def test_convert_to_hls_layers_synthetic(ch, ifmdim, idt):
model = make_model(ch, ifmdim)
model.save(export_onnx_path)
model = ModelWrapper(export_onnx_path)
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
model = model.transform(InferDataLayouts())
# model.save("golden.onnx")
# generate test vectors of correct shape
if ifmdim == -1:
input_tensor_shape = (1, ch)
else:
input_tensor_shape = (1, ch, ifmdim, ifmdim)
x = gen_finn_dt_tensor(idt, input_tensor_shape)
# generate expected value from streamlined net
input_dict = {model.graph.input[0].name: x}
output_dict = oxe.execute_onnx(model, input_dict, True)
produced_sum = output_dict[model.graph.output[0].name]
chw_mul = model.get_initializer(model.graph.node[-1].input[1])
chw_mul = 1
expected_sum = chw_mul * np.sum(2 * (2 * x + 15.0),
axis=(2, 3)) / (ifmdim * ifmdim)
assert (produced_sum.flatten() == expected_sum.flatten()).all()
model = model.transform(InferDataLayouts())
# convert to hls
model.set_tensor_datatype(model.graph.input[0].name, idt)
# extra streamlining
model = model.transform(MoveScalarLinearPastInvariants())
model = model.transform(MoveAddPastMul())
model = model.transform(CollapseRepeatedMul())
model = model.transform(CollapseRepeatedAdd())
# insert top-k node, which should absorb linear ops before it
model = model.transform(InferShapes())
model = model.transform(InferDataLayouts())
model = model.transform(InferDataTypes())
model = model.transform(to_hls.InferChannelwiseLinearLayer())
model = model.transform(to_hls.InferAddStreamsLayer())
model = model.transform(to_hls.InferGlobalAccPoolLayer())
model = model.transform(MoveScalarLinearPastInvariants())
model = model.transform(InsertTopK())
model = model.transform(AbsorbScalarMulAddIntoTopK())
model = model.transform(InferDataTypes())
model = model.transform(to_hls.InferLabelSelectLayer())
model = model.transform(AbsorbConsecutiveTransposes())
model = model.transform(InferDataTypes())
model = model.transform(to_hls.InferLabelSelectLayer())
model = model.transform(to_hls.InferDuplicateStreamsLayer())
model = model.transform(SortGraph())
# model.save("golden_hls.onnx")
# check topology status
finn_nodes = model.get_finn_nodes()
assert len(finn_nodes) == 9
add_nodes = model.get_nodes_by_op_type("AddStreams_Batch")
assert len(add_nodes) == 1
pool_nodes = model.get_nodes_by_op_type("GlobalAccPool_Batch")
assert len(pool_nodes) == 1
label_nodes = model.get_nodes_by_op_type("LabelSelect_Batch")
assert len(label_nodes) == 1
channelwise_nodes = model.get_nodes_by_op_type("ChannelwiseOp_Batch")
assert len(channelwise_nodes) == 5
dup_nodes = model.get_nodes_by_op_type("DuplicateStreams_Batch")
assert len(dup_nodes) == 1
model = model.transform(PrepareCppSim())
model = model.transform(CompileCppSim())
model = model.transform(SetExecMode("cppsim"))
output_dict = oxe.execute_onnx(model, input_dict, True)
produced_topk_hls = output_dict[model.graph.output[0].name]
topk_input = output_dict[model.graph.node[-1].input[0]]
assert soft_verify_topk(topk_input, produced_topk_hls, 5)
os.remove(export_onnx_path)
def test_convert_to_hls_layers_cnv_w1a1(fused_activation):
cnv = get_test_model_trained("CNV", 1, 1)
bo.export_finn_onnx(cnv, (1, 3, 32, 32), export_onnx_path_cnv)
model = ModelWrapper(export_onnx_path_cnv)
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
model = model.transform(Streamline())
model = model.transform(LowerConvsToMatMul())
model = model.transform(MakeMaxPoolNHWC())
model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
model = model.transform(ConvertBipolarMatMulToXnorPopcount())
model = model.transform(Streamline())
model = model.transform(InferDataLayouts())
# model.save("golden.onnx")
# load one of the test vectors
fn = pk.resource_filename("finn.qnn-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)
# generate expected value from streamlined net
input_dict = {"global_in": input_tensor}
expected_ctx = oxe.execute_onnx(model, input_dict, True)
expected = expected_ctx[model.graph.output[0].name]
# if we infer thresholding first, all MultiThresholds get converted to HLS
# subsequently, the FC inference will generate passthrough MVAUs
if not fused_activation:
model = model.transform(to_hls.InferThresholdingLayer())
model = model.transform(to_hls.InferBinaryStreamingFCLayer())
model = model.transform(to_hls.InferQuantizedStreamingFCLayer())
for node in model.graph.node:
if node.op_type == "StreamingFCLayer_Batch":
inst = getCustomOp(node)
inst.set_nodeattr("mem_mode", "decoupled")
mw = inst.get_nodeattr("MW")
mh = inst.get_nodeattr("MH")
if mh % 4 == 0:
pe = mh // 4
else:
pe = mh
inst.set_nodeattr("PE", pe)
if mw % 16 == 0:
simd = mw // 16
else:
simd = mw
inst.set_nodeattr("SIMD", simd)
model = model.transform(to_hls.InferConvInpGen())
model = model.transform(to_hls.InferStreamingMaxPool())
# check topology status
finn_nodes = model.get_finn_nodes()
if fused_activation:
assert len(finn_nodes) == 18
else:
assert len(finn_nodes) == 26
thr_nodes = model.get_nodes_by_op_type("Thresholding_Batch")
assert len(thr_nodes) == 8
non_finn_nodes = model.get_non_finn_nodes()
assert len(non_finn_nodes) == 4
exp_non_finn_nodes = ["Transpose", "Reshape", "Mul", "Add"]
assert [x.op_type for x in non_finn_nodes] == exp_non_finn_nodes
fc_nodes = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
assert len(fc_nodes) == 9
swg_nodes = model.get_nodes_by_op_type("ConvolutionInputGenerator")
assert len(swg_nodes) == 6
mp_nodes = model.get_nodes_by_op_type("StreamingMaxPool_Batch")
assert len(mp_nodes) == 2
# model.save("cnv-pre-compile.onnx")
model = model.transform(PrepareCppSim())
model = model.transform(CompileCppSim())
model = model.transform(SetExecMode("cppsim"))
# model.save("cnv-post-compile.onnx")
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()
assert np.argmax(produced) == 3
os.remove(export_onnx_path_cnv)
def test_fpgadataflow_upsampler(dt, IFMDim, scale, NumChannels, exec_mode):
atol = 1e-3
# Create the test model and inputs for it
torch_model = PyTorchTestModel(upscale_factor=scale)
input_shape = (1, NumChannels, IFMDim, IFMDim)
test_in = torch.arange(0, np.prod(np.asarray(input_shape)))
# Limit the input to values valid for the given datatype
test_in %= dt.max() - dt.min() + 1
test_in += dt.min()
# Additionally make sure we always start with 0, for convenience purposes.
test_in = torch.roll(test_in, dt.min())
test_in = test_in.view(*input_shape).type(torch.float32)
# Get golden PyTorch and ONNX inputs
golden_torch_float = torch_model(test_in)
export_path = f"{tmpdir}/Upsample_exported.onnx"
FINNManager.export(torch_model,
input_shape=input_shape,
export_path=export_path,
opset_version=11)
model = ModelWrapper(export_path)
input_dict = {model.graph.input[0].name: test_in.numpy().astype(np.int32)}
input_dict = {model.graph.input[0].name: test_in.numpy()}
golden_output_dict = oxe.execute_onnx(model, input_dict, True)
golden_result = golden_output_dict[model.graph.output[0].name]
# Make sure PyTorch and ONNX match
pyTorch_onnx_match = np.isclose(golden_result, golden_torch_float).all()
assert pyTorch_onnx_match, "ONNX and PyTorch upsampling output don't match."
# Prep model for execution
model = ModelWrapper(export_path)
# model = model.transform(TransposeUpsampleIO())
model = model.transform(MakeInputChannelsLast())
model = model.transform(InferDataLayouts())
model = model.transform(absorb.AbsorbTransposeIntoResize())
model = model.transform(InferShapes())
model = model.transform(ForceDataTypeForTensors(dType=dt))
model = model.transform(GiveUniqueNodeNames())
model = model.transform(InferUpsample())
model = model.transform(InferShapes())
model = model.transform(InferDataTypes())
# Check that all nodes are UpsampleNearestNeighbour_Batch nodes
for n in model.get_finn_nodes():
node_check = n.op_type == "UpsampleNearestNeighbour_Batch"
assert node_check, "All nodes should be UpsampleNearestNeighbour_Batch nodes."
# Prep sim
if exec_mode == "cppsim":
model = model.transform(PrepareCppSim())
model = model.transform(CompileCppSim())
model = model.transform(SetExecMode("cppsim"))
elif exec_mode == "rtlsim":
model = model.transform(GiveUniqueNodeNames())
model = model.transform(PrepareIP("xc7z020clg400-1", 10))
model = model.transform(HLSSynthIP())
model = model.transform(SetExecMode("rtlsim"))
model = model.transform(PrepareRTLSim())
else:
raise Exception("Unknown exec_mode")
# Run sim
test_in_transposed = test_in.numpy().transpose(_to_chan_last_args)
input_dict = {model.graph.input[0].name: test_in_transposed}
output_dict = oxe.execute_onnx(model, input_dict, True)
test_result = output_dict[model.graph.output[0].name]
output_matches = np.isclose(golden_result, test_result, atol=atol).all()
if exec_mode == "cppsim":
assert output_matches, "Cppsim output doesn't match ONNX/PyTorch."
elif exec_mode == "rtlsim":
assert output_matches, "Rtlsim output doesn't match ONNX/PyTorch."
