diff --git a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
index a221b510ab8d22f4daca1c32e717a9b482246712..797e8f22a006b405df1351c4f5e48b1b6d76620c 100644
--- a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
+++ b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
@@ -64,30 +64,28 @@ class InferConvInpGen(Transformation):
warnings.warn("Input is not int. Can't infer ConvInpGen")
continue
i2c_inst = getCustomOp(n)
- stride = i2c_inst.get_nodeattr("stride")
- k_attr = i2c_inst.get_nodeattr("kernel_size")
- k_h = k_attr[0]
- k_w = k_attr[1]
+ stride_h, stride_w = i2c_inst.get_nodeattr("stride")
+ k_h, k_w = i2c_inst.get_nodeattr("kernel_size")
pad_attr = i2c_inst.get_nodeattr("pad_amount")
pad_h = pad_attr[0] + pad_attr[2]
pad_w = pad_attr[1] + pad_attr[3]
+ dilation_h, dilation_w = i2c_inst.get_nodeattr("dilations")
# temporary checks until non-square conv support is finalized
- assert pad_h == pad_w, "Non-square images not yet supported."
- assert k_h == k_w, "Non-square kernels not yet supported."
- k = k_h
- pad = pad_attr[0]
pad_val = i2c_inst.get_nodeattr("pad_value")
depthwise = i2c_inst.get_nodeattr("depthwise")
ifm_ch = i2c_in_shape[-1]
- ifm_dim = i2c_in_shape[1]
- ofm_dim = i2c_out_shape[1]
+ ifm_dim_h = i2c_in_shape[1]
+ ifm_dim_w = i2c_in_shape[2]
+ ofm_dim_h = i2c_out_shape[1]
+ ofm_dim_w = i2c_out_shape[2]
# default params for ConvolutionInputGenerator
ConvInpGen_node_idx = node_ind
ConvInpGen_input = i2c_input
- ConvInpGen_idim = ifm_dim
+ ConvInpGen_idim_h = ifm_dim_h
+ ConvInpGen_idim_w = ifm_dim_w
- if pad > 0:
+ if pad_h > 0 or pad_w > 0:
# if padding enabled, ensure pad_val supported by DataType
# assert dt.allowed(pad_val),"""FMPadding_Batch DataType
# must support pad_val"""
@@ -95,12 +93,13 @@ class InferConvInpGen(Transformation):
pad_val == 0
), "FMPadding_Batch doesn't currently support pad_val!= 0"
- odim_padding = ifm_dim + 2 * pad
+ odim_padding_h = ifm_dim_h + pad_h
+ odim_padding_w = ifm_dim_w + pad_w
padding_out = helper.make_tensor_value_info(
model.make_new_valueinfo_name(),
TensorProto.FLOAT,
- (1, odim_padding, odim_padding, ifm_ch),
+ (1, odim_padding_h, odim_padding_w, ifm_ch),
)
graph.value_info.append(padding_out)
padding_out = padding_out.name
@@ -108,7 +107,8 @@ class InferConvInpGen(Transformation):
ConvInpGen_node_idx += 1
ConvInpGen_input = padding_out
- ConvInpGen_idim = odim_padding
+ ConvInpGen_idim_h = odim_padding_h
+ ConvInpGen_idim_w = odim_padding_w
padding_node = helper.make_node(
"FMPadding_Batch",
@@ -116,15 +116,31 @@ class InferConvInpGen(Transformation):
[padding_out],
domain="finn.custom_op.fpgadataflow",
backend="fpgadataflow",
- ImgDim=ifm_dim,
- Padding=2 * pad,
+ ImgDim=[ifm_dim_h, ifm_dim_w],
+ Padding=pad_attr,
NumChannels=ifm_ch,
inputDataType=dt.name,
SIMD=ifm_ch,
)
graph.node.insert(node_ind, padding_node)
- if stride > 1 and k == 1:
+ # Ensure that only supported HLS nodes are inserted
+ is_square_image = ConvInpGen_idim_h == ConvInpGen_idim_w
+ is_square_kernel = k_h == k_w
+ is_kernel_pointwise = k_h == 1 and k_w == 1
+ is_equal_stride = stride_h == stride_w
+ is_1d_convolution = (k_h == 1 and k_w > 1 and ifm_dim_h == 1) or (
+ k_h > 1 and k_w == 1 and ifm_dim_w == 1
+ )
+
+ 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
+ along different axes."""
+ ConvInpGen_idim = ConvInpGen_idim_h
+ stride = stride_h
# create DownSampler node
ConvInpGen_node = helper.make_node(
"DownSampler",
@@ -141,22 +157,58 @@ class InferConvInpGen(Transformation):
graph.node.insert(ConvInpGen_node_idx, ConvInpGen_node)
else:
# create equivalent ConvolutionInputGenerator node
- ConvInpGen_node = helper.make_node(
- "ConvolutionInputGenerator",
- [ConvInpGen_input],
- [i2c_output],
- domain="finn.custom_op.fpgadataflow",
- backend="fpgadataflow",
- ConvKernelDim=k,
- IFMChannels=ifm_ch,
- IFMDim=ConvInpGen_idim,
- OFMDim=ofm_dim,
- SIMD=ifm_ch,
- Stride=stride,
- inputDataType=dt.name,
- outputDataType=dt.name,
- depthwise=depthwise,
- )
+ 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
+ for (non-)square convolutions"""
+ assert (
+ dilation_h == 1 and dilation_w == 1
+ ), """Dilation value != 1 is not supported
+ for square convolutions"""
+ ConvInpGen_node = helper.make_node(
+ "ConvolutionInputGenerator",
+ [ConvInpGen_input],
+ [i2c_output],
+ domain="finn.custom_op.fpgadataflow",
+ backend="fpgadataflow",
+ ConvKernelDim=[k_h, k_w],
+ IFMChannels=ifm_ch,
+ IFMDim=[ConvInpGen_idim_h, ConvInpGen_idim_w],
+ OFMDim=[ofm_dim_h, ofm_dim_w],
+ SIMD=ifm_ch,
+ Stride=[stride_h, stride_w],
+ Dilation=[dilation_h, dilation_w],
+ inputDataType=dt.name,
+ outputDataType=dt.name,
+ depthwise=depthwise,
+ )
+ else: # non-square images and/or kernels
+ assert (
+ is_1d_convolution
+ ), "ConvultionInputGenerator1D works only for 1D convolutions"
+ 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
+ with dilation value greater than 1"""
+ ConvInpGen_node = helper.make_node(
+ "ConvolutionInputGenerator1D",
+ [ConvInpGen_input],
+ [i2c_output],
+ domain="finn.custom_op.fpgadataflow",
+ backend="fpgadataflow",
+ ConvKernelDim=[k_h, k_w],
+ IFMChannels=ifm_ch,
+ IFMDim=[ConvInpGen_idim_h, ConvInpGen_idim_w],
+ OFMDim=[ofm_dim_h, ofm_dim_w],
+ SIMD=ifm_ch,
+ Stride=[stride_h, stride_w],
+ Dilation=[dilation_h, dilation_w],
+ inputDataType=dt.name,
+ outputDataType=dt.name,
+ depthwise=depthwise,
+ )
graph.node.insert(ConvInpGen_node_idx, ConvInpGen_node)
# remove old nodes
graph.node.remove(n)
@@ -684,7 +736,7 @@ class InferVVAU(Transformation):
):
sparsity = model.get_tensor_sparsity(n.input[1])
try:
- k = sparsity["dw"]["kernel_shape"]
+ k_h, k_w = sparsity["dw"]["kernel_shape"]
except KeyError:
raise Exception(
"""Sparsity doesn't indicate that MatMul
@@ -702,25 +754,25 @@ class InferVVAU(Transformation):
mm_output = n.output[0]
W = model.get_initializer(mm_weight)
# infer dense weight tensor from sparse weight matrix
- # kernel size k which was extracted above and the value of
+ # kernel size (k_h, k_w) which was extracted above and the value of
# the channels is used.
- # the weight matrix has a shape of (k * k * Channels, Channels)
+ # the weight matrix has a shape of (k_h * k_w * Channels, Channels)
# we need to reverse the creation of the sparse weight matrix
- # to achieve a weight tensor of shape (Channels, 1, k, k)
+ # to achieve a weight tensor of shape (Channels, 1, k_h, k_w)
channels = int(W.shape[1])
- # transpose to achieve a shape of (k * k * Channels, Channels)
+ # transpose to achieve a shape of (k_h * k_w * Channels, Channels)
W = W.T
- # reshape to (Channels, k, k, Channels) to transpose afterwards
- # to (Channels, Channels, k, k)
- W = W.reshape(channels, k, k, channels)
+ # reshape to (Channels, k_h, k_w, Channels) to transpose afterwards
+ # to (Channels, Channels, k_h, k_w)
+ W = W.reshape(channels, k_h, k_w, channels)
W = W.transpose(0, 3, 1, 2)
# now we can extract the values using a for loop over the channels
# and fill a zero numpy array in the correct shape
- w_tensor = np.zeros((channels, 1, k, k))
+ w_tensor = np.zeros((channels, 1, k_h, k_w))
for ch in range(channels):
w_tensor[ch][0] = W[ch][ch]
model.set_initializer(mm_weight, w_tensor)
- model.set_tensor_shape(mm_weight, (channels, 1, k, k))
+ model.set_tensor_shape(mm_weight, (channels, 1, k_h, k_w))
# create node with pe=channels as default
pe = channels
assert (
@@ -762,9 +814,9 @@ class InferVVAU(Transformation):
backend="fpgadataflow",
resType="lut",
PE=pe,
- Dim=mm_in_shape[1],
+ Dim=[mm_in_shape[1], mm_in_shape[2]],
Channels=channels,
- Kernel=k,
+ Kernel=[k_h, k_w],
inputDataType=idt.name,
weightDataType=wdt.name,
outputDataType=odt.name,
@@ -790,9 +842,9 @@ class InferVVAU(Transformation):
backend="fpgadataflow",
resType="lut",
PE=pe,
- Dim=mm_in_shape[1],
+ Dim=[mm_in_shape[1], mm_in_shape[2]],
Channels=channels,
- Kernel=k,
+ Kernel=[k_h, k_w],
inputDataType=idt.name,
weightDataType=wdt.name,
outputDataType=odt.name,
@@ -1345,7 +1397,11 @@ class InferGlobalAccPoolLayer(Transformation):
)
model.graph.value_info.append(mul_value)
model.set_initializer(mul_value.name, np.array(1 / (vecs[1] * vecs[2])))
- new_mul = helper.make_node("Mul", [pool_out, mul_value.name], [result],)
+ new_mul = helper.make_node(
+ "Mul",
+ [pool_out, mul_value.name],
+ [result],
+ )
graph.node.insert(insert_point, new_pool)
graph.node.insert(insert_point + 1, new_mul)
node_ind += 1