diff --git a/src/finn/custom_op/fpgadataflow/thresholding_batch.py b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
index 7b8d09c4ada1c062b4db6d5829f0bc480e751cda..fa33c70218fab16f106da45e296f0d59ae4ea606 100644
--- a/src/finn/custom_op/fpgadataflow/thresholding_batch.py
+++ b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
@@ -222,12 +222,7 @@ class Thresholding_Batch(HLSCustomOp):
inp_hls_str = self.get_input_datatype().get_hls_datatype_str()
out_hls_str = self.get_output_datatype().get_hls_datatype_str()
# fill in TSrcI
- # TODO checks/adjustments for bipolar inputs
- inp_is_bipolar = self.get_input_datatype() == DataType.BIPOLAR
- if inp_is_bipolar:
- ret["TSrcI"] = "Recast<Binary>"
- else:
- ret["TSrcI"] = "Slice<%s>" % inp_hls_str
+ ret["TSrcI"] = "Slice<%s>" % inp_hls_str
# fill in TDstI
ret["TDstI"] = "Slice<%s>" % out_hls_str
@@ -237,7 +232,7 @@ class Thresholding_Batch(HLSCustomOp):
"""Convert the original numpy weight matrix orig_weight_matrix into
a form suitable for passing to the hlslib call:
* ensure MH % PE == 0
- * for bipolar weights&inputs, ensure thresholds are positive
+ * for unsigned inputs, ensure thresholds are positive
* interleave rows between PEs
* reshape into (PE, TMEM, n_thres_steps) and return
"""
@@ -250,12 +245,11 @@ class Thresholding_Batch(HLSCustomOp):
), """Threshold matrix dimension is
not as expected (2)."""
n_thres_steps = orig_thres_matrix.shape[1]
- inp_is_bipolar = self.get_input_datatype() == DataType.BIPOLAR
- if inp_is_bipolar:
+ if not self.get_input_datatype().signed():
# ensure all thresholds are nonnegative
assert (orig_thres_matrix >= 0).all()
- # ensure all thresholds are integer
- assert (orig_thres_matrix.astype(np.int32) == orig_thres_matrix).all()
+ # ensure all thresholds are integer
+ assert (orig_thres_matrix.astype(np.int32) == orig_thres_matrix).all()
ret = orig_thres_matrix
# ensure channels = mh , duplicating if necessary
if ret.shape[0] == 1:
@@ -286,10 +280,6 @@ class Thresholding_Batch(HLSCustomOp):
threshold_tensor = self.get_hls_compatible_threshold_tensor(thresholds)
tdt = DataType.INT32
- # use UINT32 threshold export for bipolar times bipolar
- inp_is_bipolar = self.get_input_datatype() == DataType.BIPOLAR
- if inp_is_bipolar:
- tdt = DataType.UINT32
thresholds_hls_code = numpy_to_hls_code(
threshold_tensor, tdt, "thresholds", False, True
)
@@ -426,9 +416,6 @@ class Thresholding_Batch(HLSCustomOp):
def read_npy_data(self):
code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
dtype = self.get_input_datatype()
- if dtype == DataType.BIPOLAR:
- # use binary for bipolar storage
- dtype = DataType.BINARY
elem_bits = dtype.bitwidth()
packed_bits = self.get_instream_width()
packed_hls_type = "ap_uint<%d>" % packed_bits
diff --git a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
index d98f927bfeb7b23d945cd612d5125e6f2751891f..3ff86cab48d365c10e69bc2c764e8083c6a36880 100644
--- a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
+++ b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
@@ -33,6 +33,7 @@ from finn.transformation import Transformation
from finn.custom_op.registry import getCustomOp
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.infer_datatypes import InferDataTypes
+import finn.core.data_layout as DataLayout
class InferConvInpGen(Transformation):
@@ -414,25 +415,24 @@ class InferThresholdingLayer(Transformation):
thl_threshold = node.input[1]
thl_output = node.output[0]
thl_in_shape = model.get_tensor_shape(thl_input)
- thl_out_shape = model.get_tensor_shape(thl_output)
idt = model.get_tensor_datatype(thl_input)
# skip conversion for layers with float input
if not idt.is_integer():
continue
- # extract weight shape, note that ONNX and finn-hlslib
- # make different assumptions about dim order here
- # ONNX assumes W has (in, out) shape
- # finn-hlslib assumes W has (out, in) shape
+ # skip conversion if input is not NHWC or NC
+ thl_in_layout = model.get_tensor_layout(thl_input)
+ if thl_in_layout != DataLayout.NHWC and thl_in_layout != DataLayout.NC:
+ continue
+
+ # now safe to assume number of channels is in last dimension
ifc = int(thl_in_shape[-1])
# create node with no parallelization first
pe = 1
assert ifc % pe == 0, "Requirement IFC divisable by PE is violated."
odt = model.get_tensor_datatype(thl_output)
- model.set_tensor_shape(thl_input, thl_in_shape)
- model.set_tensor_shape(thl_output, thl_out_shape)
# create and insert new StreamingFCLayer node
new_node = helper.make_node(
"Thresholding_Batch",
diff --git a/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py b/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
index be32a365e6c05d92075877b95b7eff50ad1beed4..48803c9614f53a3a149c6eaac4289d10086513a5 100644
--- a/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
+++ b/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
@@ -39,6 +39,7 @@ from finn.core.modelwrapper import ModelWrapper
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_data_layouts import InferDataLayouts
from finn.transformation.streamline import Streamline
from finn.util.test import get_test_model_trained
from finn.transformation.double_to_single_float import DoubleToSingleFloat
@@ -71,6 +72,7 @@ def test_convert_to_hls_layers_cnv_w1a1(fused_activation):
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", "data/cifar10/cifar10-test-data-class3.npz")
diff --git a/tests/fpgadataflow/test_fpgadataflow_thresholding.py b/tests/fpgadataflow/test_fpgadataflow_thresholding.py
index 4975b06a4c4f051f4906e63d3bbad9951483ebbd..50b990f13494f22e985406791445b406e9946147 100644
--- a/tests/fpgadataflow/test_fpgadataflow_thresholding.py
+++ b/tests/fpgadataflow/test_fpgadataflow_thresholding.py
@@ -86,25 +86,16 @@ def make_single_thresholding_modelwrapper(T, pe, idt, odt):
return model
-def prepare_inputs(input_tensor, idt):
- if idt == DataType.BIPOLAR:
- # convert bipolar to binary
- return {"inp": (input_tensor + 1) / 2}
- else:
- return {"inp": input_tensor}
-
-
-# TODO binary/bipolar inputs/outputs
# activation: None or DataType
-@pytest.mark.parametrize("act", [DataType.INT4])
+@pytest.mark.parametrize("act", [DataType.INT4, DataType.BIPOLAR])
# input datatype
-@pytest.mark.parametrize("idt", [DataType.INT2, DataType.INT4])
+@pytest.mark.parametrize("idt", [DataType.INT16, DataType.UINT16])
# folding, -1 is maximum possible
@pytest.mark.parametrize("nf", [-1, 2, 1])
# number of input features
@pytest.mark.parametrize("ich", [16])
# execution mode
-@pytest.mark.parametrize("exec_mode", ["rtlsim", "cppsim"])
+@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
@pytest.mark.vivado
@pytest.mark.slow
def test_fpgadataflow_thresholding(idt, act, nf, ich, exec_mode):
@@ -121,11 +112,6 @@ def test_fpgadataflow_thresholding(idt, act, nf, ich, exec_mode):
T = np.random.randint(idt.min(), idt.max() + 1, (ich, n_steps)).astype(np.float32)
# provide non-decreasing thresholds
T = np.sort(T, axis=1)
- # generate thresholds for activation
- if idt == DataType.BIPOLAR:
- # bias thresholds to be positive
- T = np.ceil((T + ich) / 2)
- assert (T >= 0).all()
model = make_single_thresholding_modelwrapper(T, pe, idt, odt)
@@ -143,8 +129,8 @@ def test_fpgadataflow_thresholding(idt, act, nf, ich, exec_mode):
else:
raise Exception("Unknown exec_mode")
- # prepare input data
- input_dict = prepare_inputs(x, idt)
+ # package input data as dictionary
+ input_dict = {"inp": x}
y = multithreshold(x, T)
if act == DataType.BIPOLAR: