diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
index 3c16e8dabeca6848daf595a6b12e14595a38581d..55b9a2753b50f76c57fb08c7a24b29b49d82c8b8 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
@@ -34,7 +34,6 @@ from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow import HLSCustomOp
from finn.custom_op.im2col import compute_conv_output_dim
from onnx import TensorProto, helper
-from finn.util.basic import roundup_to_integer_multiple
from finn.util.data_packing import npy_to_rtlsim_input, rtlsim_output_to_npy
# ONNX i/o tensor shape assumptions for ConvolutionInputGenerator:
@@ -136,7 +135,7 @@ class ConvolutionInputGenerator(HLSCustomOp):
"""Returns FINN DataType of output."""
return DataType[self.get_nodeattr("outputDataType")]
- def get_instream_width(self, axi_strm_padding=False):
+ def get_instream_width(self):
"""Returns stream width, input and output stream width are equal for
the sliding window function"""
ibits = self.get_input_datatype().bitwidth()
@@ -144,15 +143,13 @@ class ConvolutionInputGenerator(HLSCustomOp):
ifm_ch = self.get_nodeattr("IFMChannels")
assert simd == ifm_ch, "SWG currently requires SIMD=IFM"
in_width = simd * ibits
- if axi_strm_padding is True:
- in_width = roundup_to_integer_multiple(in_width, 8)
return in_width
- def get_outstream_width(self, axi_strm_padding=False):
+ def get_outstream_width(self):
"""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."""
- return self.get_instream_width(axi_strm_padding)
+ return self.get_instream_width()
def get_number_output_values(self):
folded_oshape = self.get_folded_output_shape()
diff --git a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
index ce4f883fa029225a5748c08463858e3bf1bfd35c..f30871909b1c70f3b5df148f1b6eae22fdbadc25 100644
--- a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
@@ -32,7 +32,6 @@ import numpy as np
from finn.custom_op.fpgadataflow import HLSCustomOp
from finn.core.datatype import DataType
from onnx import TensorProto, helper
-from finn.util.basic import roundup_to_integer_multiple
from finn.util.data_packing import npy_to_rtlsim_input, rtlsim_output_to_npy
# does not do anything at the ONNX node-by-node level, and input-output
@@ -151,16 +150,12 @@ class StreamingDataWidthConverter_Batch(HLSCustomOp):
folded_ishape = self.get_folded_input_shape()
return np.prod(folded_ishape[:-1])
- def get_instream_width(self, axi_strm_padding=False):
+ def get_instream_width(self):
in_width = self.get_nodeattr("inWidth")
- if axi_strm_padding is True:
- in_width = roundup_to_integer_multiple(in_width, 8)
return in_width
- def get_outstream_width(self, axi_strm_padding=False):
+ def get_outstream_width(self):
out_width = self.get_nodeattr("outWidth")
- if axi_strm_padding is True:
- out_width = roundup_to_integer_multiple(out_width, 8)
return out_width
def make_shape_compatible_op(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index f04ee7ca7830760f4ed2804b8b71f8fe5d29325f..994c17e688337bc4ef7b9f47700197370377631a 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -279,27 +279,21 @@ class StreamingFCLayer_Batch(HLSCustomOp):
"""Returns FINN DataType of output."""
return DataType[self.get_nodeattr("outputDataType")]
- def get_instream_width(self, axi_strm_padding=False):
+ def get_instream_width(self):
i_bits = self.get_input_datatype().bitwidth()
in_width = i_bits * self.get_nodeattr("SIMD")
- if axi_strm_padding is True:
- in_width = roundup_to_integer_multiple(in_width, 8)
return in_width
- def get_outstream_width(self, axi_strm_padding=False):
+ def get_outstream_width(self):
o_bits = self.get_output_datatype().bitwidth()
out_width = o_bits * self.get_nodeattr("PE")
- if axi_strm_padding is True:
- out_width = roundup_to_integer_multiple(out_width, 8)
return out_width
- def get_weightstream_width(self, axi_strm_padding=False):
+ def get_weightstream_width(self):
pe = self.get_nodeattr("PE")
simd = self.get_nodeattr("SIMD")
wp = self.get_weight_datatype().bitwidth()
w_width = pe * simd * wp
- if axi_strm_padding is True:
- w_width = roundup_to_integer_multiple(w_width, 8)
return w_width
def get_ap_int_max_w(self):
@@ -982,13 +976,13 @@ class StreamingFCLayer_Batch(HLSCustomOp):
"{}_{}".format(self.onnx_node.name, self.onnx_node.name)
]
# make instream width a multiple of 8 for AXI stream interface
- in_width = roundup_to_integer_multiple(self.get_instream_width(), 8)
+ in_width = self.get_instream_width_padded()
self.code_gen_dict["$IN_RANGE$"] = ["[{}:0]".format(in_width - 1)]
self.code_gen_dict["$OUT_RANGE$"] = [
- "[{}:0]".format(self.get_outstream_width(axi_strm_padding=True) - 1)
+ "[{}:0]".format(self.get_outstream_width_padded - 1)
]
# make weight stream width a multiple of 8 for AXI stream interface
- weight_width = roundup_to_integer_multiple(self.get_weightstream_width(), 8)
+ weight_width = self.get_weightstream_width_padded()
self.code_gen_dict["$WEIGHT_RANGE$"] = ["[{}:0]".format(weight_width - 1)]
self.code_gen_dict["$WEIGHT_WIDTH$"] = [str(weight_width)]
self.code_gen_dict["$WSTREAM_DEPTH$"] = [str(self.calc_wmem())]
diff --git a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
index ef1a5ee1bdc0bbe5c773aa375bf4402a8cb16ddb..83bc19030ebba66907e08c5b1e52d7c0ff9207a6 100644
--- a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
@@ -33,7 +33,6 @@ from finn.custom_op.fpgadataflow import HLSCustomOp
from finn.custom_op.im2col import compute_conv_output_dim
from finn.core.datatype import DataType
from onnx import TensorProto, helper
-from finn.util.basic import roundup_to_integer_multiple
from finn.util.data_packing import npy_to_rtlsim_input, rtlsim_output_to_npy
@@ -88,17 +87,15 @@ class StreamingMaxPool_Batch(HLSCustomOp):
folded_oshape = self.get_folded_output_shape()
return np.prod(folded_oshape[:-1])
- def get_instream_width(self, axi_strm_padding=False):
+ def get_instream_width(self):
dt_bits = self.get_input_datatype().bitwidth()
ifm_ch = self.get_nodeattr("NumChannels")
in_width = int(dt_bits * ifm_ch)
- if axi_strm_padding is True:
- in_width = roundup_to_integer_multiple(in_width, 8)
return in_width
- def get_outstream_width(self, axi_strm_padding=False):
+ def get_outstream_width(self):
"""For streaming maxpool out stream with is the same as in stream width"""
- return self.get_instream_width(axi_strm_padding)
+ return self.get_instream_width()
def make_shape_compatible_op(self, model):
exp_ishape = self.get_normal_input_shape()
diff --git a/src/finn/custom_op/fpgadataflow/tlastmarker.py b/src/finn/custom_op/fpgadataflow/tlastmarker.py
index a04b2a886984f3f98bd765ce617be6ca7c0170a8..4d4dee6506f04909c53cd05e4898a7ad77e4a83a 100644
--- a/src/finn/custom_op/fpgadataflow/tlastmarker.py
+++ b/src/finn/custom_op/fpgadataflow/tlastmarker.py
@@ -27,7 +27,6 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from finn.custom_op.fpgadataflow import HLSCustomOp
-from finn.util.basic import roundup_to_integer_multiple
class TLastMarker(HLSCustomOp):
@@ -134,16 +133,12 @@ class TLastMarker(HLSCustomOp):
def get_folded_output_shape(self):
return self.get_folded_input_shape()
- def get_instream_width(self, axi_strm_padding=False):
+ def get_instream_width(self):
stream_width = self.get_nodeattr("StreamWidth")
- if axi_strm_padding is True:
- stream_width = roundup_to_integer_multiple(stream_width, 8)
return stream_width
- def get_outstream_width(self, axi_strm_padding=False):
+ def get_outstream_width(self):
stream_width = self.get_nodeattr("StreamWidth")
- if axi_strm_padding is True:
- stream_width = roundup_to_integer_multiple(stream_width, 8)
return stream_width
def strm_decl(self):