From 564f4205350cc3644de7cbace9b9456ed4091d41 Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Fri, 1 May 2020 10:07:17 +0100
Subject: [PATCH] [StreamingFIFO] Add comments and adapt to new stream_width
functions
---
.../custom_op/fpgadataflow/streamingfifo.py | 25 +++++++++++++------
1 file changed, 17 insertions(+), 8 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/streamingfifo.py b/src/finn/custom_op/fpgadataflow/streamingfifo.py
index 6e004c47b..eb96c6c04 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfifo.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfifo.py
@@ -33,7 +33,6 @@ import subprocess
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
from . import templates
@@ -110,7 +109,7 @@ class StreamingFIFO(HLSCustomOp):
"{}_{}".format(self.onnx_node.name, self.onnx_node.name)
]
# make instream width a multiple of 8 for axi interface
- in_width = self.get_instream_width(axi_strm_padding=True)
+ 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(in_width - 1)]
self.code_gen_dict["$WIDTH$"] = [str(in_width)]
@@ -164,6 +163,8 @@ class StreamingFIFO(HLSCustomOp):
def get_normal_input_shape(self):
depth = self.get_nodeattr("depth")
+ # depth has to be between 2 and 256 with the current
+ # StreamingFIFO implementation
assert (
depth >= 2
), """Depth is too low. Please set node attribute "depth" to a value
@@ -172,10 +173,22 @@ class StreamingFIFO(HLSCustomOp):
depth <= 256
), """Depth is too high. Please set node attribute "depth" to a value
between 2 and 256"""
+ # derive normal shape from folded shape
+ # StreamingFIFOs are inserted in between fpgadataflow nodes
+ # the folded shape could be for example (1, nf, pe)
+ # with nf (neuron folding): mh // pe
+ # the normal input shape is in this case (1, mh)
+ # so to achieve this the two inner dimensions are multiplied
+ # and together with all previous dimensions
+ # this gives the normal input shape
+
folded_shape = self.get_nodeattr("folded_shape")
+ # extract inner dimension
inner_dim = folded_shape[-1]
+ # multiply with the next inner dimension
folding_factor = folded_shape[-2] * inner_dim
normal_ishape = []
+ # create the normal_ishape
for i in range(len(folded_shape) - 2):
normal_ishape.append(folded_shape[i])
normal_ishape.append(folding_factor)
@@ -191,20 +204,16 @@ class StreamingFIFO(HLSCustomOp):
def get_folded_output_shape(self):
return self.get_nodeattr("folded_shape")
- def get_instream_width(self, axi_strm_padding=False):
+ def get_instream_width(self):
dtype = DataType[self.get_nodeattr("dataType")]
folded_shape = self.get_nodeattr("folded_shape")
in_width = folded_shape[-1] * dtype.bitwidth()
- 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):
dtype = DataType[self.get_nodeattr("dataType")]
folded_shape = self.get_nodeattr("folded_shape")
in_width = folded_shape[-1] * dtype.bitwidth()
- if axi_strm_padding is True:
- in_width = roundup_to_integer_multiple(in_width, 8)
return in_width
def execute_node(self, context, graph):
--
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