diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index a7c31cb3e995080680fdd75beec0122c4c6c073f..983b1b8fa681ac64e5b82839441b71a5d6a38c4a 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -280,13 +280,13 @@ class StreamingFCLayer_Batch(HLSCustomOp):
out_hls_str = self.get_output_datatype().get_hls_datatype_str()
wt_hls_str = self.get_weight_datatype().get_hls_datatype_str()
inp_is_binary = self.get_input_datatype() == DataType.BINARY
- out_is_binary = self.get_output_datatype() == DataType.BINARY
+ # out_is_binary = self.get_output_datatype() == DataType.BINARY
wt_is_binary = self.get_weight_datatype() == DataType.BINARY
bin_xnor_mode = self.get_nodeattr("binaryXnorMode") == 1
if (inp_is_binary or wt_is_binary) and (not bin_xnor_mode):
raise Exception("True binary (non-bipolar) inputs not yet supported")
inp_is_bipolar = self.get_input_datatype() == DataType.BIPOLAR
- out_is_bipolar = self.get_output_datatype() == DataType.BIPOLAR
+ # out_is_bipolar = self.get_output_datatype() == DataType.BIPOLAR
wt_is_bipolar = self.get_weight_datatype() == DataType.BIPOLAR
# reinterpret inp/wt as bipolar if bin_xnor_mode is iset
inp_is_bipolar = inp_is_bipolar or (inp_is_binary and bin_xnor_mode)
@@ -312,19 +312,19 @@ class StreamingFCLayer_Batch(HLSCustomOp):
elif mem_mode == "decoupled":
if inp_is_bipolar and wt_is_bipolar:
ret["TSrcI"] = "Recast<XnorMul>"
- ret["TWeightI"] = "Identity"
+ ret["TWeightI"] = "Identity"
elif (not inp_is_bipolar) and wt_is_bipolar:
ret["TSrcI"] = "Slice<%s>" % inp_hls_str
ret["TWeightI"] = "Recast<Binary>"
- #ret["TWeightI"] = "Recast<Binary>"
+ # ret["TWeightI"] = "Recast<Binary>"
elif inp_is_bipolar and (not wt_is_bipolar):
ret["TSrcI"] = "Recast<Binary>"
- ret["TWeightI"] = "Slice<%s>" % wt_hls_str
- #ret["TWeightI"] = "Slice<%s>" % wt_hls_str
+ ret["TWeightI"] = "Slice<%s>" % wt_hls_str
+ # ret["TWeightI"] = "Slice<%s>" % wt_hls_str
elif (not inp_is_bipolar) and (not wt_is_bipolar):
ret["TSrcI"] = "Slice<%s>" % inp_hls_str
ret["TWeightI"] = "Slice<%s>" % wt_hls_str
-
+
# fill in TDstI
ret["TDstI"] = "Slice<%s>" % out_hls_str
@@ -453,7 +453,9 @@ class StreamingFCLayer_Batch(HLSCustomOp):
else:
f_weights.write(
"static BinaryWeights<{},{},{}> weights = ".format(
- self.get_nodeattr("SIMD"), self.get_nodeattr("PE"), self.calc_wmem()
+ self.get_nodeattr("SIMD"),
+ self.get_nodeattr("PE"),
+ self.calc_wmem(),
)
)
f_weights.write(weight_hls_code)
@@ -469,16 +471,16 @@ class StreamingFCLayer_Batch(HLSCustomOp):
# reshape weight tensor to desired shape
pe = self.get_nodeattr("PE")
simd = self.get_nodeattr("SIMD")
- weight_tensor = weight_tensor.reshape(1, -1, pe*simd)
+ weight_tensor = weight_tensor.reshape(1, -1, pe * simd)
weight_tensor = weight_tensor.copy()
np.save(
- os.path.join(code_gen_dir, "weights.npy"),
- weight_tensor,
- )
+ os.path.join(code_gen_dir, "weights.npy"), weight_tensor,
+ )
else:
- raise Exception("""Please set mem_mode to "const"i or "decoupled", currently no other
- parameter value is supported!""")
-
+ raise Exception(
+ """Please set mem_mode to "const"i or "decoupled", currently no other
+ parameter value is supported!"""
+ )
# save thresholds in thresh.h
if len(self.onnx_node.input) > 2:
@@ -645,20 +647,21 @@ class StreamingFCLayer_Batch(HLSCustomOp):
def global_includes(self):
self.code_gen_dict["$GLOBALS$"] = ['#include "weights.hpp"']
self.code_gen_dict["$GLOBALS$"] += ['#include "activations.hpp"']
-
+
mem_mode = self.get_nodeattr("mem_mode")
if mem_mode == "const":
self.code_gen_dict["$GLOBALS$"] += ['#include "params.h"']
elif mem_mode == "decoupled":
self.code_gen_dict["$GLOBALS$"] += ['#include "mvau.hpp"']
else:
- raise Exception("""Please set mem_mode to "const" or "decoupled", currently no other
- parameter value is supported!""")
+ raise Exception(
+ """Please set mem_mode to "const" or "decoupled", currently no other
+ parameter value is supported!"""
+ )
if self.calc_tmem() != 0:
# TODO find a better way of checking for no pregenerated thresholds
self.code_gen_dict["$GLOBALS$"] += ['#include "thresh.h"']
-
def defines(self, var):
mem_mode = self.get_nodeattr("mem_mode")
numReps = 1
@@ -682,9 +685,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
if mem_mode == "decoupled":
wdt = self.get_weight_datatype()
self.code_gen_dict["$DEFINES$"].append(
- "#define WP1 {}\n".format(
- wdt.bitwidth()
- )
+ "#define WP1 {}\n".format(wdt.bitwidth())
)
def read_npy_data(self):
@@ -734,10 +735,9 @@ class StreamingFCLayer_Batch(HLSCustomOp):
if mem_mode == "decoupled":
self.code_gen_dict["$STREAMDECLARATIONS$"].append(
'hls::stream<ap_uint<{}>> weights ("weights");'.format(
- self.get_weightstream_width()
+ self.get_weightstream_width()
)
)
-
def docompute(self):
mem_mode = self.get_nodeattr("mem_mode")
@@ -770,13 +770,13 @@ class StreamingFCLayer_Batch(HLSCustomOp):
threshs,
self.get_nodeattr("resType"),
)
- ]
+ ]
else:
- raise Exception("""Please set mem_mode to "const" or "decoupled", currently no other
- parameter value is supported!""")
-
-
+ raise Exception(
+ """Please set mem_mode to "const" or "decoupled", currently no other
+ parameter value is supported!"""
+ )
def dataoutstrm(self):
code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
@@ -823,14 +823,18 @@ class StreamingFCLayer_Batch(HLSCustomOp):
)
]
else:
- raise Exception("""Please set mem_mode to "const", currently no other
- parameter value is supported!""")
+ raise Exception(
+ """Please set mem_mode to "const", currently no other
+ parameter value is supported!"""
+ )
def pragmas(self):
mem_mode = self.get_nodeattr("mem_mode")
if mem_mode == "const":
self.code_gen_dict["$PRAGMAS$"] = ["#pragma HLS INTERFACE axis port=in0"]
- self.code_gen_dict["$PRAGMAS$"].append("#pragma HLS INTERFACE axis port=out")
+ self.code_gen_dict["$PRAGMAS$"].append(
+ "#pragma HLS INTERFACE axis port=out"
+ )
in_fifo_depth = self.get_nodeattr("inFIFODepth")
out_fifo_depth = self.get_nodeattr("outFIFODepth")
# insert depth pragmas only if specified
@@ -854,7 +858,8 @@ class StreamingFCLayer_Batch(HLSCustomOp):
)
)
# the threshold tensor is acc_type [PE][TMEM][N_THRES]
- # partition for parallel access along PE and N_THRES dimensions (dims 1 and 3)
+ # partition for parallel access along PE and N_THRES
+ # dimensions (dims 1 and 3)
if self.calc_tmem() != 0:
# TODO find a better way of checking for no pregenerated thresholds
self.code_gen_dict["$PRAGMAS$"].append(
@@ -870,5 +875,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
)
)
else:
- raise Exception("""Please set mem_mode to "const", currently no other
- parameter value is supported!""")
+ raise Exception(
+ """Please set mem_mode to "const", currently no other
+ parameter value is supported!"""
+ )
diff --git a/tests/fpgadataflow/test_fpgadataflow_fclayer.py b/tests/fpgadataflow/test_fpgadataflow_fclayer.py
index c53b735dd92eb2b700846aa0526f193059e0fef2..4201f64c963cb506305fd9b9a9fa32f66ae74226 100644
--- a/tests/fpgadataflow/test_fpgadataflow_fclayer.py
+++ b/tests/fpgadataflow/test_fpgadataflow_fclayer.py
@@ -27,7 +27,6 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import pytest
-import csv
import numpy as np
from onnx import TensorProto, helper
@@ -131,7 +130,7 @@ def prepare_inputs(input_tensor, idt, wdt):
# mem_mode: const or decoupled
-@pytest.mark.parametrize("mem_mode", ["const", "decoupled"])
+@pytest.mark.parametrize("mem_mode", ["const", "decoupled"])
# activation: None or DataType
@pytest.mark.parametrize("act", [None, DataType.BIPOLAR, DataType.INT2])
# weight datatype
@@ -209,8 +208,8 @@ def test_fpgadataflow_fclayer_npysim(mem_mode, idt, wdt, act, nf, sf, mw, mh):
y_expected = y.reshape(oshape)
# execute model
y_produced = oxe.execute_onnx(model, input_dict)["outp"]
-
- y_produced =y_produced.reshape(y_expected.shape)
+
+ y_produced = y_produced.reshape(y_expected.shape)
assert (y_produced == y_expected).all(), "npysim failed"