolutionInputGenerator(HLSCustomOp):
assert ret[0] == ret[1] == 1, "Only dilation=1 supported"
return ret
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
ifm_dim_h, ifm_dim_w = self.get_nodeattr("IFMDim")
ifm_ch = self.get_nodeattr("IFMChannels")
ishape = (1, ifm_dim_h, ifm_dim_w, ifm_ch)
return ishape
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
ifm_dim_h, ifm_dim_w = self.get_nodeattr("IFMDim")
ifm_ch = self.get_nodeattr("IFMChannels")
simd = self.get_nodeattr("SIMD")
@@ -114,7 +114,7 @@ class ConvolutionInputGenerator(HLSCustomOp):
folded_ishape = (1, ifm_dim_h, ifm_dim_w, wf, simd)
return folded_ishape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
k_h, k_w = self.get_nodeattr("ConvKernelDim")
ifm_dim_h, ifm_dim_w = self.get_nodeattr("IFMDim")
ifm_ch = self.get_nodeattr("IFMChannels")
@@ -126,7 +126,7 @@ class ConvolutionInputGenerator(HLSCustomOp):
oshape = (1, ofm_dim_h, ofm_dim_w, k_h * k_w * ifm_ch)
return oshape
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
k_h, k_w = self.get_nodeattr("ConvKernelDim")
ifm_dim_h, ifm_dim_w = self.get_nodeattr("IFMDim")
ifm_ch = self.get_nodeattr("IFMChannels")
@@ -158,11 +158,11 @@ class ConvolutionInputGenerator(HLSCustomOp):
def verify_node(self):
pass
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
return DataType[self.get_nodeattr("inputDataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output."""
return DataType[self.get_nodeattr("outputDataType")]
@@ -176,7 +176,7 @@ class ConvolutionInputGenerator(HLSCustomOp):
in_width = simd * ibits
return in_width
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
"""Returns stream width, input and output stream width are equal for
the sliding window function, so the function to determine the input
stream width can be reused."""
diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
index aba74baec..f1c84662c 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
@@ -91,13 +91,13 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
my_attrs.update(super().get_nodeattr_types())
return my_attrs
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
ifm_dim_h, ifm_dim_w = self.get_nodeattr("IFMDim")
ifm_ch = self.get_nodeattr("IFMChannels")
ishape = (1, ifm_dim_h, ifm_dim_w, ifm_ch)
return ishape
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
ifm_dim_h, ifm_dim_w = self.get_nodeattr("IFMDim")
ifm_ch = self.get_nodeattr("IFMChannels")
simd = self.get_nodeattr("SIMD")
@@ -106,7 +106,7 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
folded_ishape = (1, ifm_dim_h, ifm_dim_w, wf, simd)
return folded_ishape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
k_h, k_w = self.get_nodeattr("ConvKernelDim")
ifm_dim_h, ifm_dim_w = self.get_nodeattr("IFMDim")
ifm_ch = self.get_nodeattr("IFMChannels")
@@ -118,7 +118,7 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
oshape = (1, ofm_dim_h, ofm_dim_w, k_h * k_w * ifm_ch)
return oshape
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
k_h, k_w = self.get_nodeattr("ConvKernelDim")
ifm_dim_h, ifm_dim_w = self.get_nodeattr("IFMDim")
ifm_ch = self.get_nodeattr("IFMChannels")
@@ -153,15 +153,15 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
def verify_node(self):
pass
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
return DataType[self.get_nodeattr("inputDataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output."""
return DataType[self.get_nodeattr("outputDataType")]
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
ibits = self.get_input_datatype().bitwidth()
simd = self.get_nodeattr("SIMD")
ifm_ch = self.get_nodeattr("IFMChannels")
@@ -169,7 +169,7 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
in_width = simd * ibits
return in_width
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
if self.use_parallel_window_output():
# feed all window pixels in parallel
k_h, k_w = self.get_nodeattr("ConvKernelDim")
diff --git a/src/finn/custom_op/fpgadataflow/downsampler.py b/src/finn/custom_op/fpgadataflow/downsampler.py
index da29a524b..e5819cccd 100644
--- a/src/finn/custom_op/fpgadataflow/downsampler.py
+++ b/src/finn/custom_op/fpgadataflow/downsampler.py
@@ -73,21 +73,21 @@ class DownSampler(HLSCustomOp):
exp_cycles = channels / simd * batch_size * idim * idim
return int(exp_cycles)
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
idim = self.get_nodeattr("ImgDim")
num_ch = self.get_nodeattr("NumChannels")
batch = self.get_nodeattr("numInputVectors")
ishape = (batch, idim, idim, num_ch)
return ishape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
odim = self.get_downsampled_odim()
num_ch = self.get_nodeattr("NumChannels")
batch = self.get_nodeattr("numInputVectors")
oshape = (batch, odim, odim, num_ch)
return oshape
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
normal_ishape = list(self.get_normal_input_shape())
ifm_ch = self.get_nodeattr("NumChannels")
simd = self.get_nodeattr("SIMD")
@@ -96,7 +96,7 @@ class DownSampler(HLSCustomOp):
folded_ishape = normal_ishape[:-1] + [fold, simd]
return tuple(folded_ishape)
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
normal_oshape = list(self.get_normal_output_shape())
ifm_ch = self.get_nodeattr("NumChannels")
simd = self.get_nodeattr("SIMD")
@@ -129,21 +129,21 @@ class DownSampler(HLSCustomOp):
def verify_node(self):
pass
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
ret = DataType[self.get_nodeattr("inputDataType")]
return ret
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output. (Same as input datatype)"""
return self.get_input_datatype()
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
ibits = self.get_input_datatype().bitwidth()
simd = self.get_nodeattr("SIMD")
return ibits * simd
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
obits = self.get_output_datatype().bitwidth()
simd = self.get_nodeattr("SIMD")
return obits * simd
diff --git a/src/finn/custom_op/fpgadataflow/duplicatestreams_batch.py b/src/finn/custom_op/fpgadataflow/duplicatestreams_batch.py
index 7aee3a401..93cde15ca 100644
--- a/src/finn/custom_op/fpgadataflow/duplicatestreams_batch.py
+++ b/src/finn/custom_op/fpgadataflow/duplicatestreams_batch.py
@@ -61,13 +61,13 @@ class DuplicateStreams_Batch(HLSCustomOp):
def get_num_output_streams(self):
return self.get_nodeattr("NumOutputStreams")
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
ch = self.get_nodeattr("NumChannels")
vecs = list(self.get_nodeattr("numInputVectors"))
ishape = tuple(vecs + [ch])
return ishape
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
ch = self.get_nodeattr("NumChannels")
pe = self.get_nodeattr("PE")
vecs = list(self.get_nodeattr("numInputVectors"))
@@ -138,22 +138,22 @@ class DuplicateStreams_Batch(HLSCustomOp):
return info_messages
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
return DataType[self.get_nodeattr("inputDataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output."""
return DataType[self.get_nodeattr("inputDataType")]
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
"""Returns input stream width."""
ibits = self.get_input_datatype().bitwidth()
pe = self.get_nodeattr("PE")
in_width = pe * ibits
return in_width
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
"""Returns output stream width."""
obits = self.get_output_datatype().bitwidth()
pe = self.get_nodeattr("PE")
diff --git a/src/finn/custom_op/fpgadataflow/fmpadding_batch.py b/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
index d69ea471e..2034fb938 100644
--- a/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
+++ b/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
@@ -90,20 +90,20 @@ class FMPadding_Batch(HLSCustomOp):
exp_cycles = (channels / simd) * batch_size * odim_h * odim_w
return int(exp_cycles)
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
idim_h, idim_w = self.get_nodeattr("ImgDim")
num_ch = self.get_nodeattr("NumChannels")
ishape = (1, idim_h, idim_w, num_ch)
return ishape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
odim_h, odim_w = self.get_padded_odim()
num_ch = self.get_nodeattr("NumChannels")
oshape = (1, odim_h, odim_w, num_ch)
return oshape
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
normal_ishape = list(self.get_normal_input_shape())
ifm_ch = self.get_nodeattr("NumChannels")
simd = self.get_nodeattr("SIMD")
@@ -112,7 +112,7 @@ class FMPadding_Batch(HLSCustomOp):
folded_ishape = normal_ishape[:-1] + [fold, simd]
return tuple(folded_ishape)
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
normal_oshape = list(self.get_normal_output_shape())
ifm_ch = self.get_nodeattr("NumChannels")
simd = self.get_nodeattr("SIMD")
@@ -144,7 +144,7 @@ class FMPadding_Batch(HLSCustomOp):
def verify_node(self):
pass
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
ret = DataType[self.get_nodeattr("inputDataType")]
# the hlslib op always pads with zeros, so ensure that the DataType
@@ -156,12 +156,12 @@ class FMPadding_Batch(HLSCustomOp):
"""Returns FINN DataType of output. (Same as input datatype)"""
return self.get_input_datatype()
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
ibits = self.get_input_datatype().bitwidth()
simd = self.get_nodeattr("SIMD")
return ibits * simd
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
obits = self.get_output_datatype().bitwidth()
simd = self.get_nodeattr("SIMD")
return obits * simd
diff --git a/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py b/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
index adafa7dcf..e7fa5bc00 100644
--- a/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
@@ -56,13 +56,13 @@ class GlobalAccPool_Batch(HLSCustomOp):
my_attrs.update(super().get_nodeattr_types())
return my_attrs
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
ch = self.get_nodeattr("NumChannels")
vecs = list(self.get_nodeattr("numInputVectors"))
ishape = tuple(vecs + [ch])
return ishape
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
ch = self.get_nodeattr("NumChannels")
pe = self.get_nodeattr("PE")
vecs = list(self.get_nodeattr("numInputVectors"))
@@ -71,7 +71,7 @@ class GlobalAccPool_Batch(HLSCustomOp):
folded_ishape = tuple(vecs + [folds, pe])
return folded_ishape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
ch = self.get_nodeattr("NumChannels")
vecs = list(self.get_nodeattr("numInputVectors"))
if len(vecs) == 1:
@@ -80,7 +80,7 @@ class GlobalAccPool_Batch(HLSCustomOp):
oshape = tuple([vecs[0]] + [1, 1, ch])
return oshape
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
ch = self.get_nodeattr("NumChannels")
pe = self.get_nodeattr("PE")
unfolded_shape = list(self.get_normal_output_shape())
@@ -139,11 +139,11 @@ class GlobalAccPool_Batch(HLSCustomOp):
return info_messages
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
return DataType[self.get_nodeattr("inputDataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output."""
# determine data type from image size and input type
idt = DataType[self.get_nodeattr("inputDataType")]
@@ -155,14 +155,14 @@ class GlobalAccPool_Batch(HLSCustomOp):
extreme_value = npixels * idt.max()
return DataType.get_smallest_possible(extreme_value)
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
"""Returns input stream width."""
ibits = self.get_input_datatype().bitwidth()
pe = self.get_nodeattr("PE")
in_width = pe * ibits
return in_width
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
"""Returns output stream width."""
obits = self.get_output_datatype().bitwidth()
pe = self.get_nodeattr("PE")
diff --git a/src/finn/custom_op/fpgadataflow/hlscustomop.py b/src/finn/custom_op/fpgadataflow/hlscustomop.py
index 7d322dc37..d6993206b 100644
--- a/src/finn/custom_op/fpgadataflow/hlscustomop.py
+++ b/src/finn/custom_op/fpgadataflow/hlscustomop.py
@@ -697,40 +697,48 @@ compilation transformations?
HLSCustomOp class but has to be filled by every node."""
pass
- def get_normal_input_shape(self):
+ def get_input_datatype(self, ind=0):
+ """Returns FINN DataType of input stream ind."""
+ raise Exception("get_input_datatype not implemented for this op")
+
+ def get_output_datatype(self, ind=0):
+ """Returns FINN DataType of output stream ind."""
+ raise Exception("get_output_datatype not implemented for this op")
+
+ def get_normal_input_shape(self, ind=0):
"""Returns normal input shape if implemented."""
raise Exception("get_normal_input_shape not implemented for this op")
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
"""Returns folded output shape if implemented."""
raise Exception("get_normal_output_shape not implemented for this op")
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
"""Returns folded input shape (according to synapse folding), if implemented."""
raise Exception("get_folded_input_shape not implemented for this op")
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
"""Returns folded output shape (according to neuron folding), if implemented."""
raise Exception("get_folded_output_shape not implemented for this op")
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
"""Returns input stream width, if implemented."""
raise Exception("get_instream_width not implemented for this op")
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
"""Returns output stream width, if implemented."""
raise Exception("get_outstream_width not implemented for this op")
- def get_instream_width_padded(self):
+ def get_instream_width_padded(self, ind=0):
"""Returns input stream width padded to a multiple of 8. This is required
by the AXI Stream spec."""
- in_width = self.get_instream_width()
+ in_width = self.get_instream_width(ind=ind)
return roundup_to_integer_multiple(in_width, 8)
- def get_outstream_width_padded(self):
+ def get_outstream_width_padded(self, ind=0):
"""Returns output stream width padded to a multiple of 8. This is required
by the AXI Stream spec."""
- out_width = self.get_outstream_width()
+ out_width = self.get_outstream_width(ind=ind)
return roundup_to_integer_multiple(out_width, 8)
def get_ap_int_max_w(self):
diff --git a/src/finn/custom_op/fpgadataflow/iodma.py b/src/finn/custom_op/fpgadataflow/iodma.py
index 33ee1d359..a80eb29a6 100644
--- a/src/finn/custom_op/fpgadataflow/iodma.py
+++ b/src/finn/custom_op/fpgadataflow/iodma.py
@@ -106,10 +106,10 @@ class IODMA(HLSCustomOp):
ishape = tuple(vecs + [num_ch])
return ishape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
return self.get_normal_input_shape()
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
if self.get_nodeattr("direction") == "in":
raise ValueError("Folded input shape not defined for input IODMA")
else:
@@ -126,7 +126,7 @@ class IODMA(HLSCustomOp):
shape.append(elems_per_word)
return tuple(shape)
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
if self.get_nodeattr("direction") == "out":
raise ValueError("Folded output shape not defined for output IODMA")
else:
@@ -166,15 +166,15 @@ class IODMA(HLSCustomOp):
def verify_node(self):
pass
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
return DataType[self.get_nodeattr("dataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output. (Same as input datatype)"""
return self.get_input_datatype()
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
if self.get_nodeattr("direction") == "in":
return self.get_nodeattr("intfWidth")
elif self.get_nodeattr("direction") == "out":
@@ -182,7 +182,7 @@ class IODMA(HLSCustomOp):
else:
raise ValueError("Invalid IODMA direction, please set to in or out")
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
if self.get_nodeattr("direction") == "out":
return self.get_nodeattr("intfWidth")
elif self.get_nodeattr("direction") == "in":
diff --git a/src/finn/custom_op/fpgadataflow/labelselect_batch.py b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
index 3e27ee011..03f89bd7e 100644
--- a/src/finn/custom_op/fpgadataflow/labelselect_batch.py
+++ b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
@@ -70,13 +70,13 @@ class LabelSelect_Batch(HLSCustomOp):
my_attrs.update(super().get_nodeattr_types())
return my_attrs
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
nlabels = self.get_nodeattr("Labels")
vecs = list(self.get_nodeattr("numInputVectors"))
ishape = tuple(vecs + [nlabels])
return ishape
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
nlabels = self.get_nodeattr("Labels")
pe = self.get_nodeattr("PE")
vecs = list(self.get_nodeattr("numInputVectors"))
@@ -85,13 +85,13 @@ class LabelSelect_Batch(HLSCustomOp):
folded_ishape = tuple(vecs + [folds, pe])
return folded_ishape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
k = self.get_nodeattr("K")
vecs = list(self.get_nodeattr("numInputVectors"))
oshape = tuple(vecs + [k])
return oshape
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
k = self.get_nodeattr("K")
vecs = list(self.get_nodeattr("numInputVectors"))
oshape = tuple(vecs + [k, 1])
@@ -152,24 +152,24 @@ class LabelSelect_Batch(HLSCustomOp):
return info_messages
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
ret = DataType[self.get_nodeattr("inputDataType")]
return ret
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output."""
ret = DataType[self.get_nodeattr("outputDataType")]
return ret
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
"""Returns input stream width."""
ibits = self.get_input_datatype().bitwidth()
pe = self.get_nodeattr("PE")
in_width = pe * ibits
return in_width
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
"""Returns output stream width."""
return self.get_output_datatype().bitwidth()
diff --git a/src/finn/custom_op/fpgadataflow/lookup.py b/src/finn/custom_op/fpgadataflow/lookup.py
index 613a91b62..fd3e2b5b1 100644
--- a/src/finn/custom_op/fpgadataflow/lookup.py
+++ b/src/finn/custom_op/fpgadataflow/lookup.py
@@ -75,21 +75,21 @@ class Lookup(HLSCustomOp):
exp_cycles = int(n_inputs)
return exp_cycles
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
return self.get_nodeattr("InputShape")
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
ishape = self.get_normal_input_shape()
emb_dim = self.get_nodeattr("EmbeddingDim")
oshape = list(ishape) + [emb_dim]
return tuple(oshape)
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
ishape = self.get_normal_input_shape()
folded_ishape = list(ishape) + [1]
return tuple(folded_ishape)
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
ishape = self.get_normal_input_shape()
mem_mode = self.get_nodeattr("mem_mode")
emb_dim = self.get_nodeattr("EmbeddingDim")
@@ -135,19 +135,19 @@ class Lookup(HLSCustomOp):
def verify_node(self):
pass
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
ret = DataType[self.get_nodeattr("InputType")]
return ret
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
ret = DataType[self.get_nodeattr("EmbeddingType")]
return ret
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
ibits = self.get_input_datatype().bitwidth()
return ibits
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
folded_oshape = self.get_folded_output_shape()
obits = self.get_output_datatype().bitwidth()
return obits * folded_oshape[-1]
diff --git a/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py b/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py
index e78a918e8..69763fbea 100644
--- a/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py
+++ b/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py
@@ -409,16 +409,16 @@ class MatrixVectorActivation(HLSCustomOp):
"""Returns FINN DataType of weights."""
return DataType[self.get_nodeattr("weightDataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output."""
return DataType[self.get_nodeattr("outputDataType")]
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
i_bits = self.get_input_datatype().bitwidth()
in_width = i_bits * self.get_nodeattr("SIMD")
return in_width
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
o_bits = self.get_output_datatype().bitwidth()
out_width = o_bits * self.get_nodeattr("PE")
return out_width
@@ -474,7 +474,7 @@ class MatrixVectorActivation(HLSCustomOp):
return folded_input_shape
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
mh = self.get_nodeattr("MH")
pe = self.get_nodeattr("PE")
nf = mh // pe
@@ -482,13 +482,13 @@ class MatrixVectorActivation(HLSCustomOp):
folded_output_shape = tuple(vecs + [nf, pe])
return folded_output_shape
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
mw = self.get_nodeattr("MW")
vecs = list(self.get_nodeattr("numInputVectors"))
normal_input_shape = tuple(vecs + [mw])
return normal_input_shape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
mh = self.get_nodeattr("MH")
vecs = list(self.get_nodeattr("numInputVectors"))
normal_output_shape = tuple(vecs + [mh])
diff --git a/src/finn/custom_op/fpgadataflow/pool_batch.py b/src/finn/custom_op/fpgadataflow/pool_batch.py
index 3bf187fa9..91cd537ba 100644
--- a/src/finn/custom_op/fpgadataflow/pool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/pool_batch.py
@@ -74,11 +74,11 @@ class Pool_Batch(HLSCustomOp):
my_attrs.update(super().get_nodeattr_types())
return my_attrs
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
return DataType[self.get_nodeattr("InputDataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output."""
fxn = self.get_nodeattr("Function")
odt = DataType[self.get_nodeattr("OutputDataType")]
@@ -98,7 +98,7 @@ class Pool_Batch(HLSCustomOp):
return odt
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
ifm_ch = self.get_nodeattr("Channels")
odims = self.get_nodeattr("OutImgDims")
batch_size = self.get_nodeattr("BatchSize")
@@ -107,7 +107,7 @@ class Pool_Batch(HLSCustomOp):
ishape = (batch_size, *odims, k_prod * ifm_ch)
return ishape
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
normal_ishape = list(self.get_normal_input_shape())
ifm_ch = self.get_nodeattr("Channels")
pe = self.get_nodeattr("PE")
@@ -116,14 +116,14 @@ class Pool_Batch(HLSCustomOp):
folded_ishape = normal_ishape[:-1] + [fold, pe]
return tuple(folded_ishape)
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
ofm_ch = self.get_nodeattr("Channels")
odims = self.get_nodeattr("OutImgDims")
batch_size = self.get_nodeattr("BatchSize")
oshape = (batch_size, *odims, ofm_ch)
return oshape
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
normal_oshape = list(self.get_normal_output_shape())
ifm_ch = self.get_nodeattr("Channels")
pe = self.get_nodeattr("PE")
@@ -147,13 +147,13 @@ class Pool_Batch(HLSCustomOp):
exp_cycles = ((ifm_ch * k_prod) / pe) * np.prod(odims) * batch_size
return int(exp_cycles)
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
dt_bits = self.get_input_datatype().bitwidth()
pe = self.get_nodeattr("PE")
in_width = int(dt_bits * pe)
return in_width
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
dt_bits = self.get_output_datatype().bitwidth()
pe = self.get_nodeattr("PE")
out_width = int(dt_bits * pe)
diff --git a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
index 1e6b72e4d..a3aa9d570 100644
--- a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
@@ -60,19 +60,19 @@ class StreamingDataWidthConverter_Batch(HLSCustomOp):
my_attrs.update(super().get_nodeattr_types())
return my_attrs
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
return DataType[self.get_nodeattr("dataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output."""
return DataType[self.get_nodeattr("dataType")]
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
ishape = self.get_nodeattr("shape")
return ishape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
oshape = self.get_nodeattr("shape")
return oshape
@@ -97,7 +97,7 @@ class StreamingDataWidthConverter_Batch(HLSCustomOp):
Please adjust PE and SIMD values so that OutWidth % InWidth = 0
or alternatively use impl_style = vivado"""
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
self.check_divisible_iowidths()
iwidth = self.get_nodeattr("inWidth")
ishape = self.get_normal_input_shape()
@@ -117,7 +117,7 @@ class StreamingDataWidthConverter_Batch(HLSCustomOp):
dummy_t = dummy_t.reshape(new_shape)
return dummy_t.shape
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
self.check_divisible_iowidths()
owidth = self.get_nodeattr("outWidth")
oshape = self.get_normal_output_shape()
@@ -142,11 +142,11 @@ class StreamingDataWidthConverter_Batch(HLSCustomOp):
folded_oshape = self.get_folded_output_shape()
return np.prod(folded_oshape[:-1])
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
in_width = self.get_nodeattr("inWidth")
return in_width
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
out_width = self.get_nodeattr("outWidth")
return out_width
diff --git a/src/finn/custom_op/fpgadataflow/streamingfifo.py b/src/finn/custom_op/fpgadataflow/streamingfifo.py
index f24cdcb93..d0accc2d3 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfifo.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfifo.py
@@ -206,7 +206,7 @@ class StreamingFIFO(HLSCustomOp):
self.set_nodeattr("ip_vlnv", vlnv)
self.code_gen_dict.clear()
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
depth = self.get_adjusted_depth()
# depth has to be between 2 and 256 with the current
# StreamingFIFO implementation
@@ -237,22 +237,22 @@ class StreamingFIFO(HLSCustomOp):
return normal_ishape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
return self.get_normal_input_shape()
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
return self.get_nodeattr("folded_shape")
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
return self.get_nodeattr("folded_shape")
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
dtype = DataType[self.get_nodeattr("dataType")]
folded_shape = self.get_nodeattr("folded_shape")
in_width = folded_shape[-1] * dtype.bitwidth()
return in_width
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
dtype = DataType[self.get_nodeattr("dataType")]
folded_shape = self.get_nodeattr("folded_shape")
in_width = folded_shape[-1] * dtype.bitwidth()
diff --git a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
index 882b40a0a..a0e60931e 100755
--- a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
@@ -57,11 +57,11 @@ class StreamingMaxPool_Batch(HLSCustomOp):
my_attrs.update(super().get_nodeattr_types())
return my_attrs
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
return DataType[self.get_nodeattr("dataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output."""
return DataType[self.get_nodeattr("dataType")]
@@ -82,13 +82,13 @@ class StreamingMaxPool_Batch(HLSCustomOp):
ifm_dim, k, ifm_ch = self.get_1d_attrs_normalized()
return (ifm_dim[0] == 1) and (k[0] == 1)
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
ifm_dim_h, ifm_dim_w = self.get_nodeattr("ImgDim")
ifm_ch = self.get_nodeattr("NumChannels")
ishape = (1, ifm_dim_h, ifm_dim_w, ifm_ch)
return ishape
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
ifm_dim_h, ifm_dim_w = self.get_nodeattr("ImgDim")
ifm_ch = self.get_nodeattr("NumChannels")
pe = self.get_nodeattr("PE")
@@ -99,7 +99,7 @@ class StreamingMaxPool_Batch(HLSCustomOp):
folded_ishape = (1, ifm_dim_h, ifm_dim_w, 1, ifm_ch)
return folded_ishape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
ifm_dim_h, ifm_dim_w = self.get_nodeattr("ImgDim")
k_h, k_w = tuple(self.get_nodeattr("PoolDim"))
ifm_ch = self.get_nodeattr("NumChannels")
@@ -116,7 +116,7 @@ class StreamingMaxPool_Batch(HLSCustomOp):
oshape = (1, ofm_dim_h, ofm_dim_w, ifm_ch)
return oshape
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
# even though there is no folding in the current hlslib op,
# insert a time multiplexing axis to remain compatible with the
# shapes produced by the rest of the dataflow pipeline
@@ -155,7 +155,7 @@ class StreamingMaxPool_Batch(HLSCustomOp):
# TODO: adjust inaccurate formula
return int(ifm_dim[1] * ifm_dim[1] * (1 + 1 / (k[1] * k[1])))
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
dt_bits = self.get_input_datatype().bitwidth()
pe = self.get_nodeattr("PE")
ifm_ch = self.get_nodeattr("NumChannels")
@@ -165,7 +165,7 @@ class StreamingMaxPool_Batch(HLSCustomOp):
in_width = int(dt_bits * ifm_ch)
return in_width
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
"""For streaming maxpool out stream width is the same as in stream width"""
return self.get_instream_width()
diff --git a/src/finn/custom_op/fpgadataflow/thresholding_batch.py b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
index 110e456cb..62e51cc7b 100644
--- a/src/finn/custom_op/fpgadataflow/thresholding_batch.py
+++ b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
@@ -185,11 +185,11 @@ class Thresholding_Batch(HLSCustomOp):
# total cost
return comparator_cost + lutram_cost
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
return DataType[self.get_nodeattr("inputDataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output."""
return DataType[self.get_nodeattr("outputDataType")]
@@ -221,11 +221,11 @@ class Thresholding_Batch(HLSCustomOp):
self.set_nodeattr("weightDataType", tdt.name)
return DataType[self.get_nodeattr("weightDataType")]
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
i_bits = self.get_input_datatype().bitwidth()
return i_bits * self.get_nodeattr("PE")
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
o_bits = self.get_output_datatype().bitwidth()
return o_bits * self.get_nodeattr("PE")
@@ -251,7 +251,7 @@ class Thresholding_Batch(HLSCustomOp):
weightstream = self.get_weightstream_width()
return max([weightstream, temp_value])
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
ich = self.get_nodeattr("NumChannels")
pe = self.get_nodeattr("PE")
fold = ich // pe
@@ -259,17 +259,17 @@ class Thresholding_Batch(HLSCustomOp):
folded_input_shape = tuple(vecs + [fold, pe])
return folded_input_shape
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
# same shape as input
return self.get_folded_input_shape()
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
ich = self.get_nodeattr("NumChannels")
vecs = list(self.get_nodeattr("numInputVectors"))
normal_input_shape = tuple(vecs + [ich])
return normal_input_shape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
# same shape as input
return self.get_normal_input_shape()
diff --git a/src/finn/custom_op/fpgadataflow/tlastmarker.py b/src/finn/custom_op/fpgadataflow/tlastmarker.py
index 7386aa7e6..1bd32442a 100644
--- a/src/finn/custom_op/fpgadataflow/tlastmarker.py
+++ b/src/finn/custom_op/fpgadataflow/tlastmarker.py
@@ -218,21 +218,21 @@ class TLastMarker(HLSCustomOp):
def get_number_output_values(self):
return self.get_nodeattr("NumIters")
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
stream_width = self.get_nodeattr("StreamWidth")
elem_width = self.get_nodeattr("ElemWidth")
n_packed_elems = stream_width // elem_width
n_iters = self.get_nodeattr("NumIters")
return (1, n_iters, n_packed_elems)
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
return self.get_folded_input_shape()
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
stream_width = self.get_nodeattr("StreamWidth")
return stream_width
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
stream_width = self.get_nodeattr("StreamWidth")
return stream_width
diff --git a/src/finn/custom_op/fpgadataflow/upsampler.py b/src/finn/custom_op/fpgadataflow/upsampler.py
index eb51fe39f..a018fd35a 100644
--- a/src/finn/custom_op/fpgadataflow/upsampler.py
+++ b/src/finn/custom_op/fpgadataflow/upsampler.py
@@ -73,7 +73,7 @@ class UpsampleNearestNeighbour_Batch(HLSCustomOp):
exp_cycles = OFMDim * reps
return int(exp_cycles)
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
IFMDim = self.get_nodeattr("IFMDim")
num_ch = self.get_nodeattr("NumChannels")
batch = self.get_nodeattr("numInputVectors")
@@ -84,7 +84,7 @@ class UpsampleNearestNeighbour_Batch(HLSCustomOp):
ishape = (batch, IFMDim, 1, num_ch)
return ishape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
OFMDim = self.get_nodeattr("OFMDim")
num_ch = self.get_nodeattr("NumChannels")
batch = self.get_nodeattr("numInputVectors")
@@ -95,11 +95,11 @@ class UpsampleNearestNeighbour_Batch(HLSCustomOp):
oshape = (batch, OFMDim, 1, num_ch)
return oshape
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
normal_ishape = list(self.get_normal_input_shape())
return tuple(normal_ishape)
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
normal_oshape = list(self.get_normal_output_shape())
return tuple(normal_oshape)
@@ -129,21 +129,21 @@ class UpsampleNearestNeighbour_Batch(HLSCustomOp):
def verify_node(self):
pass
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
ret = DataType[self.get_nodeattr("inputDataType")]
return ret
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output. (Same as input datatype)"""
return self.get_input_datatype()
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
ibits = self.get_input_datatype().bitwidth()
ifm_ch = self.get_nodeattr("NumChannels")
return ibits * ifm_ch
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
obits = self.get_output_datatype().bitwidth()
ifm_ch = self.get_nodeattr("NumChannels")
return obits * ifm_ch
diff --git a/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py b/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py
index 6391f27bb..f9d09907e 100644
--- a/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py
+++ b/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py
@@ -176,7 +176,7 @@ class VectorVectorActivation(HLSCustomOp):
def verify_node(self):
pass
- def get_input_datatype(self):
+ def get_input_datatype(self, ind=0):
"""Returns FINN DataType of input."""
return DataType[self.get_nodeattr("inputDataType")]
@@ -184,21 +184,21 @@ class VectorVectorActivation(HLSCustomOp):
"""Returns FINN DataType of weights."""
return DataType[self.get_nodeattr("weightDataType")]
- def get_output_datatype(self):
+ def get_output_datatype(self, ind=0):
"""Returns FINN DataType of output."""
return DataType[self.get_nodeattr("outputDataType")]
- def get_instream_width(self):
+ def get_instream_width(self, ind=0):
i_bits = self.get_input_datatype().bitwidth()
in_width = i_bits * self.get_nodeattr("PE")
return in_width
- def get_outstream_width(self):
+ def get_outstream_width(self, ind=0):
o_bits = self.get_output_datatype().bitwidth()
out_width = o_bits * self.get_nodeattr("PE")
return out_width
- def get_folded_input_shape(self):
+ def get_folded_input_shape(self, ind=0):
k_h, k_w = self.get_nodeattr("Kernel")
sf = k_h * k_w
dim_h, dim_w = self.get_nodeattr("Dim")
@@ -208,7 +208,7 @@ class VectorVectorActivation(HLSCustomOp):
folded_input_shape = tuple([1, dim_h, dim_w, sf * nf, pe])
return folded_input_shape
- def get_folded_output_shape(self):
+ def get_folded_output_shape(self, ind=0):
ch = self.get_nodeattr("Channels")
pe = self.get_nodeattr("PE")
nf = ch // pe
@@ -216,14 +216,14 @@ class VectorVectorActivation(HLSCustomOp):
folded_output_shape = tuple([1, dim_h, dim_w, nf, pe])
return folded_output_shape
- def get_normal_input_shape(self):
+ def get_normal_input_shape(self, ind=0):
dim_h, dim_w = self.get_nodeattr("Dim")
ch = self.get_nodeattr("Channels")
k_h, k_w = self.get_nodeattr("Kernel")
normal_input_shape = tuple([1, dim_h, dim_w, k_h * k_w * ch])
return normal_input_shape
- def get_normal_output_shape(self):
+ def get_normal_output_shape(self, ind=0):
ch = self.get_nodeattr("Channels")
dim_h, dim_w = self.get_nodeattr("Dim")
normal_output_shape = tuple([1, dim_h, dim_w, ch])
--
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