diff --git a/src/finn/custom_op/fpgadataflow/__init__.py b/src/finn/custom_op/fpgadataflow/__init__.py
index 755a0e056858e4db5c4392a77fc458a267948912..2c7c86c64ea1279cb18cf8342aa20fb2792bdaf5 100644
--- a/src/finn/custom_op/fpgadataflow/__init__.py
+++ b/src/finn/custom_op/fpgadataflow/__init__.py
@@ -28,7 +28,7 @@
from finn.custom_op.fpgadataflow.addstreams_batch import AddStreams_Batch
from finn.custom_op.fpgadataflow.channelwise_op_batch import ChannelwiseOp_Batch
-from finn.custom_op.fpgadataflow.checksum import checksum
+from finn.custom_op.fpgadataflow.checksum import CheckSum
from finn.custom_op.fpgadataflow.concat import StreamingConcat
from finn.custom_op.fpgadataflow.convolutioninputgenerator import (
ConvolutionInputGenerator,
@@ -84,4 +84,4 @@ custom_op["StreamingDataflowPartition"] = StreamingDataflowPartition
custom_op["UpsampleNearestNeighbour_Batch"] = UpsampleNearestNeighbour_Batch
custom_op["Lookup"] = Lookup
custom_op["StreamingConcat"] = StreamingConcat
-custom_op["checksum"] = checksum
+custom_op["CheckSum"] = CheckSum
diff --git a/src/finn/custom_op/fpgadataflow/checksum.py b/src/finn/custom_op/fpgadataflow/checksum.py
index fee14d3c9ac5f62590c68b7e7949b2cf593ea832..a17e2418ca80f4118733c7e0f2245a5bc6ef72a0 100644
--- a/src/finn/custom_op/fpgadataflow/checksum.py
+++ b/src/finn/custom_op/fpgadataflow/checksum.py
@@ -35,7 +35,7 @@ from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
from finn.util.data_packing import npy_to_rtlsim_input, rtlsim_output_to_npy
-class checksum(HLSCustomOp):
+class CheckSum(HLSCustomOp):
"""Class that corresponds to custom_hls checksum function."""
def __init__(self, onnx_node):
diff --git a/src/finn/transformation/fpgadataflow/insert_hook.py b/src/finn/transformation/fpgadataflow/insert_hook.py
index d762911e0db1409c1685749336f9fcc77838df2e..21ec3f049fa66b66644f1c79286d1e495d97e7a3 100644
--- a/src/finn/transformation/fpgadataflow/insert_hook.py
+++ b/src/finn/transformation/fpgadataflow/insert_hook.py
@@ -37,7 +37,7 @@ from finn.util.fpgadataflow import is_fpgadataflow_node
def _is_hook_node(node):
- if node.op_type in ["checksum"]:
+ if node.op_type in ["CheckSum"]:
return True
else:
return False
@@ -82,7 +82,7 @@ class InsertHook(Transformation):
if n0_hook in list_supported_hooks:
if n0_hook == "checksum":
if len(consumers) == 1:
- if consumers[0].op_type == "checksum":
+ if consumers[0].op_type == "CheckSum":
continue
n0_normal_oshape = n0.get_normal_output_shape()
n0_folded_oshape = n0.get_folded_output_shape()
@@ -100,7 +100,7 @@ class InsertHook(Transformation):
[1],
)
chk_node = oh.make_node(
- "checksum",
+ "CheckSum",
[output_name],
outputs=[chk_otensor.name, chk_result.name],
domain="finn.custom_op.fpgadataflow",
@@ -122,6 +122,7 @@ class InsertHook(Transformation):
else:
model.graph.output.pop()
model.graph.output.append(chk_otensor)
+ model.graph.value_info.remove(chk_otensor)
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
graph_modified = True
diff --git a/tests/fpgadataflow/test_fpgadataflow_checksum.py b/tests/fpgadataflow/test_fpgadataflow_checksum.py
new file mode 100644
index 0000000000000000000000000000000000000000..62d73a9b0fa5771e6ed8b58ada2cfb9d399bb49d
--- /dev/null
+++ b/tests/fpgadataflow/test_fpgadataflow_checksum.py
@@ -0,0 +1,201 @@
+# Copyright (c) 2022, Advanced Micro Devices, Inc.
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import pytest
+
+import numpy as np
+from onnx import TensorProto, helper
+from pyverilator.util.axi_utils import axilite_read
+from qonnx.core.datatype import DataType
+from qonnx.core.modelwrapper import ModelWrapper
+from qonnx.custom_op.registry import getCustomOp
+from qonnx.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from qonnx.transformation.infer_shapes import InferShapes
+from qonnx.util.basic import gen_finn_dt_tensor
+
+import finn.core.onnx_exec as oxe
+from finn.core.rtlsim_exec import rtlsim_exec
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
+from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
+from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO
+from finn.transformation.fpgadataflow.insert_hook import InsertHook
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
+
+test_fpga_part = "xczu3eg-sbva484-1-e"
+target_clk_ns = 5
+
+
+def create_two_fc_model():
+ # create a model with two MatrixVectorActivation instances
+ wdt = DataType["INT2"]
+ idt = DataType["INT32"]
+ odt = DataType["INT32"]
+ m = 4
+ actval = 0
+ no_act = 1
+ binary_xnor_mode = 0
+ pe = 2
+ simd = 2
+
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, m])
+ mid = helper.make_tensor_value_info("mid", TensorProto.FLOAT, [1, m])
+ outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [1, m])
+
+ fc0 = helper.make_node(
+ "MatrixVectorActivation",
+ ["inp", "w0"],
+ ["mid"],
+ domain="finn.custom_op.fpgadataflow",
+ backend="fpgadataflow",
+ MW=m,
+ MH=m,
+ SIMD=simd,
+ PE=pe,
+ inputDataType=idt.name,
+ weightDataType=wdt.name,
+ outputDataType=odt.name,
+ ActVal=actval,
+ binaryXnorMode=binary_xnor_mode,
+ noActivation=no_act,
+ mem_mode="decoupled",
+ )
+
+ fc1 = helper.make_node(
+ "MatrixVectorActivation",
+ ["mid", "w1"],
+ ["outp"],
+ domain="finn.custom_op.fpgadataflow",
+ backend="fpgadataflow",
+ MW=m,
+ MH=m,
+ SIMD=simd,
+ PE=pe,
+ inputDataType=idt.name,
+ weightDataType=wdt.name,
+ outputDataType=odt.name,
+ ActVal=actval,
+ binaryXnorMode=binary_xnor_mode,
+ noActivation=no_act,
+ mem_mode="decoupled",
+ )
+
+ graph = helper.make_graph(
+ nodes=[fc0, fc1],
+ name="fclayer_graph",
+ inputs=[inp],
+ outputs=[outp],
+ value_info=[mid],
+ )
+
+ model = helper.make_model(graph, producer_name="fclayer-model")
+ model = ModelWrapper(model)
+
+ model.set_tensor_datatype("inp", idt)
+ model.set_tensor_datatype("mid", idt)
+ model.set_tensor_datatype("outp", odt)
+ model.set_tensor_datatype("w0", wdt)
+ model.set_tensor_datatype("w1", wdt)
+
+ # generate weights
+ w0 = np.eye(m, dtype=np.float32)
+ w1 = np.eye(m, dtype=np.float32)
+ model.set_initializer("w0", w0)
+ model.set_initializer("w1", w1)
+
+ return model
+
+
+@pytest.mark.fpgadataflow
+def test_fpgadataflow_checksum():
+ # use a graph consisting of two fc layers to test
+ # checksum node insertion
+ model = create_two_fc_model()
+
+ # set checksum output hook
+ for n in model.graph.node:
+ n0 = getCustomOp(n)
+ n0.set_nodeattr("output_hook", "checksum")
+
+ model = model.transform(InsertHook())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+ model = model.transform(InferShapes())
+
+ assert (
+ len(model.get_nodes_by_op_type("CheckSum")) == 2
+ ), """Insertion of
+ checksum layers was unsuccessful"""
+
+ # to verify the functionality of the checksum layer
+ # cppsim and rtlsim will be compared
+
+ x = gen_finn_dt_tensor(DataType["INT32"], (1, 4))
+
+ # cppsim
+ model = model.transform(SetExecMode("cppsim"))
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ inp = {"global_in": x}
+ y_cppsim = oxe.execute_onnx(model, inp, return_full_exec_context=True)
+ checksum0_cppsim = y_cppsim["CheckSum_0_out1"]
+ checksum1_cppsim = y_cppsim["CheckSum_1_out1"]
+
+ # in this test case scenario the checksums are equal
+ assert checksum0_cppsim == checksum1_cppsim, "CheckSums are not equal"
+
+ # rtlsim
+ model = model.transform(InsertFIFO(True))
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))
+ model = model.transform(HLSSynthIP())
+ model = model.transform(CreateStitchedIP(test_fpga_part, target_clk_ns))
+ model.set_metadata_prop("exec_mode", "rtlsim")
+
+ # define function to read out the checksums from axilite
+ checksums = []
+
+ def read_checksum(sim):
+ addr = 16
+ for i in range(len(model.get_nodes_by_op_type("CheckSum"))):
+ axi_name = "s_axi_checksum_{}_".format(i)
+ checksums.append(axilite_read(sim, addr, basename=axi_name))
+
+ rtlsim_exec(model, inp, post_hook=read_checksum)
+ checksum0_rtlsim = int(checksums[0])
+ checksum1_rtlsim = int(checksums[1])
+ assert (
+ checksum0_rtlsim == checksum0_cppsim
+ ), """The first checksums do not
+ match in cppsim vs. rtlsim"""
+ assert (
+ checksum1_rtlsim == checksum1_cppsim
+ ), """The second checksums do not
+ match in cppsim vs. rtlsim"""