diff --git a/Dockerfile b/Dockerfile
index 7780d3fd4e630af7a6395b84858211fb93c2b834..734a8fd3f4b6493246f1c7e522da2934d09e849b 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -25,7 +25,6 @@ ENV PYTHONPATH "${PYTHONPATH}:/workspace/brevitas_cnv_lfc/training_scripts"
ENV PYTHONPATH "${PYTHONPATH}:/workspace/brevitas"
ENV PYTHONPATH "${PYTHONPATH}:/workspace/pyverilator"
ENV PYNQSHELL_PATH "/workspace/PYNQ-HelloWorld/boards"
-ENV PYNQ_BOARD "Pynq-Z1"
ARG GID
ARG GNAME
diff --git a/run-docker.sh b/run-docker.sh
index 77441ed955c8a055ac57a7328f2998f8855c20e9..aadc4c78717d85bde3bdf3dcedd48824f5ba483b 100755
--- a/run-docker.sh
+++ b/run-docker.sh
@@ -17,8 +17,11 @@ DOCKER_TAG="finn_${DOCKER_UNAME}"
# uncomment to run multiple instances with different names
# DOCKER_INST_NAME="finn_${DOCKER_UNAME}_${DOCKER_RND}"
DOCKER_INST_NAME="finn_${DOCKER_UNAME}"
+# the settings below will be taken from environment variables if available,
+# otherwise the defaults below will be used
: ${JUPYTER_PORT=8888}
: ${NETRON_PORT=8081}
+: ${PYNQ_BOARD="Pynq-Z1"}
# Absolute path to this script, e.g. /home/user/bin/foo.sh
SCRIPT=$(readlink -f "$0")
@@ -40,7 +43,7 @@ PYVERILATOR_LOCAL=$SCRIPTPATH/pyverilator
PYNQSHELL_LOCAL=$SCRIPTPATH/PYNQ-HelloWorld
BUILD_LOCAL=/tmp/$DOCKER_INST_NAME
VIVADO_HLS_LOCAL=$VIVADO_PATH
-: ${VIVADO_IP_CACHE=$BUILD_LOCAL/vivado_ip_cache}
+VIVADO_IP_CACHE=$BUILD_LOCAL/vivado_ip_cache
# clone dependency repos
git clone --branch feature/finn_onnx_export $BREVITAS_REPO $BREVITAS_LOCAL || git -C "$BREVITAS_LOCAL" pull
@@ -67,6 +70,7 @@ echo "Mounting $VIVADO_PATH into $VIVADO_PATH"
echo "Port-forwarding for Jupyter $JUPYTER_PORT:$JUPYTER_PORT"
echo "Port-forwarding for Netron $NETRON_PORT:$NETRON_PORT"
echo "Vivado IP cache dir is at $VIVADO_IP_CACHE"
+echo "Using default PYNQ board $PYNQ_BOARD"
if [ "$1" = "test" ]; then
echo "Running test suite"
@@ -107,6 +111,7 @@ docker run -t --rm --name $DOCKER_INST_NAME -it \
-e FINN_INST_NAME=$DOCKER_INST_NAME \
-e FINN_ROOT="/workspace/finn" \
-e VIVADO_IP_CACHE="$VIVADO_IP_CACHE" \
+-e PYNQ_BOARD=$PYNQ_BOARD \
-p $JUPYTER_PORT:$JUPYTER_PORT \
-p $NETRON_PORT:$NETRON_PORT \
$DOCKER_TAG bash -c "$DOCKER_CMD"
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index be9b51e6a7b1b3e255cd2ee8baf10937b95f8665..6ed4df512725eebd14fd0df80040d82b802e48ad 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -41,6 +41,9 @@ class StreamingFCLayer_Batch(HLSCustomOp):
"binaryXnorMode": ("i", False, 0),
# no-activation mode (produce accumulators)
"noActivation": ("i", False, 0),
+ # input and output FIFO depths
+ "inFIFODepth": ("i", False, 0),
+ "outFIFODepth": ("i", False, 0),
}
my_attrs.update(super().get_nodeattr_types())
return my_attrs
@@ -99,6 +102,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
info_messages.append('Attribute backend should be set to "fpgadataflow"')
# verify that all necessary attributes exist
+ # TODO collect automatically from get_nodeattr_types
try:
self.get_nodeattr("code_gen_dir_npysim")
self.get_nodeattr("executable_path")
@@ -161,6 +165,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
Y. Umuroglu, M. Leeser and K. Vissers
- 12. Sep 2018
"""
+ # TODO add in/out FIFO contributions
P = self.get_nodeattr("PE")
Q = self.get_nodeattr("SIMD")
wdt = self.get_weight_datatype()
@@ -178,6 +183,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
Y. Umuroglu, M. Leeser and K. Vissers
- 12. Sep 2018
"""
+ # TODO add in/out FIFO contributions
P = self.get_nodeattr("PE")
Q = self.get_nodeattr("SIMD")
wdt = self.get_weight_datatype()
@@ -642,6 +648,17 @@ class StreamingFCLayer_Batch(HLSCustomOp):
def pragmas(self):
self.code_gen_dict["$PRAGMAS$"] = ["#pragma HLS INTERFACE axis port=in0"]
self.code_gen_dict["$PRAGMAS$"].append("#pragma HLS INTERFACE axis port=out")
+ in_fifo_depth = self.get_nodeattr("inFIFODepth")
+ out_fifo_depth = self.get_nodeattr("outFIFODepth")
+ # insert depth pragmas only if specified
+ if in_fifo_depth != 0:
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS stream depth=%d variable=in0" % in_fifo_depth
+ )
+ if out_fifo_depth != 0:
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS stream depth=%d variable=out" % out_fifo_depth
+ )
self.code_gen_dict["$PRAGMAS$"].append(
"#pragma HLS INTERFACE ap_ctrl_none port=return"
)
diff --git a/tests/fpgadataflow/test_fpgadataflow_ip_stitch.py b/tests/fpgadataflow/test_fpgadataflow_ip_stitch.py
index 40b272efb9d6b00126312a9934f28c2a899bd942..11b6dc63065fb376f95f44dd92319439321c78f8 100644
--- a/tests/fpgadataflow/test_fpgadataflow_ip_stitch.py
+++ b/tests/fpgadataflow/test_fpgadataflow_ip_stitch.py
@@ -11,6 +11,7 @@ from finn.core.modelwrapper import ModelWrapper
from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen
from finn.transformation.fpgadataflow.codegen_ipstitch import CodeGen_ipstitch
from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen
+from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver
from finn.transformation.fpgadataflow.make_pynq_proj import MakePYNQProject
@@ -26,7 +27,7 @@ from finn.util.basic import (
test_pynq_board = os.getenv("PYNQ_BOARD", default="Pynq-Z1")
test_fpga_part = pynq_part_map[test_pynq_board]
-ip_stitch_model_dir = make_build_dir("test_fpgadataflow_ipstitch")
+ip_stitch_model_dir = make_build_dir("test_fpgadataflow_ipstitch_")
def create_one_fc_model():
@@ -39,10 +40,11 @@ def create_one_fc_model():
no_act = 1
binary_xnor_mode = 0
actval = 0
+ simd = 2
+ pe = 2
inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, m])
outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [1, m])
- outp_tlast = helper.make_tensor_value_info("outp_tlast", TensorProto.FLOAT, [1, m])
fc0 = helper.make_node(
"StreamingFCLayer_Batch",
@@ -53,8 +55,8 @@ def create_one_fc_model():
resType="ap_resource_lut()",
MW=m,
MH=m,
- SIMD=m,
- PE=m // 2,
+ SIMD=simd,
+ PE=pe,
inputDataType=idt.name,
weightDataType=wdt.name,
outputDataType=odt.name,
@@ -63,23 +65,8 @@ def create_one_fc_model():
noActivation=no_act,
)
- tlastmarker = helper.make_node(
- "TLastMarker",
- ["outp"],
- ["outp_tlast"],
- domain="finn",
- backend="fpgadataflow",
- NumIters=2,
- ElemWidth=odt.bitwidth(),
- StreamWidth=odt.bitwidth() * m,
- )
-
graph = helper.make_graph(
- nodes=[fc0, tlastmarker],
- name="fclayer_graph",
- inputs=[inp],
- outputs=[outp_tlast],
- value_info=[outp],
+ nodes=[fc0], name="fclayer_graph", inputs=[inp], outputs=[outp],
)
model = helper.make_model(graph, producer_name="fclayer-model")
@@ -87,7 +74,6 @@ def create_one_fc_model():
model.set_tensor_datatype("inp", idt)
model.set_tensor_datatype("outp", odt)
- model.set_tensor_datatype("outp_tlast", odt)
model.set_tensor_datatype("w0", wdt)
# generate weights
@@ -108,11 +94,12 @@ def create_two_fc_model():
actval = odt.min()
no_act = 0
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])
- outp_tlast = helper.make_tensor_value_info("outp_tlast", TensorProto.FLOAT, [1, m])
fc0 = helper.make_node(
"StreamingFCLayer_Batch",
@@ -123,8 +110,8 @@ def create_two_fc_model():
resType="ap_resource_lut()",
MW=m,
MH=m,
- SIMD=1,
- PE=1,
+ SIMD=simd,
+ PE=pe,
inputDataType=idt.name,
weightDataType=wdt.name,
outputDataType=odt.name,
@@ -142,8 +129,8 @@ def create_two_fc_model():
resType="ap_resource_lut()",
MW=m,
MH=m,
- SIMD=1,
- PE=1,
+ SIMD=simd,
+ PE=pe,
inputDataType=idt.name,
weightDataType=wdt.name,
outputDataType=odt.name,
@@ -152,23 +139,12 @@ def create_two_fc_model():
noActivation=no_act,
)
- tlastmarker = helper.make_node(
- "TLastMarker",
- ["outp"],
- ["outp_tlast"],
- domain="finn",
- backend="fpgadataflow",
- NumIters=m,
- StreamWidth=2,
- ElemWidth=odt.bitwidth(),
- )
-
graph = helper.make_graph(
- nodes=[fc0, fc1, tlastmarker],
+ nodes=[fc0, fc1],
name="fclayer_graph",
inputs=[inp],
- outputs=[outp_tlast],
- value_info=[mid, outp],
+ outputs=[outp],
+ value_info=[mid],
)
model = helper.make_model(graph, producer_name="fclayer-model")
@@ -177,7 +153,6 @@ def create_two_fc_model():
model.set_tensor_datatype("inp", idt)
model.set_tensor_datatype("mid", idt)
model.set_tensor_datatype("outp", odt)
- model.set_tensor_datatype("outp_tlast", odt)
model.set_tensor_datatype("w0", wdt)
model.set_tensor_datatype("w1", wdt)
@@ -206,12 +181,13 @@ def create_two_fc_model():
@pytest.mark.dependency()
def test_fpgadataflow_ipstitch_gen_model():
model = create_one_fc_model()
+ model = model.transform(InsertTLastMarker())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(CodeGen_ipgen(test_fpga_part, 5))
model = model.transform(HLSSynth_IPGen())
assert model.graph.node[0].op_type == "StreamingFCLayer_Batch"
# assert model.graph.node[1].op_type == "StreamingFCLayer_Batch"
- assert model.graph.node[1].op_type == "TLastMarker"
+ assert model.graph.node[-1].op_type == "TLastMarker"
model.save(ip_stitch_model_dir + "/test_fpgadataflow_ipstitch_gen_model.onnx")
diff --git a/tests/fpgadataflow/test_layer_streaming_maxpool_batch.py b/tests/fpgadataflow/test_layer_streaming_maxpool_batch.py
index 32b1c60fc714794e39fe1ade2d0252895bb33025..a7a096d6a69de743a318c0a514a2f24da5d7a29f 100644
--- a/tests/fpgadataflow/test_layer_streaming_maxpool_batch.py
+++ b/tests/fpgadataflow/test_layer_streaming_maxpool_batch.py
@@ -7,6 +7,7 @@ from finn.core.modelwrapper import ModelWrapper
from finn.transformation.fpgadataflow.cleanup import CleanUp
from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim
from finn.transformation.fpgadataflow.compile import Compile
+from finn.transformation.fpgadataflow.set_sim_mode import SetSimMode
def test_layer_streaming_maxpool_batch():
@@ -112,6 +113,7 @@ def test_layer_streaming_maxpool_batch():
).reshape(2, 2, 4, 4)
print(input_tensor)
+ model = model.transform(SetSimMode("npysim"))
model = model.transform(CodeGen_npysim())
model = model.transform(Compile())