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Commit b5ca2f70 authored by Felix Jentzsch's avatar Felix Jentzsch
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Initial support for new 1D SWG in case of full SIMD unfolding

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src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
+ 89
− 46
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...@@ -127,8 +127,12 @@ class ConvolutionInputGenerator1D(HLSCustomOp): ...@@ -127,8 +127,12 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
ofm_dim_h = compute_conv_output_dim(ifm_dim_h, k_h, stride_h, pad, dilation_h) ofm_dim_h = compute_conv_output_dim(ifm_dim_h, k_h, stride_h, pad, dilation_h)
ofm_dim_w = compute_conv_output_dim(ifm_dim_w, k_w, stride_w, pad, dilation_w) ofm_dim_w = compute_conv_output_dim(ifm_dim_w, k_w, stride_w, pad, dilation_w)
assert ifm_ch % simd == 0, "SIMD must divide IFMChannels" assert ifm_ch % simd == 0, "SIMD must divide IFMChannels"
wf = int((k_h * k_w * ifm_ch) // simd) if self.use_parallel_window_output():
folded_oshape = (1, ofm_dim_h, ofm_dim_w, wf, simd) wf = int((ifm_ch) // simd)
folded_oshape = (1, ofm_dim_h, ofm_dim_w, wf, k_h * k_w * simd)
else:
wf = int((k_h * k_w * ifm_ch) // simd)
folded_oshape = (1, ofm_dim_h, ofm_dim_w, wf, simd)
return folded_oshape return folded_oshape
def make_shape_compatible_op(self, model): def make_shape_compatible_op(self, model):
...@@ -156,8 +160,6 @@ class ConvolutionInputGenerator1D(HLSCustomOp): ...@@ -156,8 +160,6 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
return DataType[self.get_nodeattr("outputDataType")] return DataType[self.get_nodeattr("outputDataType")]
def get_instream_width(self): def get_instream_width(self):
"""Returns stream width, input and output stream width are equal for
the sliding window function"""
ibits = self.get_input_datatype().bitwidth() ibits = self.get_input_datatype().bitwidth()
simd = self.get_nodeattr("SIMD") simd = self.get_nodeattr("SIMD")
ifm_ch = self.get_nodeattr("IFMChannels") ifm_ch = self.get_nodeattr("IFMChannels")
...@@ -166,10 +168,13 @@ class ConvolutionInputGenerator1D(HLSCustomOp): ...@@ -166,10 +168,13 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
return in_width return in_width
def get_outstream_width(self): def get_outstream_width(self):
"""Returns stream width, input and output stream width are equal for if self.use_parallel_window_output():
the sliding window function, so the function to determine the input # feed all window pixels in parallel
stream width can be reused.""" k_h, k_w = self.get_nodeattr("ConvKernelDim")
return self.get_instream_width() return self.get_instream_width() * k_h * k_w
else:
# if parallel variant not in use: same width for output and input stream
return self.get_instream_width()
def get_number_output_values(self): def get_number_output_values(self):
folded_oshape = self.get_folded_output_shape() folded_oshape = self.get_folded_output_shape()
...@@ -205,6 +210,22 @@ class ConvolutionInputGenerator1D(HLSCustomOp): ...@@ -205,6 +210,22 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
return (ifm_ch, ifm_dim, ofm_dim, k, stride, dilation) return (ifm_ch, ifm_dim, ofm_dim, k, stride, dilation)
def use_parallel_window_output(self):
# Check if simple "ConvolutionInputGenerator_1D_parallel" variant can be used to
# feed window in parallel to the following layer, enabling full SIMD unfolding.
stride = self.get_nodeattr("Stride")
dilation = self.get_nodeattr("Dilation")
stride_h, stride_w = stride
dilation_h, dilation_w = dilation
if self.get_nodeattr("SIMD") == self.get_nodeattr("IFMChannels"):
if self.get_nodeattr("depthwise") == 0:
if stride_h == 1 and stride_w == 1:
if dilation_h == 1 and dilation_w == 1:
return True
else:
return False
def get_exp_cycles(self): def get_exp_cycles(self):
simd = self.get_nodeattr("SIMD") simd = self.get_nodeattr("SIMD")
( (
...@@ -224,12 +245,15 @@ class ConvolutionInputGenerator1D(HLSCustomOp): ...@@ -224,12 +245,15 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
# since mmv != 1 is not supported yet, we set mmv for now to 1 # since mmv != 1 is not supported yet, we set mmv for now to 1
mmv = 1 mmv = 1
# see https://github.com/Xilinx/finn-hlslib/blob/master/slidingwindow.h # see https://github.com/Xilinx/finn-hlslib/blob/master/slidingwindow.h
cycles_write_block = (ofm_dim_w * k_w * k_h * (ifm_ch / simd)) / mmv if self.use_parallel_window_output():
cycles_read_block = stride_w * ifm_dim_w * (ifm_ch / simd) exp_cycles = k_w + ofm_dim_w
max_cycles = max(cycles_write_block, cycles_read_block) else:
exp_cycles = ( cycles_write_block = (ofm_dim_w * k_w * k_h * (ifm_ch / simd)) / mmv
ifm_dim_w * k_h * dilation_h * (ifm_ch / simd) + ofm_dim_h * max_cycles cycles_read_block = stride_w * ifm_dim_w * (ifm_ch / simd)
) max_cycles = max(cycles_write_block, cycles_read_block)
exp_cycles = (
ifm_dim_w * k_h * dilation_h * (ifm_ch / simd) + ofm_dim_h * max_cycles
)
return int(exp_cycles) return int(exp_cycles)
...@@ -522,39 +546,49 @@ class ConvolutionInputGenerator1D(HLSCustomOp): ...@@ -522,39 +546,49 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
"ultra": "ap_resource_uram()", "ultra": "ap_resource_uram()",
} }
hls_ram_style = map_to_hls_ram_style[ram_style] hls_ram_style = map_to_hls_ram_style[ram_style]
hls_call = "ConvolutionInputGenerator"
# check which ConvolutionInputGenerator is needed
dilation_h, dilation_w = self.get_nodeattr("Dilation")
hls_call += "_NonSquare" # check which ConvolutionInputGenerator is needed
if dilation_h > 1 or dilation_w > 1: if self.use_parallel_window_output():
hls_call += "_Dilated" hls_call = "ConvolutionInputGenerator_1D_parallel"
if self.get_nodeattr("depthwise") == 1:
hls_call += "_dws"
self.code_gen_dict["$DOCOMPUTE$"] = [
"""{}<ConvKernelDim1_x, ConvKernelDim1_y, IFMChannels1, Input_precision1,
IFMDim1_x, IFMDim1_y, OFMDim1_x, OFMDim1_y, SIMD1, Stride1_x, Stride1_y,
Dilation1_x, Dilation1_y> (in0, out, numReps, {});""".format(
hls_call, hls_ram_style
)
]
elif self.get_nodeattr("depthwise") == 1:
hls_call += "_dws"
self.code_gen_dict["$DOCOMPUTE$"] = [ self.code_gen_dict["$DOCOMPUTE$"] = [
"""{}<ConvKernelDim1_x, ConvKernelDim1_y, IFMChannels1, Input_precision1, """{}<ConvKernelDim1_x, IFMChannels1, Input_precision1,
IFMDim1_x, IFMDim1_y, OFMDim1_x, OFMDim1_y, SIMD1, Stride1_x, Stride1_y> IFMDim1_x, OFMDim1_x, SIMD1, Stride1_x>
(in0, out, numReps, {});""".format( (in0, out, numReps, {});""".format(
hls_call, hls_ram_style hls_call, hls_ram_style
) )
] ]
else: else:
self.code_gen_dict["$DOCOMPUTE$"] = [ hls_call = "ConvolutionInputGenerator_NonSquare"
"""{}<ConvKernelDim1_x, ConvKernelDim1_y, IFMChannels1, Input_precision1, dilation_h, dilation_w = self.get_nodeattr("Dilation")
IFMDim1_x, IFMDim1_y, OFMDim1_x, OFMDim1_y, SIMD1, Stride1_x, Stride1_y> if dilation_h > 1 or dilation_w > 1:
(in0, out, numReps, {});""".format( hls_call += "_Dilated"
hls_call, hls_ram_style if self.get_nodeattr("depthwise") == 1:
) hls_call += "_dws"
] self.code_gen_dict["$DOCOMPUTE$"] = [
"""{}<ConvKernelDim1_x, ConvKernelDim1_y, IFMChannels1,
Input_precision1, IFMDim1_x, IFMDim1_y, OFMDim1_x, OFMDim1_y,
SIMD1, Stride1_x, Stride1_y, Dilation1_x, Dilation1_y>
(in0, out, numReps, {});""".format(
hls_call, hls_ram_style
)
]
elif self.get_nodeattr("depthwise") == 1:
hls_call += "_dws"
self.code_gen_dict["$DOCOMPUTE$"] = [
"""{}<ConvKernelDim1_x, ConvKernelDim1_y, IFMChannels1,
Input_precision1, IFMDim1_x, IFMDim1_y, OFMDim1_x, OFMDim1_y,
SIMD1, Stride1_x, Stride1_y> (in0, out, numReps, {});""".format(
hls_call, hls_ram_style
)
]
else:
self.code_gen_dict["$DOCOMPUTE$"] = [
"""{}<ConvKernelDim1_x, ConvKernelDim1_y, IFMChannels1,
Input_precision1, IFMDim1_x, IFMDim1_y, OFMDim1_x, OFMDim1_y,
SIMD1, Stride1_x, Stride1_y> (in0, out, numReps, {});""".format(
hls_call, hls_ram_style
)
]
def dataoutstrm(self): def dataoutstrm(self):
code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim") code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
...@@ -587,12 +621,21 @@ class ConvolutionInputGenerator1D(HLSCustomOp): ...@@ -587,12 +621,21 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
self.code_gen_dict["$SAVEASCNPY$"] = [] self.code_gen_dict["$SAVEASCNPY$"] = []
def blackboxfunction(self): def blackboxfunction(self):
self.code_gen_dict["$BLACKBOXFUNCTION$"] = [ if self.use_parallel_window_output():
"""void {}(hls::stream<ap_uint<SIMD1*Input_precision1>> &in0, self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
hls::stream<ap_uint<SIMD1*Input_precision1>> &out)""".format( """void {}(hls::stream<ap_uint<SIMD1*Input_precision1>> &in0,
self.onnx_node.name hls::stream<ap_uint<ConvKernelDim1_x*SIMD1*Input_precision1>>
) &out)""".format(
] self.onnx_node.name
)
]
else:
self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
"""void {}(hls::stream<ap_uint<SIMD1*Input_precision1>> &in0,
hls::stream<ap_uint<SIMD1*Input_precision1>> &out)""".format(
self.onnx_node.name
)
]
def pragmas(self): def pragmas(self):
self.code_gen_dict["$PRAGMAS$"] = ["#pragma HLS INTERFACE axis port=in0"] self.code_gen_dict["$PRAGMAS$"] = ["#pragma HLS INTERFACE axis port=in0"]
......
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