diff --git a/src/finn/custom_op/fpgadataflow/duplicatestreams_batch.py b/src/finn/custom_op/fpgadataflow/duplicatestreams_batch.py
index 3b0fa55b0065e6ceeb8ad2eb7282a413adf443d7..08e48f435f7bf86e538c1b67ead2ec0f555eb727 100644
--- a/src/finn/custom_op/fpgadataflow/duplicatestreams_batch.py
+++ b/src/finn/custom_op/fpgadataflow/duplicatestreams_batch.py
@@ -29,7 +29,6 @@
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -46,6 +45,8 @@ class DuplicateStreams_Batch(HLSCustomOp):
my_attrs = {
"NumChannels": ("i", True, 0),
"PE": ("i", True, 0),
+ # how many duplicated output streams to create
+ "NumOutputStreams": ("i", True, 0),
# FINN DataTypes for input
"inputDataType": ("s", True, ""),
# number of input vectors, examples:
@@ -57,6 +58,9 @@ class DuplicateStreams_Batch(HLSCustomOp):
my_attrs.update(super().get_nodeattr_types())
return my_attrs
+ def get_num_output_streams(self):
+ return self.get_nodeattr("NumOutputStreams")
+
def get_normal_input_shape(self):
ch = self.get_nodeattr("NumChannels")
vecs = list(self.get_nodeattr("numInputVectors"))
@@ -82,26 +86,13 @@ class DuplicateStreams_Batch(HLSCustomOp):
exp_ishape = self.get_normal_input_shape()
ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
assert ishape == exp_ishape, "Unexpected input shape."
+ num_out = self.get_num_output_streams()
+ assert len(self.onnx_node.output) == num_out, "Unexpected number of outputs"
oshape = self.get_normal_output_shape()
- values = np.zeros(oshape).astype(np.float32)
- split_input = np.concatenate((values, values), axis=0)
-
- split_in = helper.make_tensor_value_info(
- model.make_new_valueinfo_name(), TensorProto.FLOAT, oshape
- )
-
- model.graph.value_info.append(split_in) # requires clean up
- model.set_initializer(split_in.name, split_input)
-
- shape_comp_node = helper.make_node(
- "Split",
- inputs=[split_in.name],
- outputs=[self.onnx_node.output[0], self.onnx_node.output[1]],
- axis=0,
- )
-
- return shape_comp_node
+ ret = super().make_const_shape_op(oshape)
+ ret.output[:] = self.onnx_node.output
+ return ret
def infer_node_datatype(self, model):
node = self.onnx_node
@@ -115,8 +106,8 @@ class DuplicateStreams_Batch(HLSCustomOp):
warnings.warn(warn_str)
self.set_nodeattr("inputDataType", idt.name)
odt = self.get_output_datatype()
- model.set_tensor_datatype(self.onnx_node.output[0], odt)
- model.set_tensor_datatype(self.onnx_node.output[1], odt)
+ for my_out in self.onnx_node.output:
+ model.set_tensor_datatype(my_out, odt)
def verify_node(self):
info_messages = []
@@ -133,6 +124,7 @@ class DuplicateStreams_Batch(HLSCustomOp):
self.get_nodeattr("executable_path")
self.get_nodeattr("NumChannels")
self.get_nodeattr("PE")
+ self.get_nodeattr("NumOutputStreams")
self.get_nodeattr("inputDataType")
info_messages.append("All necessary attributes exist")
except Exception:
@@ -165,12 +157,46 @@ class DuplicateStreams_Batch(HLSCustomOp):
return out_width
def get_number_output_values(self):
- return 2 * np.prod(self.get_folded_output_shape()[1:-1])
+ return self.get_num_output_streams() * np.prod(
+ self.get_folded_output_shape()[1:-1]
+ )
def get_exp_cycles(self):
# Channels/PE * batch size * fmdim * fmdim
return np.prod(self.get_folded_output_shape()[:-1])
+ def generate_params(self, model, path):
+ n_outputs = self.get_num_output_streams()
+ inp_streams = []
+ commands = []
+ o_stream_w = self.get_outstream_width()
+ i_stream_w = self.get_instream_width()
+ in_stream = "hls::stream<ap_uint<%d> > &in0" % (i_stream_w)
+ inp_streams.append(in_stream)
+ commands.append("ap_uint<%d> e = in0.read();" % i_stream_w)
+ iters = self.get_number_output_values() // self.get_num_output_streams()
+ for i in range(n_outputs):
+ out_stream = "hls::stream<ap_uint<%d> > &out%d" % (o_stream_w, i)
+ inp_streams.append(out_stream)
+ cmd = "out%d.write(e);" % i
+ commands.append(cmd)
+
+ impl_hls_code = []
+ impl_hls_code.append("void DuplicateStreamsCustom(")
+ impl_hls_code.append(",".join(inp_streams))
+ impl_hls_code.append(") {")
+ impl_hls_code.append("for(unsigned int i = 0; i < %d; i++) {" % iters)
+ impl_hls_code.append("#pragma HLS PIPELINE II=1")
+ impl_hls_code.append("\n".join(commands))
+ impl_hls_code.append("}")
+ impl_hls_code.append("}")
+ impl_hls_code = "\n".join(impl_hls_code)
+
+ impl_filename = "{}/duplicate_impl.hpp".format(path)
+ f_impl = open(impl_filename, "w")
+ f_impl.write(impl_hls_code)
+ f_impl.close()
+
def execute_node(self, context, graph):
mode = self.get_nodeattr("exec_mode")
node = self.onnx_node
@@ -178,6 +204,7 @@ class DuplicateStreams_Batch(HLSCustomOp):
exp_oshape = self.get_normal_output_shape()
folded_ishape = self.get_folded_input_shape()
folded_oshape = self.get_folded_output_shape()
+ n_outputs = self.get_num_output_streams()
if mode == "cppsim":
code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
@@ -205,17 +232,15 @@ class DuplicateStreams_Batch(HLSCustomOp):
# execute the precompiled model
super().exec_precompiled_singlenode_model()
# load output npy file
- super().npy_to_dynamic_outputs(context, ["output0.npy", "output1.npy"])
- assert (
- context[node.output[0]].shape == folded_oshape
- ), "cppsim \
- did not produce expected ofolded utput shape"
- assert (
- context[node.output[1]].shape == folded_oshape
- ), "cppsim \
- did not produce expected ofolded utput shape"
- context[node.output[0]] = context[node.output[0]].reshape(*exp_oshape)
- context[node.output[1]] = context[node.output[1]].reshape(*exp_oshape)
+ super().npy_to_dynamic_outputs(
+ context, ["output%d.npy" % i for i in range(n_outputs)]
+ )
+ for i in range(n_outputs):
+ assert (
+ context[node.output[i]].shape == folded_oshape
+ ), "cppsim \
+ did not produce expected ofolded utput shape"
+ context[node.output[i]] = context[node.output[i]].reshape(*exp_oshape)
elif mode == "rtlsim":
sim = self.get_rtlsim()
nbits = self.get_instream_width()
@@ -226,41 +251,30 @@ class DuplicateStreams_Batch(HLSCustomOp):
super().toggle_clk(sim)
rtlsim_dict = {
"inputs": {"in0": rtlsim_inp},
- "outputs": {"out0": [], "out1": []},
+ "outputs": {},
}
+ for i in range(n_outputs):
+ rtlsim_dict["outputs"]["out%d" % i] = []
self.rtlsim_multi_io(sim, rtlsim_dict)
odt = self.get_output_datatype()
target_bits = odt.bitwidth()
packed_bits = self.get_outstream_width()
out_shape = self.get_folded_output_shape()
+ for i in range(n_outputs):
+ out_npy_path = "%s/output%d.npy" % (code_gen_dir, i)
+ rtlsim_output_to_npy(
+ rtlsim_dict["outputs"]["out%d" % i],
+ out_npy_path,
+ odt,
+ out_shape,
+ packed_bits,
+ target_bits,
+ )
+ # load and reshape output 0
+ output = np.load(out_npy_path)
+ output = np.asarray([output], dtype=np.float32).reshape(*exp_oshape)
+ context[node.output[i]] = output
- out_npy_path = "{}/output0.npy".format(code_gen_dir)
- rtlsim_output_to_npy(
- rtlsim_dict["outputs"]["out0"],
- out_npy_path,
- odt,
- out_shape,
- packed_bits,
- target_bits,
- )
- # load and reshape output 0
- output = np.load(out_npy_path)
- output = np.asarray([output], dtype=np.float32).reshape(*exp_oshape)
- context[node.output[0]] = output
-
- out_npy_path = "{}/output1.npy".format(code_gen_dir)
- rtlsim_output_to_npy(
- rtlsim_dict["outputs"]["out1"],
- out_npy_path,
- odt,
- out_shape,
- packed_bits,
- target_bits,
- )
- # load and reshape output 1
- output = np.load(out_npy_path)
- output = np.asarray([output], dtype=np.float32).reshape(*exp_oshape)
- context[node.output[1]] = output
else:
raise Exception(
"""Invalid value for attribute exec_mode! Is currently set to: {}
@@ -277,7 +291,7 @@ class DuplicateStreams_Batch(HLSCustomOp):
), """Output1 shape doesn't match expected shape."""
def global_includes(self):
- self.code_gen_dict["$GLOBALS$"] = ['#include "streamtools.h"']
+ self.code_gen_dict["$GLOBALS$"] = ['#include "duplicate_impl.hpp"']
def defines(self, var):
self.code_gen_dict["$DEFINES$"] = []
@@ -298,24 +312,23 @@ class DuplicateStreams_Batch(HLSCustomOp):
)
def strm_decl(self):
+ n_outputs = self.get_num_output_streams()
self.code_gen_dict["$STREAMDECLARATIONS$"] = []
self.code_gen_dict["$STREAMDECLARATIONS$"].append(
'hls::stream<ap_uint<{}>> in0 ("in0");'.format(self.get_instream_width())
)
- self.code_gen_dict["$STREAMDECLARATIONS$"].append(
- 'hls::stream<ap_uint<{}>> out0 ("out0");'.format(self.get_outstream_width())
- )
- self.code_gen_dict["$STREAMDECLARATIONS$"].append(
- 'hls::stream<ap_uint<{}>> out1 ("out1");'.format(self.get_outstream_width())
- )
+ for i in range(n_outputs):
+ out_name = "out%d" % i
+ self.code_gen_dict["$STREAMDECLARATIONS$"].append(
+ 'hls::stream<ap_uint<%d>> %s ("%s");'
+ % (self.get_outstream_width(), out_name, out_name)
+ )
def docompute(self):
- self.code_gen_dict["$DOCOMPUTE$"] = [
- """DuplicateStreams_Batch<{}, {}> (in0, out0, out1, 1);""".format(
- self.get_outstream_width(),
- self.get_number_output_values() // 2,
- )
- ]
+ n_outputs = self.get_num_output_streams()
+ ostreams = ["out%d" % x for x in range(n_outputs)]
+ dc = "DuplicateStreamsCustom(in0, %s);" % (",".join(ostreams))
+ self.code_gen_dict["$DOCOMPUTE$"] = [dc]
def dataoutstrm(self):
code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
@@ -325,62 +338,67 @@ class DuplicateStreams_Batch(HLSCustomOp):
packed_hls_type = "ap_uint<%d>" % packed_bits
elem_hls_type = dtype.get_hls_datatype_str()
npy_type = "float"
- npy_out = "%s/output0.npy" % code_gen_dir
- npy_out1 = "%s/output1.npy" % code_gen_dir
+ n_outputs = self.get_num_output_streams()
oshape = self.get_folded_output_shape()
oshape_cpp_str = str(oshape).replace("(", "{").replace(")", "}")
-
- self.code_gen_dict["$DATAOUTSTREAM$"] = [
- 'apintstream2npy<%s, %s, %d, %s>(out0, %s, "%s");'
- % (
- packed_hls_type,
- elem_hls_type,
- elem_bits,
- npy_type,
- oshape_cpp_str,
- npy_out,
+ outstrm_code = []
+
+ for i in range(n_outputs):
+ out_name = "out%d" % i
+ npy_out = "%s/output%d.npy" % (code_gen_dir, i)
+ outstrm_code.append(
+ 'apintstream2npy<%s, %s, %d, %s>(%s, %s, "%s");'
+ % (
+ packed_hls_type,
+ elem_hls_type,
+ elem_bits,
+ npy_type,
+ out_name,
+ oshape_cpp_str,
+ npy_out,
+ )
)
- ]
- self.code_gen_dict["$DATAOUTSTREAM$"] += [
- 'apintstream2npy<%s, %s, %d, %s>(out1, %s, "%s");'
- % (
- packed_hls_type,
- elem_hls_type,
- elem_bits,
- npy_type,
- oshape_cpp_str,
- npy_out1,
- )
- ]
+ self.code_gen_dict["$DATAOUTSTREAM$"] = outstrm_code
def save_as_npy(self):
self.code_gen_dict["$SAVEASCNPY$"] = []
def blackboxfunction(self):
+ n_outputs = self.get_num_output_streams()
+ inp_streams = []
+ o_stream_w = self.get_outstream_width()
+ i_stream_w = self.get_instream_width()
+ in_stream = "hls::stream<ap_uint<%d> > &in0" % (i_stream_w)
+ inp_streams.append(in_stream)
+ for i in range(n_outputs):
+ out_stream = "hls::stream<ap_uint<%d> > &out%d" % (o_stream_w, i)
+ inp_streams.append(out_stream)
+
self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
- """void {}(hls::stream<ap_uint<{}>> &in0,
- hls::stream<ap_uint<{}>> &out0,
- hls::stream<ap_uint<{}>> &out1)""".format(
+ """void {}({})""".format(
self.onnx_node.name,
- self.get_instream_width(),
- self.get_outstream_width(),
- self.get_outstream_width(),
+ ",".join(inp_streams),
)
]
def pragmas(self):
+ n_outputs = self.get_num_output_streams()
self.code_gen_dict["$PRAGMAS$"] = ["#pragma HLS INTERFACE axis port=in0"]
- self.code_gen_dict["$PRAGMAS$"].append("#pragma HLS INTERFACE axis port=out0")
- self.code_gen_dict["$PRAGMAS$"].append("#pragma HLS INTERFACE axis port=out1")
+ for i in range(n_outputs):
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS INTERFACE axis port=out%d" % i
+ )
self.code_gen_dict["$PRAGMAS$"].append(
"#pragma HLS INTERFACE ap_ctrl_none port=return"
)
def get_verilog_top_module_intf_names(self):
intf_names = super().get_verilog_top_module_intf_names()
- intf_names["m_axis"] = [
- ("out0_V_V", self.get_outstream_width_padded()),
- ("out1_V_V", self.get_outstream_width_padded()),
- ]
+ n_outputs = self.get_num_output_streams()
+ intf_names["m_axis"] = []
+ for i in range(n_outputs):
+ intf_names["m_axis"].append(
+ ("out%d_V_V", self.get_outstream_width_padded())
+ )
return intf_names
diff --git a/tests/fpgadataflow/test_fpgadataflow_duplicatestreams.py b/tests/fpgadataflow/test_fpgadataflow_duplicatestreams.py
index 73bf1165afa9418be0c89f77797de538275fd220..4592fc2f8a2e908a91dd74fef58fb03467f9e7c0 100644
--- a/tests/fpgadataflow/test_fpgadataflow_duplicatestreams.py
+++ b/tests/fpgadataflow/test_fpgadataflow_duplicatestreams.py
@@ -61,6 +61,7 @@ def make_dupstreams_modelwrapper(ch, pe, idim, idt):
domain="finn.custom_op.fpgadataflow",
backend="fpgadataflow",
NumChannels=ch,
+ NumOutputStreams=2,
PE=pe,
inputDataType=idt.name,
numInputVectors=[1, idim, idim],