diff --git a/src/finn/core/execute_custom_node.py b/src/finn/core/execute_custom_node.py
index 0294e9ff6e0c59692a3e93d574bf8e95d9fad9e0..e8ad421e6bff58048e53a59ab3e0a527d7b67f4f 100644
--- a/src/finn/core/execute_custom_node.py
+++ b/src/finn/core/execute_custom_node.py
@@ -8,7 +8,7 @@ def execute_custom_node(node, context, graph):
try:
# lookup op_type in registry of CustomOps
inst = registry.custom_op[op_type](node)
- inst.execute_node(node, context, graph)
+ inst.execute_node(context, graph)
except KeyError:
# exception if op_type is not supported
raise Exception("Custom op_type %s is currently not supported." % op_type)
diff --git a/src/finn/custom_op/__init__.py b/src/finn/custom_op/__init__.py
index c15bc8eb0b4d5a6230240180d171f174e17e1dd5..e187ba5704cedd13e4bfe0d7241cdd6b18dfc2c9 100644
--- a/src/finn/custom_op/__init__.py
+++ b/src/finn/custom_op/__init__.py
@@ -8,13 +8,13 @@ class CustomOp(ABC):
# TODO consider specifying a list of allowed attributes
@abstractmethod
- def make_shape_compatible_op(self, node):
+ def make_shape_compatible_op(self):
pass
@abstractmethod
- def infer_node_datatype(self, node, model):
+ def infer_node_datatype(self, model):
pass
@abstractmethod
- def execute_node(self, node, context, graph):
+ def execute_node(self, context, graph):
pass
diff --git a/src/finn/custom_op/fpgadataflow/__init__.py b/src/finn/custom_op/fpgadataflow/__init__.py
index 3dfab19a01b31f3eddfb79938d4f15a2433cc3cc..dbceefb30f7886cfe481c236766c25a56ab3efa1 100644
--- a/src/finn/custom_op/fpgadataflow/__init__.py
+++ b/src/finn/custom_op/fpgadataflow/__init__.py
@@ -38,18 +38,19 @@ class HLSCustomOp(CustomOp):
self.tmp_dir = " "
@abstractmethod
- def get_attributes(self, node):
+ def get_attributes(self):
pass
- def code_generation(self, node):
- self.get_attributes(node)
- self.global_includes(node)
- self.defines(node)
- self.read_npy_data(node)
- self.strm_decl(node)
- self.docompute(node)
- self.dataoutstrm(node)
- self.save_as_npy(node)
+ def code_generation(self):
+ node = self.onnx_node
+ self.get_attributes()
+ self.global_includes()
+ self.defines()
+ self.read_npy_data()
+ self.strm_decl()
+ self.docompute()
+ self.dataoutstrm()
+ self.save_as_npy()
template = self.docompute_template
@@ -63,29 +64,29 @@ class HLSCustomOp(CustomOp):
f.close()
@abstractmethod
- def global_includes(self, node):
+ def global_includes(self):
pass
@abstractmethod
- def defines(self, node):
+ def defines(self):
pass
@abstractmethod
- def read_npy_data(self, node):
+ def read_npy_data(self):
pass
@abstractmethod
- def strm_decl(self, node):
+ def strm_decl(self):
pass
@abstractmethod
- def docompute(self, node):
+ def docompute(self):
pass
@abstractmethod
- def dataoutstrm(self, node):
+ def dataoutstrm(self):
pass
@abstractmethod
- def save_as_npy(self, node):
+ def save_as_npy(self):
pass
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index e8f75ce0219e2b2977b3f77629c12c4919a52ad2..b8540afcaccb4d73fecf9267adc009d5f7f78a98 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -16,13 +16,14 @@ class StreamingFCLayer_Batch(HLSCustomOp):
self.WMEM = 0
self.TMEM = 0
- def make_shape_compatible_op(self, node):
+ def make_shape_compatible_op(self):
pass
- def infer_node_datatype(self, node, model):
+ def infer_node_datatype(self, model):
pass
- def execute_node(self, node, context, graph):
+ def execute_node(self, context, graph):
+ node = self.onnx_node
# make temporary directory for generated files
self.tmp_dir = tmp.mkdtemp(prefix=str(node.op_type) + "_")
@@ -31,7 +32,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
temp_files = []
# get attributes for correct packing of weights and thresholds
- self.get_attributes(node)
+ self.get_attributes()
# create a npy file fore each input of the node (in_ind is input index)
in_ind = 0
@@ -89,7 +90,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
in_ind += 1
# code generation
- self.code_generation(node)
+ self.code_generation()
# c++ compilation and execution flow
temp_files.append("{}/execute_{}.cpp".format(self.tmp_dir, node.op_type))
@@ -114,7 +115,8 @@ class StreamingFCLayer_Batch(HLSCustomOp):
# for temp_file in temp_files:
# os.remove(temp_file)
- def get_attributes(self, node):
+ def get_attributes(self):
+ node = self.onnx_node
self.resType = utils.get_by_name(node.attribute, "resType").s.decode("utf-8")
self.MW = utils.get_by_name(node.attribute, "MW").i
self.MH = utils.get_by_name(node.attribute, "MH").i
@@ -124,7 +126,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
"utf-8"
)
- def global_includes(self, node):
+ def global_includes(self):
self.code_gen_dict["$GLOBALS$"] = '#include "weights.hpp" \n'
self.code_gen_dict["$GLOBALS$"] += '#include "activations.hpp" \n'
if self.WMEM != 0:
@@ -134,7 +136,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
# TODO find a better way of checking for no pregenerated thresholds
self.code_gen_dict["$GLOBALS$"] += '#include "thresh.h" \n'
- def defines(self, node):
+ def defines(self):
numReps = 2
self.code_gen_dict["$DEFINES$"] = [
"""#define MW1 {}\n #define MH1 {}\n #define SIMD1 {}\n
@@ -144,7 +146,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
)
]
- def read_npy_data(self, node):
+ def read_npy_data(self):
# c++ code to read out an npy file
# and put it in hls::stream in the correct order
self.code_gen_dict["$READNPYDATA$"] = []
@@ -175,7 +177,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
self.code_gen_dict["$READNPYDATA$"].append("in0 << dat0;")
self.code_gen_dict["$READNPYDATA$"].append("}")
- def strm_decl(self, node):
+ def strm_decl(self):
self.code_gen_dict["$STREAMDECLARATIONS$"] = []
self.code_gen_dict["$STREAMDECLARATIONS$"].append(
'hls::stream<ap_uint<{}>> in0 ("in0");'.format(self.SIMD)
@@ -184,7 +186,8 @@ class StreamingFCLayer_Batch(HLSCustomOp):
'hls::stream<ap_uint<{}>> out ("out");'.format(self.PE)
)
- def docompute(self, node):
+ def docompute(self):
+ node = self.onnx_node
self.code_gen_dict["$DOCOMPUTE$"] = [
"""{}<MW1, MH1, SIMD1, PE1, {}>
(in0, out, weights, threshs, numReps, {});""".format(
@@ -192,7 +195,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
)
]
- def dataoutstrm(self, node):
+ def dataoutstrm(self):
self.code_gen_dict["$DATAOUTSTREAM$"] = [
"ap_uint<{}> out_data;\n std::vector<ap_uint<{}>> out_data_vector;".format(
self.PE, self.PE
@@ -222,7 +225,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
)
self.code_gen_dict["$DATAOUTSTREAM$"].append("}")
- def save_as_npy(self, node):
+ def save_as_npy(self):
self.code_gen_dict["$SAVEASCNPY$"] = [
"""cnpy::npy_save("{}/output.npy",&output_data_vector[0],
{{1,{},{}}},"w");""".format(
diff --git a/src/finn/custom_op/fpgadataflow/streamingmaxpool.py b/src/finn/custom_op/fpgadataflow/streamingmaxpool.py
index e137951eaa63e9ec50c4cbf173fa4bf040d70a07..212f91ca5eaa6778e871e6b4bce1bb548eae60d2 100644
--- a/src/finn/custom_op/fpgadataflow/streamingmaxpool.py
+++ b/src/finn/custom_op/fpgadataflow/streamingmaxpool.py
@@ -9,13 +9,14 @@ from finn.custom_op.fpgadataflow import HLSCustomOp
class StreamingMaxPool(HLSCustomOp):
- def make_shape_compatible_op(self, node):
+ def make_shape_compatible_op(self):
pass
- def infer_node_datatype(self, node, model):
+ def infer_node_datatype(self, model):
pass
- def execute_node(self, node, context, graph):
+ def execute_node(self, context, graph):
+ node = self.onnx_node
# make temporary directory for generated files
self.tmp_dir = tmp.mkdtemp(prefix=str(node.op_type) + "_")
@@ -34,7 +35,7 @@ class StreamingMaxPool(HLSCustomOp):
in_ind += 1
# code generation
- self.code_generation(node)
+ self.code_generation()
# c++ compilation and execution flow
temp_files.append("{}/execute_{}.cpp".format(self.tmp_dir, node.op_type))
@@ -59,22 +60,24 @@ class StreamingMaxPool(HLSCustomOp):
# for temp_file in temp_files:
# os.remove(temp_file)
- def get_attributes(self, node):
+ def get_attributes(self):
+ node = self.onnx_node
self.ImgDim = get_by_name(node.attribute, "ImgDim").i
self.PoolDim = get_by_name(node.attribute, "PoolDim").i
self.NumChannels = get_by_name(node.attribute, "NumChannels").i
- def global_includes(self, node):
+ def global_includes(self):
self.code_gen_dict["$GLOBALS$"] = ['#include "maxpool.h"']
- def defines(self, node):
+ def defines(self):
self.code_gen_dict["$DEFINES$"] = [
"#define ImgDim {}\n #define PoolDim {}\n #define NumChannels {}".format(
self.ImgDim, self.PoolDim, self.NumChannels
)
]
- def read_npy_data(self, node):
+ def read_npy_data(self):
+ node = self.onnx_node
# c++ code to read out an npy file
# and put it in hls::stream in the correct order
self.code_gen_dict["$READNPYDATA$"] = []
@@ -113,7 +116,8 @@ class StreamingMaxPool(HLSCustomOp):
self.code_gen_dict["$READNPYDATA$"].append("}")
input_ind += 1
- def strm_decl(self, node):
+ def strm_decl(self):
+ node = self.onnx_node
self.code_gen_dict["$STREAMDECLARATIONS$"] = []
input_ind = 0
for inputs in node.input:
@@ -127,12 +131,13 @@ class StreamingMaxPool(HLSCustomOp):
'hls::stream<ap_uint<{}>> out ("out");'.format(self.NumChannels)
)
- def docompute(self, node):
+ def docompute(self):
+ node = self.onnx_node
self.code_gen_dict["$DOCOMPUTE$"] = [
"{}<ImgDim, PoolDim, NumChannels>(in0, out);".format(node.op_type)
]
- def dataoutstrm(self, node):
+ def dataoutstrm(self):
self.code_gen_dict["$DATAOUTSTREAM$"] = [
"ap_uint<{}> out_data;\n std::vector<ap_uint<{}>> out_data_vector;".format(
self.NumChannels, self.NumChannels
@@ -162,7 +167,7 @@ class StreamingMaxPool(HLSCustomOp):
)
self.code_gen_dict["$DATAOUTSTREAM$"].append("}")
- def save_as_npy(self, node):
+ def save_as_npy(self):
self.code_gen_dict["$SAVEASCNPY$"] = [
"""cnpy::npy_save("{}/output.npy",&output_data_vector[0],
{{{},{},{}}},"w");""".format(
diff --git a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
index c9b5b0b37726173c6a35179ec9107fb11fcdd8a3..11705e34c06cd045bc24a34eff14c548559e515b 100644
--- a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
@@ -9,13 +9,14 @@ from finn.custom_op.fpgadataflow import HLSCustomOp
class StreamingMaxPool_Batch(HLSCustomOp):
- def make_shape_compatible_op(self, node):
+ def make_shape_compatible_op(self):
pass
- def infer_node_datatype(self, node, model):
+ def infer_node_datatype(self, model):
pass
- def execute_node(self, node, context, graph):
+ def execute_node(self, context, graph):
+ node = self.onnx_node
# make temporary directory for generated files
self.tmp_dir = tmp.mkdtemp(prefix=str(node.op_type) + "_")
@@ -34,7 +35,7 @@ class StreamingMaxPool_Batch(HLSCustomOp):
in_ind += 1
# code generation
- self.code_generation(node)
+ self.code_generation()
# c++ compilation and execution flow
temp_files.append("{}/execute_{}.cpp".format(self.tmp_dir, node.op_type))
@@ -59,15 +60,16 @@ class StreamingMaxPool_Batch(HLSCustomOp):
# for temp_file in temp_files:
# os.remove(temp_file)
- def get_attributes(self, node):
+ def get_attributes(self):
+ node = self.onnx_node
self.ImgDim = get_by_name(node.attribute, "ImgDim").i
self.PoolDim = get_by_name(node.attribute, "PoolDim").i
self.NumChannels = get_by_name(node.attribute, "NumChannels").i
- def global_includes(self, node):
+ def global_includes(self):
self.code_gen_dict["$GLOBALS$"] = ['#include "maxpool.h"']
- def defines(self, node):
+ def defines(self):
numReps = 2
self.code_gen_dict["$DEFINES$"] = [
"""#define ImgDim {}\n #define PoolDim {}\n
@@ -76,7 +78,8 @@ class StreamingMaxPool_Batch(HLSCustomOp):
)
]
- def read_npy_data(self, node):
+ def read_npy_data(self):
+ node = self.onnx_node
# c++ code to read out an npy file
# and put it in hls::stream in the correct order
self.code_gen_dict["$READNPYDATA$"] = []
@@ -115,7 +118,8 @@ class StreamingMaxPool_Batch(HLSCustomOp):
self.code_gen_dict["$READNPYDATA$"].append("}")
input_ind += 1
- def strm_decl(self, node):
+ def strm_decl(self):
+ node = self.onnx_node
self.code_gen_dict["$STREAMDECLARATIONS$"] = []
input_ind = 0
for inputs in node.input:
@@ -129,12 +133,13 @@ class StreamingMaxPool_Batch(HLSCustomOp):
'hls::stream<ap_uint<{}>> out ("out");'.format(self.NumChannels)
)
- def docompute(self, node):
+ def docompute(self):
+ node = self.onnx_node
self.code_gen_dict["$DOCOMPUTE$"] = [
"{}<ImgDim, PoolDim, NumChannels>(in0, out, numReps);".format(node.op_type)
]
- def dataoutstrm(self, node):
+ def dataoutstrm(self):
self.code_gen_dict["$DATAOUTSTREAM$"] = [
"ap_uint<{}> out_data;\n std::vector<ap_uint<{}>> out_data_vector;".format(
self.NumChannels, self.NumChannels
@@ -164,7 +169,7 @@ class StreamingMaxPool_Batch(HLSCustomOp):
)
self.code_gen_dict["$DATAOUTSTREAM$"].append("}")
- def save_as_npy(self, node):
+ def save_as_npy(self):
numReps = 2
self.code_gen_dict["$SAVEASCNPY$"] = [
"""cnpy::npy_save("{}/output.npy",&output_data_vector[0],
diff --git a/src/finn/custom_op/multithreshold.py b/src/finn/custom_op/multithreshold.py
index 16c0c302725b539dc8a110b9a8d3e5dcc8b2102b..5d471db8e5e5df198b42403247f9a7b3b44fba59 100644
--- a/src/finn/custom_op/multithreshold.py
+++ b/src/finn/custom_op/multithreshold.py
@@ -58,10 +58,12 @@ def multithreshold(v, thresholds, out_scale=None, out_bias=None):
class MultiThreshold(CustomOp):
- def make_shape_compatible_op(self, node):
+ def make_shape_compatible_op(self):
+ node = self.onnx_node
return helper.make_node("Relu", [node.input[0]], [node.output[0]])
- def infer_node_datatype(self, node, model):
+ def infer_node_datatype(self, model):
+ node = self.onnx_node
try:
odt = get_by_name(node.attribute, "out_dtype").s.decode("utf-8")
model.set_tensor_datatype(node.output[0], DataType[odt])
@@ -72,7 +74,8 @@ class MultiThreshold(CustomOp):
odtype = DataType.get_smallest_possible(n_thres)
model.set_tensor_datatype(node.output[0], odtype)
- def execute_node(self, node, context, graph):
+ def execute_node(self, context, graph):
+ node = self.onnx_node
# save inputs
v = context[node.input[0]]
thresholds = context[node.input[1]]
diff --git a/src/finn/custom_op/xnorpopcount.py b/src/finn/custom_op/xnorpopcount.py
index 98ff123dad7bed0f87ed2ac3b03bb4727f5cb62e..134a299c1cad8a9433e0c101018960d7640b1fbf 100644
--- a/src/finn/custom_op/xnorpopcount.py
+++ b/src/finn/custom_op/xnorpopcount.py
@@ -31,19 +31,22 @@ def xnorpopcountmatmul(inp0, inp1):
class XnorPopcountMatMul(CustomOp):
- def make_shape_compatible_op(self, node):
+ def make_shape_compatible_op(self):
+ node = self.onnx_node
return helper.make_node(
"MatMul", [node.input[0], node.input[1]], [node.output[0]]
)
- def infer_node_datatype(self, node, model):
+ def infer_node_datatype(self, model):
+ node = self.onnx_node
# ensure inputs are binary
assert model.get_tensor_datatype(node.input[0]) == DataType["BINARY"]
assert model.get_tensor_datatype(node.input[1]) == DataType["BINARY"]
# XNOR-popcount produces unsigned integers, assume uint32
model.set_tensor_datatype(node.output[0], DataType["UINT32"])
- def execute_node(self, node, context, graph):
+ def execute_node(self, context, graph):
+ node = self.onnx_node
# save inputs
inp0 = context[node.input[0]]
inp1 = context[node.input[1]]
diff --git a/src/finn/transformation/infer_datatypes.py b/src/finn/transformation/infer_datatypes.py
index 83da0baacb04217c9ba56cd427f75b34ca5f764d..eaade9ebb7f09e39442e9cb8baba7c257720b45b 100644
--- a/src/finn/transformation/infer_datatypes.py
+++ b/src/finn/transformation/infer_datatypes.py
@@ -14,7 +14,7 @@ def _infer_node_datatype(model, node):
try:
# lookup op_type in registry of CustomOps
inst = registry.custom_op[op_type](node)
- inst.infer_node_datatype(node, model)
+ inst.infer_node_datatype(model)
except KeyError:
# exception if op_type is not supported
raise Exception("Custom op_type %s is currently not supported." % op_type)
diff --git a/src/finn/transformation/infer_shapes.py b/src/finn/transformation/infer_shapes.py
index eaf91dd7955fff68427f13d0b5d84388839d0ff9..2bddc44673bcd09305401a9d50033141795b00c9 100644
--- a/src/finn/transformation/infer_shapes.py
+++ b/src/finn/transformation/infer_shapes.py
@@ -13,7 +13,7 @@ def _make_shape_compatible_op(node):
try:
# lookup op_type in registry of CustomOps
inst = registry.custom_op[op_type](node)
- return inst.make_shape_compatible_op(node)
+ return inst.make_shape_compatible_op()
except KeyError:
# exception if op_type is not supported
raise Exception("Custom op_type %s is currently not supported." % op_type)