diff --git a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
index f4c31eb495ce352bb1e593b1f4dfc62e77ac924f..cf25567a85f04e778901be3e7484075022cdadf9 100644
--- a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
+++ b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
@@ -61,7 +61,9 @@ class InferConvInpGen(Transformation):
i2c_out_shape = model.get_tensor_shape(i2c_output)
dt = model.get_tensor_datatype(i2c_input)
if not dt.is_integer():
- warnings.warn("Input is not int. Can't infer ConvInpGen")
+ warnings.warn(
+ "%s : Input is not int. Can't infer ConvInpGen." % n.name
+ )
continue
i2c_inst = getCustomOp(n)
stride_h, stride_w = i2c_inst.get_nodeattr("stride")
@@ -89,9 +91,10 @@ class InferConvInpGen(Transformation):
# if padding enabled, ensure pad_val supported by DataType
# assert dt.allowed(pad_val),"""FMPadding_Batch DataType
# must support pad_val"""
- assert (
- pad_val == 0
- ), "FMPadding_Batch doesn't currently support pad_val!= 0"
+ assert pad_val == 0, (
+ "%s : FMPadding_Batch doesn't currently support pad_val!= 0"
+ % n.name
+ )
odim_padding_h = ifm_dim_h + pad_h
odim_padding_w = ifm_dim_w + pad_w
@@ -121,6 +124,7 @@ class InferConvInpGen(Transformation):
NumChannels=ifm_ch,
inputDataType=dt.name,
SIMD=ifm_ch,
+ name="FMPadding_Batch_" + n.name,
)
graph.node.insert(node_ind, padding_node)
@@ -134,11 +138,15 @@ class InferConvInpGen(Transformation):
)
if (stride_h > 1 or stride_w > 1) and is_kernel_pointwise:
- assert (
- is_square_image
- ), "DownSampler currently only supports square input images."
- assert is_equal_stride, """DownSampler currently only supports equal stride value
+ assert is_square_image, (
+ "%s : DownSampler currently only supports square input images."
+ % n.name
+ )
+ assert is_equal_stride, (
+ """%s : DownSampler currently only supports equal stride value
along different axes."""
+ % n.name
+ )
ConvInpGen_idim = ConvInpGen_idim_h
stride = stride_h
# create DownSampler node
@@ -153,6 +161,7 @@ class InferConvInpGen(Transformation):
SIMD=ifm_ch,
Stride=stride,
inputDataType=dt.name,
+ name="DownSampler_" + n.name,
)
graph.node.insert(ConvInpGen_node_idx, ConvInpGen_node)
else:
@@ -160,12 +169,16 @@ class InferConvInpGen(Transformation):
if (
is_square_image and is_square_kernel
): # square images and square kernels
- assert is_equal_stride, """Non-equal strides along different axes is not supported
+ assert is_equal_stride, (
+ """%s: Non-equal strides along different axes is not supported
for (non-)square convolutions"""
- assert (
- dilation_h == 1 and dilation_w == 1
- ), """Dilation value != 1 is not supported
+ % n.name
+ )
+ assert dilation_h == 1 and dilation_w == 1, (
+ """%s: Dilation value != 1 is not supported
for square convolutions"""
+ % n.name
+ )
ConvInpGen_node = helper.make_node(
"ConvolutionInputGenerator",
[ConvInpGen_input],
@@ -182,16 +195,19 @@ class InferConvInpGen(Transformation):
inputDataType=dt.name,
outputDataType=dt.name,
depthwise=depthwise,
+ name="ConvolutionInputGenerator_" + n.name,
)
else: # non-square images and/or kernels
- assert (
- is_1d_convolution
- ), "ConvultionInputGenerator1D works only for 1D convolutions"
+ assert is_1d_convolution, (
+ "%s: ConvolutionInputGenerator1D works only for 1D convs"
+ % n.name
+ )
if dilation_h > 1 or dilation_w > 1:
- assert (
- stride_h == 1 and stride_w == 1
- ), """Stride value of greater than 1 is not supported for convolutions
+ assert stride_h == 1 and stride_w == 1, (
+ """%s: Stride value of greater than 1 is not supported for convolutions
with dilation value greater than 1"""
+ % n.name
+ )
ConvInpGen_node = helper.make_node(
"ConvolutionInputGenerator1D",
[ConvInpGen_input],
@@ -208,6 +224,7 @@ class InferConvInpGen(Transformation):
inputDataType=dt.name,
outputDataType=dt.name,
depthwise=depthwise,
+ name="ConvolutionInputGenerator1D_" + n.name,
)
graph.node.insert(ConvInpGen_node_idx, ConvInpGen_node)
# remove old nodes
@@ -242,41 +259,49 @@ class InferUpsample(Transformation):
dt = model.get_tensor_datatype(n.input[0])
if not dt.is_integer():
warnings.warn(
- "Input is not int. Can't infer UpsampleNearestNeighbour_Batch."
+ "%s: Input not int. Can't infer UpsampleNearestNeighbour."
+ % n.name
+ )
+ continue
+
+ if model.get_tensor_layout(n.input[0]) != DataLayout.NHWC:
+ warnings.warn(
+ "%s: Input not NHWC. Can't infer UpsampleNearestNeighbour."
+ % n.name
)
continue
# Check that the parameters are okay
- assert (
- mode == "nearest"
- ), "Upsampling is only supported for the mode nearest."
+ assert mode == "nearest", (
+ "%s: Upsampling is only supported for the mode nearest." % n.name
+ )
assert len(in_shape) == 4, "Upsampling is only supported for 4D inputs."
- assert scales.shape == (
- 4,
- ), "Upsampling is only supported for 4D scales."
+ assert scales.shape == (4,), (
+ "%s: Upsampling is only supported for 4D scales." % n.name
+ )
assert (scales >= 1).all(), (
- "Upsampling is only supported for sacles "
+ n.name + ": Upsampling is only supported for scales "
"which are larger or equal 1 in all dimensions."
)
- # Assumes nchw layout for scales parameter
- # ToDo: Might need a change to nhwc later on?
- assert (
- scales[2] == scales[3]
- ), "Upsampling is only supported for qadratic scales."
- assert scales[0] == scales[1] == 1, (
- "Upsampling is only supported for sacles with "
- "the first two dimensions being 1."
+ # Assumes nhwc layout for scales and input
+ assert scales[1] == scales[2], (
+ "%s: Upsampling is only supported for quadratic scales." % n.name
+ )
+ assert scales[0] == scales[3] == 1, (
+ n.name + ": Upsampling is only supported for scales with "
+ "the first and last dimensions being 1."
)
+ spatial_scale = scales[1]
- # Assumes nhwc layout
- assert (
- in_shape[1] == in_shape[2]
- ), "Upsampling is only supported for qadratic input shapes."
+ assert in_shape[1] == in_shape[2], (
+ "%s: Upsampling is only supported for quadratic input shapes."
+ % n.name
+ )
# Extract information for HLS node
IFMDim = in_shape[1]
- OFMDim = int(round(in_shape[1] * scales[2]))
+ OFMDim = int(round(in_shape[1] * spatial_scale))
NumChannels = in_shape[-1]
numInputVectors = in_shape[0]
inputDataType = dt.name
@@ -293,6 +318,7 @@ class InferUpsample(Transformation):
NumChannels=NumChannels,
inputDataType=inputDataType,
numInputVectors=numInputVectors,
+ name="UpsampleNearestNeighbour_Batch_" + n.name,
)
# Remove the old node
@@ -338,6 +364,7 @@ class InferStreamingMaxPool(Transformation):
NumChannels=ifm_ch,
ImgDim=ifm_dim,
dataType=dt.name,
+ name="StreamingMaxPool_Batch_" + n.name,
)
graph.node.insert(node_ind, new_node)
# remove old nodes
@@ -360,7 +387,7 @@ class InferPool_Batch(Transformation):
graph_modified = False
for n in graph.node:
node_ind += 1
- if n.op_type in ["MaxPool", "QuantAvgPool2d"]:
+ if n.op_type in ["MaxPool", "QuantAvgPool2d", "MaxPoolNHWC"]:
# extract pool parameters
if n.op_type == "MaxPool":
@@ -373,6 +400,15 @@ class InferPool_Batch(Transformation):
k = inst.get_nodeattr("kernel")
stride = inst.get_nodeattr("stride")
dlayout = inst.get_nodeattr("data_layout")
+ elif n.op_type == "MaxPoolNHWC":
+ inst = getCustomOp(n)
+ k_shape = inst.get_nodeattr("kernel_shape")
+ strides = inst.get_nodeattr("strides")
+ assert k_shape[0] == k_shape[1]
+ assert strides[0] == strides[1]
+ k = k_shape[0]
+ stride = strides[0]
+ dlayout = "NHWC"
try:
pad = get_by_name(n.attribute, "pads").ints[-1]
except AttributeError:
@@ -389,7 +425,8 @@ class InferPool_Batch(Transformation):
continue
elif k == stride:
warnings.warn(
- """Inferring Pool_Batch node for k == stride.
+ n.name
+ + """: Inferring Pool_Batch node for k == stride.
This case can be optimized.
For example, for MaxPool run InferStreamingMaxPool before
InferPool_Batch """
@@ -450,7 +487,7 @@ class InferPool_Batch(Transformation):
accum_bits = 0
pool_size_param = k
pad_value = 0
- if n.op_type == "MaxPool":
+ if n.op_type in ["MaxPool", "MaxPoolNHWC"]:
pool_fxn = "MaxPool"
odt = idt
pad_value = idt.min()
@@ -480,6 +517,7 @@ class InferPool_Batch(Transformation):
pad_value=pad_value,
depthwise=1,
input_shape="(1,{},{},{})".format(ifm_dim, ifm_dim, ifm_ch),
+ name="Im2Col_" + n.name,
)
# Warning PE has to be equal to ifm_ch until Im2Col is replaced by
@@ -502,6 +540,7 @@ class InferPool_Batch(Transformation):
AccumBits=accum_bits,
Size=pool_size_param,
BatchSize=1,
+ name="Pool_Batch_" + n.name,
)
if dlayout == "NCHW":
@@ -550,14 +589,16 @@ class InferBinaryStreamingFCLayer(Transformation):
mm_output = n.output[0]
mm_in_shape = model.get_tensor_shape(mm_input)
mm_out_shape = model.get_tensor_shape(mm_output)
- assert (
- model.get_tensor_datatype(mm_input) == DataType.BINARY
- ), """First
+ assert model.get_tensor_datatype(mm_input) == DataType.BINARY, (
+ n.name
+ + """: First
input for xnorpopcount is not set to FINN DataType BINARY."""
- assert (
- model.get_tensor_datatype(mm_weight) == DataType.BINARY
- ), """Second
+ )
+ assert model.get_tensor_datatype(mm_weight) == DataType.BINARY, (
+ n.name
+ + """: Second
input (weights) for xnorpopcount is not set to FINN DataType BINARY."""
+ )
idt = DataType.BINARY
wdt = DataType.BINARY
mm_output = n.output[0]
@@ -571,13 +612,12 @@ class InferBinaryStreamingFCLayer(Transformation):
# create node with no parallelization first
pe = 1
simd = 1
- assert mh % pe == 0, "Requirement MH divisable by PE is violated."
- assert mw % simd == 0, "Requirement MW divisable by SIMD is violated."
wmem = mw * mh // (pe * simd)
- assert (
- mw * mh == wmem * pe * simd
- ), """Requirement (MW * MH) divisiable by
+ assert mw * mh == wmem * pe * simd, (
+ n.name
+ + """: Requirement (MW * MH) divisiable by
(WMEM * PE * SIMD) is violated."""
+ )
# see if we have any following thresholds
consumer = model.find_consumer(mm_output)
if consumer is not None and consumer.op_type == "MultiThreshold":
@@ -587,10 +627,11 @@ class InferBinaryStreamingFCLayer(Transformation):
mt_out_shape = model.get_tensor_shape(mt_output)
mt_thres = consumer.input[1]
T = model.get_initializer(mt_thres)
- assert (
- T.shape[0] == 1 or T.shape[0] == mh
- ), """First dimension of
+ assert T.shape[0] == 1 or T.shape[0] == mh, (
+ consumer.name
+ + """: First dimension of
thresholds neither 1 nor MH."""
+ )
odt = model.get_tensor_datatype(mt_output)
if odt.bitwidth() == 1:
# covers both bipolar and binary
@@ -618,6 +659,7 @@ class InferBinaryStreamingFCLayer(Transformation):
noActivation=0,
numInputVectors=list(mm_in_shape[:-1]),
mem_mode=self.mem_mode,
+ name=n.name,
)
graph.node.insert(node_ind, new_node)
# remove old nodes
@@ -648,6 +690,7 @@ class InferBinaryStreamingFCLayer(Transformation):
noActivation=1,
numInputVectors=list(mm_in_shape[:-1]),
mem_mode=self.mem_mode,
+ name=n.name,
)
graph.node.insert(node_ind, new_node)
# remove old node
@@ -695,15 +738,12 @@ class InferQuantizedStreamingFCLayer(Transformation):
# create node with no parallelization first
pe = 1
simd = 1
- assert mh % pe == 0, "Requirement MH divisable by PE is violated."
- assert (
- mw % simd == 0
- ), "Requirement MW divisable by SIMD is violated."
wmem = mw * mh // (pe * simd)
- assert (
- mw * mh == wmem * pe * simd
- ), """Requirement (MW * MH) divisible by
+ assert mw * mh == wmem * pe * simd, (
+ n.name
+ + """: Requirement (MW * MH) divisible by
(WMEM * PE * SIMD) is violated."""
+ )
# see if we have any following thresholds
consumer = model.find_consumer(mm_output)
if consumer is not None and consumer.op_type == "MultiThreshold":
@@ -713,27 +753,30 @@ class InferQuantizedStreamingFCLayer(Transformation):
mt_out_shape = model.get_tensor_shape(mt_output)
mt_thres = consumer.input[1]
T = model.get_initializer(mt_thres)
- assert (
- T.shape[0] == 1 or T.shape[0] == mh
- ), """First dimension of
+ assert T.shape[0] == 1 or T.shape[0] == mh, (
+ consumer.name
+ + """: First dimension of
thresholds neither 1 nor MH."""
+ )
odt = model.get_tensor_datatype(mt_output)
scale = getCustomOp(consumer).get_nodeattr("out_scale")
actval = getCustomOp(consumer).get_nodeattr("out_bias")
- assert (
- int(actval) == actval
- ), "out_bias must be integer for HLS conversion."
+ assert int(actval) == actval, (
+ consumer.name
+ + ": out_bias must be integer for HLS conversion."
+ )
actval = int(actval)
odt_is_bipolar = odt == DataType.BIPOLAR
bipolar_ok = (
odt_is_bipolar and (scale == 2.0) and (actval == -1)
)
- assert (
- scale == 1.0 or bipolar_ok
- ), "out_scale = 1.0 or bipolar output needed for conversion."
- assert (not odt.signed()) or (
- actval < 0
- ), "Signed output requres actval < 0"
+ assert scale == 1.0 or bipolar_ok, (
+ consumer.name
+ + ": out_scale=1 or bipolar output needed for conversion."
+ )
+ assert (not odt.signed()) or (actval < 0), (
+ consumer.name + ": Signed output requres actval < 0"
+ )
model.set_tensor_shape(mm_input, mm_in_shape)
model.set_tensor_shape(mt_output, mt_out_shape)
if bipolar_ok:
@@ -759,6 +802,7 @@ class InferQuantizedStreamingFCLayer(Transformation):
noActivation=0,
numInputVectors=list(mm_in_shape[:-1]),
mem_mode=self.mem_mode,
+ name="StreamingFCLayer_Batch_" + n.name,
)
graph.node.insert(node_ind, new_node)
# remove old nodes
@@ -789,6 +833,7 @@ class InferQuantizedStreamingFCLayer(Transformation):
noActivation=1,
numInputVectors=list(mm_in_shape[:-1]),
mem_mode=self.mem_mode,
+ name="StreamingFCLayer_Batch_" + n.name,
)
graph.node.insert(node_ind, new_node)
# remove old node
@@ -823,7 +868,8 @@ class InferVVAU(Transformation):
k_h, k_w = sparsity["dw"]["kernel_shape"]
except KeyError:
raise Exception(
- """Sparsity doesn't indicate that MatMul
+ n.name
+ + """: sparsity annotation doesn't indicate that MatMul
belongs to a depthwise convolution."""
)
@@ -859,9 +905,6 @@ class InferVVAU(Transformation):
model.set_tensor_shape(mm_weight, (channels, 1, k_h, k_w))
# create node with pe=channels as default
pe = channels
- assert (
- channels % pe == 0
- ), "Requirement Channels divisable by PE is violated."
# see if we have any following thresholds
consumer = model.find_consumer(mm_output)
if consumer is not None and consumer.op_type == "MultiThreshold":
@@ -870,23 +913,26 @@ class InferVVAU(Transformation):
mt_out_shape = model.get_tensor_shape(mt_output)
mt_thres = consumer.input[1]
T = model.get_initializer(mt_thres)
- assert (
- T.shape[0] == 1 or T.shape[0] == channels
- ), """First dimension of
+ assert T.shape[0] == 1 or T.shape[0] == channels, (
+ consumer.name
+ + """: First dimension of
thresholds neither 1 nor Channels."""
+ )
odt = model.get_tensor_datatype(mt_output)
scale = getCustomOp(consumer).get_nodeattr("out_scale")
- assert (
- scale == 1.0
- ), "out_scale must be equal to 1.0 for HLS conversion."
+ assert scale == 1.0, (
+ consumer.name
+ + ": out_scale must be equal to 1.0 for HLS conversion."
+ )
actval = getCustomOp(consumer).get_nodeattr("out_bias")
- assert (
- int(actval) == actval
- ), "out_bias must be integer for HLS conversion."
+ assert int(actval) == actval, (
+ consumer.name
+ + ": out_bias must be integer for HLS conversion."
+ )
actval = int(actval)
- assert (not odt.signed()) or (
- actval < 0
- ), "Signed output requres actval < 0"
+ assert (not odt.signed()) or (actval < 0), (
+ consumer.name + ": Signed output requres actval < 0"
+ )
model.set_tensor_shape(mm_input, mm_in_shape)
model.set_tensor_shape(mt_output, mt_out_shape)
# create and insert new Vector_Vector_Activate_Batch node
@@ -906,6 +952,7 @@ class InferVVAU(Transformation):
outputDataType=odt.name,
ActVal=actval,
noActivation=0,
+ name="Vector_Vector_Activate_Batch_" + n.name,
)
graph.node.insert(node_ind, new_node)
# remove old nodes
@@ -934,6 +981,7 @@ class InferVVAU(Transformation):
outputDataType=odt.name,
ActVal=0,
noActivation=1,
+ name="Vector_Vector_Activate_Batch_" + n.name,
)
graph.node.insert(node_ind, new_node)
# remove old node
@@ -991,21 +1039,22 @@ class InferThresholdingLayer(Transformation):
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)
scale = getCustomOp(node).get_nodeattr("out_scale")
- assert (
- scale == 1.0
- ), "MultiThreshold out_scale must be equal to 1.0 for HLS conversion."
+ assert scale == 1.0, (
+ node.name
+ + ": MultiThreshold out_scale must be 1 for HLS conversion."
+ )
actval = getCustomOp(node).get_nodeattr("out_bias")
- assert (
- int(actval) == actval
- ), "MultiThreshold out_bias must be integer for HLS conversion."
+ assert int(actval) == actval, (
+ node.name
+ + ": MultiThreshold out_bias must be integer for HLS conversion."
+ )
actval = int(actval)
- assert (not odt.signed()) or (
- actval < 0
- ), "Signed output requres actval < 0"
+ assert (not odt.signed()) or (actval < 0), (
+ node.name + ": Signed output requres actval < 0"
+ )
# create and insert new Thresholding_Batch node
new_node = helper.make_node(
"Thresholding_Batch",
@@ -1022,6 +1071,7 @@ class InferThresholdingLayer(Transformation):
numInputVectors=list(thl_in_shape[:-1]),
ActVal=actval,
mem_mode=self.mem_mode,
+ name="Thresholding_Batch_" + node.name,
)
graph.node.insert(insert_point, new_node)
# remove old node
@@ -1095,9 +1145,6 @@ class InferAddStreamsLayer(Transformation):
num_channels = int(in0_shape[-1])
# create node with no parallelization first
pe = 1
- assert (
- num_channels % pe == 0
- ), "Requirement Channels divisable by PE is violated."
# create and insert new StreamingFCLayer node
new_node = helper.make_node(
@@ -1110,6 +1157,7 @@ class InferAddStreamsLayer(Transformation):
PE=pe,
inputDataType=idt.name,
numInputVectors=in0_shape[:-1],
+ name="AddStreams_Batch_" + node.name,
)
graph.node.insert(insert_point, new_node)
# remove old node
@@ -1156,9 +1204,6 @@ class InferDuplicateStreamsLayer(Transformation):
# create node with no parallelization first
pe = 1
- assert (
- num_ch % pe == 0
- ), "Requirement channels divisable by PE is violated."
dup_node = helper.make_node(
"DuplicateStreams_Batch",
@@ -1170,6 +1215,7 @@ class InferDuplicateStreamsLayer(Transformation):
PE=pe,
inputDataType=dt.name,
numInputVectors=vecs,
+ name="DuplicateStreams_Batch_" + node.name,
)
graph.node.insert(node_ind, dup_node)
@@ -1338,6 +1384,7 @@ class InferChannelwiseLinearLayer(Transformation):
paramDataType=pdt.name,
outputDataType=odt.name,
numInputVectors=list(ll_in_shape[:-1]),
+ name="ChannelwiseOp_Batch_" + node.name,
)
graph.node.insert(insert_point, new_node)
# remove old node
@@ -1380,9 +1427,6 @@ class InferLabelSelectLayer(Transformation):
num_inp_vecs = list(fc_in_shape[:-1])
# create node with no parallelization first
pe = 1
- assert (
- num_labels % pe == 0
- ), "Requirement Labels divisable by PE is violated."
k = model.get_initializer(k_input)[0]
@@ -1398,6 +1442,7 @@ class InferLabelSelectLayer(Transformation):
K=k,
inputDataType=idt.name,
numInputVectors=num_inp_vecs,
+ name="LabelSelect_Batch_" + node.name,
)
graph.node.insert(node_ind, new_node)
# remove old node
@@ -1451,9 +1496,6 @@ class InferGlobalAccPoolLayer(Transformation):
vecs = in0_shape[:-1]
# create node with no parallelization first
pe = 1
- assert (
- num_ch % pe == 0
- ), "Requirement Labels divisable by PE is violated."
# create an additional tensor of the same shape and layout as result
out_shape = model.get_tensor_shape(result)
@@ -1474,6 +1516,7 @@ class InferGlobalAccPoolLayer(Transformation):
PE=pe,
inputDataType=idt.name,
numInputVectors=vecs,
+ name="GlobalAccPool_Batch_" + node.name,
)
mul_value = helper.make_tensor_value_info(