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# 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 pytest
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 onnx.numpy_helper as nph
import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
import finn.transformation.streamline.absorb as absorb
from finn.custom_op.registry import getCustomOp
from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.fpgadataflow.create_dataflow_partition import (
CreateDataflowPartition,
)
from finn.transformation.general import (
RemoveUnusedTensors,
RemoveStaticGraphInputs,
GiveReadableTensorNames,
GiveUniqueNodeNames,
)
from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import Streamline
from finn.transformation.streamline.round_thresholds import RoundAndClipThresholds
from finn.util.basic import alveo_part_map, alveo_default_platform
from finn.util.test import get_test_model_trained, load_test_checkpoint_or_skip
from finn.transformation.fpgadataflow.vitis_build import VitisBuild
from finn.transformation.infer_data_layouts import InferDataLayouts
from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
from pkgutil import get_data
from finn.core.onnx_exec import execute_onnx
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build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
test_alveo_board = os.getenv("ALVEO_BOARD", default="U250")
test_fpga_part = alveo_part_map[test_alveo_board]
test_platform = alveo_default_platform[test_alveo_board]
target_clk_ns = 10
mem_mode = "decoupled"
def test_end2end_vitis_tfc_w1a1_export():
import brevitas.onnx as bo
tfc = get_test_model_trained("TFC", 1, 1)
bo.export_finn_onnx(
tfc, (1, 1, 28, 28), build_dir + "/end2end_vitis_tfc_w1a1_export.onnx"
)
def test_end2end_vitis_tfc_w1a1_import_and_tidy():
model = load_test_checkpoint_or_skip(
build_dir + "/end2end_vitis_tfc_w1a1_export.onnx"
)
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
model = model.transform(InferDataTypes())
model = model.transform(RemoveStaticGraphInputs())
model.save(build_dir + "/end2end_vitis_tfc_w1a1_tidy.onnx")
def test_end2end_vitis_tfc_w1a1_streamline():
model = load_test_checkpoint_or_skip(
build_dir + "/end2end_vitis_tfc_w1a1_tidy.onnx"
)
model = model.transform(Streamline())
model = model.transform(RemoveUnusedTensors())
model.save(build_dir + "/end2end_vitis_tfc_w1a1_streamlined.onnx")
def test_end2end_vitis_tfc_w1a1_convert_to_hls_layers():
model = load_test_checkpoint_or_skip(
build_dir + "/end2end_vitis_tfc_w1a1_streamlined.onnx"
)
model = model.transform(ConvertBipolarMatMulToXnorPopcount())
model = model.transform(absorb.AbsorbAddIntoMultiThreshold())
model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
model = model.transform(RoundAndClipThresholds())
model = model.transform(to_hls.InferBinaryStreamingFCLayer(mem_mode))
model = model.transform(InferDataLayouts())
model.save(build_dir + "/end2end_vitis_tfc_w1a1_hls_layers.onnx")
def test_end2end_vitis_tfc_w1a1_create_dataflow_partition():
model = load_test_checkpoint_or_skip(
build_dir + "/end2end_vitis_tfc_w1a1_hls_layers.onnx"
)
parent_model = model.transform(CreateDataflowPartition())
parent_model.save(build_dir + "/end2end_vitis_tfc_w1a1_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_vitis_tfc_w1a1_dataflow_model.onnx")
def test_end2end_vitis_tfc_w1a1_fold():
model = load_test_checkpoint_or_skip(
build_dir + "/end2end_vitis_tfc_w1a1_dataflow_model.onnx"
)
fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
# (PE, SIMD, in_fifo_depth, out_fifo_depth, ramstyle) for each layer
config = [
(16, 49, 16, 64, "block"),
(8, 8, 64, 64, "auto"),
(8, 8, 64, 64, "auto"),
(10, 8, 64, 10, "distributed"),
]
for fcl, (pe, simd, ififo, ofifo, ramstyle) in zip(fc_layers, config):
fcl_inst = getCustomOp(fcl)
fcl_inst.set_nodeattr("PE", pe)
fcl_inst.set_nodeattr("SIMD", simd)
fcl_inst.set_nodeattr("inFIFODepth", ififo)
fcl_inst.set_nodeattr("outFIFODepth", ofifo)
fcl_inst.set_nodeattr("ram_style", ramstyle)
model.save(build_dir + "/end2end_vitis_tfc_w1a1_folded.onnx")
@pytest.mark.slow
@pytest.mark.vitis
def test_end2end_vitis_tfc_w1a1_build():
if "VITIS_PATH" not in os.environ:
pytest.skip("VITIS_PATH not set")
model = load_test_checkpoint_or_skip(
build_dir + "/end2end_vitis_tfc_w1a1_folded.onnx"
)
model = model.transform(VitisBuild(test_fpga_part, target_clk_ns, test_platform))
# TODO post-synth resources
model.save(build_dir + "/end2end_vitis_tfc_w1a1_build.onnx")
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def test_end2end_vitis_tfc_w1a1_deploy_on_pynq():
model = load_test_checkpoint_or_skip(
build_dir + "/end2end_vitis_tfc_w1a1_build.onnx"
)
try:
ip = os.environ["ALVEO_IP"] # no fault for this one; skip if not defined
if ip == "":
pytest.skip("PYNQ board IP address not specified")
username = os.getenv("ALVEO_USERNAME", "xilinx")
password = os.getenv("ALVEO_PASSWORD", "xilinx")
port = os.getenv("ALVEO_PORT", 22)
target_dir = os.getenv("ALVEO_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_vitis_tfc_w1a1_pynq_deploy.onnx")
except KeyError:
pytest.skip("PYNQ board IP address not specified")
def test_end2end_vitis_tfc_w1a1_run_on_pynq():
# use the streamlined model as the "golden" model for right answers
golden = load_test_checkpoint_or_skip(
build_dir + "/end2end_vitis_tfc_w1a1_streamlined.onnx"
)
iname = golden.graph.input[0].name
oname = golden.graph.output[0].name
raw_i = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/input_0.pb")
input_tensor = onnx.load_tensor_from_string(raw_i)
x = nph.to_array(input_tensor)
# x = np.zeros(ishape, dtype=np.float32)
# 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_vitis_tfc_w1a1_dataflow_parent.onnx"
)
iname = parent_model.graph.input[0].name
oname = parent_model.graph.output[0].name
try:
ip = os.environ["ALVEO_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_vitis_tfc_w1a1_pynq_deploy.onnx"
)
sdp_node.set_nodeattr(
"model", build_dir + "/end2end_vitis_tfc_w1a1_pynq_deploy.onnx"
)
ret = execute_onnx(parent_model, {iname: x}, True)
y = ret[oname]
assert np.isclose(y, y_golden).all()
except KeyError:
pytest.skip("PYNQ board IP address not specified")