diff --git a/src/finn/analysis/fpgadataflow/hls_synth_res_estimation.py b/src/finn/analysis/fpgadataflow/hls_synth_res_estimation.py
index c7db5b1d9d22ea89740f4c82633c96746a6fa5ee..7b4ca37cd78c6299fa824ecfc16d79ae013bab37 100644
--- a/src/finn/analysis/fpgadataflow/hls_synth_res_estimation.py
+++ b/src/finn/analysis/fpgadataflow/hls_synth_res_estimation.py
@@ -30,7 +30,7 @@ import os
import xml.etree.ElementTree as ET
import finn.custom_op.registry as registry
-import finn.util.basic as util
+from finn.util.fpgadataflow import is_fpgadataflow_node
def hls_synth_res_estimation(model):
@@ -40,36 +40,30 @@ def hls_synth_res_estimation(model):
res_dict = {}
for node in model.graph.node:
- if node.domain == "finn":
- backend_attribute = util.get_by_name(node.attribute, "backend")
- if backend_attribute is None:
- continue
- backend_value = backend_attribute.s.decode("UTF-8")
- if backend_value == "fpgadataflow":
- op_type = node.op_type
- inst = registry.custom_op[op_type](node)
- code_gen_dir = inst.get_nodeattr("code_gen_dir_ipgen")
- if code_gen_dir == "":
- raise Exception(
- """Please run "CodeGen_ipgen" transformation and
- "HLSSynth_IPGen" first to generate the report files"""
- )
+ if is_fpgadataflow_node(node) is True:
+ op_type = node.op_type
+ inst = registry.custom_op[op_type](node)
+ code_gen_dir = inst.get_nodeattr("code_gen_dir_ipgen")
+ if code_gen_dir == "":
+ raise Exception(
+ """Please run "CodeGen_ipgen" transformation and
+ "HLSSynth_IPGen" first to generate the report files"""
+ )
+ else:
+ xmlfile = "{}/project_{}/sol1/syn/report/{}_csynth.xml".format(
+ code_gen_dir, node.name, node.name
+ )
+
+ if os.path.isfile(xmlfile):
+ res_dict[node.name] = dict()
+ tree = ET.parse(xmlfile)
+ root = tree.getroot()
+ for item in root.findall("AreaEstimates/Resources"):
+ for child in item:
+ res_dict[node.name][child.tag] = child.text
else:
- xmlfile = "{}/project_{}/sol1/syn/report/{}_csynth.xml".format(
- code_gen_dir, node.name, node.name
+ raise Exception(
+ """Please run "HLSSynth_IPGen" first
+ to generate the report files"""
)
-
- if os.path.isfile(xmlfile):
- res_dict[node.name] = dict()
- tree = ET.parse(xmlfile)
- root = tree.getroot()
- for item in root.findall("AreaEstimates/Resources"):
- for child in item:
- res_dict[node.name][child.tag] = child.text
- else:
- raise Exception(
- """Please run "HLSSynth_IPGen" first
- to generate the report files"""
- )
-
return res_dict
diff --git a/src/finn/analysis/fpgadataflow/res_estimation.py b/src/finn/analysis/fpgadataflow/res_estimation.py
index 3585868906fb2c66aef045f49f0da919f933d012..c190059eceb0cc111477c84f843f4a9f9bf2f393 100644
--- a/src/finn/analysis/fpgadataflow/res_estimation.py
+++ b/src/finn/analysis/fpgadataflow/res_estimation.py
@@ -27,7 +27,7 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import finn.custom_op.registry as registry
-import finn.util.basic as util
+from finn.util.fpgadataflow import is_fpgadataflow_node
def res_estimation(model):
@@ -37,14 +37,9 @@ def res_estimation(model):
res_dict = {}
for node in model.graph.node:
- if node.domain == "finn":
- backend_attribute = util.get_by_name(node.attribute, "backend")
- if backend_attribute is None:
- continue
- backend_value = backend_attribute.s.decode("UTF-8")
- if backend_value == "fpgadataflow":
- op_type = node.op_type
- inst = registry.custom_op[op_type](node)
- res_dict[node.name] = inst.node_res_estimation()
+ if is_fpgadataflow_node(node) is True:
+ op_type = node.op_type
+ inst = registry.custom_op[op_type](node)
+ res_dict[node.name] = inst.node_res_estimation()
return res_dict
diff --git a/src/finn/core/modelwrapper.py b/src/finn/core/modelwrapper.py
index 3ddcaa03c3d62daaf1ffd9f5ae6c3857460994fc..e99a6ef4cd40d6323d77354d3c9b4be341d7649c 100644
--- a/src/finn/core/modelwrapper.py
+++ b/src/finn/core/modelwrapper.py
@@ -253,14 +253,12 @@ class ModelWrapper:
return None
def find_producer(self, tensor_name):
- """Finds and returns the node that produces the tensor with given name.
- Currently only works for linear graphs."""
- all_outputs = [x.output[0] for x in self._model_proto.graph.node]
- try:
- producer_ind = all_outputs.index(tensor_name)
- return self._model_proto.graph.node[producer_ind]
- except ValueError:
- return None
+ """Finds and returns the node that produces the tensor with given name."""
+ ret = None
+ for x in self._model_proto.graph.node:
+ if tensor_name in x.output:
+ ret = x
+ return ret
def find_upstream(self, tensor_name, finder_fxn):
"""Follow the producer chain upstream, calling finder_fxn on each upstream
diff --git a/src/finn/core/onnx_exec.py b/src/finn/core/onnx_exec.py
index 0f47a9104e3d2ef3ee06ef908e302344d78e0b17..172ba25b223fd087df134add460a42d0a9935e0e 100644
--- a/src/finn/core/onnx_exec.py
+++ b/src/finn/core/onnx_exec.py
@@ -61,6 +61,10 @@ def execute_node(node, context, graph):
# onnxruntime unfortunately does not implement run_node as defined by ONNX,
# it can only execute entire models -- so we create a model which solely
# consists of our current node.
+ # note: ensure that the same ValueInfo does not appear both in
+ # graph.value_info as well as graph.output or graph.input
+ # nodes with multiple outputs that are a mix of value_info and
+ # input/outputs may get them reordered below
node_inputs = list(filter(lambda x: x.name in node.input, graph.input))
node_inputs += list(
filter(lambda x: x.name in node.input, graph.value_info)
@@ -84,17 +88,25 @@ def execute_node(node, context, graph):
output_list = sess.run(None, input_dict)
for output_ind in range(len(node.output)):
+ # get the name of the target buffer from node.output
outp = node.output[output_ind]
- if output_list[output_ind].shape != context[outp].shape:
+
+ # retrieve the index of that name in node_outputs
+ for i in range(len(node_outputs)):
+ if outp == node_outputs[i].name:
+ list_ind = i
+
+ # use that index to index output_list
+ if output_list[list_ind].shape != context[outp].shape:
raise Exception(
"""Output shapes disagree after node execution:
found %s vs expected %s"""
% (
- str(output_list[output_ind].shape.shape),
+ str(output_list[list_ind].shape.shape),
str(context[outp].shape),
)
)
- context[outp] = output_list[output_ind]
+ context[outp] = output_list[list_ind]
def execute_onnx(model, input_dict, return_full_exec_context=False):
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index 6d63f3b0888c2b17e9bf5c766ce382649fa6f2be..eab3decc696cb86622bbdd8f22f015515ea936d5 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -97,7 +97,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
# block -- use BRAM
# distributed -- use LUTRAM
# see also https://www.xilinx.com/support/answers/38070.html
- "decoupled_ram_style": ("s", False, "auto"),
+ "ram_style": ("s", False, "auto"),
}
my_attrs.update(super().get_nodeattr_types())
return my_attrs
@@ -989,9 +989,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
self.code_gen_dict["$MEM_DEPTH$"] = [
str(roundup_to_integer_multiple(self.calc_wmem(), 1024))
]
- self.code_gen_dict["$RAM_STYLE$"] = [
- self.get_nodeattr("decoupled_ram_style")
- ]
+ self.code_gen_dict["$RAM_STYLE$"] = [self.get_nodeattr("ram_style")]
template = self.decoupled_wrapper
diff --git a/src/finn/transformation/fpgadataflow/cleanup.py b/src/finn/transformation/fpgadataflow/cleanup.py
index e1bf53f7ef53c986fffe3dcc507e6886660eb611..a31cbfa7dd30eff37ceb2d7bf3c162093a5a3a1c 100644
--- a/src/finn/transformation/fpgadataflow/cleanup.py
+++ b/src/finn/transformation/fpgadataflow/cleanup.py
@@ -30,7 +30,7 @@ import os
import shutil
import finn.custom_op.registry as registry
-import finn.util.basic as util
+from finn.util.fpgadataflow import is_fpgadataflow_node
from finn.transformation import Transformation
@@ -53,36 +53,33 @@ class CleanUp(Transformation):
model.set_metadata_prop("vivado_stitch_proj", "")
for node in model.graph.node:
op_type = node.op_type
- if node.domain == "finn":
- backend_attribute = util.get_by_name(node.attribute, "backend")
- backend_value = backend_attribute.s.decode("UTF-8")
- if backend_value == "fpgadataflow":
- try:
- # lookup op_type in registry of CustomOps
- inst = registry.custom_op[op_type](node)
- # delete code_gen_dir from npysim
- code_gen_dir = inst.get_nodeattr("code_gen_dir_npysim")
- if os.path.isdir(code_gen_dir):
- shutil.rmtree(code_gen_dir)
- inst.set_nodeattr("code_gen_dir_npysim", "")
- inst.set_nodeattr("executable_path", "")
- # delete code_gen_dir from ipgen and project folder
- code_gen_dir = inst.get_nodeattr("code_gen_dir_ipgen")
- ipgen_path = inst.get_nodeattr("ipgen_path")
- if os.path.isdir(code_gen_dir):
- shutil.rmtree(code_gen_dir)
- if os.path.isdir(ipgen_path):
- shutil.rmtree(ipgen_path)
- inst.set_nodeattr("code_gen_dir_ipgen", "")
- inst.set_nodeattr("ipgen_path", "")
- # delete Java HotSpot Performance data log
- for d_name in os.listdir("/tmp/"):
- if "hsperfdata" in d_name:
- shutil.rmtree("/tmp/" + str(d_name))
+ if is_fpgadataflow_node(node) is True:
+ try:
+ # lookup op_type in registry of CustomOps
+ inst = registry.custom_op[op_type](node)
+ # delete code_gen_dir from npysim
+ code_gen_dir = inst.get_nodeattr("code_gen_dir_npysim")
+ if os.path.isdir(code_gen_dir):
+ shutil.rmtree(code_gen_dir)
+ inst.set_nodeattr("code_gen_dir_npysim", "")
+ inst.set_nodeattr("executable_path", "")
+ # delete code_gen_dir from ipgen and project folder
+ code_gen_dir = inst.get_nodeattr("code_gen_dir_ipgen")
+ ipgen_path = inst.get_nodeattr("ipgen_path")
+ if os.path.isdir(code_gen_dir):
+ shutil.rmtree(code_gen_dir)
+ if os.path.isdir(ipgen_path):
+ shutil.rmtree(ipgen_path)
+ inst.set_nodeattr("code_gen_dir_ipgen", "")
+ inst.set_nodeattr("ipgen_path", "")
+ # delete Java HotSpot Performance data log
+ for d_name in os.listdir("/tmp/"):
+ if "hsperfdata" in d_name:
+ shutil.rmtree("/tmp/" + str(d_name))
- except KeyError:
- # exception if op_type is not supported
- raise Exception(
- "Custom op_type %s is currently not supported." % op_type
- )
+ except KeyError:
+ # exception if op_type is not supported
+ raise Exception(
+ "Custom op_type %s is currently not supported." % op_type
+ )
return (model, False)
diff --git a/src/finn/transformation/fpgadataflow/codegen_ipgen.py b/src/finn/transformation/fpgadataflow/codegen_ipgen.py
index 45db9db04f17325cafe04aad1016580054daf554..fa7725ae1fa03cc204aa58969d6fbc6cf71e7d97 100644
--- a/src/finn/transformation/fpgadataflow/codegen_ipgen.py
+++ b/src/finn/transformation/fpgadataflow/codegen_ipgen.py
@@ -30,7 +30,8 @@ import os
import finn.custom_op.registry as registry
from finn.transformation import Transformation
-from finn.util.basic import get_by_name, make_build_dir
+from finn.util.basic import make_build_dir
+from finn.util.fpgadataflow import is_fpgadataflow_node
def _codegen_single_node(node, model, fpgapart, clk):
@@ -77,11 +78,6 @@ class CodeGen_ipgen(Transformation):
def apply(self, model):
for node in model.graph.node:
- if node.domain == "finn":
- backend_attribute = get_by_name(node.attribute, "backend")
- if backend_attribute is None:
- continue
- backend_value = backend_attribute.s.decode("UTF-8")
- if backend_value == "fpgadataflow":
- _codegen_single_node(node, model, self.fpgapart, self.clk)
+ if is_fpgadataflow_node(node) is True:
+ _codegen_single_node(node, model, self.fpgapart, self.clk)
return (model, False)
diff --git a/src/finn/transformation/fpgadataflow/codegen_npysim.py b/src/finn/transformation/fpgadataflow/codegen_npysim.py
index fe758ec28d67bd2f46edc864574d2edddfe6e3a3..02200e76db3f9c8207605bb93c4b07f0ebc76cab 100644
--- a/src/finn/transformation/fpgadataflow/codegen_npysim.py
+++ b/src/finn/transformation/fpgadataflow/codegen_npysim.py
@@ -30,7 +30,8 @@ import os
import finn.custom_op.registry as registry
from finn.transformation import Transformation
-from finn.util.basic import get_by_name, make_build_dir
+from finn.util.basic import make_build_dir
+from finn.util.fpgadataflow import is_fpgadataflow_node
def _codegen_single_node(node, model):
@@ -67,11 +68,6 @@ class CodeGen_npysim(Transformation):
def apply(self, model):
for node in model.graph.node:
- if node.domain == "finn":
- backend_attribute = get_by_name(node.attribute, "backend")
- if backend_attribute is None:
- continue
- backend_value = backend_attribute.s.decode("UTF-8")
- if backend_value == "fpgadataflow":
- _codegen_single_node(node, model)
+ if is_fpgadataflow_node(node) is True:
+ _codegen_single_node(node, model)
return (model, False)
diff --git a/src/finn/transformation/fpgadataflow/compile.py b/src/finn/transformation/fpgadataflow/compile.py
index a76ab683209bbb1219517075ff29a75540dc7bfc..40c7da8f77efeaa655459402699a401b642b776c 100644
--- a/src/finn/transformation/fpgadataflow/compile.py
+++ b/src/finn/transformation/fpgadataflow/compile.py
@@ -27,7 +27,7 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import finn.custom_op.registry as registry
-import finn.util.basic as util
+from finn.util.fpgadataflow import is_fpgadataflow_node
from finn.transformation import NodeLocalTransformation
@@ -49,31 +49,27 @@ class Compile(NodeLocalTransformation):
def applyNodeLocal(self, node):
op_type = node.op_type
- if node.domain == "finn":
- backend_attribute = util.get_by_name(node.attribute, "backend")
- if backend_attribute is not None:
- backend_value = backend_attribute.s.decode("UTF-8")
- if backend_value == "fpgadataflow":
- try:
- # lookup op_type in registry of CustomOps
- inst = registry.custom_op[op_type](node)
- # ensure that code is generated
- assert (
- inst.get_nodeattr("code_gen_dir_npysim") != ""
- ), """Node
- attribute "code_gen_dir_npysim" is not set. Please run
- Transformation CodeGen_npysim first."""
- # call the compilation function for this node
- inst.compile_singlenode_code()
- # ensure that executable path is now set
- assert (
- inst.get_nodeattr("executable_path") != ""
- ), """Transformation
- compile was not successful, there is no path to executables set
- in node attribute "executable_path"."""
- except KeyError:
- # exception if op_type is not supported
- raise Exception(
- "Custom op_type %s is currently not supported." % op_type
- )
+ if is_fpgadataflow_node(node) is True:
+ try:
+ # lookup op_type in registry of CustomOps
+ inst = registry.custom_op[op_type](node)
+ # ensure that code is generated
+ assert (
+ inst.get_nodeattr("code_gen_dir_npysim") != ""
+ ), """Node
+ attribute "code_gen_dir_npysim" is not set. Please run
+ Transformation CodeGen_npysim first."""
+ # call the compilation function for this node
+ inst.compile_singlenode_code()
+ # ensure that executable path is now set
+ assert (
+ inst.get_nodeattr("executable_path") != ""
+ ), """Transformation
+ compile was not successful, there is no path to executables set
+ in node attribute "executable_path"."""
+ except KeyError:
+ # exception if op_type is not supported
+ raise Exception(
+ "Custom op_type %s is currently not supported." % op_type
+ )
return (node, False)
diff --git a/src/finn/transformation/fpgadataflow/hlssynth_ipgen.py b/src/finn/transformation/fpgadataflow/hlssynth_ipgen.py
index 2a40b3c2302a432937d45e807515e795f02e0365..2af623818fe0e830883ef5065e5e7c9c7364ef1e 100644
--- a/src/finn/transformation/fpgadataflow/hlssynth_ipgen.py
+++ b/src/finn/transformation/fpgadataflow/hlssynth_ipgen.py
@@ -27,7 +27,7 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import finn.custom_op.registry as registry
-import finn.util.basic as util
+from finn.util.fpgadataflow import is_fpgadataflow_node
from finn.transformation import NodeLocalTransformation
@@ -49,33 +49,27 @@ class HLSSynth_IPGen(NodeLocalTransformation):
def applyNodeLocal(self, node):
op_type = node.op_type
- if node.domain == "finn":
- backend_attribute = util.get_by_name(node.attribute, "backend")
- if backend_attribute is None:
- return (node, False)
- backend_value = backend_attribute.s.decode("UTF-8")
- if backend_value == "fpgadataflow":
- try:
- # lookup op_type in registry of CustomOps
- inst = registry.custom_op[op_type](node)
- # ensure that code is generated
- assert (
- inst.get_nodeattr("code_gen_dir_ipgen") != ""
- ), """Node
- attribute "code_gen_dir_ipgen" is empty. Please run
- transformation CodeGen_ipgen first."""
- # call the compilation function for this node
- inst.ipgen_singlenode_code()
- # ensure that executable path is now set
- assert (
- inst.get_nodeattr("ipgen_path") != ""
- ), """Transformation
- HLSSynth_IPGen was not successful. Node attribute "ipgen_path"
- is empty."""
- except KeyError:
- # exception if op_type is not supported
- raise Exception(
- "Custom op_type %s is currently not supported." % op_type
- )
-
+ if is_fpgadataflow_node(node) is True:
+ try:
+ # lookup op_type in registry of CustomOps
+ inst = registry.custom_op[op_type](node)
+ # ensure that code is generated
+ assert (
+ inst.get_nodeattr("code_gen_dir_ipgen") != ""
+ ), """Node
+ attribute "code_gen_dir_ipgen" is empty. Please run
+ transformation CodeGen_ipgen first."""
+ # call the compilation function for this node
+ inst.ipgen_singlenode_code()
+ # ensure that executable path is now set
+ assert (
+ inst.get_nodeattr("ipgen_path") != ""
+ ), """Transformation
+ HLSSynth_IPGen was not successful. Node attribute "ipgen_path"
+ is empty."""
+ except KeyError:
+ # exception if op_type is not supported
+ raise Exception(
+ "Custom op_type %s is currently not supported." % op_type
+ )
return (node, False)
diff --git a/src/finn/transformation/fpgadataflow/insert_dwc.py b/src/finn/transformation/fpgadataflow/insert_dwc.py
index 7121434dedef428cbd20c324d39193469e9b1a04..3fe60292e8f54a8cdf394b5e09f8a3d2bca7605c 100644
--- a/src/finn/transformation/fpgadataflow/insert_dwc.py
+++ b/src/finn/transformation/fpgadataflow/insert_dwc.py
@@ -3,7 +3,7 @@ from onnx import helper as oh
from finn.custom_op.registry import getCustomOp
from finn.transformation import Transformation
-from finn.util.basic import get_by_name
+from finn.util.fpgadataflow import is_fpgadataflow_node
def _is_dwc_node(node):
@@ -13,21 +13,9 @@ def _is_dwc_node(node):
return False
-def _is_fpgadataflow_node(node):
- if node.domain == "finn":
- n_backend = get_by_name(node.attribute, "backend")
- if n_backend is None:
- return False
- backend_value = n_backend.s.decode("UTF-8")
- if backend_value == "fpgadataflow":
- return True
- else:
- return False
-
-
def _suitable_node(node):
if node is not None:
- if _is_fpgadataflow_node(node) is True:
+ if is_fpgadataflow_node(node) is True:
if _is_dwc_node(node) is False:
return True
else:
diff --git a/src/finn/transformation/fpgadataflow/prepare_rtlsim.py b/src/finn/transformation/fpgadataflow/prepare_rtlsim.py
index 4474831381425268d2a59e7de835bba31c55a733..baac3e7bdf5ac936a963ac8346f01638657b042c 100644
--- a/src/finn/transformation/fpgadataflow/prepare_rtlsim.py
+++ b/src/finn/transformation/fpgadataflow/prepare_rtlsim.py
@@ -27,7 +27,8 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import finn.custom_op.registry as registry
-import finn.util.basic as util
+from finn.util.fpgadataflow import is_fpgadataflow_node
+
from finn.transformation import NodeLocalTransformation
try:
@@ -55,22 +56,18 @@ class PrepareRTLSim(NodeLocalTransformation):
def applyNodeLocal(self, node):
op_type = node.op_type
- if node.domain == "finn":
- backend_attribute = util.get_by_name(node.attribute, "backend")
- if backend_attribute is not None:
- backend_value = backend_attribute.s.decode("UTF-8")
- if backend_value == "fpgadataflow":
- try:
- # lookup op_type in registry of CustomOps
- inst = registry.custom_op[op_type](node)
- inst.prepare_rtlsim()
- # ensure that executable path is now set
- assert (
- inst.get_nodeattr("rtlsim_so") != ""
- ), "Failed to prepare RTLSim, no rtlsim_so attribute found."
- except KeyError:
- # exception if op_type is not supported
- raise Exception(
- "Custom op_type %s is currently not supported." % op_type
- )
+ if is_fpgadataflow_node(node) is True:
+ try:
+ # lookup op_type in registry of CustomOps
+ inst = registry.custom_op[op_type](node)
+ inst.prepare_rtlsim()
+ # ensure that executable path is now set
+ assert (
+ inst.get_nodeattr("rtlsim_so") != ""
+ ), "Failed to prepare RTLSim, no rtlsim_so attribute found."
+ except KeyError:
+ # exception if op_type is not supported
+ raise Exception(
+ "Custom op_type %s is currently not supported." % op_type
+ )
return (node, False)
diff --git a/src/finn/transformation/fpgadataflow/replace_verilog_relpaths.py b/src/finn/transformation/fpgadataflow/replace_verilog_relpaths.py
index dce62c20b99097feee7208cbf57aa8921ddb3566..e63ae4e0203188d9664f432f75e36994e8a71ac5 100644
--- a/src/finn/transformation/fpgadataflow/replace_verilog_relpaths.py
+++ b/src/finn/transformation/fpgadataflow/replace_verilog_relpaths.py
@@ -29,7 +29,7 @@
import os
import finn.custom_op.registry as registry
-import finn.util.basic as util
+from finn.util.fpgadataflow import is_fpgadataflow_node
from finn.transformation import Transformation
@@ -42,32 +42,27 @@ class ReplaceVerilogRelPaths(Transformation):
def apply(self, model):
for node in model.graph.node:
op_type = node.op_type
- if node.domain == "finn":
- backend_attribute = util.get_by_name(node.attribute, "backend")
- if backend_attribute is None:
- continue
- backend_value = backend_attribute.s.decode("UTF-8")
- if backend_value == "fpgadataflow":
- try:
- # lookup op_type in registry of CustomOps
- inst = registry.custom_op[op_type](node)
- # find the IP gen dir
- ipgen_path = inst.get_nodeattr("ipgen_path")
- if ipgen_path is not None and os.path.isdir(ipgen_path):
- for dname, dirs, files in os.walk(ipgen_path):
- for fname in files:
- if fname.endswith(".v"):
- fpath = os.path.join(dname, fname)
- with open(fpath, "r") as f:
- s = f.read()
- old = '$readmemh(".'
- new = '$readmemh("%s' % dname
- s = s.replace(old, new)
- old = '"./'
- new = '"%s/' % dname
- s = s.replace(old, new)
- with open(fpath, "w") as f:
- f.write(s)
- except KeyError:
- pass
+ if is_fpgadataflow_node(node) is True:
+ try:
+ # lookup op_type in registry of CustomOps
+ inst = registry.custom_op[op_type](node)
+ # find the IP gen dir
+ ipgen_path = inst.get_nodeattr("ipgen_path")
+ if ipgen_path is not None and os.path.isdir(ipgen_path):
+ for dname, dirs, files in os.walk(ipgen_path):
+ for fname in files:
+ if fname.endswith(".v"):
+ fpath = os.path.join(dname, fname)
+ with open(fpath, "r") as f:
+ s = f.read()
+ old = '$readmemh(".'
+ new = '$readmemh("%s' % dname
+ s = s.replace(old, new)
+ old = '"./'
+ new = '"%s/' % dname
+ s = s.replace(old, new)
+ with open(fpath, "w") as f:
+ f.write(s)
+ except KeyError:
+ pass
return (model, False)
diff --git a/src/finn/transformation/fpgadataflow/set_exec_mode.py b/src/finn/transformation/fpgadataflow/set_exec_mode.py
index 1f9c4c42189950e456da2dda77dee98fda49d522..83dda7ceccfd26fa1c43ab517ade2e19ccae4a61 100644
--- a/src/finn/transformation/fpgadataflow/set_exec_mode.py
+++ b/src/finn/transformation/fpgadataflow/set_exec_mode.py
@@ -27,7 +27,7 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import finn.custom_op.registry as registry
-import finn.util.basic as util
+from finn.util.fpgadataflow import is_fpgadataflow_node
from finn.transformation import Transformation
@@ -42,25 +42,20 @@ class SetExecMode(Transformation):
def apply(self, model):
for node in model.graph.node:
op_type = node.op_type
- if node.domain == "finn":
- backend_attribute = util.get_by_name(node.attribute, "backend")
- if backend_attribute is None:
- continue
- backend_value = backend_attribute.s.decode("UTF-8")
- if backend_value == "fpgadataflow":
- try:
- # lookup op_type in registry of CustomOps
- inst = registry.custom_op[op_type](node)
- # set sim_mode accordingly to argument mode
- inst.set_nodeattr("exec_mode", self.mode)
- # ensure that sim_mode is now set
- assert (
- inst.get_nodeattr("exec_mode") != ""
- ), """Transformation
+ if is_fpgadataflow_node(node) is True:
+ try:
+ # lookup op_type in registry of CustomOps
+ inst = registry.custom_op[op_type](node)
+ # set sim_mode accordingly to argument mode
+ inst.set_nodeattr("exec_mode", self.mode)
+ # ensure that sim_mode is now set
+ assert (
+ inst.get_nodeattr("exec_mode") != ""
+ ), """Transformation
was not successful. Node attribute "exec_mode" is not set"""
- except KeyError:
- # exception if op_type is not supported
- raise Exception(
- "Custom op_type %s is currently not supported." % op_type
- )
+ except KeyError:
+ # exception if op_type is not supported
+ raise Exception(
+ "Custom op_type %s is currently not supported." % op_type
+ )
return (model, False)
diff --git a/src/finn/transformation/insert_topk.py b/src/finn/transformation/insert_topk.py
new file mode 100644
index 0000000000000000000000000000000000000000..213d2cedf92c0276e33fcf2b50e6966aeee8c847
--- /dev/null
+++ b/src/finn/transformation/insert_topk.py
@@ -0,0 +1,96 @@
+# Copyright (c) 2020, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import numpy as np
+
+from onnx import TensorProto
+from onnx import helper as oh
+
+from finn.transformation import Transformation
+from finn.core.datatype import DataType
+
+
+class InsertTopK(Transformation):
+ """Add TopK node at the network output and replace the graph output with
+ the TopK indices."""
+
+ def __init__(self, k=5, axis=-1, largest=1, sorted=1):
+ super().__init__()
+ self.k = k
+ self.axis = axis
+ self.largest = largest
+ self.sorted = sorted
+
+ def apply(self, model):
+ # get name of output tensor
+ graph_out_name = model.graph.output[0].name
+ # find final node
+ final_node = model.find_producer(graph_out_name)
+ # if a top-select op is already present, do nothing
+ if final_node.op_type == "TopK":
+ return (model, False)
+ else:
+ out_shape = model.get_tensor_shape(graph_out_name)
+ out_dtype = model.get_tensor_datatype(graph_out_name)
+ # adjust shape
+ out_shape[self.axis] = self.k
+ # make new buffer
+ k_tensor = np.array([self.k]).astype(np.int64)
+ k_value = oh.make_tensor_value_info(
+ model.make_new_valueinfo_name(), TensorProto.INT64, [1]
+ )
+ topk_values = oh.make_tensor_value_info(
+ model.make_new_valueinfo_name(), TensorProto.FLOAT, out_shape
+ )
+ topk_indices = oh.make_tensor_value_info(
+ model.make_new_valueinfo_name(), TensorProto.INT64, out_shape
+ )
+ model.graph.value_info.append(k_value)
+ model.set_tensor_datatype(k_value.name, out_dtype) # TODO set to int64
+ model.graph.value_info.append(topk_values)
+ model.set_tensor_datatype(topk_values.name, out_dtype)
+ # create and append topk node
+ model.set_initializer(k_value.name, k_tensor)
+ topk_node = oh.make_node(
+ "TopK",
+ inputs=[graph_out_name, k_value.name],
+ outputs=[topk_values.name, topk_indices.name],
+ axis=self.axis,
+ largest=self.largest,
+ sorted=self.sorted,
+ )
+ model.graph.node.append(topk_node)
+ # replace the existing output definition with topk indices
+ model.graph.output.insert(0, topk_indices)
+ model.graph.output.pop(1)
+ # set quantization annotation for indices
+ # minimal output dtype for TopK indices dependens on num. classes
+ # assuming UINT32 is large enough for now (FINN has currently no
+ # DataType.INT64)
+ model.set_tensor_datatype(topk_indices.name, DataType.UINT32)
+ return (model, True)
diff --git a/tests/end2end/test_end2end_tfc_w1a1.py b/tests/end2end/test_end2end_tfc_w1a1.py
index 7db8bedd64619198aa60d289bf78451932289fd6..8a670fce2e7e6585c98efa9e4a6e27a660edf925 100644
--- a/tests/end2end/test_end2end_tfc_w1a1.py
+++ b/tests/end2end/test_end2end_tfc_w1a1.py
@@ -137,6 +137,7 @@ def test_end2end_tfc_w1a1_fold_and_tlastmarker():
fc0w.set_nodeattr("SIMD", 16)
fc0w.set_nodeattr("PE", 16)
fc0w.set_nodeattr("outFIFODepth", 4)
+ fc0w.set_nodeattr("ram_style", "block")
fc1w.set_nodeattr("SIMD", 8)
fc1w.set_nodeattr("PE", 8)
fc1w.set_nodeattr("outFIFODepth", 4)
@@ -146,6 +147,7 @@ def test_end2end_tfc_w1a1_fold_and_tlastmarker():
fc3w.set_nodeattr("SIMD", 16)
fc3w.set_nodeattr("PE", 10)
fc3w.set_nodeattr("outFIFODepth", 50)
+ fc3w.set_nodeattr("ram_style", "distributed")
model = model.transform(InsertDWC())
model = model.transform(InsertTLastMarker())
model = model.transform(GiveUniqueNodeNames())
diff --git a/tests/transformation/test_topk_insert.py b/tests/transformation/test_topk_insert.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac32c30edbbf466b2b441bcc92975a7d50f42bda
--- /dev/null
+++ b/tests/transformation/test_topk_insert.py
@@ -0,0 +1,58 @@
+import onnx
+from finn.util.test import get_test_model_trained
+import brevitas.onnx as bo
+import numpy as np
+import onnx.numpy_helper as nph
+import torch
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.fold_constants import FoldConstants
+from finn.transformation.insert_topk import InsertTopK
+
+import finn.core.onnx_exec as oxe
+from pkgutil import get_data
+
+import pytest
+
+export_onnx_path = "test_output_lfc.onnx"
+
+
+@pytest.mark.parametrize("k", [1, 5, 10])
+def test_topk_insert(k):
+ tfc = get_test_model_trained("TFC", 1, 1)
+ bo.export_finn_onnx(tfc, (1, 1, 28, 28), export_onnx_path)
+ model = ModelWrapper(export_onnx_path)
+
+ # do transformations (no topk)
+ model = model.transform(InferShapes())
+ model = model.transform(FoldConstants())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+ model = model.transform(InferDataTypes())
+
+ # verification: generate random input, run through net, streamline,
+ # run again, check that output is top-k
+ raw_i = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/input_0.pb")
+ input_tensor = onnx.load_tensor_from_string(raw_i)
+ input_brevitas = torch.from_numpy(nph.to_array(input_tensor)).float()
+ output_golden = tfc.forward(input_brevitas).detach().numpy()
+ output_golden_topk = np.flip(output_golden.flatten().argsort())[:k]
+ output_golden_topk = output_golden_topk.flatten()
+
+ input_dict = {"global_in": nph.to_array(input_tensor)}
+
+ # insert top-k
+ model = model.transform(InsertTopK(k))
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+ model = model.transform(InferShapes())
+
+ # verify output of top-k
+ output_dict_topk = oxe.execute_onnx(model, input_dict)
+ output_pysim_topk = output_dict_topk[list(output_dict_topk.keys())[0]]
+ output_pysim_topk = output_pysim_topk.astype(np.int).flatten()
+
+ assert np.array_equal(output_golden_topk, output_pysim_topk)