diff --git a/docs/finn/example_networks.rst b/docs/finn/example_networks.rst
index 9f221871f09bf655db9d81988d6fa83e53473634..86bb2bd11fd805a23a3bdf6da8a8ed686259ecc1 100644
--- a/docs/finn/example_networks.rst
+++ b/docs/finn/example_networks.rst
@@ -20,17 +20,17 @@ version, this is indicated by an x mark in the table.
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
| Export/Import | x | x | x | x | x | x | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| Streamlining | x | x | x | x | x | | |
+| Streamlining | x | x | x | x | x | | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| Convert to HLS layers | x | x | x | x | x | | |
+| Convert to HLS layers | x | x | x | x | x | | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| Stitched IP | x | x | x | x | x | | |
+| Stitched IP | x | x | x | x | x | | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| Hardware test | x | x | x | | x | | |
+| Hardware test | x | x | x | | x | | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| cppsim | x | x | x | x | x | | |
+| cppsim | x | x | x | x | x | | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| rtlsim node-by-node | x | x | x | x | x | | |
+| rtlsim node-by-node | x | x | x | x | x | | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
-| rtlsim stitched IP | x | x | x | x | x | | |
+| rtlsim stitched IP | x | x | x | x | x | | x |
+-----------------------+------------+----------+----------+----------+----------+----------+----------+
diff --git a/docs/finn/getting_started.rst b/docs/finn/getting_started.rst
index 5beabeb2980840fd1dbd2ee5b058738fa8553152..f4fa7a13dcbe4fe8ab9667a111df00c605747710 100644
--- a/docs/finn/getting_started.rst
+++ b/docs/finn/getting_started.rst
@@ -31,6 +31,7 @@ Getting an interactive shell for development or experimentation
sh run_docker.sh
Simply running sh run-docker.sh without any additional arguments will clone the dependency repos, create a Docker container and give you a terminal with you can use for development for experimentation.
+If you want a new terminal on an already-running container, you can do this with `docker exec -it finn_dev_<username> bash`.
.. warning:: The Docker container is spawned with the `--rm` option, so make sure that any important files you created inside the container are either in the /workspace/finn folder (which is mounted from the host computer) or otherwise backed up.
diff --git a/src/finn/custom_op/fpgadataflow/__init__.py b/src/finn/custom_op/fpgadataflow/__init__.py
index a688898f4a43b33fd3f07cda12144b84829e451f..14ba1b813c8d61145f7d221deee9c184aeb9bddc 100644
--- a/src/finn/custom_op/fpgadataflow/__init__.py
+++ b/src/finn/custom_op/fpgadataflow/__init__.py
@@ -171,9 +171,15 @@ class HLSCustomOp(CustomOp):
of the node as a dictionary."""
ret = dict()
ret["BRAM_18K"] = self.bram_estimation()
+ ret["BRAM_efficiency"] = self.bram_efficiency_estimation()
ret["LUT"] = self.lut_estimation()
return ret
+ def bram_efficiency_estimation(self):
+ """Function for BRAM efficiency estimation: actual parameter storage
+ needed divided by the allocated BRAM storage (from estimation)"""
+ return 1
+
def bram_estimation(self):
"""Function for BRAM resource estimation, is member function of
HLSCustomOp class but has to be filled by every node"""
@@ -219,7 +225,6 @@ class HLSCustomOp(CustomOp):
self.code_gen_dict["$CLKPERIOD$"] = [str(clk)]
self.code_gen_dict["$EXTRA_DIRECTIVES$"] = self.ipgen_extra_directives()
-
template = self.ipgentcl_template
for key in self.code_gen_dict:
@@ -235,7 +240,7 @@ class HLSCustomOp(CustomOp):
def ipgen_extra_directives(self):
"Return a list of extra tcl directives for HLS synthesis."
return []
-
+
def ipgen_singlenode_code(self):
"""Builds the bash script for ip generation using the IPGenBuilder from
finn.util.fpgadataflow."""
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index b6c992e4b5ea1ced088928b3d9a4db381d82db22..a7ebff68749120868cae9ce5ac18d2856fe2cb8a 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -240,11 +240,21 @@ class StreamingFCLayer_Batch(HLSCustomOp):
Q = self.get_nodeattr("SIMD")
wdt = self.get_weight_datatype()
W = wdt.bitwidth()
- D_in = self.get_instream_width()
- D_out = self.get_outstream_width()
+ D_in = self.get_nodeattr("MW")
+ D_out = self.get_nodeattr("MH")
omega = (D_in * D_out) / (Q * P)
return P * (math.ceil(omega / 512)) * (math.ceil((Q * W) / 36))
+ def bram_efficiency_estimation(self):
+ wdt = self.get_weight_datatype()
+ W = wdt.bitwidth()
+ D_in = self.get_nodeattr("MW")
+ D_out = self.get_nodeattr("MH")
+ bram16_est = self.bram_estimation()
+ wbits = W * D_in * D_out
+ bram16_est_capacity = bram16_est * 36 * 512
+ return wbits / bram16_est_capacity
+
def lut_estimation(self):
"""Calculates resource estimations for LUTs based on:
- FINN-R: An End-to-End Deep-Learning Framework for Fast
diff --git a/src/finn/transformation/fpgadataflow/annotate_resources.py b/src/finn/transformation/fpgadataflow/annotate_resources.py
index 207075b00de1871da19ea78472125d435449ed6e..62ee92df54eee2b63d84657515d7fbc3a8808b81 100644
--- a/src/finn/transformation/fpgadataflow/annotate_resources.py
+++ b/src/finn/transformation/fpgadataflow/annotate_resources.py
@@ -69,6 +69,9 @@ class AnnotateResources(Transformation):
total_dict[r_type] += r_amount
else:
total_dict[r_type] = r_amount
+ for k in total_dict.keys():
+ if "efficiency" in k:
+ total_dict[k] = total_dict[k] / len(graph.node)
model.set_metadata_prop("res_total_" + self.mode, str(total_dict))
for node in graph.node:
if _is_fpgadataflow_node(node) and node.name in res_dict.keys():
diff --git a/src/finn/transformation/remove_identity.py b/src/finn/transformation/remove_identity.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7a58d59c1bb8ff643e691442e7eda3c0516aa5c
--- /dev/null
+++ b/src/finn/transformation/remove_identity.py
@@ -0,0 +1,62 @@
+# Copyright (c) 2020, Xilinx
+# 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.
+
+from finn.transformation import Transformation
+
+
+def _is_identity(node, model):
+ if node.op_type == "Mul":
+ scale = model.get_initializer(node.input[1])
+ if scale is not None:
+ return (scale == 1).all()
+ elif node.op_type == "Add":
+ bias = model.get_initializer(node.input[1])
+ if bias is not None:
+ return (bias == 0).all()
+ return False
+
+
+class RemoveIdentity(Transformation):
+ """Remove nodes that apply identity ops from the graph, including:
+ * Multiply by 1
+ * Add 0
+ ."""
+
+ def apply(self, model):
+ graph = model.graph
+ node_ind = 0
+ graph_modified = False
+ for node in graph.node:
+ node_ind += 1
+ if _is_identity(node, model):
+ node_src = node.input[0]
+ node_dst = node.output[0]
+ graph.node.remove(node)
+ model.rename_tensor(node_dst, node_src)
+ graph_modified = True
+ return (model, graph_modified)
diff --git a/src/finn/transformation/streamline/__init__.py b/src/finn/transformation/streamline/__init__.py
index c9c73fa4c8303ee28bc1cc6aee879d633740e01e..d9c12a20975084705b801c0ff027d4b99aff9490 100644
--- a/src/finn/transformation/streamline/__init__.py
+++ b/src/finn/transformation/streamline/__init__.py
@@ -41,6 +41,7 @@ from finn.transformation.streamline.absorb import (
FactorOutMulSignMagnitude,
Absorb1BitMulIntoMatMul,
Absorb1BitMulIntoConv,
+ AbsorbSignBiasIntoMultiThreshold,
)
from finn.transformation.streamline.collapse_repeated import (
@@ -59,6 +60,7 @@ from finn.transformation.streamline.reorder import (
from finn.transformation.streamline.round_thresholds import RoundAndClipThresholds
from finn.transformation.streamline.sign_to_thres import ConvertSignToThres
from finn.transformation.batchnorm_to_affine import BatchNormToAffine
+from finn.transformation.streamline.remove import RemoveIdentityOps
class Streamline(Transformation):
@@ -70,6 +72,7 @@ class Streamline(Transformation):
ConvertDivToMul(),
BatchNormToAffine(),
ConvertSignToThres(),
+ AbsorbSignBiasIntoMultiThreshold(),
MoveAddPastMul(),
MoveScalarAddPastMatMul(),
MoveScalarAddPastConv(),
@@ -87,6 +90,7 @@ class Streamline(Transformation):
]
for trn in streamline_transformations:
model = model.transform(trn)
+ model = model.transform(RemoveIdentityOps())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
model = model.transform(InferDataTypes())
diff --git a/src/finn/transformation/streamline/absorb.py b/src/finn/transformation/streamline/absorb.py
index 4266488c7d1b86f2997d4c77d70b80f88bf37442..dc01eea411fc1f640e481c9be02a92acdd59533f 100644
--- a/src/finn/transformation/streamline/absorb.py
+++ b/src/finn/transformation/streamline/absorb.py
@@ -38,6 +38,70 @@ from finn.transformation.infer_shapes import InferShapes
from finn.transformation.infer_datatypes import InferDataTypes
+class AbsorbSignBiasIntoMultiThreshold(Transformation):
+ """Absorb scalar bias originating from signed int export back into
+ MultiThreshold and re-evaluate the output datatype."""
+
+ def apply(self, model):
+ graph = model.graph
+ node_ind = 0
+ graph_modified = False
+ for n in graph.node:
+ # search for (MultiThreshold, Add) pair
+ node_ind += 1
+ if (
+ n.op_type == "MultiThreshold"
+ and not model.is_fork_node(n)
+ and not model.is_join_node(n)
+ ):
+ consumer = model.find_consumer(n.output[0])
+ if consumer is not None and consumer.op_type == "Add":
+ mt_node = n
+ add_node = consumer
+ threshold_name = mt_node.input[1]
+ add_weight_name = add_node.input[1]
+ T = model.get_initializer(threshold_name)
+ A = model.get_initializer(add_weight_name)
+ if (A is None) or (T is None):
+ warnings.warn("Threshold or add bias not constant, skipping")
+ continue
+ end_name = add_node.output[0]
+ # we can only absorb scalar adds
+ is_scalar = A.ndim == 0 or all(x == 1 for x in A.shape)
+ if not is_scalar:
+ continue
+ bias = A.flatten()[0]
+ # set MultiThreshold bias property
+ mt_inst = getCustomOp(mt_node)
+ bias += mt_inst.get_nodeattr("out_bias")
+ mt_inst.set_nodeattr("out_bias", bias)
+ graph_modified = True
+ # compute new DataType for MultiThreshold output
+ steps = T.shape[-1]
+ new_min = bias
+ new_max = steps + bias
+ odt = DataType.get_smallest_possible(steps).name.replace(
+ "UINT", "INT"
+ )
+ odt = DataType[odt]
+ assert odt.allowed(new_max) and odt.allowed(
+ new_min
+ ), """Could
+ not compute new MultiThreshold DataType (min = %d max = %d)""" % (
+ new_min,
+ new_max,
+ )
+ mt_inst.set_nodeattr("out_dtype", odt.name)
+ # remove Add node, rewire MultiThreshold
+ graph.node.remove(add_node)
+ mt_node.output[0] = end_name
+ # set datatype
+ model.set_tensor_datatype(end_name, odt)
+ if graph_modified:
+ model = model.transform(InferDataTypes())
+ return (model, graph_modified)
+
+
class AbsorbAddIntoMultiThreshold(Transformation):
"""Absorb preceding Add ops into MultiThreshold by updating the threshold
values. Only scalar/1D add vectors can be absorbed."""
diff --git a/tests/end2end/test_end2end_cnv_w2a2.py b/tests/end2end/test_end2end_cnv_w2a2.py
new file mode 100644
index 0000000000000000000000000000000000000000..31ccebd4c175ad2badef17499bf113d978b637f7
--- /dev/null
+++ b/tests/end2end/test_end2end_cnv_w2a2.py
@@ -0,0 +1,377 @@
+# Copyright (c) 2020, Xilinx
+# 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 os
+
+import numpy as np
+
+# as of Feb'20 there is a bug that segfaults ONNX shape inference if we
+# import pytorch before onnx, so we make sure to import onnx first
+import onnx # NOQA
+
+import pytest
+import pkg_resources as pk
+from finn.custom_op.registry import getCustomOp
+from finn.core.onnx_exec import execute_onnx
+from finn.transformation.double_to_single_float import DoubleToSingleFloat
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.move_reshape import RemoveCNVtoFCFlatten
+from finn.transformation.fold_constants import FoldConstants
+from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.streamline import Streamline
+from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
+import finn.transformation.streamline.absorb as absorb
+from finn.transformation.streamline.reorder import MakeMaxPoolNHWC
+import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
+from finn.transformation.fpgadataflow.create_dataflow_partition import (
+ CreateDataflowPartition,
+)
+from finn.transformation.fpgadataflow.insert_dwc import InsertDWC
+from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
+from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
+ ReplaceVerilogRelPaths,
+)
+from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
+from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
+from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver
+from finn.transformation.fpgadataflow.make_pynq_proj import MakePYNQProject
+from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject
+from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
+from finn.util.basic import pynq_part_map
+from finn.util.test import get_test_model_trained, load_test_checkpoint_or_skip
+from finn.transformation.fpgadataflow.annotate_resources import AnnotateResources
+from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
+from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO
+from finn.core.throughput_test import throughput_test_rtlsim
+
+build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
+test_pynq_board = os.getenv("PYNQ_BOARD", default="Pynq-Z1")
+test_fpga_part = pynq_part_map[test_pynq_board]
+target_clk_ns = 10
+mem_mode = "decoupled"
+
+
+def test_end2end_cnv_w2a2_export():
+ import brevitas.onnx as bo
+
+ cnv = get_test_model_trained("CNV", 2, 2)
+ bo.export_finn_onnx(
+ cnv, (1, 3, 32, 32), build_dir + "/end2end_cnv_w2a2_export.onnx"
+ )
+
+
+def test_end2end_cnv_w2a2_import_and_tidy():
+ model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_export.onnx")
+ model = model.transform(DoubleToSingleFloat())
+ model = model.transform(InferShapes())
+ model = model.transform(FoldConstants())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+ model.save(build_dir + "/end2end_cnv_w2a2_tidy.onnx")
+
+
+def test_end2end_cnv_w2a2_streamline():
+ model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_tidy.onnx")
+ model = model.transform(Streamline())
+ model = model.transform(LowerConvsToMatMul())
+ model = model.transform(MakeMaxPoolNHWC())
+ model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
+ model = model.transform(Streamline())
+ model.save(build_dir + "/end2end_cnv_w2a2_streamlined.onnx")
+
+
+def test_end2end_cnv_w2a2_convert_to_hls_layers():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_streamlined.onnx"
+ )
+ model = model.transform(to_hls.InferQuantizedStreamingFCLayer(mem_mode))
+ model = model.transform(to_hls.InferConvInpGen())
+ model = model.transform(to_hls.InferStreamingMaxPool())
+ model = model.transform(RemoveCNVtoFCFlatten())
+ model.save(build_dir + "/end2end_cnv_w2a2_hls_layers.onnx")
+
+
+def test_end2end_cnv_w2a2_create_dataflow_partition():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_hls_layers.onnx"
+ )
+ parent_model = model.transform(CreateDataflowPartition())
+ parent_model.save(build_dir + "/end2end_cnv_w2a2_dataflow_parent.onnx")
+ sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
+ sdp_node = getCustomOp(sdp_node)
+ dataflow_model_filename = sdp_node.get_nodeattr("model")
+ dataflow_model = load_test_checkpoint_or_skip(dataflow_model_filename)
+ dataflow_model.save(build_dir + "/end2end_cnv_w2a2_dataflow_model.onnx")
+
+
+def test_end2end_cnv_w2a2_fold_and_tlastmarker():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_dataflow_model.onnx"
+ )
+ fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
+ # each tuple is (PE, SIMD, in_fifo_depth) for a layer
+ folding = [
+ (8, 3, 256, "auto"),
+ (16, 16, 256, "auto"),
+ (8, 16, 256, "auto"),
+ (8, 16, 256, "block"),
+ (4, 8, 214, "auto"),
+ (1, 8, 2, "auto"),
+ (1, 2, 126, "distributed"),
+ (2, 2, 62, "block"),
+ (5, 1, 6, "distributed"),
+ ]
+ for fcl, (pe, simd, ififodepth, ramstyle) in zip(fc_layers, folding):
+ fcl_inst = getCustomOp(fcl)
+ fcl_inst.set_nodeattr("PE", pe)
+ fcl_inst.set_nodeattr("SIMD", simd)
+ fcl_inst.set_nodeattr("inFIFODepth", ififodepth)
+ fcl_inst.set_nodeattr("ram_style", ramstyle)
+
+ swg_layers = model.get_nodes_by_op_type("ConvolutionInputGenerator")
+ swg_idepth = [2, 51, 9, 106, 2, 2]
+ for i in range(len(swg_layers)):
+ swg_inst = getCustomOp(swg_layers[i])
+ simd = folding[i][1]
+ swg_inst.set_nodeattr("SIMD", simd)
+ swg_inst.set_nodeattr("inFIFODepth", swg_idepth[i])
+
+ model = model.transform(InsertDWC())
+ model = model.transform(InsertFIFO())
+ model = model.transform(InsertTLastMarker())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(AnnotateResources("estimate"))
+ model.save(build_dir + "/end2end_cnv_w2a2_folded.onnx")
+
+
+@pytest.mark.slow
+@pytest.mark.vivado
+def test_end2end_cnv_w2a2_gen_hls_ip():
+ model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_folded.onnx")
+ model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))
+ model = model.transform(HLSSynthIP())
+ model = model.transform(AnnotateResources("hls"))
+ model.save(build_dir + "/end2end_cnv_w2a2_ipgen.onnx")
+
+
+@pytest.mark.vivado
+def test_end2end_cnv_w2a2_ip_stitch():
+ model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_ipgen.onnx")
+ model = model.transform(ReplaceVerilogRelPaths())
+ model = model.transform(CreateStitchedIP(test_fpga_part, target_clk_ns))
+ model.save(build_dir + "/end2end_cnv_w2a2_ipstitch.onnx")
+
+
+@pytest.mark.vivado
+def test_end2end_cnv_w2a2_verify_dataflow_part():
+ model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_ipstitch.onnx")
+ x = np.zeros((1, 32, 32, 3), dtype=np.float32)
+ inp_name = model.graph.input[0].name
+ out_name = model.graph.output[0].name
+ inp_dict = {inp_name: x}
+ # cppsim
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ model = model.transform(SetExecMode("cppsim"))
+ model.save(build_dir + "/end2end_cnv_w2a2_ipgen_cppsim.onnx")
+ ret_cppsim = execute_onnx(model, inp_dict, True)
+ res_cppsim = ret_cppsim[out_name]
+ # node-by-node rtlsim
+ model = model.transform(SetExecMode("rtlsim"))
+ model = model.transform(PrepareRTLSim())
+ model.save(build_dir + "/end2end_cnv_w2a2_ipgen_nodebynode_rtlsim.onnx")
+ ret_rtlsim_nodebynode = execute_onnx(model, inp_dict, True)
+ res_rtlsim_nodebynode = ret_rtlsim_nodebynode[out_name]
+ # whole-network (ip-stitched) rtlsim
+ model.set_metadata_prop("exec_mode", "rtlsim")
+ model.save(build_dir + "/end2end_cnv_w2a2_ipstitch_whole_rtlsim.onnx")
+ # this is a particularly long-running test, set liveness thr. to unlimited
+ os.environ["LIVENESS_THRESHOLD"] = "-1"
+ ret_rtlsim_whole = execute_onnx(model, inp_dict, True)
+ res_rtlsim_whole = ret_rtlsim_whole[out_name]
+ assert np.isclose(res_cppsim, res_rtlsim_nodebynode).all()
+ assert np.isclose(res_cppsim, res_rtlsim_whole).all()
+
+
+@pytest.mark.vivado
+def test_end2end_cnv_w2a2_throughput_test_rtlsim():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_ipstitch_whole_rtlsim.onnx"
+ )
+ model.set_metadata_prop("rtlsim_trace", "rtlsim_trace.vcd")
+ # os.environ["RTLSIM_TRACE_DEPTH"] = "4"
+ # run through IP-stitched rtlsim with increasing batch sizes and
+ # check the number of cycles it takes to execute
+ ret = throughput_test_rtlsim(model, 10)
+ # TODO check for expected performance
+ assert ret["cycles"] > 0
+
+
+@pytest.mark.vivado
+def test_end2end_cnv_w2a2_verify_all():
+ # use the streamlined model as the "golden" model for right answers
+ golden = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_streamlined.onnx"
+ )
+ iname = golden.graph.input[0].name
+ oname = golden.graph.output[0].name
+ # load one of the test vectors
+ fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
+ input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+ input_tensor = input_tensor / 255
+ assert input_tensor.shape == (1, 3, 32, 32)
+ x = input_tensor
+ # x = np.zeros(ishape, dtype=np.float32)
+ ret_golden = execute_onnx(golden, {iname: x}, True)
+ y_golden = ret_golden[oname]
+ # set up parent+child graph to test
+ # we'll use models from the previous step as the child model
+ parent_model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_dataflow_parent.onnx"
+ )
+ iname = parent_model.graph.input[0].name
+ oname = parent_model.graph.output[0].name
+ # produce results with cppsim
+ sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
+ sdp_node = getCustomOp(sdp_node)
+ load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_ipgen_cppsim.onnx")
+ sdp_node.set_nodeattr("model", build_dir + "/end2end_cnv_w2a2_ipgen_cppsim.onnx")
+ ret_cppsim = execute_onnx(parent_model, {iname: x}, True)
+ y_cppsim = ret_cppsim[oname]
+ # produce results with node-by-node rtlsim
+ load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_ipgen_nodebynode_rtlsim.onnx"
+ )
+ sdp_node.set_nodeattr(
+ "model", build_dir + "/end2end_cnv_w2a2_ipgen_nodebynode_rtlsim.onnx"
+ )
+ ret_nodebynode_rtlsim = execute_onnx(parent_model, {iname: x}, True)
+ y_nodebynode_rtlsim = ret_nodebynode_rtlsim[oname]
+ # produce results with whole-network (stitched ip) rtlsim
+ load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_ipstitch_whole_rtlsim.onnx"
+ )
+ sdp_node.set_nodeattr(
+ "model", build_dir + "/end2end_cnv_w2a2_ipstitch_whole_rtlsim.onnx"
+ )
+ # this is a particularly long-running test, set liveness thr. to unlimited
+ os.environ["LIVENESS_THRESHOLD"] = "-1"
+ ret_whole_rtlsim = execute_onnx(parent_model, {iname: x}, True)
+ y_whole_rtlsim = ret_whole_rtlsim[oname]
+ assert np.isclose(y_golden, y_cppsim).all()
+ assert np.isclose(y_golden, y_nodebynode_rtlsim).all()
+ assert np.isclose(y_golden, y_whole_rtlsim).all()
+ assert np.argmax(y_golden) == 3
+
+
+@pytest.mark.vivado
+def test_end2end_cnv_w2a2_make_pynq_proj():
+ model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_ipstitch.onnx")
+ model = model.transform(MakePYNQProject(test_pynq_board))
+ model.save(build_dir + "/end2end_cnv_w2a2_pynq_project.onnx")
+
+
+@pytest.mark.slow
+@pytest.mark.vivado
+def test_end2end_cnv_w2a2_synth_pynq_project():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_pynq_project.onnx"
+ )
+ model = model.transform(SynthPYNQProject())
+ model = model.transform(AnnotateResources("synth"))
+ model.save(build_dir + "/end2end_cnv_w2a2_synth.onnx")
+
+
+def test_end2end_cnv_w2a2_make_driver():
+ model = load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_synth.onnx")
+ model = model.transform(MakePYNQDriver())
+ model.save(build_dir + "/end2end_cnv_w2a2_pynq_driver.onnx")
+
+
+def test_end2end_cnv_w2a2_deploy_on_pynq():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_pynq_driver.onnx"
+ )
+ try:
+ ip = os.environ["PYNQ_IP"] # no fault for this one; skip if not defined
+ if ip == "":
+ pytest.skip("PYNQ board IP address not specified")
+ username = os.getenv("PYNQ_USERNAME", "xilinx")
+ password = os.getenv("PYNQ_PASSWORD", "xilinx")
+ port = os.getenv("PYNQ_PORT", 22)
+ target_dir = os.getenv("PYNQ_TARGET_DIR", "/home/xilinx/finn")
+ model = model.transform(DeployToPYNQ(ip, port, username, password, target_dir))
+ # save the model to be able to link it to the parent
+ model.save(build_dir + "/end2end_cnv_w2a2_pynq_deploy.onnx")
+ except KeyError:
+ pytest.skip("PYNQ board IP address not specified")
+
+
+def test_end2end_cnv_w2a2_run_on_pynq():
+ # use the streamlined model as the "golden" model for right answers
+ golden = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_streamlined.onnx"
+ )
+ iname = golden.graph.input[0].name
+ oname = golden.graph.output[0].name
+ # load one of the test vectors
+ fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
+ input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+ input_tensor = input_tensor / 255
+ assert input_tensor.shape == (1, 3, 32, 32)
+ x = input_tensor
+ # run using FINN-based execution
+ ret_golden = execute_onnx(golden, {iname: x}, True)
+ y_golden = ret_golden[oname]
+ # set up parent+child graph to test
+ # we'll use models from the previous step as the child model
+ parent_model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_cnv_w2a2_dataflow_parent.onnx"
+ )
+ iname = parent_model.graph.input[0].name
+ oname = parent_model.graph.output[0].name
+ try:
+ ip = os.environ["PYNQ_IP"] # NOQA
+ if ip == "":
+ pytest.skip("PYNQ board IP address not specified")
+ # produce results with cppsim
+ sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
+ sdp_node = getCustomOp(sdp_node)
+ load_test_checkpoint_or_skip(build_dir + "/end2end_cnv_w2a2_pynq_deploy.onnx")
+ sdp_node.set_nodeattr("model", build_dir + "/end2end_cnv_w2a2_pynq_deploy.onnx")
+ ret = execute_onnx(parent_model, {iname: x}, True)
+ y = ret[oname]
+ assert np.isclose(y, y_golden).all()
+ assert np.argmax(y) == 3
+
+ except KeyError:
+ pytest.skip("PYNQ board IP address not specified")
diff --git a/tests/pynq/test_pynq_performance_end2end.py b/tests/pynq/test_pynq_performance_end2end.py
index 66a93a190061e0142637be19bb2ea841d192745a..3b6ea86741b8adefce4faaa65b791f1d213cf3ae 100644
--- a/tests/pynq/test_pynq_performance_end2end.py
+++ b/tests/pynq/test_pynq_performance_end2end.py
@@ -10,7 +10,7 @@ from finn.core.throughput_test import throughput_test
build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
-@pytest.mark.parametrize("end2end_example", ["tfc_w1a1", "cnv_w1a1"])
+@pytest.mark.parametrize("end2end_example", ["tfc_w1a1", "cnv_w1a1", "cnv_w2a2"])
@pytest.mark.slow
def test_pynq_performance_end2end(end2end_example):
model = load_test_checkpoint_or_skip(
diff --git a/tests/transformation/streamline/test_streamline_cnv.py b/tests/transformation/streamline/test_streamline_cnv.py
index 56dcd26076ec0a5fba6e9be6acac7f5e13572c3d..103967dfb6b86cc6e2ce2bc9ab78249d8945d47d 100644
--- a/tests/transformation/streamline/test_streamline_cnv.py
+++ b/tests/transformation/streamline/test_streamline_cnv.py
@@ -44,9 +44,9 @@ from finn.transformation.double_to_single_float import DoubleToSingleFloat
export_onnx_path = make_build_dir("test_streamline_cnv_")
# act bits
-@pytest.mark.parametrize("abits", [1])
+@pytest.mark.parametrize("abits", [1, 2])
# weight bits
-@pytest.mark.parametrize("wbits", [1])
+@pytest.mark.parametrize("wbits", [1, 2])
# network topology / size
@pytest.mark.parametrize("size", ["CNV"])
def test_streamline_cnv(size, wbits, abits):
@@ -74,6 +74,7 @@ def test_streamline_cnv(size, wbits, abits):
# model.save("orig_cnv.onnx")
model = model.transform(Streamline())
# model.save("streamlined_cnv.onnx")
+ assert len(model.graph.node) == 23
produced_ctx = oxe.execute_onnx(model, input_dict, True)
produced = produced_ctx[model.graph.output[0].name]
assert np.isclose(expected, produced, atol=1e-3).all()